Proceedings of the 2nd International Conference on Emerging Technologies and Intelligent Systems: ICETIS 2022 Volume 1 303125273X, 9783031252730

Table of contents :
Preface
Organization
Conference General Chairs
Conference Organizing Chair
Program Committee Chair
Publication Committee Chairs
Conference Tracks Chairs
Members of Scientific Committee
Publicity and Public Relations Committee
Finance Chair
Contents
Management Information Systems
Keys to Successful Product and Process Innovation Through Soft TQM: An ANN Approach
1 Introduction
2 Theoretical Background and Literature Review
2.1 DC, Soft TQM Practices, PD, and PC
3 Research Model Development
3.1 TMC, PD, and PC
3.2 EM, PD, and PC
3.3 TR, PD, and PC
3.4 IN, PD, and PC
3.5 TE, PD, and PC
4 Methodology
4.1 Sampling Method and Data Collection
4.2 Operationalization of Research Instruments
5 Data Analysis
5.1 Sampling Method and Data Collection
5.2 Artificial Neural Networks (ANN) Results
6 Discussion
7 Implications, Limitations, Future Works, and Conclusions
References
Big Data Techniques and Internal Control: Evidence from Egypt
1 Introduction
2 Literature Review
3 Methodology
4 Results and Discussion
5 Conclusion
References
What is Stopping You from Using Mobile Payment in Oman?
1 Introduction
2 Literature Review
2.1 Theory of Planned Behaviour (TPB)
2.2 Mobile Technology Acceptance Model (MTAM)
3 Hypotheses Development
3.1 Perceived Value (PV)
3.2 Digital Social Media (DSM)
3.3 Facilitation Condition (FC)
3.4 Technical Feasibilities (TF)
3.5 Mobile Perceived Trust (MPT)
3.6 Mobile Usefulness (MU)
3.7 Mobile Ease of Use (MEoU)
3.8 Mobile Perceived Security Risk (MPSR)
3.9 Attitude (ATT)
3.10 Subjective Norms (SN)
3.11 Perceived Control Behaviour (PCB)
3.12 Mobile Payment Use Intention (IU)
4 Methodology
4.1 Demographic Profile
4.2 Measurement Model Assessment
4.3 Structural Model Assessment
5 Conclusion and Discussion
References
How Does User-Generated Content Affect Users’ Obsessive Use of Lifestyle-Sharing Mobile Social Networking Site?
1 Introduction
2 Literature Review
2.1 Studies in Social Networking Sites
2.2 Consumers’ Decision-Making Process
2.3 Obsessive Technology Use
3 Hypotheses Development
3.1 Perceptions of Platform’s Content
3.2 Perceived Value of Lifestyle-Sharing Platform
3.3 Positive Affects
3.4 Research Framework
4 Methodology
4.1 Research Instrument
4.2 Data Collection and Respondent Profile
5 Data Analysis
5.1 Statistical Analysis
5.2 Normality Test
5.3 Assessing Reflective Constructs
5.4 Assessing Formative Constructs
5.5 Assessing the Structural Model
5.6 Effect Size and Predictive Relevance
6 Discussion
References
Adoption of DeLone and McLean’s Model of Information System Success to Explore Customers’ Repurchase Intention in a Chinese Cross-Border E-commerce Platform
1 Introduction
2 Literature Review
2.1 Delone and McLean’s Model of IS Success (D&M Model)
2.2 The Commitment-Involvement Theory
2.3 Stimulus-Organism-Response (S-O-R) Theory
3 Hypotheses Development
3.1 System Quality
3.2 Information Quality
3.3 Enduring Involvement
3.4 User Satisfaction
4 Methodology
5 Analysis
5.1 Demographic Profile
5.2 Measurement Model Assessment
5.3 Structural Model Assessment
6 Discussion and Conclusion
References
Social Media Influencer Marketing: How Influencer Content Help?
1 Introduction
2 Literature Review
2.1 Stimulus-Organism-Response Model
3 Hypotheses Development
3.1 Content Attractiveness
3.2 Content Prestige
3.3 Content Expertise
3.4 Brand Trust and Purchase Intention
3.5 Research Framework
4 Methodology
4.1 Data Collection and Respondent Profile
5 Data Analysis
5.1 Measurement Model Assessment
5.2 Structural Model Assessment
6 Discussion
References
Impulsive Buying Behaviour on Social Commerce: Moderated by Cultural Dimension, and Perceived Trust
1 Introduction
2 Literature Review
2.1 Impulse Buying Behaviour
2.2 Stimulus-Organism-Response (SOR) Model
3 Proposition Development
3.1 Cognitive and Affective
3.2 Emotion
3.3 Social Influence
3.4 Cultural Dimension
3.5 Perceived Trust
4 Implications
4.1 Theoretical Implication
4.2 Practical Implication
5 Conclusion and Future Study
6 Declaration
References
Conceptualizing Business Intelligence (BI) Adoption in SME to Gain Competitive Advantage: Effects of Organizational Mindfulness, Inter-organizational Relationship and Technology Orientation
1 Introduction
1.1 Background
1.2 Research Gaps
1.3 Research Objective
2 Literature Review
2.1 Research Context
2.2 Theatrical Background
2.3 Associations and Propositions
3 Discussion and Significance
4 Conclusion
References
Backpackers’ Adoption Intention of Mobile Hotel Reservation (MHR): Are Costs Important?
1 Introduction
2 Literature Review
2.1 Perceived Usefulness, Perceived Ease of Use and Attitude
2.2 Perceived Cost-Saving
3 Research Methodology
4 Data Analysis
4.1 Measurement Model and Structural Model
5 Discussions and Implications
References
Analyzing Purchasing Behavior of NIO’s Customers Based on Theory of Planned Behavior in China
1 Introduction
2 Literature Review
2.1 Theory of Planned Behaviour (TPB)
3 Hypotheses Development
3.1 Attitude
3.2 Subjective Norms
3.3 Perceived Behavioral Control
3.4 Awareness of the New Energy Industry
3.5 Past Consuming Behavior
4 Methodology
4.1 Demographic Profile
4.2 Reliability Results
4.3 Related Analysis
4.4 Multiple Linear Regression
5 Conclusion and Discussion
References
Digital Marketing in the Perspective of Omnichannel Retailing for Customer Engagement
1 Introduction
2 Literature Review
2.1 Customer Engagement Behavior
2.2 Stimulus-Organism-Response (SOR) Model
3 Hypotheses Development
3.1 Channel Integration Quality as Stimulus
3.2 Customer Satisfaction as Internal Organism
3.3 Customer Engagement Behavior as Response
3.4 Omnichannel Compatibility
4 Research Methodology
5 Conclusion
References
What Drives Consumer’s Self-disclosure in Mobile Payment: An Investigate in China
1 Introduction
2 Literature Review
2.1 Consumer’s Self-disclosure
2.2 Attribution Theory
2.3 Justification of the Conceptual Framework
3 Hypothesis Development
3.1 Consumer’s Cognitive Trust (CCT)
3.2 Consumer’s Emotional Trust (CET)
3.3 Technology Anxiety (TA)
3.4 Social Pressure (SP)
3.5 Perceived Benefits of MP (PB)
4 Research Methodology
4.1 Data Collection and Demographic Profile
4.2 Measurement Model Assessment
4.3 Examining Inner Structural Model
5 Conclusion and Discussion
References
Identifying the Factors that Influence Users’ Intentions to Use Mobile Payment Services
1 Introduction
2 Literature Review
2.1 M-payment Adoption
2.2 Information Quality
2.3 System Quality
2.4 Service Quality
2.5 Trust and BI
2.6 Satisfaction and BI
3 Methodology
4 Results
4.1 Descriptive Statistics
4.2 Measurement Model
4.3 Structural Model
5 Discussion
6 Conclusion
References
Behavioral Intention and Actual Use of Mobile Learning During the COVID-19 Pandemic in the Higher Education System
1 Introduction
2 Literature Review and Hypotheses Development
2.1 Mobile Learning
2.2 The UTAUT Theory
2.3 Performance Expectancy
2.4 Effort Expectancy
2.5 Social Influence
2.6 Facilitating Conditions
2.7 Hedonic Motivation
2.8 Price Value
2.9 Habit
3 Methodology
4 Results
4.1 Measurement Model
4.2 Model Fit Indices
4.3 Structural Model
5 Discussion
6 Conclusion
References
Effective Risk Management as a Mediator to Enhance the Success of Construction Projects
1 Introduction
2 Literature Review
3 Methodology
4 Results and Discussion
5 Conclusion
References
A Stimulus-Organism-Response Paradigm to Word-of-Mouth and Continuance Intention of Mobile Application
1 Introduction
2 Literature Review
2.1 Stimulus-Organism-Response Theory
3 Hypotheses Development
3.1 Perceived Complementarity
3.2 Perceived Usefulness
3.3 Satisfaction
4 Methodology
5 Analysis
5.1 Common Method Bias (CMB)
5.2 Measurement Model Assessment
5.3 Structural Model Assessment
6 Discussion
References
Determining the Factors that Affect Resistance to Digital News Subscription During the COVID-19 Pandemic
1 Introduction
2 Literature Review
2.1 Innovation Resistance Theory
3 Hypotheses Development
3.1 Usage Barrier
3.2 Value Barrier
3.3 Risk Barrier
3.4 Image Barrier
3.5 Tradition Barrier
4 Methodology
5 Analysis
5.1 Demographic Profile
5.2 Common Method Bias (CMB)
5.3 Measurement Model Assessment
5.4 Structural Model Assessment
5.5 Importance-Performance Map Analysis (IPMA)
6 Discussion
References
Adoption Decision and Alleviation of Mobile Payment
1 Introduction
2 Literature Review
3 Methodology
4 Analysis and Discussion
5 Conclusion and Implications
References
Critical Success Factors of Knowledge Management in Higher Education: A Systematic Review
1 Introduction
2 Literature Review
3 Methodology
3.1 Inclusion/Exclusion Criteria
3.2 Data Sources and Search Strategies
3.3 Quality Assessment
3.4 Data Coding and Analysis
4 Discussion of Results
4.1 RQ1—Which KM Processes Play a Dominant Role in the Reviewed Studies?
4.2 RQ2—What is the Main Area of Activity for KM Studies in HEI?
4.3 RQ3—What Critical Success Factors are Identified in HEI for KM Integration?
5 Conclusion
References
Experience with Mobile Phone Technology: A Comparison Between Two Brands
1 Introduction
2 Literature Review and Hypothesis Development
2.1 Brand Loyalty
2.2 Perceived Value
2.3 Customer Experience
3 Methodology
4 Findings and Results
5 Discussion and Implications
6 Conclusion and Recommendations
References
The Influence of FinTech on Financial Sector and Economic Growth: An Analysis of Recent Literature
1 Introduction
2 Literature Review
2.1 The FinTech Ecosystem
2.2 FinTech and Financial Sector
2.3 FinTech and the Economy
3 Research Method
4 Discussion and Conclusion
4.1 FinTech Lending
4.2 FinTech Risk
4.3 FinTech Regulation
References
Social Media Technology: The Influences on Online Impulse Buying Behaviour
1 Introduction
2 Literature Review and Hypotheses Development
2.1 S-O-R Model
3 Methodology
4 Findings and Results
5 Discussion and Implications
6 Conclusion, Limitations and Recommendations
References
Automated Project Progress Monitoring in Construction Projects: A Review of Current Applications and Trends
1 Introduction
2 Questions to Highlight
3 Research Aim and Objectives
4 Research Methodology
5 Necessity of Automated Progresses Monitoring
6 Project Progress Monitoring in Construction Projects
7 Issues and Challenges of Traditional Monitoring Methods
8 Trends And Applications of Automated Project Progress Monitoring
9 Conclusion
References
Understanding the Efficiency of Gamification on the Engagement Intention of the Customers with Mobile Payment Systems
1 Introduction
2 Literature Review
2.1 Gamification and Customer Engagement with Mobile Payment Systems
3 Theoretical Foundation and Hypotheses Development
3.1 Social Influence of Gamified Mobile Payment Systems and Customers Engagement Intention
3.2 Effort Expectancy of Gamified Mobile Payment Systems and Customers Engagement Intention
3.3 Facilitating Conditions of Gamified Mobile Payment Systems and Customers Engagement Intention
3.4 Performance Expectancy of Gamified Mobile Payment Systems and Customers Engagement Intention
3.5 Trust of Gamified Mobile Payment Systems and Customers Engagement Intention
4 Research Methodology
4.1 Research Design and Measurement
4.2 Sample and Survey Administration
5 Data Analysis and Results
5.1 Evaluation of the Measurement Model
5.2 Evaluation of the Structural Model
6 Discussion
7 Conclusion, Future Research, & Limitations
References
Customer Engagement in Instagram: The Mediating Role of Perceived Advertising Value
1 Introduction
2 Literature Review
2.1 Informativeness and Perceived Value of Advertising
2.2 Entertainment Features and Perceived Value of Advertising
2.3 Credibility and Perceived Value of Advertising
2.4 Perceived Value and Customer Engagement
2.5 Mediating Roles of Perceived Value
3 Research Method
4 Results
5 Discussion and Conclusion
References
Artificial Intelligence (AI), Blockchain, and Cryptocurrency in Finance: Current Scenario and Future Direction
1 Introduction
2 Literature Review
2.1 Artificial Intelligence (AI) and Perceived Benefits to Financial Sectors
2.2 Blockchain and Perceived Benefits to Financial Sectors
2.3 Cryptocurrency and Perceived Benefits to Financial Sectors
3 Research Method
4 Discussion
4.1 Blockchain and Cryptocurrencies in Finance
4.2 Artificial Intelligence in Finance
4.3 Concluding Remark
References
Common Data Environment: Bridging the Digital Data Sharing Gap Among Construction Organizations
1 Introduction
2 The Construction Industry Common Data Environment (CDE) Context
2.1 The Construction Industry Digital Transformation and Data Sharing
2.2 Common Data Environment (CDE)
2.3 Data Creation in Construction Industry
2.4 Common Data Environment (CDE) Platform Value in the Construction Industry
3 Methodology
4 Findings and Discussion
4.1 Data Types in CDE
4.2 The Potentials of Digital Data Sharing in CDE
5 Conclusion
References
A Study of the Perceptions of Last-Mile Delivery Towards the Adoption of IoT
1 Introduction
2 Literature Review
2.1 Factors Affecting Readiness in Adopting New Technology in Last-Mile Delivery
2.2 Technology Readiness
3 Methods
3.1 Methodology and Case Selection
3.2 Analyzing the Interview Data
4 Data Analysis
5 Findings
6 Conclusions
References
Blockchain Technology in Malaysian Estate Distribution: A Systematic Review
1 Introduction
2 Methodology
3 Factors Causing Arrears in the Claiming Process
3.1 Lengthy and Costly in Inheritance Procedure
3.2 Complicated Jurisdiction in Inheritance Procedure
3.3 Untraceable Documents of Inheritance
4 Critical Issues of the Estate Distribution Chain to Solves
4.1 Enhance Transactional Transparency
4.2 Integrated Property Development System
4.3 Eradication of Corruption
4.4 Immutability
5 Applicability of Blockchain Decision
6 Result and Discussion
6.1 Smart Contract
6.2 InterPlanetary File System (IPFS)
6.3 Elliptic Curve Digital Signature Algorithm (ECDSA)
6.4 ERC(Ethereum Request for Comments)-20
6.5 Resilient Distributed Dataset (RDD)
7 Conclusion
References
How to Become King? Insights from the Importance Performance Map Analysis of User-Based Authenticity
1 Introduction
2 Conceptual Model Development
2.1 Digital Content Marketing
2.2 Authenticity and Digital Content Marketing
3 Research Design and Methodology
3.1 Data Collecting and Instrument
3.2 Data Analysis
4 Results and Discussion
4.1 Measurement Model
4.2 IPMA Analysis
5 Conclusion
References
The Impact of Organ Donation Information Dissemination on Social Media Towards Registration of Organ Donors: A Moderating Role of Family Discussion
1 Introduction
2 Literature Review
2.1 Information Adoption
2.2 Intention to Register as Organ Donors
2.3 Family Discussion
2.4 Hypotheses
3 Research Methodology
4 Findings
4.1 Demographics
4.2 Measurement Model Evaluation
4.3 Hypothesis Testing Results
4.4 Moderator Analysis
5 Discussion
6 Conclusion
References
Does Electronic Word-of-Mouth Still Contribute to Boosting Purchase Intention? Understanding the Role of Gender as a Moderator
1 Introduction
2 Literature Review and Hypotheses Development
2.1 Electronic Word-of-Mouth (e-WOM)
2.2 Interpersonal Influence
2.3 Trust
2.4 Purchase Intention
2.5 The Moderating Role of Gender
3 Research Methodology
4 Data Analysis
5 Discussion
6 Conclusion
References
Is the Elderly Prepared for Robotics Adoption at Home Care Centers? Evidence from an Empirical Study
1 Introduction
2 Research Model and Hypotheses Development
3 Research Methodology
4 Results
4.1 Measurement Model Assessment
4.2 Structural Model Assessment
5 Discussion and Conclusion
References
Advances in Information Security and Networking
Low Bit Error Rate and Complexity GMSK Demodulator Based on Soft Decision Decoder
1 Introduction
2 Materials and Methods
3 Results and Discussion
4 Conclusion
References
Design and Simulation of DVB Channel Coding and Modulation Using MATLAB
1 Introduction
2 DVB System Design
2.1 DVB Transmitter
2.2 DVB Receiver
3 Conclusions
References
Compact Multi Band Antenna Using DGS Technique
1 Introduction
2 Related Work
3 Antenna Design and Analysis
4 Simulation Result
5 Conclusion
References
Image De-Blurring and De-Noising by Using a Wiener Filter for Different Types of Noise
1 Introduction
2 Problem Statement and the Methodology
3 Basic Principles of the Wiener Filter
4 The Simulation Results
4.1 Simulate a Motion Blur-Image
4.2 Simulate a Motion Blur Images with Gaussian Noise
4.3 Simulate a Motion Blur Images with Salt & Peppers Noise
4.4 Simulate a Motion Blur Images with Speckle Noise
4.5 Simulate a Motion Blur Images with Poisson Noise
5 Performance Parameter to Measure Image Quality
6 Conclusions
References
An Overview of the Smart Grid Attributes, Architecture and Components
1 Introduction
2 Smart Grid Definition
3 Smart Grid vs Traditional Grid
4 The Attributes and Benefits of SG
5 Architecture of SG
5.1 Operations
5.2 Service Provider
5.3 Markets
5.4 Generation
5.5 Transmission
5.6 Distribution
5.7 End Users or Customer
6 Applications of SG
6.1 Advanced Metering Infrastructure (AMI)
6.2 Home Energy Management System (HEMS)
6.3 Distributed Energy Resources DERs
6.4 Electric Vehicles (EVs)
7 The Challenges to Smart Grid Implementation
7.1 Complexity
7.2 Efficiency
7.3 Consistency
7.4 Security
7.5 Standardization
7.6 Scalability
7.7 Interoperability
7.8 Self-healing
8 Conclusion
References
Advanced Security Technique in Presence of Open Communication System and Cyber Era
1 Introduction
2 Cyber Security
3 Confidence Policy Environment (CPE)
4 Suggested Trusted Model
5 Reputation Algorithms
6 Results
7 Conclusion
References
High Electromagnetic Protecting Features of Eco-Foamed Concrete Wall for 5G and LTE Users Service
1 Introduction
2 Materials and Method
2.1 Foamed Concrete Manufacturing
2.2 Foam Concrete Dielectric Properties
2.3 Measurement
3 Results and Discussion
4 Conclusion
References
Optimal Power Flow Based on Grey Wolf Optimizer: Case Study Iraqi Super Grid High Voltage 400 kV
1 Introduction
2 Optimal Power Flow (OPF) Problems
2.1 Generation Cost (GS) /h
2.2 Real Power Loss (RPL) MW
2.3 Voltage Deviation (VD) p.u.
2.4 Voltage Stability Index (VSI)
3 Grey Wolf Optimizer (GWO)
4 The Results and Discussions
4.1 Case #1: Minimization of Generation Cost on ISGHV
4.2 Case #2: Minimization of Power Loss on ISGHV
4.3 Case #3: Minimization of Voltage Deviation on ISGHV
4.4 Case #4: Minimization of Voltage Stability Index (L-index) on ISGHV
5 Conclusion
Appendix A
References
Developing a Cipher Algorithm Based on Chaotic Neural Network
1 Introduction
2 Related Works
3 Chaotic Neural Networks (CNN)
4 Overall System Model
5 Performance of Chaotic Neural Network (CNN) Cipher
6 Conclusions
References
Intrusion Detection System Using Ensemble Machine Learning in Cloud Environment
1 Introduction
1.1 Related Work
2 Intrusion Detection System
3 Proposed Methodology
3.1 UNSW-NB15 Dataset
3.2 Dataset Pre-processing and Feature Selection
3.3 Training the Model
4 Performance Results of Proposed Model
5 Conclusions
References
The Mobile Attacks Under Internet of Things Networks
1 Introduction
1.1 RPL Overview
2 Related Work
3 Methodology
4 Results and Evaluation
4.1 DIS Attack Evaluation
4.2 Rank Decreased Attack Evaluation
5 Conclusion
References
Performance and Complexity Tradeoffs of Feature Selection on Intrusion Detection System-Based Neural Network Classification with High-Dimensional Dataset
1 Introduction
2 Background
2.1 CSE-CICIDS2018 of AWS Dataset
2.2 Feature Selection Method
2.3 Neural Network Classifier
2.4 Evaluation Measurement
3 Our Methodology
4 Results
4.1 First Experiment
4.2 Second Experiment
5 Comparison with Other Methods
6 Conclusion and Future Work
References
IoT Wireless Protocol with 802.11AH: A Study of Interference Mitigation Techniques
1 Introduction
2 Related Work
2.1 Studies on Interference Mitigation Techniques
2.2 PHY Structure of IEEE 802.11ah
2.3 MAC Structure of IEEE 802.11ah
2.4 Interference of IEEE 802.11ah
3 Methodology
4 Results and Discussion
5 Conclusion
References
Management of IoT Devices Data Security Using Blockchain and Proxy Re-encryption Algorithm
1 Introduction
2 Literature Survey
3 Proposed System Design
3.1 Implementation Procedure:
4 Algorithm Used
5 Results and Discussions
6 Conclusion
References
Proposed Website Architecture with Embedded Augmented Reality to Discover Cultural Heritage
1 Introduction
2 Research Background
2.1 The Web and AR
2.2 AR for Tourism
3 Methodology
3.1 Proposed System Architecture
3.2 Design Structure
3.3 Implementation
4 Evaluation and Result
4.1 Website Performance Evaluation
4.2 Recognition Evaluation Results
5 Conclusion
References
On-Demand Model and Smart Contract Design for Monetizing IoT Data
1 Introduction
2 Related Work
3 Proposed Model
4 Process and Smart Contract Design
4.1 Device Registration on the Blockchain
4.2 Device Data Sharing
4.3 Buyer Data Access and Payment
4.4 Smart Contract Interface Methods
5 Conclusion and Future Work
References
Grasshopper Optimization Algorithm Based Spam Detection System Using Multi-Objective Wrapper Feature Selection and Neural Network Classification
1 Introduction
2 Background
2.1 Algorithm Proposed
2.2 Multiple Criteria Decision
3 Related WorK
4 Methodology of the Study
4.1 Pre-processing Stage
4.2 The Feature Selection stage
4.3 The Classification Stage
5 Performance Evaluation
6 Evaluation of Proposed and Discussion
7 Conclusion
References
A Blockchain and Proxy Re-encryption Based Approach for IoT Data Security: A Review
1 Introduction
2 Literature Review
3 Analysis of Literature
4 Conclusion
References
An Overview of Blockchain-Based IoT Architectures and Designs
1 Introduction
2 Overview on Blockchain
2.1 Nodes
2.2 Transactions
2.3 Block
3 IoT Layered Architecture
3.1 Application Layer
3.2 Application Support Layer
3.3 Network Layer
3.4 The Devices Layer
4 IoT on the Blockchain
4.1 Centralised IoT Architecture
4.2 Blockchain-Based IoT Architectures
5 Conclusion
References
A Supervised Model to Detect Suspicious Activities in the Bitcoin Network
1 Introduction
2 Related Work
3 Proposed Model
3.1 Data Preparation
3.2 Feature Extraction
4 Experimental Settings
4.1 Experimental Results
5 Conclusion
References
Awareness of Phishing Attacks in the Public Sector: Review Types and Technical Approaches
1 Introduction
2 Related Studies
3 Phishing Types
3.1 Phishing Media
3.2 Page Hijacking
3.3 Calendar Phishing
4 Technical Approaches
4.1 Spear Phishing
4.2 Whaling
4.3 Cross-Site Malicious CAPTCHA Attack
4.4 Social Engineering
4.5 Drive-By Download
4.6 Typo Squatting
4.7 Sound Squatting
4.8 Click Jacking
4.9 Malicious Browsing Extensions
4.10 Session Fixation
5 Conclusion
References
Color Image Encryption and Decryption by Using Optical Scanning Cryptography Method
1 Introduction
2 Optical Image Encryption
3 Optical Scanning Cryptography (OSC)
3.1 Image Encryption Stage
3.2 Image Decryption Stage
4 Experimental Work
5 Simulation and Discussion
6 Steps Involved in Achieving OSC Algorithm
7 Results and Discussion
8 Conclusions
References
Modeling and Performance Evaluation of Solar Cells Using I-V Curve Analysis
1 Introduction
2 Solar Cell Characteristics and Parasitic Resistances
2.1 Parasitic Resistances in Solar Cell
3 Methodology
3.1 Simulation Parameters
3.2 Simulation Modeling
4 Result and Discussion
4.1 Comparison Results of I-V Curve with Considering Rs in Solar Cell Model
5 Conclusions
References
Author Index

Citation preview

Lecture Notes in Networks and Systems 584

Mohammed A. Al-Sharafi Mostafa Al-Emran Mohammed Naji Al-Kabi Khaled Shaalan   Editors

Proceedings of the 2nd International Conference on Emerging Technologies and Intelligent Systems ICETIS 2022 Volume 1

Lecture Notes in Networks and Systems Volume 584

Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Advisory Editors Fernando Gomide, Department of Computer Engineering and Automation—DCA, School of Electrical and Computer Engineering—FEEC, University of Campinas— UNICAMP, São Paulo, Brazil Okyay Kaynak, Department of Electrical and Electronic Engineering, Bogazici University, Istanbul, Turkey Derong Liu, Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, USA Institute of Automation, Chinese Academy of Sciences, Beijing, China Witold Pedrycz, Department of Electrical and Computer Engineering, University of Alberta, Alberta, Canada Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Marios M. Polycarpou, Department of Electrical and Computer Engineering, KIOS Research Center for Intelligent Systems and Networks, University of Cyprus, Nicosia, Cyprus Imre J. Rudas, Óbuda University, Budapest, Hungary Jun Wang, Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

The series “Lecture Notes in Networks and Systems” publishes the latest developments in Networks and Systems—quickly, informally and with high quality. Original research reported in proceedings and post-proceedings represents the core of LNNS. Volumes published in LNNS embrace all aspects and subfields of, as well as new challenges in, Networks and Systems. The series contains proceedings and edited volumes in systems and networks, spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems, Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems, Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution and exposure which enable both a wide and rapid dissemination of research output. The series covers the theory, applications, and perspectives on the state of the art and future developments relevant to systems and networks, decision making, control, complex processes and related areas, as embedded in the fields of interdisciplinary and applied sciences, engineering, computer science, physics, economics, social, and life sciences, as well as the paradigms and methodologies behind them. Indexed by SCOPUS, INSPEC, WTI Frankfurt eG, zbMATH, SCImago. All books published in the series are submitted for consideration in Web of Science. For proposals from Asia please contact Aninda Bose ([email protected]).

More information about this series at https://link.springer.com/bookseries/15179

Mohammed A. Al-Sharafi Mostafa Al-Emran Mohammed Naji Al-Kabi Khaled Shaalan •

•

•

Editors

Proceedings of the 2nd International Conference on Emerging Technologies and Intelligent Systems ICETIS 2022 Volume 1

123

Editors Mohammed A. Al-Sharafi Universiti Teknologi Malaysia Johor, Malaysia

Mostafa Al-Emran The British University in Dubai Dubai, United Arab Emirates

Mohammed Naji Al-Kabi Al-Buraimi University College Al Buraimi, Oman

Khaled Shaalan The British University in Dubai Dubai, United Arab Emirates

ISSN 2367-3370 ISSN 2367-3389 (electronic) Lecture Notes in Networks and Systems ISBN 978-3-031-25273-0 ISBN 978-3-031-25274-7 (eBook) https://doi.org/10.1007/978-3-031-25274-7 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

Over the past ten years, no one has disputed the contribution that intelligent systems and emerging technologies have made to developing digital societies and transforming the knowledge-based economy. The high number of practical new technologies is causing a rapid increase in experimental and theoretical outcomes. These technologies have played a crucial part in many industries, such as health care, education, tourism, and marketing. The main aim of the 2nd International Conference on Emerging Technologies and Intelligent Systems (ICETIS 2022) is to provide a forum for academics, researchers, and developers from both academia and industry to share and exchange their latest research contributions and identify practical implications of emerging technologies to advance the wheel of these solutions for global impact. In line with the Fourth Industrial Revolution goals and its impact on sustainability development, ICETIS 2022 is devoted to increase the understanding and impact of emerging technologies on individuals, organizations, and societies, and how intelligent systems have recently reshaped these entities. ICETIS 2022 focuses on the recent innovations in Artificial Intelligence (AI) and Data Science, Advances in Information Security and Networking, Intelligent Health Informatics, Management Information Systems, Educational Technologies, and recent trends in Software Engineering. The ICETIS 2022 was able to attract 200 submissions from 33 different countries across the globe. From the 200 submissions, we accepted 117 submissions, which represents an acceptance rate of 58.5%. Out of the 117 accepted submissions, 56 were selected to be published in this volume. The accepted papers in this volume were categorized into two main themes: Management Information Systems and Advances in Information Security and Networking. Each submission is reviewed by at least two reviewers, who are considered experts in the related submitted paper. The evaluation criteria include several issues, such as correctness, originality, technical strength, significance, quality of presentation, interest, and relevance to the conference scope. The conference proceedings is published in Lecture Notes in Networks and Systems Series by Springer, which has a high SJR impact.

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Preface

We acknowledge all those who contributed to the success of ICETIS 2022. We would also like to express our gratitude to the reviewers for their valuable feedback and suggestions. Without them, it was impossible to maintain the high quality and success of ICETIS 2022. As gratitude for their efforts, ICETIS 2022 is partnered with Publons to recognize the reviewers’ contribution to peer-review officially. This partnership means that reviewers can opt-in to have their reviews added to their Publons profile. Mohammed A. Al-Sharafi Mostafa Al-Emran Mohammed N. Al-Kabi Khaled Shaalan

Organization

Conference General Chairs Mostafa Al-Emran Khaled Shaalan

The British University in Dubai, UAE The British University in Dubai, UAE

Conference Organizing Chair Mohammed A. Al-Sharafi

Universiti Teknologi Malaysia, Malaysia

Program Committee Chair Mohammed N. Al-Kabi

Al Buraimi University College, Oman

Publication Committee Chairs Mohammed A. Al-Sharafi Mostafa Al-Emran

Universiti Teknologi Malaysia, Malaysia The British University in Dubai, UAE

Conference Tracks Chairs Garry Tan Wei Han Vitaliy Mezhuyev Abdullah B. Nasser Ahmed Shihab Albahri Amr Yassin Baraq Ghaleb Ibrahim Arpaci

UCSI University, Malaysia FH Joanneum University of Applied Sciences, Austria University of Vaasa, Finland University of Information Technology and Communications, Iraq Ibb University, Yemen Edinburgh Napier University, UK Bandirma Onyedi Eylul University, Turkey

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Organization

Members of Scientific Committee Abdallah Namoun Abdullah Nasser Abdulmajid Mohammed Aldaba Abdul Rahman Al-Sewari Ahmed M. Mutahar Aisyah Ibrahim Akhyari Nasir Alaa A. D. Taha Ali Nasser Ali AL-Tahitah Ali Qasem Saleh Al-Shetwi Ameen A. Ba Homaid Amir A. Abdulmuhsin Amr Abdullatif Yassin Baraq Ghaleb Basheer Mohammed Al-haimi Bokolo Anthony Jnr Dalal Abdulmohsin Hammood Eissa M. Alshari Fadi A. T. Herzallah Fathey Mohammed Garry Wei Han Tan Gonçalo Marques Hasan Sari Heider A. M. Wahsheh Hussam S. Alhadawi Hussein Mohammed Esmail Abu Al-Rejal Ibrahim Arpaci Joseph Ng Joshua A. Abolarinwa Kamal Mohammed Alhendawi Kamal Karkonasasi Khaled Shaalan Marwah Alian Marwan Saeed Saif Moqbel

Islamic University of Medina, Saudi Arabia University of Vaasa, Finland International Islamic University Malaysia, Malaysia Universiti Malaysia Pahang, Malaysia Management and Science University, Malaysia Universiti Malaysia Pahang, Malaysia University College TATI, Kemaman, Terengganu, Malaysia University of Mosul, Iraq Universiti Sains Islam Malaysia, Malaysia Fahad Bin Sultan University, Saudi Arabia Universiti Malaysia Pahang, Malaysia University of Mosul, Iraq Ibb University, Yemen Edinburgh Napier University, UK Hebei University, Baoding, China Norwegian University of Science and Technology, Norway Middle Technical University, Iraq Ibb University, Yemen Palestine Technical University–Kadoorie, Palestine Universiti Utara Malaysia, Malaysia UCSI University, Malaysia Universidade da Beira Interior, Portugal Universiti Tenaga Nasional, Malaysia King Faisal University, Saudi Arabia Dijlah university college, Iraq University Utara Malaysia, Malaysia Gaziosmanpasa University, Turkey UCSI University, Malaysia Namibia University of Science and Technology, Namibia Al-Quds Open University, Faculty of Management, Palestine Universiti Malaysia Kelantan, Malaysia The British University in Dubai, UAE Hashemite University, Jordan Ibb University, Yemen

Organization

Mikkay Wong Ei Leen Mohamed Elwakil Mohammed A. Al-Sharafi Mohammed A. Alsaih Mohammed Ahmed Talab Mohammed Adam Kunna Azrag Mohammed N. Al-Kabi Mostafa Al-Emran Mukhtar A. Kassem Nejood Hashim Al-Walidi Noor Akma Abu Bakar Noor Al-Qaysi Noor Suhana Sulaiman Osama Mohammad Aljarrah Osamah A. M. Ghaleb Qasim AlAjmi Samer Ali Alshami Taha Sadeq Tang Tiong Yew Vitaliy Mezhuyev

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Sunway University, Malaysia University of Cincinnati, USA Universiti Teknologi Malaysia, Malaysia University Putra Malaysia, Malaysia Almaarif University College, Iraq Universiti Teknologi MARA (UiTM), Malaysia Al Buraimi University College, Oman The British University in Dubai, UAE Universiti Teknologi Malaysia, Malaysia Sanaa University, Yemen Tunku Abdul Rahman University College (TARC), Malaysia Universiti Pendidikan Sultan Idris, Malaysia University College TATI, Kemaman, Terengganu, Malaysia University of Massachusetts Dartmouth, USA Mustaqbal University, Saudi Arabia A’ Sharqiyah University, Oman Universiti Teknikal Malaysia Melaka, Malaysia Universiti Tunku Abdul Rahman, Malaysia Sunway University, Malaysia FH Joanneum University of Applied Sciences, Austria

Publicity and Public Relations Committee Hasan Sari Noor Akma Abu Bakar

Universiti Tenaga Nasional, Malaysia Universiti Malaysia Pahang, Kuantan, Malaysia

Finance Chair Taha Sadeq

Universiti Tunku Abdul Rahman, Malaysia

Contents

Management Information Systems Keys to Successful Product and Process Innovation Through Soft TQM: An ANN Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ai-Fen Lim

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Big Data Techniques and Internal Control: Evidence from Egypt . . . . . Ahmed Elmashtawy and Mohamed Salaheldeen

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What is Stopping You from Using Mobile Payment in Oman? . . . . . . . Mohamed Musallam Khasib Al Rawahi, Hooi Cheng Eaw, and Garry Wei Han Tan

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How Does User-Generated Content Affect Users’ Obsessive Use of Lifestyle-Sharing Mobile Social Networking Site? . . . . . . . . . . . . . . . . . Yun-Peng Yuan, Garry Wei-Han Tan, Eugene Cheng-Xi Aw, Tat-Huei Cham, and Keng-Boon Ooi Adoption of DeLone and McLean’s Model of Information System Success to Explore Customers’ Repurchase Intention in a Chinese Cross-Border E-commerce Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhiying Hou, Yet Mee Lim, and Garry Wei-Han Tan Social Media Influencer Marketing: How Influencer Content Help? . . . Shuzhen Liu, Eugene Cheng-Xi Aw, and Garry Wei-Han Tan Impulsive Buying Behaviour on Social Commerce: Moderated by Cultural Dimension, and Perceived Trust . . . . . . . . . . . . . . . . . . . . . . . Alexa Min-Wei Loi, Keng-Boon Ooi, and Garry Wei-Han Tan Conceptualizing Business Intelligence (BI) Adoption in SME to Gain Competitive Advantage: Effects of Organizational Mindfulness, Inter-organizational Relationship and Technology Orientation . . . . . . . Farzana Naznen and Wei Lee Lim

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Backpackers’ Adoption Intention of Mobile Hotel Reservation (MHR): Are Costs Important? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 WeiLee Lim, YouSheng Tan, and BoonKiat Ang Analyzing Purchasing Behavior of NIO’s Customers Based on Theory of Planned Behavior in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 Han Fengfan and Choy Johnn Yee Digital Marketing in the Perspective of Omnichannel Retailing for Customer Engagement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 Xi Wang and Ganesh A. L. Ramasamy What Drives Consumer’s Self-disclosure in Mobile Payment: An Investigate in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 Shao Min Wu, Garry Wei-Han Tan, and Eugene Cheng-Xi Aw Identifying the Factors that Influence Users’ Intentions to Use Mobile Payment Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 Alaa S. Jameel, Sameer S. Hamdi, and Abdul Rahman Ahmad Behavioral Intention and Actual Use of Mobile Learning During the COVID-19 Pandemic in the Higher Education System . . . . . . . . . . . . . . 155 Alaa S. Jameel, Mohammed A. Karem, and Ahmed S. Alheety Effective Risk Management as a Mediator to Enhance the Success of Construction Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168 Mukhtar A. Kassem and Kherun Nita Ali A Stimulus-Organism-Response Paradigm to Word-of-Mouth and Continuance Intention of Mobile Application . . . . . . . . . . . . . . . . . . . . . 192 Yee Von Lim, Shi Ling Ng, Wei Lin Oh, Wan Ying Tan, Yi Zhe Too, Xiu Ming Loh, and Garry Wei Han Tan Determining the Factors that Affect Resistance to Digital News Subscription During the COVID-19 Pandemic . . . . . . . . . . . . . . . . . . . . 205 Luk Sheng Chin, Wei Heng Loh, Ming Fong Tan, Zhi Hui Tan, Xiu Ming Loh, Voon Hsien Lee, and Garry Wei Han Tan Adoption Decision and Alleviation of Mobile Payment . . . . . . . . . . . . . . 218 Kok Lin Gan, Chuen Khee Pek, Yet Mee Lim, and Fang Ee Foo Critical Success Factors of Knowledge Management in Higher Education: A Systematic Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228 Ghazala Bilquise and Khaled Shaalan Experience with Mobile Phone Technology: A Comparison Between Two Brands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240 Yet-Mee Lim, Choi-Meng Leong, Teck-Chai Lau, and Chuen-Khee Pek

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The Influence of FinTech on Financial Sector and Economic Growth: An Analysis of Recent Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251 Mosharrof Hosen, Tat-Huei Cham, Hooi-Cheng Eaw, Vasanthan Subramaniam, and Hassanudin Mohd Thas Thaker Social Media Technology: The Influences on Online Impulse Buying Behaviour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264 Hanyang Zhang, Yet-Mee Lim, Choi-Meng Leong, and Chuen-Khee Pek Automated Project Progress Monitoring in Construction Projects: A Review of Current Applications and Trends . . . . . . . . . . . . . . . . . . . 274 Yaser Gamil, Hamed Alhajlah, and Mukhtar A. Kassem Understanding the Efficiency of Gamification on the Engagement Intention of the Customers with Mobile Payment Systems . . . . . . . . . . . 294 Mohannad Moufeed Ayyash and Fadi Herzallah Customer Engagement in Instagram: The Mediating Role of Perceived Advertising Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311 Poh Kiong Tee, Deeparechigi Vashu, Ling Chai Wong, and Chee Pung Ng Artificial Intelligence (AI), Blockchain, and Cryptocurrency in Finance: Current Scenario and Future Direction . . . . . . . . . . . . . . . . . . 322 Mosharrof Hosen, Hassanudin Mohd Thas Thaker, Vasanthan Subramaniam, Hooi-Cheng Eaw, and Tat-Huei Cham Common Data Environment: Bridging the Digital Data Sharing Gap Among Construction Organizations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333 Yong Jia Tan, Zafira Nadia Maaz, Shamsulhadi Bandi, and Prescilla Anak Palis A Study of the Perceptions of Last-Mile Delivery Towards the Adoption of IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343 Aimi Amirah Khairuddin, Emelia Akashah P. Akhir, Nurul Aida Osman, and Norshakirah Aziz Blockchain Technology in Malaysian Estate Distribution: A Systematic Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 352 Syahirah Balqis Anuar, Fatin Afiqah Md Azmi, and Syaza Nur Syazwana Sidek How to Become King? Insights from the Importance Performance Map Analysis of User-Based Authenticity . . . . . . . . . . . . . . . . . . . . . . . 366 F.-E. Ouboutaib, A. Aitheda, and S. Mekkaoui

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Contents

The Impact of Organ Donation Information Dissemination on Social Media Towards Registration of Organ Donors: A Moderating Role of Family Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377 Faerozh Madli, Stephen Laison Sondoh Jr., Andreas Totu, Sharifah Nurafizah Syed Annuar, Yuzainy Janin, Rudy Ansar, Yusman Yacob, and Tat-Huei Cham Does Electronic Word-of-Mouth Still Contribute to Boosting Purchase Intention? Understanding the Role of Gender as a Moderator . . . . . . . . 392 Boon-Liat Cheng, Tat-Huei Cham, Yuan Feng Cai, Anuja Chalke, and Michael M. Dent Is the Elderly Prepared for Robotics Adoption at Home Care Centers? Evidence from an Empirical Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404 Abdulkarim Rashed and Mostafa Al-Emran Advances in Information Security and Networking Low Bit Error Rate and Complexity GMSK Demodulator Based on Soft Decision Decoder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419 Ali Mohammed Kadhim, Wisam Subhi Al-dayyeni, and Majid S. Naghmash Design and Simulation of DVB Channel Coding and Modulation Using MATLAB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 428 Wisam Subhi Al-dayyeni, Ali Mohammed Kadhim, Ola Hussein Abd Ali Alzuabidi, and Majid S. Naghmash Compact Multi Band Antenna Using DGS Technique . . . . . . . . . . . . . . 438 Zinah Tareq Nayyef, Zena Hussain Fahad, Wisam Raad, and Ayman Mohammed Ibrahim Image De-Blurring and De-Noising by Using a Wiener Filter for Different Types of Noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 451 Daniya Amer Jassim, Sabbar Insaif Jassim, and Nazar Jabbar Alhayani An Overview of the Smart Grid Attributes, Architecture and Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 461 Murtadha Al-Kaabi, Bahaa Hussein Al Igeb, and Sarah Yahya Ali Advanced Security Technique in Presence of Open Communication System and Cyber Era . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 472 Oday Kamil Hamid, Riyadh Bassil Abduljabbar, Aya Qusay Dawood, Majid Salal Naghmash, and Nazar Jabbar Alhyani High Electromagnetic Protecting Features of Eco-Foamed Concrete Wall for 5G and LTE Users Service . . . . . . . . . . . . . . . . . . . . . . . . . . . 481 Amenah Emad Mohammed Redha, Majid S. Naghmash, and Yasameen M. Mohammed

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Optimal Power Flow Based on Grey Wolf Optimizer: Case Study Iraqi Super Grid High Voltage 400 kV . . . . . . . . . . . . . . . . . . . . . . . . . 490 Murtadha AL-Kaabi, Sinan Q . Salih, Al Igeb Bahaa Hussein, Virgil Dumbrava, and Mircea Eremia Developing a Cipher Algorithm Based on Chaotic Neural Network . . . . 504 Tariq Adnan Fadil, Hilal A. Fadhil, and Sabbar Insaif Jasim Intrusion Detection System Using Ensemble Machine Learning in Cloud Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 513 Saba Manzoor, Musheer Ahmad, and Hussam S. Alhadawi The Mobile Attacks Under Internet of Things Networks . . . . . . . . . . . . 523 Colin Kean, Baraq Ghaleb, Bruce Mcclelland, Jawad Ahmad, Isam Wadhaj, and Craig Thomson Performance and Complexity Tradeoffs of Feature Selection on Intrusion Detection System-Based Neural Network Classification with High-Dimensional Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 533 Qutaiba Alasad, Maytham M. Hammood, and Shahad Alahmed IoT Wireless Protocol with 802.11AH: A Study of Interference Mitigation Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 543 Azamuddin Abdul Rahman, Wei Siang Hoh, Siti Aishah Zakaria, and Noor Akma Abu Bakar Management of IoT Devices Data Security Using Blockchain and Proxy Re-encryption Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 551 Nachiket Mahamuni, Hrishikesh Nikam, Gaurav Pattewar, Omkar Loka, and Rachana Patil Proposed Website Architecture with Embedded Augmented Reality to Discover Cultural Heritage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 559 Harith A. Hussein, Saif Muhannad Maher, Moceheb Lazam Shuwandy, Armaneesa Naaman Hasoon, M. A. Ahmed, and Mahmood M. Salih On-Demand Model and Smart Contract Design for Monetizing IoT Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 570 Rowanda Ahmed, Mansoor Abdulhak, and Mohammed S. Hassan Grasshopper Optimization Algorithm Based Spam Detection System Using Multi-Objective Wrapper Feature Selection and Neural Network Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 576 Sanaa A. A. Ghaleb, Mumtazimah Mohamad, Waheed A. H. M. Ghanem, Akibu Mahmoud Abdullahi, Abdullah B. Nasser, Sami Abdulla Mohsen Saleh, Humaira Arshad, Abiodun Esther Omolara, Oludare Isaac Abiodun, and Mohamed Ghetas

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A Blockchain and Proxy Re-encryption Based Approach for IoT Data Security: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 587 Nachiket Mahamuni, Gaurav Pattewar, Hrishikesh Nikam, Omkar Loka, and Rachana Patil An Overview of Blockchain-Based IoT Architectures and Designs . . . . . 596 Mwrwan Abubakar, Hisham Ali, Baraq Ghaleb, Isam Wadhaj, and William J. Buchanan A Supervised Model to Detect Suspicious Activities in the Bitcoin Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 606 Khaled Gubran Al-Hashedi, Pritheega Magalingam, Nurazean Maarop, Ganthan Narayana Samy, Fiza Binti Abdul Rahim, Mohana Shanmugam, and Mohammad Kamrul Hasan Awareness of Phishing Attacks in the Public Sector: Review Types and Technical Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 616 Mohammed Fahad Alghenaim, Nur Azaliah Abu Bakar, and Fiza Abdul Rahim Color Image Encryption and Decryption by Using Optical Scanning Cryptography Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 630 Mohamed J. Albermani, Eman M. Al-Hilo, and Kawther H. Al-khafaji Modeling and Performance Evaluation of Solar Cells Using I-V Curve Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 643 Abdulwahab A. Q. Hasan, Ammar Ahmed Alkahtani, and Nowshad Amin Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 651

Management Information Systems

Keys to Successful Product and Process Innovation Through Soft TQM: An ANN Approach Ai-Fen Lim(B) UCSI University, 56000 Cheras, Kuala Lumpur, Malaysia [email protected]

Abstract. To transform the nation’s manufacturing industry into one that is robust and inventive, soft TQM must be implemented. Soft TQM practices are viewed as a vital management strategy for product (PD) and process (PC) innovation that will enable firms to successfully navigate their digital transition. The goal of this research is to build a holistic knowledge of the impact of five soft TQM practices including top management commitment (TMC), empowerment (EM), training (TR), involvement (IN), and teamwork (TE) on PD and PC among manufacturers. Using 222 valid data, the two-hidden-layer deep learning ANN results show that IN has the significant impact on PD with a 100% normalized relative importance (NRI), followed by EM (78.35%), whereas TR (100%), IN (95.63%), TE (91.36%), have a significant influence on PC. Firm size, interestingly, had a significantly influenced on PD, but an insignificant effect on PC. The study’s findings offer for a more thorough understanding of the value of soft TQM, with the goal of promoting the development of novel knowledge using DC theory for PD and PC. Keywords: Total quality management · Product innovation · Process innovation · Artificial neural network · Dynamic capability

1 Introduction Business innovation remains a dynamic driver for business development and a substantial contributor to a country’s economic growth [22]. As part of the 12th Malaysia plan, which is the strategic direction for Malaysia’s development from 2021 to 2025, the Malaysian government places a high priority on enhancing the innovative capabilities of manufacturers. To ensure the success of innovation, excellent human capital management, such as the deployment of soft TQM practices, is deemed vital [12]. However, many manufacturing firms have been unwilling to invest in human capital development despite its importance [9]. According to the Malaysian shared prosperity vision 2030, the manufacturing sector, continues to lag in implementing knowledge-based activities and technology programs [9]. It can be noted that the prevalence of innovation among Malaysia’s manufacturers remains low, accounting for 69.60% in terms of product, process, marketing, and administration in 2018 [9]. As such, there is an urgent need to © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (Eds.): ICETIS 2022, LNNS 584, pp. 3–13, 2023. https://doi.org/10.1007/978-3-031-25274-7_1

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bring the debate concerning management strategies, practices, and investments to the center of public and academic attention to enhance manufacturer’s innovation. Seeing that Malaysia’s manufacturing industry continues to be an important sector, accounting for approximately 22.6% of Malaysia’s Gross Domestic Product (GDP) in 2019 [9]. When compared to 2018, the manufacturing sector boosted its contribution to GDP by approximately RM11.5 billion in 2019. The correlation between soft TQM practices and product (PD) and process (PC) innovation is supported by dynamic capacity (DC) theory. Seeing as enhancing organizational competencies (i.e., the development of human capital), obtaining new knowledge, and boosting the company’s inventiveness, all can significantly promote innovation [10]. This research employed DC as a supporting theory to develop a research model that examines the relationship between soft TQM practices, PD and PC. Although [1] and [18] have demonstrated that TQM practices improve innovation performance, these studies have not tended to concentrate on the soft TQM for innovation success. Additionally, the target population for several studies was not exclusively comprised of manufacturing firm [11]. [2] investigated the relationships between soft TQM and innovation, but they focused on the soft TQM as a whole rather than the soft dimensions of TQM separately. Hence, the following research questions are raised in this study: RQ1. Do soft TQM practices have a significant effect on PD and PC? RQ2. Does the size of the firm affect the outcome of PD and PC? These questions are addressed by developing a research model that connects multidimensional soft TQM practices on PD and PC and validating the model using an ANN approach.

2 Theoretical Background and Literature Review 2.1 DC, Soft TQM Practices, PD, and PC DC refers to a business’s ability to integrate, build, and restructure internal and external skills in response to quickly changing circumstances [10]. Seeing that innovation could well be promoted by strengthening organizational capabilities, acquiring new knowledge, and fostering the creativity of the business [10]. With well-developed DC, firms can respond to new business opportunities and challenges through successful integration and restructuring of existing resources [11]. Furthermore, emphasize DC, a business is in its capacity to create internal capabilities, such as human behavioral-related QM practices, to accomplish PD and PC [11]. Soft TQM refers to the management of individuals, their behaviors, perceptions, interrelations, and leadership [2]. Indeed, people who make quality a reality [12]. Recognizing the necessity of implementing soft TQM practices for organizational development, this study illuminates the proposed five soft TQM practices that can assist narrow the TQM literature gap. Top management commitment (TMC) refers to a leader’s ability to inspire and motivate subordinates to achieve the organization’s objectives [19]. Empowerment (EM) is the delegation of responsibility and authority to employees for them to develop and maximize the use of their abilities and expertise for the benefit

Keys to Successful Product and Process Innovation

5

of the organization and themselves [19]. Training (TR) equips and supports employees before allowing them to progress their careers and produce high-quality results [19]. Involvement (IN) entails the construction of both top-down and bottom-up communication networks that enable employees provide feedback on quality control issues and direct them in decision-making processes [19]. Teamwork (TE) provides encouragement and self-efficacy to employees, which in turn satisfies workplace social requirements [8]. Innovation is believed to be an accelerator for the development of manufacturers. The pair of PD and PC is used as its widespread recognition for competitiveness and sustainability performance among manufacturers [7, 15]. PD is described as the introduction of a new product or service that extends the life cycle or boosts the competitiveness of existing products or services. While PC is described as the introduction of new or valueadded manufacturing, administrative processes, and supply chain in an organization for increasing production capabilities, lowering production costs, and gaining a competitive edge [11].

3 Research Model Development 3.1 TMC, PD, and PC Effective leaders’ ways to generating new information and carrying out their responsibilities should foster innovation by significantly influencing employees’ work practices [7]. Strong leadership will naturally close the gap between various levels structure, enabling for excellent information exchange among employees [13]. Furthermore, effective leadership skills can expedite the flow of information, resulting in increased PD. Likewise, to achieve innovation goals, leaders build an environment that encourages creativity and implements innovative approaches [15]. Leaders must ensure that the organization has an integrated framework that encourages all employees to participate in the PC. This can be accomplished through emphasizing discovery, fostering a learning environment, empowering employees, and encouraging open-mindedness in accepting differences, all of which contribute to a conducive innovation climate. 3.2 EM, PD, and PC [12] shown that EM can influence OI, including PD, since when employees are given sufficient autonomy to make more innovative improvements, they experience satisfaction from making these changes. Additionally, EM has had a great impact on employees’ propensity to participate in innovative activities such as PD, where people demonstrate their passion for exploring new opportunities and approaches while performing their job [11]. Given that empowered employees are more likely to feel increased freedom and possibilities for discovering and sharing new approaches and processes to improve innovation performance [24]. However, a study from [12] does not conclusively establish a direct link between EE and PC. Thus, it is necessary to analyses EE to optimize the PC.

6

A.-F. Lim

3.3 TR, PD, and PC According to [13], adequate training facilitates the process of delivering new ideas and the ability to apply new knowledge to the development of new products. [11] discovered that well-trained personnel exhibit increased confidence, are more receptive to change, and are more eager to share new information with others to create new products. [7] highlighted that when people are empowered appropriately, the likelihood of them recognizing new opportunities and inventing new products is significantly improved. [11] stressed the necessity of training for all employees to appropriately implement learned organizational techniques and foster new knowledge development. Given that well-trained personnel have valuable information and experience to contribute to the development of more efficient working techniques [11]. Employees will be able to share new ideas, skills, and expertise more freely to achieve PC with a better and more favorable learning environment. 3.4 IN, PD, and PC IN has aided in the introduction of innovative products in manufacturing firms [6]. [1] demonstrated how incorporating IN into human resource management could help foster a culture of knowledge sharing and improve innovation performance in manufacturing businesses. Additionally, IN is seen as a critical factor that promotes both gradual and radical PD [6]. IN fosters a positive interaction atmosphere that facilitates the flow of information and knowledge, enabling employees to develop better PC capabilities [11]. Because employees have the freedom to freely exchange ideas and information, they will act constructively to translate ideas and knowledge into more effective activities that will stimulate innovation [7]. Consequently, successful employee participation inherently improves PC. 3.5 TE, PD, and PC Meanwhile, TE is claimed to be a critical factor in determining the success of an OI, as human development is interwoven into the process of innovation product creation. TE can increase OI by integrating the employee’s creative problem-solving abilities into product development [10]. To attain PC, organizations should optimize business process design by fostering TE [13]. Knowledge and process management within a team entail directing all employees’ knowledge and efforts toward achieving PC [5]. Organizational objectives can be met by carefully synchronizing all operational team operations to achieve the specified level of innovation, such as PC [13].

4 Methodology 4.1 Sampling Method and Data Collection The conceptual framework is shown in Fig. 1. The study’s respondents were Malaysia’s manufacturers who were listed in the Federation of Malaysian Manufacturers (FMM)

Keys to Successful Product and Process Innovation

7

Directory (2018 edition). Manufacturers from four states and one federal territory, including Selangor, Perak, Kuala Lumpur, Pulau Pinang, and Johor, were chosen. Given that the sampled areas had the highest industrialization density and the most manufacturing firms [9, 17]. The intended respondents are managers who have adequate expertise about TQM. A questionnaire was created, pre-tested, revised, and then distributed 32 samples for pilot test. After confirming the reliability of survey instrument from pilot test, actual test was conducted. Actual data was screened, providing a valid of 222 samples that could be used for statistical analysis. 4.2 Operationalization of Research Instruments The instrument for the five soft TQM practices consisted of a total of 25 items [19]. While 10 items were employed to gain access to PD and PC [11]. All items in this research were measured using a seven-point Likert Scale. The size of the firm is used as a control variable in this study, which is divided into three categories based on the number of employees: 200. Given that [23] believe that larger organizations are better equipped in terms of facilities and have more innovative capability, company size is an adequate control. [16], while, argue that small businesses can be more innovative since they have greater flexibility and adaptability. Furthermore, larger firms may work, and function differently as compared to smaller firms, explaining the disparities in TQM practices implemented by different firm sizes [17].

Fig. 1. Conceptual framework

8

A.-F. Lim

5 Data Analysis 5.1 Sampling Method and Data Collection For demographic information, there were 49.10% males and 50.90% females among the respondents. Most respondents (52.25%) were in middle management, followed by junior (24.32%) and top (23.42%) management. Most of the firms had been in operation for 5–20 years (48.65%), followed by more than 20 years (34.68%), and less than 5 years (16.67%). The study found that medium-sized (75–200) firms more than small (75) (30.63%) and large (>200) firms (24.77%). 56.31% of the firms had ISO 9001 certification, followed by those planning for ISO 9001 certification (29.28%), and non-ISO 9001 certification but with TQM practices. Majority of these manufacturers were in Selangor (28.38%), followed by those in Pulau Pinang (23.42%), Kuala Lumpur (19.82%), Johor (18.92%), and Perak (9.46%). The manufacturers selected produce a wide range of products, including electrical and electronics, computer, information technology, technological items, and those on the FMM list that were classified as manufacturing. 5.2 Artificial Neural Networks (ANN) Results ANN is an intelligent analytical tool, with many neurons dispersed in the multi-layers of a huge network [3]. [14] compared an ANN to the human brain in that it acquires knowledge and retains it as synaptic weights via repeated training techniques. ANN is excellent for evaluating non-compensatory decision-making processes [4]. Recognizing the value of ANN, this research used a two-hidden-layer deep learning algorithm of ANN to examine the significant predictors that affect PD and PC (see Fig. 2 and 3).

Fig. 2. ANN model (Output neuron: PD)

Fig. 3. ANN model (Output neuron: PC)

A multilayer perceptron with feed-forward back-propagation was used in ANN. Additionally, a tenfold cross-validation technique using 10 NN (i.e., 90% of data for network training and 10% of data for testing) was applied to address model over-fitting [14]. In addition, the root means square error (RMSE) in Table 1 was performed to

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9

measure the prediction accuracy of the ANN models for both PD and PC. Because the RMSE values for PD (training: 0.085; testing: 0.087) and PC (training: 0.107; testing: 0.100) were significantly low, the ANN result produced a higher prediction accuracy and reliable data fitness. Also, the number of nonzero synaptic weights linked with the two hidden neurons indicated the validity of the input neurons. Table 1. RMSE mean of ANN model. Input neurons: TMC, EM, TR, IN, TE Output neuron: PD Neural No.

Training

Output neuron: PC Testing

Training

Testing

N

SSE

RMSE

N

SSE

RMSE

N

SSE

RMSE

N

SSE

RMSE

1

204

1.332

0.081

18

0.162

0.095

197

2.288

0.108

25

0.202

0.090

2

199

1.476

0.086

23

0.145

0.079

198

2.249

0.107

24

0.241

0.100

3

196

1.144

0.076

26

0.271

0.102

188

2.179

0.108

34

0.416

0.111

4

198

1.464

0.086

24

0.228

0.097

199

2.373

0.109

23

0.333

0.120

5

201

1.620

0.090

21

0.175

0.091

193

2.030

0.103

29

0.232

0.089

6

197

1.306

0.081

25

0.152

0.078

194

2.259

0.108

28

0.291

0.102

7

201

1.486

0.086

21

0.080

0.062

206

2.363

0.107

16

0.161

0.100

8

197

1.780

0.095

25

0.324

0.114

194

2.237

0.107

28

0.340

0.110

9

197

1.425

0.085

25

0.228

0.095

196

2.200

0.106

26

0.237

0.095

10

198

1.331

0.082

24

0.088

0.061

197

2.476

0.112

25

0.184

0.086

Mean

1.436

0.085

Mean

0.185

0.087

Mean

2.265

0.107

Mean

0.264

0.005

SD

0.017

SD

0.002

SD

SD

0.100 0.011

Table 2. Sensitivity analysis. Output neuron: PD TR

Output neuron: PC

Neural network

TMC EM

TE

IN

TMC EM

TR

TE

IN

1

0.162 0.345 0.067 0.033 0.393

0.048 0.025 0.357

0.239 0.331

2

0.036 0.348 0.150 0.135 0.332

0.036 0.071 0.257

0.241 0.394

3

0.167 0.322 0.069 0.040 0.402

0.064 0.210 0.236

0.184 0.306

4

0.110 0.183 0.263 0.022 0.422

0.256 0.183 0.249

0.231 0.082

5

0.160 0.249 0.081 0.058 0.452

0.156 0.128 0.304

0.243 0.169

6

0.113 0.238 0.115 0.156 0.378

0.051 0.054 0.328

0.207 0.360

7

0.172 0.240 0.122 0.085 0.380

0.074 0.127 0.230

0.335 0.234

8

0.235 0.239 0.121 0.222 0.184

0.082 0.051 0.304

0.352 0.211

9

0.185 0.288 0.173 0.152 0.203

0.132 0.089 0.324

0.200 0.256

10

0.228 0.281 0.061 0.088 0.342

0.188 0.233 0.109

0.233 0.237 (continued)

10

A.-F. Lim Table 2. (continued) Output neuron: PD TR

Output neuron: PC

Neural network

TMC EM

TE

IN

Mean importance

0.157 0.273 0.122 0.099 0.349

TMC EM

TR

0.109 0.117 0.270

TE

IN

0.247 0.258

Normalized 44.95 78.35 35.03 28.41 100.00 40.29 43.40 100.00 91.36 95.63 importance (%) Ranking

3

2

4

5

1

5

4

1

3

2

In Table 2, the normalized importance value (NRI) from the sensitivity analysis was used to prioritize the relative relevance of each input neuron on output neuron. With a NRI of 100%, IN has the highest effect on PD, followed by EM (78.35%), TMC (44.95%), TR (35.03%), and TE (28.41%). While TR is the most significant predictor on PC with 100% NRI, tailed by IN (95.63%), TE (91.36%), EM (43.40%), and TMC (40.29%). Interestingly, the R2 values of PD = 52.8% and PC = 61.9% demonstrate that both ANN models exhibit a higher degree of predictive power and accuracy. Furthermore, results from SEM showed that firm size had a significant influence on the effect of PD (β = 0.066, p < 0.05) but insignificant on PC (β = 0.018, p > 0.05).

6 Discussion This study reveals that IN and EM have a positive and significant correlation with PD, with a NRI greater than 50%, whereas TMC, TR, and TE have a less significant correlation with PD, with 44.95%, 35.03%, and 28.41%, respectively. In line with the finding of [6], IN can build a collaborative atmosphere conducive to PD. Like [8]’s finding, employees express their passion when given the authority to design new products. However, Malaysia’s leadership structure remains rigid, influenced by a conservative organizational system [20], with less emphasis on PD. Also, there is a dearth of attention paid to the important training benefits that could encourage PD among Malaysia’s firms. Moreover, due to the rigidity and inflexibility of these manufacturer’s hierarchy cultural structure, cooperation among employees to develop new products has been stifled. Intriguingly, TR, IN, and TE were positively and significantly connected with PC, given that their NRI exceeds 50%. While EM (43.40%) and TMC (40.29%) appeared to have a lesser effect on PC. This demonstrates that high-quality training programs enable employees to acquire new skills and knowledge necessary for PC, which agrees with [11]. Additionally, IN inclined the process of knowledge sharing to optimize the newly established strategy and method. Meanwhile, in line with the findings of [5], TE is facilitating all employees’ understanding and efforts toward reaching PC. Nevertheless, inefficiency EM in Malaysia’s manufacturing firms, with a proclivity for employing low-skilled people with less expertise [21], making them less successful at creating PC. Furthermore, Malaysian leaders rarely foster an innovation culture that encourages employees to think creatively about process improvement. Notably, firm size had a significant influence on

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PD but had no effect on PC. This implies that different sized manufacturing firms will have a higher tendency for PD but not differ for PC.

7 Implications, Limitations, Future Works, and Conclusions The study model filled research gaps by demonstrating the importance of five soft TQM practices in improving PD and PC, with firm size serving as the control variable. The application of DC theory to business management can enhance a firm’s ability to integrate and restructure internal competences in response to a rapidly changing market. In addition, the impact of a firm’s size can have varying effects on PD and PC. However, empirical studies investigating the relationship between these determinants remain largely unexplored. This study can therefore serve as a basis for future research. This study offers manufacturers with valuable practical insights by highlighting a set of the most influential soft TQM practices that can achieve a higher PC and PD. To succeed in a competitive market, manufacturers must maintain their business growth through innovation capabilities. Considering this, manufacturers should reevaluate their initiative to improve the quality of IN and EM to stimulate PD, while simultaneously improving the quality of TR, IN, and TE for PC. This study has several limitations and recommendations for future research. First, this study did not consider other organizational contexts. Future research should expand its scope to determine the impact of the predictors on other aspects of business performance. Second, this study ignores other moderating and mediating effects that future research could consider. All in all, this research established a platform for future researchers to conduct deeper research on the relationship between soft TQM, PD, PC, and firm size. This allows the significance of TQM to be better understood, with the goal of stimulating the generation of new knowledge through the incorporation of the most recent theory. Acknowledgement. This research is part of the author’s PhD works and was conducted while the author is a Ph.D. candidate at UCSI Graduate Business School, UCSI University. The author is grateful to Senior Professor Dr. Ooi Keng Boon (Main Supervisor), Professor Dr. Garry Tan Wei-Han (Co-Supervisor) and Associate Professor Dr. Lee Voon Hsien (External Co-Supervisor) for their valuable suggestions on an earlier draft of this paper.

References 1. Abu Salim, T., Sundarakani, B., Lasrado, F.: The relationship between TQM practices and organisational innovation outcomes: Moderating and mediating the role of slack. TQM J. 31(6), 874–907 (2019) 2. Albuhisi, A.M., Abdallah, A.B.: The impact of soft TQM on financial performance: The mediating roles of non-financial balanced scorecard perspectives. Int. J. Qual. Reliab. Manage. 35(7), 1360–1379 (2018) 3. Al-Sharafi, M. A., Al-Qaysi, N., Iahad, N. A., Al-Emran, M.: Evaluating the sustainable use of mobile payment contactless technologies within and beyond the COVID-19 pandemic using a hybrid SEM-ANN approach. Int. J. Bank Market. 40, 1071–1095 (2021)

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4. Al-Emran, M., Abbasi, G. A., Mezhuyev, V.: Evaluating the impact of knowledge management factors on M-learning adoption: A deep learning-based hybrid SEM-ANN approach. In Recent advances in technology acceptance models and theories, pp. 159–172. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-64987-6https://doi.org/10.1007/978-3-030-64987-6 5. Cinar, O., Altuntas, S., Alan, M. A.: Technology transfer and its impact on innovation and firm performance: empirical evidence from Turkish export companies. Kybernetes 50, 2179–2207 (2020) 6. El Manzani, Y., Sidmou, M. L., Cegarra, J. Jack.: Does IS0 9001 quality management system support product innovation? An analysis from the sociotechnical systems theory. Int. J. Qual. Reliab. Manage. 36(6), 951–982 (2019) 7. Elrehail, H., Emeagwali, O.L., Alsaad, A., Alzghoul, A.: The impact of transformational and authentic leadership on innovation in higher education: The contingent role of knowledge sharing. Telematics Inform. 35(1), 55–67 (2018) 8. Escrig, A.B., Segarra-Ciprés, M., García, B., Beltrán, I.: The impact of hard and soft quality management and proactive behaviour in determining innovation performance. Int. J. Prod. Econ. 200(March), 1–14 (2018) 9. FMM.: Business In Action FMM. www.fmm.org.my (2020) 10. Gonzalez, R.V.D., Melo, T.M.: Analyzing dynamic capability in teamwork. J. Knowl. Manag. 23(6), 1196–1217 (2019) 11. Honarpour, A., Jusoh, A., Md, K.: Total quality management, knowledge management, and innovation: an empirical study in R&D units. Total Qual. Manag. Bus. Excell. 29(7–8), 798– 816 (2017) 12. Jiménez-Jiménez, D., Martínez, M., Para-Gonzalez, L.: Implications of TQM in firm’s innovation capability. Int. J. Qual. Reliab. Manage. 37(2), 279–304 (2020) 13. Kafetzopoulos, D., Gotzamani, K., Gkana, V.: Relationship between quality management, innovation and competitiveness. Evidence from Greek companies. J. Manuf. Technol. Manage. 26(8), 1177–1200 (2015) 14. Lim, A.F., Lee, V.H., Foo, P.Y., Ooi, K.B., Tan, G.W.H.: Unfolding the impact of supply chain quality management practices on sustainability performance: an artificial neural network approach. Supply Chain Manage. Int. J. (2021) 15. Lim, A.F., Nair, R.K., Foo, P.Y.: TQM and organisational innovation: A systematic review and research framework. Int. J. Innov. Learn. 26(3), 273–300 (2019) 16. Lopez-, A., Pérez-, A., Valle-Cabrera, R.: Knowledge as a mediator between HRM practices and innovative activity. Hum. Resour. Manage. 48(4), 485–503 (2009) 17. Ooi, K.-B.: TQM: A facilitator to enhance knowledge management? A structural analysis. Expert Syst. Appl. 41(11), 5167–5179 (2014) 18. Ooi, K.B., Lin, B., Teh, P.L., Chong, A.Y.L.: Does TQM support innovation performance in Malaysia’s manufacturing industry? J. Bus. Econ. Manag. 13(2), 366–393 (2012) 19. Prajogo, D.I., Cooper, B.K.: The individual and organizational level effects of TQM practices on job satisfaction. Int. J. Manpow. 38(2), 215–225 (2017) 20. Salleh, K.M., Sulaiman, N.L.: Malaysia leadership competencies: A model for effective performance. Int. J. Hum. Resour. Manage. Res. 3(2), 63–70 (2013) 21. Tay, S.I., Alipal, J., Lee, T.C.: Industry 4.0: Current practice and challenges in Malaysian manufacturing firms Technology in Society Industry 4.0: Current practice and challenges in Malaysian manufacturing firms. Tech. Soc. 67, 101749 (2021)

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22. The Star.: Country needs new generation of manufacturers. The Star Online. https://www. thestar.com.my/business/business-news/2021/05/05/country-needs-new-generation-of-man ufacturers (2021). Accessed 30 September 2021 23. Turulja, L., Bajgoric, N.: Innovation, firms’ performance and environmental turbulence: Is there a moderator or mediator? Eur. J. Innov. Manag. 22(1), 213–232 (2019) 24. Wang, W.T., Wang, Y.S., Chang, W.T.: Investigating the effects of psychological empowerment and interpersonal conflicts on employees’ knowledge sharing intentions. J. Knowl. Manag. 23(6), 1039–1076 (2019)

Big Data Techniques and Internal Control: Evidence from Egypt Ahmed Elmashtawy1,3

and Mohamed Salaheldeen2,3(B)

1 Faculty of Business and Economics and Social Development, Universiti Malaysia Terengganu

(UMT), Kuala Terengganu, Malaysia 2 Faculty of Economics and Muamalat, Universiti Sains Islam Malaysia (USIM), Nilai, Malaysia

[email protected] 3 Faculty of Commerce, Menoufia University, Menoufia, Egypt

Abstract. The purpose of this paper is to examine the impact of using big data techniques on the effectiveness of the internal control for companies listed on the Egyptian Stock Exchange in 2020. A sample of study contains 102 companies listed in six sectors on the Egyptian Stock Exchange. The Pearson correlation coefficient and multiple linear regression were used to analyze the relationships between the variables. The results revealed a significant effect for the use of big data techniques and the effectiveness of internal control (effective control environment and the provision of effective communication channels between internal management and external parties). The study concluded that the integration of business intelligence tools and blockchain databases with big data is critical to support accounting practices and develop the roles of accountants and auditors. It is recommended that all big data techniques be expanded in order to maintain the security and dependability of data in businesses. Also, it is suggested to rely more on big data techniques in the decision-making process, particularly Hadoop and machine learning. Keywords: Big Data techniques · Hadoop techniques · Machine learning techniques · Internal control

1 Introduction Big data is a relative concept for companies and institutions; some may see that the data they deal with is very big, while others do not see it in comparison to other companies and institutions. This is determined by the dimensions of big data, which measure the extent of its size (Volume), diversity of this data, different patterns and complexity (Variants), speed of data processing, and performance of its processing (Velocity) [1]. There are several gaps that prevent big data from working effectively, including those related to data consistency, data integrity, data identification, aggregation, and confidentiality [2]. Companies are dealing with a massive increase in data, which comes from various sources and in various forms, and it changes rapidly, making it difficult to analyze. Big data, on the other hand, has the ability to access and analyze massive amounts of data [3]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (Eds.): ICETIS 2022, LNNS 584, pp. 14–23, 2023. https://doi.org/10.1007/978-3-031-25274-7_2

Big Data Techniques and Internal Control: Evidence from Egypt

15

There were several issues with this data, including ensuring the validity of these data, particularly given that large amounts of data came from outside the company. The data will not be a source of trust for the auditors unless this security guarantee is provided [4]. That is, big data techniques are used to address issues in the internal control environment in order to ensure its effectiveness through risk management and internal control governance [5]. Besides, it could also assess risks by designing risk management and internal control system, as well as detect fraudulent practices as they occur[6]. Unfortunately, there is a gap between the practice and application of big data systems in the internal control environment [7–9]. Hence, controlling information confidentiality has become inextricably linked to internal control [10]. Hadoop and machine learning techniques, as one of the big data technologies, have worked to maintain the security and confidentiality of this data, and companies cannot guarantee the effectiveness of internal control and the integrity of their financial data without an appropriate level of privacy and security related to data use [11]. A risk management and internal control system have been established to meet the establishments’ planned strategic goals. Hadoop and machine learning approaches also depended on integrated risk management and internal control strategy to ensure effective risk management based on several control mechanisms and then prepared clear reports. In light of the foregoing, it is clear that there are a number of issues surrounding big data, including the nature of big data analysis and the resulting massive amounts of data, which lead to poor audit quality [5]. The issue with inappropriate information is a large amount of irrelevant information that auditors must deal with.That is, there is a pattern of problem recognition, where big data gives decision makers the ability to search for many patterns by providing a data set for the community as a whole that is undetectable in samples or smaller data sets [12]. In addition to the problem of ambiguity, the presence of unstructured data in various forms may cause ambiguity. This ambiguity may arise from the amount and quality of available data, as well as differences in the reliability of the source. Moreover, the lack of adequate training and skills required to analyze big data, doing data analysis necessitates the availability of highly qualified and effective auditors in a way that allows the company to control massive amounts of data.

2 Literature Review The ability to operate information through the use of the best modern technological means for information systems represented in big data methods helps managers increase the ability to plan and modify the plan on a continuous and frequent basis by transferring information from its source to the decision-making position [1]. On the other hand, internal control systems are one of the systems that seek to face potential risks related to the company’s internal systems [13]. These systems often face a high level of uncertainty that requires a high level of information operation, which can be provided through big data methods. Therefore, the informational advantage obtained by companies applying big data methods allows them to mitigate the shortcomings of their internal control systems [11, 14].The rise of big data has resulted in the supply of real time information as well as predictive analyses on massive volumes of data in order to forecast the future [15].

16

A. Elmashtawy and M. Salaheldeen

Hadoop and machine learning techniques are regarded as two of the most essential data methods, as they aid in the integration of structured and unstructured data to enhance decision-making [13].The study aimed to investigate the effect of using big data techniques on the effectiveness of internal control in companies listed on the Egyptian Stock Exchange by using Hadoop and machine learning techniques, as big data techniques and studying its impact on two of the five basic components of internal control, which are: increasing the effectiveness of the control environment and providing effective communication channels between internal and external departments. Figure 1 illustrates the theoretical framework for this study. The figure shows all the variables that are examined to accomplish the study objectives. Thus, the study hypotheses can be formulated as follows: H1: There is a statistically significant effect of applying big data techniques on the effectiveness of the control environment. H2: There is a statistically significant effect of applying big data techniques on providing effective communication channels between stakeholders.

Fig. 1. Theoretical framework

3 Methodology The study population is represented by all the companies listed on the Egyptian Stock Exchange, and the Cross-Sectional analysis method was used for the year 2020, and given that the companies that use big data techniques are limited to only six sectors of the Egyptian Stock Exchange, the final sample of the study will be the companies listed in those six sectors in 2020, which recorded 102 observations, the Table 1 provides a summary of the sample selection.

Big Data Techniques and Internal Control: Evidence from Egypt

17

Table 1. Sample selection No

Sectors

Observations No

%

1

Construction and materials

25

24.5%

2

Healthcare and pharmaceuticals

16

15.7%

3

Real estate

26

25.5%

4

Industrial goods and services and automobiles

17

16.7%

5

Personal and household products

10

9.8%

6

Basic resources

8

7.8%

102

100%

Total

The independent variable is reflected in the company’s big data Techniques, which are classified into Hadoop techniques and machine learning techniques [16]. As a result, this variable can be measured using a dummy variable that takes the value of 1 when one of these techniques is used and zero otherwise. The study’s dependent variables are the effectiveness of internal control, as represented by the effectiveness of the control environment, and the provision of effective channels of communication between internal departments and each other and with external parties. Concerning the effectiveness of the control environment, the study used the auditor’s modified opinion (as a reverse measure) regarding the deficiencies or deficiencies related to the control environment (Going Concern Modified Opinions), which is any qualified auditor’s opinion issued on the internal control environment, by using a dummy variable that takes the value 1 in the absence of a qualified opinion on the internal control environment and the value zero when there is a qualified opinion on the internal control environment [16, 17]. With regard to providing effective channels of communication between internal departments and each other and with external parties, all of these parties are called stakeholders, and the presence of these parties requires a kind of internal organizational responsibility that motivates managers to provide the best possible information to all of these parties. This is fully reflected in the trends of current and prospective investors’ decisions regarding investing in stocks. The lower the level of that information, the greater the confusion of investors’ decisions, which leads to the dispersion of stock prices and vice versa [18]. Therefore, the study uses the level of dispersion of stock prices from their mean to measure the effectiveness of communication between internal and external channels and all other stakeholders [19]. Based on the empirical literature, control variables that are closely related to the independence and dependent variables include firm size, audit firm, return on assets, and firm value [19]. Table 2 explains the measurement of the control variables.

18

A. Elmashtawy and M. Salaheldeen Table 2. Operationalization of control variables

Variable

Acronym Measurement

Firm size

LSize

The natural logarithm of total assets at the end of the year

Audit firm

Big-4

A dummy variable that takes the value of 1 when the company is audited by big-4 firms and the value is zero otherwise

Return of assets (Profitability) ROA Firm value

Ratio of net income to total assets

Tobins’Q The ratio of the sum of the market value of equity plus total debt divided by book value of total assets

4 Results and Discussion Descriptive statistics seek to present a summary of the nature of the study sample observation and its organization in order to identify the nature of the study sample, which was studied and tested, how it was distributed, and to judge the applicability of the study’s results for comparison with other studies. The mean, median, and standard deviation are the most significant descriptive statistics. As a result, the following Table 3 displays descriptive statistics for all observations in the study sample: Table 3. Descriptive statistics for the study sample Variable

Observations

Mean

Control environment

102

0.3137255

Information and communication

102

Big Data Firm size

Standard deviation

Minimum

Maximum

0.4662977

0

1

3.314202

4.693195

0

27.71936

102

0.2745098

0.4484707

0

1

102

8.8184

0.8538526

5.3919

10.80494

Audit firm

102

0.6568627

0.4771014

0

1

Return of assets (Profitability)

102

0.0374875

0.2388698

– 1.82003

0.3939048

Firm value

102

0.525175

10.64524

– 91.26396 34.12062

According to the previous table, the average number of observations in the sample that use big data methods is 0.2745098, which equates to 27.45%, indicating that big data methods have not received sufficient spread in those six sectors, possibly due to the high cost of applying those methods. On the other hand, we note that the average of the companies in which the auditor did not express any conservative opinions regarding the internal control systems is 0.3137255, which is equivalent to 31.37%, indicating the

Big Data Techniques and Internal Control: Evidence from Egypt

19

presence of a large number of observations that drew the attention of the external auditor to express conservative opinions regarding its control environment. Furthermore, the average of the observations audited by one of the big 4 firms is 0.6568627, which is comparable to 65.68 percent, showing a high degree of quality in the audit process for many of the observations included in the study sample. The Pearson Correlation The study attempts to analyze the impact of big data techniques on the two components of internal control represented by the effectiveness of the control environment and effective communication channels between internal management and external parties by applying it to a sample of companies listed in the Egyptian stock market. Therefore, we aim in this part of the study to present the Pearson correlation matrix between the variables included in the statistical hypothesis test model through the Table 4 to identify the nature of the relationship between the independent and dependent variables in the models for testing the statistical hypotheses of the study and to form an initial opinion about the problem of multicollinearity between those variables. We have measured the parameter (VIF) to confirm that there are no problems with multicollinearity. Table 4. The Pearson correlation Variable

Control Information Big environment and Data communication

Big 4

Size

Control environment

1.000

Information and communication

0.3013

1.000

Big Data

0.5262

0.7109

1.000

Big 4

–0.0071

0.1083

0.0281 1.000

Size

–0.1111

0.0335

–0.0347 0.1982 1.000

ROA

–0.0097

0.0074

–0.1123 0.0644 0.0474

Tobins’ Q

–0.0328

–0.0640

ROA

Tobins’ Q

1.000

–0.1843 0.0928 0.0383 –0.0979 1.000

The correlation coefficients in the preceding table show that there is a positive association between the independent variable (big data) and the dependent variables (control environment/information and communication). Regression Analysis Results The first hypothesis is that using big data techniques has a direct and statistically significant effect on the effectiveness of the control environment. We describe the regression model A to examine this relationship. In Panel A, the results are provided in the table below. The model’s explanatory power is 82%, which is a good percentage. Suggesting that the proposed model can efficiently describe the link between the study’s distinct

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A. Elmashtawy and M. Salaheldeen

variables. When the significance of the coefficients within the model is considered, the significance of the variable coefficient of big data related to the application of big data techniques has a positive sign, indicating the existence of a direct, statistically significant effect between the application of big data techniques and the effectiveness of the control environment as one of the components of internal control. In other words, the increased use of big data techniques leads to an increase in the efficacy of the control environment, which is one of the internal control components of firms listed on the Egyptian stock exchange. Therefore, the first hypothesis can be accepted. The second hypothesis asserts that using big data techniques has a direct, statistically significant influence on providing effective communication channels amongst stakeholders. We describe the regression model No. B to examine this relationship. The results shown in panel B of the following Table 5 were obtained. It turns out that the model’s explanatory power is 50%, which is a decent proportion. Indicating that the proposed model can describe the relationship between the different variables of the study with a reasonable level of efficiency. Considering the significance of the coefficients within the model, the significance of the variable coefficient of big data related to the application of big data techniques bears a positive sign, which indicates the existence of a statistically significant relationship between the application of big data techniques and the provision of effective communication channels between stakeholders as one of the components of internal control. In other words, the increased apply big data techniques leads to an increase in the effectiveness of communication channels between internal management and external parties as one of the components of internal control for companies listed in the Egyptian stock market. Thus, the second hypothesis can be accepted. The values of the control variables (size and the big 4) are positive, and this means that there is a positive relationship between both the independent variable (big data techniques) and the dependent variables (the internal control environment and information and communication), and it is also clear that the rate of return on assets (ROA) has increased for the dependent variables (the internal control environment and information and communications), which means an increase in the profitability of the companies under study as a result of the optimal exploitation of their assets, which shows the positive impact of big data techniques as an independent variable on the dependent variables. It is also noted that the ratio of firm value (Tobin’s Q) to dependent variables increased. A higher ratio of one indicates that the market value of the assets is greater than their replacement value, indicating a high return on assets, and a lower ratio of one indicates that the company is undervalued. Table 5. Regression analysis results Variable

Constant

Panel: A

Panel: B

Control environment

Information and communication

Coef.

t-stat

p-value

0.0421

1.20

0.235

VIF

Coef.

t-stat

p-value

0.6170

1.04

0.30

VIF

(continued)

Big Data Techniques and Internal Control: Evidence from Egypt

21

Table 5. (continued) Variable

Panel: A

Panel: B

Control environment

Information and communication

Coef.

t-stat

p-value

VIF

Coef.

t-stat

p-value

VIF

BD

0.9378

20.93

0.000

1.06

7.6798

10.16

0.000

1.06

Big 4

0.0136

0.32

0.747

1.06

0.6639

0.93

0.352

1.06

Size

2.2700

1.08

0.283

1.04

1.9100

0.54

0.593

1.04

ROA

−0.0622

−0.75

0.457

1.04

1.7835

1.27

0.207

1.04

Tobins’ Q

−0.0013

−0.69

0.492

1.06

0.0319

1.00

0.320

1.06

N

102

102

Adj. R2

82.28%

50.17%

F. value

94.79

21.34

F. sig

0.00

0.00

5 Conclusion In this research study, we contributed to offering new insights into big data techniques and internal control by using data from listed firms in Egypt. The objective of the study is to investigate the effect of using big data techniques (Hadoop and Machine learning techniques) on the effectiveness of internal control (effective control environment and the provision of effective communication channels between internal management and external parties) in companies listed on the Egyptian Stock Exchange. The analysis conducted shows that there is a statistically significant effect of the use of big data techniques (Hadoop and Machine learning techniques) on the effectiveness of internal control (effective control environment and the provision of effective communication channels between internal management and external parties). The study concluded that big data gives businesses a competitive advantage in terms of operational efficiency, risk reduction, cost reduction, technical and nontechnical innovation, increased sales volume, asset control, operational automation, and precrash preventive maintenance. In addition to assisting businesses to adapt to market changes, it improves supply chain management efficiency, enhances customer relationship management, and forecasts future changes. The study also stated that integrating business intelligence tools and blockchain databases with big data are crucial to supporting financial reporting and developing accountants’ and auditors’ responsibilities. It is recommended that all big data techniques be expanded in order to maintain the security and dependability of data in businesses. Also, It is suggested to rely more on big data techniques in the decision-making process, particularly Hadoop and machine learning. Finally, this study acknowledges certain limitations, for example, the use of a small sample size of 6 sectors for one year period and from only one country. Next, we cannot control all variables that may affect the analysis. And, a limited period sample may restrict the generalizability of the results. Then, this study can be extended by increasing

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the number of years of data and conducting cross-country research to provide a more comprehensive understanding of big data techniques and internal control. Furthermore, the study only investigated the impact of some of the big data techniques on two of the five components of internal control. In addition, future research may focus on Investigating the impact of various big data techniques on internal control effectiveness.

References 1. Pappas, I.O., et al.: Big data and business analytics ecosystems: paving the way towards digital transformation and sustainable societies. Inf. Syst. E-Bus Manage. 16, 479–491 (2018). https://doi.org/10.1007/s10257-018-0377-z 2. Salaheldeen, M., Artificial intelligence in business research: Trends and future. In: Emerging Issues and Challenges in Management Conference, Faculty of Commerce, Menoufia University, Egypt (2017) 3. Noureldeen, A., Salaheldeen, M., Battour, M.: Critical Success Factors for ERP Implementation: A Study on Mobile Telecommunication Companies in Egypt. In: Al-Emran, M., AlSharafi, M.A., Al-Kabi, M.N., Shaalan, K. (eds.) ICETIS 2021. LNNS, vol. 299, pp. 691–701. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-82616-1_57 4. Pouyanfar, S., et al.: Multimedia big data analytics: A survey. ACM Comput. Surv. 51(1), 1–34 (2018) 5. Elmashtawy, A., Salaheldeen, M.: Big data and business analytics: Evidence from Egypt. In: Proceedings of International Conference on Information Systems and Intelligent Applications, ICISIA 2022. Lecture Notes in Networks and Systems, Springer, Cham (2022). https://doi. org/10.1007/978-3-031-16865-9 6. Tetteh, L.A., et al.: The impact of internal control systems on corporate performance among listed firms in Ghana: The moderating role of information technology. J. Afr. Bus. 23(1), 104–125 (2022) 7. Hopkins, J. and P. Hawking, Big Data Analytics and IoT in logistics: a case study. The International Journal of Logistics Management, 2018 8. Mikalef, P., et al.: Big data analytics capabilities and innovation: The mediating role of dynamic capabilities and moderating effect of the environment. Br. J. Manag. 30(2), 272–298 (2019) 9. Chen, F.-H., Hsu, M.-F., Hu, K.-H.: Enterprise’s internal control for knowledge discovery in a big data environment by an integrated hybrid model. Inf. Technol. Manage. 23, 213–231 (2021). https://doi.org/10.1007/s10799-021-00342-8 10. Steinbart, P.J., et al.: The influence of a good relationship between the internal audit and information security functions on information security outcomes. Acc. Organ. Soc. 71, 15–29 (2018) 11. Sadgali, I., Sael, N., Benabbou, F.: Performance of machine learning techniques in the detection of financial frauds. Pro. Comput. Sci. 148, 45–54 (2019) 12. Salaheldeen, M.: Opportunities for Halal entrepreneurs in the Islamic digital economy: future and trends from a cultural entrepreneurship perspective. In: Ratten, V. (ed.) Cultural Entrepreneurship: New Societal Trends, pp. 95–107. Springer Nature, Singapore (2022) 13. Duan, Y., Edwards, J.S., Dwivedi, Y.K.: Artificial intelligence for decision making in the era of Big Data-evolution, challenges and research agenda. Int. J. Inf. Manage. 48, 63–71 (2019) 14. Salaheldeen, M.: Management control systems as a package: An application to science & technology parks: UPTEC case study. In: 8th Conference On Performance Measurement And Management Control, Nice, France (2015)

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15. Ibrahim, A.E.A., Elamer, A.A., Ezat, A.N.: The convergence of big data and accounting: Innovative research opportunities. Technol. Forecast. Soc. Chang. 173, 121171 (2021) 16. Müller, O., Fay, M., Vom Brocke, J.: The effect of big data and analytics on firm performance: An econometric analysis considering industry characteristics. J. Manag. Inf. Syst. 35(2), 488–509 (2018) 17. Carey, P., Kortum, S., Moroney, R.: Auditors’ going-concern-modified opinions after 2001: Measuring reporting accuracy. Accounting & Finance 52(4), 1041–1059 (2012) 18. Healy, P.M., Palepu, K.G.: Information asymmetry, corporate disclosure, and the capital markets: A review of the empirical disclosure literature. J. Account. Econ. 31(1–3), 405–440 (2001) 19. Deng, C.-M., Xiao, Z., Zhou, L.: Information systems and internal control: Evidence from China. Electron. Commer. Res. 17(3), 361–377 (2016). https://doi.org/10.1007/s10660-0169228-5

What is Stopping You from Using Mobile Payment in Oman? Mohamed Musallam Khasib Al Rawahi1(B) , Hooi Cheng Eaw1 , and Garry Wei Han Tan1,2 1 Graduate Business School, UCSI University, Kuala Lumpur, Malaysia [email protected], {eawhc,GarryTan}@ucsiuniversity.edu.my 2 Department of Business Administration, IQRA University, Karachi, Pakistan

Abstract. Mobile payment has revolutionised the payment industry. It employs features of convenience, effectiveness and cost-saving. This study investigates why consumers do not use mobile payment as their primary payment instrument in the Sultanate of Oman. This study extends the mobile payment technology acceptance model (MTAM) with consumer behavioural factors of the theory of planned behaviour (TPB). Finding confirm the significant association of perceived value, attitude, perceived control behaviour and subjective norms with the mobile payment use intention. 308 qualified responses from the questionnaire were analysed using SEM-PLS. This study enlightens mobile payment providers to understand factors influencing mobile payment usage in a developing and multi diversity culture country such as Oman. Moreover, this study presents national and international firms working in the region with deeper clarification and understanding of the mobile payment influencing factors, which can assist in enhancing and improving the marking plans. Keywords: Mobile payment technology model · Theory of planned behaviour · Mobile payment intention actual use · Oman

1 Introduction Financial payment services have evolved from traditional to mobile electronic payments via the internet. ICT plays a major role in influencing modern technologies worldwide as internet users reached 53.6 per cent of the global population [1]. The development and fast advancement of technology and mobile enabling capabilities resulted in the mobile devices’ mobile payment service. Mobile payment is a cutting-edge technology that has revolutionised the payment industry. Mobile payment offers convenience, effectiveness and cost-saving [2]. Therefore, making payments through mobile devices such as wireless phones, personal digital assistants (PDAs), radio frequency (RF) devices, and NFC-based devices is called mobile payment [3, 4]. A survey study conducted in the year 2017 in Europe reported that 92 per cent of teenagers would use mobile payments in the next three years [3]. Most Americans (89%) prefer to pay with cash rather than debit or credit cards. Less than 8 per cent © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (Eds.): ICETIS 2022, LNNS 584, pp. 24–39, 2023. https://doi.org/10.1007/978-3-031-25274-7_3

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of people prefer to pay with their phones [4]. Security concerns (fraud and identity theft) were mentioned by approximately 40% of those who responded to the survey. Furthermore, 70% of respondents reported that not using mobile payment is due to a lack of confidence [4] 22.9 per cent are not tech-savvy, with 17.7 per cent putting them at risk of losing their devices. 10.7 per cent of people forget their passwords, 9.1 per cent of people make mistakes. 9.1% and due to dissatisfaction with the new payment option. Customers in Singapore, Malaysia, and Thailand use mobile payment as a choice if cash and bank cards are not available, and only 4% of customers use mobile payment frequently [5]. Global mobile payment adoption has been sluggish, and mobile payment has yet to be introduced in Lebanon [6]. In Malaysia, cash is still dominant, and citizens rely on it [9, 10], while m-payment is still in its infancy [7]. In Bangladesh, the case is similar. The adoption rate is scary [8]. In Oman, the central bank of Oman licensed Thawani Technologies, the first Non-bank Company to become a payment service provider, “Thawani Technologies” in 2020 [9]. Currently, 60K users downloaded their mobile payment application [10]. However, the active users did not exceed 12% of those who downloaded the application on their mobile devices [10]. Cash payment plays the biggest part in Oman in most people’s daily lives [11]. The total active mobile telephone subscriptions in Oman reached 6,082,741, with a penetration rate of 131.7% per inhabitant. 9.3% of the population is served by at least one 3G mobile network [12]. Although mobile payment carries many advantages, the adoption rate is unsatisfactory worldwide, as confirmed by [2]. As a result, both researchers and practitioners must consider the factors influencing a customer’s adoption intention. Ultimately, it would increase financial inclusion and enhance banking facilities, positively impacting the economy. Mobile payments failed to reach initial expectations for many reasons, including intense competition among the various parties involved in the financial ecosystem [13]. Thus, examining the influencing factors of mobile usage’s actual behaviour in Oman is the goal of this study. Overall, this study concentrates on a payment method that supports contactless payment. Moreover, this study integrated the MTAM (mobile payment technology model) with TPB (the theory of planned behaviour) and included additional constructs to examine mobile payment actual usage in Oman; hence, the present study’s contribution is important to understand why mobile payment is not being used from technology adoption viewpoint as well examining the consumer behaviour. Knowing the impact of the proposed variable to examine mobile payment services in Oman will shape mobile payment’s strategic decision. Similarly, the study’s findings and conclusions will make people more aware of mobile payment by encouraging people to adopt it through proper means. Consequently, this study will give national and international firms operating in the region better know the elements that influence mobile payment intention and usage. This research will also aid them in developing efficient advertising campaigns and enhance their marketing strategies.

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2 Literature Review 2.1 Theory of Planned Behaviour (TPB) TPB is a developed version of the Theory of Reasoned Action (TRA) of Ajzen and Fishbein’s (1980). It is appropriate for predicting and comprehending particular behaviours in contexts [14]. The TPB has been commonly used in studies to describe factors influencing potential users’ intentions to use a new system or technology [15]. An actual individual’s behaviour is influenced by their behaviour intention and influenced by attitude, subjective norms, and perceived behavioural control to achieve the behaviour in the TPB [14]. Many recent studies have used the TPB to explain the consumption mechanism, such as purchasing counterfeit luxury products and in the healthcare industry [15, 17]. The TPB relies on its power to explain the individual intention by including internal and external factors of an individual. The attitude and perceived behavioural control variables are used to explain an individual’s internal process of deciding whether to move forward with the behaviour of interest or not. On the other hand, the subjective norms encourage or discourage the behaviour of interest in society’s opinion and important people around the user opinion from supporting or not. Due to discrepancies in the findings and a lack of exploratory data, there have been calls to replicate and expand the TPB’s usage [16]. Accordingly, continuing the process beyond intention to the actual behaviour and continuing use phase determines the service provider’s success [17]. The customer and the service provider usually have a long-standing partnership [17]. In mobile payment, the adoption is an initial phase toward the use intention, actual use, and continued use. The proposed study would combine TPB constructs with other constructs important to understanding mobile payment’s real use to investigate its actual use. 2.2 Mobile Technology Acceptance Model (MTAM) The Technology Acceptance Model (TAM) has been criticised for being designed to justify the individual implementation of an electronic mail system in an organisational context [18]. This was due to the inconsistency with the study context. TAM was invented and used to examine the electronic mail system in an organisation that bears the deployment cost of the electronic mail system. Therefore, it was unsuitable for explaining individual adoption using personal smart mobile. The individual entirely bears the cost of the smart mobile. Innovation adoption requires training and technical support, part of the deployment project within the organisation’s context. However, the TAM model did not differentiate between such requirements for organisations and individuals. The MTAM model was created to investigate the smartphone Credit Card (SCC), which connects two devices using short-range wireless technology within a 10-cm range or more minor [18]. Since this study focuses on mobile remote commerce, it is more appropriate to use MTAM. To strengthen the conceptual framework, additional variables were added to accommodate the shortfalls of MTAM, which was invented for investigating proximate mobile payment. Thus, it has become more comprehensive and appropriate for investigating remote mobile payment or peer-to-peer payment.

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3 Hypotheses Development 3.1 Perceived Value (PV) The most commonly used concept of perceived value is “the consumer’s overall assessment of the utility of a product based on perceptions of what is received and what is given” [19]. The significance of the perceived value was proven in a study related to mobile banking [20]. Additionally, perceived value is the variable that significantly impacts the intention to use the proposed payment system [21]. As a result, we developed the first hypothesis as below: H1: PV has a positive effect on mobile payment use intention. 3.2 Digital Social Media (DSM) Digital social media is “a collection of online services that support social interactions among users and allows them to co-create, find, share and evaluate the online information repository” [22]. Digital social media is used by 79 per cent of Fortune 100 best businesses [22]. It is worth knowing that 55 per cent of the worldwide population uses digital social media platforms [23]. Hence, Businesses can easily connect and engage with millions of people through digital social media [22]. Digital social media users have confirmed that it influences customers’ intentions in credit card acceptance in Vietnam [24]. Furthermore, in recent research on mobile banking adoption in Bangladesh, it was shown that social influence had a substantial direct impact on intended usage behavior [8]. Accordingly, we put the following hypothesis into account. H2. DSM has a positive effect on mobile payment use intention. 3.3 Facilitation Condition (FC) The level of customer’s belief in the organisation and infrastructure to support service users are facilitating conditions [25]. A study included 660 respondents and reported a significant relationship with a p-value less than 0.001 [25]. Mobile payment requires a certain kind of resources and systems infrastructure. As a result, customers are more willing to use the mobile system if guidance and support assistance are in place [25]. Additionally, configuring and operating mobile phones needs service providers’ participation from a mobile banking viewpoint [26]. They want to control their mobile banking and mobile payment systems [26]. In a study conducted in Bangladesh, facilitation conditions was confirmed that a direct and significant impact mobile banking’s intention to use behavior [8]. Based on the above, this study developed the following hypothesis: H3. FC has a positive effect on mobile payment use intention.

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3.4 Technical Feasibilities (TF) Consumer capabilities and technology savvy are referred to as technical feasibility. Mobile payment adds a new degree of difficulty because of the nature of the exchanged data during the payment transaction from a privacy and sensitivity point of view [27]. The limitation of mobile devices, such as typing on the screen, small screens, and the fear of security protection, might also be obstacles for some users. Consumers will be encouraged to know that the service provider offers the necessary experience and tools to perform banking services and transactions on a mobile device, such as software systems, expertise, technical infrastructure, and skills [28]. Therefore, the following hypothesis was developed (Fig. 1): H4: TF has a positive effect on mobile payment use intention.

Fig. 1. Proposed research conceptual framework

3.5 Mobile Perceived Trust (MPT) Adopting any new technology requires the feeling of a safe environment. The customer needs to feel secure while using the service. Trusting the electronic wallet service is considered a hedonic value dimension [29]. Hence, the belief that all nodes connecting mobile payment services are trustworthy, sincere in upholding commitments and willing to take full responsibility for identity theft, fraud, or security issues will always encourage the actual use of m-payment [18]. Consequently, we positioned the fifth hypothesis of this study H5. MPT has a positive effect on mobile payment use intention.

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3.6 Mobile Usefulness (MU) In this research, perceived usefulness refers to how much a customer feels mobile payment can help them accomplish their goals or enhance their tasks [30]. The study analysed 459 respondents’ data and reported that the perceived usefulness is significant, with a p-value of less than 0.05. Moreover, a study conducted in Malaysia to examine the QR payment confirmed the association of usefulness with behavioural Intention [31]. Additionally, another study related to mobile payment adoption [32], and study examining the adoption of wearable payment both studies confirmed the relationship with behavioural intention [33]. Thus, the hypothesis below was developed: H6. MU has a positive effect on mobile payment use intention. 3.7 Mobile Ease of Use (MEoU) The perceived ease of use refers to how simple or complicated a system is to understand and execute [24]. The relationship between perceived ease of use and behaviour intention is confirmed using SEM analytical tool in a study that involved 485 participants [24]. Individual intention is influenced and affected by the perceived ease of use of creative solutions or facilities, which means that perceived ease of use stands out as an obstacle to the intention to use e-banking services in Jordan [32]. Moreover, a study examining the mobile wallet in the hospitality industry found a significant relationship and association of the perceived ease of use with behavioural intention [33]. Accordingly, the study developed the following hypothesis:H7: MEoU has a positive effect on mobile payment use intention. 3.8 Mobile Perceived Security Risk (MPSR) Adopting any new technology requires the feeling of a safe environment. According to a study, a sense of security is linked to the emotions they feel when their money is secure [29]. Furthermore, they noted that a sense of security is not associated with a safe method of conducting banking transactions but with positive feelings associated with money. Another research study focused on the intention to use e-banking in Jordan; reported that the security risk effect on consumer attitude is significant [32]. Moreover, a study related to new innovative technology (wearable payment found that security risk significantly affects the adoption intention [34]. Customers’ private data and financial assets are still in their thoughts since data on their mobile devices might be lost or stolen if they use the wrong app, spyware, dangerous virus software, or are hacked [35]. The customer inferences about the newly adopted payment channel’s reliability and security level from their cumulative satisfaction with the old channels [35]. Hence, the hypothesis below was developed: H8: MPSR has a positive effect on mobile payment use intention.

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3.9 Attitude (ATT) The overall evaluation of the behaviour is referred to as attitude [14]. It can negatively or positively affect behavior [36]. The seriousness of the behaviour and assumptions about the likelihood of the outcome positively or negatively affect people’s attitudes [15, 39]. A study confirmed that attitude significantly influences users’ attitudes toward using mobile payment [37]. Hence, the hypothesis below was developed: H9: ATT has a positive effect on mobile payment use intention. 3.10 Subjective Norms (SN) Subjective norms presume that an individual’s intention to engage in a particular behaviour highly predicted whether the surrounding atmosphere or significant people in his or her life approve of the behavior [38]. It connects the individual to the social network surrounding that can inspire more people to use mobile payment systems. A study involving 196 respondents supported the relationship between subjective norms and consumer intention [15]. Hence, the hypothesis below was developed: H10: SN has a positive effect on mobile payment use intention. 3.11 Perceived Control Behaviour (PCB) The level of control that a person perceives over performing a particular behaviour is perceived control behavior [39]. As a result, the more individuals have the necessary abilities, resources, opportunities, and convenience to do the behaviour, the more inclined they are to use mobile payments. A study on saving electricity intention confirmed the relationship with the intention [39]. Moreover, a study involved 196 respondents in assessing their intention to attend events using social network websites reported a significant relationship to influence the intention use to attend events using social network websites [15]. Another study involved 269 respondents adopting fintech innovation, which confirmed the relationship [38]. Hence, the hypothesis below was developed: H11:- PCB has a positive effect on mobile payment use intention. 3.12 Mobile Payment Use Intention (IU) According to TPB (Ajzan 1985), the intention to conduct given behaviour is considered an immediate antecedent of actual behavior [17]. Several studies have shown that actual technology use is primarily guided by an individual’s behavioural intention and assessment [40]. As a result, adoption intention has been regarded as one of the most closely related cognitive precedents of behaviour since breed behaviour. In several other studies, the intention was used as a surrogate for actual use [40]. Thus, the hypothesis below was developed: H12: IU has a positive effect on actual use behaviour.

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4 Methodology This quantitative non-experimental approach study aims to see if there is a connection between technological constructs and human behaviour when adopting mobile payment technology and using it. A developed questionnaire is delivered to research participants to collect primary data in this strategy. The outer layer is identified via quantitative data collecting to generalise exploratory. There will be an opportunity for acquiring quality data by addressing these persons in private and public sector organisations working in the Sultanate of Oman. The specialised authority could not release sampling frames; therefore, non-probability convenient sampling was adopted for effective cost, time, and convenience. A total of 308 valid responses were collected and used in data analysis. The sample size of this study exceeded the rule of thumb stated by Hair et al. (2017); the minimum sample size should be ten times the maximum number of arrowheads pointing at latent variable anywhere in the PLS path model [41]. Using the rule of thumb of Hair et al. [43], the minimum sample size must not be less than 120. To overcome the biased result caused by the non-responded questionnaires or poorly responded (example of all answers set to the first or last option or following a trend, additional responses are needed. Therefore a sample size of 308 is justified. The data for this study was collected utilising a created questionnaire survey using Google Forms as a collecting tool. A sevenpoint Likert scale will be used in the study (ranging from one to seven, with one being “Strongly agree” and seven being “Strongly disagree”). The structural equation model will be employed as the statistical software for this project (SEM). A review of existing research was used to develop the tools used in this study’s questionnaire. To pre-test the questionnaire, two ways were used: the participating approach and the undeclared approach. 4.1 Demographic Profile Based on Table 1 (Descriptive Analysis), 256 respondents selected the Arabic version of the questionnaire. The respondent’s ages vary in all age groups. From education wise, 176 of the respondents were undergraduate (Bachelor), 37 were high secondary school or less, 49 were diplomas, and 46 were postgraduate (master and above). Table 1. Descriptive analysis

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4.2 Measurement Model Assessment This study adapted the partial least square-structure Equation Modelling (PLS-SEM) method to analyse the conceptual model using SmartPLS 3.3.3. The validity and reliability of the pilot phase’s data were conducted; all results are shown in Table 2. For testing the internal consistency reliability, the result demonstrated that Cronbach’s Alpha ranges from 0.732 to 0.967, where the threshold for Cronbach’s Alpha is 0.7 [42]. Table 2 also shows composite reliability (CR) values ranging from 0.878 to 0.976. All passed this test at the suggested point value above 0.7; therefore, the internal consistency reliability and Cronbach’s alpha and composite reliability are established. Convergent validity, Factor loading and average variance extracted (AVE) were tested to examine the convergent validity. Table 2 Results shows the instrument loading factor where all items have fulfilled the minimum loading requirement of 0.7. The results also indicate that the AVE values ranging from 0.713 to 0.910 are higher than the suggested value of 0.5 [43]. Therefore convergent validity in this study was ascertained. Table 2. Factor loading, Cronbach’s alpha, rho_A, composite reliability and average variance extracted (AVE)

The threshold for Cronbach’s Alpha is suggested to ≥ 0.7, Composite reliability (CR) ≥ 0.6, and Average Variance Extracted (AVE) ≥ 0.5 [22]. The measurement of discriminant validity could be established if each indicator’s loading on its constructs is higher than the cross-loading on other constructs [22], which is confirmed by results shown in Table 3, Discriminant Validity -Fornell-Larcker Criterion.

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Discriminant Validity - Fornell-Larcker Criterion has also shown that it has been attained for this study. Hetrotrait-Mono-Trait discriminant validity was also proven because none were above the 0.90 cut-offs [41]. As a result, Table 4 demonstrates that HTMT has been accomplished as all the values fall below 0.90. Table 3. Discriminant validity – Fornell-Larcker criterion

Table 4. Heterotrait-monotraito (HTMT)

4.3 Structural Model Assessment SRMR threshold suggested for model goodness of fitness should be less than or equal to 0.08 [44]. The assessment report of SRMR values for the structured Model and the estimated Model were below the threshold of 0.080 (0.055, 0.069, respectively). The predictive accuracy analysis of the proposed model was checked through R square (R2). The predictive accuracy can be read as substantial if the value is more than 0.75, moderate if the value is above 0.5, and weak if the value is less than 0.25 [41]. Our result reported that model prediction accuracy for the mobile payment intention use is 72.15% which is considered substantial. While, mobile payment actual use behaviour is 36.02% which is moderate. Regarding hypothesis testing, H1, H9, H10, H11, H12 were significant with p values of 0.000, 0.017, 0.028, and 0.026, respectively. Overall, five hypotheses were accepted and supported out of 12 hypotheses (Tables 5 and 6). F2 is used to assess whether the omitted variable significantly impacts the endogenous variable. Cohen (1988) has set the thresholds to measure the level of effect of F2 . The

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M. M. K. Al Rawahi et al. Table 5. R square of the endogenous latent variables

Table 6. Hypotheses testing

Table 7. F square

values less than 0.02 are no effect, between 0.02 and 0.15 are small effects, and between 0.15 and 0.35 are medium effects. In contrast, values above 0.35 are considered larges effect [8]. Therefore as shown in Table 7, the intention to use mobile payment has a large effect with an f2 value = 0.514, while Attitude, Perceived Control Behaviour, Perceived Usefulness, Perceived value, and subjective effects are small with f2 value (0.044, 0.031, 0.021, 0.091, 0.030) respectively. No effect was calculated for digital social media, facilitation condition, mobile ease of use/ usefulness, mobile trust, and technical feasibilities.

5 Conclusion and Discussion This study aimed to examine the elements influencing people’s intentions and actual use of mobile payment. This research will help strategic decision-makers understand the factors that significantly affect the individual’s intention to use m-payment in Oman.

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The conceptual framework consists of 12 constructs. This study found that perceived value significantly impacts consumer intention to use mobile payment, supported by the qualitative data collected. The supporting data suggest that quick access, time-saving, loyalty point redemption, and discount on the fees were relevant in enhancing consumers’ perceived value of the mobile payment. Additionally, the influence of perceived value on Mobile payment intention use may also be achieved by ensuring that mobile payment is a pleasant experience. Increased customer participation in the product or service throughout the consumption phase might lead to a more enjoyable experience [45]. Moreover, earlier research found that service quality substantially influences the perceived value and that corporate image has fair connections with perceived value. This means that service quality influences the realisation of value in financial services, affecting consumers’ behavioural intentions [46]. The consumer’s attitude to mobile payment was also a significant influencer for mobile payment intention use. This finding indicates that consumers are affected by the surrounding people. Utilising such a marketing tool can boost the usage of mobile payment. Subjective norms influence individuals from the surrounding people like friends, family members and other close people’s acceptance of the behavior [38]. This influence is similar to the social norms of (TRA). Subjective norms are the most important and influential factor in predicting the use and acceptance of new technology [47]. Therefore, mobile payment intention and actual usage can be promoted by people if they are satisfied with the service. As the finding shows, perceived control behaviour significantly influences mobile payment intention use. Like previous studies, “the control behaviour is determinant key for student banking intention” [36]. As in the mobile payment context, the better-perceived control behaviour, the more use intention is achieved. Therefore, mobile payment providers must take a flexible approach to accommodate different consumer groups like older adults, young people, and special needs. The more skills, resources, opportunities, and simplicity individuals can conduct the behaviour, the more inclined they are to use mobile payments. Earlier studies confirmed that mobile payment intention uses a surrogate for actual use [40]. This study also confirmed this relationship. This study found perceived value, attitude, subjective norms and perceived control behaviour to have a significant relationship with mobile payment intention use. Therefore, to boost the adoption and use of mobile payment, payment providers and the government need to set the required plans to enhance the perceived value of the mobile payment, the perceived control behaviour, attitude, and subjective norms to achieve the best incline of mobile payment adoption in the region. On the other hand, this study also concluded that digital social media does not significantly influence user intention. In Oman, a study was conducted to study student attitudes toward using social media in the education field [48]. It matches our results that it does not significantly influence the intention to use mobile payment. This could explain that individuals are interested in using applications regardless of the social activity performed under these applications. Moreover, facilitating conditions were found not significant in influencing the use of mobile payment intention; however, such finding contradict another study, such as the adoption of mobile zakat payment application in Indonesia [49], where they found it to be a very important determent. This could be when the study collection data falls during the worldwide pandemic where people

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practice social distancing. With limited resources, they can manage their daily activities with less interest in depending on the service provider’s support. Similarly to ease of use factor was found not significant. In a country such as Oman, where there is high mobile device penetration, people find it no anymore an obstacle. Mobile devices have become part of people’s daily life. Suppose an individual is interested in specific application weather. In that case, it is the ease or not he will find his way to proceed in. Ease of Use as an influencing factor was also found insignificant in this study is in line with a study evaluating ease of use during the COVID-19 pandemic to use electronic money applications [50], this contradiction is a result of human behaviour changes along with the technology development advancement and the high use of mobile devices among Omani people [51]. Mobile perceived security risk was found insignificant in influencing the use of mobile payment intention; this could be because when compared with the traditional payment methods, digital payment was found to be less risky as it employs advanced encryption techniques. Thus individuals have confidence that their payment transaction is secured by using mobile payment. Therefore, they do not see a security risk as an issue. This is in line with another study assessing the adoption of smart credit cards [18]. Perceived trust was found insignificant in this study; this can be explained because people already established trust with the payment providers. This is because of the government’s imposed rules and regulations and payment industry compliance which protects personal and sensitive information, prompting total security of users’ credentials. Therefore users have no more issues with trust as long as the government is blessing and compliance with international rules and regulations in operating payment systems. This finding contradicts another study conducted in Oman [51]. This contraction is new knowledge for Oman’s context and needs further exploration. Mobile usefulness does not have a significant impact on influencing the individual’s intention to use the mobile payment, and such a result can such determent is not an issue as users value the perceived value more the usefulness; therefore, if the gained value is more, users tend to accept and adopt the technology regardless it is usefulness. This is in line with the finding of a study to assess purchase intention via mobile app technology [52].

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How Does User-Generated Content Affect Users’ Obsessive Use of Lifestyle-Sharing Mobile Social Networking Site? Yun-Peng Yuan1 , Garry Wei-Han Tan1,2(B) , Eugene Cheng-Xi Aw1,2 , Tat-Huei Cham1,2 , and Keng-Boon Ooi1,2,3 1 UCSI Graduate Business School, UCSI University, Cheras, Malaysia

[email protected]

2 Department of Business Administration, IQRA University, Karachi, Pakistan 3 College of Management, Chang Jung Christian University, Tainan City, Taiwan

Abstract. Recently, lifestyle-sharing mobile social networking sites (m-SNS) are becoming more and more popular among young users in China. However, like other SNS platforms such as Facebook and WeChat, lifestyle-sharing m-SNS users also suffer from obsessive technology use issues. Therefore, this study aims to understand users’ obsessive use of lifestyle-sharing m-SNS by exploring the role of users’ perceptions of the platform’s content in users’ decision-making process. The data yielded 734 effective responses collected through an online survey and analyzed by Partial Least Squares-Structural Equation Modelling (PLS-SEM). The results revealed that users’ perceptions of content on lifestyle-sharing platforms significantly affect both users’ cognitive and affective assessments on the platform, and users’ affective assessment has a strong positive influence on four types of obsessive SNS usage behaviors. This study fills the theoretical gap of SNS obsessive use by introducing content perceptions as antecedents and offers practical guidelines to both SNS providers and users in reducing problematic technology use. Keywords: Lifestyle-sharing m-SNS · Obsessive technology use · Stimulus-organism-response framework · PLS-SEM

1 Introduction In China, the emergence of lifestyle-sharing m-SNSs such as ‘little red book’ (i.e., ‘Xiaohongshu’) has changed the market landscape shaped by traditional leading players, including ‘Wechat’, ‘Weibo’ and ‘Tiktok’. Lifestyle-sharing m-SNS is the SNS allowing users to share, engage and discover diversified lifestyles. Using photos, text, videos, and live-streaming, the users generate and share showcases regard to their experiences on different lifestyles such as cosmetics, fashion clothing, traveling, and entertainment that visually inspire other community members. In addition, the platform adopts an innovative business model that combines its user-generated content with e-commerce features, enabling its users to monetize their goods shared by adding purchasing links © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (Eds.): ICETIS 2022, LNNS 584, pp. 40–55, 2023. https://doi.org/10.1007/978-3-031-25274-7_4

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directly to their content [1]. Therefore, innovative platforms such as ‘little red book’ quickly become popular and support young consumers purchasing decisions and social interaction. However, like other popular m-SNSs, lifestyle-sharing m-SNS users may also face addictive use issues. Researchers have focused on addictive behaviors on the topics such as alcohol addiction [2], drug abuse [3], problematic online gaming [4], and generic smartphone addictive usage [5]. Balcerowska and Sawiciki [6] emphasized the roles of personality and individual differences in problematic SNS usage in terms of SNS addiction. Therefore, the external factors attribute to SNS addiction are yet to be fully understood. In addition, unlike conventional SNSs, which operate purely based on users’ social and communication needs (e.g., Facebook, Wechat), the lifestyle-sharing SNS ‘little red book’ focuses explicitly on lifestyle sharing and content monetization, making this SNS platform fulfills user’s social needs and being a shopping tool at the same time. Therefore, utilitarian and affective values should be considered when understanding consumers’ behaviors toward such innovative m-SNS [7]. In response to the discussions mentioned earlier, this study develops a stimulusorganism-response (SOR) based model that integrates consumers’ perceptions of SNS contents with psychological assessments and typical obsessive usage behaviors to understand the potentially problematic use of lifestyle sharing m-SNS users in China. Overall, this study is believed to extend the current knowledge of SNS addiction by introducing an innovative type of SNS into the topic and integrating utilitarian and affective factors into users’ decision-making. This study raises consumers’ attention to potential SNS addictive usage behavior and facilitates SNS providers’ engagement with corporate social responsibility.

2 Literature Review 2.1 Studies in Social Networking Sites The studies on SNS topics can be traced back to 2008, when researchers started to investigate how self-esteem and social capital influence online social networking sites and compare the differences between online and offline social networks [8]. After the first glance at this emerging online business model, the studies on SNS have undertaken a smooth transition over time. Recently, Bailey et al. [9] investigated the role of attitudinal and motivational factors in affecting SNS users’ engagement behavior. In addition, Khan et al. [10] attempted to understand mobile users’ SNS usage during the COVID-19 pandemic. Sun et al. [11] found that adolescents’ socioeconomic status is negatively related to SNS addiction, implying that adolescents with weak socioeconomic status are more likely to be addicted to using SNS to fulfill social needs. Although prior studies have discussed SNS from various perspectives, the SNSs discussed mainly refer to conventional SNSs, which are social-oriented rather than the one that combines social interaction with e-commerce features. Besides, since the primary research focuses on SNS addiction studies were children and adolescents, the SNS addiction of adults was neglected. Therefore, the research focusing on innovative SNS addiction among adults is

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essential to provide a more comprehensive understanding of the current SNS industrial landscape. 2.2 Consumers’ Decision-Making Process Many prior studies have used the Stimulus-Organism-Response (SOR) framework proposed by Mehrabian and Russell [12] to describe an individual’s decision-making process. The SOR framework consists of three core parts: stimulus, organism, and response. Stimulus represents external factors that a person perceives to trigger a decision-making process. In addition, the organism reflects the cognitive and affective assessment of the stimulus, while the response is the result of the organism and the actual behavioral reaction of the stimulus [13]. SOR framework has been widely used in consumers’ intention study. For example, Yang et al. [14] applied the SOR framework to explain Chinese solo travellers’ outbound travelling behavior, and Bigne et al. [15] examined the causal effect of online reviews and consumers’ purchasing intention. At the same time, little has been done using the SOR framework to explain an individual’s obsessive technology usage. Therefore, in this study, we attempt to explain lifestyle sharing m-SNS users’ obsessive usage from the SOR perspective. 2.3 Obsessive Technology Use According to James et al. [16], obsessive technology use is a behavioral pattern that could threaten users’ well-being. Inspired by the dual-system theory [17], Gong et al. [18] opined that obsessive technology usage behavior should be distinguished between the state of obsessive usage (urge versus usage) and the awareness of consequences (unawareness versus awareness). They summarized obsessive technology usage into four types, namely impulsive use, compulsive use, excessive use, and addictive use. Zheng et al. [19] pointed out that impulsive behavior is unplanned action resulting from exposure to the stimulus. While compulsive behavior, according to Muela et al. [20], is the repetitive, and self-enhance action that may be a consequence of events or feelings. Shen and Wang [21] described excessive use as when users invest more time in using products or services that exceed their original plan. Lastly, Gong et al. [18] defined addictive use as an obsessive pattern that results in harmful consequences such as interpersonal issues and academic failures. Considering prior studies on problematic SNS use neglect the differences among the obsessive patterns (i.e., patterns of addiction), this study aims to investigate the typology of obsessive lifestyle sharing m-SNS usage triggered by the user-generated content.

3 Hypotheses Development 3.1 Perceptions of Platform’s Content Inspired by Sharma et al. [22], we propose the perception of the platform’s content (PPC) as a higher-order reflective-formative construct with lower-order reflective construct as measurements which include credibility (CR), Entertainment (EN), and Informativeness

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(INF). According to Thien [23], adopting a higher-order construct reduces the number of hypotheses, allowing the proposed model to be more concise. Prior studies on advertising and SNS have applied dimensions such as CR, EN, and INF to represent consumers’ perceptions of advertisement and investigate such perceptions’ effect on advertising value (see, e.g., [22]). Zhang et al. [24] define CR as consumers’ perceived expertise and trustworthiness of the target source. EN in this study refers to users’ perception of whether the engagement with user-generated content is enjoyable and recognized as an intrinsic motivation for using the platform [25]. Lastly, McClure and Seock [26] argued that users’ commitment is strongly associated with the quality of the information provided on the website. Therefore, INF is adopted to evaluate users’ perceived information quality of content on the platform. Considering the SNS and e-commerce features of the lifestylesharing platform, the successful provision of content on the platform should meet CR, EN, and INF to maintain users’ perceived value and positive image of the platform. As such, this study adopts PPC as the higher-order reflective formative construct with lowerorder reflective constructs to measure the stimulus that triggers consumers’ decisionmaking, and the following hypotheses are proposed: H1: Users’ perceptions of the platform’s content (PPC) positively affect users’ perceived value of the lifestyle-sharing platform (PV). H2: Users’ perceptions of the platform’s content (PPC) positively affect users’ perceived positive affects (PA) from the lifestyle-sharing platform. 3.2 Perceived Value of Lifestyle-Sharing Platform Hsiao [27] argued that value refers to the incentives or reasons for performing a particular task, thus reflecting how the person values the desired outcomes. Specifically, Karjaluoto et al. [28] believed that the perceived value is consumers’ evaluation of the information system’s utility. Karjaluoto et al. [28] commented that consumers’ perceived value from the service provider is the aggregation of benefits that they are seeking, expecting, or experiencing. From the SOR perspective, utilitarian value is shaped by users’ cognitive organism, while the latter is formed based on an effective organism. In this study, users’ perceived value of the lifestyle-sharing platform (PV) is proposed to reflect users’ perceived utilitarian value resulting from their cognitive assessment of the lifestyle-sharing platform. Karjaluoto et al. [28] found that technology’s more excellent utilitarian value successfully contributes to a higher degree of user engagement. Therefore, it is assumed that the higher PV from lifestyle-sharing m-SNS would enhance users’ obsessive usage behaviors, and the following hypotheses are made: H3a: users’ perceived value of lifestyle-sharing platform (PV) positively affects impulsive use (IU). H3b: users’ perceived value of lifestyle-sharing platform (PV) positively affects compulsive use (CU). H3c: users’ perceived value of lifestyle-sharing platform (PV) positively affects excessive use (EU). H3d: users’ perceived value of lifestyle-sharing platform (PV) positively affects addictive use (AU).

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3.3 Positive Affects Affective assessment is another type of intrinsic mental process triggered by a stimulus. An affective organism captures the emotional psychological response that reacts to the environmental stimulus [29]. Islam and Rahman [30] argued that under exposure to a stimulus, a mental process is occurred to convert the perceptions into meaningful information that supports consumers in making a decision. Prior studies have found that positive affect is an essential predictor of urge behaviors [31]. Therefore, this study posits that the positive effects such as enthusiasm, happiness, and arousing would enhance users’ obsessive usage behaviors on lifestyle-sharing m-SNS, hypothesizing the following: H4a: The positive affects (PA) of a lifestyle-sharing platform positively affects impulsive use (IU). H4b: The positive affects (PA) of a lifestyle-sharing platform positively affects compulsive use (CU). H4c: The positive affects (PA) of a lifestyle-sharing platform positively affects excessive use (EU). H4d: The positive affects (PA) of a lifestyle-sharing platform positively affects the addictive use (AU). 3.4 Research Framework Based on the above-discussed hypotheses, the following research model is conceptualized to investigate how users’ perceptions of user-generated content trigger their cognitive and affective assessment, resulting in the potential obsessive uses of lifestyle-sharing m-SNS under the framework of SOR (Fig. 1).

Fig. 1. Research model

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4 Methodology 4.1 Research Instrument Since this study aims to examine the antecedents that affect users’ lifestyle-sharing m-SNS obsessive use behaviors, a non-probability convenience sampling approach is employed. Sharma et al. [22] opined that the convenience sampling approach is suitable for responding to the lack of a sampling frame. Table 1 presents the sources of past literature that were used to construct the questionnaire items of this study. All the research instruments are measured on a seven-point Likert scale from (1) ‘strongly disagree’ to (7) ‘strongly agree’. In addition, items for recording respondents’ demographic data are also included in the questionnaire. Table 1. Variables and source of items Constructs & dimensions

Items

Sources

Credibility (CR)

4

[24]

Entertainment (EN)

3

[32]

Informativeness (INF)

3

[22]

Perceived value of lifestyle-sharing platform (PV)

3

[22, 28]

Positive Affects (PA)

4

[31]

Impulsive use (IU)

4

[18]

Compulsive use (CU)

3

[32]

Excessive use (EU)

3

[18]

Addictive use (AU)

4

[18]

4.2 Data Collection and Respondent Profile A self-administered online-based questionnaire is deployed as a data collection tool. We adopt the online survey approach for two reasons: firstly, the contact-less data collection is to respond to the hygiene needs under the COVID-19 context. Moreover, the online data collection approach effectively obtains a large sample size with limited time and cost. Before the formal data collection, a pilot test was performed with 30 participants to secure item reliability and validity. The digital questionnaire was distributed through China’s largest online survey platform, ‘Sojump’. A total of 734 effective responses, which exceed the minimum effective sample of 92 calculated by G*power (v 3.1.9.2) with a power level of 0.8, an alpha value of 0.05, and five predictors, were collected [33]. The questionnaire contains two separate sections, of which the first section records respondents’ demographic characteristics (Table 2).

46

Y.-P. Yuan et al. Table 2. Demographic profile

Demographic characteristics

Frequency

Percentage (%)

390

53.2

Gender

Male Female

344

46.8

Age

Below 19

91

12.4

19 to 23

314

42.8

24 to 28

224

30.5

29 to 34

61

8.3

35 and above

44

6.0

Less than 2

59

8.0

2 to 5

314

42.8

6 to 9

255

34.7

Mobile device daily use frequency (hours)

Number of devices owned

Monthly living expenses (CHY)

More than 9

106

14.4

1

351

47.8

2

298

40.6

3

58

7.9

More than 3

27

3.7

Less than 1500

204

27.8

1500 to 2000

329

44.8

2001 to 2500

137

18.7

More than 2500

64

8.7

5 Data Analysis 5.1 Statistical Analysis This study employs Partial Least Squares-Structural Equation Modelling (PLS-SEM) to predict the proposed research model with multiple constructs. The PLS-SEM approach has high accuracy and is more effective in predicting complex research models than covariance-based SEM techniques [34, 35]. 5.2 Normality Test An online tool named ‘Web power’ was used to examine the multivariate normality of data (Sharma et al., 2021). Sharma et al. [22] argued that multivariate normality is an important criterion for accurate model prediction. Both Mardia’s multivariate skewness (β = 225.559, p < 0.001) and Mardia’s multivariate kurtosis (β = 1327.375, p < 0.001) suggesting multivariate non-normality.

How Does User-Generated Content Affect Users’ Obsessive Use

47

5.3 Assessing Reflective Constructs The reliability of the outer measurement model is confirmed by Dijkstra-Henseler’s rho (rho_A) and composite reliability (CR). As per Table 3, the rho_A value of each construct exceeds the recommended minimum threshold of 0.7, and all CR values are above 0.81, suggesting the outer measurement data are of good reliability [36, 37]. In addition, the outer loadings and the average variance extracted (AVE) are used to measure convergent data validity. The study is confirmed with convergent validity since all the outer loadings are above the recommended value of 0.7 [38, 39], and all AVE values exceed 0.5 [40]. In addition, Hetero-Trait-Mono-Trait (HTMT) ratio [41] is adopted to test data discriminant validity. The HTMT results (Table 4) indicated that all the values are below the threshold of 1, suggesting the data provide acceptable discriminant validity. 5.4 Assessing Formative Constructs This study proposed perceptions of platform content (PPC) as a reflective-formative construct. In order to test PPC, we adopted a two-step approach suggested by Hair et al. [42]. In the first step, a repeated indicator approach is used to obtain latent variable scores from the PLS algorithm, and then the variable weight, loading, and significance level were calculated in the second step. Based on Table 5, the results of VIF for all indicators are below the threshold of 5 [42], suggesting collinearity is not a severe issue in this study. Moreover, indicator weights and loadings were assessed based on 5000 bootstrap samples, and all weights and loadings were at significant levels. The results demonstrate that this study’s formative construct effectively establishes the higher-order reflective-formative construct. Table 3. Loadings, Dijkstra Henseler’s rho, composite reliability, and average variance extracted Construct

Items

Loadings

rho_A

Composite reliability

Average variance extracted (AVE)

AU

AU1

0.962

0.972

0.977

0.895

AU2

0.961

AU3

0.924

AU4

0.953

AU5

0.929

CR1

0.888

0.942

0.954

0.804

CR2

0.891

CR3

0.908

CR4

0.896

CR5

0.900

CR

(continued)

48

Y.-P. Yuan et al. Table 3. (continued)

Construct

Items

Loadings

rho_A

Composite reliability

Average variance extracted (AVE)

CU

CU1

0.971

0.975

0.981

0.93

CU2

0.961

CU3

0.969 0.959

0.968

0.858

0.981

0.986

0.946

0.969

0.977

0.913

0.981

0.985

0.929

0.952

0.964

0.871

0.948

0.966

0.905

EN

EU

INF

IU

PA

PV

CU4

0.956

EN1

0.924

EN2

0.932

EN3

0.928

EN4

0.899

EN5

0.947

EU1

0.972

EU2

0.977

EU3

0.976

EU4

0.966

INF1

0.949

INF2

0.964

INF3

0.959

INF4

0.949

IU1

0.957

IU2

0.949

IU3

0.977

IU4

0.967

IU5

0.97

PA1

0.919

PA2

0.924

PA3

0.94

PA4

0.951

PV1

0.962

PV2

0.947

PV3

0.946

How Does User-Generated Content Affect Users’ Obsessive Use

49

Table 4. Hetero-trait-mono-trait AU

CR

CU

EN

EU

INF

IU

PA

PV

AU CR

0.5

CU

0.918

0.596

EN

0.513

0.936

EU

0.967

0.55

0.97

0.582

INF

0.513

0.888

0.63

0.954

0.579

IU

0.841

0.65

0.95

0.677

0.899

PA

0.761

0.766

0.872

0.81

0.822

0.82

0.932

PV

0.691

0.772

0.805

0.805

0.748

0.838

0.849

0.62

0.684 0.962

Table 5. Assessment of higher-order construct Higher-order construct

Formative indicator

Outer weight

VIF

Outer loading

Perceptions of platform’s content (PPC)

Credibility (CR)

0.163***

0.710

0.906***

Entertainment (EN)

0.247**

1.581

0.950***

Informativeness (INF)

0.626***

3.613

0.986***

5.5 Assessing the Structural Model According to Table 6, the hypothesis testing results indicated that H1, H2 and H4a–c are supported, of which PPC is significant to PV (β = 0.805, p < 0.001) and PA (β = 0.796, p < 0.001), and PA have strong positive effect on IU (β = 0.912, p < 0.001), CU (β = 0.736, p < 0.001), EU (β = 0.821, p < 0.001) and AU (β = 0.754, p < 0.001). However, the testing result revealed that PV is insignificant to all four types of obsessive use behaviors. Overall, the model is able to explain 64.8% of changes in PV, 63.3% of Table 6. Hypothesis testing Hypothesis

Original sample (O)

Sample mean (M)

Standard deviation (STDEV)

T statistics (|O/STDEV|)

P values

H1: PPC → PV

0.805

0.805

0.018

43.862

0

H2: PPC → PA

0.796

0.796

0.017

47.731

0 (continued)

50

Y.-P. Yuan et al. Table 6. (continued)

Hypothesis

Original sample (O)

Sample mean (M)

Standard deviation (STDEV)

T statistics (|O/STDEV|)

P values

H3a: PV → IU

–0.019

–0.019

0.053

0.365

0.715

H3b: PV → CU

0.075

0.074

0.052

1.445

0.149

H3c: PV → EU

–0.03

–0.03

0.059

0.507

0.612

H3d: PV → AU

–0.028

–0.027

0.065

0.433

0.665

H4a: PA → IU

0.912

0.912

0.05

18.324

0

H4b: PA → CU

0.763

0.764

0.051

15.093

0

H4c: PA → EU

0.821

0.821

0.056

14.723

0

H4d: PA → AU

0.754

0.754

0.061

12.376

0

changes in PA, 80% of changes in IU, 69.2% of changes in CU, 63% of changes in EU, and 52.9% of changes in AU. 5.6 Effect Size and Predictive Relevance The effect size (f2 ) is calculated and presented in Table 7. A value above 0.35 indicates a large effect. The intervals between 0.35 to 0.15 and 0.15 to 0.02 specify medium and small effects, respectively. Moreover, a value less than 0.02 suggests no effect on the relationship [43, 44]. The results show that PPC has a large effect on both PV and PA. Also, PA was observed with medium to large effects on IU, CU, EU, and AU, while no effects between PY and obsessive use variables were found. Further, Table 6 indicates the predictive relevance of the model. By calculating Stone-Geisser’s Q2 , all the results are greater than zero value, suggesting the model in this study has predictive relevance [45, 46] (Table 8).

How Does User-Generated Content Affect Users’ Obsessive Use

51

Table 7. Effect size (f2 ) AU

CU

EU

IU

0

0.003

0

0

PV

PPC

PA

AU CU EU IU PV PPC PA

1.843 0.2

0.313

0.302

1.729

0.689

Table 8. Predictive Relevance (Q2 ) SO

SSE

Q2 (=1 – SSE/SSO)

AU

2,936.00

1,606.54

0.453

CU

2,202.00

848.554

0.615

EU

2,202.00

959.571

0.564

IU

2,936.00

859.633

0.707

979.076

0.555

PV

2,202.00

PPC

2,202.00

2,202.00

PA

2,936.00

1,410.94

0.519

6 Discussion This study aims to examine antecedents that affect users’ possible obsessive usage behaviors on emerging SNS named lifestyle-sharing mobile social networking sites (lifestylesharing m-SNS). As hypothesized, users’ PPC is positively related to PV, supporting H1. The finding is in line with Sharma et al. [22], who found that consumers’ perceptions of advertising strongly and positively impact advertising value. In addition, PPC is also found to be significant with PA, supporting H2. Sharma et al. [22] revealed that advertising perceptions positively affect consumers’ attitudes toward advertising services. Since the main characteristics of user-generated content on lifestyle-sharing SNS are to share and recommend products and services to other SNS users, it is safe to argue that the usergenerated content on the platform plays a role similar to an advertisement. Therefore, it can be concluded that the positive stimulus users perceived from the platform could successfully be led to positive outcomes for both users’ cognitive and affective organisms. Significant relationships are also observed between PA and four types of obsessive usage behavior, supporting H4a-d. The findings agreed with Wang and Lee [31], who opined that positive attitudes toward technology lead to compulsive usage behaviors. Similarly,

52

Y.-P. Yuan et al.

addiction studies on various topics indicated that people’s affective states such as Loneliness [47], anxiety [5], enthusiasm, and arousing [18] are determinants of obsessive behaviors. Therefore, in this study, we argue that users’ four types of obsessive usage behaviors are affected by their affective evaluations of the platform’s content. The relationship between PV and obsessive usage behaviors is rejected apart from the supported hypotheses. The findings on H3a to H3d contradict prior studies, which revealed that consumers’ perceived value could enhance their usage intention, commitment, and satisfaction with mobile-based services [22, 28]. One explanation is that PV is the outcome of consumers’ cognitive assessment, which usually relates to their rational thoughts on the SNS platform’s usage intention and satisfaction. At the same time, obsessive behaviors are more likely to be a person’s urge, which is usually irrational [18]. In other words, the results implied that users’ obsessive uses of lifestyle-sharing m-SNS are more likely to be determined by users’ subjective feelings rather than a rational assessment of the features of the shopping tool. In terms of theoretical implications, this study first time focuses on the innovative lifestyle-sharing m-SNS, which is different from the conventional types of SNS such as Facebook or WeChat. In addition, unlike prior studies on addictive behaviors, which focused on specific obsessive behaviors such as compulsive behavior [48] and impulsive behavior [49, 50], this study extends the existing knowledge by examining the antecedents of four different types of obsessive technology use as a whole, and attempt to understand whether the same set of attributes would lead to different obsessive outcomes. This study is also with practical implications. Firstly, the work is believed to alert SNS users by investigating the potential obsessive technology use behaviors. Moreover, the finding of this study implies that obsessive SNS usage can be controlled by controlling users’ emotional incentives obtained from the content on the platform. Finally, this study provides a guideline for SNS service providers to enhance corporate social responsibility to reduce customers’ obsessive use and gain positive reputations. However, this study is also with limitations. This study focused on an innovative type of SNS. Thus, few prior studies on the same subject provide direct literature support. Therefore, we recommend future studies to keep track of the topic and contribute new knowledge to this field. Moreover, this study uses cross-sectional data and cannot detect changes regarding the time difference. Thus, future studies are encouraged to obtain research data on a longitudinal basis to overcome this limitation.

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36. Loh, X.-K., Lee, V.-H., Loh, X.-M., Tan, G.-H., Ooi, K.-B., Dwivedi, Y.K.: The dark side of mobile learning via social media: How bad can it get? Inf. Syst. Front. 24, 1887–1904 (2021). https://doi.org/10.1007/s10796-021-10202-z 37. Aw, E.C.-X., Tan, G.W.-H., Cham, T.-H., Raman, R., Ooi, K.-B.: Alexa, what’s on my shopping list? Transforming customer experience with digital voice assistants. Technol. Forecast. Soc. Chang. 180, 121711 (2022). https://doi.org/10.1016/j.techfore.2022.121711 38. Wong, L.-W., Lee, V.-H., Tan, G.W.-H., Ooi, K.-B., Sohal, A.: The role of cybersecurity and policy awareness in shifting employee compliance attitudes: Building supply chain capabilities. Int. J. Inf. Manage. 66, 102520 (2022). https://doi.org/10.1016/j.ijinfomgt.2022. 102520 39. Wong, L.-W., Tan, G.W.-H., Hew, J.-J., Ooi, K.-B., Leong, L.-Y.: Mobile social media marketing: A new marketing channel among digital natives in higher education? J. Mark. High. Educ. 32(1), 113–137 (2022). https://doi.org/10.1080/08841241.2020.1834486 40. Tew, H.T., Tan, G.W.H., Loh, X.M., Lee, V.H., Lim, W.L., Ooi, K.B.: Tapping the next purchase: Embracing the wave of mobile payment. J. Comput. Inf. Syst. 62(3), 527–535 (2021). https://doi.org/10.1080/08874417.2020.1858731 41. Henseler, J., Ringle, C.M., Sarstedt, M.: A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 43(1), 115–135 (2014). https://doi.org/10.1007/s11747-014-0403-8 42. Hair, J.F.H., Ringle, G.T., Sarstedt, M.C.: A primer on partial least squares structural equation modeling (PLS-SEM). Int. J. Res. Method Educ. 38, 220–221 (2017) 43. Yuan, Y. P., Wei Han Tan, W. H. G., Ooi, K. B., & Lim, W. L.: Can COVID-19 pandemic influence experience response in mobile learning? Tele. Inf. 64, 101676 (2021). https://doi. org/10.1016/j.tele.2021.101676 44. Wang, G., Tan, G.W.-H., Yuan, Y., Ooi, K.-B., Dwivedi, Y.K.: Revisiting TAM2 in behavioral targeting advertising: A deep learning-based dual-stage SEM-ANN analysis. Technol. Forecast. Soc. Chang. 175, 121345 (2022). https://doi.org/10.1016/j.techfore.2021.121345 45. Ooi, K.B., Foo, F.E., Tan, G.W.H., Hew, J.J., Leong, L.Y.: Taxi within a grab? A genderinvariant model of mobile taxi adoption. Ind. Manag. Data Syst. 121(2), 312–332 (2020). https://doi.org/10.1108/IMDS-04-2020-0239 46. Wong, L.-W., Tan, G.W.-H., Ooi, K.-B., Lin, B., Dwivedi, Y.K.: Artificial intelligence-driven risk management for enhancing supply chain agility: A deep-learning-based dual-stage PLSSEM-ANN analysis. Int. J. Prod. Res., 1–21 (2022). https://doi.org/10.1080/00207543.2022. 2063089 47. Chatterjee, S., Chaudhuri, R., Thrassou, A., Vrontis, D.: Social network games (SNGs) addiction: Psychological dimensions and impacts on life quality and society. Technol. Forecast. Soc. Chang. 177, 121529 (2022). https://doi.org/10.1016/j.techfore.2022.121529 48. Islam, T., Wei, J., Sheikh, Z., Hameed, Z., Azam, R.I.: Determinants of compulsive buying behavior among young adults: The mediating role of materialism. J. Adolesc. 61, 117–130 (2017). https://doi.org/10.1016/j.adolescence.2017.10.004 49. Parsad, C., Prashar, S., Vijay, T.S., Kumar, M.: Do promotion and prevention focus influence impulse buying: The role of mood regulation, shopping values, and impulse buying tendency. J. Retail. Consum. Serv. 61, 102554 (2021). https://doi.org/10.1016/j.jretconser.2021.102554 50. Lo, P.-S., Dwivedi, Y.K., Wei-Han Tan, G., Ooi, K.-B., Cheng-Xi Aw, E., Metri, B.: Why do consumers buy impulsively during live streaming? A deep learning-based dual-stage SEM-ANN analysis. J. Bus. Res. 147, 325–337 (2022). https://doi.org/10.1016/j.jbusres.2022. 04.013

Adoption of DeLone and McLean’s Model of Information System Success to Explore Customers’ Repurchase Intention in a Chinese Cross-Border E-commerce Platform Zhiying Hou1(B) , Yet Mee Lim1 , and Garry Wei-Han Tan1,2 1 UCSI Graduate Business School, UCSI University, 56000 Cheras, Malaysia

[email protected] 2 Department of Business Administration, IQRA University, Karachi, Pakistan

Abstract. Following the emergence of cross-border e-commerce (CBEC) in China, examining Chinese consumers’ future behavioral intention has become imperative. The research objective was to investigate the effect of CBEC platforms’ information system quality on customers’ repurchase intention via their enduring involvement and user satisfaction. Data was gathered from 511 respondents who had used a CBEC platform and was analyzed using Smart PLS. For the theoretical contribution, this research expands the commitment-involvement theory and Delone and McLean’s Information Systems Success Model to the CBEC area. For the practical contribution, this study explains customers’ repurchase intention based on their past shopping experience, which provides evidence for sellers to forecast customers’ future behavior. Keywords: Cross-border e-commerce · Repurchase intention · Information system success model · Enduring involvement · User satisfaction

1 Introduction A customer’s purchase process via an online platform from another country is known as cross-border e-commerce (hereafter CBEC). Because CBEC provides developed and developing countries various opportunities to benefit from global transactions [1], it is an essential channel in international business [2]. China, in particular, is a large market that offers many business opportunities for other countries [3]. For the past five years, there has been rapid development in the Chinese CBEC platform, including policy improvements and related platform technology development [2]. It is therefore valuable to conduct research on customers’ behavioral intentions in the CBEC platform. Given that the success of CBEC depends on buyers’ satisfied purchase experiences on the platform [4], understanding customer satisfaction is a central issue in the CBEC research area. Specifically, it is imperative to explore customers’ repeat purchase intention based on their satisfaction with past purchase experiences. Until now, however, there has been limited research on consumers’ repeat purchase intention in this context [2]. This research © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (Eds.): ICETIS 2022, LNNS 584, pp. 56–67, 2023. https://doi.org/10.1007/978-3-031-25274-7_5

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sought to identify variables from Delone and McLean’s Model of IS Success (hereafter the D&M Model) to explore the factors that influence customer satisfaction on the CBEC platform. While the D&M Model was anchored in the commitment-involvement theory to clarify how customer involvement impacts their satisfaction, this study adopted the SOR model to hypothesize how CBEC platform features affect customers’ repurchase intention via customer satisfaction.

2 Literature Review 2.1 Delone and McLean’s Model of IS Success (D&M Model) Delone and Mclean [5] reviewed the different measurements of IS success and developed a model with three main measurement dimensions: system-related features, use of the system, and the impact of users. The CBEC platform is an information system, wherein its users are sellers and consumers. This research applied the D&M Model to investigate how CBEC platform-related features affect consumer repurchase intention on the CBEC platform, whereby repurchase intention reflects the impact of the user. 2.2 The Commitment-Involvement Theory The commitment-involvement theory combines involvement theory and the concept of commitment [1]. Involvement is a motivational construct [6] applied to study people’s attitudes [7], while commitment is a long-term relationship [8] typically applied to research consumers’ consistent behavior [1]. On the CBEC platform, customers pay attention not only to the product, but also to the shopping experience. In this regard, the notion of enduring involvement reflects customers’ long-term interest in a platform, such that a high level of enduring involvement indicates the platform’s importance for customers [9]. 2.3 Stimulus-Organism-Response (S-O-R) Theory The S-O-R theory, developed from the Stimulus-Response (S-R) theory [10], is widely applied in explaining consumer behavior [11]. In this model, Stimulus (S) refers to the external environment’s impact on consumers’ internal state. This internal state is the Organism (O) component that includes physiology, feelings, and emotions. Finally, Response (R) refers to consumers’ behavioral reaction to their internal state [12]. This research posited system quality and information quality as external environmental variables that constitute the Stimulus (S) stage. Subsequently, enduring involvement and customer satisfaction were designed as the Organism (O) aspects to explore consumers’ internal state, while repurchase intention was selected as the Response (R).

3 Hypotheses Development 3.1 System Quality According to Gorla et al., system quality encompasses a website’s technical and functional features that create user satisfaction [13]. In other words, the level of user satisfaction with a website’s technical and functional performance represents system quality [4].

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The impact of system quality on behavioral intention has been found to be significant in previous research [14]. However, given the relatively new and rapid development of the CBEC platform, it is important to clarify how system quality affects user satisfaction in this context. Thus, it was hypothesized that: H1: System quality has a significant positive effect on user satisfaction. 3.2 Information Quality Information quality is customers’ perceived value from the information provided by a website [15]. Scholars have suggested that the degree of users’ perceived understanding of a website determines the site’s information quality [16]. Accordingly, Molinillo [17] indicated that information quality is essential for an e-commerce site’s success, as the information quality of shopping websites can contribute to customers’ perceived value of the website [18]. In fact, previous research has proven that information quality can enhance consumers’ usage of a system [19]. It was therefore hypothesized that: H2: Information quality has a significant positive effect on user satisfaction. 3.3 Enduring Involvement Seddon and Kiew [20] expanded the D&M Model [5] by adding an important part of the system to it, i.e., involvement. Mou et al. [1] divided involvement in a platform into enduring and situational dimensions, wherein enduring involvement reflects a customer’s long-term interest in a product or platform [21]. Huang [22] suggested that customers’ involvement influences their self-image and pleasure in purchasing on a platform. As such, this research selected enduring involvement as an independent variable that enhances user satisfaction. H3: Enduring involvement has a significant positive effect on user satisfaction. 3.4 User Satisfaction Udo et al. [23] defined user satisfaction as customers’ positive feelings about the purchasing process that is triggered by their purchasing experience [24]. Satisfaction is an essential factor in an online shopping website [25] as it influences customers’ future behavior [26]. Therefore, this hypothesized that: H4: User satisfaction has a significant positive effect on repurchase intention. Figure 1 depicts the framework of this research.

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Fig. 1. Research framework

4 Methodology The population of this research was Chinese consumers who have used a CBEC platform to purchase a foreign product. Data collected through social media channels. This research use judgement sampling method to select the target population: people who have shopping experience on CBEC platform. The first question of questionnaire is asked people’s shopping experience on CBEC platform, if they select “No, I haven’t.” then the questionnaire will be completed and submit automatically. If they select “Yes, I have.” then the questionnaire will continue show the remaining questions. Table 1 illustrates the variable measures of this study. The items for all variables were rated on a 7-point Likert scale [27]. G*Power statistical software [28] provide the minimum sample size required for this research was 85, which was achieved. Smart PLS was selected as the data analysis tool in this research [29]. Table 1. Measurement items and sources Construct

Number of items

Sources

System quality

4

[30]

Information quality

5

[30]

Enduring involvement

5

[1]

User satisfaction

3

[31]

Repurchase intention

3

[32]

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5 Analysis 5.1 Demographic Profile Table 2 shows the profile of the study participants. Of the 511 respondents, females accounted for two-thirds while males comprised one-third. A majority of them were between 18 and 24 years old, typically comprising young, college-aged individuals in China. It was also observed that most of the respondents spent less than 1000 CNY per month on the CEBC platform. The most popular CEBC platforms were shown to be TM Global and JD Worldwide. Table 2. Descriptive analysis Characteristics

Description

Count

Percentage

Gender

Male

170

28.45

Female

341

71.55

6

1.17

18–24 years old

490

95.89

25–30 years old

5

0.98

31–40 years old

3

0.58

Age

Less than 18 years old

41 years old and above Shopping experience on CBEC platform

Average expenditure on CBEC platform (CNY per month)

7

1.37

Less than 1 year

100

19.75

1

149

29.16

2

88

17.22

3

85

16.63

4

27

5.28

5

29

5.68

More than 5 years

33

6.46

Less than 200

168

32.88

200–500

211

41.29

501–1000

54

10.57

1001–2000

14

2.74

2000–5000

12

2.35

More than 5000

15

2.94

Not sure Most frequently used CBEC platform

37

7.24

Tmall Global

232

45.4

JD Worldwide

124

24.27 (continued)

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Table 2. (continued) Characteristics

Description

Count

Percentage

VIP International

23

4.5

Xiaohongshu

39

7.63

4

0.78

Kaola

16

3.13

Others

73

14.29

YMatou

5.2 Measurement Model Assessment With reference to Table 3, Cronbach’s Alpha, Dillon-Goldstein’s rho and Composite Reliability (CR) values were 0.935 and higher, indicating that all the variables in the study research had satisfactory reliability [33, 34, 41, 42]. Next, the factor loadings were all above 0.708, while the average variance extracted (AVE) values were above 0.50, confirming that the latent variable items could explain more than half of their indicator’s variance [43–46]. Thus, convergent validity was established for the data [35, 47–49]. This research used the Heterotrait-Monotrait Ratio of Correlations (HTMT) to assess the items’ discriminant validity. Table 4 shows HTMT.85 criterion, wherein all values were above 0.85. This study additionally evaluated the HTMT inference criterion at the 95 percent level of confidence (see Table 5) [36]. The results indicate that none of the bootstrap confidence intervals included the value 1.0. Therefore, the research model was confirmed to have satisfactory discriminant validity [37]. Table 3. Reliability and convergent validity results Construct

Measurement Factor Cronbach’s Dillon-Goldstein’s Composite Average item loading alpha rho reliability variance extracted

Information IQ1 quality IQ2

0.915

IQ3

0.946

IQ4

0.956

SQ1

0.797

SQ2

0.889

SQ3

0.914

SQ4

0.934

System quality

0.977

0.978

0.977

0.895

0.935

0.938

0.935

0.783

0.933

(continued)

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Z. Hou et al. Table 3. (continued)

Construct

Measurement Factor Cronbach’s Dillon-Goldstein’s Composite Average item loading alpha rho reliability variance extracted

Enduring EI1 involvement EI2

0.895

EI3

0.938

EI4

0.957

EI5

0.903

US1

0.959

US2

0.939

US3

0.956

PRI1

0.968

PRI2

0.917

PRI3

0.929

User satisfaction

Repurchase intention

0.968

0.969

0.968

0.860

0.966

0.966

0.966

0.905

0.956

0.957

0.957

0.880

0.940

Table 4. Heterotrait-monotrait ratio of correlations criterion (HTMT.85) Enduring involvement

Information quality

Repurchase intention

System quality

User satisfaction

Enduring involvement Information quality

0.905

Repurchase intention

0.929

0.876

System quality 0.836

0.917

0.836

User satisfaction

0.882

0.978

0.937

0.827

5.3 Structural Model Assessment Table 6 presented the results of hypothesis testing. At the 95 percent confidence level (α = 0.05), H1 and H2 were rejected, H3 and H4 were supported. Specifically, the results show that enduring involvement significantly enhances user satisfaction, while user satisfaction significantly increases repeat purchase intention. However, system quality did not exhibit a significant impact on user satisfaction. Information quality also didn’t shows positive impact on customer satisfaction. Hair et al. [38] suggested that a model exhibits predictive relevance when Q-square value is higher than zero. Based on Table 7, this research model exhibited sufficient predictive relevance [39, 50–52]. According to

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Table 5. Heterotrait-monotrait ratio of correlations criterion (HTMT.inference) Original sample (O)

Sample mean (M)

Information quality → Enduring involvement

0.905

0.905

Repurchase intention → Enduring involvement

0.929

Repurchase intention → Information quality

Bias

0.025

0.975

0.000

0.867

0.937

0.929

0.000

0.887

0.959

0.876

0.875

0.000

0.825

0.915

System quality → Enduring Involvement

0.836

0.835

0.000

0.755

0.894

System quality → Information Quality

0.917

0.917

0.000

0.870

0.951

System quality → Repurchase 0.836 intention

0.836

0.000

0.751

0.891

User satisfaction → Enduring 0.937 involvement

0.937

0.000

0.897

0.967

User satisfaction → Information quality

0.882

0.882

–0.001

0.834

0.919

User satisfaction → Repurchase Intention

0.978

0.979

0.000

0.960

0.993

User satisfaction → System quality

0.827

0.827

0.000

0.739

0.888

R-square in Table 7, all R-square value above 0.8, indicated that the predictive accuracy of this research is substantial [38]. Table 8 presents the results of effect size. A variable has a small effect if its f-square value ranges from 0.020 to 0.149, a moderate effect if it ranges from 0.150 to 0.349, and a large effect if it is 0.350 or higher [40]. An f-square value below 0.020 indicates no effect [36]. As can be seen in Table 8, user satisfaction recorded a large effect on repurchase intention. Likewise, enduring involvement showed a large effect on user satisfaction. However, system quality and information quality demonstrated no effect on user satisfaction.

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Z. Hou et al. Table 6. Hypothesis testing

Path coefficient

Original sample (O)

Sample mean (M)

Standard deviation (STDEV)

T statistics (|O/STDEV|)

P values

Remark

EI → US

0.666

0.661

0.076

IQ → US

0.197

0.203

0.106

8.728

0.000

Supported

1.856

0.064

Unsupported

SQ → US

0.085

0.084

0.098

0.864

0.388

Unsupported

US → RPI

0.941

0.941

0.010

98.395

0.000

Supported

Note: EI = enduring involvement, IQ = information quality, SQ = system quality, US = user satisfaction, RPI = repurchase intention Table 7. Predictive relevance (Q2 ) R Square

Q2 (=1-SSE/SSO)

User satisfaction

0.885

0.780

Repurchase intention

0.839

0.811

Table 8. Effect size (f2 ) Enduring involvement

Information quality

User satisfaction System quality

Repurchase intention

System quality

User satisfaction

22.305 0.011

Repurchase intention Information quality

0.010

Enduring involvement

0.919

6 Discussion and Conclusion CBEC platforms are new and popular in Chinese market, which calls for more research in this area. This preliminary research has revealed that the quality of the information and system of a CBEC platform does not affect user satisfaction. Nevertheless, customers’ enduring involvement increases their satisfaction, which in turn, enhances their repurchase intention on the CBEC platform. For the practical implications, this study provides evidence on the factors that increase customers’ future behaviors, such as repurchase

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intention. Customer involvement will play an important role on customer satisfactions and furthermore impact customer repurchase intention. This finding may lead to a better understanding of how customer involvement impact on their repurchase intention. From the theoretical perspective, this research applied the commitment-involvement theory and the S-O-R model to validate that enduring involvement has a significant positive impact on CBEC user repurchase intention. This finding provides further evidence that commitment-involvement theory and be used to research customer intention. Since the two variables adopted from the D&M Model were unsupported in this research, future studies are suggested to conduct more in-depth research to clarify this phenomenon. This research was limited by its homogenous sample of young consumers as well as its evaluation of only specific variables. In the future, it is advised that scholars expand their sample’s diversity and investigate more determinants of customer purchase behavior on CBEC platforms, such as website features and website design.

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37. Loh, X.M., Lee. V.H., Tan, G.W.H, Hew, J.J., Ooi, K.B.: Towards a cashless society: The imminent role of wearable technology, J. Comput. Inf. Syst. 62(1), 39–49 (2022) 38. Hair, J.F., Hult, G.T.M., Ringle, C.M., Sarstedt, M.: A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 2nd edn. Sage, Thousand Oaks (2017) 39. Foo, P.Y., Lee, V.H., Ooi, K.B., Tan, G.W.H., Sohal, A.S.: Unfolding the impact of leadership and management on sustainability performance: Green and lean practices and Guanxi as the dual mediators’, green and lean practices: Turbo-driver of sustainability performance, with Guanxi as the moderator. Bus. Strategy Environ. 30(8), 4136–4153 (2021) 40. Gefen, D., Straub, D.: A practical guide to factorial validity using PLS-graph: tutorial and annotated example. Commun. Assoc. Inf. Syst. 16(July) (2005). https://doi.org/10.17705/ 1cais.01605 41. Wan, S.M., Cham, L.N, Tan, G.W.H., Lo, P.S, Ooi, K. B., Chatterjee, R.S.: What’s stopping you from migrating to mobile tourism shopping? J. Comput. Inf. Syst. 62, 1223–1238 (2022) 42. Lee, V.H., Hew, J.J, Leomg, L.Y., Tan, G.W.H., Ooi, K.B.: The dark side of compulsory e-education: Are students really happy and learning during the COVID-19 pandemic? Int. J. Hum. Comput. Interact. 38, 1168–1181 (2022) 43. Loh,.X.K., Lee, V.H., Loh, X.M., Tan, G.W.H., Ooi, K.B., Dwivedi, Y.K.: The dark side of mobile learning via social media: How bad can it get?’ Inf. Syst. Front. 24, 1887–1904 (2022) 44. Wong, L.W., Tan, G.W.H., Lee V.H., Ooi K.B., Sohal, A.S.: Psychological and system related barriers to adopting Blockchain for operations management: An artificial neural network approach. IEEE Trans. Eng. Manage. 70, 67–81 (2022) 45. Wong, LW., Tan, G.W.H., Hew, J.J., Ooi, K.B., Leong, L.Y.: Mobile social media marketing adoption: A new marketing channel among digital natives? J. Market. Higher Educ. 32, 113–137 (2022) 46. Guoqiang W., Tan, G.W.H., Yuan, Y.P., Ooi, K.B., Dwivedi Y.K.: Revisiting TAM2 in behavioral advertising: A deep learning-based dual-stage SEM-ANN analysis. Technol. Forecast. Soc. Change 175(February), 1–15 (2022) 47. Ng, F.Z.X., Yap, H.Y., Tan, G.W.H., Lo, P.S., Ooi, K.B.: Fashion shopping on the go: A dual-stage predictive-analytics SEM-ANN analysis on usage behaviour, experience response and cross-category usage. J. Retail. Consum. Serv. 65(March), 1–15 (2022) 48. Loh, X.M., Lee. V.H., Tan, G.W.H, Hew. J.J., Ooi, K.B.: Towards a cashless society: The imminent role of wearable technology. J. Comput. Inf. Syst. 62(1), 39–49 (2022) 49. Loh, X.M., Lee. V.H., Tan, G.W.H., Ooi, K.B., Dwivedi, Y.K.: Switching from cash to mobile payment: What’s the hold-up? Int. Res. 31(1), 376–399 (2021) 50. Lew, S., Tan, G.W.H., Loh X.M., Hew. J.J., Ooi, K.B.: The disruptive mobile wallet in hospitality industry. Technol. Soc. 63(November), 101430 (2020) 51. Ooi, K.B., Tan, G.W.H.: Mobile technology acceptance model: An investigation using mobile users to explore smartphone credit card. Expert Syst. Appl. 59(October), 33–46 (2016) 52. Wong, L.W., Tan, G.W.H., Lee, V.H., Ooi, K.B, Sohal, A.S.: Unearthing the determinants of Blockchain adoption in the supply chain management. Int. J. Prod. Res. 58(7), 2100–2123 (2020)

Social Media Influencer Marketing: How Influencer Content Help? Shuzhen Liu1 , Eugene Cheng-Xi Aw1,2(B) , and Garry Wei-Han Tan1,2 1 UCSI Graduate Business School, UCSI University, 56000 Cheras, Malaysia

[email protected] 2 Iqra University, Karachi, Pakistan

Abstract. Although social media influencers are increasingly in favor of for endorsements, understanding of influencer content characteristics on endorsement outcomes remains relatively under-researched. Therefore, this study aims to understand the effective influencer content characteristics that promote brand trust and purchase intention. By conducting an online survey, a total of 235 usable responses were collected and the data was examined using partial least squares-structural equation model (PLS-SEM). The findings found that the content characteristics of social media influencers (i.e. attractiveness and expertise) have positive effects on brand trust, which further promote the development of purchase intention. Based on the research findings, theoretical and practical implications are discussed. Keywords: Influencer marketing · Social media influencers · Content characteristics · Brand trust · Purchase intention

1 Introduction Brands are increasingly looking for individuals with large social followings to endorse their products and services through experiential images, content, and live broadcasts of their products and services (Aw and Chuah 2021). According to statistics released in 2022, the number of users of global social media platforms has reached 3.78 billion at the end of 2021 and the upward trend is expected to continue. By 2025, the number of social media users will increase to about 4.41 billion, and people spend an average of 144 minutes a day using social media. Social media is intensively integrated into our daily lives (Statista 2022a). By the end of 2021, at least nine out of ten users across the globe will have watched online videos, with music videos being the most popular category. In recent years, video content has grown in popularity among users, Content creation is a digital career that is growing in popularity among users in leading global markets such as the United States. Six out of ten YouTubers based in the country stated in a 2020 survey that the platform provided them with unprecedented opportunities they would not have had access to through traditional media. In 2021, approximately 30 percent of online users in the United States claimed to have created video content for the internet (Statista 2022b). In contrast to celebrities or public figures who gain fame through traditional media, social media influencers achieve fame through the creation and publication of content © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (Eds.): ICETIS 2022, LNNS 584, pp. 68–78, 2023. https://doi.org/10.1007/978-3-031-25274-7_6

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on social media platforms. They are referred to as “ordinary people” who become “influencers” in fields such as beauty, travel, cooking, and design. Typically, they have a large following on one or more social media platforms (such as YouTube, Instagram, or Tiktok) and exert influence over their followers (Varsamis 2018; Aw and Chuah 2021). Social media influencers are heavily consulted by consumers for product recommendations. Gen Z consumers are 32% more likely than baby boomers to discover products and brands through influencers (Oracle 2022). With the growing prevalence of social media, the brand marketing practices have changed where the promotion has largely occurred on social media, partnering with opinion leaders (i.e., influencers). Authors of prior studies have examined that opinion leaders such as social media influencers who have high social status and a large number of followers can strongly influence individuals’ purchase decisions through their social media content (i.e., product recommendation and opinion sharing) (Aw and Chuah 2021; Aw & Labrecque, 2020; Ki & Kim, 2019). Although positive implications of social media influencer marketing have been evidenced, there are instances where consumers question the motive of social media influencers (Shan et al. 2019). To further explain, it has been pointed out that when the brand endorsement of social media influencers is regarded by consumers as a behavior motivated by economic interests, the trust in the brand deteriorates, which could eventually lead to the decline in willingness to purchase endorsed products (Shan et al. 2019). Social media influencers are known for their frequent interactions and relational exchanges with fans, which can be crucial for endorsed brand trust development (Aw and Lauren 2020). The consumer-influencer interactions are largely based on the social media influencer’s content. However, the brand-building role of social media influencers’ content has not been thoroughly studied in the literature (Cheung et al. 2022). Therefore, this study aimed to identify (1) the influence of social media influencers’ content characteristics (i.e. attractiveness, prestige, expertise) on consumers’ purchase intention and (2) the mediating role of brand trust in the relationship between content characteristics and consumer purchase intention.

2 Literature Review 2.1 Stimulus-Organism-Response Model Mehrabian and Russell (1974) pioneered the S-O-R model which consists of stimuli (S), organisms (O) and response (R). Stimulus are the external factors that influence an individual’s decision-making process. The organism reflects the cognitive and affective outcome engendered from the stimulus, while the response is the final reaction derived from the organism. This model explains the formation of behavioral outcome arises from individual’s perceptions and emotions, due to the exposure to external stimuli. The SO-R model has been applied in understanding shopping behavior (Hew et al. 2018), and recently applied into the influencer marketing context (Aw et al. 2022). In the influencer marketing context, social media influencers’ influence attempts, which are their content, can be seen as stimulus to consumers in the persuasive communication process (Jun and Yi 2020). Next, there have been many studies support brand trust as an organism. Brand trust is considered a psychological and perceptually positive expectation which

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mediates between consumers and brands (Tatar and Eren 2016). Lastly, response is an outcome trigged to purchase based on brand trust.

3 Hypotheses Development 3.1 Content Attractiveness Attractiveness is the degree to which a person is visually or aesthetically dependent on social media influencers for the content posted on social media (Patzer 1983). Based on previous research in the field, social media influencers’ content can influence how consumers perceive the influencer and their behavior, which in turn has a knock-on effect on the brands they endorse (Casaló and Ibá 2020; Ki & Kim, 2019; Sokolova & Kefi, 2020). In early research on source attractiveness and source credibility, the relationship between attractiveness and trust/credibility has been verified (Chung and Cho 2017). Attractive social media content posted by the influencers plays a vital role in fostering relationships between influencers and fans (Aw and Chuah 2021). The “what is beautiful is good” perception explicated in social psychology explains why people attribute positive qualities to attractive others, either persons or objects (Aw et al. 2022; Eagly, Ashmore, Makhijani, & Longo, 1991). Thus, we posit the hypothesis as follows: H1: Attractiveness is positively related to brand trust. 3.2 Content Prestige People inherently are attracted by and inclined to associated themselves with prestigious and reputable entities (Abrams and Hogg 2001). According to Bairrada et al. (2018), consumers leverage prestigious corporate brands in fulling their self-expressive needs, prompting them to integrate their self-identity with and form identification with prestigious brands. Consumers identify with social media influencers who exhibit status and display a prestigious identity and image (Hu et al. 2020). Consumers are more likely to rate influencers more positively for the prestige conveyed through social media influencer content (Ki and Kim 2019), which triggers their desire to imitate social media influencers and accept products or services endorsed by social influencers (Ki and Kim 2019). The prestige attribute signals the social capital of social media influencers, which in turn translates into consumers’ perception towards them and the brands they endorsed (Aw et al. 2022). H2: Prestige is positively related to brand trust. 3.3 Content Expertise Hoveland and Janis (1953) defined expertise as the knowledge and understanding a person has in a particular field which allow him or her to make correct assertions. Consumers gain benefits from social media influencers’ content sharing, especially their professional knowledge in a particular field such as fashion and travel (Aw et al. 2022; Aw

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and Chuah 2021; Ki and Kim 2019). Prior research suggests that consumers develop more trust towards social media influencers’ opinion (i.e., brand recommendation) when their endorsements are in line with their areas of expertise (Lou and Yuan 2019). Similarly, it has been shown that social media users develop trust in celebrities’ product reviews based on the expertise and knowledge celebrities have with the reviewed products (Djafarova and Rushworth 2017). H3: Expertise is positively related to brand trust. 3.4 Brand Trust and Purchase Intention Rooted in the social psychology literature, trust denotes the willingness and confidence of a person to rely on his or her exchange partner (Moorman et al. 1993). Embedded in the brand context, brand trust refers to the willingness of consumers to rely on the brand to perform as expected (Chaudhuri and Holbrook 2001). Brand trust promotes positive consumer attitudes and brand love (Drennan et al. 2015; Matzler et al. 2008). Building brand trust has an important role in social media marketing in which it exerts positive effect on purchase intention (Kim and Ko 2012; Chung and Cho 2017). Therefore, the following hypotheses is postulated: H4: Brand trust is positively related to purchase intention. 3.5 Research Framework Based on the SOR theory and proposed hypotheses, the following research model is constructed to understand consumers’ perceptions of social media influencers’ content and their effects on cognitive and affective assessment towards the endorsed brand (i.e., brand trust), and ultimately the intention to purchase (Fig. 1).

Fig. 1. Research model

4 Methodology 4.1 Data Collection and Respondent Profile Partial Least Square-Structural equation modeling (PLS-SEM) is a well-established forecasting method that has gained popularity in various fields, including business. Compared

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to covariance-based SEM (CB-SEM), PLS-SEM can accommodate both theory testing and predicting purposes. In addition, PLS-SEM is an effective analytical technique when sample sizes are small, data do not match distributional assumptions, models are complex, and the goal of the research is exploratory. As a result, PLS-SEM was chosen in this study (Hair et al. 2017). An online questionnaire survey was used to collect data. Judgmental sampling was applied to ensure that the respondents are Chinese consumers who have followed at least a social media influencer account (Aw and Chuah 2021). The questionnaire is developed through the Wenjuanxing survey platform. Then, the link of the questionnaire was shared and distributed through social media. The first part of the survey asked respondents to provide their demographic information (i.e., age, gender, income, and education level). Next, respondents were required to name one of their favorite social influencers. The second part of the survey included content characteristics attractiveness (3 items), content characteristics prestige (3 items) content characteristics expertise (3 items), brand trust (3 items) and customer purchase intention (3 items). The measurement items were adapted from previous studies with minor medications made based on the study context. The items of content characteristics of social media influencers (i.e., attractiveness, prestige, expertise) were adopted from the research of Aw and Chuah (2021). Items of brand trust were adopted from Habbi et al. (2014). We adopted items measuring consumer purchase intentions from Chung and Cho (2017). All measurement items were anchored on a seven-point Likert scale. In sum, a total number of 235 responses was obtained. Respondents’ profile was shown in Table 1. Table 1. Demographic profile Characteristics

Description

Count

Percentage

Gender

Male

99

42.1

Female

136

57.9

18–24 years old

82

34.9

25–29 years old

58

24.4

30–34 years old

40

16.9

35–39 years old

20

8.4

40–44 years old

24

10

45–49 years old

9

3.9

50 years old and above

2

1.5

Less than RMB 500

7

3

RMB 500–RMB 1000

12

5.1

Age

Personal total income (per month)

(continued)

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Table 1. (continued) Characteristics

Professions

Marital status

Education background

Description

Count

Percentage

RMB 1001–RMB 1500

22

9.3

RMB 1501–RMB 2000

25

10.8

RMB 2001–RMB 3000

29

12.5

RMB 3001–RMB 5000

49

20.8

RMB 5001–RMB 8000

44

18.7

RMB 8001–RMB 10000

27

11.4

RMB 10001–RMB 20000

13

5.5

RMB 20000 and above

7

2.9

Employed

108

46.2

Student

77

32.6

Freelance

39

16.6

Unemployed

6

2.6

Others

5

2.0

Dating

36

15.4

Single

84

35.6

Married

91

38.8

Others

24

10.2

Primary/middle school

18

7.5

High school

37

15.7

College degree

44

18.6

Bachelor degree

98

41.9

Master / PhD degree

38

16.3

5 Data Analysis 5.1 Measurement Model Assessment According to Table 2, the results showed that all constructs’ Cronbach alpha values are above the cut-off value of 0.7 (Hew et al. 2019; Hew et al. 2020), thus all constructs indicated strong internal consistency (Hair et al. 2006). In addition, in Table 2, all factor loadings were between 0.862 and 0.946. All values of average variance extracted were greater than 0.5, thus the convergent validity was established (Hew et al. 2020). Finally, as shown in Table 3, the discriminant validity was established as the AVE of all constructs exceeded the square of their correlations with other constructs (Fornell and Larcker 1981) (Table 3).

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S. Liu et al. Table 2. Factor loading, reliability and convergent validity

Construct

Measurement item

Factor loading

Cronbach’s alpha

Composite reliability

Average variance extracted

Attractiveness

CCA1

0.933

0.925

0.952

0.869

CCA2

0.946 0.890

0.932

0.820

0.875

0.923

0.800

0.844

0.906

0.762

0.905

0.941

0.841

Prestige

Expertise

Brand trust

Customer Purchase Intention

CCA3

0.918

CCP1

0.881

CCP2

0.908

CCP3

0.927

CCE1

0.922

CCE2

0.873

CCE3

0.888

BT1

0.890

BT2

0.866

BT3

0.862

CPI1

0.931

CPI2

0.931

CPI3

0.888

Notes: CCA = Content characteristics attractiveness, CCP = Content characteristics prestige, CCE = Content characteristics expertise, BT = Brand trust, CPI = Customer purchase intention Table 3. Discriminant validity Construct

BT

CCA

CCE

CCP

BT

0.873

CCA

0.577

0.932

CCE

0.589

0.788

0.895

CCP

0.564

0.728

0.852

0.906

CPI

0.646

0.744

0.786

0.722

CPI

0.917

Notes: CCA = Content characteristics attractiveness, CCP = Content characteristics prestige, CCE = Content characteristics expertise, BT = Brand trust, CPI = Customer purchase intention

5.2 Structural Model Assessment The study used the bootstrapping technique to test the proposed hypotheses. Results showed that attractiveness (β = 0.276; p < 0.05) and expertise (β = 0.256; p < 0.05) had significant positive impacts on brand trust. However, prestige had an insignificant impact on brand trust (p > 0.05). Furthermore, brand trust (β = 0.659, p < 0.05) had a

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positive significant impact on consumer purchase intention. Summarizing the above, all hypotheses except H2 were supported (Fig. 2 and Table 4). Table 4. Hypotheses testing Hypotheses

Relationship

Path coefficient

T-value

P-value

Remark

H1

CCA → BT

H2

CCP → BT

0.276

3.217

0.001*

Supported

0.152

1.528

0.062 n.s

Unsupported

H3

CCE → BT

0.256

1.970

0.015 *

Supported

H4

BT → CPI

0.659

13.976

0.000**

Supported

Notes: * p < 0.05, ** p < 0.01, n.s. = not significant. CCA = Content characteristics attractiveness, CCP = Content characteristics prestige, CCE = Content characteristics expertise, BT = Brand trust, CPI = Customer purchase intention

Fig. 2. Structural model Table 5. Q2 Predictive relevance Construct

SSO

SSE

Attractiveness

705.000

705.000

Q2 (=1-SSE/SSO)

Prestige

705.000

705.000

Expertise

705.000

705.000

Brand trust

705.000

504.698

0.284

Purchase intention

705.000

461.789

0.345

From the result shown in Table 5, Q2 values of brand trust and purchase intention were 0.284 and 0.345, respectively, which were greater than 0. Thus, the structural model showed predictive relevance.

6 Discussion Brand marketers are turning to social media influencers because of their large followings and increasing social influence. Nevertheless, current research mainly focused on

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traditional celebrity endorsement but overlooked social media influencers as the emerging tool of influencer marketing (Ki and Kim 2019). In order to better understand the endorsement impact of social media influencers, we construct and test a model encompassing the inter-relationships between social media influencer content characteristics (i.e., attractiveness, prestige and expertise), brand trust, and purchase intention. In this study, we found that the content characteristics of social media influencers such as attractiveness and expertise have significant roles in building brand trust. Following the interaction with social media influencer content, consumers build brand trust by not only attractive but also professional social media content from social media influencers. However, the prestige attribute of social media influencer content has an insignificant role in developing brand trust. The reason for the insignificant result is that consumers’ initial expectations of social media influencers’ content to be prestigious, which is a premise for consumers to pay attention to social media influencers. Another possible reason for the finding is attributed to the facts that prestige can signal exclusiveness, which may engender the feeling of distance between consumers and the social media influencers, eventually extended to the endorsing brand. In terms of theoretical implications, firstly, compared to the past research in influencer marketing which emphasized celebrity endorsement (Aw and Labrecque 2022), the present study takes a further step to understand the modern influencer marketing that founded upon the social media influencers. Secondly, current research has largely focused on the personal characteristics of social media influencers but overlooked the importance of the content characteristics. The present study recognizes the importance of social media influencers’ content as the main medium influencers interact with consumers and expands the theoretical knowledge by examining how the content characteristics of social media influencers can influence brand-related outcomes. The study provides empirical evidence for the effect of content characteristics such as attractiveness and expertise as the drivers of brand trust, which ultimately foster purchase intention. The findings suggest that social media influencers can endorse products or services with more engaging and professional content. Importantly, social media influencers are suggested to craft their social media endorsement content as accurate, professional and attractive. Improving the attractiveness and expertise of social media influencers’ content can help build brand trust and promote consumer purchase intentions, and finally improve endorsement effects. To this end, social media influencers may want to fully utilize the new features embedded in social media platforms, such as reels, stories, live streaming to improve their content. In terms of expertise, it would be good for social media influencers to demonstrate their ample knowledge in the endorsing product category through a thorough personal experience sharing, backing up with the sharing of proper evidence whenever needed. Despite the richness in terms of findings, some study limitations should be acknowledged. First, this study is limited by its cross-sectional design that cannot take into accounts of the impact yielded by change in time on the power of influencer content. Future research should conduct longitudinal design-based studies to address this issue. Moreover, the research is conducted in a single country (i.e. China), which may limit its generalizability to other countries with different cultures and demographics background. Future studies should address this issue by conducting a cross-country comparison to

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yield richer insights. Furthermore, social media influencers’ interactional stimuli, such as interactivity and self-disclosure can be integrated into the model to yield further insights. In addition, future studies could examine other behavioral outcomes such as engagement and co-creation as well as the application of social media influencer marketing in another context such as brand crisis (Aw and Labrecque 2022).

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Impulsive Buying Behaviour on Social Commerce: Moderated by Cultural Dimension, and Perceived Trust Alexa Min-Wei Loi1,2(B) , Keng-Boon Ooi1 , and Garry Wei-Han Tan1,3 1 UCSI Graduate Business School, UCSI University, No. 1 Jalan Menara Gading, UCSI

Heights, 56000 Cheras, Wilayah Persekutuan Kuala Lumpur, Malaysia [email protected] 2 Faculty of Business and Management, UCSI University Sarawak Campus, Lot 2976, Block 7, Muara Tebas Land District, Sejingkat, 93450 Kuching, Sarawak, Malaysia 3 Department of Business Administration, IQRA University, Karachi, Pakistan Abstract. Driven by technology and globalisation, consumers are switching from physical to online shopping, with the growth of social commerce being foreseeable. Impulse buying behaviour has contributed to a big percentage of ecommerce. The study adapted the stimulus-organism-response (SOR) model with the internal stimuli (emotion) and external stimuli (social influence) being the independent variables. The study aimed to contribute theoretically to the stimulus-organismresponse (SOR) model with the introduction of psychological and demographic factors (perceived trust and cultural dimension) as moderating effects, while also contributing practically to e-retailers and marketers with insights into the key stimulus affecting consumers’ behaviour for strategic marketing. The study has contributed to the body of knowledge for academicians and practitioners. Academically, the conceptual research framework contributes to the impulse buying and consumer behaviour literatures by taking into consideration the stimulusorganism-response (SOR) model, psychological and demographic factors. Practically, practitioners can develop effective marketing strategies by focusing on the important factors affecting consumer’s impulse buying behaviour. Keywords: Social commerce · Impulsive buying behaviour · Stimulus-organism-response (SOR) model

1 Introduction Online shopping behaviour and experience have evolved with the advancement of technology and many consumers preferred purchasing via online store as shopping can be performed without the restriction of time and place [1]. Social media has become an important internet marketing tool [2]. Understanding the behaviour of online shoppers has become more important by the day for e-retailers and marketers to maintain competitiveness [3]. Globalisation has also increased competitiveness and poses challenges to many Malaysian retailers [4]. Online shopping churns a higher possibility of impulse purchases with the removal of time and location restrictions [1] and social media’s viability affects the consumer’s purchase decision, including impulsive purchases [5–7]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (Eds.): ICETIS 2022, LNNS 584, pp. 79–89, 2023. https://doi.org/10.1007/978-3-031-25274-7_7

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Social interaction in social commerce has provided different consumer experiences and has contributed to the parallel development of impulsive buying behaviour [8, 9] which account for a large portion of social commerce revenue [10]. Recent studies of social commerce impulse buying focus mainly on the environmental cues such as advertisements, celebrities’ endorsements, social interaction, and personalisation [11–13], however, the traditional findings of impulsive buying behaviour in general should be disregarded in the social commerce context. The stimulus-organismresponse (SOR) model used often in the study of consumer buying behaviour has suggested that internal and external stimuli cue the consumers’ cognitive and emotional reaction, resulting in buying behaviour [14, 15]. The study of environmental cues has addressed the external stimulus, while there is paucity in the study of internal stimulus or personal trait. Following the aforesaid gap, this research devotes its attention to impulsive buying behaviour by incorporating both the external and internal stimuli with personal traits in the social commerce environment to offer valuable insights.

2 Literature Review 2.1 Impulse Buying Behaviour The action of consumer buying with little or no deliberation after a sudden, powerful urge is known as impulse buying [16]. Impulse buying is defined as an unplanned purchase [17], resulting from a stimulus and a purchase decision is made on the spot [18, 19], when the time lapse between the desire to purchase and the actual purchase is short, it is thus classified as unplanned [20–22]. Such unplanned purchases fall into four main categories, such as: pure impulse buying, reminder impulse buying, suggestion impulse buying, and planned impulse buying [12, 22]. Impulsive buying behaviour on electronic and social commerce platforms is the desired outcome for online retailers [23] as impulse buying is found to be contributing to approximately 60 to 80 percent of all purchases varied by the type of products [22, 24]. The event of impulse buying is commonly described as an experience of strong temptation for an object and the individual has little behavioural constraint to resist the temptation of making the purchase [25–28]. The behaviour is difficult to control and accompanied by strong emotional conflict most of the time, and thus the consumption outcome is neglected by the individual [21]. However, later research found that impulsiveness can be controlled to some extent, where impulse buying behaviour is not a process done completely without thinking [29]. Empirical studies have proven consumer impulsive buying behaviour is associated with many stimuli, including consumption and sales promotion stimuli [3] while the hedonic and utilitarian value perceived is also found to be related to impulsive buying behaviour [19]. 2.2 Stimulus-Organism-Response (SOR) Model The stimulus-organism-response (SOR) model, that is often used in the study of consumer behaviour, suggested that internal and external stimuli cue the consumers’ cognitive and affective reactions, resulting in buying behaviour [14, 15]. The model is

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frequently utilized by researchers in interpreting the consumer decision-making process [30], including in the context of social commerce and impulse buying [1, 31] where online buying is a result of reacting to the internal cognitive or affective state triggered by environmental stimuli [15]. Social influences were believed to be stimuli to impulse buying urge [21, 32]. The relationship between consumers’ emotional state and their intention to purchase were highlighted by researchers in 2011 using the SOR model [33, 34]. Thus, the SOR model is adapted as the underlying framework.

3 Proposition Development 3.1 Cognitive and Affective Three concepts of impulse buying are identified, where impulse buying is usually unplanned, being a behaviour prompted by external stimuli and with little consideration on information search or evaluation of choices [35]. Consumers can experience an urge to buy impulsively with promotional incentives [28], which is aligned with findings that impulse buying begins with a consumer’s sensation and perception, driven by external stimulus, and furthered by urge [36]. Consumers are expected to make more informed purchases with the growing use of the online platforms where information searches are more convenient with the empowerment of the internet [37]. However, impulse buying in ecommerce has increased with the development of online stores [38], along with technology innovation which has claimed to make the purchasing process easier [21]. Consumer behaviour is believed to be based on the influences of cognitive and affection reaction towards an object [39]. The values and thoughts are believed to be part of cognitive reaction [39–41], while feelings and attitudes are the affective reactions [39, 41–43]. Cognitive reaction can be triggered by an external stimulus [30, 44] where information is evaluated based on information usefulness and hence leads to information adoption before the buying intention is formed [45]. Information and perceptual constraints must be evaluated before internal constraints such as emotional attachment (affective reaction) can be addressed [44, 46]. Affective reaction involves a complex set of processes guided by emotions [21, 44], at the same time, it is established with cognitive reaction as a basis [44, 46]. On one hand, affective reaction has been found to have significant influences on impulsive buying urge, cognitive reaction is believed to be preceding it, which is aligned to the earlier argument that online purchasing decision is a rational process based on problem-solving and information processing [47]. Thus, the following is the proposition for the study. Proposition 1: Cognitive and affective state (perceived enjoyment) of an individual confirmed or denied the online impulsive buying urge of an individual. 3.2 Emotion Emotion is the direct stimulus developed internally that has immediate impact on an individual’s behaviour. Internal stimulus is found to be related to the various personalityrelated factors that characterize an individual to experience urge in impulse buying during shopping [16]. Individuals enhance their positive mood or repair their negative mood

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through shopping [48] as shopping provides a multi-sensory fantasy and emotional experience [49, 50]. Hedonic shopping realizes an individual’s fantasies where a sense of fun is initiated during the process of buying, rather than the buying behaviour itself [51]. Hedonic shopping stimulates the tendency of purchasing for adventure, gratifying curiosity, socialising, and declining boredom [52, 53]. The tendency of impulse buying due to emotion is driven by excitement and enthusiasm [19, 54, 55]. It can also be considered as a form of an act for immediate self-fulfilment through consumption [22, 28, 47, 56]. Thus, it is believed that the emotional state of an individual can affect the buying decision and is a vital set of affective factors influencing consumer behaviour [57, 58]. Accordingly, the following proposition is derived: Proposition 2: Emotion state of an individual possess as a stimulus to trigger online impulse buying intention in an individual. 3.3 Social Influence Impulsive buying is found to be influenced by an individuals’ cognitive and affective reactions that are easily affected by the environmental cues [14, 15]. The urge of impulse buying is possible through visual cues such as promotional incentives or other sensory information through interaction and visual media [3, 21, 28]. Media information, professional opinions, and word-of-mouth from peers are forms of social influences [59] which affect an individual’s behaviour as demonstrated in the theory of planned behaviour. Peer opinion and experience available online have become useful information sources for consumers, rather than company generated information [60–63] as the consumer’s assessment and opinions shared on social media are believed to attribute to greater authenticity, perceived credibility, and diminished bias [64–67]. The adoption of electronic word of mouth (eWOM) information in social media does affect purchase intention of consumers [68]. Earlier studies have confirmed that social norms are influential to an individual’s behaviour [69]. Thus, it is possible to believe that peer behaviour may act as a signal to consumer acceptance of impulse buying behaviour [70]. An industry marketing report in 2014 found that personalised advertisements are better in engaging customers and are more memorable to customers compared to general advertisements [23]. This further confirmed that perceived personalisation in advertisements is influential in brand evaluation and click intention [71]. These are aligned with the theory of planned behaviour where subjective norms are found to be contributing to an individual’s behaviour [69]. Thus, the following proposition is derived: Proposition 3: Social influence is a stimulus to trigger online impulse buying intention in an individual. 3.4 Cultural Dimension Impulsive buying is believed to be shared by interactive influences of not only the individual’s environment but also other traits [72]. This indicates that consumer impulse buying behaviour could be reflected by the influence of prevailing cultural mind-sets [72, 73]. Hofstede’s six cultural dimensions theory reflected that culture—value as a broad tendency to prefer certain states of affairs over other ways of behaving [73]. Cultural

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difference is an important context of consumer behaviour where it could have significant influences on the consumer’s impulse buying behaviour [73]. Cultural dimensions are a part of the social pattern of individual’s perception of themselves [73] and are being reinforced and encouraged since young within their community, which has resulted in some individuals having better control over their impulse behaviour and emotions [73]. Furthermore, culture is found to account for the behaviour and underlying actions by individuals, where the rules, traditions, behavioural traits and actions observed show how they perceived or interpreted the world [74] and have significant influences on impulsive buying [58]. Studies has also highlighted cultural differences being important in the context of consumer behaviour and are believed to have significant influences on the consumer’s impulsive buying behaviour [73]. Accordingly, the following is the proposition: Proposition 4: Individual cultural background moderates the relationship between perceived enjoyment (the cognitive or affective state) and the online impulsive buying decision of an individual.

Fig. 1. Proposed conceptual framework

3.5 Perceived Trust Perceived trust is an important element for online commerce as customers are dealing with virtual stores where at some extent, the level of uncertainty is higher as compared to physical stores [75]. It is normal that an individual would hesitate to complete the transaction when there is lack of confidence in the virtual platform [76–78] and there are also many studies that have been conducted in which proven perceived trust has been shown to affect online purchases [79–82]. Consumers could be reluctant to perform transactions online if risk is perceived as there are fewer verifications and control for exchange of product and money from the customer’s perspective [10]. There are several underlying factors that have been commonly studied in the matter of customer’s trust to the online store [79–82]. Hence, it is postulated:

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Proposition 5: Perceived trust in the online purchase platform moderates the relationship between perceived enjoyment (the cognitive or affective state) and the online impulsive buying decision of an individual.

4 Implications 4.1 Theoretical Implication First, the study proposed a new research framework by integrating perceived trust and cultural dimension to the study of impulse buying behaviour triggered by external and internal stimuli. From the theoretical point of view, this study expands the impulsive buying behaviour literature using the stimulus-organism-response (SOR) model through addressing the psychological and demographic factors as moderators to the cognitive and affective state of impulse purchase urge triggered by stimulus. The integrated research framework can be a useful source of reference for other social science scholars who are keen to replicate impulse buying behaviour in other countries with strong national cultural or cyber security. Secondly, the application of cultural dimensions in purchase behaviour is not common and thus this study contributes to the scarce literatures of purchase behaviour from the perspective of national culture. Additionally, the study of impulse buying behaviour is popular with external stimulus, however, internal stimuli, such as the emotional state of the individual, are often neglected. Thus, the inclusion of internal stimuli in the study may provide a different perspective to the interpretation of consumer impulse behaviour in decision making. 4.2 Practical Implication The shift of shopping from physical to online platforms (electronic commerce, social commerce and live commerce) has resulted in the need for practitioners (retailers, marketers, and entrepreneurs) to understand the consumer behaviour from a different perspective, especially regarding the trigger of impulse behaviour for it has contributed to the majority of sales. The investment and speed of practitioners adopting big data have revealed the fact that the study of consumer behaviour is of high value for them. The performance of businesses relies greatly on the success of triggering buying behaviour of a prospective customer. Besides being able to reach out to as many prospects in a short timeframe, being able to close sales is the key to all the effort spent in strategic planning. Practitioners have been focusing on developing all kinds of promotional campaigns, giving the impression of value for money to trigger purchase behaviour. While public relations are often another area of focus with the belief that positive word of mouth and brand awareness can build brand equity; in a long run, prospects will choose to purchase their products over the other. Often times, when an urge of purchase is triggered, the consumer may not be relating to brand reputation in the process of decision making, especially in impulse buying where the period of cognitive analysis is very short. Psychological (perceived trust) and demographic (cultural dimensions) factors however are imprinted in an individual’s behaviour and affect an individual’s decision making without much cognitive process. Thus, a better understanding of how these factors influence

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consumers’ decision making would provide the practitioners with valuable perspective in designing their marketing activities so that it is better associated to the customer.

5 Conclusion and Future Study Consumer buying experience has changed with the introduction of technology and social media applications and has resulted in a shift of marketers’ strategies in motivating consumer buying behaviour. The impulsive buying behaviour under the influence of internal stimuli, and the moderating effect of perceived trust and cultural dimensions is less studied. This study has proposed a new research framework combining both internal and external stimuli with moderating psychological and demographic factors to study the impulsive buying behaviour in the social commerce setting. The framework provides useful insights to practitioners in the better understanding of consumers in strategies development and product offering, which allows the practitioner to be more competitive in the market. There are several limitations that can be considered for future studies. First, the research model only takes into consideration of social influences as the only external stimuli. With consideration that consumers could be influenced by other factors such as government policy, perceived ease of use and usefulness of the social commerce platform and other promotional discounts, future researchers may consider incorporating the earlier mentioned to facilitate their study of impulse buying behaviour. Secondly, this study is confined from the perspective of the current status of social commerce in Malaysia, the generalizability might be limited. Future studies can be extended to other developing or developed countries where the social commerce acceptance and national culture are different to have better understanding of consumer behaviour across different countries. Lastly, the study is conceptual in nature, researchers should validate and test the proposed proposition developed in the model empirically using the Partial Least Square-Structural Equation Modelling (PLS-SEM) approach.

6 Declaration This research is part of the author’s PhD works and has been conducted while the author is a Ph.D. candidate at UCSI Graduate Business School, UCSI University. The author is grateful to Professor Ooi Keng Boon (Main Supervisor), Dr Garry Tan Wei-Han (Co-Supervisor) for their valuable suggestions on an earlier draft of this paper.

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Conceptualizing Business Intelligence (BI) Adoption in SME to Gain Competitive Advantage: Effects of Organizational Mindfulness, Inter-organizational Relationship and Technology Orientation Farzana Naznen(B) and Wei Lee Lim Graduate Business School, UCSI University, UCSI Heights, 1, Jalan Puncak Menara Gading, Taman Connaught, 56000 Cheras, Kuala Lumpur, Malaysia [email protected], [email protected]

Abstract. Business Intelligence (BI) is an advanced technology for producing highly intellectual information to ease decision-making, which strengthens growth in revenue and enables obtaining competitive advantages. BI is used in organizations across many industries, however, most struggle to properly implement it and get the desired benefits. Due to a variety of factors, BI adoption among SMEs has remained low, particularly in developing countries. This conceptual paper attempts to critically assess socio-technological network factors by developing a holistic framework for measuring the effects of various interdependent variables of BI adoption on achieving competitive advantages. The proposed model is the pioneer to combine Diffusion of Innovation (DOI) and Actor-Network Theory (ANT) theories for integrating different actors of innovation adoption processes in the organizational context. The study would provide academics and industry experts with detailed insights into the interrelated components of inter-organizational relationship (IOR) in which all players should be treated equally important in the BI adoption process. Keywords: Business intelligence · SME · Technology orientation · Organizational mindfulness · Inter-organizational relationship · Actor-network theory

1 Introduction 1.1 Background Given today’s modern and ultra-competitive business environment, it is crucial for organizations to succeed in identifying methods that may differentiate themselves from the competitors. Recent times, a wide range of technologies are employed as decisionmaking tools across businesses and industries [1]. As a consequence, organizations require sophisticated technological developments in order to respond rapidly to competitive marketplaces [2]. Organizations must work harder to obtain information to © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (Eds.): ICETIS 2022, LNNS 584, pp. 90–100, 2023. https://doi.org/10.1007/978-3-031-25274-7_8

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enhance effective business decision-making processes in such intense competition and dynamism [3]. During the previous decade, the business environment experienced significant changes in client demands, uncertainties in the economies, and shorter product lifecycle [4]. To alleviate these complications, Business Intelligence (BI) systems have a significant beneficial impact on business performance and efficiency [5]. BI system are able to identify competitive performance assessment, differentiate competitive pressures, integrate data from market, anticipate future revenue, and make decisions accordingly to achieve the goals [6]. The preference for BI is not limited to company’s size, despite the fact that it has traditionally been affiliated with larger organizations [7]. This is evident in the worldwide BI sector, which expanded by about 10.4% in 2019 compare to 2018, with revenues approximately $24.8 billion [8] and 2.82% in 2020 with a revenue more than $25.5 billion [9]. It is predicted that by 2023, BI adoption growth would be over 7.8%, with sales reaching $26.66 billion [10]. Numerous firms, particularly in emerging countries, have yet to implement BI tools due to a lack of awareness of its benefits, a scarcity of skills and expertise, and an inadequate funding [11]. For instance, most garment and textile businesses are trailing and unwilling to accept this reliable technology causing a high rate of failure (70%–80%), particularly in developing economies [2]. Despite the fact that small and medium-sized enterprises (SMEs) increasingly have the same need for BI systems as like major corporates, their adoption rates remain very low [12]. The difficulties that SMEs encounter are not how to actively handle current data, but how to extract strategically significant new data through BI. The poor use of BI thus is hampering SMEs’ capacity to compete with larger companies in the respective industry, resulting in a lack of gaining competitive advantage [12]. Thus, the adoption of BI systems, thus, need to be effectively as well as the factors related to technology, people, organization and environment must be analyzed and evaluated [5]. 1.2 Research Gaps In the implementation of BI, the intersection and interrelation between human and nonhuman resources, and organizational and environmental resources, and organization to human resources have yet to be thoroughly conceptualized and investigated. This study emphasized a theoretical deficit in the collaboration of socio-technological network variables, which may have critical intersecting associations among different level players in organization for adopting BI. For instance, most research on the association between organizational mindfulness and innovation adoption are conceptualized on generalized IT adoption or organizational digitalization [13]. There is a dearth of studies concerning influence of organizational mindfulness along with other socio-technological factors specifically in the adoption of BI. Furthermore, numerous research investigated individual technology-related determinants for BI adoption. Technology maturity, IT assets, facilitating conditions, IT readiness, data quality, and so on are key factors among those [2, 7]. Despite the existence of individual components of technology enablement, rare studies have focused on the role of Technology Orientation as a collective IT strategic force (i.e., technology innovation characteristics, IT resources, IT training, and

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IT budgets) to investigate its integrated influence on BI adoption. Furthermore, interorganizational relationships have been a significant component in information assimilation and knowledge exchange in the adoption of new technology in organizational contexts [43]. Surprisingly, current BI literature disregarded this strong element in BI adoption investigations. All of these aspects can be aligned through a robust theoretical framework known as Actor-Network Theory (ANT), which, to the best of the researchers’ knowledge, has not been utilized yet in the conceptualization and investigation of BI adoption. More significantly, the preceding determinants haven’t been studied in the context of SMEs’ BI adoption, indicating a population gap. 1.3 Research Objective To address the aforementioned research gaps, the following research objectives are set by proposing a holistic conceptual framework illustrating the associations among the factors that logically apparent in BI system alignment to get enhanced competitive advantages for the SMEs in the developing country context: • RO1: To elicit the impacts of Technology Orientation in adoption of BI system among SMEs. • RO2: To determine the roles of organizational mindfulness in influencing BI system adoption among SMEs. • RO3: To identify the effect of inter-organizational relationship in adoption of BI system among SMEs. • RO4: Finally, to illustrate the benefits of adopting BI system in achieving competitive advantages.

2 Literature Review 2.1 Research Context Business Intelligence (BI) and its Adoption in SMEs BI encompasses a range of operations, procedures, and technology for obtaining, preserving, and interpreting data in order to enhance decision-making [14]. Two features are in common: firstly to being the essential element of BI, which comprises gathering, organizing, interpreting, and disseminating information both internally and externally and secondly to enhance the firm’s strategic decision-making process generating meaningful data through BI [12]. Undoubtedly, BI is intended to depict an organization’s data assets in order to generate an exact picture of business dynamics that aids better decisions making by aggregating information from numerous sources [15]. BI systems are characterized as information systems (IS), but they differ from traditional IS systems in that they place a strong focus on data and its sources, as well as the analytical tools provided, with the purpose of directly assisting the decision-making process [16]. BI system consists of three primary components: technology components, a business process, and human factors that facilitate the translation of data and information into knowledge

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[5]. BI might be viewed as a structured approach to management, not merely a tool for SMEs [17]. BI enables the SME to consolidate data for studying, reporting, obtaining key performance indicators (KPIs), and market updates, among other activities [19]. As a consequence of having access to historical data and capturing current industry data through BI, SMEs may realize previously undiscovered business patterns and obtain a better insight of company operations and future market trends [18]. SMEs may increase the quality of their information systems through BI by gaining accessibility to latest advanced computing technologies, and reallocate financial resources to other parts of the business [19]. Competitive Advantage in SMEs through Adopting BI Competitiveness is a fundamental capability that offers a company a competitive edge over its rivals [20]. According to [21], competitive advantage is a capability or asset that is hard to replicate and facilitate organizations to outperform their rivals. Porter [22] proposed two basic attributes of Competitive Advantage: Cost Leadership, in which the business produces products at a reduced cost, and differentiation, in which the business delivers unique innovative products and services to consumers that are not available with competitors. The biggest issue for SMEs is determining how to exploit existing data to provide value to the business [23]. As a result, SMEs that can improve their BI capacities and use them through various applications would be able to fit the information together to get a competitive advantage over their competitors [19]. If BI vendors can furnish data in a useable format, SMEs may appropriately match the data with all of its resources and acquire an advantage over their rivals [23]. Technology Orientation Technology Orientation is one of the most highlighted strategic orientations, and it has been thoroughly researched in the development of new technology in organizations [24]. According to Day [25], Technology Orientation may be described as a systematic bundle of resources of organizations bonded along with learning and unlearning procedures to integrate all technological assets and enable those to be deployed successfully. Innovation characteristics, IT policies, IT processes, IT budgets, IT Training Process, and IT resources may be used to assess technology orientation [26]. Organizational Mindfulness Organizational mindfulness has been defined as the integrated ability to recognize radical changes, manage unrealistic goals, stay focused on occurrences as they develop, and accomplish key business processes even in the midst of disruptions or instabilities [27]. Mindful organizations proactively utilize market intelligence to remain aware to changing digital technologies, selecting suitable technology platforms such as hardware, applications, and network that might facilitate the changes, and keeping management fully updated on the most pertinent options that enhances the strategic decisions of digitalization [28]. Weick [29] highlighted five major aspects of organizational mindfulness in technology adoption, which Barros [30] substantially operationalized in BI adoption: • Fear of failure: Detecting and managing risks, monitoring competition and business performance, and strengthening competencies.

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• Reluctance to simplify: managing diversity and inclusiveness, as well as fostering sense-making and dispute resolution. • Operational sensitivity: managing procedures and improving quality, successfully executing operational changes, and assuring agility. • Resilience commitment: dealing with emergencies and crises, building resilience, and fostering continuous learning. • Deference to expertise: managing human capital effectively, fostering leadership, as well as navigating knowledge and innovations. Inter-organizational Relationship An inter-organizational relationship comprises a group of players, both large and small, that work together to achieve a common objective by trading resources and engaging in collaborative activities [31]. Inter-organizational relationships between firms, customers, suppliers, partners, competitors, research institutions, and training centers promote collective learning, open innovation, information and resource transfer, data sharing and dissemination, all of which are crucial for establishing technological competences [32]. There must be trust among the players in the inter-organizational network, which conceptualizes having faith across all parties, since they involve in sharing and accessing valuable sensitive information and resources [33]. 2.2 Theatrical Background Based on a literature review by Ahmad [2] (p. 9), “the majority of the studies have used diffusion of innovation (DOI) theory (48.50%), technology organization environment (TOE) framework (35.40%) and institutional theory (32.25%) regarding the BIS adoption in the literature, while for the BIS acceptance; unified theory of acceptance and use of technology (UTAUT) (12.90%), theory of planned behavior (TPB) (9.67%) and technology acceptance model (TAM) (9.67%) are deployed frequently”. This research proposes an integrative holistic research model (shown in Fig. 1) that encompasses the two prominent firm-level innovation adoption models, namely, the Actor-Network Theory (ANT) and the Diffusion of Innovation (DOI) model. To get a deeper understanding of the BI adoption process, this model identifies BI adoption and competitive advantage as dependent variables, which are consistent with the DOI phases of innovation. Moreover, the innovative characteristics (Relative advantage; Perceived Complexity; Compatibility) as dimensions of technology orientation are associated with the DOI phases of technology adoption, which is one of the independent variables in this study model. ANT was developed on the basis of the notion that the success of any technical innovation and adoption is dependent on the players of the socio-technological networks, such as engagement and collaboration of all stakeholders [34]. In that respect, for a firm to adopt any new technology, the players and their related networks (i.e., internal resources, consumers, rivals, partners, IT providers, other suppliers, other industry participants, and government) must be involved [35]. Small firms, in particular, can benefit from various network actors’ commitment to articulating the relevant information to other players, allowing them to make intelligent decisions and optimize operations [35]. In this study, the Organizational Mindfulness and Inter-organizational Relationships are regarded as independent variables under ANT factors.

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(+)

(+)

(+) BI Adoption

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Fig. 1. Conceptual framework (source: author)

2.3 Associations and Propositions Technology Orientation (TO) and BI Adoption A large internal technology portfolio as a result of a strong technology orientation is likely to facilitate technology identification and absorption [36]. According to Masa’deh et al. [37], technology-orientation aids firms to be more proactive in acquiring, integrating, and applying new technologies to improve their business. Earlier studies empirically established that numerous technological characteristics, including as technology complexity, compatibility with existing procedures, and relative advantage might impact an organization’s adoption of BI [12, 38]. Furthermore, Technology readiness, technology resources, IS competence, IT infrastructure, Technological capability, data quality and technological challenges all the factors are established to be associated with the BI adoption in earlier studies [2]. Hence, this study proposes the following association between several dimensions of Technology Orientation and BI adoption. P1: Technology Orientation is related to adoption of BI among SMEs.

Organizational Mindfulness and BI Adoption Organizational mindfulness facilitates employees in scanning the business surroundings and expanding their understanding of the associated sociotechnical settings, which enhances the adoption of organizational digitalization [28]. The presence of mindfulness in businesses enhances the likelihood that managers would make proper digital transformation decisions while using their resources to integrate digital technologies in an efficient manner [39]. Organizational mindfulness comprises anticipating, preparing for, and navigating technology advancements, which are a fundamental component of organizational digitalization [40]. The more organizational mindfulness grows, the more favorable association establishes among competitive pressure and adoption of new technology [13]. Addressing the importance of organizational mindfulness in adoption of new technologies this study proposed the following:

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P2: Organizational Mindfulness is related to adoption of BI among SMEs.

Inter-organizational Relationship and BI Adoption Because of the high degree of inter-firm collaboration and coordination activities, it is understandable that firms usually seek technology implementation skills from their trusted acquaintances [41]. Firms are increasingly engaging in co-innovation to share R&D costs and risks, expedite innovation cycles, and improve quality, and efficiency of technology adoptions through inter-organizational partnerships [42]. Li et al. [43] empirically established that Inter-organizational information exchange improves the success of ERP adoption. Managers and policymakers who allow and encourage web-based applications adoption must consider the inter-organizational associations that is existing in the industries in which they operate [44]. Concurrently, it is also critical to establish trustworthy alliances to explain the use of shared information as well as privacy issues openly, with the goal of balancing independent data confidentiality [45]. This study thus proposed the following association: P3: Inter-organizational relationships is related to adoption of Bi among SMEs.

BI Adoption and Competitive Advantage BI is regarded as a key strategic tool for attaining a competitive advantage [1, 17]. The only way to develop a really sustainable competitive advantage is to establish an organization that is constantly aware and nimble enough to recognize advantages, opportunities, and risks, crisis, and market changes where BI systems playing a critical role [20]. BI gives a long-term competitive advantage by “raising revenues,” “controlling expenses,” “achieving stable profitability,” and “providing forecasting market and economic realities” [46]. By providing data integration and analytical capabilities that may offer useful decision-making information to stakeholders at various organizational levels, BI adoption facilitates firms’ strategic planning, performance improvement, and competitive advantage [38]. BI is remarkably useful in gaining information about the competitive market, particularly market forces, public policy, new technology, new competitors, new customers, and valuable innovation information in order to forecast the future environment, develop innovative products, or continuously improve methods in which a business will operate to gain competitive advantages [47]. Realizing the need of BI adoption in gaining decision-making excellence this study proposed the following: P4: Adoption of BI systems enhances competitive advantages for SMEs.

3 Discussion and Significance Theoretically, this study is the pioneer in integrating socio-technical network factors with technological, people, organizational, and environmental drivers, designed to address all the players are individually incomplete without the interaction among themselves for the adoption of BI systems. Boonsiritomachai et al. [12] empirically test a model

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where they found relative advantage, complexity, vendor selection, competitive pressure, owner-managers’ innovativeness, and organizational resource availability are the significant factors in adoption of BI among Thai SMEs. The perception of environmental uncertainty is framed by intellectual characteristics underpinned in organizational mindfulness. As a result, in order to efficiently use BI in any impending recession, a thorough understanding of these elements is required. The study may theoretically guide firms in the right allocation of technical and organizational resources, as well as take proper measures in different deployment phases to improve BI system acceptance. Another significance of this research is to look at inter-organizational relationships from a socio-technological network perspective to consider the accumulated effects of learning mechanisms, information sharing among partners, co-innovation, and friendly assistance as intermediary actions for the capability-building process for BI adoption. A study by Eidizadeh et al. [47] revealed that business intelligence substantially influence knowledge sharing, organizational innovation and aids in gaining competitive advantage. It is anticipated that this conceptual paper would encourage researchers to enhance their perspective beyond traditional technology adoption theories and focusing on the practical interrelationships between the various players in the organization for a more holistic overview of the BI adoption process. It is expected that this research would not only add value to the current body of knowledge, but would also stimulate the interest of industry specialists by successfully establishing correlations among the proposed factors and achieving desired outcomes from adopting BI. IT suppliers and consultants may establish strong bond by adhering to proposed theoretical model for successful deployment of BI for their clients and increase organizational competitive advantages by transforming business information into usable intelligence.

4 Conclusion BI appeared to be a paradigm shifter, not a passing trend and became a necessity for all enterprises. As a consequence, there are several prospects for expanded research in the fields of BI for SMEs or how it would interact across other academic areas. The literature review indicated a few gaps in the literature focusing on factors that may be important for SMEs’ BI adoption. The study developed an integrated model that incorporates the DOI and ANT models to elicit associations between technology, people, and organizational factors in order to thoroughly apprehend the adoption of BI by SMEs in developing countries, as well as addressed how BI adoption can help to obtain a competitive advantage. Although the DOI framework has indeed been largely deployed in technology adoption literature, to the best of the researchers’ knowledge, no study attempted to establish it in conjunction with ANT, given the significance of BI adoption in SMEs in developing countries. The study emphasized the importance of SMEs acquiring critical market knowledge rapidly and accurately. It is difficult to make a final judgment on the successful adoption of BI for SMEs since simply having access to the relevant data would not guarantee a success without determining the most effective strategy to deploy BI. The discussion also warrants into further detail on the strategic importance of this fruitful area for future rigorous research.

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22. Porter, M.E.: Industry structure and competitive strategy: Keys to profitability. Financ. Anal. J. 36(4), 30–41 (1980) 23. Vidgen, R., Sarah, S., David, B.G.: Management challenges in creating value from business analytics. Eur. J. Oper. Res. 261(2), 626–639 (2017) 24. Halac, D.S.: Multidimensional construct of technology orientation. Procedia. Soc. Behav. Sci. 195, 1057–1065 (2015) 25. Day, G.S.: The capabilities of market-driven organizations. J. of Mkt. 8(4), 37 (1994) 26. Renko, M., Alan, C., Malin, B.: The effect of a market orientation, entrepreneurial orientation, and technological capability on innovativeness: A study of young biotechnology ventures in the United States and in Scandinavia. J. Small Bus. Manage. 47(3), 331–369 (2009) 27. Gärtner, C.: Enhancing readiness for change by enhancing mindfulness. J. Chang. Manag. 13(1), 52–68 (2013) 28. Singh, S., Meenakshi, S., Sanjay, D.: Modeling the effects of digital transformation in Indian manufacturing industry. Technol. Soc. 67, 101763 (2021) 29. Weick, K.E., Kathleen, M.S.: Mindfulness and the quality of organizational attention. Organ. Sci. 17(4), 514–524 (2006) 30. Barros, V.F., Isabel, R.: Organizational mindfulness to innovation at an organization in the cork sector. Inf. Technol. People (2022) 31. Mokhtarzadeh, N.G., Mahdiraji, H.A., Jafarpanah, I., Jafari-Sadeghi, V., Cardinali, S.: Investigating the impact of networking capability on firm innovation performance: Using the resource-action-performance framework. J. Intell. Cap. 21(6), 1009–1034 (2020) 32. Eiriz, V., Miguel, G., João, S.A.: Inter-organizational learning within an institutional knowledge network. European J. Innov. Manage. 20(2), 230–249 (2017) 33. Asare, A.K., Thomas, G.B.-A., Jun, K.: B2B technology adoption in customer driven supply chains. J. Bus. Indus. Market. 31(1), 1–12 (2016) 34. Benqatla, M.S., Chikhaoui, D., Bounabat, B.: Actor network theory a framework of IT collaboration. In: 2017 International Conference on Wireless Networks and Mobile Communications (WINCOM), pp. 1–6. IEEE (2017) 35. Eze, S.C., Vera, C.C.-E.: Strategic roles of actors in emerging information communication technology (EICT) adoption in SMEs. Bottom Line 31(2), 114–136 (2018) 36. Lichtenthaler, U.: Determinants of absorptive capacity: The value of technology and market orientation for external knowledge acquisition. J. Bus. Indus. Market. 31(5), 600–610 (2016) 37. Masa’deh, R., Jawaher, A.-H., Ali, T., Bader, Y.O.: The associations among market orientation, technology orientation, entrepreneurial orientation and organizational performance. Benchmark. Int. J. 25(8), 3117–3142 (2018) 38. Puklavec, B., Tiago, O., Aleš, P.: Understanding the determinants of business intelligence system adoption stages. Indus. Manage Data Syst. 118(1), 236–261 (2018) 39. Li, H., Yun, W., Dongmei, C., Yichuan, W.: Organizational mindfulness towards digital transformation as a prerequisite of information processing capability to achieve market agility. J. Bus. Res. 122, 700–712 (2021) 40. McAvoy, J., Tadhg, N., David, S.: Using mindfulness to examine ISD agility. Inf. Syst. J. 23(2), 155–172 (2012) 41. Andersson, A.: Communication barriers in an inter-organizational ERP-project. Int. J. Manag. Proj. Bus. 9(1), 214–233 (2016) 42. Ford, J.A., Martie, V., John, S.: Limits to networking capabilities: Relationship trade-offs and innovation. Ind. Mark. Manage. 74, 50–64 (2018) 43. Li, Y., Feng, W., Wei, Z., Bo, L.: Supply chain collaboration for ERP implementation. Int. J. Opt. Prod. Manage. 37(10), 1327–1347 (2017) 44. Tan, J., Stephan, L.: Regional adoption of business-to-business electronic commerce in China. Int. J. Electron. Commer. 20(3), 408–439 (2016)

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Backpackers’ Adoption Intention of Mobile Hotel Reservation (MHR): Are Costs Important? WeiLee Lim1(B) , YouSheng Tan2 , and BoonKiat Ang2 1 Graduate Business School , UCSI University , 56000 Kuala Lumpur, Malaysia

[email protected] 2 Faculty of Business and Management, UCSI University, 56000 Kuala Lumpur, Malaysia

Abstract. With mobile technology advancements, the distribution channelof the hotel industry’s distribution channel has evolved in tandem with changes in purchasing behaviour. Mobile technology offers mobility and accessibility in making hotel reservations. Especially for backpackers who like to travel on their own, being able to make a custom travel plan on the spot is a big part of the experience. The study aims to extend TAM by examining the effect of perceived cost-saving on backpackers’ intention to adopt mobile hotel reservations. This cross-sectional design employs partial least squares structural equation modelling to test 296 data collected from backpackers in Malaysia. Findings revealed perceived cost-saving, attitude, perceived ease of use and perceived usefulness to be significant predictors of backpackers’ intention to adopt mobile hotel reservations. Backpackers are unique in their demands and expectations. With perceived cost-saving indicating a strong influence on intention to adopt mobile hotel reservations, information on pricing and promotions should be highlighted and personalised for a better customer experience. Keywords: Backpacker · Adoption model · Mobile technology · Mobile hotel reservation · Cost-savings

1 Introduction In recent years, one of the most impactful trends to affect humans is the technological trend in the development of mobile or smartphone devices [1]. With mobile technology, business operators benefit from a more cost-effective method of building customer relationships. It was observed by [2] that mobile apps helped travel-related companies in providing exciting, engaging, and innovative experiences for travellers. According to a survey by [3], there are currently 6.64 bil smartphone users worldwide, which translates as 83.72% of the world’s population. This is an impressive growth of 49.89% from 2017-to 2022. A study by [4] revealed that individual self-guided tours have moved to direct bookings and payments through mobile apps rather than through traditional travel agents or official websites. Through accommodation apps, self-guided travellers such as backpackers can search for various product offerings while saving © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (Eds.): ICETIS 2022, LNNS 584, pp. 101–110, 2023. https://doi.org/10.1007/978-3-031-25274-7_9

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costs. Backpackers are generally defined as self-guided or independent travellers with more extended stays and flexible itineraries [5]. Small- to medium-sized accommodation operators gain access to a cost-efficient and convenient marketplace, which overcomes their inadequacy of marketing budget and skills [6]. The most significant difference between mobile technology and other forms of technology in hotel reservations is mobility and accessibility. With mobile technology such as global navigation satellite system (GNSS), geographic information system (GIS)and global positioning system (GPS), service providers can provide location-based services during critical times [7]. To remain competitive in this landscape, hotels need to develop m-commerce technology for their customers through mobile hotel reservations (MHR). MHR is “a location-based online distribution information system that enables customers worldwide to reserve hotel rooms anytime, anywhere through the wireless Internet, GNSS, GIS, GPS, and mobile phones/devices” [7]. Users of mobile devices are characterised by shorter user sessions, no idle time, preoccupied users, specific goals and a low tolerance for slow-loading sites or inaccurate information compared to conventional device users [8]. The popularity of using a mobile device in making hotel reservations has risen steadily. As predicted by [7], mobile will be the travellers’ choice when they book online. This is supported by [9] which showed that post-loosening of border restrictions in Malaysia, solo and small group travellers have increased from 75.45% (2018) to 96.35% (2022). Fuelled by increased digital literacy, this segment prefers to plan their own itineraries. For backpackers whose expenses are self-borne, their motivation to adopt mobile technology may defer from conventional technology adopters. The traditional technology adopters are either corporate users or one-time fee for a period type of users [7] which are less sensitive towards costs. Hence, the cost variable must be considered in improving our understanding of backpackers’ intention to adopt MHR. With the rising importance of mobile technology in tourism, few and limited studies have on mobile apps in tourism [10], particularly MHR. The combination of a modern backpacker wanting flexibility in choosing an array of accommodation and accommodation operators having direct access to wider clientele is worth investigating. Further investigation is needed on consumer acceptance and continuous use of mobile technology in tourism. Against the backdrop of the subject matter, the extended TAM (Technology Acceptance Model) was utilised in this study with the construct of Perceived Cost Saving (PCS) to study their effect on backpackers’ intention to adopt accommodation apps. Hence, this study aims to validate the extended TAM with perceived cost-saving as a new variable and understand its impact on backpackers’ intention to adopt mobile hotel reservations.

2 Literature Review With the emergence of mobile technologies, the distribution channel of the hotel industry evolved with the changes in purchase behaviour. New technologies were introduced to improve efficiency and productivity, provide more convenient channels [11], provide customised service tailored for customer preferences, and support personalised service [12]. Mobile technology delivers innovative services and communication, and promotes

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new product offerings. Hence, mobile technology as a distribution channel gained popularity garnering 39% of total hotel reservations made on a smartphone through online travel agents [13]. Technology adoption models such as the Technology Acceptance Model (TAM) [14] and Theory of Reasoned Action (TRA) [15] have been used in the research of the adoption of online hotel reservations. However, MHR cannot be fully explained by the models as each is limited in explaining the adoption of new technology. TAM is an extension of TRA, based on the theory that a person’s beliefs influence attitudes (ATT), affect intention (INT), and finally manifest as behaviour. Using TRA as a foundation, the TAM was expanded to add external factors for perceived ease of use (PEU) and perceived usefulness (PU) to forecast users’ acceptance of technology. In tourism literature, TAM has been applied to the studies of marketing through digital media [16], online review systems [17], tour planning applications [18], mobile hotel services apps [19] and social media advertising [20]. Studies have further extended the TAM to incorporate various dimensions and theories in improving the robustness of the new model to predict users’ intention to adopt mobile technology. A study by [21] has added perceived playfulness to TAM on customers’ intention to adopt online hotel reservations. Another study [22] extended TAM with variables such as credibility and subjective norms in their research on hotel guests’ intention to use hotel applications. 2.1 Perceived Usefulness, Perceived Ease of Use and Attitude TAM’s two core variables are PU and PEU, the belief segment leading to ATT. PU refers to the extent a person believes the system can improve their performance, while PEU refers to the easiness of using the system. In this study, the system is mobile hotel reservations, and the variables focus on the effectiveness and ease of use of such reservation systems for backpackers. PU indicates the effectiveness of backpackers in making reservations through mobile devices. On the other hand, PEU differs where it considers the intrinsic motivations of the user. When backpackers make a reservation through a mobile device, the system’s process and interface must be easy to use, requiring minimal physical and mental effort. In the study by [16] on the impact of Facebook on tourists’ intention to embark on medical tourism in Jordan, both PU and PEU are significant predictors of the tourists’ attitudes. Likewise, in a study on generation Y’s adoption intention towards user-generated content (UGC) review websites, PU and PEU significantly affect attitude and intention to use review websites [17]. Trustworthiness was introduced as an antecedent to PU and PEU in that study. Interestingly, trustworthiness was not significant in its relationship to PU. The general findings were also supported in another study [19] on mobile hotel services apps. The results suggested that leisure travellers displayed a more substantial effect of PEU on ATT and PU on ATT compared to the business traveller groups. An explanation for this is that leisure groups typically have a longer time at hotels and expect a higher level of experiential outcome where function-related constructs are likely to show stronger effects over business travellers. Past studies have confirmed the effect of PU and PEU on INT in various contexts. Hence, in this study, we infer that the PU and PEU of using the MHR system will positively affect backpackers’ intention to make hotel reservations through a mobile device. This led to the following hypothesis:

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H1: PEU towards MHR has a positive relationship with backpackers’ intention in adopting MHR H2: PU towards MHR has a positive relationship with backpackers’ intention in adopting MHR The third construct in TAM is attitude (ATT) which refers to the preposition towards technology. The ATT component develops the behavioural intention of the user to perform a behaviour. In this study, ATT is an overall positive feeling that backpackers have regarding mobile hotel reservations. Most TAM studies agree that PU and PEU are strong predictors of behavioural intentions. However, ATT is sometimes omitted because it is a weak predictor in some instances [23], showing inconsistent results in its role as a mediator between the predictor variables and INT [24]. In a recent study by [17] in Korea, ATT was found to be a strong predictor of INT for generation Y in using review websites. In addition, ATT as a mediator is also established on relationships between PEU and ATT and PU and ATT. Types of travellers have also been found to impact differently on ATT [19]. Leisure travellers display higher levels of ATT when functional and hedonistic benefits are present. This leads to a higher intention to use hotel services apps. The association between ATT and INT to adopt technologies has been studied with positive results. Drawing from the discussions above, this study posits the following hypotheses: H3: ATT towards MHR has a positive relationship with backpackers’ intention in adopting MHR 2.2 Perceived Cost-Saving For backpackers, cost-saving is an essential criterion for these travellers. The cost has to be managed to plan for a longer stay at each destination. In this study, PCS refers to the backpacker’s perception of how MHR provides a cost-effective hotel reservation. The costs are beyond monetary costs to include time and emotional costs. With decreasing costs of mobile technology, the capabilities of mobile devices in performing business functions while offering the advantage of mobility is an essential advantage to backpackers. Few studies have focused on PCS and its effect on technology adoption. In a study by [25], PCS was studied on its impact on behavioural intention to use smartphones and found to be of a significant relationship. As attitude is a general precursor to behaviour, it is expected that PCS impacts INT through ATT and on ATT itself. Hence, this study hypothesis as the following: H4: PCS towards MHR has a positive relationship with backpackers’ intention in adopting MHR H5: PCS towards MHR has a positive relationship with backpackers’ ATT H6: ATT mediates the relationship between PCS and backpackers’ intention to adopt MHR

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3 Research Methodology The questionnaire used in this study consists of two parts: the demographic information and the variables influencing the adoption of mobile hotel reservation systems (PEU, PU, ATT, PCS and INT). In the first part of the questionnaire, demographic information about the respondents was obtained. These include questions on age, gender, highest education level, income and usage of mobile hotel reservations. For model constructs, the measurement items were adapted from past studies by [25, 26] to ascertain the scales’ validity. A five-point Likert scale (1–5) with anchors ranging from “strongly disagree” to “strongly agree” was used for all construct items. This study was conducted during the Covid pandemic, where strict movement controls restricted our ability to meet face to face. Hence, the data was gathered via an online survey using Google Forms. A self-administered questionnaire was prepared using a quantitative survey approach, and links were forwarded to target respondents through social media channels and messenger apps. As there is no sampling frame available while requiring specific criteria for respondents, the non-probability sampling technique was employed. A combination of purposive and snowball technique is used where respondents who have knowledge and experience as a backpacker and owns a mobile device are selected. G*Power was used to determine the number of respondents. With statistical power of 0.8, a margin of error of 0.05, the effect size of 0.15 and 4 predictors, the calculated number required is 85 respondents. The study received 296 responses which are above what is needed for analysis.

4 Data Analysis The proposed model was examined by employing Partial Least Square–Structural Equation Modelling (PLS-SEM) by the SmartPLS3 software. PLS-SEM is deemed appropriate as it makes no presumptions on data normality and can handle different sample volumes. As a multivariate analysis tool, PLS-SEM is widely used to determine path models with latent constructs. Harman’s single factor test was conducted to determine the presence of common method variance (CMV). A single factor explains just 37.35 percent of the total variance in this study, according to Harman’s single factor test. This is below the 50% threshold [27], and hence, CMV is not an issue in this study. An analysis was performed on the demographic information of the 296 respondents, and the findings are; that 64.19% are male, and the largest age group are represented as between 20 to 30 years old. (33.45%). A large majority (88.85%) is of at least tertiary education level, with 37.84% largest earning income group of between RM4000 to RM8000. The large majority of respondents (51.35%) travel once a year and have used a mobile device to make hotel reservations for between 3 to 5 years (64.86%). 4.1 Measurement Model and Structural Model The measurement model focuses on the reliability and validity of the constructs in the model. Results of the reliability are presented in Table 1. To confirm the reliability of the constructs, Cronbach’s Alpha is employed to check for internal consistencies. The

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reading shows all constructs’ coefficients above 0.7, which is the recommended threshold [28]. The variance inflation factor is then used to investigate the multicollinearity issue (VIF). All values were below 3.3, which showed that no serious multicollinearity violations had occurred [29]. Convergent validity and divergent validity are evaluated for the constructs’ validity. Convergent validity is examined based on the average variance extracted (AVE) results, whose values should be above the 0.5 level. The factor loadings are also examined for values above the 0.7 thresholds. The constructs and items satisfy the requirement, and hence convergent validity is established. To confirm discriminant validity, the study employs the Fornell-Larcker criterion and Hetero-Trait-Mono-Trait (HTMT). The results of the Fornell-Larcker criterion are shown in Table 2, the square root of the construct’s AVE indicates a higher value than the correlation values of the other constructs [30]. HTMT also confirms the discriminant validity with values under 0.90 (0.080–0.0869) As noted in the results, discriminant validity is established. Table 1. Reliability and validity Constructs

Items

Loadings

Cronbach’s alpha

rho_A

Composite reliability

Average variance extracted (AVE)

VIF

ATT

ATT1

0.884

0.895

0.982

0.934

0.826

1.350

ATT2

0.964

ATT3

0.876

PCS2

0.873

0.774

0.815

0.897

0.813

1.390

PCS4

0.929

PEU

PEU1

0.918

0.865

0.926

0.935

0.879

1.610

PEU3

0.956

PU

PU2

0.81

0.796

1.409

0.89

0.804

1.582

PU4

0.976

INT1

0.857

0.741

0.764

0.851

0.656

INT2

0.748

INT4

0.821

PCS

INT

In assessing the fitness of the structural model, three analyses are used to determine the quality. The coefficient determination (R2 ), cross-validated redundancy (Q2 ), and effect size are analysed and reported in Table 3. R2 is a measure to test the structural model’s predictive power on the endogenous variable, while Q2 is examined for values greater than zero to indicate predictive relevance. The predictive values indicated as 0.75, 0.50, and 0.25 are substantial, moderate, and weak [31]. The effect size f2 measures the relative impact of an exogenous latent variable on an endogenous latent variable. The effect sizes with values of 0.02, 0.15 and 0.35 are described as small, medium, and large, respectively [32].

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Table 2. Fornell-Larcker criterion ATT

INT

PCS

PEU

ATT

0.909

INT

0.537

0.81

PCS

0.518

0.559

0.902

PEU

0.114

0.234

0.246

0.937

PU

0.207

0.183

0.369

0.621

PU

0.897

Overall, the proposed model shows a weak-moderate predictive power on INT as PEU, PU, ATT, and PCS combined explained 41.11% of the variance on INT. The predictive relevance of the model is also established, with all the Q2 values being more than zero. Hence, the model has predictive relevance to all endogenous variables. The hypotheses are tested using the bootstrapping technique with 5000 subsamples at a 95% confidence level. The path analysis results are shown in Table 3. With a p-value of 0.05, 4 of the 5 (i.e. H2, H3, H4 and H5) hypotheses are supported. Both PCS (β = 0.389, p < 0.001) and ATT (β = 0.345, p < 0.001) were found to be the strongest predictor of backpackers’ intention to adopt MHR. While PU (β = 0.153, p = 0.002) is significant but has a small effect, H1 is not supported where PEU (β = –0.194, p < 0.001) is significant but negative towards INT. Hence, H1 is not supported while H2 to H4 are supported. H5 is also supported with PCS (β = 0.518, p < 0.001), showing a significant effect on ATT. Table 3. Path analysis and quality criteria Hypothesis H1

PEU → INT

H2

Coefficient

t-values

Sig

r2

Q2

f2

Decision

–0.194

4.142

0.000

0.040

Not supported

PU → INT

0.153

3.133

0.002

0.023

Supported

H3

ATT → INT

0.345

7.913

0.000

0.150

Supported

H4

PCS → INT

0.389

8.403

0.000

0.411

0.264

0.171

Supported

H5

PCS → ATT

0.518

10.285

0.000

0.265

0.205

0.366

Supported

The mediation analysis is further examined for H6 with bootstrapping procedure of checking the spread of upper limit and lower limits of 95% confidence interval [33]. Referring to Table 4 for the results, the analysis showed that the bootstrapped confidence interval does not straddle zero in between, indicating the presence of mediation (β = 0.178, p < 0.05). In addition, the Variance Accounted For (VAF) is 31% implying a partial mediation effect of ATT between PCS and INT [34]. Hence, H6 is also supported.

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Indirect 95% Direct effect Bootstrapped effect confidence interval

H6 0.567*** 0.178* PCS → ATT → INT

(0.135, 0.220)

95% VAF Bootstrapped confidence interval

0.389*** (0.292, 0.475)

31% Partial mediation

Note: ***p < 0.001, **p < 0.01, *p < 0.05, NS p > 0.05

5 Discussions and Implications This study examines the backpacker’s attitudes and behavioural intentions toward adopting mobile hotel reservations. The study was carried out given the increasing interest in mobile technology in the hotel industry. Leveraging TAM, the study further extended TAM to include a new construct (i.e., PCS) to advance the understanding of the literature. The analysis results confirm five hypotheses to be supported, thus providing empirical evidence for the proposed model. All predictors (PU, ATT, and PCS) of backpackers’ intention to adopt mobile hotel reservations (INT) are positive and significant except PEU, a significant but negative relationship. The results are consistent with previous studies, which found significant relationships in the predictor factors (PEU, PU, ATT, and PCS) with INT [19, 22, 23, 25, 35]. Backpackers intend to adopt mobile hotel reservations due to their usefulness, the right attitude, cost-saving, and specific easy-to-use applications. The strongest predictor of INT is PCS. This indicates the importance of cost-saving functionality to them. Not just in monetary terms but emotional and time effort as opportunity costs. Backpackers place high value in experiencing life through travel and are known to find ways to cost-save wherever they can to travel further. The results also indicate a negative relationship between PEU on INT. This is similar to other studies where PEU was found to be negative and not significant to INT to adopt hotel apps [23], and in [35], PEU was negative and significant towards ATT on carsharing apps. A plausible explanation for this is that backpackers emphasise cost-saving factors over the easy-to-use functions of the hotel reservation apps. Simple to use system may be seen as too simplistic with basic functions that do not appeal to the needs of the backpackers. The mediation relationship of ATT mediating between PCS and INT is supported. This result further validates the construct of PCS and ATT. The focus on cost-saving is a strong need that impacts backpackers’ adoption intention of mobile hotel reservations through attitudes. A critical contribution of the current study is in the enrichment of our understanding of an extended TAM with a new variable, perceived cost-saving. In the fast-paced technological change environment, hotels need to respond quickly with targeted strategic actions to attract and maintain high occupancy rates for the hotel. Particularly, hotel guests’ personalised experience through hotel mobile technology is the key. For targeted groups of guests such as a backpacker, unique demands and expectations should be

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accounted for. Results of the study have shown that backpackers are sensitive to costsaving functionality. Hence, information on product pricing or promotions should be considered in designing personalised messages or communications to this group. Some limitations to this study should be addressed in future studies. The study was conducted based on Malaysian backpacker samples. Hence, it is acknowledged that the findings may not be able to be generalised to backpackers from other cultures which are different from the Asian region [36]. Future research is encouraged to verify further if the model applies to different cultures and values. In addition, to further enhance the predictive power of the model, future studies may explore other variables such as perceived enjoyment, perceived reliability and perceived privacy [37].

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17. Bae, S.Y., Han. J.H.: Considering cultural consonance in trustworthiness of online hotel reviews among generation Y for sustainable tourism: An extended TAM model. Sustainability 12(7), 2942 (2020) 18. Pillai, R., Sivathanu, B.: Adoption of AI-based chatbots for hospitality and tourism. Int. J. Contemp. Hosp. Manag. 32(10), (2020) 19. Zhang, T.T., Seo, S.B., Ahn. J.A.: Why hotel guests go mobile? Examining motives of business and leisure travelers. J. Hosp. Mark. Manag. 28, 621–644 (2019) 20. Tan, G.W.H, Lee V.H., Hew, J.J., Ooi, K.B., Wong. L.W.: The interactive mobile social media advertising: An imminent approach to advertise tourism products and services?. Telemat. Informatics, 35(8), 2270–2288 (2018) 21. Morosan, C., Jeong, M.: Users’ perceptions of two types of hotel reservation Web sites. Int. J. Hosp. Manage. 27(2), 284–292 (2008) 22. Kim, J.: An extended technology acceptance model in behavioral intention toward hotel tablet apps with moderating effects of gender and age. Int. J. Contemp. Hosp. Manage. 28, 1535–1553 (2016) 23. Huang, Y.C., Lan, L.C., Yu, C.P., Chen. J.: Examining an extended technology acceptance model with experience construct on hotel consumers’ adoption of mobile applications. J. Hosp. Mark. Manag. 28(8), 957–980 (2019) 24. Abdullah, D., Jayaraman, K., Shariff, D.N., Bahari, K.A., Norfezah, M.N.: The effects of perceived interactivity, perceived ease of use and perceived usefulness on online hotel booking intention: A conceptual framework. Int. Acad. J. Soc. Sci 3, 16–23 (2017) 25. Kim, S.H.: Moderating effects of job relevance and experience on mobile wireless technology acceptance: Adoption of a smartphone by individuals. Inf. Manag. 45, 387–393 (2008) 26. Kim, D.Y., Park, J., Alastair, M.M.: A model of traveller acceptance of mobile technology. Int. J. Tour. Res 10, 393–407 (2008) 27. Lim, W., Lee, Y., Abdullah, A.M.: Delineating competency and opportunity recognition in the entrepreneurial intention analysis framework. J. Entrepr. Emerg. Econ. (2021) 28. Hair, J.F., William, C., Barry, J.B., Rolph, E.A.: Multivariate Data Analysis, 7th edn. Prentice Hall, Upper Saddle River (2009) 29. Diamantopoulos, A., Siguaw, J.A.: Formative versus reflective indicators in organizational measure development: A comparison and empirical illustration. Br. J. Manage. 17, 263–282 (2006) 30. Tew, H.T., Garry, T.W.H., Loh, X.M., Lee, V.S., Lim, W., Ooi, K.B.: Tapping the next purchase: Embracing the wave of mobile payment. J. Comput. Inf. Syst. 62(3), 1–9 (2021) 31. Hair, J.F., Hult, G.T.M., Ringle, C., Sarstedt, M.: A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Sage, Thousand Oaks, CA (2017) 32. Yuan, Y.P., Garry T.W.H., Ooi, K.B., Lim, W.: Can COVID-19 pandemic influence experience response in mobile learning? Telemat. Inf. 64, 101676 (2021) 33. Lim, W.: Ideas and opportunities: Impact of technology knowledge through entrepreneurial alertness. In: IEEE International Symposium of Innovation and Entrepreneurship, IEEE, pp. 1–5 (2019). 34. Hair, Jr.J.F., Sarstedt, M., Hopkins, L., Kuppelwieser, V.G.: Partial least squares structural equation modeling ( PLS-SEM ) An emerging tool in business research. Eur. Bus. Rev. 26, 106–121 (2014) 35. Haldar, P., Goel, P.: Willingness to use carsharing apps: An integrated TPB and TAM. Int. J. Indian Cult. Bus. Manage. 19, 129–146 (2019) 36. Fam, K.S., Liat-Cheng, B., Cham, T.H., Tan, C.Y.M., Ting, H.: The role of cultural differences in customer retention: Evidence from the high-contact service industry. J. Hosp. Tour. Res. 47(1), 257–288 (2023) 37. Zhang, T., Lu, C., Kizildag, M.: Banking “on-the-go”: Examining consumers’ adoption of mobile banking services. Int. J. Qual. Serv. Sci. 10(3), 279–295 (2018)

Analyzing Purchasing Behavior of NIO’s Customers Based on Theory of Planned Behavior in China Han Fengfan1(B) and Choy Johnn Yee2 1 UCSI University, Cheras, Malaysia

[email protected] 2 Universiti Tunku Abdul Rahman (UTAR), Kampar, Malaysia

Abstract. With the explosive growth of the number of vehicles, the use of new energy vehicles is becoming one of the ways to alleviate the auto industry’s dependence on petroleum, therebyreducing pollutant emissions. As one of the leading new energy vehicle start-ups in the Chinese market, Nio still has great potential for growth in its product sales. Therefore, it is necessary to analyze the main factors that affect Chinese consumers’ purchase of new energy vehicles in order to accelerate and increase the sales of NIO. Based on the theory of planned behavior, this article analyzes the factors that may affect consumers’ purchase of NIO products, so as to analyze consumers’ purchasing behavior and provide suggestions for NIO. The author set up a survey scale based on the theory of planned behavior, conducted a sample survey of 410 effective samples of potential consumers, and analyze the data obtained. Finding confirm that the understanding of the new energy industry, purchase attitude, subjective norms and perceptual behavior control are the key factors that affect the purchase intention of NIO cars, while past consumer behavior has no positive effect on consumers’ purchase intention. This study will help NIO company understand the factors that affect the willingness of potential consumers to buy their products in China. In addition, this study also provides other companies working in the region with the influencing factors of consumers’ willingness to buy electric vehicles as a reference. Keywords: Electric vehicle · Theory of planned behavior · Green consumption behavior · Purchase intention · China

1 Introduction The use of new energy vehicles is becoming one of the ways to alleviate the auto industry’s dependence on petroleum [1]. NIO, as one of the leading new energy vehicle (NEV) startups in the Chinese market, has delivery vehicles more than doubled in 2020. It was established in November 2014 and listed on the New York Stock Exchange in September 2018. As a newly born auto company, NIO directly positioned itself as a mid-to-high-end auto brand, and launched its first model es8 in December 2017, priced at around 450,000 to 550,000 CNY (about 70,000 to 77,000 US dollars). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (Eds.): ICETIS 2022, LNNS 584, pp. 111–121, 2023. https://doi.org/10.1007/978-3-031-25274-7_10

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However, there is still a big growing potential opportunity for NIO in NEV industry. According to information released by the Transportation Administration of the Ministry of China, in 2021, the number of motor vehicles in the country has reached 3.95 million, and the population of car drivers has been 4.44 million. In 2020, 27.50 million new drivers nationwide have obtained driving licenses [2]. It is shown that the study of consumer behavior is not very active in NEV markets. Therefore, it is essential to analyze the main factors that influence Chinese consumers’ behavior to purchase NEVs, in order to accelerate and improve the sales volume of NIO. If the sales are not improved, the performance of the company will not satisfy the investors, it will make NIO into a dilemma, such as the market value goes down, no one else wants to invest in it so the cash flow will decrease and the company will have no source to continue the business. Thus, it is very important for NIO to understand better about the potential customer. So in this report, the author aims to provide a framework based on the Theory of Planned Behavior to reach this goal. Theory of Planned Behavior is used to figure out the psychological factors that link beliefs to behavior, hopefully, some positive results can be got to help NIO predict the consumers’ behavior, additionally, based on the result, give some marketing suggestions, such as online advertising campaigns, public relations, for NIO to improve the sales performance. This project has five independent variables, and three of them are based on TPB. In addition, two new variables, “awareness of new energy industry” and “past consuming behavior,” are added based on previous research. This article takes NIO as an example, and analyses the possible factors that affect the purchase intention of potential consumers of NIO. The ultimate goal of these actions is to help the company adjust the strategy to improve the situation of capital shortage, which has very important application significance. Secondly, this project sheds new light on green purchasing behavior based on the Theory of Planned Behavior. This research can add further evidence to consumers’ purchasing behavior, and it could provide new evidence for future studies.

2 Literature Review 2.1 Theory of Planned Behaviour (TPB) The Theory of Planned Behavior (TPB) was proposed as an extension of the Theory of Reasoned Action (TRA) [3]. TRA was proposed in social psychology, which is one of the most influential and fundamental theories for predicting social activities [4]. The TPB is based on the idea that an individual’s behavior intention is determined by more than just their attitude and subjective norm. This is because research found that behavioral intentions may not lead to an individual’s actual behavior when a person’s control over the behavior is incomplete [3]. For this reason, based on the TRA, the third factor was mentioned—Perceived Behavioral Control, which is a better way to predict an individual’s actual behavior. Perceived Behavioral Control is conceptually similar to self-efficacy. It refers to an individual’s belief that he or she can accomplish a goal or an individual’s confidence in executing a given behavior [3]. Existing literature indicates that three constructs of TPB can support an individual’s purchase behavior. Specifically, some researchers analyze young individuals’ intentions toward buying green products. Results show that all TPB variables are significantly related to individuals’ intention

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to buy green products [5]. Similarly, findings show that the TPB can fully support an individual’s green purchase intention, and the two additional constructs can improve the predicted power of the TPB in predicting an individual’s behavior to buy green products [5]. Some researchers explore the effects of the TPB constructs on ecologically conscious consumer behavior (ECCB). Their findings indicate that attitudes and perceived consumer effectiveness significantly impact ECCB. However, the subjective norm does not have a significant impact on ECCB [7]. Kassim, Arokiasamy & Ping investigate the effects of attitude, subjective norms, and perceived behavioral control on safer car purchasing behavior in Indonesia. Their findings imply that all three TPB factors could affect customers’ intentions to buy safer cars, especially having a positive attitude, which could increase the intention to purchase [8].

3 Hypotheses Development 3.1 Attitude In the theory of planned behavior, attitude is one of three conceptually independent determinants of intention. Attitude toward the behaviour refers to how a person has a favorable or unfavorable evaluation or appraisal of the behavior in question [3]. These attitudes are influenced by two factors: the strength of behavioral beliefs regarding the outcomes of the performed behavior and the evaluation of the potential outcomes [4]. Existing research shows that attitudes were significantly correlated with individuals’ willingness to buy green products [5]. Research shows, that for electric vehicles, attitudes towards electric vehicles are positively correlated with Indian consumers’ willingness to adopt them [9]. This leads to the following hypotheses: H1:Attitude is positively related to the potential customers’ intention to buy NIO’s products. 3.2 Subjective Norms The second predictor in TPB is a social factor called subjective norm, which refers to the perceived social pressure to perform or not to perform the behavior (Ajzen, 1991). According to a study related to green purchase intention, normative factors in TPB theory can fully support individuals’ green purchase intention [6]. Researchers explored the impact of the TPB structure on eco-conscious consumer behavior (ECCB). However, they found that subjective norms had no significant effect on ECCB [7]. Thus, the following hypothesis can be developed: H2: Subjective norms are positively related to the potential customers’ intention to buy NIO’s products. 3.3 Perceived Behavioral Control Icek Ajzen believes that behavioral intentions are not only determined by attitudes and subjective norms but also influenced by Perceived behavioral control [3]. Perceived

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Behavioral Control refers to an individual’s belief that he or she can accomplish a goal, or an individual’s confidence in executing a given behavior. After analyzing young people’s willingness to buy green products, some researchers found that all TPB variables were significantly correlated with individuals’ willingness to purchase green products [5]. Some researchers found that perceived behavioral control is positively associated with safer car purchase behavior intentions in Indonesia [8]. So the following hypothesis can be developed: H3:Perceived behavioral control is positively related to potential customers’ intention to buy NIO’s products. 3.4 Awareness of the New Energy Industry Although existing literature shows that three constructs of the TPB can support an individual’s purchase behavior, a growing body of research advocates for incorporating new constructs that are not included in the TPB model [10, 11]. Customers’ awareness can promote purchasing intention [12–14]. For instance, Novansa & Ali use a multiple linear regression model to show that brand awareness is positively related to customers’ decisions to purchase the products of SMEs [12]. Similarly, Scholz’s group find that user-generated content and marketer-generated content can create awareness of potential customers, attracting online shop visitors and increasing their purchasing intention [13]. The evidence shows that customers’ awareness can make them more likely to buy products. This leads to the following hypotheses: H4: Awareness of new energy industry is positively related to the potential customers’ intention to buy NIO’s products. . 3.5 Past Consuming Behavior Evidence shows that an individual’s past consuming experience plays an important role in future purchasing behavior [15–17]. For example, Jun finds that customers consider the prior experiences of Airbnb users, implying that good past experience has a significant positive effect on consumer decision-making[15]. In addition, some founds show that past purchases can predict future intentions to purchase, indicating a positive correlation between past and future purchases [16]. Since previous evidence shows that customers’ past consuming experiences are positively related to their future purchasing intentions in purchasing products, it leads to the following hypotheses (Fig. 1): H5: Past consuming behavior is positively related to the potential customers’ intention to buy NIO’s products.

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Fig. 1. Proposed conceptual model.

4 Methodology An online questionnaire survey will collect data. To avoid the selection bias problem, respondents will be randomly selected. This means that the samples are collected in different areas and different groups by demographic and household characteristics such as education level, gender, marital status, age, and wealth. In the absence of data from authoritative organizations, it is necessary to obtain data on the purchase intention and behavior of potential customers for Nio Auto through questionnaire surveys. Therefore, data collection will be mainly conducted by way of questionnaire surveys [18–24], and finally, the main research will be conducted on the collected 384 or higher number of fully answered questionnaires. The survey is divided into two parts, (1) demographics and (2) measurement items. The demographics section covers respondents’ age, gender, and the highest level of educational qualification attained. The next section measures attitude (5 items), awareness of the new energy industry (5 items), past consuming behavior (5 items), subjective norms (5 items), perceived behavioral control (5 items), potential customers’ intention to buy NIO’s products (5 items). All questionnaire items were measured using a 5-point Likert scale. The result of the survey was used to confirm the hypotheses of this project. Overall, this study collected 410 responses. Common method bias is also not a problem in this studies [25–29]. 4.1 Demographic Profile Due to China’s huge population, the number of new energy vehicle users in China far exceeds 1,000,000. According to the Morgan Sample Scale [30], the target sample size

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is 384 respondents. However, the author distributed and collected 420 questionnaires for better research. Among the 420 questionnaires collected, 10 were invalid, so the total number of completed questionnaires after statistical testing was 410.Table 1 shows that compared with female respondents (43.4%), more male respondents (56.6%) participated in the questionnaire survey. Approximately 40.7% of the respondents were between 21 and 35 years old, and the rest were between 36 and 60 years old. About half of the respondents (52.4%) are studying or have obtained a bachelor’s or professional degree. The interviewees were mainly residents of the central provinces of China. Table 1. Distribution of demographic profile Frequency count

Percent (%)

Valid percent (%)

Cumulative percent (%)

Male

232

56.6

56.6

56.6

Female

178

43.4

43.4

100

Total

410

100

100

21–35

167

40.7

40.7

40.7

36–60

243

59.3

59.3

100

Total

410

100

100

Gender

Age

Highest level of educational qualification attained High school and Lower

63

15.4

15.4

15.4

Diploma and Foundation

132

32.2

32.2

47.6

Bachelor Degree

173

42.2

42.2

89.8

Master and Higher

42

10.2

10.2

100

Total

410

100

100

4.2 Reliability Results With reference to the reliability factor standard, the minimum reliability factor of the scale should be greater than 0.7, preferably greater than 0.8 [30–34]. Initially, the reliability coefficient of past-consuming behaviour was lower than the threshold value of 0.7 (see Table 2), and the deleted Cronbach’s α coefficients were all items greater than 0.460. Finally, the Cronbach’s α coefficient of past consumption behaviour was 0.790. For other variables, the corresponding Cronbach alpha score is higher than the threshold

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of 0.7 [35]. It means that the reliability of the questionnaire is better when the reliability coefficient of most questions is greater than 0.7. Table 2. Reliability test’s result Variables

Number of items

Cronbach Alpha score

Attitude

5

0.865

Subjective norm

5

0.792

Perceived behavioral control

5

0.773

Awareness of new energy industry

5

0.961

Past consuming behaviour

5

0.790

Customers’ intention to buy NIO’s products

5

0.958

4.3 Related Analysis Before confirming causality, the author of this article has tested whether IV is related to DV. For the sake of simplicity, the correlation relationship just means that the data of the two test variables are executed in a synchronized/coordinated manner. The correlation coefficient score can take any value between positive and negative. Table 3 shows that the Person correlation coefficient of each IV and DV is greater than 0.3, and is significantly positive at the accuracy level of 0.05. Table 3. The Pearson’s correlation coefficient scores A

SN

PBC

ANEI

PCB

A

–

SN

.694**

–

PBC

.702**

.738**

–

ANEI

.500**

.593**

.813**

–

PCB

.452**

.711**

.719**

.501**

–

AIBN

.633**

.554**

.735**

.689**

.397**

AIBN

–

** Correlation is significant at the 0.01 level (2-tailed).

As shown in the above table, SN and A show a significant positive correlation. PBC has a significant positive correlation with SN and A. ANEI has a significant positive correlation with SN and A, and PBC. PCB has a significant positive correlation with SN, A, PBC, and ANEI. AIBN has a significant positive correlation with SN, A, PBC, ANEI and PCB.

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4.4 Multiple Linear Regression According to Table 4, Attitude (p = < 0.001), Perceived Behavioral Control (p = < 0.001), Awareness of New Energy Industry (p = < 0.001), Past Consuming Behaviour (p = < 0.001) are all related to Customers’ Intention to Buy NIO’s Products have a significant relationship as the p-values are all below 0.01. In addition, Attitude, Perceived Behavioral Control, Awareness of New Energy Industry have a positive impact on Customers’ Intention to Buy NIO’s Products, while Perceived Behavioral Control has a negative impact on Customers’ Intention to Buy NIO’s Products, where Attitude (β = 0.660) The largest impact is followed by Awareness of New Energy Industry (β = 0.394), Past Consuming Behaviour (β = 0.301), Perceived Behavioral Control (β = –0.286). Therefore, four hypotheses are supported. Table 4. Hypotheses testing Hypothesis

Relationship

(Constant)

Parameter estimates

Standardized estimates

– .050

t-value

p-value

– .170

.865

Remark

H1

A → AIBN

.660

.480

4.247

< 0.001

Supported

H2

SN → AIBN

.101

.091

2.001

.192

Not supported

H3

PBC → AIBN

– .286

– .198

– 3.094

< 0.001

Supported

H4

ANEI → AIBN

.394

.249

4.019

< 0.001

Supported

H5

PCB → AIBN

.301

.274

3.548

< 0.001

Supported

Overall, the equation of multiple linear regression is as follows. AIBN = −0.050 − 0.660(A) − 0.286(PBC) + 0.394(ANEI ) + 0.301(PCB)

5 Conclusion and Discussion This study mainly aimed to look at the influencing factors of potential customers’ intention to purchase NIO products. This study helps policy makers understand the significant factors that affect the purchase intention of potential consumers for NIO’s products. This study found that attitude is positively related to customers’ intention to buy NIO’s products, which means that customers have a certain attitude toward purchasing new energy vehicles, and they value cost-effectiveness, brand, and social status. Attitude is positively related to customers’ intention to buy NIO’s products, which means that customers have a certain attitude toward purchasing new energy vehicles, and they value cost-effectiveness, brand, and social status. The results do not support H2 (interpersonal relationship or the relationship between KOL and the intention to purchase new energy vehicles) and are consistent with some past research results. Perceived Behavioral Control and Awareness of New Energy Industry are positively related to customers’ intentions

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to buy NIO’s products. Past Consuming Behaviour is negatively related to Customers’ Intention to Buy NIO’s Products. In short, the promotion of opinion leaders, advertising, subsidies, and taxes have little effect on purchase intentions. It shows that consumers are more cautious about the propaganda and guidance given by manufacturers and the media. At the same time, they seem to care more about personal experience and consumer reputation. Consumers pay more attention to pragmatism when buying new energy vehicles, and will focus on their own needs, such as commuting and picking up children. According to the research results, in order to encourage potential consumers to purchase NIO’s electric vehicle products, service providers need to refer to variables that are significantly related to consumer Intentional Behavior. Awareness of the new energy automobile industry and the attitude, subjective norms and perceived behavior control that affect the willingness of potential customers to purchase NIO’s products. The survey also found that most of the interviewees have poor participation in using new energy vehicles, and many people have never used new energy vehicles. Therefore, policy makers need to better allow potential consumers to use new energy electric vehicles. Norway is known as the “capital” of Electric Vehicles (EVs) because of its large number of electric vehicle users [36]. Some Norwegian scholars believe that government actions can lead to a significant increase in the use of electric vehicles, such as exemption from purchase tax when consumers buy cars, and allowing road users to use bus lanes to induce consumers to buy and use electric vehicles [37]. This behavior has certain reference value. Consumers’ own perceived behavior control will affect consumers’ intention to purchase new energy vehicles. The stronger the consumer’s control over their own behavior, the stronger their intention to buy new energy vehicles. Therefore, Nio must make potential consumers feel that they have the ability to buy and use Nio’s products, so it is necessary to set up some relatively low-priced models. At the same time, Nio should do a good job of after-sales service to let consumers know that they can easily maintain their cars. Although this research has completed the research content, there are still some limitations in the research process and research results. The main limitation is in the selection of the questionnaire objects. Due to some factors, the survey is only conducted in individual places. This study has certain limitations if residents in different regions cannot be equally selected for the questionnaire survey.

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24. Wong, L.W., Tan, G.W.H., Hew, J.J., Ooi, K.B., Leong, L.Y.: Mobile social media marketing adoption: A new marketing channel among digital natives? J. Mark. High. Educ. 32, 113–137 (2022) 25. Loh, X.M., Lee, V.H., Tan, G.W.H., Ooi, K.B., Dwivedi, Y.K.: Switching from cash to mobile payment: What’s the hold-up? Internet Res. 31(1), 376–399 (2021) 26. Al-Sharafi, M.A., Al-Emran, M., Iranmanesh, M., Al-Qaysi, N., Iahad, N.A., Arpaci, I.: Understanding the impact of knowledge management factors on the sustainable use of AIbased chatbots for educational purposes using a hybrid SEM-ANN approach. Interact. Learn. Environ. 1–20 (2022) 27. Lee, V.H., Hew. J.J., Leong, L.Y., Tan, G.W.H., Ooi, K.B.: Wearable payment: A deep learningbased dual-stage SEM-ANN analysis. Expert Syst. Appl. 157, 11347 (2020) 28. Al-Matari, A.S., Amiruddin, R., Aziz, K.A., Al-Sharafi, M.A.: The impact of dynamic accounting information system on organizational resilience: The mediating role of business processes capabilities. Sustainability 14(9), 4967 (2022) 29. Wong, L.W., Tan, G.W.H., Lee, V.H., Ooi, K.B., Sohal, A.S.: Unearthing the determinants of Blockchain adoption in the supply chain management. Int. J. Prod. Res. 58(7), 2100–2123 (2020) 30. Krejcie, R., Morgan, D.W.: Determining sample size for research activity. J. Comput. Technol. (1970). http://www.emoderators.com/ipct-j/1998/n3-4/hill.hmtl. Accessed 21 Dec 2020 31. Guoqiang, W., Tan, G.W.H., Yuan, Y.P., Ooi, K.B., Dwivedi, Y.K.: Revisiting TAM2 in behavioral advertising: A deep learning-based dual-stage SEM-ANN analysis. Technol. Forecast. Soc. Chang. 175, 1–15 (2022) 32. Al-Sharafi, M.A., Al-Qaysi, N., Iahad, N.A., Al-Emran, M.: Evaluating the sustainable use of mobile payment contactless technologies within and beyond the COVID-19 pandemic using a hybrid SEM-ANN approach. Int. J. Bank Market. 40(5), 1071–1095 (2021) 33. Loh, X.M., Lee, V.H., Tan, G.W.H., Hew, J.J., Ooi, K.B.: Towards a cashless society: The imminent role of wearable technology. J. Comput. Inf. Syst. 62(1), 39–49 (2022) 34. Yuan, Y.P., Tan, G.W.H., Ooi, K.B., Lim, W.L.: Can COVID-19 pandemic influence experience response in mobile learning? Tele. Inf. 64, 1–14 (2021) 35. Nunnally, K., Bernstein, A.: Psychometric Theory, 3rd edn. McGraw-Hill, New York (1994) 36. Haugneland, P., Kvisle, H.H.: Norwegian electric car user experiences. Int. J. Automot. Technol. Manage. 15(2), 194–221 (2015) 37. Aasness, M.A., Odeck, J.: The increase of electric vehicle usage in Norway—Incentives and adverse effects. Eur. Transp. Res. Rev. 7(4), 1–8 (2015). https://doi.org/10.1007/s12544-0150182-4

Digital Marketing in the Perspective of Omnichannel Retailing for Customer Engagement Xi Wang(B) and Ganesh A. L. Ramasamy Faculty of Business and Management, UCSI University, Kuala Lumpur, Malaysia [email protected]

Abstract. In the digital era, advanced information and communication technologies (ICTs) have steered pure offline and online retailers towards omnichannel retailing. This conceptual paper tries to explore the customer engagement behavior, a critical factor in establishing customer-firm relationship, in the context of omnichannel retailing. The study selects channel integration quality and customer satisfaction to explore their effects on customer engagement behavior based on Stimulus-Organism-Response (SOR) model. In addition, omnichannel compatibility is included as a boundary condition to the relationship between channel integration quality and its consequences. The proposed model will possibly help omnichannel retailers achieve a high level of customer support and enrich the existing research on omnichannel retailing. Keywords: Digital economy · Omnichannel retailing · Channel integration quality · Customer engagement behavior

1 Introduction In today’s digital economy, the increasing pervasiveness of advanced technologies has given the continuous growth of electronic, mobile and social commerce in shopping norms. This has given rise to omnichannel retailing, a hybrid retailing format which refers to a coordinated and integrated process, enabling consumers to freely transit across multiple channels [1]. Customer engagement is defined as “customer’s behavioral manifestations that have a brand or firm focus, beyond purchase, resulting from motivational drivers” [2, 3]. A study highlights that “fully engaged” and “engaged” consumers accounted for sales increases of 23% and 7%, whereas the “not engaged”and “actively disengaged”customers lead to declining sales form 1% and 13%, respectively [4]. Omnichannel retailing can help to develop a solid customer-firm partnership and achieve competitive edge by enhancing customer engagement within and across all available channels. While omnichannel can provide more opportunities to engage shoppers, it also poses some challenges as inconsistent information or responses from different channels may frustrate the consumers [5, 6]. In order to overcome the challenges, it is essential to © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (Eds.): ICETIS 2022, LNNS 584, pp. 122–131, 2023. https://doi.org/10.1007/978-3-031-25274-7_11

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operate with channel integration quality that refers to “the quality of the overall service experienced by a customer, encompassing all the existing physical and virtual components” [7]. A well-integrated omnichannel service can effectively realize synergies across channels, provide a higher variety of products and services, and improve consumer experience [8]. The positive effect of channel integration on customer engagement has been less discussed except for Lee et al. [5] who verified the positive relationship based on social exchange theory with a sample size of 269 Apple shoppers and 221 Kroger buyers. But they only discussed about the context-specific variable and neglected the consumer-initiated factors. To further their studies, this study intends to combine both firm-initiated factor (i.e., channel integration quality) and customer-centered element (i.e., customer satisfaction) to capture a holistic picture of customer engagement behavior in omnichannel context. Compatibility in business refers to the degree to which business innovation is perceived to be consistent with the potential users’ existing values, previous experiences, and needs [9]. The concept has always been considered as an important determinant of adoption and acceptance. Since consumers may differ in their shopping habits and preferences, compatibility may exert varied impacts on their responses and behaviors with an omnichannel retailer, thus, it is logical to conjecture that omnichannel compatibility may play a role in controlling the dynamics among channel integration quality, customer satisfaction and customer engagement behavior. In order to show theoretical importance, it is essential to (1) delve into the impacts of channel integration quality on customer satisfaction and customer engagement behavior as well as the mediating effect of customer satisfaction. Based on stimulus-organismresponse paradigm (SOR), channel integration quality (S) may transform into customer satisfaction (O) which subsequently provokes customer engagement behavior (R) [10]; (2) explore the moderating role of omnichannel compatibility in relations to channel integration quality, customer satisfaction and customer engagement behavior. In light to the aforementioned, this study makes contribution to the existing literature from two perspectives: first, we proffer an integrative research model that provides a holistic picture of what may affect customer engagement behavior that lacks of sufficient attention in the relevant studies; second, we complement the omnichannel literature by introducing omnichannel compatibility as a moderator that would likely have an impact on the relationships among channel integration quality and its consequences. Meanwhile, the proposed model will provide guidance for omnichannel retailers to design and implement proper channel strategies to better interact with their customers.

2 Literature Review 2.1 Customer Engagement Behavior As the literature on customer engagement is yet mature, the definition of the concept is still evolving [11]. Most recent research emphasizes on the different dimensions of customer engagement, that is, cognitive, emotional, and behavioral aspects [12, 13]. Given that, behavioral engagement distinguishes from other constructs as it can offer a better understanding of the way in which customer behaviors influence [11, 14]. The

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emphasis suggests that behavioral dimension of customer engagement is “a customer’s behavioral interactions towards an omnichannel brand or firm beyond transactions” [2]. Generally, studies categorize the prerequisites of customer engagement into three parts: shopper characteristics, which represent consumers’ attitude (e.g., trust, satisfaction, commitment, brand identification), their affective, behavioral or/and cognitive state (e.g., customer emotions, flow, involvement), and their consumption goals; firm initiatives that are under control of the organization (e.g., brand attributes, firm reputation and size). Apart from that there are contextual elements that primarily exist in the outside environment (e.g., socioeconomical and technological forces) [2, 13]. Among the studies on customer engagement in different research contexts, the effects of the firm-based and consumer-based customer predispositions have been consistently explored, such as service environment [15], store attributes [16], extraversion–introversion [17], customer attachment styles [18]. In the context of omnichannel, Lee et al. [5] put forth channel integration (i.e., firm initiative) as the precursor of customer engagement which induces repurchase intention of Apple and Kroger shoppers. 2.2 Stimulus-Organism-Response (SOR) Model The stimulus-organism-response (SOR) framework was proposed by Mehrabian and Russell [10] in environmental psychology. It describes a process where the external environmental factors (stimulus) influence consumers’ internal state (organism) that actuates their approach or averting behaviors (response). Stimulus refers to the impetus within the environment that can affect consumers’ cognitive, affective, consciousness, and value processes, whereas organism is the inner state of perceptions, feelings, and thinking exercises [19], and it is the mediating process between the stimulus and consumers’ response. The SOR model is quite a popular framework developed to investigate the effect of atmosphere/environment on customer behavior and it continues to be extensively tested in marketing literature, such as traditional retail, online shopping, mobile commerce, and social commerce, in an effort to help comprehend the reasons as to why a person behaves in a particular manner, and hence, it is relevant in analyzing behavior-related matters [20, 21]. Table 1 depicts previous relevant studies based on SOR model. Table 1. Previous relevant studies based on SOR model. Author

Industry

Stimulus

Fang et al. [22]

Mobile travel app

Mobile travel app Psychological attributes engagement and perceived benefits

Behavioral engagement intention

Channel integration

Repatronage intention

Zhang et al. [23] Omnichannel retailing

Organism

Customer empowerment, trust, satisfaction

Response

(continued)

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Table 1. (continued) Author

Industry

Stimulus

Organism

Response

Choi and Kandampully [14]

Upscale hotels

Atmosphere (social elements, public design, room design, ambience)

Customer satisfaction

Engagement (willingness to suggest and WOM)

Chopdar and Balakrishnan [24]

Mobile apps

Perceived Impulsiveness and ubiquity, perceived value contextual offering, visual attractiveness and app incentives

Satisfying experience and repurchase intention

Cheah et al. [25] Omnichannel retailing

Consumer perception of channel integration

Customer Patronage empowerment and intention trust

This study

Channel integration quality

Customer satisfaction

Omnichannel retailing

Customer engagement behavior

The findings show that the stimulus mainly falls on physical and social cues (e.g., store atmosphere, design, ambience), or specific technological characteristics (e.g., app attributes). Further, Zhang et al. [23] and Cheah et al. [25] concur on the stimulus of (perceived) channel integration at an omnichannel retailer. In addition, as for consumers’ internal state, studies can enrich on the pleasure, arousal, and dominance elements by adding new variables into the model, such as perceived value, satisfaction and attitude. With respect to responses, purchase intention, engagement intention, repurchase intention and loyalty intention are widely discussed. The SOR model can be appropriately for two reasons. First, as noted earlier, the model has been extensively used in previous studies on offline, online, mobile, and multichannel consumers’ behaviors. Second, the SOR model provides a structured manner by which to examine why and how consumers choose to engage with a retailer, accordingly, representing the theoretical foundation of the whole framework in this study as it allows to bring in omnichannel factors as channel integration quality to capture its holistic impact on users’ organismic state (i.e., customer satisfaction).

3 Hypotheses Development 3.1 Channel Integration Quality as Stimulus Channel integration quality refers to the degree to which a retailer coordinates its multiple channels to create synergy [23]. It also relates to the level of seamless customer experience a company can deliver across all channels [7]. Scholars consider channel

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integration quality as the most important component of omnichannel service quality because while the levels of online and offline service provided by the same retailer independently may be good, the overall evaluation of service quality may be low due to poor integration between different channels and their attributes [7]. Previous studies have applied the concept to explain different measures of behavioral outcomes, however, the direct and indirect linkage between channel integration quality and customer engagement behavior has remained largely unvisited. Customer engagement is primarily generated by the consumer in response to an overall experience received from a firm/retailer [26]. In the case of omnichannel retailing, when consumers receive the benefits from the seamless, frictionless, and enjoyable shopping experience enabled by integrated channels, they will reciprocate by engaging more with the focal firm/retailer [5, 27]. H1. Channel integration quality is positively related customer engagement behavior. 3.2 Customer Satisfaction as Internal Organism Customer satisfaction is explored continually by many scholars across different fields. It originates from the expectancy-disconfirmation paradigm [28] which asserts that customer satisfaction expresses the individual’s perception of the performance of the product or service compared to the expectations. In omnichannel environment, highly integrated channels provide consistent information of products, prices, and promotions online and offline, thus, can effectively reduce the time for customers to search and compare products and make customers feel delighted and satisfied [23]; it can also provide value-added offerings such as buy online and pick up offlline that are inaccessible from transacting with single-channel retailers [29] or that are pertinent to the buyers’ specific needs [30]. Such good performance will equal to or even exceed customers’ expectation which will result in customer satisfaction. This has been revealed from previous studies that channel integration quality exerts positive influence on satisfaction in product category [31], banking industry [32], consumer electronic products [23], and local service platform [33]. H2. Channel integration quality is positively related to customer satisfaction. 3.3 Customer Engagement Behavior as Response Satisfied customers tend to show the enthusiasm and pleasure typical of high customer engagement [34], which signals the possibility that a satisfied customer would be more proactively engaged with the firm/brand [2, 13, 35]. This has been confirmed in Brodie et al.’s [36] study hat highly engaged consumers have experienced higher level of satisfaction. In the same context, Simon and Tossan [37] highlighted that customer satisfaction is a precursor of brand Facebook page engagement. Apart from that, in retail banking industry, satisfaction is found to be the most important variable in generating customer engagement [35]. Further to the above, in investigating the firm-initiated on customer engagement in shareholder value, Beckers et al. [11] found that customer engagement

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initiatives show more effectiveness among satisfied customers who are more likely to respond positively in future. In other words, customers with higher degree of satisfaction tend to act positively towards the company when invited to participate in customer engagement activities. H3. Customer satisfaction is positively related to customer engagement behavior. Based on SOR model, organism behaves as a mediator in the stimulus-response link. Thus, the paper proposes that customer satisfaction mediates the relationship between channel integration quality and customer engagement behavior. The reason is that although channel integration quality as the core element of omnichannel service quality has a positive impact on customer engagement, some studies did not reveal the same. Thus, it is possible that when channel integration is not able to drive customer engagement, good relationships can help strengthen consumers’ engaging behavior. Therefore, it is reasonable to suggest that positive effect of channel integration on customer engagement behavior takes place through building stronger relationships with customers [19]. H4. Customer satisfaction mediates the relationship between channel integration quality and customer engagement behavior. 3.4 Omnichannel Compatibility Compatibility is one of the critical innovation characteristics in innovation that help predicting how people make decisions to adopt certain patterns [38]. As omnichannel shopping deals with different electronic devices and technologies (e.g., mobile phone, tablets, laptop, electronic kiosks), it is considered as an innovative shopping format and may require shoppers to shift from their current routine of shopping [39]. Omnichannel compatibility is defined as the degree to which omnichannel shopping fits the lifestyles, needs and shopping experiences of individuals [40]. It may have an influence on the relationships between channel integration quality and its outcomes, namely, customer satisfaction and customer engagement behavior. According to the technology-fit theory, if the requirement of tasks matches with the available technology, users will likely use the technology to perform the tasks [41]. Following this, if shoppers believe that purchasing through different online and offline channels fits the way they would like to shop, they tend to accept this kind of shopping format easily and naturally. With that, higher level of omnichannel compatibility may increase the effect of channel integration quality on positive customer responses and behaviors (i.e., customer satisfaction and customer engagement behavior) [42]. H5. The association between channel integration quality and customer satisfaction is stronger for consumers with higher levels of omnichannel compatibility. H6. The association between channel integration quality and customer engagement behavior is stronger for consumers with higher levels of omnichannel compatibility.

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On the basis of the above hypotheses, our study proposes a consolidated theoretical framework based on S-O-R model as showed in Fig. 1

Fig. 1. The proposed conceptual framework. H4: Mediated relationship (channel integration quality → customer satisfaction → customer engagement behavior)

4 Research Methodology A survey method is employed to test the proposed model. The online self-administered survey is designed on WJX [http://www.wenjuanxing.com], the most popular online survey platform in China. The convenience sampling is adopted to enroll respondents trough social mediums. A screening question “Have you ever made purchases through different channels (e.g. physical stores, website, mobile apps) of a focus retailer in the past six months?” is listed in the first part of the survey to screen out those who have no experience of omnichannel shopping. The measurement items in the questionnaire are generated from the literature with minor modifications to ensure the scales’ fit in our study context. The demographic information of the participants will be analyzed via Excel and the measurement and structural model assessment will be processed using PLS-SEM with SMART PLS 3.0 software.

5 Conclusion In the context of digital marketing, the advancement of various technologies has produced multiple channels like in-store kiosks, website, mobile apps, social platforms, and so on. Connecting all these touchpoints with highest degree of channel integration to create a seamless omnichannel customer experience can improve a customer’s interaction with the organization which demotes more sales and profits. More particularly, it will produce the customer personal experience wherein customers interact with retailer through their favorite method, for instance, they can search online and buy offline or vise versa, buy online and pick up in store, or return the items bought online in physical stores. In doing so, their personal needs can be satisfied, accordingly, they are more willing to actively

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participate in the activities of the focal brand/retailer and recommend it to their friends. Future research can verify the proposed model via survey or experiment method. Acknowledgement. This work is a part of a project submitted to UCSI University.

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What Drives Consumer’s Self-disclosure in Mobile Payment: An Investigate in China Shao Min Wu1(B) , Garry Wei-Han Tan1,2 , and Eugene Cheng-Xi Aw1,2 1 UCSI Graduate Business School, UCSI University, 56000 Cheras, Malaysia

[email protected] 2 Iqra University, Karachi, Pakistan

Abstract. Mobile payment has emerged as a new payment option for both consumers and businesses, a result of the rapid progress of mobile technology. When compared to traditional cash and/or ATM card transactions, mobile payment services can benefit consumers and businesses by providing a convenient, simple, secure, and quick way to conduct financial transactions. This study investigates what drives consumers’ self-disclosure in mobile payments in China. This study explores how internal structure (consumer’s cognitive trust and consumer’s emotional trust) and external structure driving force (technology anxiety and social pressure) can influence perceived benefits of mobile payment and consumer’s self-disclosure in mobile payment. The result shows that the perceived benefits of mobile payment have a significant relationship with consumers’ selfdisclosure of mobile payment. Moreover, there is a significant effect between the consumer’s emotional trust and perceived benefits of mobile payment. A total of 202 qualified responses from the questionnaire were analysed using PLS-SEM. This study enlightens mobile payment providers to understand factors affecting consumer’s self-disclosure in China, which can assist in enhancing and improving the marketing strategies. Keywords: Mobile payment · Consumer’s self-disclosure · Perceived benefits · Trust

1 Introduction As a result of advancements in mobile communication technology, mobile payment (MP) services have emerged [1, 2]. Mobile payment is a form of payment that integrates business transactions and financial transfer technology over mobile networks or any other wireless technology [3]. MP application which allows users to conduct transactions using mobile payment devices [4] has caught the interest of both individuals and business [5]. As people’s lifestyles become more dynamic, they have lesser free time, mobile payment allows people to take place at any time, from any location, during the period of online transactions [6]. Mobile payment increasing become a new mode of payment around the world as more and more people connected via smartphones [7], tablets, and other mobile devices in recent years, and has begun to replace the traditional credit card © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (Eds.): ICETIS 2022, LNNS 584, pp. 132–143, 2023. https://doi.org/10.1007/978-3-031-25274-7_12

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[8]. By the end of 2017, the worldwide mobile payment market have grown by 25.8% annually to US$780 billion [9]. In comparison to other areas of the world, MPSs are still in their infancy in the United States, despite their benefits and their expansion [10]. According to statistics, 86% of Internet users in the United States have never heard of mobile payment services, and 74% have heard of them but have never used them [11]. In fact, in Europe, the use of mobile payment (MP) is substantially lower [12]. India, on the other hand, presents a huge opportunity for mobile payment because it is the world’s second largest mobile market with 616 million subscribers. Despite the fact that India’s smartphone penetration is 26% of the total population, statistics show that only 7.6% of India’s population uses mobile payments for routine transactions [13]. Mobile payment technology it has also developed booming in China [2]. According to data, the total value of transactions in China’s mobile payment sector reached 83 trillion yuan in 2021, with over 852.5 million mobile Internet payment users in 2020 (Statista, 2022) [14]. Although mobile payment has many advantages, statistics show that the percentage of global acceptance of mobile payment is not ideal. As a result, both scholars and practitioners must evaluate the elements influencing a customer’s self-disclosure in mobile payment. In the final analysis, it would improve financial inclusion and banking facilities, which would have a good impact on the economy. For a variety of reasons, including intense competition among the numerous stakeholders involved in the financial ecosystem, mobile payments have fallen short of initial expectations. Therefore, this study aims to identify factors influencing Chinese consumers’ self-disclosure. In general, this research focuses on a payment technique that allows for contactless transactions. Furthermore, we created a model to study the impact of perceived benefits of mobile payment on customers’ self-disclosure of MP services. The spillover effect illustrates the connection between internal and external structures in mobile payment. In this study, internal structure refers to cognitive and emotional trust in MP services, whereas external structure refers to technology anxiety and social pressure. As a result, the current study is important for understanding why mobile payment isn’t widely used from the perspective of technology adoption as well as user behavior. In actuality, analyzing the effect of the proposed variable would help in formulating strategic selections regarding MP while researching mobile payment services in China. Similarly, the result of this research brings up public awareness of mobile payment by encouraging individuals to use it properly as well local and foreign companies operating in the country will gain a good understanding of the elements in driving the willingness and use of mobile payments. This study will also help them design effective advertising campaigns and improve their marketing strategy.

2 Literature Review 2.1 Consumer’s Self-disclosure Mobile payment is a complex social technology occurrence that must be fully comprehended [15]. Self-disclosure is the most familiar behavior in protecting consumer’s privacy in online environment [16]. Self-disclosure is the voluntary disclosure of individual information to others when using mobile payment applications in this paper [17]. Based on previous research on self-disclosure in information privacy literature review,

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understand the current research status of self-disclosure. Researchers listed some variables in the interpretation of consumer self-disclosure in this literature review, namely perceived benefits, consumer’s cognitive trust, consumer’s emotional trust, technology anxiety and social pressure. The perceived benefit of information disclosure is a total evaluation of the utility of risk perception of personal privacy information disclosure [18]. 2.2 Attribution Theory Heider’s attribution theory [19] was designed to describe how non-scientific or naive people interpret everyday happenings and how these explanations (or attributions) affect their perceptions and happiness with these events. According to the theory, a person’s personal views of success or failure in any activity will impact the amount of effort they put in for upcoming activities. In this study, attribution theory is utilized to explain the internal structure (consumer’s cognitive trust and consumer’s emotional trust) and external structure (technology anxiety and social pressure) (Fig. 1). 2.3 Justification of the Conceptual Framework

Fig. 1. Proposed research conceptual framework

3 Hypothesis Development 3.1 Consumer’s Cognitive Trust (CCT) In this research, consumer’s cognitive trust refers to whether the mobile payment is dependable, competent, and capable of delivering on its promises [20]. According to previous studies, accurate, reliable, safe and effective in relying on MP services are captured by consumer’s cognitive trust [21]. If consumers feel reliable and secure when using MP services, they are more likely to believe they will receive the desired benefits. In a study conducted in the US, it was also found that there is a significant effect of consumer’s cognitive trust on perceived benefits [11]. Therefore, we hypothesize the following:

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H1: There is a positive relationship between consumer’s cognitive trust in MP and perceived benefits of MP 3.2 Consumer’s Emotional Trust (CET) Consumers’ emotional trust refers to users’ willingness to conduct mobile payment transactions and expect the platform to meet their expectations [22] in spites of the consumer has the power to track or hold in the actions of the payment mode [23]. According to previous studies, confident and comfort in relying on MP services are captured by the consumer’s emotional trust [24]. If consumers feel relaxed and confident when accessing MP services, they are more likely to believe they will receive the desired benefits. In a study conducted in the US, it was also found that there is a significant affect on a consumer’s emotional trust towards perceived benefits [11]. Therefore, we hypothesize the following: H2: There is a positive relationship between consumer’s emotional trust in MP and perceived benefits in MP 3.3 Technology Anxiety (TA) Technology anxiety refers to a psychological state that describes a consumer’s beliefs about his ability to adapt and his willingness to worry when faced with new technology for the first time [11]. Although mobile payment service providers may rely on the convenience of mobile devices [25], consumers consider mobile transactions less secure and worry about privacy [18] and information loss [13]. Based on previous research, one study confirmed a negative correlation between technology anxiety and perceived benefits [11]. Therefore, we hypothesize the following: H3: There is a negative relationship between technology anxiety and perceived benefits of MP. 3.4 Social Pressure (SP) Social pressure refers to people’s tendency to accept a new technology [2, 26] that emerges when a large number of people accept the innovation [27]. In this study, social pressure refers to participants’ views of pressure from their social community to accept mobile payment [28]; thus, it is an important factor. It ties a person to their virtual community, which may motivate more people to adopt mobile payment services. A study conducted on users’ MP in the US provides support to the relationship between social pressure and perceived benefit [11].Therefore, we hypothesize the following: H4: There is a positive relationship between social pressure and perceived benefits of MP.

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3.5 Perceived Benefits of MP (PB) Perceived benefits describe how much potential consumers expect mobile payment to improve their transaction performance [29]. Consumers are hesitant to use a system if the individual’s perceived risk is greater than the perceived advantage [11]. The significance of perceived benefits was confirmed in a study connected to mobile payment [30]. Therefore, we hypothesize the following: H5: There is a positive relationship between perceived benefits and consumer’s selfdisclosure

4 Research Methodology This study adopted a quantitative method, with the objective to study whether internal and external constructs affect perceived benefits in MP customer self-disclosure. Consumer’s cognitive trust and consumer’s emotional trust are internal variables to investigate the relationship; external constructs to investigate the relationship are (consumer’s cognitive trust, consumer’s emotional trust), (technology anxiety, social pressure). Furthermore, the present study investigates the relationship between perceived benefits and customer self-disclosure in MP. In order to collect data, recruited research participants were requested to fill up a questionnaire.The confidence level at 95% and the recommended confidence interval at 90% was set to achieve a reasonable balance between cost and precision and population size. The minimum sample size calculated was 202. This research used questionnaires to collect the data. The study applied a seven-point Likert scale ranging from one to seven, with one being from “Strongly agree” to seven being “Strongly disagree.“ The SmartPLS software was utilized as an analysis tool in this study. The instruments utilized in this study’s questionnaire were developed after an assessment of previous literature. 4.1 Data Collection and Demographic Profile The data gathering technique is a self-administered online questionnaire. The largest online survey platform in China, So jump, was used to distribute the digital questionnaire. 202 actual responses in total. According to Table 1, 202 respondents were chosen to fill up the Chinese version of the questionnaire. 42.08% of the respondents were male, whereas 57.92% were female. Most of the respondents were between 30 to 34 years old, and more than half of them bachelor’s degrees/professional qualifications. 4.2 Measurement Model Assessment Partial least squares structural equation model (PLS-SEM) was used to study the research conceptual model. It examines the outer measurement of the reliability and validity of the model with the reliability, convergent validity as well discriminant validity. Cronbach’s Alpha is the most often used scale reliability measurement method. If Cronbach’s alpha, composite reliability as well rho value are above 0.7, a measured variable is assumed to

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Table 1. Descriptive analysis Variables

Selection

Frequency

Percent

Gender

Male

85

42.08%

Female

117

57.92%

Age

20–24

51

25.25%

30–34

53

26.24%

Education

Others

98

50.49%

Degree

75

37.13%

Master

52

25.74%

Others

75

37.13%

Table 2. Cronbach’s alpha, rho_A, composite reliability and average variance extracted (AVE) Cronbach’s alpha

rho_A

Composite reliability

Average variance extracted (AVE)

0.877

0.886

0.915

0.73

CD

0.818

0.84

0.916

0.845

CET

0.88

0.885

0.918

0.738

PB

0.76

0.764

0.85

0.59

SP

0.769

0.903

0.827

0.553

TA

0.768

2.776

0.855

0.751

CCT

Note: CCT = consumer’s cognitive trust, CD = consumer’s self-disclosure, CET = consumer’s emotional trust, PB = perceived benefit, SP = social pressure, TA = technology anxiety Table 3. Hetero-trait-mono-trait (HTMT.85) Hetero-trait-mono-trait (HTMT.85) CCT

CD

CET

PB

SP

TA

CCT CD

0.269

CET

0.713

0.267

PB

0.592

0.162

0.831

SP

0.212

0.386

0.382

0.397

TA

0.161

0.341

0.179

0.102

0.581

Note: CCT = consumer’s cognitive trust, CD = consumer’s self-disclosure, CET = consumer’s emotional trust, PB = perceived benefit, SP = social pressure, TA = technology anxiety

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S. M. Wu et al. Table 4. Discriminant validity

Fornell-Larcker criterion CCT

CD

CET

PB

SP

CCT

0.854

CD

0.231

0.761

CET

0.632

0.214

0.859

PB

0.509

0.208

0.745

0.768

SP

0.219

0.265

0.379

0.49

0.744

TA

0.185

0.118

0.244

0.203

0.303

TA

0.551

Note: CCT = consumer’s cognitive trust, CD = consumer’s self-disclosure, CET = consumer’s emotional trust, PB = perceived benefit, SP = social pressure, TA = technology anxiety

have internal consistency [31]. Table 2 shows that the reliability values are greater than 0.80. A validity test is used to determine the correlation between a person’s score and other variables. Kline (1988) believed that AVE should not be less than 0.50 [33], while Fornell and Larcker (1981) believed that item loading should be at least 0.70 [34]. It is concluded that the measurement model shows convergence validity. Finally, according to the study of Kim and Park (2013), discriminant validity is achieved when the square root of AVE is greater than the correlation between components [35]. Convergent validity and discriminant validity to evaluate the construct validity in this study. Convergent validity, and discriminant validity, respectively describe “how different measured values of the same construct are correlated” and “how different measured values of different constructs are not correlated” [36]. Average-variance-extraction (AVE) used criterion to assess the convergence validity in Table 2. A general rule of thumb shows that AVE above 0.5 is accepted [31]. If other high load indicators can explain half of the AVE, projects with an outer load between 0.4 and 0.7 can be recognized, but it should delete the item when loaded before 0.4 [31]. Therefore, in Table 5, TA2 was included, while TA1, TA3, and TA4 were excluded as bad outer loading. Furthermore, all AVE values were more than the 0.5 thresholds. Although Fornell and Larcker’s (1981) criteria are often used in lot of mobile payment studies [2], discriminant validity (DV) is lacking in common study situations [37]. Therefore, the Hetero- Trait-Mono-Trait (HTMT) correlation coefficient was used to evaluate dependent variable [37]. The finding shows that DV was not a problem through evaluating HTMT(HTMT,0.85) in Table 3 [38]. All structures show empirically different, and DV has been created in Table 4.

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Table 5. Outer loading CCT C1

CD

CET

PB

SP

TA

0.857

C2

0.965

CCT1

0.832

CCT2

0.881

CCT3

0.824

CCT4

0.879

CET1

0.891

CET2

0.909

CET3

0.882

CET4

0.742

PB1

0.874

PB2

0.903

PB3

0.776

SP2

0.707

SP3

0.756

SP4

0.906

TA2

1

Note: CCT = consumer’s cognitive trust, CD = consumer’s selfdisclosure, CET = consumer’s emotional trust, PB = perceived benefit, SP = social pressure, TA = technology anxiety

4.3 Examining Inner Structural Model The internal structural model uses 5000 bootstrap subsamples without sign change options and performs bias correction and acceleration (BCa) bootstrap at 95% confidence intervals [31]. The t values of path coefficients were 10.394, 3.073, and 4.62, respectively, and the significance levels were 1.288 and 0.354, respectively, to verify the proposed hypothesis. In recent years, scholars have used besides the t value [39, 40], also began to use deviation correction (bias, corrected the confidence interval) as part of the report. Hair et al.(2017) explained that if “the confidence interval does not contain zero, the path coefficient is significant.“ Table 6 reported the hypothesis test results of PLS-SEM, which showed that CET, CD, and SP (ρ < 0.05) were significantly positively correlated with PB, as assumed. But the negative correlation between TA and PB (ρ > .05) is not supported. PB (ρ > .05) is negatively correlated with CCT (ρ > .05).

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S. M. Wu et al. Table 6. Outcome of the Structural Model Examination Original sample (O)

Sample mean (M)

Standard deviation (STDEV)

CCT → PB

0.075

0.085

0.058

CET → PB

0.611

0.582

PB → CD

0.208

SP → PB TA → PB

T statistics (|O/STDEV|)

P values

Bias corrected confidence intervals

Remark

1.288

0.198

[–0.028, 0.528]

No

0.059

10.394

0.000

[0.035, 0.126]

Yes

0.223

0.068

3.073

0.002

[0.019, 0.035]

Yes

0.253

0.292

0.055

4.620

0.000

[0.018, –0.049]

Yes

–0.036

–0.112

0.103

0.354

0.723

[–0.085, –0.121]

No

Note: CCT = consumer’s cognitive trust, CD = consumer’s self-disclosure, CET = consumer’s emotional trust, PB = perceived benefit, SP = social pressure, TA = technology anxiety

5 Conclusion and Discussion This study explored the influencing factors consumers’ mobile payment self-disclosure. This study will help strategic decision-makers better understand the factors influencing the self-disclosure of Mobile payment users in China. There are six constructs in the conceptual framework. The result indicates consumers’ cognitive trust as a nonsignificant negative effect on the perceived benefits of MP, thus H3 was supported but not H1. In addition, this study found a significant relationship between the perceived benefits of mobile payment and consumers’ self-disclosure of MP. It implies the impact of consumers’ perceived benefits of mobile payments on consumers’ self-disclosure in an enjoyable mobile payment experience. Consumers’ emotional trust has a direct positive impact on the perceived benefits of mobile payment. It means that people who have more emotional trust in mobile payments and get more benefits from mobile payments. Perceived benefits of mobile payment are directly and positively affected by social pressure. The results suggest that consumers’ acceptance of mobile payments is affected by their surroundings others, such as friends, family, and other nearby relatives. Thus, social pressures play an important role in predicting the adoption and use of new technologies. Therefore, the better the perceived benefit, the more self-disclosure in the mobile payment areas. Therefore, mobile payment providers must adopt practical methods to adapt to different customer groups such as the elderly, the young, and people with special needs. The more skills, resources, and opportunities, the more likely consumers are going to use mobile payments. In addition, there is a substantial link between the perceived advantages of mobile payments and consumers’ self-disclosure in this study. Therefore, payment providers and government must formulate the necessary measures to

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improve the perceived benefits of mobile payment for achieving the best mobile payment index in the region. The theoretical model was evaluated in the developing mobile payment context, and as a result, generalizations of the research context’s findings will theoretically add to the existing body of knowledge on mobile payment. This is since the bulk of currently available studies have focused on the adoption of mobile payments, whilst relatively few studies have focused on customer self-disclosure in mobile payments. This study also has applications in real life. First, by looking into possible obsessive technology usage behaviors, the work is thought to warn consumers of mobile payments. This study also offers recommendations for improving corporate social responsibility on the part of mobile payment service providers to boost customer confidence in mobile payments and build a solid reputation.

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Identifying the Factors that Influence Users’ Intentions to Use Mobile Payment Services Alaa S. Jameel1(B) , Sameer S. Hamdi1 , and Abdul Rahman Ahmad2 1 Department of Public Administration, Cihan University-Erbil, Kurdistan Region Erbil, Iraq

{alaa.salam,Sameer.hamdi}@cihanuniversity.edu.iq

2 Faculty of Technology Management and Business, Universiti Tun Hussein Onn, Parit Raja,

Malaysia [email protected]

Abstract. Mobile payments with significant economic potential have emerged due to the advancement of mobile communication technology and the widespread use of intelligent gadgets. However, the rate of mobile payment adoption is insufficient. This study aims to measure behavioral intention to use Mobile payments. The target populations of the current study are mobile device users in Iraq. The study used Partial Least Square to measure 281 usable questioners. The results indicated that information quality, system quality, and service quality have a significant impact on trust and user satisfaction with mobile payment. Besides, trust and user satisfaction significantly impact behavioral intention to use m-payment. Therefore, Mobile payments services providers should constantly make sure that the information they give is correct and up to date. Furthermore, developers of mobile service systems must ensure that the Mobile payments system is dependable, simple to use, and responsive. Keywords: Mobile payment · Trust · System quality · Services quality

1 Introduction The emergence of mobile technology, particularly the mobile internet, has hugely impacted people’s lives and activities. The adoption of mobile devices has resulted in substantial changes in how individuals go about their everyday lives. Many people’s lives have increasingly dependent on mobile gadgets, particularly smartphones. As the usage of smartphones has grown, a new method of payment known as Mobile payments (MP) has emerged. MP is a service that allows users to initiate, approve, and execute money payments utilizing a mobile device [1–3]. For retailers, MP has the possibility to be a competitive advantage. This is due to the multiple advantages that MP may provide for both users and retailers [1]. For example, users benefit from mobile payment in retail outlets since it streamlines the payment process, speeds up services, and eliminates the need to carry currency. In addition, mobile payment enhances automation for retailers by quickly collecting data and providing statistics such as regular incomings and outgoings and average client spending. Few © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (Eds.): ICETIS 2022, LNNS 584, pp. 144–154, 2023. https://doi.org/10.1007/978-3-031-25274-7_13

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studies measured the factors that led to the intention to use mobile payments in Iraq [4]. However, in Iraq, m-payment is in the early stages; despite the growing number of mobile subscribers, mobile phone users’ use of m-payment services remains limited. This is due to the fact that people may be hesitant to make purchases using their mobile devices, maybe due to security and trust concerns. Nevertheless, the prevalence of mobile phones, notwithstanding their limited usage, can boost m-payment. The study used three main elements of quality from DeLone and McLean [5] information system success (D&M ISS): Information Quality (IQ), System Quality (SYQ), Service Quality (SEQ), and users satisfaction (US). Additionally, the study used users’ trust besides the US to predict the behavioral intention (BI) to use MP in Iraq. The study employed trust in the proposed model because the MP service incorporates transaction information that impacts user privacy; many users are concerned about individual performance and SEQ. Therefore, it is critical that customers have faith in mobile payments, recognize that the service is high quality, offer relevant information, and feel compelled to use it more frequently. This study investigates the impact on intention to use MP using the D&M ISS model and extends by trust.

2 Literature Review 2.1 M-payment Adoption MP has been characterized as any payment transaction expressly started, granted, and verified by a mobile. In recent years, advances in mobile technology and the underlying mobile networks have resulted in a significant rise in m-commerce transactions [6]. mpayment is a service that allows users to make payments, check balances, and transfer money conveniently, at any time, and from anywhere [7]. As a result, M-payment technology has expanded significantly worldwide, and its expanding strength and cross-border effect are rising [8, 9]. The most crucial stage in achieving technology acceptability and success in technological advances like mobile payments is for the target users to take adoption’s first and most vital step [10]. Furthermore, not all consumers have adopted and accepted payment through mobile as an essential service. Consequently, scholars have been concentrating on discovering the elements impacting usage intention [11–14]. When using MP, retailers are frequently concerned about security. However, on the other hand, retailers consider retailer trust in payment service providers and the safety of MP solutions to be crucial requirements for MP adoption [15–17]. The skill is regarded as the capability credibility in the mobile payment scenario, allowing sellers to believe that the mobile payment can achieve the expected goal and execute the required degree of performance [10]. 2.2 Information Quality IQ is usually related to the availability of useful, accurate information quickly. It requires that the material be error-free and feature up-to-date, exact, and comprehensive information. Users become dissatisfied when they have insufficient information.

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In the m-payment context, ensuring excellent information quality is critical [18]. Users may be able to conduct mobile payments more efficiently and avoid commercial issues if they have access to sufficient, reliable, and timely information regarding account balances and transaction records. Empirically and in the context of m-payment, Yuan et al. [18] reported trust and satisfaction are positively and significantly impacted by IQ. Yang et al. [1] and Franque et al. [7] reported IQ significantly affects users satisfaction in the Mobile Payment context. IQ can significantly increase the user’s trust to use mpayment [6]. Based on mobile banking, Tam and Oliveira [17] reported IQ significantly enhances the BI. H1: IQ significantly impacts user satisfaction. H2: IQ significantly impacts trust. 2.3 System Quality SYQ refers to users’ perceptions and evaluations of the MP system, including access speed, simplicity of use, navigation, and visual attractiveness [18]. In the scope of MP Services, system quality refers to the availability of certain attributes, including ease of use, fast connectivity, flexibility, and a visually pleasing look [19]. Empirically, Yuan et al. [18] SYQ’s most crucial factor predict the user satisfaction of m-payment and SYQ significantly predicts user trust. According to Gao and Waechter [20], this revealed that early trust in m-payment and online payment trust were significant predictors of usage intention. In the context of MP, Yang et al. [1] found that customer satisfaction significantly impacts BI. Therefore, SYQ can significantly enhance the user’s trust to use m-payment. In the mobile banking context, Tam and Oliveira [21] indicated BI was significantly impacted by SYQ. On the other hand, SYQ insignificantly predicted the satisfaction of the m-payment context [7]. H3: SYQ significantly impacts user satisfaction. H4: SYQ significantly impacts trust. 2.4 Service Quality Users who have access to high-quality services are more likely to be satisfied, which leads to the continuation of services and a greater belief that service providers have the capacity and willingness to provide as promised [15, 21]. Mobile payment service providers must deliver exact, dependable, and tangible services to users. Statistically, SEQ’s most crucial factor impacts Trust, followed by M-payment user satisfaction [18]. Similarly, SEQ can positively enhance users’ trust to use mobile payment [6]. In addition, SEQ can improve the BI to use mobile banking [21]. While, Yang et al. [1] and Franque et al. [7] reported, SEQ had insignificant impact on costumer’s satisfaction in the context of m-payment. H5: SEQ significantly impacts user satisfaction H6: SEQ significantly impacts Trust.

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2.5 Trust and BI The perceived risk possibilities associated with mobile-based payments are significant for both adoption and continuing intentions. In this case, consumers’ early trust may operate as a motivator to embrace mobile-based technologies. As a result of the potential risks and financial damage, users may be hesitant to conduct important transactions through mobile [22]. However, their perception that mobile payment businesses are trustworthy and trusted to conduct secure transactions due to their abilities, talents, and knowledge, on the other hand, increases the likelihood that these users would use such services [23].According to Zhou [24], the intention to use MP impacts directly or indirectly by users’ trust. Trust of users significantly impacts on intention to use e-wallet [25]. In the context of mobile payment Yang et al., [1] reported trust has a significant impact on BI to use mobile payments. According to [6], trust significantly enhances and increases the BI to use m-payment. H7: Trust significantly impact BI. 2.6 Satisfaction and BI BI is the level or strength of a person’s desire to engage in a particular action [11, 26]. BI is defined as the certainty with which people want to employ a particular technology. The most important and vital aspect determining consumers’ actual behavior is their BI. One of the most important deciding variables in assessing the effectiveness of an IS implementation is customer satisfaction [27]. Studies have shown that satisfaction is a strong antecedent of continued BI to use in several IS situations. Lin et al. [8] reported usage intention of mobile payment is significantly impacted by users’ satisfaction. Yang et al. [1] indicated BI use of M-payment is influenced by customer satisfaction. Satisfaction significantly impacts BI to use mobile payment [7]. According to Phuong et al.,[25], User satisfaction and BI to utilize an E-wallet payment system heavily influence customer trust. Based on the discussion above, the studies prove that users’ satisfaction with ISS is highly linked to BI to use. H8: user satisfaction significantly impacts BI.

3 Methodology The target populations of the current study are mobile payment users in Iraq, and all the users had online activities experiences. The current study used a convenience sample due to not being able to reach all users who have mobile payment experience. 400 questionnaires were distributed by self-administrative in several shopping malls located in Erbil. However, choosing the shopping malls is due to the large traffic of Iraqi customers from various demographics. 312 questionnaires were returned, which means the response rate is 78%; after checking the missing values and outliers, 281 questionnaires were valid to analyze. However, the data was analyzed using Smart-PLS. As mentioned above, the data was collected by questionnaire, and the questionnaire consisted of 31 items among 6 constructs. All the items adopted from previous works

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and as follows; 6 items measured the BI [19, 21], the IQ, SYQ, SEQ and user satisfaction measured by 5 items for each construct [19, 21], the Trust measured by 5 items [1, 19].

4 Results 4.1 Descriptive Statistics The majority of respondents were male, 70% and 30% female. However, the majority of responds Age between 21–25 years old 31%, while the Age 20 years old and below was 25%, Aged between 26–30 years old shown 16%, and Age between 31–35 years old and 36–40 years old 12% and 10% respectively, only 6% was 40 years old and above. 4.2 Measurement Model This step will measure the reliability, validity, Convergent validity (CV), and Discriminant validity (DV). The loadings of an item should be 0.70 and above according to Hair et al. [28] reliability scale. As depicted in Table 1, the outer loadings show all the items loadings above 0.7, except two items, that were removed (SEQ5 and TR5) because they showed poor loadings. In addition, to assess the internal consistency reliability of each construct should measure Cronbach’s Alpha (CA) and Composite Reliability (CR); the cut-off level for both criteria is 0.7 and above [28]. According to Table 1, both criteria CA and CR constructs are above 0.70 this is means the construct has good internal consistency. Thus, this step has been achieved. To evaluate the CV, according to Hair et al. [28] should be measuring the variance extracted (AVE) should be above 0.50. it is based on Table 1, all the constructs AVE above 0.50. Table 1. Construct reliability and validity Constructs

Items

Outer loadings

CA

CR

AVE

Behavioural intention

BI1 BI2 BI3 BI4 BI5 BI6

0.786 0.861 0.809 0.789 0.898 0.864

0.913

0.933

0.698

Information quality

IQ1 IQ2 IQ3 IQ4 IQ5

0.878 0.866 0.861 0.876 0.883

0.922

0.941

0.762

(continued)

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Table 1. (continued) Constructs

Items

Outer loadings

CA

CR

AVE

Service quality

SEQ1 SEQ2 SEQ3 SEQ4

0.842 0.869 0.845 0.815

0.864

0.907

0.710

System quality

SYQ1 SYQ2 SYQ3 SYQ4 SYQ5

0.814 0.888 0.915 0.891 0.831

0.918

0.939

0.755

Trust

TR1 TR2 TR3 TR4

0.849 0.864 0.891 0.847

0.886

0.921

0.745

User satisfaction

US1 US2 US3 US4 US5

0.844 0.843 0.896 0.790 0.853

0.901

0.926

0.715

The DV assessing by Fornell-Larcker Criterion. According to Fornell and Larcker [29], the AVE for each construct should correlate above other constructs, as shown in Table 2, each construct correlated above than other as highlighted in bold font. Thus, this criterion was achieved. Table 2. Fornell-Larcker criterion BI

IQ

SEQ

SYQ

TR

BI

0.836

IQ

0.483

0.873

SEQ

0.480

0.377

0.843

SYQ

0.541

0.595

0.440

0.869

TR

0.523

0.503

0.490

0.525

0.863

US

0.502

0.456

0.530

0.485

0.475

US

0.846

4.3 Structural Model After ensuring there are no issues in validity, reliability, and DV, the next step is to test the hypotheses, which is called the structural model. This step has been run by bootstrapping with 5000 subsamples as recommended by Hair et al. [30].

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Fig. 1. Structural model

Table 3. Hypotheses outcomes Hypotheses

Original sample

Sample mean

Standard deviation

T statistics

P values

Supported

IQ → US

0.195

0.196

0.068

2.863

0.004

YES

IQ → TR

0.244

0.247

0.071

3.411

0.001

YES

SYQ → US

0.209

0.209

0.065

3.221

0.001

YES

SYQ → TR

0.254

0.253

0.064

3.935

0.000

YES

SEQ → US

0.364

0.366

0.053

6.922

0.000

YES

SEQ → TR

0.287

0.287

0.064

4.459

0.000

YES

TR → BI

0.368

0.367

0.067

5.456

0.000

YES

US → BI

0.328

0.329

0.068

4.804

0.000

YES

The R2 was evaluated to show the variance and reported how much the exogenous explained the endogenous. The R2 for TR and US to IB is 0.357; this mean TR and US 35% explained the BI, which could be a moderate variance [31]. IQ, SYQ, and SEQ explained 39% and 38% variance to Trust and US, respectively, which could be a moderate variance [31]. However, the Q2 purpose is to measure the predictive relevance and, according to Hair et al. [28], should be above zero. As depicted in Table 3, the Q2 is BI 0.250, TR 0.290, and US 0.264; these results are considered medium [28]. Table 3

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and Fig. 1 depict the Hypotheses’ results. All eight hypotheses are supported. IQ has significant impact on US and TR (T-value 2.863 and 3.411 > 1.96; P-value 0.004 and 0.001 < 0.05) respectively. Thus, H1 and H2 are supported. Additionally, SYQ showed a significant impact on US and TR due to the P-value being less than 0.05 for both, and the T-value is higher than 1.96, 3.22, and 3.93, respectively. Thus, H3 and H4 are supported. Besides, SEQ significantly predicted the US and TR with p-value < 0.05 and t-value 6.922 and 4.459 higher than 1.96. Thus, H5 and H6 are supported. The TR and US also significantly impact BI to use MP with t-value 5.456 and 4.804, respectively, and the p-value < 0.05. Thus, H7 and H8 are supported.

5 Discussion The results indicated user satisfaction and trust were significantly impacted by IQ. This results in line with prior results [1, 6, 18]. This indicates that users may be encouraged to make m-payments if they access relevant, adequate, reliable, and timely information. High-quality information is a sign of a service provider’s competitiveness that can’t be readily replicated or counterfeit; greater information quality indicates a service provider’s skill, trustworthiness, and integrity. Users are more likely to be satisfied and trust the provider’s reliability if they believe a high IQ is supplied via an MP. SYQ significantly impacts user satisfaction and trust. These results are in line with previous findings [1, 18, 21]. Users acquire discontent and uncertainty about the provider’s capacity in every usage experience if they discover an m-payment system’s efficiency is weak, challenging to use, or sluggish to display text and graphics—resulting in lesser trust. As a result, the providers of m-payment services should strive to improve the dependability of their systems by reducing user inputs and simplifying the procedure. Given the limited screen size of mobile devices, a well-designed interface that is both appealing and easy to browse may be a suitable option for providers. Integration of the MP system with current financial and internet infrastructures is also required. However, Poor system quality, shown in poor imagery, smaller screen size, inflexibility, and complexity of use, on the other hand, would frustrate consumers and erode their faith in service providers. They may interpret this as the suppliers’ failure to deliver excellent services, resulting in dissatisfied users. User satisfaction and trust were significantly impacted by SEQ. These results are in line with previous results [6, 18, 21]. The MP platform is essentially a service product based on the mobile network, and since SEQ is a crucial aspect of payment products, it is logical to assume that consumers will be more concerned with SEQ. As a result, companies should build prompt, dependable, and professional services to value users. Furthermore, companies may supply customized services based on users’ interests and locations, presuming that users have given their consent. Current results refer to the fact that boosting service quality will increase a person’s BI to use MP. Additionally, if the Product information, enjoyment, convenience of use, and relative to usage all impact the quality of a system. For example, suppose the information supplied to consumers is free of inaccuracies, omissions, errors, irrelevant material, and out-of-date content. Then, it will have a favorable influence on the customers’ intention to utilize the services offered. Trust significantly impact BI to use mobile payment. Similar results were reported by Yang et al. [1], Phuong et al., [25],

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Pal et al.[6]. Another critical element that the authors intended to investigate was the extent to which payment trust influences customer intention to use MP. The result indicated that payment trust is a critical component of customer retention; improving SYQ, SEQ, and IQ will boost consumers’ trust and encourage them to use M-payment in the future. Different facets of trust include trust in services, service suppliers, initial trust, and continuing trust. Because m-payments rely on wireless networks, which are more unreliable and riskier, increasing trust is critical to enhancing the behavioral intention to use the payment through mobile. As a result, service providers must exercise caution when building encrypted services that include numerous security checks and effective fraud detection methods to alleviate privacy concerns and increase customer trust during transactions. BI to use MP is positively impacted by user satisfaction; these results are in line with similar previous findings [1, 7, 8, 32–36]. In addition, the results show that when a user is extremely satisfied with their m-payment experience, satisfaction becomes a key predictor of their intention to use m-payment in the future. However, In the retailing business, customer satisfaction in BI to use mobile payment is essential in determining whether or not to use it. In addition, because MP is a financial service, customers place a premium on trust and satisfaction when deciding whether or not to utilize it.

6 Conclusion Mobile payment service providers should constantly make sure that the information they give is correct and up to date. Furthermore, developers of mobile service systems must ensure that the MP system is dependable, simple to use, and responsive. Finally, users must be able to trust mobile payment service providers. This may be accomplished by enhancing the mobile payment services’ reputation and security. Overall, the results showed that IQ, SYQ, and SEQ impact trust and user satisfaction. Additionally, trust and user satisfaction significantly impact BI to use m-payment.

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Behavioral Intention and Actual Use of Mobile Learning During the COVID-19 Pandemic in the Higher Education System Alaa S. Jameel1(B) , Mohammed A. Karem1 , and Ahmed S. Alheety2 1 Department of Public Administration, Cihan University-Erbil, Kurdistan Region Erbil, Iraq

{alaa.salam,mohammad.abdulkarem}@cihanuniversity.edu.iq 2 Arts, Sciences and Technology University in Lebanon, Beirut, Lebanon

Abstract. This study aims to investigate the impact on Behavioral Intention and Actual use of Mobile Learning in the Higher Education (HE). The survey was conducted at Cihan University Erbil, and the data was collected by questionnaire. 207 valid questionnaires have been analyzed by Structural equation modelling (SEM). The results indicated that performance expectancy, Effort Expectancy, Facilitating Conditions, Hedonic Motivation, and Habit had a positive and significant impact on Behavioral Intention to use Mobile learning among the students. On the other hand, Social influence and Price Value had an insignificant impact on Behavioral Intention to use Mobile learning among the students. Besides that, the current study reported that behavioral intention directly impacts user behavior (UB). At the same time, facilitating conditions and Habit had an insignificant impact on user behavior. Keywords: UTAUT · Use behavior · Mobile learning · Higher education · Behavioral intention

1 Introduction Nowadays, Mobile learning (ML) has grown in popularity due to the pandemic of COVID 19. Furthermore, the low cost of telecommunications and the high quality of mobile devices (MDs) led to extensive use of ML among learners, particularly in the HE. Education institutions during the COVID-19 pandemic have been more open to the use of mobile learning, offering students and educators a better learning environment during the lockdown and postponed attendance at the university campus. Meanwhile, Due to the unique capabilities of mobile learning has been universally recognized as a valuable educational system [1]. However, integrating educational technology into the teaching and learning process is becoming commonplace in today’s curriculum. It has been shown that such innovations can aid in the improvement of learning outcomes and learner expectations [2]. During and after the COVID-19 pandemic, universities are moving away from the hierarchical and regular teaching rule favoring technology-assisted mass teaching © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (Eds.): ICETIS 2022, LNNS 584, pp. 155–167, 2023. https://doi.org/10.1007/978-3-031-25274-7_14

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programs, adaptive learning pathways, and student-centered learning approaches that promote a symbiotic relationship with society [3–5]. Mobile learning, According to Al-Emran et al. [6] can be described from three viewpoints. To begin with, it refers to learning that occurs through the use of smart devices. Furthermore, it is a form of learning originating from the wider term "distance learning." Finally, it reflects the next wave of mobile-based e-learning. Thus, in addition to the intrinsic advantages of MDs, such as accessibility and durability, ML may be a more innovative way of collaboration and information sharing for learners and educators. The Unified Theory of Acceptance and Use of Technology (UTAUT) and UTAUT2 has been used in different area of information technology. Such as E-learning systems [4], mobile banking [7, 8], and mobile shopping [9, 10]. Many universities Nowadays have incorporated ML into their work environments. However, universities face issues that hinder students from using ML entirely, particularly in the developing world. These barriers may be technological or non-technological, such as system capability, network reach, enabling circumstances, social impact, and so on [11–13]. The sluggish adoption of ML, on the other hand, has been seen as a technological problem rather than an educational problem. As a result, core technical reasons that could inspire learners to use ML systems in their education must be identified. Furthermore, before implementing new technologies, such as m-learning, it is essential to gain community approval. Following multiple suspensions of academic activities in Iraq due to the COVID-19 epidemic, maintaining public education in Iraq became exceedingly difficult on various levels due to a lack of technology and consistency in the online teaching experience. Even though students are rapidly using mobile devices [14], there are limited studies has been conducted in Iraqi HE and Arab countries [2, 10, 15]. Nowadays, the use of technology tools such as e-learning and ML has been rising in the Iraqi education system due to the COVID-19 pandemic [16]. As a result, Iraqi universities have shifted their practices in teaching and learning to the portals provided by various educational technology, allowing students to receive learning material at home during the self-isolate and lockdown in the country. These portals include learning management systems (LMSs) such as Moodle platform, and the students efficiently use various electronic devices such as tablets, computers, and smartphones. This study aims to investigate the impact of UTAUT2 dimensions on ML among undergraduate students in the Iraqi HE.

2 Literature Review and Hypotheses Development 2.1 Mobile Learning ML is described as disseminating teaching resources through mobile devices, allowing students to learn whenever and wherever they are. As a result, m-learning has provided new methods to reach learning materials via mobile devices such as tablets, notebooks, and computers [17, 18]. M-learning is widely regarded as one of the most significant new developments in modern instructional activities [19]. ML refer to the Learners may conduct authentic practices in the sense of their learning because they have access to information at any time and from any location through mobile technologies [1]. Alghazi

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et al. [10] described Mobile learning as the transmission of any instructional material created and used by MDs to the user, whether it is basic knowledge or a complete curriculum. Since m-learning enables students to study outside of the university or schools and the students have the advantage to learn at any time and from any place [1, 2]. M-learning is a combination of mobile communication and online learning that allows users to learn whenever and wherever they want and share what they’ve learned with others [20, 21]. On the other hand, Kim and Rha [22] describe the ML as a tool that incorporates many emerging tools, such as internet networking and MDs. It has a greater degree of complexity and danger in people’s minds, which may cause them to deny or defer accepting it. As a result, learner adoption of mobile technology is regarded as critical to optimizing the benefits of M-learning [21, 23, 24]. M-learning enhances the acceptability of the process of education, particularly for younger generations who tend to follow and use new technologies [15]. Furthermore, According to Ng, Lui, and Ngao [25] m-learning enables users to learn autonomously without teachers, increasing the extent of their communication with others and knowledge. 2.2 The UTAUT Theory Several theories have been developed to identify the elements that may lead to accepting the technology among users. However, when it comes to individual technology acceptance, UTAUT has been recognized as a fully-fledged chain of information system (IS) science, implying a cohesive view of the art of information technology [26, 27] One of the most widely adopted technology is the UTAUT theory. Several previous studies confirmed that UTAUT2 models have significant and more explained the acceptance of technology compared to other technology theories [1, 4]. Venkatesh et al. [26] proposed the UTAUT theory, which consisted of three factors that has a direct impact on “behavioral intention” (BI), namely; (“Performance Expectancy” (PE), “Effort Expectancy” (EE), “Social Influence” (SI)). At the same time, two factors are (“Facilitating Conditions” (FC) and “behavioral Intention” (BI) has a direct impact on “user behavior” (UB). Later in 2012, Venkatesh, Thong, and Xu [28] extended the UTAUT theory by adding three new factors, namely, “Hedonic Motivation” (HM), “Price Value” (PV), and “Habit” (HA) extended known as UTAUT2. The HM, PV, and HA have a direct impact on BI. Besides, HA, FC and BI has a direct effect on UB. BI refers to the degree to which user intend to use MDs in learning and execute a certain behavior [29]. Several prior studies reported that BI in the setting of ML had a direct effect on UB [1, 23, 30, 31]. H1: BI Has a Significant Effect on UB Using Mobile Learning Among Students. 2.3 Performance Expectancy PE Refers to the extent to which a person’s use of a device will help them in improving their job performance [26]. To put it another way, PE refers to a student’s ability to complete an assignment and duties successfully in the future. It’s a measure of a student’s belief that using mobile devices can help them perform better. [15] define PE as the extent

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to which a student assumes that ML can increase their learning ability. Empirically several prior studies confirmed that PE significantly impacts on BI to use ML [1, 2, 4, 15, 20, 23, 30]. H2: PE Has a Significant Effect on BI to Use Mobile Learning Among Students. 2.4 Effort Expectancy EE refers to user interpretation of how simple a device is to use [26]. EP refers to the student’s impression level of ease of use when using the M-Learning to complete the university tasks and duties. Venkatesh & Davis [32] Confirmed that conceptions of EE could only be wellconstructed after a direct, realistic experience. The degree of comfort with students using mobile devices to study. EE refers to the degree of comfort in which mobile devices can be used in the learning process [15]. The study conducted by Shukla [20] among mlearning users reported that EE can enhance the intention to use ML. However, based on the empirical studies, there is an inconsistency of results. That EE significantly impacts on BI to use ML [1, 2, 4, 15, 20, 23]. On the other hand, Arain et al. [30] reported that EE had an insignificant impact on BI to use of the technology. This inconsistency of results is one of the main motivations to conduct this study to examine the impact of EE on BI in the Iraqi higher education context. H3: EE Has a Significant Effect on BI to Use Mobile Learning Among Students. 2.5 Social Influence SI refers to the level of effect from others such as (friends and family) to students to use mobile technology in learning. SI refers to the ability of students’ friends and family to persuade them to use ML during university study, particularly nowadays during the pandemic of COVID-19. Furthermore, SI refers to the extent to which a student feels that the essential culture around them supports using mobile learning [15]. Alowayr and Al-Azawei [2] defined SI as the user’s decision impacted by social pressures to do a particular action and depending on the behavior in question. Empirically BI to use ML is impacted by SI [1, 20, 23]. On the other hand, BI does not impact by SI [2, 15, 30, 31]. Based on what was mentioned, there is an inconsistency in previous results, and this study tries to solve this inconsistency in the Iraqi context. H4: SI Has a Significant Effect on BI to Use Mobile Learning Among Students. 2.6 Facilitating Conditions FC refers to users who believe that systemic and technological infrastructural support is required to maintain device use [26]. In other words, FC refers to the technology infrastructure and support that receive it the students when they use ML. Users’ impressions of the resources and assistance accessible to them in order to carry out a behavior

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[4]. Alowayr and Al-Azawei [2] define the FC as a user’s get from their institutes or organizations assist when using technology. Statistically, FC examined to impact on BI and BU, and the results showed FC had a significant impact on BI [1, 4, 20] and BU [1, 23]. On the other hand, BI does not impacted by FC [2, 30, 31] and BU [31]. H5: FC Has a Significant Effect on BI to Use Mobile Learning Among Students. H6: FC Has a Significant Effect on UB to Use Mobile Learning Among Students. 2.7 Hedonic Motivation HM refers to the enjoyment or gratification gained from using MDs for educational purposes. According to Jameel et al. [4] If the student enjoys utilizing technology, there is a greater possibility that he or she will continue to use that technology. Several previous studies confirmed that using Mobile increased users’ joy and pleasure [4, 30]. Empirically BI impacted by HM [1, 7, 30]. While [4] reported HM had an insignificant impact on BI. H7: HM Has a Positive Effect on BI to Use Mobile Learning Among Students. 2.8 Price Value The students’ perceptual trade-off between the supposed educational gains of mobile apps during the learning process. However, it means that the perceived gain compared to costs from using technology [4]. PV refers to the ueser’s perception that the cost of the MD is appropriate in the learning process [15]. Empirically, PV impact on BI to use ML [1, 15]. While other studies reported, PV had an insignificant impact on BI [4]. H8: PV Has a Positive Effect on BI to Use Mobile Learning Among Students. 2.9 Habit According to Venkatesh, the amount users learn causes them to do things automatically and describes the Habit as a perceptual construct. It applies to the degree to which students tend to naturally learn from using MD. According to Jameel et al. [4], Users who have used similar technology in the past have been demonstrated to be easily affected by new similar technology when adopting it. The habits are a significant predictor of BI, which may redirect BU. It has a statistically significant influence on behavioral intention [30]. Previous studies reported BI impacted by HA [1, 4, 30] and BU [1]. H9: HA Has a Positive Effect on BI to Use Mobile Learning Among Students. H10: HA Has a Positive Effect on Using UB Among Students.

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3 Methodology The data was collected by a questionnaire, which is considered an appropriate method for data collection—the study was conducted on the Moodle users of Cihan university Erbil. The reasons behind choosing Cihan University; First, the university of Cihan has implemented the Moodle Platform since 2018. Second, Cihan university is one of the best rank universities in the Kurdistan Region and Iraq as a private university. Third, the Authors are academic staff at Cihan university which facilitates the Data collection and communication with students as a respondent. The Constructs items were adapted from prior studies, and the study used a five-point Likert scale because it eliminates cognitive biases and responder confusion [33]. The number of items and Sources for each construct has been depicted in Table 1. The population of the current study is undergraduate students from Cihan University Erbil, Located in Iraq. AMOS has analyzed 207 valid questionnaires after conducting the missing value, outliers and Checking the Normality and Multicollinearity by SPSS.

4 Results 4.1 Measurement Model The aim of this step is to ensure the validity and reliability of the model the outcomes of the measurement model of the current study are illustrated in Table 1. The composite reliability (CR) for all constructs has exceeded the cut-off level of 0.7 and ranged from 0.818 to 0.960. All the items reflect high internal constancy and are reliable to measure the constructs. Further, the average variance extracted (AVE) indicated that all the constructs exceeded the cut-off level of 0.50 as recommended by [34]. Factor loadings for all the items exceeded the recommended value by Hair et al. [34] 0.60, except EE4 showed poor loadings and was removed to enhance the model fit. As a result, all of the underlying variables obtained an acceptable variability with convergent validity. Table 1. Results of measurement model assessment Constructs

Items

FL

CR

AVE

Sources

HA

HA1 HA2 HA3 HA4

.974 .973 .900 .738

0.945

0.813

[4, 9]

HM

HM1 HM2 HM3 HM4 HM5

.789 .918 .897 .813 .716

0.917

0.690

[4, 9]

(continued)

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Table 1. (continued) Constructs

Items

FL

CR

AVE

Sources

FC

FC1 FC2 FC3 FC4 FC5

.842 .845 .762 .823 .758

0.903

0.652

[2, 31]

BI

BI1 BI2 BI3 BI4 BI5

.690 .855 .845 .836 .750

0.897

0.637

[2, 15]

PE

PE1 PE2 PE3 PE4

.851 .909 .939 .661

0.909

0.718

[15, 31]

SI

SI1 SI2 SI3 SI4 SI5

.886 .966 .879 .887 .930

0.960

0.829

[2, 15]

EE

EE1 EE2 EE3 EE5

.699 .878 .644 .673

0.818

0.533

[2, 15]

PV

PV1 PV2 PV3

.690 .799 .670

0.761

0.517

[15]

UB

UB1 UB2 UB3 UB4 UB5

.766 .946 .844 .745 .691

0.900

0.646

[9, 31]

4.2 Model Fit Indices The results of indices showed the RMSEA = 0.063, CFI = 0.907, TLI = 0.900, IFI = .908 and ChiSq/df = 1.791. According to Hair et al., [34] the RMSEA value should be ≤ 0.08, CFI, TLI, and IFI should be garter or equal to 0.9, and ChiSq/df should be 1.0 ≤ χ2 / df ≤ 5. Thus, the proposed model of the current study achieved the model fit (see Fig. 1).

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Fig. 1. Structural model

4.3 Structural Model As depicted in Table 2 and Fig. 1, The results indicated that BI has a positive and significant impact on BI to use the ML among students due to the p-value 0.320 < 0.05 and the T-value 2.143 > 1.96 Thus, H1 is supported. PE, EE, FC, HM and HA has significant impact on BI to use ML among students due to the p-values .0301,.008,.013, .041, .001 < 0.05 respectively and the t-values > 1.96 see Table 2. Thus H2, H3, H5, H7, H9 are supported. On the other hand, the results reported SI and PV had an insignificant impact on BI to use ML among the students due to the p-values .409 and .235 respectively > 0.05 and the t-values less than 1.96 Thus, H4 and H8 are not supported.

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At the same time, FC and HA had an insignificant impact on UB among students due to the p-values .730 and .0508 > 0.05 respectively. Thus, H6 and H10 are not supported. Table 2. Hypotheses results Hypotheses

Estimate

S.E

T-value

P-value

Supported

H1

UB ← BI

.247

.115

2.143

.0320

YES

H2

BI ← PE

.119

.054

2.168

.0301

YES

H3

BI ← EE

.212

.080

2.642

.0082

YES

H4

BI ← SI

.042

.052

0.824

.4096

NO

H5

BI ← FC

.217

.087

2.482

.0130

YES

H6

UB ← FC

.037

.108

0.344

.7304

NO

H7

BI ← HM

.181

.089

2.035

.0417

YES

H8

BI ← PV

.204

.171

1.187

.2350

NO

H9

BI ← HA

.139

.043

3.220

.0012

YES

H10

UB ← HA

.116

.059

1.952

.0508

NO

5 Discussion ML method in teaching and learning is unique. It allows students to view learning resources at any time and in various media, including audio, visual, and textual types, according to their tastes. The provision of the Internet, human capital, and the desire of teachers and students to use it are both factors in the progress of m-learning. As a result, M-learning has the potential to improve self-learning and possibilities tools for practice. PE and EE significantly predict the IB to use ML this result is consistent with previous results [1, 2, 4, 20, 23]. This means the students believe using the Mobile in the learning process could be helpful and valuable to do their tasks and assignments and increase their knowledge and productivity. This result refers to the student using the Mobile in education easily and free of effort. The interaction with Mobile is understandable and accessible for the students to be skillful when adopting the ML. However, if students believe that universities provide them with the necessary technical assistance, they will put in more effort to do a particular behavior. Furthermore, users’ perceptions of a technology’s utility may be favorably influenced if they perceive that enacting a specific behavior would not require much effort. Most of the previous results obtained the SI able to increase BI to use ML [1, 20, 23]. At the same time, the current study’s findings revealed that students do not consider SI as a factor that may influence BI to use the ML; this finding in line with [2, 15]. May explain the negligible impact of SI on ML uptake in underdeveloped nations due to the lack of sustaining technology and the expensive cost of Mobile. FC significantly impacts on BI to use ML among students. Similar results reported by [1, 20]. When students believe their knowledge, abilities, and resources are conducive

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to using mobile learning, they are more likely to use mobile technology to study. However, this result indicated the students have the necessary resources, knowledge, and technology to use ML. HM significantly impact on BI to use ML among students. Similar results reported by [1, 7, 30]. When students’ enjoyment of utilizing mobile devices in the classroom grows, so does their desire to utilize these devices. Students find the mobile app entertaining, which may encourage them to participate in ML activities. Thus, students are motivated to join in m-learning activities through fun and pleasure. This result indicates that PV has an insignificant effect on the intention to use ML among students, similar finding reported by Jameel et al. [4]. The insignificant impact may be due to the fact the study was conducted at private universities, and usually, the students who enroll at private universities have a good economic level, and the price does not impact their intention to use ML. HA reported as a predictor on BI to use ML. Similar findings reported by [1, 4, 30]. Usually, the students utilize mobile devices regularly, and the technology becomes used daily [4]. Thus, students have a more habitual behavior toward utilizing ML technology, and the technology adoption rate is expected to be more significant. UB does not impacted by FC among students this result inconsistent with most of the previous findings, which reported the FC and HA has a significant impact on UB [1, 4, 23] and consistent with the finding reported by [31] reported FC had an insignificant impact on UB. According to Venkatesh [26] usually, the FC shift to insignificant when the PE and EE are significant. HA did not lead to an increase in the BU among students and had an insignificant impact on BU consistent with previous findings [4] and inconsistent with the previous finding which is reported HA had a significant impact on BU [1]. This could be due to the students must use the system to be in touch with the instructor and submit tasks and assignments as well as download the materials, particularly during the pandemic of COVID 19 and most of the universities postponed the physically attending or shifted to the blended learning process BI statistically predicted to UB and able to increase the actual use of ML among students, this finding in line with prior findings [1, 23].

6 Conclusion The study aim has been achieved, and the results demonstrated that PE, EE, FC, HM, and HA are capable of enhancing the BI to use ML among the students. The universities should improve the performance and facilitating at the university to motivate the students to engage with ML, particularly nowadays with the pandemic of COVID19.as well the study reported that SI and PV do not impact students’ Intention to use mobile learning. Practitioners and university administrators should clarify to persuade students of the advantages of using ML in university classes. In addition, some students who have a lower level of personal innovation may require motivation during the first stages of mlearning implementation. A mobile learning system’s simplicity of use and utility can add value to an existing LMS by improving learning and increasing users’ acceptance of ML.

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Effective Risk Management as a Mediator to Enhance the Success of Construction Projects Mukhtar A. Kassem(B) and Kherun Nita Ali Department of Quantity Surveying, Faculty of Built Environment and Surveying, University of Technology Malaysia, 81310 Skudai, Johor Bahru, Malaysia [email protected]

Abstract. Risk management is seen as a critical organizational capability for gaining a competitive advantage in the construction sector. The importance of risk management in minimizing the impacts of external risk factors in construction projects, and therefore achieving project success, was investigated in this study. Using partial least squares structural equation modelling and survey data from 348 project members in oil and gas construction projects, the study statistically examined the relationships between external risk variables and project success. The findings show that, in cases where risk management plays a mediating role, effective risk management can minimize the influence of external risk factors on project success and, as a result, improve construction project success. Time overruns have the most significant impact on project performance, with a factor loading of 0.842, while risk analysis is the essential phase, with a factor loading of 0.851. In terms of risk management, the study contributes to closing a gap in knowledge on how external risk mitigation may help a construction project succeed. In contrast to the usual technique of establishing a sophisticated management system, it also gives new theoretical recommendations for employing project risk management to promote integration in construction companies. Keywords: Risk management · Construction projects · Project success · PLS-SEM

1 Introduction Managing risks efficiently avoids construction projects and many unexpected losses, whether in the cost or time of the project. This study aims to research in-depth the role that efficient risk management plays by the project team and its reflection on the mitigation of the effects of external risk factors on the construction project’s success. The importance of this research comes as a case study for construction projects in the oil and gas sector in Yemen due to the economic importance of this sector and the Yemeni government’s heavy dependence on oil export revenues as one of the most important financial resources. Failure to implement infrastructure projects for the oil sector, such as refineries, pipelines, and central processing facilities, will automatically reflect on the number of exports and thus on the expected financial revenues. According to [1], © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (Eds.): ICETIS 2022, LNNS 584, pp. 168–191, 2023. https://doi.org/10.1007/978-3-031-25274-7_15

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a project’s risk management is one of the leading roles played by a project manager. Nevertheless, if the project had an inadequate risk management plan, this function is particularly complex and inefficient. Yemen’s oil and gas industry has been in steep decline for years due to sabotage, oilfield degradation, and underinvestment, despite remaining the primary source of government funding. Although most production has been halted and many international investors have been terrified of external risk, oil and gas assets and infrastructure remain essential contention items in the continuing conflict. Yemen has 3 billion barrels of oil and 17 trillion cubic feet of gas reserves based on [2]. Oil revenues are critical in addressing the poverty that underpins much of the country’s instability. In general, the oil and gas sector’s contribution to GDP fluctuates and evolves. The fluctuation in the oil and gas sector’s contribution to GDP is attributable primarily to fluctuations in the global oil price and a decrease in production quantities. While higher world oil prices contributed to a rise in the contribution of oil and gas to GDP in 2011 compared to 2010, the proportion fell in 2012 and 2013 and continued in 2014 owing to a severe drop in production quantities, according to [3]. However, despite the significant decline in crude oil production, the oil sector’s contribution to GDP growth remained a significant concern due to the increase in world oil prices (before the fall in oil prices in the recent period), as well as the entry of the world gas market into the world gas market, as well as the entry of the world gas market. Liquefied natural gas was exported in 2009. The positive effects of the improvement in gas prices, which have been agreed upon between the proper government and contracting companies in the sale of Natural Gas, should also be taken into account by raising the sale price starting in 2014 [4]. However, opportunities are always followed by threats, and multinational construction companies will be exposed to new external risks if they start a business overseas based on [5]. The Greek, Venezuelan, and Congo financial crises; hostilities in southern Sudan, Iraq, Afghanistan, and Libya; terrorist assaults in Europe, the Middle East, Central Asia, and South Asia; and coups in Niger, Thailand, and Honduras have all witnessed substantial increases in global political and economic threats according to [6]. Such risks substantially negatively impact the worldwide market, resulting in significant losses for international construction businesses in the oil and gas sector. The study’s focus is limited to oil and gas firms that work in Yemen on projects. This study is relevant because there has been little research on the external risks experienced when entering Yemen. Novice and prospective international firms interested in investing in Yemen’s oil and gas sector will learn about the many dangers to consider and how to plan for them. This paper’s assessment of the methods used to minimize and manage political, economic, and security concerns may assist international businesses in planning their entry into Yemen [7]. Yemen is one of the less developing countries with a lack of infrastructure and has a weak political and economic system. Whether political, social, cultural, economic, legal, security, logistical, or force majeure, external risk elements depend on the business environment in the least developed nations like Yemen. At the same time, the suggested established external risk management framework provides a guide for multinational firms to detect better, classify, and respond to external threats. Studying the role of risk management as a mediator to reduce the impact of risk factors on project success is an important topic that needs to

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be studied to increase awareness among workers in the construction sector of the importance of studying risks during the project stages and developing response strategies to confront them and mitigate their effects as much as possible.

2 Literature Review According to [8] in the absence of any project achievement and overruns costs or overruns time, it is common to investigate the project to determine the causes of its recurrence. Often it can be easy to identify the reasons for not reaching a specific goal. Moreover, it can be pretty easy to answer why a project failed rather than to answer the question as to why a project was successful, and this question is never resolved in a single simple way. Nonetheless, the issue is essential and must be addressed to constantly discuss what drives contractors’ construction and road projects to succeed. Furthermore, risk management is a strategy of recognizing, classifying, examining, evaluating, and responding to all risks in any project and is a necessary step for success [9, 10]. In Nigeria, [11] carried out a study and reported, risk management is an important role to be undertaken by a project manager. However, if effective risk management measures were not implemented right from the outset of the project, the position of a project manager is primarily challenging and inefficient. Applying a systematic framework, mainly to practice and expertise, requires an accessible and reliable risk assessment approach. Some work in Nigeria has shown that owners, contractors, and consultants are not regularly using Nigerian construction industry risk management techniques, which would, in the long term, have adverse impacts on project performance based on [12]. In the same way, Nigerian construction firms have not stressed risk during construction projects. These risks have led to project losses in the construction industry when not handled adequately, as per [13]. According to [13], the project manager has relatively uncontrollable external risk factors such as interest rates, inflation, volatility, and significant force injuries. Given the uncontrollable complexity, the consequences of external forces need constant scanning and forecasting. However, this previous work does not identify risk factors based on their effect on the project’s success. The development of oil and gas facilities is distinct from those of traditional mining companies or service providers. Construction projects, however, are part and parcel of the oil and gas industry. Different issues have to be faced as the risks from the development of general offices, processing facilities to the development of oil platforms based on [14]. Studied the modelling of global variables impacting construction cost performance risk, comprehensively evaluated several research on construction risk factors and created a list of essential components (e.g. risks related to the availability of labour skills, material delivery and quality, reliability of equipment and availability and management efficiency), based on [15]. Earlier researchers presented a general risk category classification containing various sub-risks, enabling new sub-classifications to be included in critical groupings. While earlier studies are comprehensive, the impacts of these risks are not adequately explained by work in the risk allocation for the project of the various stakeholders. Furthermore, the oil and gas sector is defined by its specific manufacturing and service activities, requiring careful project management for the associated building

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projects. Furthermore, in an oil and gas construction project, the focus is on meeting the owner’s expectations regarding the stated scope of work, the available money, and the stipulated timeline and attendance. In addition, management must address the challenging categories of quality, schedule and cost. During the project life cycle, specialist resources are needed: design, assessment, test, practice, identify, calculate, assess, develop, control; suppliers, inputs, processes, outputs, and customer design, evaluate, justify and integrate as explained by [16]. As stated by [17], the risk classification was used to differentiate between risks beyond the control of the company and those which construction companies could handle, pass, or avoid. Moreover, all 27 external risks were divided into five main groups (political, economic, legal, social and natural risks). In the Vietnamese oil and gas sector, [18] investigated the risks connected with construction projects, interviewing and surveying local companies. Furthermore, the top ten significant risks were: government bureaucracy polity and lengthy and complex approval procedure, poorly designed, the ineffectiveness of project team, insufficient tendering, late internal authorizations from the owner, poor project organization structure, inappropriate pre-feasibility study, unproductive and inadequate performance of the contractor, and preliminary project plan. Moreover, [19] confirmed that many risks that cannot be reported in the project risk registry had been proposed for further study because their effects cannot be easily defined, e.g. labour injuries, fire hazards, and absence of site staff. Nevertheless, identifying these risks can be allowed where there is a facility that impacts the length of the project or the expense of the project, not the duration or costs of the particular operations. The seven most common causes of lateness have been identified. The problem mentioned above (lack of involvement with suppliers throughout the engineering and procurement stages) is unique in oil and gas construction projects. The focus of this study was on the contractor’s obligations, as this was the link between "subcontractor problems" and "project failure," a typical complaint in many construction projects [20]. Similarly, the influence of risk factors on project success was examined by [21–23] in the context of project delays in oil and gas construction, and similar results were obtained. Imported materials, unrealistic project durations, vendor materials, property expropriation, reimbursement to the supplier, change orders, contractual selection methods, permit implementation, suppliers, and contractual cash flow have all been recognized as main risks in the Iranian gas pipeline projects. More study on these risk factors should be done since they have a high potential to be the fundamental causes of failure, and addressing them will result in beneficial outcomes for owners, contractors, and the project as a whole, according to [24]. Based on literature review, statistical analysis and using expert judgement, [25] collected the most significant external risk factors in six main groups, namely economic, political, local people, environment & safety, security and force majeure, which include 20 risk factors in total as shown in Table 1:

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M. A. Kassem and K. N. Ali Table 1. External risk factors according to [26]

No

Code

Risk factors

1: Economic risk factors—EC 1

EC1

Financial and economic crisis

2

EC2

Fluctuation of Foreign currency

3

EC3

Insurance and transport prices

4

EC4

Infrastructure projects

2: Political risk factors—PO 5

PO1

Instability in politics

6

PO2

Change the law and regulations

7

PO3

Conditions in the Country During Construction

8

PO4

Nepotism and illegal support

3: Risk factors related to local community—LP 9

LP1

Society’s responsibilities

10

LP2

Locals are being recruited

11

LP3

Various cultures

4: Safety and environment—EN 12

EN1

Environmental protection requirements

13

EN2

Safety and Health

14

EN3

Treatment of waste

5: Security risk factors—SE 15

SE1

The length of the oil sector’s borders

16

SE2

Armed groups pose a threat

17

SE3

Roads that are unsafe for travelling

6: Force majeure factors—FM 18

FM1

Windy weather, floods, fire, landslides

19

FM2

Unexpected circumstances

20

FM3

War in the country

The external risk factors in the above table have been compiled through previous studies in construction projects, especially in the oil and gas field in countries similar to the situation in Yemen, according to [26]. This research will examine the role played by effective risk management by identifying and evaluating risks and selecting the appropriate strategy to respond to them, and finally follow up control and control of risk factors during the life cycle of the project and its impact in reducing the impact of these risks on the success of these risks. Construction projects in Yemen’s oil and gas sector. According

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to [27], risk management in the construction sector significantly affects the project’s success. The achievement of schedule, cost, quality, and other project objectives is strongly contingent on an effective approach to risk management. Risk management is the systematic process of analyzing, identifying, and responding to project risks. According to [28], it includes raising the chance and impact of positive events while lowering the possibility and impact of negative events to meet project objectives. Risk management may be defined as a decision-making process that entails having a comprehensive understanding of known risk and/or taking the necessary steps to reduce the impact and probability of such risks arising in order to avoid problems and increase the chances of success, as stated by [29]. The techniques for managing risk in the construction business are divided into three stages: a) risk identification, b) risk analysis and evaluation, and c) risk response [30]. Furthermore, the term "risk identification" refers to the process of identifying and recording the dangers that are associated with it. On the other hand, risk assessment entails thoroughly evaluating the detected risks, improving the risk description, and estimating the risks’ likelihood and impact on the project. The project’s assessment, allocation, inspection, and reaction are part of the risk response [31].

3 Methodology The method used for this research is based on structural equation modelling (SEM), using the PLS-SEM approach and the research model was ascertained through the SmartPLS 3.0 software. Through conducting a structural questionnaire distributed to oil & gas companies in Yemen and containing questions that achieve the goals of the research by knowing the importance of the selected external risk factors and the extent of their influence on the success of the construction project, as well as inquiring about the role of effective risk management as a mediator in reducing the relationship between risks and their effects on the construction project. A conceptual model framework was developed to explain the relationship between independent and dependent variables through moderators under study, including eight main hypotheses as Fig. 1. The research contains eight hypotheses that are based on the conceptual model shown in Figure: H1: Factors related to economic risks have significantly contributed as external risk factors. H2: Factors related to political risks have significantly contributed as external risk factors. H3: Factors related to local community risks have significantly contributed as external risk factors. H4: Factors related to safety and environmental risk have significantly contributed as external risk. H5: Factors related to security risk have significantly contributed as external risk. H6: Factors related to force Majeure risk have significantly contributed as external risk. H7: The external risks have a significant impact on construction project success. H8: Effective risk management as moderator plays a significant role to mitigate the impact of external risk on construction project success.

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Fig. 1. A conceptual model framework

According to their previous research, the study focused on studying six groups of external risk factors that contain 20 risk factors that the authors believe are crucial in influencing the success of construction projects in the oil and gas sector. The study population was defined as it includes all the producing oil companies operating in Yemen and their number according to the statistics of the Yemen Petroleum Exploration Authority, there are 11 oil companies and one liquefied natural gas company and according to the statements and official figures of the Ministry of Oil in Yemen for the number of workers in this sector are 4812 employees. By verifying this number using the Krejcie & Morgan equation and sample size table, we found 357 employees. Furthermore, [32] proposed for any sort of study, sample sizes more than 30 and fewer than 500 are recommended, while [33] recommended having a minimum sample size of 200 for any SEM analysis, [34] suggest for SEM, a sample size of 200 to 400 is recommended. A sample size of 360 individuals was chosen for this study, and the questionnaire was disseminated appropriately.

4 Results and Discussion 1. Demographic Distribution of the Respondents In surveys, the response rate is the ratio of the number of people who completed surveys to the number of people invited to participate. It is not expected that a standard number of answers will be obtained because they will differ between questionnaires to achieve an acceptable response rate. In total, 390 questionnaires were distributed to Yemen’s oil and gas companies, including all related participants in construction and oilfield development

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projects. Of the 390 questionnaires given, 359 were returned, representing an identical ratio of 92% of all individuals invited, 348 were accepted, and three incomplete replies were discovered and rejected. Therefore, the response rate of 92% is considered adequate for analysis in this study because [35] suggested that a response rate of 30% is sufficient for surveys. Most importantly, the acceptance rate was calculated. The minimum number of data instances required to verify the study’s PLS-SEM research model could be anticipated to be ten times the number of predictive variables, according to [36]. Table 2. Summary of participants demographics Years of experience

Frequency

The job title

Frequency

Oil and gas sector

Frequency

< 5 years

45

Construction manager

37

Petro Masila sector

67

5–10 years

103

Project manager

49

Safer sector

61

10–20 years

118

Project coordinator

36

YLNG sector

77

20–30 years

64

Project engineers

137

Total sector

47

>30 years

18

Site supervisor

46

OMV sector

57

Others

43

Other sectors

39

The demographic analysis of the participants is shown in the previous Table 2, which shows that the study covered all oil and gas sectors operating in Yemen, and that the majority of the participants are directly related to project management and have a long work experience of more than ten years for the most part. According to the respondents’ experience levels, the majority of them had extensive experience. A total of 118 participants (33%) have worked for 10–20 years, followed by 103 people (29%) who have worked for 5–10 years, 64 people (13%) who have worked for 20–30 years, and 18 people (5%), who have worked for more than 30 years. Furthermore, it was shown that the majority of the respondents held roles closely connected to project management at the job title level. A total of 137 with 39% of respondents are construction engineers (civil, mechanical and electrical), 46 with 13%, site supervisors, 49 with 14% are project managers, and 37 with 10% are construction managers. 2. PLS-SEM Analysis PLS-SEM is a statistical modelling methodology that maximizes the explained variance of endogenous latent constructs (dependent variables). It’s been around for a while, and there are several papers on it [37–40] and [41] However, there is no complete literature that describes the method’s essential elements, especially in a way that nonstatisticians can understand. Because it can identify critical driving components, PLSSEM was used; it is also able to deal with data that don’t have normality based on and has

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minimum demand for sample size [42]. Moreover, The SmartPLS software programme incorporated Partial Least Square-Structural Equation Modeling (PLS-SEM) [43]. The step of structural equation modelling analysis using partial least square (PLSSEM) can be seen in Table 3. Table 3. PLS-SEM model analysis steps

The structures that researchers want to look at can be extremely complicated in some cases and can also be operationally defined at various levels of abstraction. Developing such higher-order models, also known as hierarchical component models (HCMs) in the context of PLS-SEM [44], the majority of cases, testing second-order structures with two layers of constructs is required. Figure 2 depicts the suggested model of the link among the sources and impact of risks on the success of oil & gas construction projects, with two tiers of risk factors. The thirteen risk factor groupings, as indicated in the framework previously, are first-order. In contrast, the primary categories of internal and external risks, which will be examined using the PLS-SEM, are second order. 2.1 Assessment of measurement model (outer model) The purpose of the measurement model evaluation is to test the assessment methods used, whether to measure external risk and their impact on the construction project’s success or to evaluate the role of effective risk management by evaluating the reliability and validity of the variables used in the measurement. Composite reliability for evaluating external correctness, Average Variance Extracted (AVE), and individual indicator reliability for measuring convergent validity are the three essential tests for evaluating reflecting measurement models. When determining discriminative validity, Fornell-Larcker and Cross Loadings are employed.

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Fig. 2. Hypothetical model of the risk factors influencing construction success

The interaction of the objects with their associated latent factors determines individual item reliability. Measurements of structured loads are used to assess it (or basic correlations). Essential elements are those that load at 0.7 or above. [45] recommended that items with a loading of less than 0.4 should be eliminated, and items with a loading of 0.4 to 0.7 should be evaluated while eliminated if they do not enhance the composite reliability value. On the other side, convergent validity (CV) is a test of internal consistency. Based on [36], it may be computed by estimating Cronbach’s alpha values, the Composite Reliability Score(C), and the Average variance derived (AVE) of the latent variables. The Cronbach alpha is a measurement of the data’s consistency (or reliability). Although the composite reliability test may be used to assess how successfully a system’s given indications are assessed. Because it employs the item loadings gathered in the theoretical model, the composite reliability rating is greater than Cronbach’s Alpha internal correctness test. Cronbach’s Alpha gives equal weight to both things without taking into account their loading factors. The definitions of the composite dependability value and Cronbach’s Alpha, on the other hand, are the same. [46] suggested that the Cronbach alpha value should be larger than 0.7 for credible findings, while [47] aid that a Cronbach alpha value of 0.6 is adequate to confirm internal correctness. Moreover, [41] also claim that 0.7 is a standard for composite dependability. In the Table 4, each criterion for evaluating reflective measurement models is listed.

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M. A. Kassem and K. N. Ali Table 4. Individual item reliability and construct validity construct

Exogenous constructs

Items

Loadings

AVE

CR

Alpha

The economic risk—EC

EC1

0.886

0.779

0.934

0.906

EC2

0.894

EC3

0.864

EC4

0.886

PO1

0.905

0.793

0.939

0.913

PO2

0.892

PO3

0.886 0.735

0.892

0.819

0.805

0.925

0.879

0.808

0.926

0.880

0.763

0.906

0.845

0.657

0.884

0.827

The political risk—PO

The local community risks—LP

The safety and environmental risks—EN

The security risk—SE

The force majeure risk—FM

The role of risk management

PO4

0.877

LP1

0.880

LP2

0.886

LP3

0.802

EN1

0.904

EN2

0.902

EN3

0.887

SE1

0.864

SE2

0.916

SE3

0.915

FM1

0.878

FM2

0.870

FM3

0.871

Identify

0.851

Analysis

0.817

Respond

0.740

Monitor

0.830

Endogenous constructs

Constructs

Loadings

AVE

CR

Alpha

Risks impact in project success

1 - Overruns of cost

0.595

0.549

0.826

0.720

2 - Achieved project objectives

0.761

3 - Project stopped

0.741

4 - Overruns of time

0.800

According to [48] the AVE test is used to assess the model’s intrinsic correctness by measuring the amount of difference that the latent variable extracts from its measurement objects vs the amount of variation ascribed to calculation mistakes. The fundamental rule is that the average covariance between variables must be positive. [41] claimed that

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AVE would be larger than 0.5. It indicates that latent variables account for at least half of the output variance. Table 4 and Fig. 3 demonstrates the proposed model’s reliability and validity, as it shows that all load factors are greater than 0.7, the Alpha Cronbach coefficient is also greater than 0.7. The Average Variance Extracted (AVE) is greater than 0.5, implying that all questionnaire questions used as variables to be measured have a high level of consistency. These findings are key signs that the structural equation modelling approach should be used to finish the research. According to the study results above, the Threat of Armed Groups component SE2 has the greatest loading factor value of 0.916. In contrast, the Security Risk group SE has the highest AVE value of 0.808, indicating the significance of security to the success of any oil and gas construction project. The discriminant validity of the model was also evaluated using AVE. According to [48], the square root of the construct AVE must be larger than the similarity of the construct to the other constructs for excellent discriminant validity. The current study demonstrates that these requirements were met in the first-order factor model, as shown in the table, which shows that the correlation between each group with itself is more extensive than their correlation with other groups, indicating that risk groupings are covariant. Table 5. The discriminant validity of the model Country Environmental Force Local Political Role of risk Security economic and safety majeure peoples risk—PO management risk Country economic—EC

0.883

Safety and 0.740 environmental—EN

0.897

Force majeure—FM 0.795

0.691

0.873

Local community—LP

0.768

0.740

0.718

0.857

Political risk—PO

0.871

0.715

0.801

0.776

0.890

Role of risk management

0.708

0.564

0.566

0.599

0.653

0.811

Security risk—SE

0.846

0.749

0.804

0.782

0.803

0.618

0.899

The values in Table 5 show that the correlation of the elements within the single dependent variable has more to do with each other than the relationship with other dependent variables. For example, the value of discriminant validity for the country economy variable is 0.884, and it exceeds all other values for the same variable and its association with other variables.

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M. A. Kassem and K. N. Ali Table 6. Cross loading among observational variables of measurement model Item EC

EN

FM

LP

PO

Role of risk management

SE

EC1

0.886

0.654

0.712

0.691

0.809

0.633

0.757

EC2

0.894

0.632

0.692

0.636

0.742

0.670

0.730

EC3

0.864

0.603

0.701

0.634

0.713

0.579

0.724

EC4

0.886

0.719

0.701

0.743

0.806

0.615

0.772

EN1

0.653

0.904

0.589

0.638

0.652

0.529

0.653

EN2

0.674

0.902

0.603

0.712

0.646

0.543

0.708

EN3

0.665

0.887

0.671

0.642

0.626

0.445

0.654

FM1

0.676

0.605

0.878

0.639

0.668

0.487

0.658

FM2

0.633

0.562

0.870

0.601

0.670

0.467

0.688

FM3

0.767

0.640

0.871

0.638

0.757

0.526

0.756

LP1

0.740

0.629

0.685

0.880

0.755

0.567

0.712

LP2

0.675

0.581

0.613

0.886

0.686

0.541

0.701

LP3

0.546

0.704

0.538

0.802

0.540

0.423

0.591

PO1

0.829

0.671

0.743

0.738

0.905

0.635

0.759

PO2

0.780

0.672

0.707

0.665

0.892

0.600

0.669

PO3

0.735

0.632

0.719

0.698

0.886

0.592

0.738

PO4

0.755

0.566

0.683

0.662

0.877

0.492

0.693

Risk analysis

0.521

0.405

0.351

0.406

0.450

0.851

0.428

Risk identify

0.515

0.381

0.353

0.429

0.420

0.817

0.470

Risk monitor

0.695

0.623

0.669

0.672

0.695

0.740

0.662

Risk respond

0.506

0.350

0.386

0.360

0.487

0.830

0.372

SE1

0.742

0.758

0.675

0.702

0.705

0.556

0.864

SE2

0.758

0.642

0.713

0.736

0.712

0.552

0.916

SE3

0.779

0.620

0.778

0.670

0.749

0.558

0.915

Note: Bold values represent loadings for items that are higher than the suggested value of 0.7 and higher than other correlation values to other components in the raw data

Table 6 shows that the inter-correlation of the configuration of all the other variables in the model was larger than the cross-loading of all the variables observed. Consequently, these findings confirmed the accuracy of cross-loading measurement and provided sufficient evidence for the measuring model’s discriminative validity. As a result, the proposed conceptual model was predicted to be suitable, with acceptable reliability, convergent and discriminating validity, and study model testing. 2.2 Assessment of Structural Model (Inner Model) To evaluate the practical direction of the coefficients for the accurate model, the authors employ a stand-by bootstrapping approach with 5,000 bootstrap tests and 238 instances

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Fig. 3. Final measurement model of the risk factors influencing construction success

[39, 41]. The main coefficient directions for this study model are shown in Fig. 4 and Table 7. It’s also possible to view the diagrammatical histrionics of the outcomes of the structural modelling research presented to evaluate the ostensible link between the latent variables. The authors used the one-tailed technique since the author’s assumptions are spatially specified, and the single-tailed test has a higher strength than the two-tailed test. Table 7. Standardized coefficient estimates (p-value) of the final structural equation model Hypothetical No

Hypotheses

Original sample (O)

Sample mean (M)

Standard deviation (STDEV)

T Statistics (|O/STDEV|)

P values

H1

Country economic-EC → External risk factors

0.192

0.193

0.004

48.672

0.000***

H2

Environmental and 0.176 safety-EN → External risk factors

0.176

0.004

45.210

0.000***

H3

Force majeure-FM → External risk factors

0.178

0.003

52.004

0.000***

0.177

(continued)

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No

Hypotheses

Original sample (O)

Sample mean (M)

Standard deviation (STDEV)

T Statistics (|O/STDEV|)

P values

H4

Local peoples-LP → External risk factors

0.184

0.184

0.003

55.544

0.000***

H5

Political risk-PO → External risk factors

0.188

0.188

0.003

54.729

0.000***

H6

Role of risk management → Effect of risks in project success

0.217

0.221

0.048

4.572

0.000***

H7

Security risk-SE → External risk factors

0.192

0.192

0.004

50.616

0.000***

H8

External risk factors → Effect of risks in project success

0.662

0.659

0.050

13.168

0.000***

Note: *** The T-value of 0.01 indicates that the T-value is significant

Similarly, with Original Sample β, standard error, t-value, and p-value of 0.662, 0.050, 13.168***, the theoretical framework’s validated findings suggest a relationship between external risk factors and their effect on construction project success. In the same vein as the risk management function, a close link was discovered between risk management rules and project performance, with beta values, standard errors, t-values, and p-values of 0.217, 0.048, and 4.572***. The link between the Moderating Effect and the Effect of Risks on Project Success was also found to be supported, with beta values, standard errors, t-values, and p-values of 0.049, 0.023, and 2.105**, respectively. Hypothesis 1 predicted that country economic risk variables would be positively connected to foreign risk factors affecting project success, as illustrated in Fig. 5. (The increase of economic risk factors will lead to more risk factors affecting construction project success). According to the findings, economic risk showed a substantial positive connection with construction risk management (β = 0.192, p < 0.00). As a result, hypothesis 1 is strongly supported. Hypothesis 2 predicted that political risk variables would be positively connected to external risk factors affecting project success. The results indicated that political risks positively impacted project success (β = 0.188, p < 0.00). As a result, hypothesis 2 confirmed the findings empirically. Hypothesis 3 similarly anticipated that local people’s risk variables were positively connected to external risks and influenced the success of building projects. However, the results revealed that local people’s risk positively impacted construction risk management (β = 0.184, p < 0.00). Hypothesis 4 predicted that environmental and safety risk factors would have a positive impact on external risk factors, and the results showed that environmental and safety risk

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Fig. 4. Final structural model of the risk factors influencing construction success

factors did have a positive impact on external risk factors that influenced project success, indicating that the hypothesis is supported (β = 0.176, p < 0.00). Hypothesis 5 anticipated that security risk factors would positively impact external risk factors, and the findings showed that this was true (β = 192, p < 0.00). Hypothesis 6 further claimed that Risk Factors Due to Force Majeure substantially impact External Risk Factors. According to the results, there is a positive association (β = 0.177, p < 0.00). Furthermore, hypothesis 7 claimed that external risk variables had a substantial impact on construction project success, concluding that a positive association (β = 0.662, p < 0.00) was confirmed. Finally, effective risk management as a moderator has a substantial impact in mitigating the influence of external risk variables on construction project success (β = 0.217, p < 0.00) with the highest value among hypotheses, indicating that hypothesis 8 is supported. Predictive Relevance and Effect Size The authors evaluated the degree of R-squared values, effect size, and prediction usefulness of the research model after establishing vital route coefficients for the genuine study template. According to the findings, the study model can explain 73% of the overall variance in construction risk management. According to the authors, the six exogenous latent factors, namely economic, political, local people’s environment and safety, security, and force majeure, explained 73% of the variation in construction hazards’ influence on project success relative impact of a given external latent variable on the endogenous latent variable(s) owing to changes in R-square values is represented by the effect size [47]. The R-squared value of the latent variable in proportion to the variance of unidentified variation in latent variables is used to determine this [47]. The following formula

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may be used to calculate the effect size based on [49–51]: f2 =

R2included − R2excluded 1 − R2included

According to (Mukhtar A. Kassem, et al. 2020), the effect size of any exogenous latent building is the degree of impact on the endogenous latent construction. Furthermore, when an independent construct is removed from the path model, it alters the value of the determination coefficient (R2). It determines if the eliminated latent exogenous construct substantially impacts the value of the latent endogenous construct. The value of 0.35 (Strong Effect), 0.15 (Moderate Effect) and 0.02 (Weak Effect) are based on [49]. Effect sizes of 0.037 for country economic risks, 0.100 for political risks, 0.023 for local people risk, 0.141 for environmental risk, 0.16 for security risks, and 0.066 for force majeure risk factors were confirmed by the findings. As a result, the latent variable effect sizes are small, small, medium, medium, small, and medium [51]. The security risk factors had the highest value since the consequence of the conflict was to halt most project operations in Yemen’s oil and gas industry. [35] validated this by removing and predicting each data point of the specified endogenous reflective variable. As a result, the blindfolded technique may compare the original and anticipated values. If the forecast is close to the actual value, the route coefficient has good predictive accuracy (i.e., a small predictive error). This error rate, coupled with a simple prediction error (specified as the mean of the remainder of the data), may be used to compute the distribution of Q2 [47], with Q2 values larger than zero indicating that the model has the predictive ability. Similarly, the research model with considerably higher Q2 values has more predictive utility. The latent endogenous variable investigated has a Q2 value of 0.385, more than zero, indicating the model’s predictive relevance [39, 47]. Moderating Effect Analysis The researchers observed and assessed the moderating influence of risk management on the link between external risk variables and their impact on the outcome of the building project using an item-indicator approach and PLS structural equation modelling [47]. To apply the item-indicator approach, the first step is to analyze direct effects in the research model by integrating all exogenous latent factors and treating the moderating variable as an independent latent variable. The next step is to define a latent interaction effect by replicating the elements of each exogenous latent variable indicator with each moderating variable indicator [39]. The final step is to calculate the standardized path coefficients, which are -0.049, 0.662, and 0.217 for moderating effects, external risk factors, and risk management roles, respectively, to see if the interaction effects are significant. The next stage is to assess the intensity of the moderating effects using [51] recommended moderator effect size calculation: f2 =

R2model with modirator − R2model without modirator 1 − R2model with modirator

Figure 5 provides additional evidence of risk management’s modest impact on external factors that affect project performance. As illustrated in Fig. 5, risk management has a beneficial impact on the connection between particular external risk variables and

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Fig. 5. The interaction between external risk factors and rule of risk management in mitigating construction risk effects on project success

project success. Individuals who have a high level of compliance with rules and regulations have a statistically significant advantage over those with a low level of compliance with rules and regulations. In the construction business, when risk management procedures (rules and regulations) are high, the influence of risks on project success is low, and vice versa. The project’s inability to meet its objectives may result in the initiative being permanently halted. This indicates that there is a greater positive influence of particular elements impacting project performance in the construction sector due to the high level of risk management. Theoretical Implications In construction engineering, the findings add to the integration of risk management and project management research. On the one hand, it fills a gap by providing a new explanation for how project management may contribute to the success of building projects from the perspective of risk management integration, even though previous academics have emphasized the importance of external risk management [35–55]and the role of risk management [56–61] to project outcomes. This study fills a gap in the literature by addressing the need for more research into the mediating factors that influence the success of construction projects. It is the first empirical attempt to explain the new role of project risk management, rather than general management, in minimizing the impact of external risk variables on project success, particularly in oil and gas construction projects in Yemen, our case study, and in third-world countries in general. On the other hand, in construction project management, this research adds to risk governance theory. It provides a novel method of mitigating risk and ensuring the success of construction projects through the project risk management process, as opposed to the old method of developing a complex project management system. The study prioritized both outputs, and the project risk management process may be viewed as formal and informal

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governance structures that facilitate the deployment of explicit and implicit risk management activities. The study answers the questions raised by stakeholders in the tradeoff between possible time and cost benefits. Furthermore, the study contributes to the knowledge group on project risk management by statistically stressing the beneficial influence of risk management with shared responsibilities of management supervision, risk response, and consensus work on project performance. Responds to several claims regarding the necessity of risk management in oil and gas construction projects and backs up some theoretical facts on the subject. Practical Implications Construction engineering’s sophisticated and tacit knowledge-intensive nature necessitates risk management in construction projects. This study offers project managers in the construction industry advice on how to successfully manage risks by leading project execution, responding to uncertainty in construction projects, and creating a shared understanding with different project teams. It builds a successful project management connection chain that leads operations to successful risk management in construction engineering with the optimum input/output ratio. According to the research, project managers should aim to improve current risk management methods rather than invest money and time in building a complex, specialized project management system. Because the function of risk management at the collective level can produce significant synergy value, members of the team must be pushed to take an active role in the project risk management process by recognizing and using their particular risk identification and experience.

5 Conclusion This study looked at the function of risk management in reducing the influence of external risks on the success of building projects. Many construction projects encounter significant delays, causing them to surpass their original timelines and budgets. Because more contractors and equipment and operations overlap, construction projects have more variables and uncertainties than product lines. External risk variables enhance the likelihood of their influence on the success of building projects, making risk mitigation techniques critical to reducing deviations from the original plan. As a result, the function of the risk management process, which includes detection, analysis, reaction, and monitoring, in reducing the impact of external risk factors on project performance has been studied in this study. The findings confirm the premise that risk management in construction projects decreases the influence of external risk factors on project success and, as a result, reduces the impacts of risks on cost overrun and project completion time exceedance. Risk management works to minimize numerous problems and overcome the difficulties and barriers that the project may face during execution, according to the findings of the field research and the opinions of experts working on the targeted projects. The analysis clearly shows that risk factors have a greater impact on the project schedule, with a load factor of "0.842," followed by failure to meet project objectives, with a load factor of "0.807," emphasizing the importance of risk management throughout the project lifecycle, from planning and preparation of designs to implementation. The results also show that the research hypotheses are achieved and that there is a positive

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relationship between the external risk factors and their negative impact on the success of projects, meaning that the increase in risks leads to an increase in the possibility of the construction project failing to succeed according to the cost and the specified schedule. The chances of the project failing to achieve its goals increase. Furthermore, hypothesis 7 claimed that external risk factors substantially influenced construction project success, and the results confirmed a positive connection (β = 0.662, p < 0.00). Finally, effective risk management as a moderator has an important role in mitigating the influence of external risk factors on construction project success (β = 0.217, p < 0.00) with the highest value among hypotheses and supporting hypothesis 8. In today’s knowledge-intensive world, with a rising number of project-based construction businesses, the findings of this study give critical managerial insights into construction project management. Furthermore, while project participants with international experience in the Yemeni oil and gas construction industry offered a reasonably comprehensive background to survey data, future research in international companies is expected to broaden the study’s potential relationship to the global construction sector. Acknowledgments. The authors are grateful to Universiti Teknologi Malaysia (UTM) Research Grant Vot No: J130000.7113.05E79 for supporting this research and providing research facilities.

Funding. This research was funded by Universiti Teknologi Malaysia (UTM) Research Grant Vot No: J130000.7113.05E79.

Data Availability Statement. The data sets during and/or analyzed during the current study available from the corresponding author on reasonable request.

Conflicts of Interest. The authors declare no conflict of interest.

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A Stimulus-Organism-Response Paradigm to Word-of-Mouth and Continuance Intention of Mobile Application Yee Von Lim1 , Shi Ling Ng1 , Wei Lin Oh1 , Wan Ying Tan1 , Yi Zhe Too1 , Xiu Ming Loh1(B) , and Garry Wei Han Tan2,3 1 Universiti Tunku Abdul Rahman, 31900 Kampar, Malaysia

{yeevon1213,shiling4990,weilin1021,twying13,tooyizhe}@1utar.my, [email protected] 2 UCSI University, 56000 Cheras, Malaysia [email protected] 3 Department of Business Administration, IQRA University, Karachi, Pakistan

Abstract. In recent years, the utilization of mobile applications has been increasing worldwide. However, the antecedents that would motivate users to engage with word of mouth and continue using mobile application are relatively understudied. Thus, this study looks to capture the variables that would drive users to continue using mobile application. In order to achieve this objective, a research model was developed via a uniquely developed Stimulus-Organism-Response framework. Following that, data was collected via an online survey which yielded 300 usable responses. Subsequently, the data was analyzed using PLS-SEM. The results show that perceived complementarity and perceived usefulness are significant facilitators of satisfaction. Besides, satisfaction was determined as a significant driver of word of mouth and continuance intention. Based on the results of this study, there were several insights proposed to the relevant stakeholders. In addition, this study filled the theoretical gap of comprehending the main antecedents influencing customers’ intention to continue using mobile application. Keywords: Word-of-mouth · Continuance intention · Mobile application · Stimulus-organism-response partial least squares-structural equation modeling

1 Introduction Over the past decade, the number of mobile applications has increased all around the world [1]. As of the first quarter of 2022, the Google Play Store has roughly 3.3 million mobile applications while the Apple App Store has about 2.1 million mobile applications [2]. Furthermore, the number of downloads for mobile applications have also been steadily rising in recent years [3]. In addition to that, the functionalities of mobile applications have constantly been evolving with the aim of making user’s lives more convenient [4]. It is undeniable that people nowadays rely heavily on mobile applications, be it for entertainment, education, gaming, banking and so on [5]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (Eds.): ICETIS 2022, LNNS 584, pp. 192–204, 2023. https://doi.org/10.1007/978-3-031-25274-7_16

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Hence, companies with mobile applications were compelled to consider how they can leverage opportunities to meet users’ demands [6]. This led to companies increasing the functionalities of single-purpose mobile applications by offering more features to their users. One example is Grab which was initially a transport hailing mobile application. As of today, users can make payment as well as order groceries and food delivery through the mobile application [7]. With that said, global mobile application retention rate at 30 days from installation was below 7% [1]. This is drastically low as it indicates that more than 93% of mobile applications would be abandoned after 30 days since they were installed. In view of the above-mentioned, this study aims to (1) establish the constructs that influence the intention to continue using mobile application and (2) develop a theoretical framework to investigate the users’ continuance intention to use mobile application. Overall, this study is postulated to contribute to numerous novel findings and practical insights to mobile application developers. From the theoretical perspective, this study can serve to expand the existing knowledge on continuance intention to use mobile application.

2 Literature Review 2.1 Stimulus-Organism-Response Theory The Stimulus-Organism-Response theory serves as a framework to investigate users’ behavioral intention from a multi-component perspective [8]. More precisely, the three components posited in this theory are stimulus, organism, and response. In summary, the theory elucidates that the environmental cues (stimulus) would have an effect on people’s internal state (organism) which would subsequently compel them to perform a certain action (response) [9]. The Stimulus-Organism-Response is among the most prominent theory when investigating online consumer behavior. In particular, this theory has been applied by past studies in a variety of digital [10, 11] and mobile [12, 13] settings. In this study, the stimuli are signified by perceived complementarity and perceived usefulness. Besides, satisfaction serve as the organism while the responses are conceptualized in the form of word of mouth and continuance intention. These constructs were selected in view of their relevance to the subject matter which are further elaborated in the next section.

3 Hypotheses Development 3.1 Perceived Complementarity Perceived complementarity denotes “the availability of complementary goods or services that provide additional value or benefits to the users” [14]. Perceived complementarity is relevant to mobile applications as the majority of them provide users with complementarity services. Hence, these complementary services would increase the users’ satisfaction [15]. This is because users’ satisfaction is tied to their perceived value which would increase from the use of complementary services [16]. Thus, the hypothesis below was developed:

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H1: Perceived complementarity has a significantly positive relationship with satisfaction. 3.2 Perceived Usefulness In this study, perceived usefulness has been adapted to denote the users’ perceptions of increased performance arising from the utilization of mobile application [17]. This is because users can derive utility from the service offered by mobile application [5]. Subsequently, the utility derived from using mobile application will facilitate the users’ satisfaction towards it. Several past studies [18, 19] have obtained empirical support for the facilitating effect of perceived usefulness on satisfaction in various mobile settings. Therefore, the hypothesis below was developed: H2: Perceived usefulness has a significantly positive relationship with satisfaction. 3.3 Satisfaction Based on the cumulative perspective, satisfaction is a result of a person’s overall evaluation between their actual experiences and initial expectations [18]. Moreover, “satisfaction has been defined as an emotional response based on users’ overall evaluation of their expectations and experiences derived from their previous interactions with an m-service” [20]. It has been postulated by Cao et al. [21] that users’ satisfaction towards a mobile application would facilitate frequent usage behavior. Several recent studies have found empirical support for the significant facilitating effect of satisfaction on continuance intention in a number of mobile settings [22, 23]. In addition, through positive interactions with the mobile application, satisfaction creates a unique position in the minds of users in terms of its value [24]. Following that, satisfied users would be inclined to share their positive experiences with others [25]. This is referred to as positive word of mouth which has been posited as a user’s way of reciprocating the satisfaction gained from the interactions [26]. Based on the above-mentioned, the hypotheses below were developed (Fig. 1): H3: Satisfaction has a significantly positive relationship with word of mouth.

Fig. 1. Proposed conceptual model.

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H4: Satisfaction has a significantly positive relationship with continuance intention.

4 Methodology The target respondents for this study were smartphone users in view of the subject matter of this study. As no sampling frame was available for this group of people, a nonprobability sampling technique was utilized [27]. More specifically, purposive sampling was employed which involved the screening of respondents to ensure better fitting with the subject matter [28, 29]. Initial participants who were not smartphone users were filtered out. In other words, only those who were smartphone users were solicited to complete the survey. Overall, a total of 300 responses were collected which is roughly similar with the samples of mobile-related past studies [5, 30]. In terms of the data collection tool, an online survey was used [31, 32]. Prior to the actual data collection, the survey’s content validity and reliability were established through a pre-test and a pilot test respectively [33, 34]. The online survey’s demographic section captured the respondents’ gender, age, occupation, and education level as shown in Table 1. More specifically, there were slightly more female (50.7%) than male respondents (49.3%). Based on each of the demographic characteristics, the highest proportion of respondents were between 20–29 years old (52.7%), students (47.0%), and pursuing their Bachelor’s Degree (58.0%). In addition, all variables had five items each and gauged with a 5-point Likert scale. Subsequently, the PLS-SEM was employed for data analysis as it is suitable to analyze complex research models [35–37]. Table 1. Descriptive analysis. Characteristics

Description

Count

Percentage

Gender

Male

148

49.3

Female

152

50.7

Age

19 years old and below

10

3.3

20–29 years old

158

52.7

30–39 years old

43

14.3

40–49 years old

38

12.7

50–59 years old

35

11.7

60 years old and above Occupation

16

5.3

Student

141

47.0

Employee

100

33.3

33

11.0

Self-employed

(continued)

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Y. V. Lim et al. Table 1. (continued)

Characteristics

Description

Count

Unemployed

17

Retiree Education level

Percentage 5.7

9

3.0

Primary/secondary school

64

21.3

Pre-U/diploma

60

20.0

174

58.0

2

0.7

Bachelor’s degree Postgraduate degree

5 Analysis 5.1 Common Method Bias (CMB) The presence of CMB was examined as there only one method utilized for this study’s data collection. It can be established that CMB is not found in the dataset of this study as demonstrated in Table 2. This is because all Ra values were significant (p < 0.001) [38, 39]. This on top of the substantially higher Ra2 average (0.806) than Rb2 average (0.000) [40, 41]. Table 2. Common method bias Construct

Item

Substantive factor loading (Ra)

Ra2

Method factor loading (Rb)

Rb2

Perceived complementarity

PC1

0.912***

0.832

– 0.165*

0.027

PC2

0.924***

0.854

– 0.149*

0.022

PC3

0.782***

0.612

– 0.058

0.003

PC4

0.583***

0.340

0.212*

0.045 0.026

Perceived usefulness

PC5

0.654***

0.428

0.160 NS

PU1

0.926***

0.857

– 0.131 NS

0.017 0.016

PU2

0.902***

0.814

PU3

0.780***

0.608

– 0.128 NS 0.055 NS

0.684

0.014 NS

0.000

PU4 Satisfaction

0.827***

0.003

PU5

0.610***

0.372

0.194*

0.038

SAT1

0.870***

0.757

– 0.091 NS

0.008

SAT2

0.900***

0.810

– 0.077 NS

0.006

0.801

– 0.069 NS

SAT3

0.895***

0.005 (continued)

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Table 2. (continued) Construct

Word of mouth

Continuance intention

Item

Substantive factor loading (Ra)

Ra2

Method factor loading (Rb)

Rb2

SAT4

0.736***

0.542

0.096 NS

0.009

SAT5

0.693***

0.480

0.141 NS

0.020 0.005

WOM1

0.919***

0.845

WOM2

0.881***

0.776

– 0.070 NS – 0.021 NS

WOM3

0.731***

0.534

0.098 NS

0.010 0.011

0.000

WOM4

0.916***

0.839

WOM5

0.735***

0.540

– 0.106 NS 0.103 NS

CI1

0.887***

0.787

– 0.067 NS

0.004 0.001

0.011

CI2

0.834***

0.696

CI3

0.801***

0.642

– 0.034 NS 0.041 NS

CI4

0.747***

0.558

0.045 NS

0.002

0.704***

0.496

0.017 NS

0.000

0.806

0.660

0.000

0.012

CI5 Average

0.002

Note: *** = p < 0.001; * = p < 0.05; NS = p > 0.05

5.2 Measurement Model Assessment According to Table 3, the reliability of all constructs was determined as the values of Cronbach’s Alpha, rho_A, and Composite Reliability were above 0.7 [42, 43]. Besides, convergent validity was established as all of the average variance extracted values (AVE) were greater than 0.5 [44, 45]. Table 3. Reliability and convergent validity Construct

Cronbach’s alpha

rho_A

Composite reliability

Average variance extracted (AVE)

Perceived complementarity

0.830

0.839

0.880

0.594

Perceived usefulness

0.869

0.870

0.905

0.656

Satisfaction

0.877

0.879

0.910

0.670

Word of mouth

0.893

0.894

0.921

0.701

Continuance intention

0.854

0.859

0.896

0.633

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Moreover, discriminant validity was validated from the Fornell-Larcker criterion and cross loadings [46, 47]. More precisely, the square root of each construct’s AVE (Table 4) is greater than the actual AVE (Table 3). This is corroborated by the cross loadings as all construct’s loadings are higher than the cross-loadings of other constructs (Table 5) [48, 49]. Table 4. Fornell-Larcker criterion PC PC

0.771

PU

0.710

PU

SAT

WOM

CI

0.810

SAT

0.673

0.683

0.819

WOM

0.667

0.677

0.715

0.837

CI

0.711

0.682

0.791

0.735

0.796

Note: PC = Perceived complementarity; PU = Perceived usefulness; SAT = Satisfaction; WOM = Word of mouth; CI = Continuance intention Table 5. Cross loadings PC

PU

SAT

WOM

CI

PC1

0.760

0.498

0.466

0.453

0.510

PC2

0.783

0.572

0.471

0.484

0.485

PC3

0.718

0.533

0.443

0.474

0.494

PC4

0.783

0.566

0.583

0.572

0.610

PC5

0.807

0.566

0.598

0.566

0.615

PU1

0.499

0.811

0.538

0.508

0.535

PU2

0.551

0.785

0.505

0.471

0.507

PU3

0.581

0.829

0.570

0.613

0.562

PU4

0.576

0.840

0.583

0.582

0.577

PU5

0.664

0.783

0.563

0.559

0.573

SAT1

0.542

0.530

0.782

0.526

0.578

SAT2

0.533

0.558

0.828

0.572

0.643

SAT3

0.558

0.518

0.832

0.585

0.661

SAT4

0.564

0.580

0.826

0.629

0.657

SAT5

0.559

0.604

0.824

0.611

0.692

WOM1

0.528

0.569

0.590

0.854

0.634

WOM2

0.556

0.575

0.630

0.864

0.628 (continued)

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Table 5. (continued) PC

PU

SAT

WOM

CI

WOM3

0.647

0.568

0.575

0.815

0.595

WOM4

0.470

0.528

0.583

0.823

0.606

WOM5

0.593

0.594

0.614

0.829

0.612

CI1

0.517

0.548

0.682

0.603

0.831

CI2

0.565

0.549

0.626

0.576

0.801

CI3

0.636

0.564

0.654

0.630

0.837

CI4

0.599

0.552

0.634

0.558

0.790

CI5

0.514

0.498

0.544

0.558

0.714

Note: PC = Perceived complementarity; PU = Perceived usefulness; SAT = Satisfaction; WOM = Word of mouth; CI = Continuance intention

5.3 Structural Model Assessment Table 6 indicates that at a significance level of 0.05, all four hypotheses were supported. In particular, perceived complementarity was found to have a significant positive correlation with satisfaction. Thus, H1 was supported. Similarly, H2 was also supported as perceived usefulness had a significantly positive influence on satisfaction. Furthermore, satisfaction demonstrated a significant positive relationship with word of mouth, thereby H3 was supported. Additionally, H4 was also supported given that satisfaction was revealed as a significant facilitator of continuance intention. Table 6. Hypotheses testing Hypothesis

Relationship

Path coefficient

H1

PC → SAT

0.380

H2

PU → SAT

0.413

H3

SAT → WOM

H4

SAT → CI

t-value

p-value

Remark

6.673

0.000

Supported

7.402

0.000

Supported

0.715

29.366

0.000

Supported

0.791

19.448

0.000

Supported

Note: PC = Perceived complementarity; PU = Perceived usefulness; SAT = Satisfaction; WOM = Word of mouth; CI = Continuance intention

Despite stating that the organism serves as a mediator between the stimulus and response [50, 51], past mobile-related studies that have used the Stimulus-OrganismResponse theory [e.g., 52, 53] did not assess the mediation effect. Given this deficiency, the present study carried out a mediation analysis to which the results are reported in Table 7. In general, it can be established that organism (satisfaction) is able to serve as a partial mediator between the stimuli (perceived complementarity and perceived usefulness) and responses (word of mouth and continuance intention).

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Relationship

Path coefficient

t-value

p-value

Remark

PC → SAT → WOM

0.272

6.219

0.000

Partial mediation

PU → SAT → WOM

0.295

6.174

0.000

Partial mediation

PC → SAT → CI

0.301

6.134

0.000

Partial mediation

PU → SAT → CI

0.327

6.745

0.000

Partial mediation

Note: PC = Perceived complementarity; PU = Perceived usefulness; SAT = Satisfaction; WOM = Word of mouth; CI = Continuance intention

Table 8 displays the results pertaining to the research model’s predictive capabilities. In particular, all Q2 values were greater than 0 which indicates that the structural model is has good predictive relevance [54]. Additionally, the research model was able to capture 53.8%, 51.2%, and 62.6% of the variance for satisfaction, word of mouth, and continuance intention respectively. Table 8. Predictive relevance (Q2 ) and power (R2 ) Q2 (=1-SSE/SSO)

R2

966.904

0.355

0.538

1500.000

968.879

0.354

0.512

1500.000

911.684

0.392

0.626

Construct

SSO

SSE

Perceived complementarity

1500.000

1500.000

Perceived usefulness

1500.000

1500.000

Satisfaction

1500.000

Word of mouth Continuance intention

6 Discussion From the results, perceived complementarity and perceived usefulness were found to be significant facilitators of satisfaction. These findings show that users would be more satisfied with mobile applications that have value added services that meet their demands. While the sought after benefits would vary between different users, this study established that they would generally value the increased performance in carrying out their daily activities [55]. As such, these benefits should result in increased convenience, faster speed and so on to the users. Besides, satisfaction was revealed to have significantly positive effects on word of mouth and continuance intention. The results for satisfaction as a significant facilitator are similar with mobile-related past studies such as Kalini´c et al. [56] for word of mouth and Marinkovi´c et al. [57] for continuance intention. With that said, when users are satisfied with their use of mobile application, they would develop the desire to continue using it. In addition, they would also want to share their positive experiences with the mobile application with others.

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Based on the findings, there are several implications that companies with mobile application can focus on to develop satisfaction as well as encourage their users to spread word of mouth and continue utilizing the mobile application. In essence, these companies should look to widen the assortment of functionalities that their mobile applications offer to the user. This can be done by providing more services in the mobile application. In addition to that, they should also ensure that users would consider the additional functionalities contained in the mobile application to be useful. This can be achieved by collecting feedback from users and providing the services that were indicated to be useful to the performance of the users’ desired actions when using the mobile application. By enhancing the quantity and quality of services, users will have higher satisfaction which would automatically increase their desire to share their positive experiences with others and continue using the mobile application. In terms of theoretical contributions, this study extends the current literature by looking into the continuance intention of mobile application. More precisely, this study proposed a unique research model with the Stimulus-Organism-Response framework as the foundational theory. Following that, the results validated the relevance of perceived complementarity on users’ continuance intention of mobile application. Furthermore, the results of the mediation analysis established the Stimulus-Organism-Response theory’s soundness as satisfaction was found to have significant mediating effects between the stimuli and responses. There are several limitations identified in this study which should be addressed by future studies. Firstly, this study looked into the continuance intention of mobile payment from a broad perspective. Hence, future studies can narrow down the scope of study to be more specific. This is because there are several categories for mobile applications such as health, finance, and so on [58, 59]. Secondly, this study only collected data from citizens in a single country. Therefore, the findings should be cautiously generalized in the context of other countries [60, 61]. As such, future studies should collect data from several countries to better reflect a broader group and capture the differences between countries.

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Determining the Factors that Affect Resistance to Digital News Subscription During the COVID-19 Pandemic Luk Sheng Chin1 , Wei Heng Loh1 , Ming Fong Tan1 , Zhi Hui Tan1 , Xiu Ming Loh1(B) , Voon Hsien Lee1 , and Garry Wei Han Tan2 1 Universiti Tunku Abdul Rahman, 31900 Kampar, Malaysia {nixonchinluksheng,lohweiheng,tanmingfong,ZHui00}@1utar.my, {lohxm,leevh}@utar.edu.my 2 Department of Business Administration, IQRA University, Karachi, Pakistan [email protected]

Abstract. The subscription of digital services has increased due to the COVID19 pandemic. However, this was not the same for digital news subscription which remained low. Therefore, this study looks to study the factors that influence the resistance to digital news subscription during the COVID-19 pandemic. In order to achieve this, the Innovation Resistance Theory was applied. Data was collected through an online survey that yielded 199 responses. Based on the results of the data analysis, two out of the five barriers were revealed to have insignificant relationships with resistance. With that said, value barrier, risk barrier, and image barrier were established as significant facilitators of resistance. Several insights were then proposed to news media companies. Moreover, this study fills the theoretical gap of comprehending the antecedents of resistance on digital news during the COVID-19 pandemic. Keywords: Digital resistance · Innovation resistance theory · Partial least squares-structural equation modeling · COVID-19 pandemic

1 Introduction Many innovations have been introduced to the general public in recent years as a result of rapid technological advancements. Some of these innovations have brought about significant changes in consumer behavior [1]. In particular, the internet has been serving as a catalyst when it comes to the digitalization of numerous everyday life activities. With that said, the widespread permeance of digital services has greatly improved our lives in many ways [2]. A digital alternative was afforded to the general public for the carrying out of daily activities which made it more convenient. Recently, the COVID-19 pandemic has further highlighted the significance of digital services in view of the rapid adoption of digital channels that were recorded all around the world [3]. Despite numerous digital services recording an overall surge in subscriptions during the COVID-19 pandemic [4], this was not the same for digital news in Malaysia. In © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (Eds.): ICETIS 2022, LNNS 584, pp. 205–217, 2023. https://doi.org/10.1007/978-3-031-25274-7_17

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particular, it was found that only 16% of Malaysians paid for digital news while in the midst of the COVID-19 pandemic [5]. This issue was further compounded by the decline in demand for printed newspapers. It was estimated that less than one million printed newspapers were sold daily which is a significant decline from its peak of 4.7 million copies sold daily in 2007 [6]. For a number of these companies, the aftermath of this decline was so significant that they ceased publication of their printed newspaper while some have totally shut down their operations [7]. The current study posits that the above-mentioned issue is attributed to people’s resistance towards digital news subscriptions. In general, resistance denotes the people’s unwillingness to take a new or different action from the status quo [8]. Following that, resistance has widely been considered to be a key reason for the failure of countless innovative technologies and digital services [9, 10]. Despite its significance, the studies on resistance has gotten comparatively lesser attention than adoption. As a result, there are substantially lesser studies on resistance in comparison to those on adoption [11]. Therefore, the objectives of this study are twofold: (1) to determine the barriers that influence the resistance to digital news subscription and (2) to establish the relevance of the Innovation Resistance Theory during the COVID-19 pandemic. Following that, this study is anticipated to provide numerous novel findings and insights. From a practical perspective, this study has significant contributions to business stakeholders. Particularly for digital news service providers, they would be able to develop business strategies that will reduce the resistance of subscriptions among their audience. Besides, this study will also contribute theoretically by extending current knowledge on resistance. More specifically, this study is among the scarce empirical studies to assess the effect of the Innovation Resistance Theory on digital news during the COVID-19 pandemic from a developing nation.

2 Literature Review 2.1 Innovation Resistance Theory The Innovation Resistance Theory was introduced by Ram and Sheth [12] to serve as a theoretical framework to look into resistance. More specifically, the theory examines the barriers that would affect people’s resistance towards innovative technologies and services. It has been postulated that people resist change because it tends to involve uncertainties and contradicts current lifestyle habits [13]. Therefore, resistance is only a natural human response that plays a significant role in a person’s behavior [14]. The Innovation Resistance Theory posits five barriers of resistance which are the usage barrier, value barrier, risk barrier, image barrier, and tradition barrier [15]. These five barriers can be further categorized into active and passive resistance. Active resistance arises from the characteristics of a technology or service while passive resistance comes from the person’s internal state [16]. With that said, the different facets of active resistance are captured by the functional barriers of usage barrier, value barrier, and risk barrier [17]. On the other hand, the different facets of passive resistance can be studied through psychological barriers which are image barriers and tradition barrier [11].

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3 Hypotheses Development 3.1 Usage Barrier Usage barrier refers to the perceived lack of usefulness by an individual towards a particular product or service [18]. Several past studies such as Kaur et al. [14] and Leong et al. [19] have found empirical support for the significant influence that usage barrier has on consumer behavior. Particularly in this research, non-subscribers may develop resistance as feel that subscribing to digital news would only result in an incremental increase of usefulness. Hence, the hypothesis below was developed: H1: Usage barrier has a significantly positive relationship with resistance. 3.2 Value Barrier Value barrier denotes the overall perceived lack of benefits when compared to the costs required to learn or use a particular service [19]. According to Mani and Chouk [20], value barrier typically refers to the perceived price of the service when it comes to the context of resistance. Value barrier is posited as relevant in this study given the fee required for the subscription of digital news. Hence, people would develop resistance if the fee is perceived to outweigh the benefits of subscribing to digital news. Overall, the hypothesis below was developed: H2: Value barrier has a significantly positive relationship with resistance. 3.3 Risk Barrier Risk barrier is related to the uncertainties that are inherent with the use of a service [21]. The risk can be categorized as financial, psychological, physical, or social. In this study, the risks involved with the subscription of digital news are financial, privacy, and security [13]. This is because resistance is higher when it involves financial transactions and high uncertainty [22]. Hence, the hypothesis below was developed: H3: Risk barrier has a significantly positive relationship with resistance. 3.4 Image Barrier Image barrier has been postulated to be present when an individual has a lack of selfimage congruence [20]. More specifically, this happens when the individual feels that there is an incompatibility between his/her image and the image of the service. Subsequently, this situation would have an effect on the consumer’s behavior [23]. In line with the above-mentioned, a number of past studies have found that image barrier has a significantly negative relationship with adoption intention [22, 24]. As such, the following hypothesis was developed: H4: Image barrier has a significantly positive relationship with resistance.

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3.5 Tradition Barrier Tradition barrier is involved when a user experience changes as a result of executing a certain action [18]. More precisely, tradition barrier is induced when the change is incompatible with the users’ current norms, habits, and lifestyle [25]. This is because traditions are strongly embedded in the person and any possible conflict would result in strong resistance [14]. As such, the change in status from being a non-subscriber to a subscriber of digital news may result in resistance. Overall, the following hypothesis was developed (Fig. 1): H5: Tradition barrier has a significantly positive relationship with resistance.

Fig. 1. Proposed conceptual model.

4 Methodology In view of the subject matter of this study, youths who were non-subscribers of digital newspapers were selected as the target respondents. In addition, the non-probability technique of purposive sampling was used as there is no sampling frame available for the target respondents [26, 27]. In particular, the screening question (“I currently subscribe to digital newspaper”) was included at the survey’s cover page. Only those who indicated

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“No” were retained to complete the survey. Besides, the minimum sample size was determined with the G*Power software [28, 29] which indicated 92 responses. Overall, 199 usable responses were collected which were higher than the recommended minimum sample sizes. Besides, an online survey was utilized to collect the data for this study [30, 31]. More specifically, the survey contained three sections which are the (1) cover page, (2) demographic, and (3) measurement items. The cover page included a brief introduction of the study and screening question while the demographic section captured the respondents’ gender, age, occupation, and personal income. The final section included items that measured the dependent variable of resistance (three items) and independent variables of usage barrier (three items), value barrier (four items), risk barrier (five items), and image barrier (three items) with a 5-point Likert scale. Subsequently, the responses collected were analyzed with the PLS-SEM technique [32, 33].

5 Analysis 5.1 Demographic Profile As per Table 1, more than half of the respondents are female (54.77%), students (59.30%), and have a monthly allowance of RM999 and below (58.29%). Table 1. Descriptive analysis. Characteristics

Description

Count

Percentage

Gender

Male

90

45.23

Female

109

54.77

Age

19 years old and below

11

5.53

20–29 years old

188

94.47

Occupation

Student

118

59.30

Employee

68

34.17

Self-Employed

6

3.02

Unemployed

7

3.52

RM999 and below

116

58.29

RM1000–RM1999

28

14.07

RM2000–RM2999

22

11.06

RM3000–RM3999

20

10.05

RM4000–RM4999

9

4.52

RM5000 and above

4

2.01

Personal income/allowance (per month)

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5.2 Common Method Bias (CMB) The issue of CMB was assessed in this study as only one data collection tool was utilized. Based on Table 2, it can be concluded that CMB is not present as all Ra values were significant (p < 0.001) [34]. This is in addition to the significantly higher average of Ra2 (0.797) when compared to Rb2 (0.001) [35, 36]. Table 2. Common method bias Construct

Indicator

Substantive factor loading (Ra)

Ra2

Method factor loading (Rb)

Rb2

Resistance

RES1

0.827***

0.684

−0.021 NS

0.000

0.508

0.077 NS

0.006 0.004

RES2 Usage barrier

Value barrier

Risk barrier

Image barrier

Tradition barrier

Average

0.713***

RES3

0.756***

0.572

−0.060 NS

UB1

0.938***

0.880

−0.244***

0.060

UB2

0.711***

0.506

0.196**

0.038 0.000

UB3

0.769***

0.591

0.018 NS

VB1

0.955***

0.912

−0.178*

0.032

VB2

0.835***

0.697

−0.046 NS

0.002 0.000

VB3

0.872***

0.760

0.013 NS

VB4

0.550***

0.303

0.227*

0.052

RB1

0.753***

0.567

0.073 NS

0.005

RB2

0.940***

0.884

−0.157**

0.025

RB3

0.855***

0.731

−0.124*

0.015 0.004

RB4

0.756***

0.572

0.067 NS

RB5

0.597***

0.356

0.162*

0.026

IB1

0.970***

0.941

−0.198**

0.039

IB2

0.752***

0.566

0.138*

0.019

IB3

0.795***

0.632

0.047 NS

0.002

0.623

0.070 NS

0.005 0.000

TB1

0.789***

TB2

0.792***

0.627

0.007 NS

TB3

0.816***

0.666

−0.085 NS

0.007

0.797

0.646

0.001

0.016

Note: *** = p < 0.001; ** = p < 0.01; * = p < 0.05; NS = p > 0.05

5.3 Measurement Model Assessment Based on Table 2, reliability was determined as all constructs recorded a Composite Reliability value of above the threshold of 0.7 [37, 38]. Moreover, convergent validity

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was established as all values for average variance extracted are above 0.5 [39, 40] whereas multicollinearity was absent as all values for variance inflation factor were lower than 5 [41, 42]. Table 3. Reliability, convergent validity, and multicollinearity Construct

Composite reliability

Average variance extracted

Variance inflation factor

Resistance

0.809

0.586

–

Usage barrier

0.836

0.632

1.946

Value barrier

0.882

0.652

2.159

Risk barrier

0.888

0.613

1.297

Image barrier

0.874

0.698

2.618

Tradition barrier

0.838

0.634

1.916

As shown in Table 3, discriminant validity was also found to be present as every value for the original sample is below 0.9 [43, 44]. This was further confirmed by the 2.5% and 97.5% confidence intervals which were all lower than 1 [45, 46] (Table 4). Table 4. Discriminant validity Path

Original sample

Mean sample

Confidence interval 2.5%

97.5%

RES → RB

0.557

0.565

0.420

0.697

RES → IB

0.746

0.754

0.622

0.879

UB → RES

0.706

0.717

0.573

0.853

UB → RB

0.478

0.484

0.324

0.641

UB → IB

0.814

0.814

0.688

0.928

UB → TB

0.676

0.671

0.489

0.823

VB → RES

0.777

0.780

0.639

0.914

VB → UB

0.801

0.800

0.669

0.923

VB → RB

0.460

0.463

0.344

0.569

VB → IB

0.858

0.858

0.763

0.942

VB → TB

0.701

0.698

0.574

0.812

RB → IB

0.474

0.480

0.357

0.602 (continued)

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L. S. Chin et al. Table 4. (continued)

Path

Original sample

Mean sample

Confidence interval 2.5%

97.5%

TB → RES

0.631

0.635

0.476

0.779

TB → RB

0.546

0.561

0.393

0.726

TB → IB

0.859

0.860

0.740

0.974

Note: RES = Resistance; UB = Usage barrier; VB = Value barrier; RB = Risk barrier; IB = Image barrier; TB = Tradition barrier

5.4 Structural Model Assessment With reference to Table 5, three out of the five hypotheses were supported at a significance level of 0.05. More specifically, the significant relationships were between value barrier (β = 0.287, p < 0.01), risk barrier (β = 0.180, p < 0.01), and image barrier (β = 0.157, p < 0.05) with resistance. These relationships correspond to H2, H3, and H4 respectively. Given the positive coefficient values for the above-mentioned hypotheses, they indicate that value barrier, risk barrier, and image barrier are significant facilitators of resistance. Contrarily, empirical support was not ascertained for H1 and H5. In other words, usage barrier and tradition barrier are insignificant antecedents of resistance. The results pertaining to the research model’s predictive capabilities are provided in Table 6. More precisely, the value of Q2 for resistance exceeds 0 which established the structural model’s predictive relevance [47, 48]. Additionally, the research model captured an R2 value of 0.408. In other words, it was able to account for 40.8% of the variance in resistance. Table 5. Hypotheses testing Hypothesis

Relationship

Path coefficient

t-value

p-value

Remark

H1

UB → RES

0.136

1.351

0.089

Not supported

H2

VB → RES

0.287

2.738

0.003

Supported

H3

RB → RES

0.180

2.718

0.003

Supported

H4

IB → RES

0.157

1.722

0.043

Supported

H5

TB → RES

0.038

0.476

0.317

Not supported

Note: RES = Resistance; UB = Usage Barrier; VB = Value Barrier; RB = Risk Barrier; IB = Image Barrier; TB = Tradition Barrier

5.5 Importance-Performance Map Analysis (IPMA) Furthermore, this study followed the footsteps of Wang et al. [49] and Yan et al. [50] to further extend the results of the PLS-SEM by carrying out the IPMA. In particular, the

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Table 6. Predictive relevance (Q2 ) and power (R2 ) Construct

SSO

SSE

Q2 (=1 – SSE/SSO)

R2

0.211

0.408

Resistance

597.000

471.320

Usage barrier

597.000

597.000

Value barrier

796.000

796.000

Risk barrier

995.000

995.000

Image barrier

597.000

597.000

Tradition barrier

597.000

597.000

aim was to determine the antecedent with a high importance has low performance for the target construct of resistance [51, 52]. According to Table 7, value barrier was determined as the exogenous construct with the highest importance but lowest performance. Table 7. Importance performance map analysis Antecedent

Importance

Performance

Usage barrier

0.136

53.115

Value barrier

0.287

47.814

0.180

63.358

Risk barrier

Target construct

Resistance

Image barrier

0.157

48.439

Tradition barrier

0.038

57.082

6 Discussion Based on the results, usage barrier was found to have an insignificant effect on resistance. This can be attributed to the business models that news companies use in the online setting. In particular, the majority of digital news platforms employ a subscription model which requires readers to pay a fee every month in order to access the articles. However, in an effort to entice the users to subscribe, they also provide a limited number of articles for free as samples [53]. Hence, usage barrier would not be an issue as youths would already be somewhat accustomed to reading digital news. Besides, tradition barrier was also found to be an insignificant antecedent of resistance. This is because reading in general and news in particular is not a typical activity of young people [54]. As such, there would be no existing tradition that would serve as a barrier to resist subscribing to digital news. Besides, value barrier was found to be the most significant facilitator of resistance. This can be attributed to the majority of youths being students and have low income/allowance. As a fee is involved when subscribing to digital news, they may

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decide to find alternative ways to read digital news. For example, digital news sites that allow users to read all the articles for free as they look to gain revenue through the advertising model. Moreover, risk barrier is also another significant facilitator of resistance. In particular, all respondents for this study did not subscribe to digital news. Thus, they would be uncertain about the risks that the subscription process may entail. These risks include privacy and security concerns [45] as users would have to provide personal details such as payment information when subscribing to digital news. Finally, image barrier was also revealed as a significant antecedent of resistance. This could be due to the perceptions of youths that reading the news is boring. Therefore, youths who subscribe to digital news and read it regularly may be seen as weird among their peers [24]. From the results, a few managerial implications can be derived to help news media companies in developing strategies that would reduce the youths’ resistance to digital news subscription. Firstly, they should provide a subscription package specifically catered for students. In particular, the subscription fee for this student package should be lower than normal packages. Additionally, the news media companies should require a picture of the student card to ensure the authenticity of those who subscribe for this package. Besides, news media companies should regularly notify their users about the privacy and security measures that are in place to safeguard their personal information. Furthermore, they should constantly look to update these measures according to the latest best practices in digital privacy and security. These could include implementing two-factor authentication with biometrics. Theoretically, this study has been successful in extending the literature of resistance in several ways. Firstly, this study was contextualized to the digital news setting. This is believed to be significant as resistance to digital news subscription is relatively understudied despite having been around for many years [55]. Furthermore, this study focused the issue of resistance to digital news subscription among youths. This is important because the general public often stereotype this group of people to be tech-savvy and always ready to embrace digitalization [56]. With that said, this study reveals that there are instances in which youths would resist certain aspects of the digital setting. Besides, this study was conducted when the COVID-19 pandemic was still a significant threat. Following that, a unique setting of this issue was captured by this study. There are several limitations that were identified in this study. In particular, this study adopted the Innovation Resistance Theory in its entirety. As 40.8% of the variance for resistance was captured in this study, this implies that there are other significant factors that were not included. Therefore, future studies should look to extend this theoretical model with other antecedents of resistance. Besides, this study employed a cross-sectional approach in which the data were only collected at one point in time [57, 58]. With that said, the data do not warrant the analysis and capture of changes between different periods of time. Therefore, future studies should use a longitudinal approach to better capture the underlying trends of this situation.

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Adoption Decision and Alleviation of Mobile Payment Kok Lin Gan1 , Chuen Khee Pek2,3(B) , Yet Mee Lim2 , and Fang Ee Foo4 1 Nottingham University Business School, Jalan Broga, 43500 Semenyih, Selangor Darul

Ehsan, Malaysia 2 Graduate Business School, UCSI University, UCSI Heights, Jalan Puncak Menara Gading,

Taman Connaught, 56000 Cheras, Malaysia [email protected] 3 UCSI Poll Research Centre, UCSI Heights, Jalan Puncak Menara Gading, Taman Connaught, 56000 Cheras, Malaysia 4 Faculty of Business & Management, UCSI Universiy, UCSI Heights, Jalan Puncak Menara Gading, Taman Connaught, 56000 Cheras, Malaysia

Abstract. This study aims at alleviating mobile payment by identifying the factors influencing the adoption decision and obstacles blocking providers in achieving competitive edge in e-businesses. A survey was commissioned, and the binary logistic regression used to predict the probability of m-payment adoption influenced by Perceived Usefulness (contributing 2.5 times higher probability in adoption decision), Perceived Ease of Use (4 times), Trust (2.5 times) and Compatibility (4.5 times). These insights aid the m-payment service providers and financial institutions to understand the need of the consumers and beneficial in strategizing business plans to achieve optimal profitability. Keywords: Mobile payment · Payment mode · Mobile adoption

1 Introduction Mobile payment (m-payment) is the type of payment carried out or made through digital mobility technologies with or without using mobile networks via portable devices allowing consumers to easily transact by scanning their mobile wallet (m-wallet) application at the merchant Point of Sale (POS) [1]. Such development has reshuffled the distribution of power as the mobile payment in Malaysia now is offered by both by traditional and non-traditional institutions. Despite the bright future, a research study by MasterCard reported that while 67% of the Malaysian millennials does value the convenience and user-friendliness of m-banking, a 30% has stopped using m-payment after single experience [2]. The m-payment adoption rate is expected to rise due to the effects of speed and convenience in mobile payment [3]. The Covid-19 had also increased the adoption of m-payment [4, 5]. M-payment services is expected to grow significantly in mobile marketing and popular in Asia and this includes Malaysia [6]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (Eds.): ICETIS 2022, LNNS 584, pp. 218–227, 2023. https://doi.org/10.1007/978-3-031-25274-7_18

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The research question is how can Malaysia expedite the m-payment behaviour of the Malaysians? To answer this question, two research objectives have been identified: (1) to determine the factors influencing the adoption of m-payment, and (2) to identify the obstacles blocking mobile payment players in achieving their competitive edge.

2 Literature Review The studies on m-payment are popular and diverse, ranging from consumers and merchant adoption, security technology development and to challenges on system standardisation/ compatibility. A widespread of merchant acceptance base was a critical success factor for consumer m-payment adoption [7]. However, Ligon et al. found that some low-income countries have low adoption rate despite significant efforts to promote mpayment [8]. Humbani and Wiese confirmed that m-payment adoption was affected by the number of merchant acceptances, which determined the space for consumers to use the service [9]. On the contrary, Harris, Guru and Avvari discovered that flexibility was the most important factor affecting the perception of the firms on the e-payment system. This study focuses on the perception and intention to adopt m-payment of the Malaysian consumers under complex environment [10]. Gao and Waechter stated that most researchers focused only on the factors affecting consumer adoption of m-payment since it was yet to be a widely used service [11]. The importance of mobile payment adoption is widely recognized by numerous researchers. These researchers have studied various factors leading to behavioral intention of adopting mobile payments in varied forms. Yang et al. found trust in mobile payment and customer satisfaction are significantly related to customers’ intention to adopt mobile payment services in the retail industry [12]. Yan et al. examined Quick Response (QR) code technology and found that perceived usefulness, ease of use, convenience, and speed are contributing factors to mobile payment adoption intention [13]. Similarly, the study by Tew et al. indicated the perceived usefulness and ease of use of Near Field Communication (NFC) system enhance smartphone users’ intention to adopt mobile payment [14]. In the hospitality industry, technology self-efficacy, perceived critical mass, mobile usefulness, mobile ease of use, mobile self-efficacy, and perceived enjoyment are positively related to intention to adopt mobile wallet payment [15]. Finally, Loh et al. investigated the use of wearable technology to make payment and found that mobile usefulness and mobile ease of use influence the intention to adopt wearable payment significantly [16]. The results of all these studies imply that Malaysia is heading to a cashless society. The consumer adoption of m-payment depends on their perception and intention to use it. The attitude of users towards specific IT system and its applications was crucial to decide whether a person adopted that system. Consumer intention reflected the driving factors, which affected their willingness to commit to a behaviour [17]. Intention was always used to apprehend the way attitude of an individual could influence the actual behaviour leading to favourable or unfavourable intentions [18]. Hence, positive intentions are a key indicator as it is the motivational factor towards the adoption of new systems, unfamiliar to the consumers [19]. Nevertheless, Cao found that customer intention does not influence the adoption of m-payment [4].

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Numerous researches have analysed multiple variables (factors) affecting consumer adoption of m-payment with different conclusions. Shin found that trust, perceived ease of use (PEU), perceived usefulness (PU) and perceived risk (PR) affected user adoption of m-payment [20]. Mallat noted that compatibility, trust and relative advantage were the main factors affecting m-payment acceptance [7]. Meanwhile, Tan et al. indicated that social influence, perceived ease of use (PEU) and perceived usefulness (PU) would determine usage intention on m-payment [21]. Perceived cost, perceived risk, compatibility and convenience are among the variables influencing consumer readiness of m-payment services usage [9]. On the other hand, Eze et al. discovered the importance of security and privacy influencing the m-payment system usages in Malaysia [22]. In this study, PU is referred as the extent an individual believes that using a specific system will boost task performance [23]. Previous studies had shown that PU strongly affected consumer perception and intention of adopting m-payment [19, 24, 25]. This means that higher consumer PU leads to a greater m-payment adoption intention. Thus: H1: Perceived Usefulness (PU) has a positive influence on consumer perception and intention to adopt m-payment. PEU is defined as the perception of a person on the complexity of a particular system in its usages following the work of Su et al. [26]. PEU is one of the critical factors which determines consumer acceptance of m-payment [9, 21, 27, 28]. If the consumer PEU is high, the perception and intention towards m-payment adoption is high. Thus: H2: Perceived Ease of Use (PEU) has a positive influence on consumer perception and intention to adopt m-payment. Trust can be explained as the confidence of consumers in quality, ability and reliability of a product or service provided by any business [29]. Prior studies have mentioned that the trust of consumers in m-payment significantly affects the adoption of this payment mode [20, 30–33]. Higher consumer trust in m-payment leads to higher intention of m-payment adoption. Thus: H3: Trust has a positive influence on consumer perception and intention to adopt mpayment. According to Thsiakis and Sthephanides [34], security is a series of processes and mechanisms applied to confirm/ verify the authenticity of information source, which protects the privacy and integrity of the information. Past researches have shown significant influence of security and privacy on the decision of consumers to adopt m-payment [9, 28, 31]. If consumers perceive that the security and privacy of m-payment is high, their perception and intention of adopting it will be high. Thus: H4: Security and Privacy has a positive influence on consumer perception and intention to adopt m-payment.

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Based on Luarn and Lin [35], perceived cost is the degree of a person believing that m-banking will incur some cost. Perceived cost is negatively correlated with the intention of consumers to adopt m-payment [31, 36, 37]. This means that consumers will repel to adopt m-payment if they perceive using m-payment is costly. Thus: H5: Perceived Cost has a negative influence on consumers’ perception and intention to adopt m-payment. Social influence is the external pressure exerted by the surroundings an individual engages or not to engage a behaviour following the work of Taylor and Todd (38). Prior studies highlighted the importance of social influence on consumer perception and intention to adopt m-payment [32, 39]. It is believed that social influence affect consumers’ perception and willingness to adopt m-payment. Thus: H6: Social Influence has a positive influence on consumer perception and intention to adopt m-payment. Compatibility was defined as the extent to which a new technology was perceived to be consistent with the experiences, behavioural pattern, existing values and needs of adopters [40]. This study adopts the similar definition. The construct had a significant influence on consumer perception and intention of m-payment adoption as reported by the works of [9, 28]. In the work of Ma et al., consumers tend to have higher pleasure of buying through m-payment than cash [41]. The higher the compatibility of m-payment with the lifestyle or value of consumers, the higher the intention of m-payment adoption. Thus: H7: Compatibility has a positive influence on consumer perception and intention to adopt m-payment.

3 Methodology A web-based self-administered questionnaire was developed for primary data collection. The questionnaire was piloted on a sizeable number of respondents, where their comments and suggestions were considered. The final set of questionnaires were then distributed to targeted participants through various communication channels. The questionnaires reached the target sample size of 264 qualified Malaysian respondents. VanVoorhis and Morgan stated that the general rule of thumb for statistics to study correlation or regression is minimum 50 respondents [42]. The sample sizes of similar researches on e-payment or m-banking in Malaysia ranged around 150 to 200 participants [3, 42]. In the absence of face-to-face contact, respondents were briefed on the survey objective, introduction of m-payment on the first page of the questionnaires. The questionnaire is divided into three main sections. Section A comprises of four questions on the demographic information of respondents while section B consists of seven items meant to understand the awareness and experiences of respondents with m-payment. Section C contains 37 items aimed to examine seven constructs/ independent factors discussed.

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These questions are the sets of four and five Likert scale questions and adapted from various literatures and studies. In Section C, a four-point Likert scale is used to measure the decisions of respondents with no midpoint in between (1 = Strongly Agree; 2 = Agree; 3 = Disagree; 4 = Strongly Disagree). The total value of the Likert scale questions for each construct of every respondent was compared to the average Likert scale score of the sample to decide if the individual agrees or disagrees with the construct in influencing the adoption of m-payment. The outcome will then be coded in the binary form with 1 = agreeing that the construct influences the adoption of m-payment and 0 = otherwise. The completed questionnaires were analysed using the SPSS 24.0 software. The econometric model used was the Binary Logistic Regression (BLR) to accommodate the dependent variable, Intention to adopt m-payment, which is a categorical data.

4 Analysis and Discussion Table 1 shows the mean and standard deviation (SD) scores for all the constructs (independent variables) and adoption decision (dependent variable). The SD scores of all items are rather close to one another reflecting the consistency of the feedback from the respondents. Table 1. Standard deviation scores for all variables Constructs PU

Mean 8.64

Standard deviation (SD) 0.85

PEU

9.31

0.82

Trust

11.50

0.81

Security & privacy

12.78

0.84

Perceived cost

10.60

0.79

Social influence

8.70

0.79

Compatibility

8.42

0.86

Adoption decision

7.51

0.83

The Cox & Snell R Square value of 0.405 indicated that the seven constructs are important from the context of m-payment adoption. Based on the BLR category prediction result, the model correctly predicted 79.5% of cases (0 = decide to adopt m-payment, 1 = decide not to adopt m-payment), where the predictions are correct 210 times out of 264 times. The sensitivity of this model is high whereby it predicted correctly 81.5% of cases (decide not to adopt m-payment) from actual observed. In addition, the specificity of this model is also high as it correctly predicted 77.1% of cases (decide to adopt m-payment) from actual observed. The results from the BLR in Table 2 show that all the parameters acquire consistent signs with the a priori expectations. The study supports that the intention to adopt m-payment by the Malaysian consumers are influenced by both PU and PEU, where

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the parameters are statistically significant at 0.05 level of significance. Besides that, the results indicate that Compatibility and Trust are strong predictors of m-payment adoption decision among the locals. The other three constructs, namely Security and Privacy, Perceived Cost and Social Influence are insignificantly associated with m-payment adoption at significance level of five percent. Evidently, PU has a significant positive influence (p = 0.024 < 0.05) on the perception and intention of Malaysian consumers to adopt m-payment. This is aligned with the BLR analysis, where the Malaysian consumers are 2.5 times more likely to adopt m-payment [Exp (B) = 2.471] if they perceive the system is useful or beneficial when other factors remained constant. The finding corresponds with prior studies [19, 25, 43]. This suggests that the Malaysian consumers are ready to use m-payment if they believe the system is useful. The swift check-out has been found to be the motivating factor in encouraging the 45% of respondents to adopt m-payment. Besides that, PEU is significantly associated (p = 0.000 < 0.05) with the perception and intention of Malaysian consumers to adopt m-payment. The Malaysian consumers, who feel that m-payment system is user-friendly and easy to use will be four times more likely [Exp (B) = 3.991] to use m-payment when other factors remained constant. The outcome is aligned with previous research [9, 21], where PEU was an important predictor for m-payment usage. This signify the usage of m-payment among the Malaysian consumers is highly driven by its user-friendliness and effort required. Based on the analysis, it is believed that the more user-friendly the m-payment system, the higher is the system adoption rate. Table 2. BLR analysis result Variables in the equation (significant at 0.05 level) B

S.E.

Wald

df

Sig

Exp(B)

PU

.905

.400

5.114

1

.024**

2.471

PEU

1.384

.390

12.585

1

.000**

3.991

Trust

.946

.371

6.512

1

.011**

2.576

Security & privacy

.246

.379

.423

1

.515

1.279

Perceived cost

– .320

.406

.621

1

.431

.726

Social influence

.663

.397

2.785

1

.095

1.941

Compatibility

1.493

.404

13.640

1

.000**

4.450

Constant

– 1.960

.300

42.552

1

.000**

.141

Trust has a positive significant relationship (p = 0.011 < 0.05) with the perception and intention of the Malaysian consumers on the m-payment usage. It is found that local consumers, who trust m-payment are 2.5 times more possible [Exp (B) = 2.576] to use m-payment when other factors remained constant. The result is supported by various past studies [9, 32, 33], where the acceptance of m-payment was heavily affected by the trust of users. The finding reflects that intention and actual adoption of m-payment among the Malaysian consumers are highly dependent on their level of trust on the m-payment

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system involving monetary transactions. Gefen stated that the trust of consumers was important in e-commerce as there were risks/ uncertainties caused by unethical online vendors like inaccurate information, unfair pricing, fraudulence and even leakage of the personal information of consumers [44]. Compatibility has proven to have a significant positive influence (p = 0.000 < 0.05) on the decisions in utilising the m-payment with 4.5 times more likelihood [Exp (B) = 4.450] when it is compatible with the daily lives and values of the local consumers when other factors remained constant. The finding is aligned with prior studies [28, 31]. Surprisingly, Security and Privacy is statistically insignificant (p = 0.515 > 0.05). The finding is inconsistent with the prior studies [9, 22, 31]. Nevertheless, Teoh et al. reported the same inconsistency [43]. Similarly, Perceived Cost is also statistically insignificant (p = 0.431 > 0.05). The finding is not consistent with the law of demand and differ from the previous works [31, 36, 37]. The central bank, Bank Negara Malaysia reported that lower financial cost was one of the major drivers in promoting wider adoption of e-payment/ m-payment among the local consumers [45]. However, the finding of this study is supported by the work of Ooi and Tan [33]. This may be explained by the usually low cost of using mobile technology in the country. Next, social influence is also insignificantly related (p = 0.095 > 0.05) to the perception and intention of Malaysian consumers to use m-payment. This is inconsistent with the past studies [32, 39]. As mentioned in the past section, most of the online activities in Malaysia are concentrated on text communication and social networking. Hence, social influence can be applied through the internet. On the contrary, Wang and Yi found no significant relationship between social influence and m-payment adoption supporting the result of this study [46].

5 Conclusion and Implications This study provided useful insights from the perspective of strategies for regulators (BNM and MCMC) and industry players (financial institutions, fintech and mobile service providers). Actions should be taken by regulators to reduce the technological barriers of entry to popularise new payment technology usage, which can then improve the effectiveness of costs through economies of scale for examples through tax relief incentives on m-payment technology research and development (R&D) and create fair and transparent pricing policy to ensure healthy competitions among the industry players. Regulators are encouraged to partner with the industry players to promote public awareness on the benefits of m-payment. Regulators should also safeguard consumer rights through sufficient controls. Adequate regulations and controls will prevent industry players with profits, which are over-burdening the consumers while sacrificing their rights and privacy. The higher the trust of local consumers on m-payment, the more and sooner they will be adopting it. High adoption of cashless transactions like m-payment will accelerate the e-payment migration strategy the central bank to displace the traditional payment modes such as cash and cheques, which will ultimately help transform Malaysia into a more efficient economy. Besides that, PU, PEU and Trust are also significant factors and focus is also needed on these areas. Specifically, on PEU, the industry players shall design more user-friendly

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m-payment system or applications with minimal learning and effort required from the signup until payment stage. This should also include simple steps on problem troubleshooting. Continuous enhancement, updates and simpler/ fewer usage steps on mpayment system is needed to improve the overall customer usage experience, which is crucial for customer retention and loyalty. The m-payment service providers can also inform and educate consumers the convenience of the system facilities, especially to nontech savvy public through the online platforms and service kiosks. Similarly, reliable and transparent information and infrastructures should be in place to build trust among the Malaysian consumers on m-payment platforms. Customer trust level is also based on the support services rendered by the m-payment service providers. Swift problem-solving skills, high reachability and empathetic customer service are the basic requirements needed to build trust. The m-payment service providers should formulate customer experience transformation roadmap, which is essential in the migration of traditional payment modes (e.g., cash and cheques) to the unfamiliar m-payment method.

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Critical Success Factors of Knowledge Management in Higher Education: A Systematic Review Ghazala Bilquise1(B)

and Khaled Shaalan2

1 Computer Information Science Department, Higher Colleges of Technology, Dubai, UAE

[email protected]

2 Informatics Department, The British University in Dubai, Dubai, UAE

[email protected]

Abstract. The growing need to stay competitively ahead in this knowledge era has drastically changed the performance expectations of Higher Education Institutions (HEIs). Knowledge Management (KM) has become crucial for institutional effectiveness, performance, and innovation as it provides academic institutions with strategies for attaining a competitive advantage. Several Critical Success Factors (CSF) have been identified in previous studies as determinants of KM Success. However, the research in this area is fragmented. This study provides a holistic view for researchers interested in understanding the factors that enable KM integration in HEI. The study is based on 17 articles retrieved from top-ranked journals and finalized by applying the process of systematic review protocols. Results show that organizational culture is the topmost factor of success in all the studies. Knowledge-sharing is the most frequently researched process, closely followed by knowledge creation. Keywords: Systematic literature review · Higher education · Academic institutions · Knowledge management · Critical success factors · Knowledge processes

1 Introduction In today’s dynamic and ever-changing information society, Higher Education Institutions (HEIs) face the demands of globalization and technological innovations to stay competitive [1]. Higher expectations from internal and external stakeholders and diminishing financial resources lead HEIs to re-evaluate their institutional practices and adopt new strategies to stay ahead [2]. To meet these challenges of the knowledge era HEIs are focusing on knowledge-based strategies for their success. The integration of KM in HEIs suffers from several challenges that impact its successful institution-wide adoption [3]. To this effect, numerous studies have investigated the integration of KM on HEI in the last few decades. These studies have attempted to identify the critical success factors required in an educational setting to adopt KM practices. However, research on this topic lacks integration [4]. To the best of our knowledge, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (Eds.): ICETIS 2022, LNNS 584, pp. 228–239, 2023. https://doi.org/10.1007/978-3-031-25274-7_19

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no study has performed a systematic review to explore the success factors of KM in the higher education context. This study attempts to contribute to the literature by providing a systematic review of relevant research that explores the factors associated with the successful adoption of KM in HEIs. In doing so, the study assimilates research findings of high-quality papers to provide researchers with an analysis of articles by analyzing reviewed content. We aim to answer the following research questions: RQ1—Which KM processes play a dominant role in the reviewed studies? RQ2—What is the main area of focus for KM studies in HEI? RQ3—What critical success factors are identified in HEI for KM integration?

2 Literature Review Although the concept of KM has been explored since the 1990s, yet there is no unified definition to date. A widely accepted definition of KM is that it is the process of acquiring, sharing, and effectively utilizing knowledge for institutional performance [5]. A more comprehensive definition is that it is the management of activities that encourage generation, sharing, and transfer of knowledge to make informed decisions, solve problems and generate value [6, 7]. So, in essence, the role of KM is to make the right knowledge available to the concerned people within an organization so that informed decisions are taken that in turn lead to organizational excellence. KM directly impacts organizational performance by reducing operational costs, improving customer service, facilitating the transfer of best practices, and more [8]. In addition to these benefits, there are several other implications of adopting KM that are specific to the higher education domain, such as improved curriculum development, reduced time and cost for interdisciplinary research [9], and enhanced pedagogical practices and learning outcomes [3]. Thus KM practices are a valuable tool to improve the academic and administrative services of HEIs and overall institutional effectiveness. Effective implementation of KM requires a well-planned integration of people, processes, and technology to generate, assemble, store, disseminate, and evaluate knowledge [10]. Managing knowledge requires creating a culture that stimulates knowledge-based activities. To this effect, numerous research has been conducted on the enablers and barriers of KM in higher education. The enablers are primarily the factors that promote KM initiatives and encourage KM activities. Several reviews have studied KM practices and enablers in domains such as Information Management [11], innovation [12]. The study by [13] focused only on the Knowledge Sharing (KS) process in HEIs. This review attempts to provide a holistic view of the most recent studies in KM integration with HEI from different perspectives. The review examines the application areas of KM, the KM processes involved, the types of studies and participants involved, and various success factors described for the successful integration of KM. After consolidating the different processes from reviewed literature, four main processes were identified within the context of HEI that support academic, administrative and research and innovation initiatives—Knowledge Sharing (KS), Knowledge Transfer (KT), Knowledge Creation (KC) and Knowledge Utilization (KU).

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3 Methodology This review was conducted through a systematic process based on the PRISMA [14] methodology to structure and document the review process. The following sub-sections explain the details of each phase of the methodology. 3.1 Inclusion/Exclusion Criteria A review criterion was adopted to include the most recent papers published in the domain of the subject. The reviewed publications were limited to the period between 2018 and 2021. This range was chosen because it represents recent studies on the success factors of KM in HEI. Second, only peer-reviewed journals, which are published in high-rank journals, were included. Studies written in English were included as the author’s primary language is English. Moreover, the search was focused on KM processes in HEIs. In this context, both public and private institutions were considered. The inclusion and exclusion criteria used to screen the papers are described in Table 1. Table 1. Inclusion exclusion criteria Inclusion criteria

Exclusion criteria

Must be based on a higher education institution

The KM context is not for a higher education institution

Must involve KM processes or tools to study the success factors

Does not study or explore the success factors of KM implementation

Must be written in English

Paper is not written in English

Must be a peer-reviewed journal

Conference papers, book chapters or articles in the press

Must be published in a high-rank journal

Journal does not meet the high rank criteria

3.2 Data Sources and Search Strategies To begin the search, the main keywords were identified. Logical operators (AND/OR) were used to filter the search results and ensure relevance. Below is a list of the keywords combination used for searching. ( "knowledge management" OR "knowledge sharing" OR "knowledge acquisition" OR "knowledge transfer" OR "knowledge creation" OR "knowledge codification" OR "knowledge storage" OR "knowledge protection") AND ( "higher education" OR "university" OR "college" OR "academic institution") Due to the multidisciplinary nature of the research topic, four main databases were used to retrieve 395 studies for this review—Scopus, Science Direct, IEEE Xplore, and Google Scholar. These studies were screened for eligibility following the PRISMA process. Figure 1 shows the PRISMA flowchart highlighting the screening steps.

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3.3 Quality Assessment Assessing the quality of eligible papers is an imperative step to ensure that the systematic review results are not biased. The quality assessment in no way criticizes any of the studies, and neither does it evaluate the impact of the findings. Instead, it is used to identify how the study has been conducted and how relevant it is to this review. The checklist was adapted from [11], and a list of ten questions was prepared for assessment, and each assessment question was evaluated on a three-point scale. A threshold of 70% was set for studies to be included in the review.

Fig. 1. PRISMA flowchart

3.4 Data Coding and Analysis A thorough full-text review was performed to extract the main characteristics of the papers based on the research questions described in Section 1. The encoding included characteristics such as (a) method of study, (b) the number of participants, (c) type of participants, (e) country the study is based in, (f) HEI area of activity, (g) CSF identified and, (h) KM process used. Among the reviewed studies, the highest number of papers were published in the year 2020 (n = 8). Nearly 60% of the studies were based in Asia

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(n = 10), showing that research on KM in the Asian higher education sector is rapidly growing. Europe (n = 3) followed next with 17% studies and only one study each in North America, South America, Australia, and Africa. Empirical results were presented by all studies with a majority of the studies (n = 9) based on quantitative data, while few studies (n = 4) used qualitative data and some studies (n = 4) used both research methods.

4 Discussion of Results 4.1 RQ1—Which KM Processes Play a Dominant Role in the Reviewed Studies? Four KM processes were studied in all the reviewed papers—knowledge sharing (KS), knowledge transfer (KT), knowledge creation (KC), and knowledge utilization (KU). Among these, knowledge sharing was found to be the most dominant process (n = 13) studied in the analyzed papers. This is consistent with the findings of previous systematic reviews in the field of KM [11], thus showing that it is the most fundamental process of KM impacting the success of all KM practices within any organization. KS was investigated by the reviewed studies to determine the factors that impact collaboration amongst academics and external organizations for research activities. Two studies focused on KT, to facilitate entrepreneurial activities and external relations. Several studies also focused on KC (n = 7) as it is an essential process for generating new ideas. The knowledge creation process was strongly linked with the HEI activities of research, curriculum development, U-I collaboration and general institutional effectiveness. 4.2 RQ2—What is the Main Area of Activity for KM Studies in HEI? The majority of studies (n = 9) investigated the integration of KM in HEI for organizational performance, although mainly academics were part of the data collection process. The next main area of focus for integrating KM was to encourage research activities (n = 3), followed by University-Industry collaboration (n = 2), and to promote innovation (n = 2). Only one study focused on curriculum development as the area of interest for integrating KM. Surprisingly there were no studies that investigated KM integration for teaching and learning (T&L), considering that this is the core function of an academic institution. This finding shows the demands of the knowledge economy are driving HEIs to diversify their activities and focus on innovation and creativity through their research and external partnerships. 4.3 RQ3—What Critical Success Factors are Identified in HEI for KM Integration? The reviewed studies have investigated the critical success factors of integrating KM practices based on one of three dimensions—institutional, personal, and support. Institutional factors involve aspects such as organizational culture, structure, communication,

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and extrinsic motivation. Personal factors include employee skills, knowledge, intrinsic motivation, trust, belief, and bonding. Support factor includes the provision of resources, training, and technology. The institutional factors were identified as the top factors of integrating KM. Among these factors were organizational culture, leadership, and rewards and incentives scheme. Figure 2 presents a summary of the critical success factors identified in the studies.

Fig. 2. Critical success factors of KM in HEI

Institutional Factors. Several studies concluded that organizational culture has a positive impact on KM [15–20]. Study [15] concluded that an adhocracy culture that focuses on employee empowerment has a positive impact on the processes of knowledge creation, knowledge sharing, and knowledge application. This finding is conclusive with previous studies which suggest that a positive organizational culture encourages knowledge sharing [21]. Several studies also revealed that an institutional strategy is essential to promote and encourage collaboration among academics as well as external partners [22–24]. Leadership was also highlighted as a critical success factor in numerous studies [17, 20, 25–27]. While [25] focused on transformational leadership for promoting innovationbased culture, several studies concluded that knowledge-oriented leadership is essential to provide support and encourage knowledge-based activities [26, 27]. These findings are also supported by an empirical study by [28], that shows that knowledge oriented leadership is crucial for enhancing KM processes. Confirming to previous research findings [29, 30], four of the reviewed studies have found that rewards and incentives are a crucial motivating factor for promoting KM activities [17, 20, 24, 31]. However, in contrast, some studies also showed that extrinsic motivation has very little or no significance in sharing among academics. Study [22] revealed that extrinsic motivation is not a determinant of knowledge sharing in their institution and explained their finding by stating that the religious beliefs of the respondents encourage knowledge sharing and are not driven by rewards. Their finding is consistent with that of a previous study [32], which showed that the use of incentives does not have a positive impact on building an attitude towards sharing in an academic environment. The results from the study by [18] was also in agreement with this finding showing that personal satisfaction is an intrinsic motivation factor required for a

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knowledge sharing behavior among academics. The results of these studies show that although extrinsic motivation may be a factor in some HEIs to promote KM activities, yet intrinsic motivation is more sustainable in cultivating knowledge sharing behavior. Communication was also highlighted as a factor for successfully integrating KM initiatives [16, 24, 31, 33, 34]. Some studies concluded that robust communication channels are crucial for KM practices and can be established by creating flatter organizational structures and reducing bureaucratic barriers [16, 31]. The study by [34] emphasized that communication channels are fostered when there is a bonding between the academics. The study further showed that similarity in age, ethnicity, and department creates a bonding that promotes knowledge sharing. Support Factors. Management support was highlighted as an essential factor of KM success by several studies [16, 20, 26, 35]. Other support factors were training [16, 20, 26] and access to resources such as library resources, provision of funds and sponsorships [19, 20, 22]. Technology was identified as an essential factor of success by three studies [18, 20, 24]. In contrast to other studies [22] showed that technology and management support do not play a crucial role in promoting knowledge sharing, mainly because it hinders the process of sharing. The study by [15] showed that technology played a negative mediating role in sharing since the Information System used by their university was not designed adequately to store and disseminate discovered knowledge. [16] found that technology may be used as a tool to promote KM activities, however, the study identified management support and training as CSFs to raise awareness. These findings showed that just the presence of technology is not enough, the effective implementation of technology is essential. Moreover, technology must be suitable for the task and if it is not implemented properly, it may act as an obstacle in sharing rather than an enabler. Personal Factors. Trust was identified as a crucial factor that plays a significant role in KS behaviour among academics [22, 23, 33, 35]. This finding is conclusive with previous studies that have highlighted the importance of trust in collaborative activities [36, 37]. Only one study investigated Emotional Intelligence (EI) as an enabling factor that significantly influences KM processes [38]. The study showed that EI facilitates selfdirected learning, thus making employees responsible and autonomous in their drive to acquire and share knowledge. This finding is consistent with a previous study which shows that EI plays a crucial role in influencing an individual’s willingness to share knowledge [39]. However, further research is required to investigate the role of EI on KM within an academic environment. Table 2 provides a summary of all reviewed papers, their objectives, KM processes investigated and main findings.

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Table 2. Summary of reviewed papers Source Research objective

KM processes Main findings

[15]

To study the impact of organizational culture on KM processes

KS, KC, KU

[22]

To study the impact of personal, KS organizational and technology factors on KS

Only University policy and trust significantly influences KS. KS is mediated by personal satisfaction, and sense of responsibility

[35]

To identify the determinants of KM integration within a HEI

KS

Trust, organizational support, and intrinsic motivation have strong influence on KS

[23]

To identify the enablers of collaboration with industry

KS

Trust, University policy and a structured communication channel fosters collaboration between university and external partners

[26]

To determine effective KM practices for HEIs to increase their ability to learn

KT

Leadership that raises awareness, provides training, internalization and externalization activities has a positive impact on KT

[16]

To determine the factors for integrating a social networking tool to promote KS

KS

Flatter organizational structure is more conducive to KS, organizational culture, training, management are also strong factors

[25]

To investigate two types of leadership styles as CSF to KS

KS

Transformational leadership plays a crucial role in KS, which has a direct impact on innovation

[31]

To analyze the KT methods for KT encouraging innovations for the underrepresented

Reward schemes are essential to motivate innovations for the underrepresented groups of society

[20]

To determine the CSF of KM tools in the KM processes

Human empowerment factors such as employee motivation, financial resources and training contribute most the success of KM

[17]

To investigate the impact of KM KS, KC, KU processes in research universities

KS, KC

Organizational culture with focus on innovation and entrepreneurship has a positive impact on KC, KS and KU

Leadership, organizational culture and incentives are crucial for the success of KM activities (continued)

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G. Bilquise and K. Shaalan Table 2. (continued)

Source Research objective

KM processes Main findings

[34]

To explore the impact of social capital on KS

KS

University professors are more likely to share when there is hemophilic bonding (similarity in age, ethnicity and dept.) as this builds trust

[18]

To examine five that factors that KS contribute to KS

Enjoyment in helping others, organizational culture and technology are the critical factors for KS

[27]

To study the influence of leadership on performance

KS, KC, KU

Knowledge oriented leadership is essential to promote knowledge based activities

[38]

To study Emotional Intelligence(EI) as a factor of KM

KS, KC, KU

Emotional Intelligence has a significant influence on all KM Processes

[19]

To explore factors that affect KC in HEIs

KC

Social capital and organizational culture influence KC. Other factors include employee’s basic skills

[33]

To explore the factors of KC in University Industry (UI) relationship

KC

Communication, shared values, trust, commitment and critical factors for KC

[24]

To examine the impact of seven independent variables on KS

KS

University policy and technology are essential for KS

5 Conclusion The objective of this study is to systematically review the papers published on the topic of KM integration in HEI to get a deeper understanding of the most recent research conducted in this field. This study reviewed 17 empirical studies on KM in Higher Education Institutions. The review was conducted using the PRISMA standard to screen papers that were retrieved from four different databases. The screening processes included defining an inclusion/exclusion and assessment criteria to get the eligible papers for this review. Table 2 summarizes the objectives and main findings of all the reviewed studies. Knowledge sharing was the most predominant KM process studied among all the reviewed papers. The next process that was studied the most is knowledge creation followed by knowledge utilization. Lastly, knowledge transfer is the least studied process. It was studied mainly in the context of curriculum development.

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The reviewed studies have investigated several KM success factors that can be broadly classified into three dimensions—institutional, personal, and support. Institutional factors such as organizational culture, leadership, university policy, and communication were emphasized as the top enablers of KM integration. Technology, training, management support, and access to resources were identified as the main support factors. However, technology is mainly a tool and the success of KM depends on the successful implementation and utilization of technology. Awareness of the tool and policies for its utilization is essential. Among personal factors trust was identified as the most significant factor of success. This finding shows that when academics trust that their hard earned knowledge will be valued, the willingness to share increases. One study also investigated emotional intelligence and showed that EI leads to self-directed learning and increases knowledge sharing. Overall personal factors seem to be ideal for cultivating a knowledge-based culture. Therefore, it is recommended for KM leadership to focus on these factors while developing strategies for KM.

References 1. Alajmi, Q., Arshah, R.A., Kamaludin, A., Al-Sharafi, M.A.: Current State of Cloud-Based E-learning Adoption: Results from Gulf Cooperation Council’s Higher Education Institutions (2019). https://doi.org/10.1109/IEMCON.2018.8614772 2. Palumbo, R., Manna, R.: Making educational organizations able to change: A literature review. Int. J. Educ. Manage. 33, 734–752 (2019) 3. Cranfield, D.: Knowledge Management and Higher Education: A UK Case Study Using Grounded Theory. University of Southampton (2011) 4. Quarchioni, S., Paternostro, S., Trovarelli, F.: Knowledge management in higher education: A literature review and further research avenues. Knowl. Manage. Res. Pract. 20, 304–319 (2020) 5. Wen, Y.-F.: An effectiveness measurement model for knowledge management. Knowl.-Based Syst. 22(5), 363–367 (2009) 6. Horwitch, M., Armacost, R.: Helping knowledge management be all it can be. J. Bus. Strat. 23, 26–31 (2002) 7. Arpaci, I., Al-Emran, M., Al-Sharafi, M. A.: The impact of knowledge management practices on the acceptance of Massive Open Online Courses (MOOCs) by engineering students: A cross-cultural comparison. Tele. Inf. 54, 101468 (2020). https://doi.org/10.1016/j.tele.2020. 101468 8. Pinto, M.: Knowledge management in higher education institutions: A framework to improve collaboration. In: 2014 9th Iberian Conference on Information Systems and Technologies (CISTI), pp. 1–4 (2014) 9. Kidwell, J.J., Vander Linde, K., Johnson, S.L.: Applying corporate knowledge management practices in higher education. Educause Q. 23(4), 28–33 (2000) 10. Evans, M., Dalkir, K., Bidian, C.: A holistic view of the knowledge life cycle: The knowledge management cycle (KMC) model. Electr. J. Knowl. Manage. 12(1), 47 (2015) 11. Al-Emran, M., Mezhuyev, V., Kamaludin, A., Shaalan, K.: The impact of knowledge management processes on information systems: A systematic review. Int. J. Inf. Manage. 43, 173–187 (2018). https://doi.org/10.1016/j.ijinfomgt.2018.08.001 12. Costa, V., Monteiro, S.: Key knowledge management processes for innovation: A systematic literature review. VINE J. Inf. Knowl. Manage. Syst. 46, 386–410 (2016)

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Experience with Mobile Phone Technology: A Comparison Between Two Brands Yet-Mee Lim1(B)

, Choi-Meng Leong1 , Teck-Chai Lau2 and Chuen-Khee Pek3

,

1 UCSI Graduate Business School, UCSI University, No. 1, Jalan Menara Gading, UCSI

Heights (Taman Connaught), 56000 Cheras, Kuala Lumpur, Malaysia {LimYM,leongcm}@ucsiuniversity.edu.my 2 Faculty of Business and Law, School of Marketing and Management, Taylor’s University Lakeside Campus, 1, Jalan Taylors, 47500 Subang Jaya, Selangor, Malaysia [email protected] 3 Faculty of Business and Management, UCSI University, No. 1, Jalan Menara Gading, UCSI Heights (Taman Connaught), 56000 Cheras, Kuala Lumpur, Malaysia [email protected]

Abstract. This study investigates the influence of customer experience on brand loyalty through perceived value by comparing Apple and Samsung users. Data collection was conducted via online platforms through self-administered questionnaire. A total of 252 Apple and 265 Samsung brand respondents were subsequently used for data analysis. Partial least square structural equation modelling was used to analyse the data. The results revealed that perceived value was determined by sense experience and relate experience for Apple users while perceived value was determined by feel experience, act experience and relate experience for Samsung users. Perceived value was the predictor of brand loyalty for both Apple and Samsung users. This study contributes to the literature of customer loyalty by confirming the impact of five customer experience dimensions on the perceived value. Further suggestions and recommendations were made regarding critical customer experience that contributed to perceived value and subsequently to brand loyalty. Keywords: Sense experience · Feel experience · Think experience · Act experience · Relate experience · Brand loyalty

1 Introduction With the advancement of new technology, customer experience is progressively being shaped and transformed [1]. Customer experience is referred to by marketing experts as the “basic underpinning” for marketing management [2]. It has become essential for marketers to place significant emphasis on customer experience and monitoring the experience dimensions [3]. This is because the value perceptions of consumer experience will directly affect the value of a product and influence customer’s intention to purchase [4]. Customers and markets are constantly shaped by businesses that create unique © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (Eds.): ICETIS 2022, LNNS 584, pp. 240–250, 2023. https://doi.org/10.1007/978-3-031-25274-7_20

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experiences that give businesses a competitive edge; resulting in business outcomes such as customer retention and profitability [5]. Many past research looked at the relationship between customer experience and perceived value. However, studies in the context of smartphone industry by comparing different smartphone vendors such as Apple and Samsung seem limited. Currently there is an aggressive competition in the mobile telecommunication industry due to the rapid technological development. Customer loyalty will be crucial as the core competency of the firm, resulting in sustainable competitive advantage in ensuring long term survival due to the short product life-cycle and increased customers’ product expectations [6]. Organisational managers must be able to recognise and quantify the value of several critical customer experience dimensions, as well as their synergy, which effects company performance, so that they can appropriately manage their time and resources [7]. The top two mobile vendor market share in Malaysia between March 2021 to March 2022 are Apple (27.37 percent) and Samsung (16.03 percent) [8]. In view of the intense competition, it is crucial for companies such as Apple and Samsung to further explore the dimensions of customer experience that could contribute positively towards perceived value and strong brand loyalty that lead to customer retention and profitability. Companies who managed to do this will be able to sustain their industry leader positions in the smartphone segment by customising suitable experiential marketing strategies.

2 Literature Review and Hypothesis Development 2.1 Brand Loyalty Brand loyalty is defined as the recurring purchase of a favoured product, regardless of environmental factors or marketing efforts [9]. Customers who have high brand loyalty would likely to engage in repeat purchase of the same brand and are more likely to be willing to spend on the products [10]. Furthermore, brand loyalty is described as a strong desire to buy or patronise a favoured product or service in the future [11]. 2.2 Perceived Value Perceived value is a consumer’s total rating of a product’s use based on what is received (benefits) and what is given (costs and sacrifice) [12]. The study further explained that perceived value can be improved in two ways, either by increasing the benefits offered or reducing the costs of products. Consumers’ trust in a brand is reinforced by higher perceived value [13]. If brand equity and brand loyalty are correlated, perceived value has effects on brand loyalty [14]. High perceived value would result in the continuance intention or repurchase intention of products [15, 16]. Such behaviours reflect brand loyalty. Thus, we hypothesise that H1: Perceived value has a positive effect on brand loyalty.

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2.3 Customer Experience Marketers can employ strategic experiential modules to provide a variety of customer experiences [17]. The experiences are expressed in five dimensions, namely sense experience, feel experience, think experience, act experience and relate experience. As the whole process of customer experience is complex, all five experiences are difficult to distinguish from each other and sometimes are interrelated [18]. Customer experience emphasises the enhancement of perceived value through interactivity [19]. Sense Experience. Sense experience is stimulated by human’s five senses, i.e. sight, sound, touch, smell and taste. These are all ways to gain sensory experiences [20]. These senses resulted from touch sensation and visual content can directly affect customer’s perception on product value, which influences customer’s desire to purchase [15]. Aesthetic is a stimuli of sense experience [21] while positive perceived value led to customer satisfaction [16]. These reflect that sense experience can influence customer’s perceived value. Thus, we hypothesise that H2: Sense experience has a positive effect on perceived value. Feel Experience. Feel experiences draw attention to customers’ inner feelings and emotions, and their impact is felt during purchasing process [20]. A customer’s feeling of pleasure or disappointment is associated with the resulted comparison between their expectation and perceived value on the product. Furthermore, positive emotions such as enjoyment and excitement were discovered to be capable of creating memorable consumption experience, and this pleasant experience was linked to customer perceived value and satisfaction [22]. Therefore, we hypothesise that H3: Feel experience has a positive effect on perceived value. Think Experience. Think experience is termed as an experience that encourages consumers to think creatively and innovatively about a company’s offerings. It has the ability to shape consumer perceptions and address problems in a unique way [17]. Think experiences respond to the intellect of the consumer, with the goal of creating cognitive, problem-solving experiences that grip consumers imaginatively [20]. Differentiation strategy encourages individuals to consider the product attributes that set their brands apart from competitors, with the goal of increasing perceived value [23]. In a recent study, customer’s individual cognitive experience is found affecting perceived value and significantly motivate their purchase intention [24]. Therefore, we hypothesise that H4: Think experience has a positive effect on perceived value. Act Experience. Act experience refers to a consumer’s experience with their lifestyle and social behaviour [17]. Act experiences are intended to have an impact on bodily and physical experiences, lifestyles, and interactions [20]. The concepts of experiential consumption and perceived value were also supported by [25]. It was revealed that regular mobile phone experiential consumption adds value. It was emphasised that mobile

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services and mobile marketing improve consumers’ daily experiences as a result of their use of phones. Thus, the following hypothesis is formed H5: Act experience has a positive effect on perceived value. Relate Experience. A relate experience is one in which consumers build rapport with one another via the purchase and consumption of specific products [17]. Relate experiences include experiences that are related to the individual, to himself or herself, to other people, institutions, or cultures in addition to the personal feelings from the aspects of sense, feel, think, and act [20]. Relate experience has a favourable impact on customers’ return on investment when the value or advantages acquired are more than the cost of investment, resulting in a higher perceived value [26]. Hence, we propose the following hypothesis (Fig. 1) H6: Relate experience has a positive effect on perceived value.

Sense Experience

H2

Feel Experience

H3

Think Experience

H4

Act Experience

Perceived Value

H1

H5

Brand Loyalty

H6 Relate Experience

Fig. 1. Research framework

3 Methodology The target respondents were students aged between 18 to 25 who were currently using any smartphones under Apple iPhone series or Samsung Galaxy Note or series. Purposive sampling is used to identify the target respondents. The minimum sample size for each of the smartphone brand is 92, by using medium effect size (f 2 ) of 0.15, the margin of error 0.5 or 5%, probability of accepting null hypothesis of 0.8, or 80% and 5 predictors. Online questionnaire via Google Form were distributed to the respondents via online platforms. The measurement items were adapted from past reliable sources: sense experience from [27] and [28]; feel experience from [27, 29] and [30]; think experience from [27] and [30]; act experience from [28, 29] and [30]; relate experience from [27, 30] and [31]; perceived value from [32]; and brand loyalty from [33] and [34]. The data was analysed using partial least square structural equation modelling (PLS-SEM) which enabled the establishment of a composite measurement model [35].

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4 Findings and Results Both Apple and Samsung smartphone users were selected in this study. Data were collected from 252 Apple smartphone users and 265 Samsung smartphone users. Based on both users, majority of the respondents were female (51.1 percent) while 48.6 percent were males. A total of 38.5 percent of the respondents were aged between 20 to 21 years, followed by 37 percent between 22 to 23 years, 20.6 percent aged 18 to 19 years and the minority (3.5 percent) between 24 to 25 years. Table 1. Composite reliability and average variance extracted Construct

Items

CR

AVE

Apple

CR

AVE

Samsung

Act experience (AE)

6

0.882

0.559

0.867

0.526

Brand loyalty (BL)

5

0.962

0.836

0.965

0.848

Feel experience (FE)

6

0.905

0.620

0.908

0.623

Perceived value (PV)

5

0.836

0.513

0.923

0.705

Relate experience (RE)

6

0.914

0.639

0.863

0.523

Sense experience (SE)

4

0.878

0.649

0.874

0.639

Think experience (TE)

4

0.860

0.605

0.866

0.618

Table 2. Indicator loadings Construct

Items Apple Samsung Construct

Items Apple Samsung

Act experience (AE)

AE1

0.640

0.648

FE1

0.549

0.739

AE2

0.854

0.824

FE2

0.656

0.768

AE3

0.779

0.769

FE3

0.852

0.841

AE4

0.832

0.814

FE4

0.858

0.784

AE5

0.587

0.534

FE5

0.892

0.759

AE6

0.755

0.720

FE6

0.856

0.839

BL1

0.905

0.922

PV1

0.780

0.858

BL2

0.900

0.920

PV2

0.623

0.823

BL3

0.896

0.893

PV3

0.822

0.869

BL4

0.934

0.943

PV4

0.808

0.861

BL5

0.936

0.925

PV5

0.489

Brand loyalty (BL)

Feel experience (FE)

Perceived value (PV)

0.784 (continued)

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Table 2. (continued) Construct

Items Apple Samsung Construct

Sense experience (SE)

SE1

0.876

0.873

SE2

0.543

SE3

0.886

SE4

Items Apple Samsung 0.785

0.833

0.588

Think experience TE1 (TE) TE2

0.783

0.830

0.846

TE3

0.727

0.737

0.866

0.856

TE4

0.815

0.739

Relate experience RE1 (RE) RE2

0.779

0.827

0.821

0.858

RE3

0.826

0.851

RE4

0.776

0.550

RE5

0.814

0.642

RE6

0.776

0.528

Assessment of measurement model was performed preliminary. In Table 1, the composite reliability (CR) for all constructs were greater than the threshold level of 0.7 and thus internal consistency was achieved for Apple and Samsung users. The values of average variance extracted (AVE) were more than 0.5 ensuring the convergent reliability for Apple and Samsung users. Table 2 showed the indicator loadings for all items. As the AVE in Table 2 indicated all constructs of Apple and Samsung users were greater than 0.5, the indicator loadings value that was greater than 0.4 were still acceptable [36]. Therefore, convergent reliability was achieved. Table 3. Heterotrait-monotrait ratio (HTMT) analysis for the Apple users AE

BL

FE

PV

RE

SE

TE

AE BL

0.617

FE

0.708

0.624

PV

0.633

0.609

0.544

RE

0.847

0.536

0.655

0.701

SE

0.738

0.596

0.862

0.583

0.619

TE

0.698

0.562

0.627

0.512

0.564

0.641

Tables 3 and 4 presented the results of Heterotrait-Monotrait Ratio (HTMT) analysis for Apple and Samsung users, respectively. As all of the constructs exhibited the threshold values less than 0.85, discriminant validity was confirmed for all constructs.

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Y.-M. Lim et al. Table 4. Heterotrait-monotrait ratio (HTMT) analysis for the Samsung users AE

BL

FE

PV

RE

SE

TE

AE BL

0.474

FE

0.731

0.591

PV

0.646

0.725

0.662

RE

0.697

0.441

0.606

0.527

SE

0.720

0.554

0.798

0.554

0.485

TE

0.608

0.394

0.572

0.468

0.428

0.629

Table 5. Structural model assessment Relation

Path coefficient (t-value) Apple

Samsung

PV → BL

0.530***(9.438)

0.677***(20.267)

SE → PV

0.208***(2.517)

0.099NS (1.372)

FE → PV

0.063NS (0.735)

0.265***(3.794)

TE → PV

0.024NS (0.390)

0.059NS (1.046)

AE → PV

0.114NS (1.532)

0.192***(2.624)

RE → PV

0.347***(4.451)

0.211***(3.354)

Table 5 presented the structural model assessment for Apple and Samsung users. The r-squared values that indicated the explanatory power of perceived value were 42.2 percent and 46.0 percent for Apple and Samsung users, respectively. On the other hand, PV explained 28.1 percent and 45.8 percent of the variations in BL for Apple and Samsung users. The Q2 analysis also indicated the predictive relevance of the structural models for Apple users (BL = 0.214; PV = 0.194) and Samsung users (BL = 0.383; PV = 0.312) as all of the values were greater than zero. Path coefficient analysis provided a better insight of the relationship analysis for Apple and Samsung users. For Apple users, PV (β = 0.530, t = 9.438) was significantly related to BL and H1 was supported. SE (β = 0.208, t = 2.517) and RE (β = 0.347, t = 4.451) were significant and positively related to PV. Hence, H2 and H5 were supported. FE (β = 0.063, t = 0.735), TE (β = 0.024, t = 0.390) and AE (β = 0.114, t = 1.532) were insignificantly related to PV. H3, H4 and H6 were not supported. For Samsung users, PV (β = 0.677, t = 20.267) was significant and positively related to BL and H1 was supported. FE (β = 0.265, t = 3.794), AE (β = 0.192, t = 2.624) and RE (β = 0.211, t = 3.354) were significant and positively related to PV. H3, H5 and H6 were supported. SE (β = 0.099, t = 1.372) and TE (β = 0.059, t = 1.046) were insignificant predictors for PV, indicating that H2 and H4 were not supported.

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5 Discussion and Implications A significant relationship was found between perceived value and brand loyalty among both Apple and Samsung users. This result was consistent with a similar study conducted by [15]. When customers’ satisfaction and their expectation of value are achieved, customers are more likely to repurchase or reuse the product. Inconsistent with the findings of [37], this study supports the importance of experience. Based on Apple’s results, the relationship between relate experience and perceived value was significant. This result was consistent with similar study of [25]. When the smartphone has high brand equity, consumers will have high perceived value. They will feel that they are being recognised in their social groups and can gain higher status when they are using branded goods. A significant relationship existed between sense experience and perceived value. The result is supported by the study of [15]. The senses resulted from touch sensation and visual content can directly affect customer’s perception on product value, which influences customer’s desire to purchase. The findings of Samsung showed that a significant relationship existed between feel experience and perceived value. This result was tallied with the finding of [15]. Feel experience may affect the overall evaluation of attitudes, which in turn affects perceived value [21]. Besides, the relationship between act experience and perceived value was also significant. This result is consistent with the finding of [25]. Behavioural actions such as using phones to coordinate everyday tasks create perceived value of the phones [15]. The relationship between relate experience and perceived value was significant. This result is consistent with the findings of [25] that high brand equity leads to high perceived value. Due to technology evolvement [38–40], past experience contributes to adoption [41]. Apple marketers could emphasise on stimulating consumers relate experience to build high perceived value to create brand loyalty. Apple marketers should ensure that their smartphones enable customers to connect with others through the usages. For instance, customers feel like they belong to the Apple group through activities, that they have a greater social status, and that they are recognised. Apple can also introduce applications that are only available in App Store (iOS). These applications might include the range of games, news, education and social apps. It may facilitate users to relate to their reference groups as users can exchange their information and idea besides building a social connection with the communities via the same iOS platform. Samsung users considered relate experience in increasing their perceived value. For Samsung to establish brand loyalty, it must produce smartphones that excite users’ feel and act experiences, which in turn serves to develop consumers’ high perceived value on the products. Participation in activities both online and offline can boost one’s sense of belonging [42]. Marketers should highlight on its communication and interaction functions as well as its ability to solve consumers’ problems. For university students, smartphone social applications such as Facebook, WhatsApp, WeChat and Instagram had become a must have in their smartphones. Marketers must ensure that their smartphones’ hardware is compatible with those social applications as well as possessing strong and fast Internet and Wi-Fi reception ability. Samsung marketers should develop a communication application that is unique and could greatly enhance interactions among users.

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This would in turn create a sustainable competitive advantage, enabling Samsung smartphones to outcompete others. Marketers can develop an application allowing users to video call or voice chat while at the same time playing interactive board games together with each other on their Samsung smartphones.

6 Conclusion and Recommendations This study used the five dimensions of customer experiences to provide a more comprehensive investigation on the customer experience [17] for Apple and Samsung users. The findings provided insights to the market players regarding the types of experience that were expected from the customers. Also, perceived value of both Apple users and Samsung users lead to brand loyalty. This study has explored five customer experience dimensions. Due to the Covid-19 pandemic, the digital experience is another area could be considered in the future. The future study may also contemplate to expand the context of the study to other countries by comparing customer experience across different countries, ethnicities and culture. Acknowledgement. The authors would like to thank the participating researchers, Chang Leong Kit, Chang Min Yee, Sin Meiyan and Yap Phay Yan, for their assistance in data collection.

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The Influence of FinTech on Financial Sector and Economic Growth: An Analysis of Recent Literature Mosharrof Hosen1(B) , Tat-Huei Cham2 , Hooi-Cheng Eaw1 , Vasanthan Subramaniam1 , and Hassanudin Mohd Thas Thaker1 1 Department of Accounting and Finance, UCSI University, Kuala Lumpur, Malaysia

[email protected], {eawhc,vasanthan, hassanudin}@ucsiuniversity.edu.my 2 UCSI Graduate Business School, UCSI University, Kuala Lumpur, Malaysia

Abstract. Even though financial technology (FinTech) has become one of the essential financial sector development strategies of many countries around the world in recent years, previous studies have analyzed the influence of financial growth from different aspects, but there are few works of literature devoted to the impact of FinTech on the financial sector and economic progress together. Fintech’s innovative banking and financial products are in high demand due to its uniqueness, cost efficiency, and user-friendly nature. This has caused disruption in traditional banking and finance sector of the economy. The question of whether the financial sector and the economy would not suffer from the incursions of FinTech, is not unreasonable. Using a review methodology, this paper finds that, Fintech has potential to influence the realization of increase in financial development and economic growth, only when appropriate regulation is put in place. Important policy implications and future directions are discussed accordingly. Keywords: FinTech · Financial growth · Economic growth · Bigtech · Banking services · Fintech regulation

1 Introduction Financial technology (FinTech) has brought innovation into the delivery of banking and financial services in recent times [1]. Its rapid growth is the reason for which academics as well as industries players, seek for inquiry into the modus operandi of the Fintech ecosystem [2]. There is increased demand for Fintech services based on attributes of easiness, speed, convenience, and low cost of delivery [3]. The development of the Fintech industry has resulted in the establishment of many Fintech start-ups, Bigtechs, and non-bank firms competing among themselves spaces in the financial industry market. The resultant disruption caused in the traditional financial sector, has forced banks to engage in competition with the Fintechs [4]. Disruptive activities aside, Fintechs are not or less regulated in many jurisdictions to the disadvantage of the traditional banking and institutions, whose operations are overly © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (Eds.): ICETIS 2022, LNNS 584, pp. 251–263, 2023. https://doi.org/10.1007/978-3-031-25274-7_21

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regulated. Unlike fintechs, the financial sector is supervised by the central bank and other international and local institutions. From the perspective of inequality in supervision between Fintechs and the traditional financial sector, the question arises whether Fintech has no negative consequences for the financial sector and the economy. The objective of this paper therefore, is to determine whether Fintech does influence the financial sector and the economy. Through a review of recent literature, this study pays attention to investigates the issues through three channels namely; Fintech lending, Fintech risk, and Fintech regulation. Our findings indicate that Fintech has the potential of causing an increase in financial and economic growth. However, an appropriate regulation that provides an even playing field for Fintechs and the financial sector is required, in order to achieve such benefits. Our contribution to literature is, to the best of our knowledge, being the first to examine how Fintech influences the finance sector and the economy in the above mentioned channels. The rest of this paper is organized as follows; Section 2 is a review of literature, Section 3 presents methodology, and Section 4 constitutes discussion and conclusion.

2 Literature Review This section undertakes a review of recent literature relevant to topic of this study. Thus, all articles under consideration cover the related areas of Fintech, Finance sector, and the economy. 2.1 The FinTech Ecosystem Without disregard to the numerous definitions provided by academics, scholars, industry players and relevant stakeholders, Fintech appears to be well understood by all. In simple terms, Fintech is understood to be a bond between technologies and finance that results in providing innovative financial services and products to various sectors and individuals in an economy. In fact, Fintech’s services transcend beyond national jurisdictions [5]. And can be accessed from any part of the globe. From the perspective of [6], Fintech consists of all high-tech set-up services that provide innovative business and digital platform models. [7] describes Fintech as consisting of a series of new business models, technology applications, and products and services that have a significant impact on finance and financial services. Thus for [7], everything output emanating from Fintech is new. Among the numerous advantages of Fintech, is its capability of ensuring the less privileged have access to financial and banking services. According to [3], Fintech is providing global citizens especially, the most disadvantaged, access to faster, cheaper, and more efficient tools to manage day-to-day transactions in many aspects including education, health, and business. As noted by [8], traditional financial institutions faced major challenges in providing service to the poor. On the other hand, the innovations brought about through Fintech, have caused disruptions to the traditional banking and finance industries. In this regard, [9] indicate that the novel communication channels of Fintechs have led to a stepping-aside of

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banks’ distribution networks, enabling Fintechs to provide financial services for which access to balance requirements is unnecessary. As a result of the disruptions caused, their fierce competition between Fintech start-ups, Fintechs and banks, and between the traditional banks [10]. [11] indicates that for a disruption to occur, the disruptive innovation must arise from an ecosystem. According to the authors, the Fintech ecosystem consists of Fintech firms, Bigtech, non-banks, technology firms, clients, and other relevant stakeholders. 2.2 FinTech and Financial Sector A review of how Fintech influences the financial sector is considered in this section. This implies covering articles featuring the relationships between Fintech on one hand, and banking and stock market on the other. In a study of how regulatory differences and technological advantages contribute to the growth of online “Fintech lenders”, [12] find that regulatory burden, capital requirements, mortgage servicing rights (MSR), mortgage-related lawsuits, and the movement of supervision to Office of Comptroller and Currency (OCC), all led to a contraction of traditional banks in markets where they faced more regulatory constraints. As a consequence, shadow banks partially filled the resultant gaps. The authors summed up their finding suggesting both increased regulatory burdens and technological improvements do contribute to the decline of traditional banks’ market share. [13] discussed conceptualization of approaches necessary to create Fintech products and services. The author observed that in the current era of new technologies, it is possible to achieve the provision of needed products and services that will be acceptable to society by simply providing services that respond to constant social change, and, to create business activities that continually transform production activities to generate new ways for society to function and for the emergence of new cultures. Additionally, [13] further suggests products and businesses must aim at achieving social creation through the use of technology. Likewise, [14] pointed out two important observations. Firstly, they observed loss of trust in central banks is an important trigger for the emergence of FinTech, and secondly, FinTech poses many regulatory challenges to the financial system. In relation to the stock market, [15] studied the impact of mergers and acquisitions of fintech firms on stock returns. They show the existence of significant positive average abnormal return after acquisition of fintech companies in the short-term. However, the average abnormal return is negative in the long-term. Extending credit or loans to clients is an important function of banks. Hence, researchers normally measure the ability of banks to perform this mandate and the associated risks, under various conditions. Thus [16] examined the impact of Fintech on the ability of banks to offer credit to SMEs. Fintech does impact banking sector’s credit supply to SMEs positively. [4] explore the relationship between bank FinTech and credit risk. They find that bank FinTech significantly reduces credit risk in Chinese commercial banks. Analysing further, the results indicate the negative effects of bank FinTech on credit risk are relatively weak among large banks, state-owned banks, and listed banks. In a related study, [17] examined determinants of Fintech loans’ default. Their findings indicate relevant contractual loan characteristics, borrower risk characteristics, and

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some relevant macroeconomic variables are important determinants of the probability of default of individual loans. Long-term loan applicants, specifically exhibit a higher likelihood of default. Other applicants with higher likelihood of default according to the findings include those with lower assigned credit scores, renters (not a homeowner) at the moment of application, those classified as elementary or machine operators and assemblers in the standard occupation classification, and those who use the loan proceeds to finance medical expenses or small businesses. On the contrary, applicants who seek loans to finance their weddings, home improvements, car purchases, and those classified as managers or professionals, exhibit a lower probability of default. Furthermore, [17] indicate the existence of a 20% default rate on loans issued to borrowers in P2P market in the period 2007–2018. In the interest of all stakeholders, understanding driving factors for customers’ willingness to embrace digital banking is necessary. [6] examined these factors in addition to studying customers which to Fintechs’ services. Their findings show that trust and comfort with new technologies, financial literacy, and overall transparency impact on household’s propensity to switch to a FinTech. They provide specifics to the results as; households with low-levels trust, good financial education, and preference for transparency are characterized by a higher probability of adopting FinTech. In contrast, household price perceptions do not appear to matter significantly on the impact switching probability. Beyond securing clients to patronize Fintechs, is the desire to obtain a developed financial sector. Unsurprisingly, [2] examined the relationship between Fintech and sustainable development of the banking and financial industries. They report that FinTech, has a significant positive impact on sustainable development of the Financial and Banking Industries in Indonesia. Disruptive capabilities and activities have closely been linked to Fintech. [11] present their knowledge on the emergence and impact of disruptive innovation ecosystems, with particular attention to the fintech ecosystem. They suggest that besides the need for disruptive innovation ecosystems, there is the need to study such phenomena further. [18] examined the implications of Fintech development for financial stability, market structure, and benefits and risks of technologically driven financial innovations. [18] findings show that despite its ability in expanding financial services, it poses additional risks to the financial system in terms of micro-financial and macro-financial risks. The question of whether Fintech impacts on the banking sector was the subject matter of a study by [19]. In their findings indicate that Fintech leads high cost productivity growth, which is higher in bank’ with a bigger share of mobile banking transactions. Also, [20] find Fintech development enhances total factor productivity of commercial banks in their study to examine relationship between the use of Fintech and commercial banks’ total factor productivity. [21] examined how Fintech affects access to the finance process. They report the existence of a positive relationship between Fintech and access to finance. However, banks’ traditional role as “first point of contact” is found to be weakening due to how Fintech is seem to be reducing barrier to entry into the banking industry [9]. This finding emanates from a study of the effect of technological change on financial intermediation by [9]. Financial inclusion plays a pivotal role in mobilizing deposits through banks. In this regard, [22] studied the impact of Fintech on financial inclusion of societies. Their

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results showing FinTech significantly improves the financial inclusion of society in EU countries. Similarly, [23] were interested in investigating how Fintech affects financial inclusion of MSMEs. Further, [3] undertook a study to examine the relationship between fintech development and demand for savings, borrowing, and remittances. They also indicate the existence of significant positive relationship between Fintech development and financial inclusion. They find a positive relationship between Fintech and financial inclusion of MSMEs. However, efficiency in banking operations cannot be sacrificed in the quest to achieve inclusiveness or Fintech’s adoption. Justifiably, [5] investigated how Fintech development impacts on cost efficiency of banks. The authors report that Fintech development influence cost efficiency of banks, and enhances their desire to adopt the use of technology. [24] studied the relationship between Fintech’s adoption and financial development in Malaysia. They conclude that the FinTech industry practitioners such as the banks can benefit by way of internalizing FinTech adoption and its effects towards Malaysia’s financial development. On the other hand, [25] studied the influence of fintech on financial developments in China’s financial sector across 290 cities and 31 provinces. Their findings establish a positive relationship between fintech and financial development. Finally, studies by [1] and [10] complete the list of the main articles whose reviews have been used for this study. [1] advanced a framework for guiding information systems’ research on fintech-led financial inclusion. They extrapolate five areas of research that can better illuminate fintech’s contributions to financial inclusion namely: business strategies for fintech-led financial inclusion, digital artifacts of fintech-led financial inclusion, business environment of fintech-led financial inclusion, micro-foundations of fintech for financial inclusion, and developmental impacts of fintech. On their part, [10] focused attention on studying FinTech and FinTech-enabled services, concentrating on the opportunities and risks for banks. The report from their findings indicates that it does not appear likely FinTech lenders will replace banks, as banks are developing their own FinTech platforms or working with FinTech start-ups. 2.3 FinTech and the Economy Fintech’s influence on the economy is reviewed in this section. The earliest among the articles under consideration as far as this study is concern, is the study by [26]. The main objective behind the study is to examine the effect of financial innovations on the economy of China, and to assess whether there are any possible associated risks. In the opinion of the authors, though the impact of Fintech’s products on the economy is uncertain, the benefits outweigh the risks. Giving cognizance to environmental, social and economic considerations, [27] were interested in examining the relationship between Fintech and sustainable development in 31 Provinces in China. They report in their study on the existence of a U-shaped relationship between FinTech and sustainable development, which depends mainly on the pattern of extensive economic growth. According to their findings Fintech restrains sustainable development until it exceeds a critical value after which, it promotes sustainable development.

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[28] investigates the relationship between Fintech and economic growth of Indonesia. The findings indicate that though FinTech startups are disruptive in the first year of their inception, they have no significant implications for Indonesia’s economic growth. In addition, the results indicate a significant positive impact of Fintech start-ups on growth in their second years of operations. One important measure of economic wellbeing is the poverty level of citizens. Consequently, [8] examined the relationship between Fintech and poverty levels in China. The authors provide results to show the existence of a negative relationship between Fintech and poverty. Further, the results indicate fintech complements economic growth and financial development, to reduce poverty in China. The review process has led to unearthing of certain issues related to Fintechs’ activities and operations. It is these activities that influence the financial sector and economy directly or otherwise. Important amongst the many such activities are Fintech lending, risk, and regulation. Thus, this study intends to undertake analysis of these 3 activities (Fintech lending, risks, and regulation) and how they help influence the financial sector and the economy. To the best of the authors’ knowledge, such analysis using recent articles is a gap in the literature.

3 Research Method Data for this study consist of articles from peer-review journals that undertake studies related to the subject matter of this paper. Therefore, a search was conducted on the databases of websites namely, Science Direct, Springer, Wiley, Emerald, and Google Scholar. Through the use of keywords including “Fintech”, “Financial sector” and “Economy”, many articles were downloaded. Articles that finally made it as part of the sample data, have at least two of the keywords or their derivatives. The final sample utilized mainly for the purposes of this review, consists of recent articles published during the period 2018–2022. Hence, the data sample of articles contains up to date information on the Fintech ecosystem. The review entailed reading through all the articles, taking note important aspects including objectives, findings, and conclusion, as well as noting the use of certain keywords. Of specific importance, is the Co-occurrence of certain keywords [7] signifying how vital they are in the body of knowledge. A few of the keywords that co-occur would be selected for analysis and discussions, in relation to their roles in determining how Fintech influences the financial sector and the economy. Three co-occurrence keywords would be discussed enabling a conclusion of this paper to be made.

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Fig. 1. Percentage used in the year. Note: percentage of articles

Fig. 2. Influence of Fintech through lending. Note: percentage of articles

Figure 1 presents the percentage of articles for each of the years used for this review. From the graph, more than 52% of the articles are from 2021 and 2022. Thus, this paper reflects current happening in the Fintech sector.

4 Discussion and Conclusion Fintech lending, Fintech risk, and Fintech regulation are keywords that have been mentioned by almost all the main articles employed for this review. This shows the importance of these words in the Fintech ecosystem. Therefore, this section undertakes to provide detailed analysis of the roles of these words in how Fintech tends to influence both the financial sector and the economy. In analyzing the issues, relevant theories and empirical studies would be relied upon where necessary.

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4.1 FinTech Lending Lending is an old-age activity that has been practiced among local traditional communities for several centuries. With the introduction of banks and financial institutions in modern and formalized societies and nations states, lending has become one of the many important functions [29, 30], and [31] of banks. As societies developed further, some financial non-banking institutions have been licensed by central banks to provide lending services for the formers’ clients. For several reasons, lending by banks and financial institutions has been placed under closed monitoring and supervision [32] by central banks in most, if not all jurisdictions. Some of the reasons include minimizing loan payment defaults, protecting customers’ deposits, controlling interest rate, undertaking monetary policy [33], and preventing financial crisis. Thus controlling lending is an important function of government or the central bank for that matter. However, the current situation with Fintech lending is that, activities are pursued at the blind side of the central bank. This phenomenon despite its numerous benefit might hurt the financial and the economy. Fintech has advantages of accessibility to many including the poor [34], unbanked, and rural dwellers. Fintech possesses the capacity to extend credit facilities to the aforementioned categories of people [35] and vulnerable businesses, who otherwise, would find it impossible to access credit from traditional banks and financial institutions. The rapid development of Fintech [36] has led to the springing up of innovative banking services originating not only from traditional banking system, but from fintech start-ups, Bigtechs, non-banks, and shadow banks. The culmination of lending activities from all fintechs (fintechs, Bigtechs, non-banks, and shadow banks) and traditional lenders (banks and financial institutions) increases “credit to the private sector”, which is an important measure of a countries’ level of financial development [37]. This phenomenon is shown in Fig. 2. In this regard, the Fintech ecosystem can be said to promote financial development through the generation of momentum in lending. With regards to the economy, there are potential benefits to be derived from Fintech lending [38]. Through access to credit made possible by the Fintech ecosystem, the poor and vulnerable, MSMEs, and rural dwellers are able to finance their respective projects. Private businesses and individuals then obtain the needed funds, to undertake investment and productive activities. Increase in investments [39] and production are important for ensuring enhancement in economic growth. Another benefit to the economy stems from the resultant increase in production. More resources including labour, would have to be deployed for the realization of increasing production. Employing more hands results in reduction in the unemployment rate which is favourable for all economies. Hence Fintech influences both the financial sector and the economy positively through the channel of Fintech lending. 4.2 FinTech Risk Many risks have been found to be associated with the Fintech ecosystem [40]. In the financial sector, the common risks worthy of noting include reputational, operational,

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and credit risks. These risks though, already threaten the financial sector, are aggravated through the advent of Fintech. The reputation of the whole financial sector is dented if any of its institutions (banks or financial) engage in misconduct. The fintech ecosystem has resulted in the mushrooming of pseud-banks, Fintech start-ups, and Bigtechs, all providing financial services [41, 42]. Most of these Fintech firms have little or no professional know-how for delivering financial services. Meanwhile, they are engaged in deposit taking, lending, and provision of investment services. Since these firms lack the required experience in the industry, the likelihood of slipping is high, resulting in clients losing their investments. However, the ordinary stakeholder is unable to decipher between Fintech firms and traditional financial institutions. Hence ills of Fintech affect the reputation of the whole financial system. Fintech has resulted in the establishment and growth of many Fintech firms. These firms together with Bigtechs compete among themselves in the provision of financial services. Traditional financial institutions have been forced into the competition [43] due to disruptive activities of Fintechs. In order to withstand the competition, traditional banks, for example, had to acquire the needed technology [25, 53] or Fintech startups. This implies incurring additional operational costs. Thus, the financial sector faces the risk of incurring additional costs to keep pace with advancements or changes in technological innovations. But there exists another form of operational risk exposed to traditional financial sector. The intense competition means that fees and charges in the financial sector would have to be adjusted in line with the new realities. To attract deposits, financial institutions would have to provide higher interest rates than Fintech is charging. The reverse suffices, if they wish to attract borrowers, where in this case, banks must lend at lower interest rates. These adjustments in rates constitute costs to banks. Thus, traditional financial institutions face the risks of making losses since Fintechs’ cost of operations is lower, and for which the latter are capable of further slashing downwards. The financial system faces credit risk as a consequence of the Fintech ecosystem. In their quest to attract borrowers, traditional financial institutions would have to abandon their stringent methods of screening potential borrowers, and adopt the Fintech way of doing things. This implies extending credits without requesting for collateral or security [45]. Hence the likelihood of falling into situations of moral hazards and adversely selection [46] would be aggravated. Hence increasing the risk of loan default. Therefore, activities of Fintechs expose the financial sector to high risks including reputational, operational, and credit risks. In the economic front, nations face mainly, the risk of contagion [47] as a result of Fintech. The economy is potentially exposed to two forms of Fintech contagions; internal and external contagions. Internally, the collapse of a domestic Fintech firm may affect several sectors, and threatens economic stability. On the other hand, external Fintech contagion arises from foreign country, affects and threatens domestic economic stability. Hence Fintech’ influences the economy exposing it to the risk of contagion and economic instability [48].

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4.3 FinTech Regulation Effective regulation of the financial sector ensures security and stability of the sector, and provide protection to depositors’ and stakeholders’ interest. While the Fintech sector is not or less regulated [49] and [50], the financial sector seems to be over regulated especially, since the subprime financial crisis of 2008 [51, 52]. Banks for example must adhere to statutory capital and mandatory requirements, and other prudential requirements for investments and lending practices. It is the belief of stakeholders, the imbalance in regulating the Fintech and financial industries, puts the latter at a disadvantage position. Thus, there is a call for equality of regulations for equal services provided by both sectors. Hence, with regard to regulation, the financial sector is under pressure to respect rules of operations from central banks and international organisations such as Bank for International Settlement (BIS). The traditional financial sector would be well placed to deal with competition occasioned by operations of the Fintech sector, through the relaxation of regulations placed on them. Or yet still, will perform better put under a new regulation to reflect the current environment of disruptive Fintech innovations. As a result of growth in Fintech development economies and the central bank is under intense pressure to regulate the Fintech industry. They must also put in place the required risk management measures to protect the citizens, businesses, the financial sector, and the economy against any crisis that may emanate from the Fintech ecosystem. From the above discussions, the following finding would suffice; 1. Fintech influences the financial sector and the economy through the channels of Fintech lending, risks, and regulation. 2. Through Fintech lending, increase financial development and economic growth can be achieved. 3. Fintech risks expose the financial sector to reputational, operational, and credit risks, whiles exposing the economy to the risk of contagion and financial crisis. 4. The influence of Fintech through regulation, is exerting pressure on the financial sector, economies, and central banks to work towards regulation that ensures a level playing field for Fintechs and the financial sector to operate evenly. The rapid growth of Fintech holds huge potential for the financial sector and the economy. It can be relied upon to engender financial development, reduce unemployment, increase investments and production for economic growth and stability. However, these benefits will be realized only if the steps are taken to mitigate risks emanating from Fintechs’ operations. Nation states, central banks, and international financial institutions should come together in formulating regulations to ensure smooth operations of Fintechs alongside traditional financial institutions. In addition to fintech regulation, policymakers must need to enhance cybersecurity so that fintech users could get rid of any kind of unexpected hacking activity.

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Social Media Technology: The Influences on Online Impulse Buying Behaviour Hanyang Zhang1

, Yet-Mee Lim1 , Choi-Meng Leong1(B) and Chuen-Khee Pek2

,

1 UCSI Graduate Business School, UCSI University, No. 1, Jalan Menara Gading, UCSI

Heights (Taman Connaught), 56000 Cheras, Kuala Lumpur, Malaysia {LimYM,leongcm}@ucsiuniversity.edu.my 2 Faculty of Business and Management, UCSI University, No. 1, Jalan Menara Gading, UCSI Heights (Taman Connaught), 56000 Cheras, Kuala Lumpur, Malaysia [email protected]

Abstract. This study investigates the impact of information exchange, information timeliness and the influence of review information on impulse buying through curiosity and attention focus. The Stimulus-Organism-Response (S-O-R) model was used for the investigation. A self-administered survey was employed to collect data from respondents online. 200 sets of questionnaires were used for the analysis by partial least square structural equation modelling (PLS-SEM). The findings revealed that information exchange and information timeliness possessed positive effects on both curiosity and attention focus. In addition, curiosity and attention focus also had a positive impact on impulse buying. This study contributes to the literature of impulse buying by incorporating the flow experience theory into the SO-R model. Industry players could formulate suitable strategies by understanding their customers’ impulse buying behaviour. Keywords: Information exchange · Information timeliness · Review information influence · Impulse buying · Curiosity · Attention focus

1 Introduction Recently, with the rapid development of the Internet, e-commerce that fulfills people’s needs has attracted a lot of users. In the same vein, social media has witnessed a speedy development and the use of social media has accelerated. By April 2021, the number of people who are using social media technology worldwide has reached 4.33 billion [1]. The increasing usage of social media is gradually exerting influence on people’s shopping tendencies as customers are consequently more actively using and perceiving the major social media networks. Social media covers a wider range of industries, including marketing and social environments that stimulate people’s propensity to buy, as social media is more informative and interactive [2]. This triggers consumers’ buying behaviour without preparing in advance, namely impulse purchases. Impulse purchase is significant in boosting sales and profits for retailers [3]. As the e-commerce and scommerce markets become increasingly saturated, online retailers contend with intense © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (Eds.): ICETIS 2022, LNNS 584, pp. 264–273, 2023. https://doi.org/10.1007/978-3-031-25274-7_22

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competition [4], especially in the emerging markets [5, 6]. Thus, it is important to study the influence of social media on impulse buying in the context of e-commerce [7, 8] to strengthen the sustainability of retailers. Previous studies of impulsive buying behaviour that incorporate social media have focused on user engagement [9]; social media co-creation [10]; and the impact of social media [11]. Recent studies of social media mainly investigate the importance of frequent use of social media technology and the future development of social media technology [12]. This study is also being conducted to investigate the use of social media to achieve customer management, or to promote the exchange of information between enterprises and customers [13]. However, there is a lack of research on the impact of the features of social media technology on consumer psychology. This study fills the research gap by studying the characteristics of social media and their impact on consumers’ impulsive consumption.

2 Literature Review and Hypotheses Development 2.1 S-O-R Model In the S-O-R model, Stimulus (S) refers to a large number of factors that can trigger or affect the organism’s state. Organism (O) denotes the states of perception, physiology, feeling and emotion. Response (R) represents an outcome and decision [14]. The S-O-R model is effective in explaining behavioural variations brought on by different marketing stimuli and cognitive components. Also, the model has been empirically investigated in numerous consumer behaviour contexts [15]. Therefore, this study examines the effect of information characteristics (stimulus) on impulse purchase (response) through flow experience (organism). Information Exchange. The exchange of information involves the search and supply of information and the sharing of important information or events. This kind of information exchange is mainly used to extract or provide information and it can strengthen the interaction between consumers and businesses [16]. The exchange of such information cannot completely direct affect consumers’ purchases, but it can influence consumer judgment. Due to the characteristics of information, people may have a series of reactions after receiving the information, which will lead to emotion change. A self-report or payment in exchange for information provides a snapshot of an individual’s curiosity at one point in time [17]. In addition, information quality influences trust beliefs in interorganizational data interchange [18] and subsequently impacts customer experience (flow), comprising perceived enjoyment, perceived control and attention focus [19]. Therefore, this study proposes that the information exchange will have a positive impact on the two factors in flow experience. These are described as two hypotheses as follows: H1 Information Exchange has a positive impact on Curiosity H2 Information Exchange has a positive impact on Attention Focus Information Timeliness. The timeliness of information is described as the latest information about a product or service, which mainly explains the current level of the product

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or service [20]. For instance, real-time information changes can affect customers [21]. Timeliness is one of the characteristics of information transparency [22] which has had an impact on curiosity [23]. Moreover, content is an individual’s opinion of a content provider’s reliability, timeliness, sufficiency, and relevance of information [24] and it is related to cognitive concentration [25]. Based on the above judgment, this study suggests the following further hypotheses: H3 Information Timeliness has a positive impact on Curiosity H4 Information Timeliness has a positive impact on Attention Focus Review Information Influence. Consumers’ comments on products (reviews) can serve as a reference for other consumers to evaluate whether such products are needed or how they treat the service [26]. Social media marketing activities include entertainment, interaction, trendiness, customization, and word-of-mouth [27]. A recent trend indicates that customers search online and buy offline [28]. Most studies also show that a large number of positive evaluations have a positive impact on the purchase of goods [29]. Especially for the current generation of consumers, online evaluations are possibly the most pertinent source of information in today’s retail environment [30]. Five dimensions of flow are affected by telepresence, namely enjoyment, concentration, challenge, control, and curiosity [31]. Based on the discussion, the following hypotheses are developed: H5 Review Information Influence has a positive impact on Curiosity H6 Review Information Influence has a positive impact on Attention Focus Impulse Purchase. Impulse buying is often accompanied by abnormal emotional states, and the time between the generation of the purchase idea and the end of the purchase behaviour is often very minimal. Impulse buying has three characteristics: firstly, the idea in the process of purchase is infinitely close to that of wanting to buy the item; secondly, buying without thinking about the outcome; thirdly, buying for emotional fulfillment [32]. Flow Experience. This is the psychological feeling when an individual is fully engaged in an activity [33]. This theory contends that the relationship between skills and challenge, which occurs online, is what causes people to experience flow the most [34]. The goal of flow theory is to understand why people behave in certain ways for pleasure and enjoyment without anticipating a reward [34]. In this study, the flow experience is expressed by curiosity and attention focus. Curiosity is referred to as the awareness of, commitment to, and enthusiasm for investigating unexpected, uncertain, complex, and ambiguous occurrences [35]. The literature on curiosity also covers many different fields such as psychology [36], and economics [37]. One of the impacts of curiosity is to enhance customer behaviours, for instance purchases [38]. Curiosity also acts as an internal stimulus of impulse purchase [39]. Concentration means that when consumers are attracted by physical objects, their attention is focused on the stimulation in a limited field, filtering out irrelevant ideas and

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opinions [40]. Attention focus or concentration is an important feature of flow experience [41]. In the flow state, people will focus their attention on the stimulation that they are receiving, while ignoring the thoughts and feelings that are not related to it. People lose their self-consciousness in the flow, and all consciousness is immersed in the stimulation that they receive. Some studies have investigated the influence of flow experience on impulse consumption [42]. Therefore, it is proposed that: H7 Curiosity has a positive impact on Impulse Buying H8 Attention Focus has a positive impact on Impulse Buying Based on this discussion, all the variables are incorporated into the S-O-R model. Therefore, the research model for this study is depicted in Fig. 1.

Information Exchange

H1 H2 H3

Information Timeliness Review Information Influence

Curiosity

H4

H5 H6

H7 H8

Impulse Buying

Attention Focus

Fig. 1. Research framework

3 Methodology Purposive sampling was used to select the respondents, who were users of the major e-commerce site Shopee in Malaysia [43]. By applying G*Power with 0.15 effect size, power of 0.80 and 6 predictors, the minimum sample size was 98. The data collected for this study was 200 and therefore exceed the minimum sample size. A self-administrated questionnaire survey was used to collect the data online. The measurement items used in the study are adapted from reliable sources in the literature: information timeliness from [44]; review information influence from [45]; information exchange from [46]; attention focus from [47]; curiosity from [48]; and impulse purchase from [49]. Respondents rated their perceptions using a six-point Likert Scale where 1 = completely disagree and 6 = completely agree. Partial least square structural equation modeling (PLS-SEM) was used to analyze the data collected, which led to the formation of a composite measurement model [50].

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4 Findings and Results Based on 200 respondents, 41.5 percent of the respondents were male while 58.5 percent of the respondents were female. Majority of the respondents (63 percent) were aged between 21 and 25 years, and 18.5 percent were aged between 26 and 30 years, 10.5 percent were aged 20 and below, followed by 4 percent aged 31 to 35 years and lastly 2 per cent were aged between 36 and 40 years old and 41 years and above. 53 percent of respondents visited Shopee at least 1 to 2 times; 38.5 percent visited Shopee 3 to 4 times, 8 percent visited Shopee 5 to 6 times and 0.5 percent visited Shopee more than 7 times in a month. Table 1. Loadings, composite reliability and average variance extracted Construct

Item

Loadings

Composite reliability

Average variance extracted (AVE)

Attention focus (ATTFO)

ATTFO1

0.893

0.911

0.773

ATTFO2

0.885 0.912

0.774

0.919

0.792

0.917

0.788

0.947

0.78

0.927

0.761

Curiousity (CURIOUS)

ATTFO3

0.860

CURIOUS1

0.891

CURIOUS2

0.874

CURIOUS3

0.874

Information exchange INFOEXC1 (INFOEXC) INFOEXC2

0.877

INFOEXC3

0.879

Information timeliness (INFOTIME)

INFOTIME1

0.857

INFOTIME2

0.905

INFOTIME3

0.899

Impulse buying (IB)

IP1

0.860

IP2

0.863

IP3

0.929

IP4

0.883

Review information influence (REVIEW)

0.913

IP5

0.878

REVIEW1

0.866

REVIEW2

0.860

REVIEW3

0.905

REVIEW4

0.858

Measurement model assessment was performed to check the reliability and validity of the data. Table 1 showed that all the loadings exceed 0.708 and thus confirmed indicator reliability. Convergent reliability was confirmed as all the average variance

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extracted values were greater than 0.5. From Table 2, the Heterotrait-Monotrait ratio of correlation (HTMT) was conducted to verify discriminant validity, in which all the values were not greater than the threshold value of 0.85 [51]. Table 2. Heterotrait-monotrait ratio of correlation (HTMT) Construct

ATTFO

CURIOUS

IB

INFOEXC

INFOTIME

REVIEW

ATTFO CURIOUS

0.736

IB

0.763

0.862

INFOEXC

0.678

0.744

0.701

INFOTIME

0.598

0.645

0.676

0.619

REVIEW

0.330

0.440

0.499

0.555

0.554

All the VIF values were less than 5.0 [52], therefore indicating no collinearity problem. Table 3 presented the results of path coefficient analysis. INFOEXC was found positively related to CURIOUS (β = 0.481, p < 0.001) and ATTFO (β = 0.463, p < 0.001). Hence, H1 and H2 were supported. INFTIME was found positively related to CURIOUS (β = 0.292, p < 0.001) and ATTFO (β = 0.309, p < 0.001). Hence, H3 and H4 were supported. The relationships between REVIEW and CURIOUS (β = 0.007, p > 0.05) as well as between REVIEW and ATTFO (β = -0.089, p > 0.05) were found insignificant. Therefore, H5 and H6 were not supported. CURIOUS (β = 0.565, p < 0.001) and ATTFO (β = 0.325, p < 0.001) was found positively related to IB. Table 3. Path coefficients Hypothesis Relationship

Coefficient Standard T P f2 deviation statistics values

Supported

H1

INFOEXC → CURIOUS

0.481

0.073

6.573

0.000

H2

INFOEXC → ATTFO

0.463

0.073

6.366

0.000

Yes

H3

INFOTIME → CURIOUS

0.292

0.074

3.942

0.000

0.104 Yes

H4

INFOTIME → ATTFO

0.309

0.079

3.931

0.000

Yes

H5

REVIEW → CURIOUS

0.000 No

H6

REVIEW → ATTFO

H7 H8

0.282 Yes

0.007

0.068

0.108

0.457

-0.089

0.086

1.026

0.152

No

CURIOUS → IB

0.565

0.081

6.994

0.000

0.560 Yes

ATTFO → IB

0.325

0.087

3.747

0.000

0.186 Yes

The r-squared value indicated that all the independent variables explained 40.4% of the variation of ATTFO while 47.4% of the variation of CURIOUS. Both ATTFO and CURIOUS explained 65.6% of the variation of IB. Q2 values (ATTFO = 0.301,

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CURIOUS = 0.345 and IB = 0.487) that were more than zero also indicated that the predictors possessed predictive relevance for the dependent variables.

5 Discussion and Implications Information exchange and information timeliness has a positive impact on curiosity. The result is consistent with [17] and [23], respectively. Social media technology is the most popular and used technology in the world, and its rich and diversified information will naturally stimulate people’s curiosity. Also, the frequency of commodity change in social shopping will gradually change people’s view on commodities over time. Information exchange and information timeliness have a positive impact on attention focus. Information exchange enables people to read and receive information more carefully and attentively [53] as well as pay attention to timely information when browsing the information via social media technology [25]. Conversely, review information influence has no impact on curiosity. The result is inconsistent with the study by [31]. To stimulate curiosity, stimulation from the out-side world is usually different from an individual’s own cognition. If the comment could not achieve such effects, review information influence has no impact on curiosity. Also, review information influence has no impact on attention focus. This is consistent with the findings of [54] who found that certain reasons in the environment could lead to less attention from people, although the activities are attractive. The reasons include sleepiness, tiredness, a lack of interest and other reasons. Curiosity has a positive impact on impulse buying. The finding is supported by the study by [23]. The emotion generated by curiosity and a stronger operational tendency coincide with the conditions needed to stimulate the emotion of impulse buying. Attention focus has a positive impact on impulse buying in which [55] also contend that with the concentration of attention, people will gradually show a sense of immersion. As impulse consumption behaviour requires emotional stimulation, high excitement will stimulate impulse consumption behaviour to a certain extent. Theoretically, this study combines the flow experience theory with social media and impulse buying, filling the research gap between social media and impulse consumption. Moreover, although many studies have also involved the S-O-R frame-work [56], few studies have added psychological theories such as flow experience into the framework, which has enriched the practicality of the S-O-R framework. Practically, this study has implications for merchants, who could utilize social media platforms to enhance information exchange or users’ interaction that could trigger curiosity and attention. Alternately, merchants could also shift the focus to provide up-to-date information of their products or services via social media platforms to better stimulate and change consumers’ emotions.

6 Conclusion, Limitations and Recommendations Impulse buying, as a relatively new topic, has emerged in recent years. This study aimed to investigate the influence of the social media on impulse consumption. This study also revealed that information timeliness and information exchange have an impact

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on impulse buying through changing emotions. As the change in emotion could be an important trigger for impulse buying, future researchers could include emotion as a moderator in the investigation of the relationship analysis. Furthermore, sales promotion could be an important stimulus for impulse buying. Therefore, to include how customers perceived the value of such offer that could lead to the impulse purchase, will be worth investigating. Future researchers could consider collecting data in a wider range of regions by comparing Malaysia and other countries and expanding the study to other online shopping platforms.

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Automated Project Progress Monitoring in Construction Projects: A Review of Current Applications and Trends Yaser Gamil1,2 , Hamed Alhajlah3 , and Mukhtar A. Kassem4(B) 1 Building Materials, Department of Civil, Environmental and Natural Resources Engineering,

Luleå University of Technology, 97187 Luleå, Sweden 2 Faculty of Civil and Environmental Engineering, University Tun Hussein Onn Malaysia,

86400 Johor, Malaysia 3 Civil Engineering Department, Faculty of Engineering, University of Birmingham, Edgbaston,

Birmingham B15 2TT, West Midlands, UK 4 Department of Quantity Surveying, Faculty of Built Environment & Surveying, Universiti

Teknologi Malaysia, 81310 Skudai, Malaysia [email protected]

Abstract. The recent advancement in technology and equipment offered many opportunities and possibilities to simplify and speed up the construction processes. One of the most relevant and important to utilize technology is in the project progress monitoring which is needed for project stakeholders to know where they stand in the project. Using an automated processes will offer many potentials for fast decision making. This article aims to offer a mini review on the current applications and developments in automated progress monitoring. It discusses the concept of Project Progress Monitoring (PPM), challenges of traditional monitoring and the progress forward automated monitoring and methodology. An explorative review method has been adopted to analyses the literature. The study found that automating the progress monitoring has helped to improve decisionmaking in terms of accuracy and cost-effectiveness. Not only that but also offered a platform for timely monitoring of the project progress which then contributes to reducing time overruns and late decision making. Keywords: Project progress · Project monitoring · Construction projects

1 Introduction Project progress monitoring is defined as a process of keeping tracking of all the projectrelated activities and it involves all the project participants and counterparts [1]. It is fundamental process in the project lifecycle to maintain track of all activities and components of the project such as budget, time and technical progress [2]. Timely and accurate project progress monitoring is an important process to the success of the construction project in a holistic view and an accurate monitoring helps to keep persons in © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (Eds.): ICETIS 2022, LNNS 584, pp. 274–293, 2023. https://doi.org/10.1007/978-3-031-25274-7_23

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charge alert of all progress and identify all problems encountered [3]. Progress monitoring helps detects any deviation of pre-planned scheduling and as-built construction drawings since it allows more time for project parties to take action, identify best solution and maintain the progress to run smoothly till the completion of the project [4]. This process is rather complex if the conventional methods are used because the project involves high volume of information and data [5]. Using as-built data is essential to have more effective monitoring plans and that requires the adoption of advanced technology used in the built environment [3]. Building Information Modelling (BIM) is a modern technology adopted in the construction industry [6]. It is an integrated and complete tool and process to administer construction projects in both aspects management and technical [7]. It brings all construction stakeholders into one effective platform [6–9]. That makes it efficient for easy project information exchange and dissemination. However, this technology is still used limitedly to create geometric modelling which in fact aims for more than just a tool of design and it is a complete process. Therefore, this technology can be utilized beyond geometric design [6, 8]. It can be used to develop a platform for accurate monitoring progress measurements of the infrastructure project. Data extracted from BIM gives more accurate project progress measurements because it gives a realistic understanding of the progressive nature and helps in taking actions toward accuracy of any associate project declination [4]. Another good attribute of BIM is being agile to accept any technology to embed in the process for easy facilitating the project progress monitoring.

2 Questions to Highlight Discussions and critics have been ongoing to study the possibility of providing project progress monitoring using conventional methods. However, these methods lack of accuracy and speed rapidity and that doesn’t solve the issue of time to get updates on the project work.. BIM as a newly established approach of project implementation has been used as just a tool of designing 3D models and it can be used beyond that. In fact, there is no specific study which concentrated on the possibility of expanding the application of modern technologies such as BIM in providing more accurate project progress monitoring. Three questions are highlighted from the problem statements which can be listed as follow: 1. How possible to use BIM beyond geometric design for accurate project progress monitoring. 2. Can project visual data can be used for frequent project progress monitoring? 3. How semantic-rich BIM models can be created for infrastructure projects progress monitoring? 4. How unmanned Aerial Vehicles (UAVs) and other virtual reality technologies can optimize the visual monitoring of construction and operation of assets?

3 Research Aim and Objectives Based on the research questions stated previously, the main aim addressed by this study is to study the possibility expanding the application of modern technology (i.e. BIM) from

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limited use in geometric design into accurate project progress monitoring. To accomplish this aim, this study outlines the following objectives: 1. To review the existing adopted methods of project progress monitoring. 2. To identify the weaknesses of current methods in project progress monitoring. 3. To streamline the usage of both conventional methods and BIM whilst discovering valuable practices for performance improvement in infrastructure projects project progress monitoring. 4. To identify the potential uses of BIM in producing more accurate project progress monitoring in infra projects. 5. To study the likelihood of using visual data produced from BIM and construction project sites in frequent project progress monitoring. 6. To propose guidelines to be used to standardize the accuracy of project progress monitoring in infrastructure projects.

4 Research Methodology In this study, an explorative review method has been adopted to analyses the literature in the topic of project progress monitoring in construction projects. Each article was analysed for the main findings presented in Table 2. Figure 1 shows the steps followed to approach this topic.

Literature search • Concepts • Methods • Screening and filtering related arƟcles

Tabulated sumaries

Data analysis

• Each arƟcle was thoroughly analysed to extract the important findings

• The outcome from the arƟcles was analysed to draw the conclusions.

Fig. 1. Methods of the research

Figure 1 shows the steps adopted to run this research. The first step is defining the research strategy by defining the keywords and terms then the articles related to PPM was thoroughly analysed to draw the final conclusions.

5 Necessity of Automated Progresses Monitoring Traditionally, project executives devote more time collecting data and reading progress reports resulted from regulated interface meetings to monitor the progress of construction projects. Detection of any issue requires more time to investigate and take correction measures, therefore, actions to consider is being late consecutively results negatively toward the refurbishment of any deviation of the project [4]. Progress monitoring is required to ensure that the deadlines are met on time, the cost as estimated and the quality as recommended [10]. Accurate progress monitoring in infrastructure is a major challenge to perform due to the complexity of projects and the

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high volume of project information [11, 12]. Conventional methods in project progress monitoring are still problematic and inefficient to produce accurate tracking of project activities [13]. Real time monitoring requires more advanced technology to detect any discrepancy of project activities [4]. Visual data is one of the advanced features produced by BIM and other associated tools [14]. These data require much analysis to produce accurate monitoring of project progress and it is time consuming and susceptible to inaccuracies. The current modern technologies are utilized to create geometric designs in different dimensions. For example, BIM is commonly used to create 3D model which allows architecture, engineering and construction professionals to work together under one platform and easily monitor the project [7, 8, 14–16]. Studies have introduced methods of using BIM exclusively to manage and plan the whole construction projects [15]. However, there is possibility to integrate the use of BIM and other tools to provide more accurate progress monitoring. The combination of visual models produced from BIM and construction project sites to produce accurate project progress monitoring is a research opportunity toward the improvement of accuracy [16]. The emergence of new technology becomes necessary to achieve high scale of accuracy in progress monitoring and measurements [1]. This requires the integration of BIM models with Unmanned Aerial Vehicles (UAVs) to optimize the monitoring of construction and operation of any assets assigned [11, 12]. Despite the studies mentioned before, there is still lack of studies concentrating the utilization of modern technology to enhance the accuracy of progress monitoring in infrastructure projects and moving beyond geometric modelling.

6 Project Progress Monitoring in Construction Projects As mentioned before, PPM in construction is meant by keeping track of the project activities from the idea to the planning and implementation of the construction project [17, 18]. It is initially a periodic process which comprises of collecting actual progress data from construction site and relate them with planned-progress data to assess the overall project progress over a specific cut-off schedule [18, 19]. This process is an important to the project managements [20] and it is described as a key element to the success of the construction project [4, 21], therefore, an early project monitoring is required to detect any discrepancy or deviation to acquire sufficient time for project participants to take correction measures in case of deviations [4, 15]. In addition to that, keeping every member in the team alert of the project progress and challenges accurately is essential to the accomplishment of the project [22, 23]. Every member has to know how the project is progressing, budget is still in the scope, safety measures are practiced, construction methods are followed [1, 22]. A successful PPM helps to perform any remedial actions and helps controlling the project activities and this is achieved by producing well-depicted baseline and effective measurement of activities deviations [18, 20]. As a matter of necessity, management of the project needs on-time progress data and frequent notices of the status of the project to make decisions on corrective actions if necessary and to control the project time and cost and avoid any failure of the project [24]. The fall behind the predefined schedule and having deviation between as-planned and as-built are undesirable issues in construction project and leads to unexpected cost

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and time overruns [16, 25]. Therefore, contractors and subcontractors are required to periodically generate report of the real progress on-site to owners and consultant to ensure effective monitoring of the progress and to detect any negative deviation of the as-planned model [26, 27]. Conventionally, several methods adopted to monitor the project and introduce decision makings in term of cost, time and construction process [28]. Cost is initially monitored based on the demand and supply and then compared to the estimated cost and bill of quantities before the commencement of project however time is monitored based on the project planned schedules and construction process is monitored by visual inspections and the regular meetings and weekly progress report [29, 30]. However, these methods are subjected to imprecision and inconsistency due to the variation of reporting skills and data acquisitions method which are required to monitor and control the projects and in consequence require more effort, time and result in less accuracy [19, 20]. These challenges have motivated researchers to bring about new methods to improve accuracy and reduce effort and time for project progress monitoring [22]. One of the common methods is the implementation of automated project progress monitoring tools.

7 Issues and Challenges of Traditional Monitoring Methods The traditional methods adopted for project progress monitoring are associated with discrepancies and malfunctions due to the complex and fragmented nature of construction project [4]. The methods imply the expression of progress in percentages of completion and not precisely articulating actual measurement progress [18]. The method is manually carried out and requires more time and effort in which site engineers need to collect actual data from site then send them to office to write up a periodic progress report and this is associated with wrong assumption of percentage accomplished in comparison with planned project and the time taken to produce the report procrastinate the application of correction measures by managers and other involving parties if deviation occurs at any cut-off schedule [4, 9]. In fact, the later and inefficient PPM leads to miscommunication and that would lead to conflict and undesirable outcome [41]. In most cases, the progress measurement is used to determine the schedule status and the sources depends on the progress measurement on site which is not always measured precisely and that causes wrong assumption of reporting the status of the project at any cut-off time. This will lead to dispute and misunderstanding between project parties [24]. In the other hand, these techniques are time consuming, prone to error and require manual data acquisitions [17, 28, 31]. Therefore this issue leads to the growing demand for an accurate and efficient method to monitor the progress of construction projects [32]. To reduce the effort and arbitrary estimation associated with manual process, automated methods are evolving to provide more efficient project progress monitoring.

8 Trends And Applications of Automated Project Progress Monitoring The evolution of technological computer aided software has made the process easier to visualize the real planned project and as built models before the inception of construction execution. The advancements of 3D CAD as a simulation tool have opened new

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opportunities to add more dimensions to facilitate the process such noble technology added to 3D which include 4D, 5D, 6D to simulate schedule, cost and project life cycle respectively and technically adding more dimensions to CAD extends the use of CAD tools from design phase to construction phase [33]. But, these methods are still limited to certain project types and costly in term of technology and skilled personnel [18]. The advancement of technological tools created opportunities for researchers to explore methods to automate the project progress monitoring. The general method of automation implied currently is to extract as-built and as-planned data and compare these data to track the progress and define the hitches associated within project activities. Of these methods, laser scanner, Radio Frequency Identification (RFID), surveillance system, barcode, visual image, augmented reality, embedded sensors, and Global Positioning System (GPS) [1, 9, 20, 34, 35]. In other approach, De Vries and Harnik [29] developed a new algorithm to automate planning using 3D model integrated with planning as the fourth dimension and this method helps to monitor generated planning during design and real planning in real time basis. But, these methods are not always compatible to all types of construction projects and are described to be time consuming, not fully automated, complex and require skilled individuals [4, 20]. These techniques mostly depend on retrieving data from digital images which is an intricate challenge [32] whereby in some cases the environmental uncontrolled interruption make it difficult to capture the scene in addition to that the images are incomplete and noisy or pale to visualize because it is taken from project site [22]. Another newly implemented technique is the use of Unmanned Aerial Vehicles (UAVs) which is equipped with cameras to acquire real data to monitor and visualize real construction progress nevertheless, there is still lack of research in this method which requires more attentions because it is considered inexpensive, safe and easy to operate in real construction site [36]. Table 1 summarizes the common methods of data acquisition tools from construction sites and their functions. Table 1. Common tools for as-built data acquisition [24, 25] Data Acquisition Tools from construction site

Geospatial (location)

1. RFID (used to calculate actual working hours) 2. Barcode (component location tracking) 3. Ultra-Wide Band (UWB) (used for material tracking and activity-based progress tracking 4. GIS &GPS: used for determining location of components

Digital image

1. Photogrammetry: used to depicts site representation and helps to get measurements from images (continued)

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Y. Gamil et al. Table 1. (continued) 2. 3D laser scanning: used to calculate quantity of work at site 3. Videogrammetry 4. Unmanned Aerials vehicles (UAVs) Augmented reality: used to overlay a computer-generated image (BIM) on a real site image

Despite the advancement of technology and research development, there are still many drawbacks and limitations of the current methods introduced by researchers and lack of awareness in regards to the impact of those technologies on the overall quality and performance of the construction projects [37]. Table 2 illustrates a chronological review on the current automated monitoring methods, their applications, and limitations. As can be concluded from the chronological review in Table 2, there have been a noticeable effort by researchers and developers to implicitly utilize the BIM approach beyond the geometric design and embed new advanced tools and methods to automate project progress monitoring. In fact, there are still more to go on this development and principally apply these developments to realize its effectiveness toward the automation of progress by taking into account how flexible and agile can be BIM to accept new embedded systems, this opens more opportunities for researchers and project practitioners to advance the project monitoring and that would lead to more successful project communication and collaborations between the stakeholders and reduce any negative outcomes.

The data was extracted using distributed data collection and centralized management, On-site control station was set up to collect information. The barcode system for the component is combined with wireless radio transmit to send data to control station automatically

Integrating barcode and GIS for monitoring construction progress

An Automatic Project Progress Monitoring Model By Integrating Auto Cad And Digital Photos

[34]

[1]

The data was extracted on-site by capturing real progress image and compare with 3D CAD then compare with schedule plan to determine the progress percentage

Data acquisition method

Author (s)/year Research theme

Building construction (tested to monitor car park project)

Precast building construction

Targeted project type

Digitalizing Construction Monitoring, photo modeler, 3D CAD

1. Wireless barcode transmit system 2. Onsite control center 3. Automated data collection using barcode 4. Video monitoring

System components

The method is comparing what has been completed on-site with 3D CAD and match with project schedule

Developing a system called ArcSched to monitor erection process in real time basis, ArchSched consists of GIS with data base management, The process is carried out by using graphics and colors to monitor the erection process. The initial method was using barcode system integrated with radio frequency transmit and then sent data to control center

How to monitor (methods)

This project introduce the method of semi automating the progress measurement

It is an automated monitoring process which is performed by scheduling the components of precast in the erection process. The process helps to monitor a real time lifting which consequently reduce conflicts and project delay by identifying the differences between the planned schedules and construction onsite

Significance of the findings

Table 2. Chronological analysis of developments in the field of automation in construction

(continued)

The method used only if the digital images are clear, and it can only be used in projects which have few number of activities besides it requires more effort and workers

The study targeted only precast building Also, the developed system can work only if the structure components has barcode, another issue this might not work with cast in-situ activities

Research limitations/gap

Automated Project Progress Monitoring in Construction Projects 281

The data are Superstructure extracted by linking graphic with schedule to determine progress at any specific time

4D dynamic construction management and visualization software: 1. Development

Visualization Of The data was Building projects Construction captured using real Progress Monitoring images and compared visually with visualization techniques

[38]

[28]

Targeted project type

Data acquisition method

Author (s)/year Research theme

Metaphor, augmented reality and colors and color gradients

Four-dimensional site management model (4DSMM), graphics for construction and site utilization (GCPSU)

System components

Table 2. (continued)

The monitoring process differs depends on the technique used such as color gradients shows the progress and comparison between real site images with preplanned drawings

The developed model is based on the schedule and building graphic using 3D, the system helps to visualize the progress at any given time

How to monitor (methods)

This project highlighted the method of monitoring using visualization techniques

The system helps to automate the progress measurement using planned schedule and 3D model

Significance of the findings

(continued)

The method is not accurate to measure the progress

The issue of the system is that it is slow and requires large amount of database in order to run the process

Research limitations/gap

282 Y. Gamil et al.

The use of real time augmented reality to capture work onsite then compare with as-planned CAD

Application of Visualization Techniques for Construction Progress Monitoring

Research in automated measurement of project performance indicators

[39]

[31]

Building projects

Targeted project type

Automated project Varies performance control is developed using automated data collection technology

Data acquisition method

Author (s)/year Research theme

Automated data collection (ADC) technologies Automated Project Performance Control, project performance indicators PPI

Fixed cameras onsite, material detection device, Communication media between site and office

System components

Table 2. (continued)

The monitoring process is carried out using automated data collection which consists of automated labor and equipment control, materials management, and control as well as monitoring safety measures

The camera capture images and then the detector detect the color code and material content for all construction elements and removing occlusion through clustering from photos. This helps to monitor the progress onsite and compare with as-planned drawings

How to monitor (methods)

The method helps to monitor the performance of the project from the perspective of labor, equipment’s, material and safety measures. The method help to automate data collection and its processing

The outcome of this research helps to monitor the construction process by comparing planned with constructed elements accordingly

Significance of the findings

(continued)

The main drawback is that the developed algorithm doesn’t work for underground work and other long-distance projects

The study depends on the material detection which is based on color and texture detection which make it complex to differentiate between building elements. The drawback of this method is that, the monitoring is semi-automated and requires more personnel involvements

Research limitations/gap

Automated Project Progress Monitoring in Construction Projects 283

Daily imagery logs (traffic light metaphor), 3D as planned models then 4D augmented reality model

System components

Superstructure of Integrated Building buildings consists of Information System (computer vision, WBS, scheduling and budgeting, progress measurement valuation and early alert)

Automating progress measurement of construction projects

[22]

The data extracted from captured images on site then using computer vision to coordinate progress using estimated schedule and cost

(Site images + 3D = Building model-4D augmented construction projects reality) The basic method underlay in the 4 steps which are collection, analysis, communication and reporting

D4AR—A 4-Dimensional Augmented Reality Model For Automating Construction Progress Monitoring Data Collection, Processing And Communication

[20]

Targeted project type

Data acquisition method

Author (s)/year Research theme

Table 2. (continued)

Computer vision technology is used to determine the progress of construction from digital images captured on site then relate them to schedule and cost information. The system helps to calculate the interim payment and detect delay using construction progress data

Collecting daily site imagery log as well as 3D models then these data are transformed into 4D augmented reality model to monitor as-planned and as-built activities

How to monitor (methods)

The system is used to coordinate the progress in a semi-auto technique. It also helps to calculate the interim payments and detect delay and finally produce an early alert to project stakeholders

4D augmented reality model is used to monitor as-built and as-planned activities using daily images collection

Significance of the findings

(continued)

The system is limited to building superstructure and fully dependent on digital images captured on site. In addition, it requires WBS for each component therefore it could be difficult to monitor complex projects. The system also can’t provide complete view of work progress and has limited success rate to detect the completion of assigned building components

The system drawbacks are time consuming, low quality, non-systematic data collection and visually complex

Research limitations/gap

284 Y. Gamil et al.

Building construction

Progress monitoring of construction projects using pattern recognition techniques

[19]

The system uses neural network with the utilization of CPM

The basic step Steel structure implied to acquire the data by using digital images with colors then using stereo vision camera to provide 2D rectified color image of construction site

3D structural component recognition and modeling method using color and 3D data for construction progress monitoring

[21]

Targeted project type

Data acquisition method

Author (s)/year Research theme

How to monitor (methods)

Pattern recognition techniques, Critical path method

Patterns were designed with the use of CPM schedules to encode planned and actual progress at any cut-off dates then the patterns are interpreted by computer program

Bumblebee® XB3 The data is used to stereo vision system, extract 3D coordinate 3D model, MATLAB for each color then match it with CAD model The integration of both 2D and 3D model linked together to allow project participants to auto assess the progress. The 3D data is then converted to STL format then imported to MATLAB for analysis purpose

System components

Table 2. (continued)

The project introduced new technique of measuring project progress by utilizing neural network

The study introduce new method of progress monitoring by using color images then compare with 3D model then analyses the data using MATLAB

Significance of the findings

(continued)

The method requires expertise to understand how NN works with CPM. Another issue it requires more time to setup, test, calibrate the data before any processing of comparisons between planned and actual schedules

The method used is still not fully automated and depends on the quality of digital images and their color transparency

Research limitations/gap

Automated Project Progress Monitoring in Construction Projects 285

The data extracted using tablet PC to snapshot and comment in each activity

Integrating automated data acquisition technologies for progress reporting of construction projects

An object-based 3D walk-through model for interior construction progress monitoring

[24]

[26]

System components

Facility construction Barcode, Radio Frequency Identification (RFID), 3D laser scanning, Laser Distance and Ranging (LADAR) photogrammetry, pen-based computers

Targeted project type

The data acquired Interior construction 3D planned model using digital images of buildings and digital image of to represent as built as built and 3D model to represent planned, the photos are stored and positioned in a 3D walkthrough model then represent the progress using color and pattern coding

Data acquisition method

Author (s)/year Research theme

Table 2. (continued)

The planned 3D model from BIM and as-built digital images are compared in a walk-through model. The as-built construction objects decomposed to automatically generate the status of construction progress

Different data acquisition methods were integrated. The data are collected from site using pen-based computer, RFID, barcode, LADAR and multimedia

How to monitor (methods)

The system automated the progress monitoring of interior construction. It helps to detect any schedule deviations comparing with planned schedule

The system helps contractors to track their project in timely manner and help client to look over the project

Significance of the findings

(continued)

The system is limited and focused only on the interior construction. The model needs more improvement to emerge capture technology to help retrieve info of user’s perspectives inside a building

The weakness of the system it requires many personnel to operate the data collection because the system consists of many methods of collection

Research limitations/gap

286 Y. Gamil et al.

Data collected from site using remote sensing and data of as planned are extracted from 4D BIM

Fully automated registration of 3D data to a 3D CAD model for project progress monitoring

Construction Project Monitoring with Site Photographs and 4D Project Models

[40]

[4]

The data acquired by overlaying constructed scene over planned model. The model allows as-built photographs to be geo-registered by 4D as-planned model

Data acquisition method

Author (s)/year Research theme

Interior and exterior construction

Four-floor concrete building under construction in South Korea

Targeted project type

Time-lapse photographs

4D BIM Data, 3D from construction site acquired by remote sensing

System components

Table 2. (continued)

A comparison between as-planned and as-built using augmented reality then the progress is visualized in 4D using quadrangle visualization scheme

The process is done by aligning the as-built data with as-planned to info in the BIM. Using this integrated info, the status of real work is computed

How to monitor (methods)

The method automated the project progress monitoring by using photographs on site then superimposing them with 4D model to monitor the progress

The findings contribute to increase the capability of BIM. It also helps to automate progress measurements

Significance of the findings

(continued)

The drawback of this technique is that it can’t monitor the nonphysical activities of interior work such as painting, plumbing etc. Another drawback is that the schedule can’t be represented in 4D model due to the discrepancy of color detection and coding

The results are incomplete and required double process to get full image of the progress onsite. The reason why the accuracy is being hampered because the sequences of activities are not defined properly

Research limitations/gap

Automated Project Progress Monitoring in Construction Projects 287

Automated progress monitoring based on photogrammetric point clouds and precedence relationship graphs

[23]

The images are Building collected using fixed construction camera in crane tower then using color pattern to represent components accomplished afterward comparing as-built with as-planned model

They used a Building combination of construction structure from motion process together with control points to create a scaled point cloud in a consistent coordinate system

Comparison of photogrammetric point clouds with BIM building elements for construction progress monitoring

[9]

Targeted project type

Data acquisition method

Author (s)/year Research theme

Fixed camera, Unmanned aerial vehicle, 4D BIM

Photogrammetric point clouds, Laser scanner

System components

Table 2. (continued)

A dense point cloud is made from images. Then the results of comparison between as-built and as-planned is used to identify the occluded elements and enhance detection algorithm

An array casting was based on the triangulated points and the camera positions. This allows identifying not existing building parts. The steps of carrying out the monitoring are data acquisition, orientation of the images, image matching and co-registration

How to monitor (methods)

The research introduced new technique of progress monitoring which is the use of photo point cloud to compare with 4D BIM model

Two approaches were developed: one for as-built data generation, the other one for the as-built—as-planed comparison For the generation of the as-built data, photogrammetry was used to create the point cloud

Significance of the findings

(continued)

Only exposed building components are detected for monitoring however occluded can’t appear in the digital image

The system’s weakness is inaccurate geometry; it’s also incorporated manual measuring of control points

Research limitations/gap

288 Y. Gamil et al.

Then the image Building patches are convolved construction with texture and color filters and their concatenated vector-quantized responses are compared with multiple discriminative material classification models that are relevant to the expected progress of that element.. For each element, a quantized histogram of the observed material types is formed and the material type with the highest appearance frequency infers the appearance and thus the state of progress

Appearance-based material classification for monitoring of operation-level construction progress using 4D BIM and site photologs

[15]

Targeted project type

Data acquisition method

Author (s)/year Research theme 4D Building Information Models (BIM) and 3D point cloud models from site photologs

System components

Table 2. (continued)

To initialize, a user manually assigns correspondences between the point cloud model and BIM, which automatically brings in the photos and the 4D BIM into alignment from all camera viewpoints. Through reasoning about occlusion, each BIM element is back projected on all images that see that element. From these back-projections, several 2D patches are sampled per element and are classified into different material types

How to monitor (methods) The study is discussed an important technique by using BIM and daily construction photo logs to monitor the progress

Significance of the findings

(continued)

Practical limitations, images do not depict the footing of a column once it is buried,

Research limitations/gap

Automated Project Progress Monitoring in Construction Projects 289

Data from site are Building collected using constructions Unmanned Ground Vehicles (UGV) then compared to BIM cad to determine missing pixels and lines

Real-Time Image-to-BIM Registration Using Perspective Alignment for Automated Construction Monitoring

[16]

Targeted project type

Data acquisition method

Author (s)/year Research theme UGV, 3D BIM

System components

Table 2. (continued)

Real time images are compared with BIM CAD to determine missing image data then comparing as-planned with as-built for progress monitoring

How to monitor (methods) The method used is relevant to project progress monitoring in construction projects

Significance of the findings

The method is limited to certain exposed components of the building and hidden and curved objects are only viewed using BIM to complete the image

Research limitations/gap

290 Y. Gamil et al.

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9 Conclusion This article addressed the topic of progress monitoring automation in construction industry by reviewing current applications and trends in the field. The automation of progress monitoring has been discussed in many publications due to its significance in producing more effective project management and executions. In fact, if the project is running behind schedule, automated progress monitoring allows decision makers to identify deviations from the expected state and take remedial action. As a matter of fact, automated progress monitoring reduces the time and expense of existing progress monitoring by allowing experts to do numerous decision-enabling activities in a virtual environment rather than in the real world. The present state of automated progress monitoring was discussed in this study, as well as what has been done and what needs to be investigated further in future research. Some conclusions are drawn from this article: 1. The advancement of technologies and equipment’s are available but then the level of acceptance to apply them remains a challenge. The cost of technology is a burden to construction companies. 2. Lack of skilled staff remains challenge for many companies. 3. Automated progress monitoring offers many possibilities such as timely decision making, reduction of cost due to real time progressing checks, quality of the work. 4. Conventional monitoring remains predominant. 5. More investigation is required targeting the hotspot of decision making and identifying the technical challenges of applying automation in progress monitoring.

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Understanding the Efficiency of Gamification on the Engagement Intention of the Customers with Mobile Payment Systems Mohannad Moufeed Ayyash(B)

and Fadi Herzallah

Department of Business Administration and E-Commerce, The College of Business and Economics, Palestine Technical University — Kadoorie, Tulkarm, Palestine {mohannad.ayyash,f.herzallah}@ptuk.edu.ps

Abstract. With the investigation of the impact of gamification on customer engagement in online communities by several kinds of research, the current work aims to pinpoint the effect of gamification on the engagement intention of the customers with mobile payment systems in various electronic banking domains. Achieving the study’s aims necessitates adopting a convenient sampling method, and also an online survey is used to obtain data from 226 Palestinian customers using mobile banking services. To test the model and hypotheses of the research, the calculation related to partial least squares (PLS) structural equation modeling is performed. This study shows that customers’ engagement intention with mobile payment systems is positively impacted by effort expectancy, facilitating conditions, performance expectancy, and trust. The study also shows that customers’ engagement intention with mobile payment systems is not affected by social influence. It is noteworthy that policymakers, related officials, and bank marketers can benefit from the results of the study in improving digital approaches and plans to increase customers’ engagement intention with gamified mobile payment systems. Keywords: Gamification · Customers engagement · Mobile payment systems · Electronic banking · Trust · Palestine

1 Introduction Within the service marketing industry, mobile payment furnishes consumers with a lowcost substitute so that they can digitally pay for services, products, and information transactions via wireless technologies [1]. As revealed by [2], banking customers are familiarized with the use of smartphones to obtain financial information, there is an appropriate moment for banking with gamification principles and gamification features with huge potential to come together to enhance financial inclusion. As a consequence, a multichannel mobile marketing experience can be spurred through the incorporation of gamification into the mobile payment systems as a thoughtful integration of the user experience design element [3, 4]. In mobile payment systems, there are various gamification elements, namely: collating badges or points and getting purchase rewards, where users can enjoy and be comfortable while buying a service or a product [1] As © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (Eds.): ICETIS 2022, LNNS 584, pp. 294–310, 2023. https://doi.org/10.1007/978-3-031-25274-7_24

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put by [5], young generations’ attention can be attracted by gamified systems, leading to raising their ambitions. As well, [6] reveals that the related data of the customer attained from social media and gamification apps assist companies to comprehend their customers’ basic requirements and preferences to support constructing customer value co-creation. Yet, it is significant to highlight those electronic transactions can share the customers’ personal information. With that being said, several researchers indicate that there is a dire need to pay much attention to the consumer’s trust, security, and privacy relating to the use of electronic payment systems [7–9]. Therefore, the service marketing industry likely confronts a challenge to select the paramount plan and policy to develop customers’ engagement with their mobile payment systems. Admitting that the effect of gamification on customer engagement’s enthusiasm in online contexts such as information systems [10], marketing [11], user experience [12], e-health [13], aviation safety [14], e-banking [15], tourism [16], and online shopping [17] has been addressed by the previous shreds of research, the studies delving into the gamification efficiency in mobile payment systems still scarce, namely: [2, 18], and thus requiring much empirical research. Additionally, it is established that assessing purposes and attitudes toward adopting the concept of gamification is an advised approach and track for future research work to assist in furnishing novel research models supportive of understanding the domain of gamification [5, 16]. To examine the previously specified gaps, this work contributes to mobile payment systems’ literature, as there is an urgent call for much empirical research to investigate gamification’s efficiency in customers’ engagement with mobile payment systems. And so, this current research aims to examine gamification’s impact on customers’ engagement intention with mobile payment systems. Within the current preliminary implementation stages of gamification in mobile payment systems, the factors influencing gamification’s efficiency and its impact on customer engagement with mobile payment systems are thoroughly explored by this study.

2 Literature Review 2.1 Gamification and Customer Engagement with Mobile Payment Systems Unlike other mobile systems designed to entertain users, the majority of mobile payment systems are intended to be transactional and provide useful services, guaranteeing the utilitarian values’ salience in these services’ initial stages [3]. The application of gamification in mobile payment systems likely has a significant effect, as it is a transformational one, happening in certain domains. It also makes the mobile payments systems a much more entertaining experience and increases the engagement of the customer in one go. Gamification is defined as employing game elements in a non-gaming domain to adjust and enhance human behaviors [4]. As put by [5], gamification has recently been on the rise, catching the attention of companies in non-gaming contexts. As gleaned from several kinds of research, gamification’s effect on understanding human behaviors in various contexts is previously considered. To give you an idea, among the positive results related to the adoption of gamification is the engagement of customers in the touristry industry [6, 8]. The gamification’s mutual effect along with broadened the Unified Theory of Acceptance and Use of Technology (UTAUT) to define the impact

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of applying the techniques of game design on the mobile banking acceptance is examined [3, 9, 10]. Besides, gamification is regarded as a major factor in adopting large mobile payment systems [11]. As revealed by [12], gamification’s possible practicality in e-banking relating to recognizing the relationship of the customer with the financial product and game is highlighted, pinpointing novel dimensions such as product and game in the method of considering and designing gamification in e-banking. Largely, gamification creates a pleasant, amusing, and inspiring experience among users. Thus, it is presumed that gamification likely has an important effect on customers’ engagement intention with mobile payment systems. Yet, the previous related pieces of research disregard conclusions, showing how consumers’ engagement intention is influenced by gamification.

3 Theoretical Foundation and Hypotheses Development The Theory of Planned Behavior (TPB), Theory of Reason Action (TRA), the Unified Theory of Acceptance and Use of Technology (UTAUT), Technology Acceptance Model (TAM), along with the developed versions of these models are largely employed by scholars and researchers reviewing the adoption of information technology in mobile payment systems. Established on eight noticeable models in the domain of information technology acceptance, [13] have developed UTAUT to identify the user adoption with the ability to elucidate two-thirds of the various behavior intentions. The four dimensions of UTAUT, i.e. performance expectancy, effort expectancy, social influence, and facilitating condition factors are used to predict technology adoption intention [13]. [14] regard UTAUT as the most distinguished and wide-ranging model in technology acceptance. Taking into account the use intention of an information system, trust is an important factor since it influences the usage intention of the users [15, 16]. Thus, the current research work incorporates perceived trust into UTAUT [13]. The model of the perceived trust and UTAUT is used to construct the current study as it suitably examines the gamification’s impact on the engagement intention of the consumers with mobile payment systems. Accordingly, the proposed model is shown in Fig. 1. 3.1 Social Influence of Gamified Mobile Payment Systems and Customers Engagement Intention Social influence refers to the friends and family members’ social pressure employed to adopt new technology. Both social influence and the behavioral intention of digital payment share a significant relationship [17]. As defined by [10], social influence refers to the degree of the perception of the customers that others such as family members and friends have a related belief that shall employ new technology. A significant association connects social influence and intention to adopt and use mobile banking [18]. Likewise, the significant impact of social influence on the technology adoption by users is suitably emphasized [19]. They show that using the same game by the majority of workmates and friends is a confirmation that social interaction is a vital driver for adopting gamification. Given the possible effect of social influence in snowballing customers’ engagement with gamified mobile payment systems, hypothesis No. 1 can be read:

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H1. Social influence positively affects the engagement intention of the customers with gamified mobile payment systems. 3.2 Effort Expectancy of Gamified Mobile Payment Systems and Customers Engagement Intention Effort expectancy refers to the degree of easiness associated with the system [13]. It is presumed that easiness assists users in adopting the system without problems, as this functions as a motivation to accept or refuse the adoption of a new system the same as the example with mobile payment systems [20]. As put by [21], easiness for users rests as the main driver to use and adopt gamification. As recommended by [22], marketers are continuously required to review their games to preserve enthusiasm and entertainment to sustain the engagement of the customers. Hereafter, it is pertinent to associate the effort expectancy of gamified mobile payment systems with the engagement intention of the customers. Given the effect of effort expectancy to increase the engagement of the customers with gamified mobile payment systems, hypothesis No. 2 can be read: H2. Effort expectancy positively affects the engagement intention of the customers with gamified mobile payment systems. 3.3 Facilitating Conditions of Gamified Mobile Payment Systems and Customers Engagement Intention Facilitating conditions refer to the certainty of the person that technical and organizational infrastructure is attainable to support using the system [13]. Of the dimensions and areas required to investigate in future research is the facilitating conditions of gamification adoption [22]. As said by [23], facilitating support furnished by organizations is one of the main necessities required by inexperienced users using games seriously as it strengthens their acceptance and game-based engagement. Given the effect of facilitating conditions in developing the engagement of the customers with gamified mobile payment systems, hypothesis No. 3 can be read: H3. Facilitating conditions positively affect the engagement intention of the customers with gamified mobile payment systems. 3.4 Performance Expectancy of Gamified Mobile Payment Systems and Customers Engagement Intention Better performance is defined as a stimulus assisting in inducing higher intention among new users to use and adopt the apps [13]. Consistent with [24], performance expectancy is regarded as a significant factor affecting non-users to adopt and use remote mobile payments. In the mobile payment context, performance expectancy refers to the capacity of the mobile payment in enhancing the consumers’ payment performance [25]. As shown by [26], intention in the Near Field Communication (NFC) mobile payment is significantly predicted by the system performance expectancy. Given the effect of performance expectancy to increase the engagement of the customers with gamified mobile payment systems, hypothesis No. 4 can be read:

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H4. Performance expectancy positively affects the engagement intention of the customers with gamified mobile payment systems. 3.5 Trust of Gamified Mobile Payment Systems and Customers Engagement Intention Of the pivotal problems affecting the use of these systems among customers are the uncertainty and risky nature of technology payment systems, particularly mobile payment systems. Within the TAM meta-analysis, trust is regarded as a significant constituent used in numerous studies demonstrating that the results are considered significant [15, 16, 27]. As postulated by [28], trust is considered a basic necessity of m-payment usage intention that is followed by both performance expectancy and social influence. The lack of trust among consumers exposes them to uncertainty, affecting the decision and intention of the consumers to use m-payment [29]. On the other hand, as suggested by [30], mobile payment’s success rests on the trust of the consumers to experience up-to-date payment methods. Consistent with [31], trust in the current research paper is related to the trust in both the service provider and the gamified mobile payment systems themselves. Therefore, hypothesis No. 5 can be read: H5. Trust positively affects the engagement intention of the customers with gamified mobile payment systems.

Fig. 1. Research model

4 Research Methodology 4.1 Research Design and Measurement Understanding the efficiency of gamification on customers’ engagement intention with mobile payment systems precisely necessitates conducting a quantitative study. With

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that being said, attaining responses from the consumers in Palestine using mobile banking requires designing a two-section structured questionnaire. The first section includes demographic questions, while the second section has questions related to the relationships among the variables of the research paper and the adopted hypotheses. The measures in the current literature adjusted to address the research objectives are adopted to process the research constructs as shown in Table 1. The use of a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree) enables us to measure all the construct items. The items of the questionnaire are first translated into the Arabic language by a qualified translator and later translated into the English language to ensure consistency. 4.2 Sample and Survey Administration The whole customers using mobile banking services in the Palestinian banks constitute the study’s statistical population. Several online media websites such as e-mail, WhatsApp, and Facebook are used to distribute the online questionnaire. The survey is carried out for six weeks from January to February 2022. With the use of a convenience sampling approach, the users of the Internet banking services are invited to fill out the questionnaire. The convenience sampling approach is the most suitable and applicable in social science studies [32, 33]. As the study only concentrates on mobile banking users’ behavior, the convenience sampling technique is adopted. Therefore, the target population includes participants possessing a mobile phone or tablet with internet access and one bank account and using mobile banking annually. In detail, 240 questionnaires are returned from the 430 distributed questionnaires with a 56% response rate. 14 out of returned 240 questionnaires are rejected because of unsuitable answers. Therefore, there are 226 valid questionnaires used for statistical data analysis. Demographic variables such as gender, age, and education are incorporated into the current study. The descriptive analysis shows that respondents with ages less than 20 are equal to 5.4%, respondents aged 21 to 30 are 14.8%, respondents with an age of 31–40 are 31.2%, and respondents with an age of 41–50 are only 30.6%. Thus, 18% of the responses are for ages 51 to 60. Concerning the education level of the respondents, (67.7%) of the respondents are educated with a high education level, while 27.5% have a bachelor’s degree and only 4.8% of the respondents have a primary education level. Also, the descriptive analysis shows that (62.4%) of the respondents are male, while females are (37.6%).

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M. M. Ayyash and F. Herzallah Table 1. Scale items of the selected constructs

Constructs

Reference

Items

Social influence (SI)

[10, 13]

Close People suggest using gamified mobile payment systems Workmates suggest using gamified mobile payment systems Gamified mobile payment systems use is a prestige symbol in my culture I think that firms are required to support using gamified mobile payment systems

Effort expectancy (EE)

[10, 13, 32]

Learning how to use gamified mobile payment systems is accessible I have a clear and logical interaction with gamified mobile payment systems Gamified mobile payment systems use is simple Being competent in using gamified mobile payment systems is doable I would find the gamified mobile payment systems to be flexible to interact with

Facilitating conditions (FC)

[10, 13]

The use of gamified mobile payment systems prevents me from showing up and queuing in bank branches Attaining the essential resources for using gamified mobile payment systems is simple as ABC Acquiring the necessary knowledge for using gamified mobile payment systems is effortless The technologies I use are suitable for gamified mobile payment systems

Performance expectancy (PE)

[10, 13]

My lifestyle benefits from using gamified mobile payment systems My productivity increases by using gamified mobile payment systems I quickly accomplish things by using gamified mobile payment systems (continued)

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Table 1. (continued) Constructs

Reference

Items Gamified mobile payment systems are very useful in my daily life

Trust (TR)

[34]

I trust the security of the gamified mobile payment systems I trust the trustworthiness of the gamified mobile payment systems I trust the efficiency of the gamified mobile payment systems I trust the reliability of the gamified mobile payment systems

Customer engagement intention (CEI)

[10, 35]

I plan using gamified mobile payment systems in the upcoming periods I daily attempt using gamified mobile payment systems in life I intend to check my balance account on the gamified mobile payment systems I plan making a wire transfer on the gamified mobile payment systems

5 Data Analysis and Results As shown by [36], the PLS-SEM is adopted to carry out the data analysis, as the PLS-SEM application necessitates a two-stage process. The first is to evaluate the reliability and validity measurement and the second is to assess the structural model [37]. Using PLS is regarded as proper to validate the structural model’s causality and test the measured model. In related literature, PLS has been deemed a suitable method for empirically validating a non-normal distributed sample [38, 39]. Likewise, research areas such as information systems, new technology research, and social science study strongly adopt PLS-SEM as an applicable and analytical technique [6, 40]. 5.1 Evaluation of the Measurement Model The validity and reliability of the employed measures are ensured by the measurement model. As shown in Fig. 2 and Table 2, the loadings of the indicators exceed 0.7, demonstrating good correlations with their constructs. Composite reliability (CR) and Cronbach’s alpha (CA) values exceed the 0.7 thresholds, guaranteeing the concerns of the reliability [36]. What’s more, as put by [37], the average variance’s extracted values (AVEs) are greater than the 0.5 thresholds, indicating the existence of the convergent validity.

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Fig. 2. Measurement model

Table 2. Results of measurement model Construct

Items

Factor loading

Cronbach’s alpha

CR

AVE

Customer engagement intention (CEI)

CEI1

0.844

0.839

0.892

0.674

CEI2

0.803

CEI3

0.814

CEI4

0.822

EE1

0.727

0.844

0.889

0.617

EE2

0.797

EE3

0.774

EE4

0.824

EE5

0.802

FC1

0.819

0.832

0.888

0.665

FC2

0.771

FC3

0.831

FC4

0.839

Performance expectancy PE1 (PE)

0.757

0.804

0.872

0.630

PE2

0.795

Effort expectancy (EE)

Facilitating conditions (FC)

(continued)

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Table 2. (continued) Construct

Social influence (SI)

Trust (TR)

Items

Factor loading

PE3

0.796

PE4

0.824

SI1

0.843

SI2

0.791

SI3

0.792

SI4

0.755

TR1

0.893

TR2

0.874

TR3

0.872

Cronbach’s alpha

CR

AVE

0.807

0.873

0.633

0.854

0.911

0.774

[41] criterion is adopted to evaluate the examined constructs’ discriminant validity. As stated by [36], the construct comprising the average square root of the excerpted variance, which is greater than the correlation values of whole variables, has such a discriminant validity. As shown in Table 3, the Heterotrait-Monotrait Ratio (HTMT) is an assessment regarding the correlation among constructs, matching the disattenuated construct score creation that adopts a 0.9 value as the path. As illustrated in Table 4, this research has found that the decline of discriminant validity is not indicated, along with all the constructs that match the standards. As an extra test for multicollinearity, generally regarded as a threat to the design of the experimental model, the variance inflation factor (VIF) is computed as the research model constructs’ VIFs values are found less than 5, demonstrating that multicollinearity is not a problem [37]. Table 3. Assessment of discriminant validity (HTMT) [43] CEI

EE

FC

PE

SI

TR

Customer engagement intention (CEI) Effort expectancy (EE)

0.683

Facilitating conditions (FC)

0.773

0.672

Performance expectancy (PE)

0.721

0.569

0.687

Social influence (SI)

0.668

0.665

0.709

0.737

Trust (TR)

0.621

0.375

0.434

0.463

0.440

The results of the measurement model show that the model includes good indicator reliability, construct reliability, discriminant validity, and convergence validity, confirming the statistical distinction of the constructs and usability for testing the structural model.

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5.2 Evaluation of the Structural Model The results demonstrate that the customer engagement intention (CEI) has a 0.593 R2 value, designating that the independent variable explains the 59.3% of the variance in CEI. As said by [36], among the adopted key scales is to determine the effect size (f 2 ), allowing to evaluate of an exogenous variable influence on an endogenous variable. Recommendations to measure f 2 elucidate that the values of 0.02 (small), 0.15 (medium), 0.35 (large), and less than 0.02 signpost that there is no effect [42]. As shown in Table 4, as H1 has no effect, H2, H3, H4, and H5 are supported with a small effect. As put by [36], the predictive power of the proposed model is represented by the evaluation test predictive relevance (Q2 ). The proposed model is characterized by sufficient predictive relevance when the values of the endogenous variable are more than zero, as a zero score and below reveals a model lacking a predictive relevance [37]. On the subject of Customer Engagement Intention (CEI), the user has a 0.389 score for Q2 , indicating the proposed model has the required predictive relevance. As presented in Table 4, the structural model’s path coefficients are measured, along with conducting bootstrap analysis (resampling = 1000) to assess the path coefficients’ statistical significance. As shown in Table 4 and Fig. 3, the findings attained from the tests of the research hypotheses are four hypotheses from five hypotheses and are acceptable. Specifically, concerning the findings of hypothesis No. 1 (H1) stipulating “social influence positively affects the engagement intention of the customers with gamified mobile payment systems”, it is shown that there is an insignificant relationship between social influence and engagement intention of the customers. This is gleaned from a piece of evidence attained from that survey data with the findings (ß = 0.054, T-Statistics = 0.867, P-value > 0.05). Regarding hypothesis No. 2 (H2) stipulating “Effort expectancy positively affects the engagement intention of the customers with gamified mobile payment systems, it is supported by the findings (ß = 0.202, T-Statistics = 3.436, p < 0.05). Similarly, concerning hypothesis No. 3 (H3) stipulating “Facilitating conditions positively affect the engagement intention of the customers with gamified mobile payment systems”, it is also supported by the survey data with values (ß = 0.292, T-Statistics = 4.875, P-value < 0.05). Moreover, as regards hypothesis No. 4 (H4) stipulating “Performance expectancy positively affects the engagement intention of the customers with gamified mobile payment systems” is supported by the survey data with values (ß = 0.207, T-Statistics = 3.672, P-value < 0.05). Hypothesis No. 5 (H5) stipulating “Trust positively affects the engagement intention of the customers with gamified mobile payment systems” is also supported by the findings (ß = 0.255, T-Statistics = 5.243, p < 0.05).

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Table 4. Hypotheses testing #

Hypothesis

Original sample (O)

T statistics (|O/STDEV|)

P-values

f2

H1

Social influence (SI) → Customer engagement intention (CEI)

0.054

0.867

0.386

0.004

H2

Effort Expectancy (EE) → Customer engagement intention (CEI)

0.202

3.436

0.001

0.060

H3

Facilitating conditions (FC) → Customer engagement intention (CEI)

0.292

4.875

0.000

0.109

H4

Performance expectancy (PE) → Customer engagement intention (CEI)

0.207

3.672

0.000

0.058

H5

Trust (TR) → Customer engagement intention (CEI)

0.255

5.243

0.000

0.128

6 Discussion Admitting that the effect of gamification on customer engagement in an online environment has been addressed in the previous shreds of research, scarce pieces of research are done to recognize gamification in mobile payment systems in the e-banking industry. Importantly, little work is written in developing countries in general and Palestine in particular. More importantly, the current work aims to pinpoint the effect of gamification on the engagement intention of the customers with mobile payment systems in the electronic banking industry. The Smart-PLS test results verify that the existing study’s four hypotheses are expected, and so validating that the adopted model is a valued instrument to examine the engagement intention of the customers with gamified mobile payment systems. Conversely, the results show an insignificant relationship between social influence and the engagement intention of the customers. To illustrate, it is found that the first relationship between social influence and the engagement intention of the customers with gamified mobile payment systems (H1) is insignificant with (ß = 0.054 =, T-Statistics = 0.867, P-value > 0.05). This result is not in line with [6], demonstrating that the mobile banking customers in the current study are not impacted by the recommendations and offers from important and close persons to them to adopt and use the gamified mobile payment systems. Additionally,

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Fig. 3. Structural model

these customers have no belief that the use of gamified mobile payment systems is regarded as a status symbol in their community and culture as the majority of the people currently own the essential knowledge and skills relating to the use of smart devices. Second, the relationship between effort expectancy and the engagement intention of the customers with gamified mobile payment systems (H2) is also supported (ß = 0.202, T-Statistics = 3.436, p < 0.05). This result is in agreement with [34, 35], signifying that once the customers believe that the gamified mobile payment systems are simple, comprehensible, applicable, and easy to use, their engagement intention with gamified mobile payment systems can increase. Therefore, financial institutions such as banks shall continuously work to evaluate their gamified mobile payment systems to keep entertaining and amusing so that the engagement of the customers is increased. Third, the association between facilitating conditions and the engagement intention of the customers with gamified mobile payment systems (H3) is also supported (ß = 0.292, T-Statistics = 4.875, P-value < 0.05). This result is along the lines of the results of [36], demonstrating that unskillful customers in using games can take note of the facilitating support furnished by banks and the various benefits of gamified mobile payment systems, i.e. saving time, avoiding showing up in the branches of banks, and lining up in queues for a long time. Thus, the minute the customers believe that they acquire the required knowledge and resources and that the gamified mobile payment systems are suitable for various technologies used by them, their engagement with gamified mobile payment systems can increase. Fourth, the association between performance expectancy and engagement intention of the customers with gamified mobile payment systems (H4) is supported (ß = 0.207, T-Statistics = 3.672, P-value < 0.05). This result is in one with [44], signifying that the gamified mobile payment systems rapidly help increase

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the productivity of the customers and enhance their performance and achieve their work and objectives. Consequently, the engagement intention of the customers with gamified mobile payment systems can increase. Fifth, the relationship between trust and engagement intention of the customers with gamified mobile payment systems is also supported (H5) (ß 0.255, T-Statistics = 5.243, p < 0.05). This result is in harmony with, [18] showing that perceived risks are regarded as an obstacle to transforming positive attitudes into an intention to adopt gamified mobile payment systems. With that being said, trust stands as the milestone of gamified mobile payment systems engagement intention.

7 Conclusion, Future Research, & Limitations In a nutshell, the aforesaid results chip in giving more insight into the efficiency of gamification on engagement intention of the customers with mobile payment systems. Besides, the results examine the factors influencing gamification efficiency and its effect on the engagement of the customers with mobile payment systems. To have the objectives of the study achieved, the quantitative method is adopted by using the method of survey. As shown by the findings, the four hypotheses are supported. Conversely, social influence and the engagement intention of the customers with gamified mobile payment systems are confirmed to be insignificant. As put together from these results, legislators, researchers, and academics are generally required to consider the impact of these factors when being frustrated to enhance the engagement intention of the customers with mobile payment systems in the Middle East and Palestine in particular. Even though plenty of studies have been recently penned to envisage the efficacy of gamification in mobile payment systems, the greatest amount of former studies disregard the context of the Arab countries, i.e. Palestine. Accordingly, the current study’s results get the feet out to identify the factors influencing gamification efficiency and its impact on the engagement of the customers with mobile payment systems in the banking sector in the state of Palestine. In simple terms, concerned specialists and scholars are required to take advantage of the current results in examining the customers’ interests, challenges, and concerns relating to their engagement intention with mobile payment systems. This study is featured with a fresh and innovative contribution as it creates a novel model gleaned from the Unified Theory of Acceptance and Use of Technology (UTAUT), and the trust-based literature reviewed to study the objectives of the research. Due to the applicability and usability of the model, it can be adopted in the related areas in the upcoming studies, as the model can be extended to address diverse unaddressed areas of research. On the subject of the limitations of the study, a convenient sampling method has been used, where a sample of Palestinian consumers using mobile banking has been selected. As well, the present study’s generalizability is limited to Palestinian consumers, and thus future studies shall consider the efficacy of gamification on the engagement intention of the customers with mobile payment systems employing a cross-cultural approach. This study rests among other related studies as a valuable guide designed to enhance the engagement intention of the customers with gamified mobile payment systems. Acknowledgements. The authors would like to thank Palestine Technical University—Kadoorie for their financial support in conducting this research.

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Customer Engagement in Instagram: The Mediating Role of Perceived Advertising Value Poh Kiong Tee1(B)

, Deeparechigi Vashu2 and Chee Pung Ng3

, Ling Chai Wong1

,

1 School of Marketing and Management, Asia Pacific University of Technology and Innovation,

Kuala Lumpur, Malaysia [email protected] 2 Faculty of Business, International University of Malaya-Wales, Kuala Lumpur, Malaysia [email protected] 3 Faculty of Business and Communication, INTI International University, Nilai, Malaysia [email protected]

Abstract. As of 2021, about 86% of Malaysian population were active in using social media. This study aims to investigate the factors driving Malaysian customer engagement on Instagram. We attempt to fill the research gap by examining the mediating role of the perceived value of advertising aside the direct predictors on customer engagement. Data was collected from a sample of 219 Instagram users in Malaysia. The findings revealed that informativeness and credibility have a significant impact on customer engagement and customer perceived value of advertising acts as a mediator in the relationship between two independent variables (informativeness, credibility of advertisement) and customer engagement. Overall, the study provides evidence and understanding of the impact of Instagram in shaping customer engagement by exploring the mediating role of customer perceived value. Keywords: Customer engagement · Instagram · Perceived value · Informativeness · Entertainment feature · Credibility

1 Introduction The development and transformation of digital marketing (i.e., social media) bring a significant impact on marketing and branding approaches in creating value for the customers [1–5]. As of January 2021, there were 4.66 billion active internet users worldwide, accounted 59.5 percent of the global population [6]. As reported [7], Malaysia ranks 5th in the world for social network penetration recently, and there are approximately 28 million active social media users in Malaysia as of 2021 which accounts for 86 percent of Malaysia’s population [8]. Instagram is one of the social net-working media that allows users to showcase digitally created content with friends and families. Correspondingly, this social networking application has the highest per-follower interaction rate among all social media platforms [9, 10]. Thus, the implication of social media advertising © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (Eds.): ICETIS 2022, LNNS 584, pp. 311–321, 2023. https://doi.org/10.1007/978-3-031-25274-7_25

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via Instagram and its influences should not be undervalued [11]. Notwithstanding the myriad preference for Instagram among the younger generation, study on the factors influencing customer engagement on Instagram remains unexplored [11]. In the digital economy, customers are socially connected with one another in horizontal webs of communities. To effectively engage with a community of customers, businesses started to recognize the importance of social networking services in connecting with the netizens who create ratings, post comments, and express their feelings and opinions about brands [12–14]. Despite past studies revealing the amount of social media content related to branding and marketing in various platforms climbed by 35%, the degree of content interaction and engagement has declined by 17% [15]. Moreover, most of the research on customer engagement focused on studying it among Facebook users [16–18] or examined customer engagement in general [11] without choosing a specific network or platform. Hence, this study aims to explore the factors influencing customer engagement on Instagram. Customer perceived advertising value was specifically introduced as a mediator, and its mediating function was further demonstrated.

2 Literature Review Advertising value is a metric for measuring the effectiveness of advertisements and is used to gauge customer satisfaction and engagement with the product or service being advertised [2, 19]. In an increasingly high-tech world, high-touch interaction is becoming the new differentiation while online businesses have taken up a significant portion of the market in recent years [3, 4]. According to [20], in a highly connected world, people have more favorable attitudes toward digital (web-based) advertising, such as social media (i.e., Instagram) than traditional advertising. Also, content-based advertising in digital (or social) media was found more effective than traditional advertising since the former exclusively targets customers who have expressed an interest in learning more about a certain product, service, or brand. Indeed, social media advertising is able to generate higher value compared to traditional irritative advertising in traditional media [9, 22]. Particularly, people like to share valuable information with others to form personal connections. Likewise, when customers find valuable product information in an advertisement, they will share it with their social network friends, leading to higher engagement with the social media advertisement. 2.1 Informativeness and Perceived Value of Advertising The ability of an advertiser (business) to deliver valuable and vital information about a product or service that is relevant to the target audience is known as the informativeness of advertising [23]. Interactivity features in the social media brand pages can boost the informative value of advertisements [3]. Likewise, most customers utilize social media to obtain complete and up-to-date information about a brand, and the informativeness of commercials, is believed to influence customer perceived value toward the advertisements, both traditional and digital [19, 21]. Thus, informativeness is critical in capturing the customer’s attention, establishing exposure to, and creating a favorable impression of the advertisement regardless of whether traditional or online advertising is used [18, 24]. Hence, the following hypothesis was stipulated:

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H1: There is a positive relationship between informativeness of advertising and customer perceived value of advertising in Instagram. 2.2 Entertainment Features and Perceived Value of Advertising Advertisement’s entertainment elements refer to its ability to provide diversion and meet audiences’ aesthetic needs [25, 26]. Advertisers use entertaining content to satisfy audiences’ hedonistic pleasure, escapism, and emotional release which enhance the perception of advertising value [27]. In the social media advertising context, entertainment value has been proven as an important construct that influences customers’ perceived value as well as their engagement with the advertisement [17]. In fact, customers are more likely to attend to online advertisements that were fun, with enjoyment and amusement features [20]. Furthermore, prior studies on social media marketing found that the majority of social media users who join social media sites expected to be entertained, amused and relax [9, 12, 20]. Therefore, online pages (in Instagram) with entertaining content will get more favorable ratings (i.e., higher perceived value) from social media users, and they are more likely to return to a social media platform. Thus, we hypothesized that: H2: There is a positive relationship between the entertainment feature of advertisement and customer perceived value of advertising on Instagram. 2.3 Credibility and Perceived Value of Advertising The credibility of an advertisement can be described as the believability, trustworthiness, and honesty of the advertisement [21]. [23] suggested that advertisement credibility can be classified; (1) the advertiser credibility: whether a company is a trustworthy source of information; (2) the advertisement content: if the customers regard the information in the advertisement as reliable or trustworthy. Customers’ perceived value of advertising was influenced by both message credibility and advertiser credibility.[21] found that the believability of an advertisement directly predicts the consumers’ appraisal of the advertising value and led to customers’ engagement with the (green) advertisement. Similarly, previous research also found the significant direct impact of credibility on customers’ attitudes toward social media advertising [24, 25, 28]. Consistently, we hypothesized that: H3: There is a positive relationship between the credibility of advertisement and customer perceived value of advertising in Instagram. 2.4 Perceived Value and Customer Engagement Customer engagement refers to a series of actions that customers take on brand message on social media platforms, such as commenting, liking, sharing with others, and uploading user-generated content [10]. Social media has become an ideal platform for customer-generated content and brand or product-related promotion due to its interactive

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feature that turns inactive viewers into active participants [28]. The survey conducted by [29] revealed that when a person has a positive attitude about an online commercial, he or she is more likely to share it with friends and family on social media. People appreciate sharing valuable information with others to form personal connections and networking [5]. Likewise, when customers find ‘useful’ or ‘interesting’ product information, they will share it with their social network friends. Hence, it was hypothesized that: H4: There is a positive relationship between customer perceived value of advertising and customer engagement in Instagram. 2.5 Mediating Roles of Perceived Value Van-Tien Dao et al. [23] discovered that the perceived value of advertising served as a mediator along the direct path between the independent variables and customer engagement in social media advertising. The perception of valuable advertising content was found to influence the degrees of sharing intention and purchase intention [30]. Similarly, previous research [21, 25] found that perceived advertising value mediated the frequency and usage of social media sites. This leads to the formulation of the following hypothesis: H5: Customer perceived value mediates the relationship between (a) informativeness, (b) entertainment features, and (c) credibility of an advertisement on customer engagement in Instagram. Figure 1 depicts the research model of this study:

Fig. 1. Research model

3 Research Method This study focuses on the quantitative aspect, cross-sectional study since most of the research studies undertaken for academic courses are time-constrained, and a selfadministrated questionnaire will be used to collect primary data. In this paper, the data

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collected are quantified to identify the relationship between independent variables (i.e., advertisement’s informativeness, entertainment feature, and credibility), mediating variable (i.e., customer perceived value of advertising) and dependent variable (i.e., customer engagement). The sampling frame for this study is young adults aged 18–44 who reside in Klang Valley. This group of people is the most active Instagram users [7]. 219 responses were collected using convenience sampling via an online survey due to the constraints of face-to-face interaction during the COVID pandemic. Majority of the respondents were aged between 18 to 24 years (64%, n = 141). The subsequent respondents consist of those between the age of 25–34 years old (30%, n = 65); 35–44 years old (5%, n = 11); and 45 and above (1%, n = 2). Also, 66% (n = 146) are students, 24% (n = 52) of the respondents are employed full-time, 4% (n = 9) employed part-time, self-employed and unemployed make 2% (n = 5) each, and 2% (n = 4) of the respondents have retired. In terms of Instagram daily usage, 28% (n = 62) log into Instagram five times a day, 26% (n = 56) check in Instagram page more than ten times a day, 23% (n = 51) log in up to ten times a day, and 11% (n = 23) of the respondent log in Instagram less than one time a day, 12% (n = 27) rarely use Instagram (one-two times a day). Most of the items employed to measure the constructs (i.e., informativeness, entertainment features, credibility and perceived value) are adapted from [23] study, and the questions pertaining to customer engagement were adapted from [11]. All items were measured using a five-point Likert scale: (1) Strongly Disagree, (2) Disagree, (3) Neutral, (4) Agree, and (5) Strongly Agree. The numerical data received from the respondents were analysed using the Statistical Package for Social Sciences (SPSS) and SmartPLS. The PLS-SEM method has been widely used in social science research [30–32]. SEM is a versatile modeling tool that may be used to do multivariate statistical analyses such as route analysis, regression analysis, and factor analysis [33–35]. Internal consistency, convergent and discriminant validity, as well as coefficient of determination (R2 ) and path coefficients, were utilised to evaluate the reflective measurement and structural models for this study using Smart PLS.

4 Results Preliminary analysis was conducted to verify the collected data and to ensure the data used is complete and valid. Data normality test using a Shapiro-Wilk test indicated that all variables have significant values less than 0.05, meaning that the data is not normally distributed. Hence, supporting the use of PLS-SEM [34]. Prior to data analysis, Harman’s single-factor test was used to detect the presence of common technique bias. The findings demonstrate that the most significant single factor only contributed 24% of the variance in the data, which is less than the 50% threshold for item covariance. As a result, common method bias was not a problem in our investigation. When constructs have an average variance extracted value of 0.5 and above, convergent validity is sufficient [36]. The convergent validity of the measurement model is assessed in this study by looking at its average variance extracted value (AVE). Furthermore, each construct’s composite reliability (CR) must be greater than 0.7 for the measurement model to have acceptable internal consistency dependability [34]. Table 1

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outlines the outcomes of the variables’ validity and reliability analysis. The findings indicate that all loadings value are greater than 0.708, all constructs have AVE ranging from 0.531 to 0.628, exceeded the suggested threshold value of 0.5 and the composite reliability for each construct was above 0.7, ranging from 0.715 to 0.886. Hence, the study’s measurement model has shown adequate convergent validity and high level of internal consistency reliability. Table 1. Result of convergent validity Items

Loadings

AVE

CR

Informativeness

3

0.723–0.796

0.541

0.778

Entertainment feature

4

0.715–0.821

0.593

0.853

Credibility

3

0.722–0.812

0.531

0.715

Perceived value

3

0.798–0.845

0.628

0.835

Customer engagement

7

0.756–0.845

0.568

0.886

Notes: CR = Compostite relaibility; AVE = Average variance extracted

In addition, the Heterotrait-Monotrait (HTMT) criterion was used to testify the discriminant validity of potentially overlapping concepts. According to [36], a threshold value of 0.90 is recommended for the HTMT approach. A number greater than 0.90 suggests that discriminant validity is absent. Table 2 shows that all values are below 0.90 and are ranging from 0.463 to 0.812. Therefore, the discriminant validity of all the items in the model was achieved. Table 2. Result of discriminant validity CE

CRED

ENT

INFO

PV

Customer engagement Credibility

0.463

Entertainment feature

0.611

0.624

Informativeness

0.622

0.575

0.812

Perceived value

0.593

0.707

0.745

0.756

Notes: CE = Customer engagement, CRED = Credibility, ENT = Entertainment feature, INFO = Informativeness, PV = Perceived value

As for the structural model, the findings reported in Table 3 indicates that six out of seven direct relationships are significant. Informativeness (β = 0.368), entertainment value (β = 0.223) and credibility (β = 0.364) significantly influence (t > 1.96, p < 0.05) customer perceived value of advertising. In similar vein, informativeness (β = 0.215), credibility (β = 0.218) and customer perceived value of advertising (β = 0.592) have significant direct impact (t > 1.96, p < 0.05) on customer engagement in Instagram,

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but entertainment feature of advertisement does not (β = 0.132, t < 1.96, p > 0.05) influence customer engagement. Overall, the model has an acceptable fit and predictive relevance since all the Q2 values (PV = 0.423, CE = 0.498) are larger than 0. Table 3. Result of path coefficients Correlations

Path coefficient (β) T-statistics p-value

Q2

Informativeness → Perceived value

0.368

2.660

0.008*

Entertainment → Perceived value

0.223

2.631

0.001*

Credibility → Perceived value

0.364

4.119

0.000** 0.423

Informativeness → Customer engagement 0.215

2.496

0.013*

Entertainment → Customer engagement

0.132

1.576

0.115

Credibility → Customer engagement

0.218

3.973

0.000**

Perceived value → Customer engagement 0.592

10.418

0.000** 0.498

Notes: **p-value < 0.001, *p-value < 0.05, ns = not signficant

As shown in Table 4, the bootstrapping analysis revealed that all of the indirect effects of INFO → CE (β = 0.484, t = 9.279), ENT → CE (β = 0.267, t = 6.431), and CRED → CE (β = 0.233, t = 7.294) are significant at t-values > 1.96 and p-value 0.05, implying the presence of mediating effects. Furthermore, zero does not appear in the 95 percent bias corrected bootstrap confidence interval for indirect effects. As a result, the presence of mediation effects is statistically significant, according to the researcher. Because the direct effect of the entertainment function on customer engagement was insignificant but the indirect influence was, a full mediation of customer perceived value was recommended. Consumer perceived value of advertisement, on the other hand, somewhat mediated the effect of informativeness and credibility of advertisement on customer engagement in Instagram. Table 4. Result of mediation analysis Hypothesis

Std beta

t-value

P value

5%

95%

INFO > PV > CE

0.484

9.279

0.000

0.284

0.744

ENT > PV > CE

0.267

6.431

0.000

0.155

0.465

CRED > PV > CE

0.233

7.294

0.000

0.185

0.314

Notes: INFO = Informativeness, ENT = Entertainment feature, CRED = Credibility, PV = Perceived value, CE = Customer engagement

5 Discussion and Conclusion The study has revealed some interesting findings. Informativeness and credibility of advertisement were found to have a significant relationship with customer perceived

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value of advertising. This finding corresponds to the past study conducted by [18, 19], where if an advertisement fulfills the utilitarian needs, precisely in providing useful and sought-after information, the same advertisement will then be considered to be credible. This will eventually shape an overall positive perception among the customers. Likewise, the perception of the relevancy and credibility of information in social media advertising tends to create value for customers. Advertisements that provide customers with relevant information about specific product benefits and comparative product information are regarded more valuable commercials [19, 21, 24]. Furthermore, previous research has shown that credibility has a significant positive impact on customers’ attitudes toward social media advertising, particularly how they evaluate these ads [25, 26]. Credible online commercials, according to previous research, have a higher likelihood of generating positive advertising outcomes and play an important part in the evaluation of advertising value among social media users [23]. Despite the non-significant link between the entertainment feature of advertising and customer engagement, the entertainment aspect did influence customer perceived value, which shown a full mediation impact along the link between the entertainment feature of advertising and customer engagement. Similarly, when compared to the entertainment element, informativeness was found to have the strongest predictive potential toward online advertising value in a study conducted by [19]. Likewise, [19] recommend brands should focus on the informative feature to generate product awareness and increase the advertising value instead of producing content that has no relevance to the target audience. Moreover, [18] also revealed that the entertainment feature of advertisement is not considered as a key predictor in enhancing customer engagement of online advertising. The current study also shows that customer perceived advertising value has a substantial association with customer engagement, which is consistent with earlier research [29]. Lutz [37] discovered that both cognitive and affective engagement might be linked to the customer’s reaction to persuasive communication, and that both are drivers of attitude toward advertisement. Similarly, [30, 38–40] discovered that people have a natural tendency to form personal connections and enjoy sharing pleasant items or news with others. As a result, when online users find a commercial “interesting’ or “worthy”, they will share it with their social network. Lastly, the findings also revealed customer perceived value of advertising partially mediated the relationship between the informativeness and credibility of advertisement toward customer engagement. This finding is consistent with previous studies in justifying the mediating roles of customer perceived value of advertising [22, 23, 25]. This study provides new insights into understanding the determinants of customer engagement among Instagram users in Malaysia. Considering the reasonably high explanatory power of the model, this research has significant theoretical and practical implications. Theoretically, the findings of this study added knowledge to the existing literature with regards to customer engagement in online advertising, specifically among the Instagram users which remains unexplored beforehand. Adding advertising value perception as a mediator provide empirical evidence on the important of enhancing advertising value to increase the customer engagement in online advertising. Practically,

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understanding customer engagement toward online media is vital in today digital economy where majority of the world population are actively involved in digital or online marketing. Moreover, customers nowadays, are actively and socially connected with one another in horizontal webs of communities [9, 23, 25]. The findings may be useful for online marketers to assess, develop and enhance their marketing communications strategies to enhance customer engagement on the web pages and the brands. As a conclusion, this study only includes the respondents from young generation which might not be generalizable to all Instagram users in Malaysia. Moreover, the present study focused only on the common predictors of online marketing, instead of specific factors related to the engagement in social media. Future researchers are recommend examining the predictors such as localization and customization of message on customer engagement, which contributes to a better understanding of this topic.

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Artificial Intelligence (AI), Blockchain, and Cryptocurrency in Finance: Current Scenario and Future Direction Mosharrof Hosen1(B) , Hassanudin Mohd Thas Thaker1 , Vasanthan Subramaniam1 , Hooi-Cheng Eaw1 , and Tat-Huei Cham2 1 Department of Accounting and Finance, UCSI University, Kuala Lumpur, Malaysia

[email protected], {hassanudin,vasanthan, eawhc}@ucsiuniversity.edu.my 2 UCSI Graduate Business School, UCSI University, Kuala Lumpur, Malaysia

Abstract. By combining artificial intelligence (AI), big data, and cloud computing, the blockchain revolutionises the production of both ascendable information technology systems and diverse applications. The combination of AI and blockchain has enormous potential for developing new financial service models that are enabled by digitalization. Although there are a few investigations on the application and conjunction of AI and blockchain, our extant literature findings on the financial services’ value of their integration are still fragmented. To close this gap, the goal of this research is to evaluate the uses and benefits of integrated AI and blockchain platforms across many financial service platforms. We further discussed the impact of decentralized cryptocurrency on financial sectors. This study revealed that AI and Blockchain have the potential of causing an increase in banking services, lending efficiency, collection and payment competency, and asset management. Furthermore, we highlight cryptocurrencies are encountering cybersecurity, mining, privacy, anonymity, and regulatory challenges that policymakers need to act on. Keywords: Artificial intelligence (AI) · Blockchain · Cryptocurrency · Financial services · Digitalization

1 Introduction Digital innovations revolutionised how we communicated in the 1990s and provides a new era where commercial activities become more convenient and well-connected [1– 3]. The World Wide Web connected us to the rest of the world and revolutionised the way we create, distribute, and utilise information. At the moment, we’re working with blockchain, artificial intelligence (AI), robotics, sensors, and a variety of other emerging digital technologies. And we frequently hear about how this or that technology, whether it’s automation, artificial intelligence, or anything else, has the potential to revolutionise the world on a similar scale to the internet. However, we must need to understand whether new digital technologies will have a far higher economic, cultural, and social influence © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (Eds.): ICETIS 2022, LNNS 584, pp. 322–332, 2023. https://doi.org/10.1007/978-3-031-25274-7_26

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than previous innovations because new technologies are intensively connected with each other [4]. Blockchain is gaining attraction to academics and nonacademic as a revolutionary technology with numerous applications in a variety of sectors including health, education, finance, banking, and business [5]. Blockchain considers a unique and distracting technology that affects the approach common people interact, increases security, traces dealings, decentralizes, automates payments, and distributed ledger. Most importantly, it gained popularity after the successful launch of bitcoin in 2008 and reached the apex of bitcoin price in 2017 [6]. Certainly, blockchain has opened up a world of possibilities by allowing for the secure and trusted movement of currency between its users and accelerating a cashless society. Furthermore, the popular blockchain technology mainly focused on a shared database that is synchronised with many sites, the checking of permissions and compliances can become more convenient with the implementation of smart contracts, as such actions can be approved by a respective member in a disseminated network [7]. More importantly, hash values and timestamps are two important approaches that are used to store data or information in a blockchain [5]. Likewise, all transactions on the blockchain are cryptographically signed, which means that all mining nodes have a carbon copy of the whole record book that can validate everything about particular operations or contracts. As a result, blockchain is both cost-effective and safe because it does not require a centralised authority to authenticate transactions [8]. Apart from the blockchain, artificial intelligence (AI) is also getting popularity as a disruptive technology that can execute difficult activities that would otherwise need individual intelligence, and it has the capability to surpass human skills [9]. AI facilitates the integration of developing technologies including cloud computing, internet of things (IoT), blockchain, cryptocurrency, and fintech in the Fourth Industrial Revolution, and it is considered one of the most important drivers of industrial development (IR 4.0). In fact, the huge amounts of data and information created by web applications, social media, and IoT devices have driven the growth of AI, which uses the data to train machine learning algorithms [10]. Nevertheless, there are some concerns about AI applications. Due to the various information revelations and misuses of individual sensitive information, privacy has become a major concern. One such example is the Cambridge Analytica affair which collected millions of Facebook users’ personal data without following proper rules of “personal data protection act” and these pieces of information were distributed to a third-party political firm. Explainability and trustworthiness are two further developing difficulties with AI because technology has no ability to contact or converse with individual operators and hence cannot be trusted or verified. AI and blockchain advancements have accelerated their integration, paving the way for the next digital generation to be ushered in by IR 4.0. According to [5], blockchain can provide AI-based applications with explainability, privacy, and trust, while artificial intelligence can improve security and scalability while resolving customization and ascendancy challenges. Although blockchain and AI are not always technically similar in many aspects, they can be utilised to compensate for each other’s flaws. In this way, blockchain and AI become substitutes for digital financial services, with AI assisting

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financial services in understanding, recognising, and making decisions, and blockchain assisting financial services in executing, verifying, and recording transactions [8]. Due to the enormous benefits of AI and blockchain technology, most entrepreneurs integrate both technologies to get maximum return from their professional activities. For example, “Chainhaus” is a blockchain and artificial intelligence (AI) consulting, teaching, and marketing organisation. From teaching and app creation to research and capital raising, the organisation offers a wide range of end-to-end solutions. Likewise, by integrating blockchain, big data, and AI, “BurstIQ” created a ‘Health Wallet’ that mostly concentrates to handle a patient’s historical data. Before inventing “BurstIQ” it was very difficult to protect patients’ information for long and it was inconvenient to find out when necessary. By using this BurstIQ application, the medical team easily finds out patients medical history and the medication process becomes easier. This application helps healthcare professionals to buy, sell, or trade patients’ information to understand certain diseases and accelerate research and development. On the other hand, the blockchain permits patients to maintain their personally identifying information confidential while distributing simply collective health data. Therefore, it is still imperative how AI and blockchain can assist the finance domain. This study mitigates the research gap by investigating how AI and blockchain impact finance. Along with the widespread acceptance of AI and blockchain technology, another newly developed technology called cryptocurrency has been getting popularity day by day [11]. A cryptocurrency is a form of electronic cash that can be used as a medium of exchange instead of fiat money. It makes use of blockchain technology, which creates a secure, traceable, and unchangeable foundation for financial transactions. Unlike digital currencies like Amazon cash and conventional central banking systems, newly invented cryptocurrency techniques do not have a central control structure [12]. Double spending is the most important problem that every single digital cash disbursement network faces. The term “double spending” refers to an entity spending the same amount twice. A central server maintains the balance track record in a digital cash disbursement network to prevent dual payout. There is no centralised structure in cryptocurrency systems; instead, it is a decentralised network. As a result, the network maintains a history and track record of all operations, and all transactions must be granted by contemporaries, which removes the possibility of duplicate expenditure [13]. [22] advocated that most scholars overlooked the benefits of cryptocurrency in economics and finance and the authors further suggested investigating more studies in this domain. Since cryptocurrency gaining prompt attention from scholars and investors, it is necessary to investigate the impact of cryptocurrency on finance. By following a thorough and systematic analysis of contemporary and relevant literature, this study gives interest to exploring the issues through three different aspects namely; AI, blockchain, and cryptocurrency impact on finance. Our findings indicate that AI and Blockchain have the potential of causing an increase in banking services, lending efficiency, collection and payment system, and asset management. Even though cryptocurrency is significantly enhancing and moderating traditional financial services we revealed that cryptocurrencies encountering challenges in cybersecurity, mining, privacy, anonymity, and proper regulations. More importantly, the policymakers should pay more attention to creating an appropriate regulation that provides an even playing field

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for cryptocurrencies and the financial sector, in order to attain such outstanding advantages. Our notable contribution to literature is, to the best of our knowledge, being the first to investigate how AI, blockchain, and cryptocurrency influence the finance sector by employing a systematic literature review. The rest of this paper is organized as follows; Sect. 2 offers a systematic review of recent literature while Sect. 3 exhibits the research method and Sect. 4 comprises a brief discussion. The final section highlights conclusion and future implications.

2 Literature Review Despite the global use of financial technologies (such as artificial intelligence, blockchain, and cryptocurrency), little is known about their potential influence on financial enterprises [14]. Due to the paucity of literature in this field, this study summarises existing practices and future discoveries in the fields of artificial intelligence, blockchain, and cryptocurrency. 2.1 Artificial Intelligence (AI) and Perceived Benefits to Financial Sectors Although the introduction of AI into the banking industry disturbed existing processes, the usage of AI in conjunction with robo-advisers is considered as a way to aid business parties in areas where humans may not be able to provide consistent guidance [14, 15]. This technique is based on financial theory and is supported by reproducible algorithms. It has the potential to simplify and speed up consumer interactions while also being far more transparent and reliable than human advisors. AI can also execute tasks that are simple for a financial advisor to complete but complex to formalise [16]. This type of work is common when a financial advisor need business forecasting data. Machine learning (ML) is currently being used widely as a method of developing AI-based systems. Information overload and constrained rationality are two behavioural biases that can be mitigated using machine learning algorithms. Without involving human cognitive skills, a machine learning algorithm can accurately determine human preferences based on the data and feedback provided to the system [16]. Humans may also use AI to train computers to forecast future consumer trends using existing data [16]. It can analyse photos of clients and determine a pattern of their behaviour over time. Not only can this system recognise images, but it can also recognise voices and translate languages [17]. AI will improve a wide range of procedures in the financial department in the future, making it more efficient. Businesses can save time and money by automating their operational processes, allowing top management to concentrate on strategic issues. The service coverage can also be extended for an entire day with minimal human interaction. 2.2 Blockchain and Perceived Benefits to Financial Sectors The blockchain revolution in financial technology has been well documented in recent literature [14]. Blockchain technology has the potential to help financial organisations

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clear payments and manage credit information systems. It can also be used to govern decentralised networks with consensus effectiveness and to coordinate smart contracts [18]. Blockchain has the potential to change some industrial companies and their business environments, resulting in a more competitive landscape. As a result, enterprises may be able to reduce knowledge asymmetry among stakeholders while also boosting consumer welfare. According to [19], Blockchain began as a mystery fiction concept (about information storage) that has now become a reality. The goal of blockchain is to improve information security and transparency by allowing network members to share encrypted data. The decentralised blockchain system, in contrast to the traditional method, can prevent information monopolisation or even fabrication by individual nodes because the same data is distributed and stored across all nodes [19]. External hacking targeting the central system is also less likely with a decentralised system. The Economists (2015) dubbed blockchain technology “The Thrust Machine,” predicting that in the future, the blockchain will be able to complete reliable transactions without the need for human contact. Because it permits direct transactions without intermediaries, it can boost trust among participants while also lowering transaction costs. For example, a provider of any good can interact directly with their consumer using this automated method. This technique can also help them avoid conflict and mistakes (eg: Smart Contract) [18]. It is possible to cut down on time and support costs (marketing costs). Blockchain technology is not a panacea for industrial development where the old operating system does not need to be totally replaced. As the current blockchain revolution focuses on using information to link consumers and suppliers, the blockchain of the future will focus on value creation, reshaping business models to increase transparency and reliability [20]. Users can share information more equally and safely than they can now. The blockchain system is also likely to merge with existing systems such as artificial intelligence (AI) and the Internet of Things (IoT) to allow additional nodes to join the network. 2.3 Cryptocurrency and Perceived Benefits to Financial Sectors Hundreds of businesses have sprung up in response to the unexpected popularity of cryptocurrencies enabled by Blockchain technology, such as mobile payment and international remittance [14]. This payment gateway keeps the clients’ preferences while reducing the anonymity of the people dealing with them. Starting with Bitcoin, Ethereum, and other cryptocurrencies have drawn a lot of interest from investors [21]. A sufficient development of the regulatory framework for cryptocurrencies is currently required in order for it to remain reliable and not be used for illegal purposes. In comparison to fiat currency, which has a central control mechanism, cryptocurrency has built a new platform to serve as a decentralised issuing organisation [22]. In sharing economies, the evolution of bitcoin and its underlying blockchain technology has the potential to establish socially adjusted solidarity-based collaborative organisations and common-oriented ecosystems [23]. This new business model can be built in the way businesses obtain funds and build their entrepreneurship abilities, particularly for Small and Medium Enterprises (SMEs).

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Cryptocurrency has the potential to become a promising type of payment platform in the future [22]. The majority of consumers will also use cryptocurrencies to save the value of speculative investments and to protect their savings. With the use of two-way transactions, it can also help consumers create and increase their trust in one another. As a result, using this democratised platform, consumers’ power can be increased [23]. The adoption of cryptocurrencies can also help to lessen the risk of volatility. The value of the currency can be maintained while waiting for a transaction to be verified by the cryptocurrency’s network (Fig. 1).

Artificial Intelligence Perceived Benefits to Finance

Blockchain Cryptocurrency Fig. 1. Research model

3 Research Method We used Google Scholar, Scopus, Web of Science, Springer Link, and other equivalent platforms to address the research question. We used different keywords such as Artificial Intelligence, AI, Blockchain, Cryptocurrency, Financial service, and Finance. There were a lot of articles came out, but we excluded all articles that are not aligned with finance or financial services. A narrative literature review tries to build a full overview and critical evaluation of a topic’s knowledge and to uncover deficiencies or problematize concepts, theories, or assertions that need more research [24, 47]. A narrative literature review isn’t exhaustive; it’s selected and aims to advance theory development [25]. Compiling a sample of data does not need to be representative, as understanding as much as possible about a topic is most important [26]. Using a narrative literature review allows us to explore how blockchain, AI, and cryptocurrencies have been conceptualized in earlier studies and to analyze their theoretical grounding. Being a novel study area [27] that needs sound elaboration and clear conceptualization, and considering the numerous perspectives from which cryptocurrencies have been studied, a narrative literature review is more appropriate to help us meet our research objectives. Narrative literature reviews are a well-established research method in management, finance, and economics [28].

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4 Discussion 4.1 Blockchain and Cryptocurrencies in Finance Blockchain improves Industry 4.0’s Anti-money laundering (AML) and Know your customer (KYC) processes [29]. First, distributed ledgers and the network’s public autonomy can trace the ins and outs of each fund to prevent monitoring. Criminals exploit vulnerabilities, laws, and regulations. Second, each node maintains network-wide data to reduce audit duplication. Third, all participants’ credit histories and transactions are shared in Blockchain’s global ledger. After KFC, all new client data is easily found, saving time and improving efficiency. Blockchain cuts AML and KYC costs. Blockchain has changed banks’ business model and technology. Financial giants and local banks are driven by: Reduced costs and value transfers. Commercial banks invest in a centralized database to reduce terminal maintenance and procurement costs. Bookkeeping and settlement raise costs and risk. Blockchain’s decentralized ledger and automation can provide a low-cost, transparent paradigm [30]. It decreases risks. Commercial banks emphasize loan monitoring, but it’s not effective. Global capital regulation can be difficult. Each Blockchain user is a node, allowing direct peer-to-peer transactions without bank credit guarantees. Information asymmetry improves fund management and decreases credit risk. Profit tactics are inventive. Banks and investment firms are investing in or partnering with Blockchain companies. In this competitive environment, banks need new profit models to generate financial products and open markets. 1,600 cryptocurrencies have developed since bitcoin’s launch [31]. Despite the hype, cryptocurrency buy real goods and services [32]. Cryptocurrencies affect financial design, management, and regulation [33]. Blockchain drives cryptocurrency [34]. Blockchain is a digital, decentralized, distributed ledger that logs and adds transactions in chronological order to build tamper-proof records. Blockchain technology is based on peer-to-peer connectivity and cryptographic security, giving decentralization, transparency, and trust. Many cryptocurrencies are used for daily payments and are analogous to precious metals [35]. Central banks and retail banks like blockchain [36]. Many banks use blockchain to develop FinTech businesses and underpin cryptocurrencies [37]. Cryptocurrencies are distributed and decentralized [38]. The technology allows direct financial transactions and value transfers between parties [39]. Blockchain solves double-spending in financial apps [40]. Digital tokens are digital files that can be copied and spent several times. Bitcoin uses the Internet for speed, resilience, and efficiency [41]. Public-key encryption, used to protect money transfers, erodes trust [42]. Most cryptocurrencies are utilized online. The development of cashless payments and credit cards has boosted crypto online. Digital currencies could influence cash flows and supply chain architecture as exchange partners trade, exchange value, and settle payments with them [43, 44]. [45] indicates that crypto can simplify payments by eliminating commissions. 4.2 Artificial Intelligence in Finance There are many applications of AI in finance. Most financial institutions’ work nowadays involves deploying AI technology to meet their needs. Credit scoring is a key

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use of machine learning in finance. Many banks and fintech firms lend money. They must accurately analyze a person’s or a company’s creditworthiness. AI-based scoring systems may make unbiased judgements because no human influences, such as a bank employee’s emotions on a given day, influence them. It may help those without a credit history prove their dependability and ability to repay a loan [46]. Next is for fraud prevention whereby in the past, businesses fought fraud with expert-designed hardcoded regulations. Fraudsters may uncover the regulations and exploit the system. AI-based systems can adapt over time to new data trends. Machine learning algorithms excel at recognizing anomalies and fraudulent transactions. An algorithm may filter through millions of transaction-related information (client behaviour, location, spending trends, etc.) and trigger a warning when something seems off. Traditional machine learning approaches such as logistic regression, SVMs, and decision trees already perform well, but the industry is always looking to improve. Complex algorithms scale better for big volumes of data (both the number of observations and potential features) [47]. Another widely AI application is under investment sector. Algorithmic trading systems incorporate advances in machine and deep learning. Other aspects of these systems may employ econometrics and asset allocation theory to anticipate asset returns (to a reasonable degree). Using alternate data sources to beat the competition is growing popularity. Advances in object recognition can aid analyze satellite photos, while NLP approaches allow accurate sentiment identification from news articles, Twitter, Reddit, etc. Individual data scientists who design their own trading systems on local workstations or in the cloud are also interested in algorithmic trading. With modifications to how easy it is to start trading and additional brokers’ APIs, more people are eager to try it. Finally, AI is used to customize banking for everyone. Chatbots are hard to distinguish from human consultants. They can grasp the customer’s intent using powerful NLP techniques. They can change passwords, check balances, arrange transactions, etc. Chatbots can also recognize a customer’s emotions and respond accordingly. If the consumer is upset, it may be best to connect them to a human consultant to remedy the problem and minimize more frustration. Smart chatbots’ growing capabilities reduce call center workload, saving money [48]. 4.3 Concluding Remark The application of blockchain and AI technology has evolved in the financial services industry which allows the automotive recognition and checking process especially in identifying on-chain loan requests by readiness on transactions ledger with validated and authenticated to overall clients and bank’s accounts. Based on the financial perspective blockchain and Al can reduce the transaction costs by cutting short on the validating and authenticating of client profiles with reducing the approval period. Besides, the automation functions enable the approval functions which can be processed within a short period with only minimum manpower required. Further, it has proven the cost reduction for manpower allocation from the checking, validating, and approval stages by automation. Next, the build-in of a public ledger tracer on the system helps easiness for auditors, accountants, and government tax authorities to validate the account transactions saving time and costs to eliminating redundant processes. Besides, blockchain also cuts the middlemen that reduce commission payment as compared to

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the conventional bank will need to hire more sales agents to deal with the client loan application. From a non-financial perspective, blockchain and Al is transparent and secure because the system is designed with functions nearly impossible for altering which increases the users’ confidence in reliability and security. The underlying technology has been applied using bitcoin or another type of cryptocurrency transactions to enable the system to trace on the peer-to-peer network. Hence, blockchain guarantees transparent information for the client users and bankers by unified data standardization. Next, the blockchain system has inherently protected user data, the individual user enables control over one’s digital information as they wish to share and how long for whom can be shared. Furthermore, as highlighted early, automation verification and authentication make speedily on the processes with confidence in the transaction via tokenization and cryptocurrency settings. However, the blockchain in using bitcoin transactions is still new in Malaysia. Based on the survey from Singapore Fintech Association (2022) indicated only 3.1% of Malaysians own cryptocurrency and only a few traders are allowed to trade the cryptocurrencies approved by the securities commission. Traders must follow strict regulations from securities of commissioners to ensure their assets and funds are protected. Furthermore, the Bank Negara Malaysia (BNM) plans to approve a maximum of five digital bank licenses in alignment with the blockchain development in March 2022. However, the Malaysian Industry-government group for high technology (MIGHT) is expected Malaysia’s financial industry is the readiness to adopt blockchain in fintech by 2025 as working more research from national R&D in ICT, MIMOS, etc. to find more data on intelligent information and cryptography feature on the fintech-related area.

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Common Data Environment: Bridging the Digital Data Sharing Gap Among Construction Organizations Yong Jia Tan(B) , Zafira Nadia Maaz , Shamsulhadi Bandi , and Prescilla Anak Palis Department of Quantity Surveying, Universiti Teknologi Malaysia, 81300 Johor, Malaysia [email protected], {zafiranadia,shamsulhadi}@utm.my, [email protected]

Abstract. Moving into the 21st century, digital data sharing is pertinent towards the construction industry technology advancement. Preeminent digital data sharing revolves around construction organizations’ effective data management and digital data utilization within the Common Data Environment (CDE). Interconnected data is the heart of the construction industry’s future digital utility. Albeit the progressive digitalization uptake, the absence of integrated digital data collaboration efforts due to working-in-silo facet impedes the Malaysian construction organizations capability to capitalize the technology potential at best. To identify the types of digital data and the potential of digital data sharing through Common Data Environment within the Malaysian construction industry, this study adopts thematic analysis methodology on five in-depth case study on CDE adoption among construction organizations. The presented case study further identified through snowball sampling method. The analysis reveals the three main data categories created by construction organization in CDE are graphical data, nongraphical data, and associated construction project documents. Findings further identifies eight potentials of CDE data sharing namely improved efficiency, productivity, collaboration, effective decision making, cost and time savings, security, and accessibility. Ultimately, this study presents insights and explorative avenues for construction stakeholders to transcend advanced technology maximization and boost the industry productivity gain. Keywords: Common Data Environment (CDE) · Big data · Data sharing · Building Information Modelling (BIM) · Digital collaboration

1 Introduction The recent pandemic creates a significant technological shift with an overnight increase of technology adoption among construction organizations. The McKinsey Global Survey 2021 postulate the rapid technology adoption as key factor to revolutionize the conventional practice within the construction industry [1]. To stay relevant, research suggests © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (Eds.): ICETIS 2022, LNNS 584, pp. 333–342, 2023. https://doi.org/10.1007/978-3-031-25274-7_27

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the need for digitalization strategies exploration namely, the Common Data Environment (CDE) adoption. According to Mordue [2], CDE is an interconnected environment which encapsulates the creation of vast digital data across different technology systems. In the effort of transcending technology maximization, CDE plays a significant role towards integrated digital collaboration among construction organizations. With CDE, construction organizations are able to manage inter and intra organization digital collaboration efficiently. Hence, instigate promising avenue to muster organizations’ productivity improvement, drive business value and transcend business sustainability [1]. Despite recent positive technology uptake, construction organizations yet to proffer significant productivity improvement [3]. While technology adoption creates a sustainable mechanism in digitalizing construction project information, these data are created and stored across diverse technology system. Research further postulates the nature of working in silo across construction organizations result to technology adoption exists as independent systems; further creates ‘’islands of automation’ [4–6]. Similarly, Raihan et al. [7] argues construction organizations’ limited capability in managing coherent technology adoption inhibits the construction industry productivity improvement. These issues believed to impede construction organizations’ future technology investment as the potential return value is insignificant in comparison to the huge investment required. Within the local construction industry context, the recent Construction 4.0 Strategic Plan 2020–2025 attests the call for strategic digital collaborative efforts across diverse modern technologies [8]. However, limited research on strategic digital collaboration through CDE impedes construction organization’s capability to strategically streamline current and future technology adoption. Critically, these issues suggest a gap in understanding the context of CDE as a strategy towards shaping an integrated digital collaboration within the construction industry. Knowledge on strategic digital collaboration particularly; the identification of digital data in the construction industry and the potential of data sharing through CDE shall create positive awareness on of the importance of digital data sharing as well as shaping the local construction organizations’ ability to maximize modern technology such as Building Information Modelling (BIM), big data and robotics.

2 The Construction Industry Common Data Environment (CDE) Context 2.1 The Construction Industry Digital Transformation and Data Sharing Digital transformation refers to technology, data science, devices, design, and business strategy adoption and organizational changes to increase business added value and agility [6]. According to Williams et al. [1], 64% of the global organizations reckon the pressing need to move towards digitalizing the overall business process in staying relevant. Within the construction industry context, digital transformation is a process of technology adoption and maximization by construction organizations, across construction projects. Digital transformation commonly favours large organizations. However, the complex nature construction projects seen to force digital transformation among

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small construction organizations [9]. This suggests the digital transformation is relevant across construction organizations, regardless of their size. Following to the unique characteristics of this industry, technology adoption at best shall transcend technology maximization across both inter and intra organization level. Succinctly, digital transformation of construction project data requires the progression from non-model based approach towards model-centricity approach [10]. 2.2 Common Data Environment (CDE) CDE is a centralized information repository hosting digital project information across multidisciplinary teams. Essentially, CDE is a platform for construction organizations to create and store digital data. It is based on idea of an integrated collaborative workspace using cloud technology [11]. As CDE matures, CDE shall function as an automated centralized repository to collect, manage, and disseminate construction project data [12]. CDE consists of two components; 1) Data standard and 2) Data platform [13]. Data standard defines the type of information to be collected and data shall be stored to assist data sharing and data integration process. Data platforms are technological platforms to store, share, and integrate digital data. While construction organizations positively embark digital transformation, data created within this industry are fragmented, chaotic, unorganised, and isolated [14]. Risks on data duplication and information loss shall result to poor decision-making [15]. Nevertheless, spatial coordination is inherently possible when a centralized system is used [16]. Thus, CDE platform allows construction organizations to access, integrate and share data. CDE incite the opportunity for construction organizations to move towards an integrated and collaborative project management approach. 2.3 Data Creation in Construction Industry Data is the new oil [17]. The construction industry generates volumes of data with varying levels of complexity throughout the entire project life cycle. Data is the core layer of the model-centricity approach as data models coincide with the physical world. Construction organizations can turn data into valuable insights using big data technology to execute real-time yet accurate decision making [18]. Construction project data can be in the form of text, such as contract clauses and specifications, or quantitative data, including variation orders and associated properties, such as value, timing, planned and actual activity durations as well as costs [19]. Table 1 summarizes the types of digital data created within the construction industry. Commonly, data are categorized into structured and unstructured characteristics [20]. Effective digital transformation allows data accumulation and accessibility across construction stages. These stages involve generating construction site data, project schedule data, architectural and structural data, utility data, cost estimating data and others. Thus, the availability of such data allows informed project planning and control through real-time analysis such as structural analysis and cost estimation [22].

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Authors

Types of data

Soibelman et al. [20] Structured data: Productivity records data, cost estimates data, network-based schedules data Unstructured data: text documents data, digital images data, web pages data, project schedules data, contracts data, specifications data, data of 2D or 3D drawings, geospatial data Baek et al. [21]

Structured data, unstructured data, text data, image, sensor data, safety document data, code and specifications data, engineering document data, bidding document data, contract document data, claim document data, construction document data, web data, academic publication data

2.4 Common Data Environment (CDE) Platform Value in the Construction Industry Effective Decision Making. Stransky [23] evident the potential of effective decision making along the avenues of schedule management and construction progress monitoring. The role of CDE is not limited to digital storage and data sharing system. Data hosted in CDE can be visualized to support automated digital reporting dashboards [24, 25]. In this sense, CDE shall provide valuable insights for effective decision-making as construction project data are stored and visualized promptly. Productivity Improvement. CDE serves as a single source of truth, creating a more reliable data set with reduced risk [26]. As collaboration between construction stakeholders are held in one place, CDE streamline interactions between construction organizations. This significantly prevents bottlenecks and data access delay. As decision making can be made in a timely manner. Hence, aids positive productivity improvement across the construction industry. Efficiency Improvement. Akob et al. [25] highlights that CDE presents opportunities for asset maintenance enhancement. As CDE fosters the easy exchange of information among construction stakeholders, this facilitates effective digital plans and model management. CDE has shown significant efficiency improvement in the area of built assets, estate planning, procurement, and maintenance [26]. Time Saving. With CDE, construction organizations do not have to rely on traditional methods of managing data; the mundene repetitive data filing process filing. CDE offers effective indexing capabilities, making it easy for users to locate every item. CDE is timesaving with its ability to transfer accurate and complete information from construction to operational stages [26]. CDE allows smooth operation in digital data sharing. This shall significantly reduce time and effort required to verify, modify, and reissue information.

3 Methodology This study adopts the qualitative research methodology. To obtain an in-depth understanding on the Malaysian context of digital data sharing, a case study research strategy

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is chosen to examine CDE utilization by the Malaysian construction organization in detail. The case study research strategy is evident in addressing ‘what’, ‘why’ and ‘how’ strategic digital data sharing can be achieved through CDE platform [27]. A fixed purposive sampling strategy was used to identify participants with capability to assist in exploring CDE in the construction industry context [28]. A set of characteristics was used to identify participants with a deep understanding of CDE through multiple perspectives. A set of characteristics was used to identify participants with a deep understanding of CDE through multiple perspectives. The following characteristics were used to identify participants with a deep understanding of CDE within the construction industry context are: i. Individuals or parties participating the development of Common Data Environment (CDE) platform, which he or she can either be in private or public sector. ii. Individuals with experience in handling CDE-based projects The exploration of the types of data and the potential of digital data sharing using CDE platform was conducted through semi-structured interview. The semi-structured interview details perspectives, views, or opinions corresponding to the objectives of this study. Data gained from the semi-structured interview sessions were further analyzed using thematic analysis. This involves data coding process to identify substance and patterns of CDE understanding in the construction industry, data types and the potential of data sharing context. NVivo software facilitates the process of identifying, tracking and reviewing codes to achieve the objectives of this study.

4 Findings and Discussion 4.1 Data Types in CDE There are three main categories of data types identified through the adoption of CDE in the construction industry namely, graphical data, non-graphical data, and associated construction project documents, as shown in Fig. 1. Graphical data are spatial data, models, and CAD drawings. BIM models and Geographic Information System (GIS) support the availability of information-rich spatial and graphical data in CDE. Findings further identify CAD drawings as the most available data created and stored in CDE. Non-graphical data consists of records such as piling records, quality records, inspection forms, specification documents and method statements. Since construction project data must be recorded for a minimum of seven years, CDE plays a vital role in maintaining the records digitally. Findings in this study further identify ten types of associated documents available in CDE, namely, request for information (RFIs), submittals, transmittals, operational manual, warrants, working files, variation orders, subcontractor back charges, progress claims, and payment documents. CDE and BIM are interrelated. BIM models represent the digital creation of construction project information while CDE acts as platform to store and integrate BIM models as well as other forms of data. CDE plays an essential role in supporting seamless data sharing across different data types.

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Fig. 1. Theme representing data types created in CDE platform

Based on the data analysis, documents in the CDE platform are vital to aligned and coherent data management across the project team members as data are managed in realtime. The availability of accurate and real-time data shall curate a better understanding of construction issues as well as the corresponding mitigation prospect. Critically, findings on the availability of three main data types across the construction project life cycle insinuate construction organizations’ awareness on the role of CDE as an integrated data management strategy. Alternatively, findings further highlight construction organizations’ limited capability to maximize CDE potential in the pursuit of improving the construction industry productivity. Critically, these findings align with Soibelman et al. [20], Baek et al. [21], insinuating the construction industry’s awareness of CDE, including the availability of three main data types across the construction project life cycle. Alternatively, understanding the types of data used in CDE improves the extraction of relevant information from large volumes of records for different purposes in a timely manner. However, construction stakeholders pose limited capability to maximize CDE potential fully. 4.2 The Potentials of Digital Data Sharing in CDE This study identifies eight potentials for data sharing in the construction industry through CDE adoption. Figure 2 details the data sharing potentials: easy accessibility, effective decision-making, improved efficiency, productivity, time-saving, efficient collaboration, improved project quality and consistency, and cost-saving. Easy accessibility through the CDE platform enables real-time and boundaryless data sharing. In terms of efficient collaboration, findings show CDE facilitates collaborative working among construction organizations as well as reduces the likelihood of data miscommunications as all the data is stored in a single platform. Thenceforward, CDE bridge the communications among stakeholders while reducing the potential of arising

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Fig. 2. Theme representing the potentials of data sharing through CDE

dispute. CDE uplifts data transparency while providing a ‘single source of truth’ avenue to construction organizations. CDE is more than just a digital storage and sharing system. Features such as data real-time data integration and interactive visualization enable construction organizations to make effective yet accurate decisions making. CDE plays a significant role in organizing structured and unstructured data as well as maintaining proper documentation. Thus, the construction work process can be improved as the right information reaches the right people at the right time. Moreover, CDE fosters data sharing through the easy exchange of information among construction organizations. This makes it possible for project team members to create digital plans and models in an effective and efficient manner. Time-saving is one of the digital data sharing potentials identified. It offers effective indexing capabilities, making it easy for users to locate complex data. As a result, the time and effort required to verify, modify, and reissue information are significantly reduced. Findings in this study highlight the potential of interactive data visualization as an extension of CDE data maximization. Next, CDE offer alternatives to solve data interoperability issues. CDE pioneers flexible data accessibility with various plug-ins data integration. While previous findings by Radl and Kaiser [14] suggest that CDE in construction projects would reduce operation costs, one notable result in this study on high licensing fees deters a great number of adoption resistance among of construction organizations. Nonetheless, findings indicate that the long term of CDE adoption affirms positive investment return. CDE cost-effectiveness is parallel to the volume of usage, context of collaboration, and maximization data as well as construction organizations’ data analytic capability. With CDE, data are shared simultaneously among construction organizations in a unified platform. The value of communication simplification and effectiveness through CDE helps prevent bottlenecks in data access while furnishing a multi-perspective

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decision making capability. This also accords with previous studies, postulating CDE as indispensable strategy in achieving data-driven decision in managing construction project [24]. Consequently, these potentials contribute toward the construction industry’s productivity improvement. CDE adoption presents a necessity for construction organizations to transform the conventional construction project information data creation norm. Hence, CDE is the precursor towards digitalization transformation within the construction industry.

5 Conclusion CDE as an alternative towards an integrated digital collaboration within the construction industry has gained traction since 2007. This study extends previous findings by detailing CDE knowledge from the construction industry context. The findings depict a deep overarching understanding of the Malaysian construction industry digitalization efforts. The study lists the digital data types created in managing construction projects across inter and intra-construction organization. Albeit positive technology adoption and government’s effort to create awareness on CDE potential, findings affix critical grounds on construction organization limited capability to leverage and turn the digital data in CDE into valuable insights. Findings in this study further suggest as the integrated digital collaboration through CDE within the construction industry is at infancy stage. Finally, the identification of CDE potential within the construction industry spark means towards improving the construction industry productivity as well as achieving construction organization’s business sustainability. In a broader context, the findings of this study inform basis for regulatory bodies such as the Construction Industry Development Board (CIDB) and Public Works Department (PWD) to create strategies in light maximizing integrated digital collaboration efforts among construction organizations. Acknowledgments. The authors would like to express their appreciation for the support from the Ministry of Higher Education under the Fundamental Research Grant Scheme (FRGS) (FRGS/1/2021/SS02/UTM/02/2) and Universiti Teknologi Malaysia under Geran Others (R.J130000.7352.4B726).

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A Study of the Perceptions of Last-Mile Delivery Towards the Adoption of IoT Aimi Amirah Khairuddin, Emelia Akashah P. Akhir(B) , Nurul Aida Osman, and Norshakirah Aziz Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak, Malaysia {emelia.akhir,nurulaida.osman,norshakirah.aziz}@utp.edu.my

Abstract. The rapid growth of Malaysia’s e-commerce industry has caused the logistics industry to serve a rapidly increasing number of customers, mainly in the last-mile delivery sector. Thus, there is a need to use advanced technology, such as IoT to handle this challenge. MIMOS has carried out an IoT roadmap, but the development is still slow. Therefore, the purpose of this paper is to investigate the perceptions and attitudes of courier companies towards the adoption of IoT. For this research study, in-depth interviews with four courier companies in Malaysia were used to gather their perceptions and attitudes towards adopting IoT using the Technology Readiness (TR) construct as a guideline. Content analysis was then utilized to determine participants’ attitudes. This paper proposes a model to help decision-makers predict the IoT’s intended use. The results show the majority appear to have a positive outlook on IoT. The respondents reacted more to positive views, optimism, innovativeness, and convenience than negative views, which are risk-taking propensity. The findings of this study provide some guidelines for courier companies to evaluate IoT adoption in their firms. Moreover, the government can also benefit from this study by encouraging the acceleration of the IoT roadmap. The technology readiness in this study has been accessed qualitatively differently from earlier studies on technology readiness that used survey methods and scale. For future research, it is encouraged to acquire research data from different sectors in courier services, which are local and international. Keywords: IoT · Technology readiness · Last-mile delivery · Courier companies · Adopting new technology

1 Introduction COVID-19 has impacted the consumer payment space in Malaysia as consumers transition to online shopping [1]. The closure of physical establishments owing to lockdown and social distancing measures led consumers to increase online buying, which increased Malaysia’s e-commerce market growth. GlobalData’s E-Commerce Analytics predicts Malaysia’s e-commerce sector will rise 24.7% in 2020. The market is estimated to reach MYR51.6bn (US$12.6bn) by 2024, growing 14.3% between 2020 and 2024 [2]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (Eds.): ICETIS 2022, LNNS 584, pp. 343–351, 2023. https://doi.org/10.1007/978-3-031-25274-7_28

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This pandemic increased the use of technology to secure corporate viability. Most industry participants are beginning to view technology as the optimal solution for replacing traditional procedures. The technological explosion gave rise to e-commerce [3]. The recently released Federal 2021 Budget allocates USD242.5 million (RM 1 billion) to Cybersecurity, Connectivity, the Internet of Things, Digital Talents, and the Digital Transformation of Malaysian SMEs [4]. The huge amount of allocation shows IoT’s importance for Malaysia’s future. Last-mile delivery refers to transferring items from a warehouse to a customer’s door, a synonym for parcel deliveries [5] Last-mile delivery is becoming very important in the supply chain because of the high demand for online orders [6]. In the last year, when the coronavirus epidemic spread rapidly, e-commerce has been a substantial source of revenue for the global courier, express, and parcel (CEP industry [7]. As a consequence of the growth in e-commerce, there are a significant rise in-home delivery packages and a massive increase in the number of orders delivered. There is a sharp rise in last-mile services for parcel distribution [8]. As e-commerce grows, so does the need for logistic service providers to keep up with the rising transparency and flexibility demanded by businesses and customers alike, including the need for enhanced traceability, shorter lead times, and the opportunity to select flexible delivery locations [9]. This new customer expectation will require courier companies to reinvent themselves. IoT real-time tracking is one of them [10]. IoT will improve customer satisfaction with the help of faster information flow that the supply chain can offer with the availability of IoT. Numerous technical devices are connected through sensors installed on these devices, which allows for obtaining realtime information on several critical parameters such as temperature, pressure, and others [5]. In conjunction with the expansion of the IoT network, Malaysia has implemented an IoT roadmap to generate RM9.5 billion in gross national income by 2020 and RM42.5 billion by 2025 [11]. However, Malaysia is still slow in embracing the digital revolution compared to neighboring countries like Singapore and Vietnam [12]. According to IT experts, costs affect the slow adoption rate of IoT [13]. Hence, there is a need to assess IoT readiness, especially in last-mile delivery operations. This paper reveals insight into courier companies’ perception and attitude towards the IoT, highlighting the contributors and inhibitors posed by this technology readiness.

2 Literature Review 2.1 Factors Affecting Readiness in Adopting New Technology in Last-Mile Delivery The retail e-commerce market is rising in today’s digital era. As e-commerce grows, logistics players utilize technology and new business models to maximize cost and time efficiency for last-mile delivery. Growth comes with an excellent chance for development. In most cases, there are two elements causing innovations in last-mile delivery: customer expectations and cost unpredictability. However, there might be risks such as

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unstable volume and volume density, inaccessibility in rural areas, cancellations and returning of orders, and others that affect last-mile delivery costs [5].

Inhibitor for building digital operations capabilities

0%

20%

40%

60%

Unresolved questions around data security & data privacy in connection with the use of external data High financial investment requirements Lack of clear digital operatios vision and support/ leadership from top management Insufficiet talent Slow expansion of basic infrastructure techologies Business partners are not able to collaborate around digital solutions Unclear economic benefit of digital investments Lack of digital standards norms and certifications Concerns around loss of control over your company's intellectual property

Fig. 1. Inhibitor for building digital operations capabilities [14]

Based on Fig. 1 above, the main challenges businesses face are a lack of clear digital operations vision and support from top management. Top management is responsible for engendering innovations by providing the appropriate environment and making decisions that develop the creation and execution of knowledge positively. Idyllic top management can encourage employees to innovate and solve problems by displaying deep responsiveness and delivering an inducement. Top management should help employees grow as people, reach their goals, and feel better about themselves. Many researchers found that top management is the main force behind organizational change. Other researchers said that top management affects how organizations use innovative activities [15]. The second inhibitor is an unclear economic benefit and digital investments. The introduction of Industry 4.0 technologies has not been planned out, and major investments have not been made. As a result of the solutions’ novelty and the necessity for substantial alterations, many businesses find it difficult to implement them. There are many ways to quantify potential, each with its unique challenges. There is a pressing need for greater openness and cross-sectoral knowledge exchange [16]. High financial investment requirements placed third in the rank. Next, unresolved questions around data privacy and insufficient talent share the same percentage (25%). The data that hold unique identification, personal information, encryption, and data integrity are considered the challenges for IoT when it is communicated and acquired.

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Henceforth, IoT identification is essential in terms of security and privacy. Five dimensions of a secure IoT system include hardware, software, operating system, networking, and data generated and maintained within the system [17]. Then another critical resource impelling information technology (IT) adoption in SMEs is knowledge in IT. The lack of IT knowledge in SMEs commonly looks like an obstacle to IT adoption since the top management of SMEs might be confused by the rapid development of IT and numerous choices [18]. One of the most vital factors that help to increase the levels of IT adoption and satisfaction in SMEs is the development of internal IT knowledge and skills. Moving to the next factor, the slow expansion of infrastructure technology has become one contributor to technology adoption in logistics. The digital telephone network, mobile phones, Internet access, Internet servers, fixed broadband, and other technologies make up what is known as “information and communication technology infrastructure.” [19]. Comparing our telecommunications services with other advanced IT countries, their systems are more advanced. This is because many individuals believe that current telecommunications network services do not adequately satisfy customers. Additionally, it is limited and typically only available in urban areas. Thus, the critical issue is modernizing the existing infrastructure and building a new one in remote and rural areas. The construction of telecommunication lines in remote areas is essential since it will facilitate the people there. However, this effort may be risky as the maximum utilization of the available infrastructure cannot be guaranteed due to the low demand in the area. It is not worth the cost of the development of infrastructures. Therefore, cheaper, complementary methods should be distinguished as the underlying infrastructure is developed step by step [20]. Therefore, Malaysia needs to move faster and embrace Industry 4.0 as a critical platform for promoting and maintaining its future manufacturing competitiveness [21]. Four overarching goals need to be focused on, which are: • • • •

Drive constant growth in manufacturing GDP An upsurge in national productivity Generate higher skill employment opportunities Increase innovation capabilities and competitiveness

2.2 Technology Readiness Parasuraman and Colby [22] described technology readiness as “people’s propensity to embrace and to use new technologies for accomplishing goals in home life at the workplace.” As shown in Fig. 2, TRI (Technology Readiness Index) has developed by Parasuraman to measure one’s beliefs and thoughts in general about technology. There are two positive perspectives on technology: optimism about technology and the propensity to be an early adopter of new technology. The other two views are unfavorable and reflect a trend toward discomfort and skepticism regarding technology. The four resulting dimensions of technological readiness are optimism, innovation, discomfort, and insecurity. The first two characteristics of technology readiness, optimism, and innovativeness, are “contributors” that can boost readiness to use technology. The optimism dimension

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represents a positive perspective of technology and perceptions of the benefits of enhancing work efficiency and performance at home and the workplace. Innovativeness refers to the extent to which a person enjoys experimenting with technology and being an early adopter of the newest technology-based products or services. In contrast, discomfort and insecurity are “inhibitors” in the last two dimensions” that might reduce the level of technological readiness. Dimensions of discomfort demonstrate a lack of technical expertise and confidence in utilizing the most advanced technologies. The insecurity dimension relates to mistrust of technology-based transactions and doubts about the technological capabilities of the technology. Although it is related to the dimensions of discomfort that indicate the inconvenience of technology in general, the insecurity dimension focuses more on mistrust of technology-based transactions and doubts about the technological capabilities of the technology.

Fig. 2. Technology readiness construct [22]

3 Methods 3.1 Methodology and Case Selection This study used an in-depth interview to understand the participants’ attitudes and opinions toward a particular topic. A total of four courier companies in Malaysia participated in the qualitative research phase. In this study, an in-depth interview was utilized to conduct rigorous individual interviews with a few respondents to determine their perspective on IoT and gain a comprehensive understanding of the situation. The company was chosen based on its active status with the Malaysian Communications and Multimedia Commission (MCMC), responsible for communications and multimedia sector regulation. In total, interviews were conducted with six individuals representing four courier companies. Interviewees in senior and top management were selected to represent the company. The interviews lasted one hour on average. The discussion has covered a few topics, such as the status of the IoT implementation in the company and the factors influencing the company’s decision to adopt IoT.

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3.2 Analyzing the Interview Data The interviews, which lasted an average of one hour, were recorded. The interviews were then transcribed, and their content was examined using content analysis. The initial step in content analysis is transcribing the interview materials, then labeling condensed meaning units by establishing codes and then categorizing these codes. The qualitative content analysis aims to translate a large amount of material into a highly organized and concise summary of essential findings. The interviewees’ expressions of readiness dimensions were coded, and the content analysis process is explained in Sect. 4. After the content analysis has been done, all the functions in the content analysis, which are condensation, codes, and categories, will be transformed into a model. The results will be further explained in the next section.

4 Data Analysis After the interviews, the content analysis identifies and draws out participants’ attitudes. Next, transcripts were read and measured against the Technology Readiness (TR) construct to establish their perceptions and attitudes toward adopting IoT. The analysis will help answer the proposed research question and interview questions as the process involves generating codes, condensations, coding, and assigning categories and themes. Once the themes were defined, the following process was to establish the names in a way that clearly explained the scope of the data. Theme names were kept short of signaling the essence and straightforward interpretation. The themes are shown in Table 1 and defined as 1) Optimism, 2) Innovativeness, 3) Convenience, 4) Risk-taking propensity. The themes that best represent respondents’ perspectives on the IoT tend to condense the four dimensions of the TR construct. Convenience is the sole theme that does not appear in the TR construct. Convenience is included as a new dimension in this article because it demonstrates the capacity of cutting-edge technology to enable customers to experience products and services without regard to time or location. Moreover, the other two TRI aspects, discomfort and insecurity, have incorporated extra concerns (for example, technology specificity). In TRI, insecurity is described as mistrust and skepticism, and discomfort is the fear of losing control over technology. Even though discomfort and insecurity are thought of separately in TRI, it has been found that the items developed by Parasuraman [22] for measuring these two concepts have some things in common, like “Many new technologies have safety risks that aren’t discovered until after people use them,” which measures discomfort, and “You don’t think it’s safe to do financial transactions online,” which measures insecurity. These strategies address the safety concerns involved with adopting innovative technologies. Thus, it has been integrated with the “Risk-taking propensity” to form a single dimension [23].

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Table 1. Themes that appeared in the data Themes

Nodes that categorized the theme

Optimism

– – – – –

Easiness of the technology Connectivity Efficiency Effectiveness Productivity

Innovativeness

– – – –

Solve Problem Independency Stimulation Strength of competition

Convenience

– Feel convenience – Convenient to use – Convenient access

Risk-taking propensity

– – – – – – – – –

Complication Difficulty Dependence Lack of Support Incompatibility Failure Threat Reducing Interaction Distraction

5 Findings The findings in this section highlighted the participants’ responses to the interview questions. These responses all contribute to shaping the overall attitudes of the courier company. During this study, it was found that the respondents reacted more to positive views, optimism, innovativeness, and convenience, compared to negative views, which are risk-taking propensity. From the research studied, it is clear, for the most part, that IoT technology satisfies customers’ wants and needs. From the research, it is evident that IoT technology fits customers’ expectations and needs for the most part. The majority appear to have a positive outlook on IoT. This, therefore, encourages the acceleration of the IoT Roadmap (Fig. 3).

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Fig. 3. The proposed model

6 Conclusions In addition, the aspects of readiness should be measured accordingly and thoroughly. Through the results of this research, it is hoped that it can help accelerate the willingness among courier companies in Malaysia to adopt IoT. The study also shows that the government should consider the company’s opinion on IoT to promote Industry 4.0 adoption in Malaysia. The study also revealed the potential challenge of adopting IoT. Employees often feel the nature of the current business might not be as relevant for adopting IoT. Thus, the government should consider changing employees’ factors when adopting new technology. For future research, it is encouraged to acquire research data from different sectors in courier services which are local and international. This comparison would assist in understanding the different sectors’ attitudes towards IoT and if there are any major differences between sectors.

References 1. Eger, L., Komárková, L., Egerová, D., Miˇcík, M.: The effect of COVID-19 on consumer shopping behaviour: generational cohort perspective. J. Retail. Consum. Serv. 61, 102542 (2021). https://doi.org/10.1016/j.jretconser.2021.102542 2. GlobalData: COVID-19 accelerates e-commerce growth in Malaysia, says GlobalData GlobalData (2020). https://www.globaldata.com/covid-19-accelerates-e-commerce-growthmalaysia-says-globaldata/. Accessed 25 Jul 2022 3. Mazlan, M.T.: Challenge E-commerce to the logistics courier services provider during MCO in Malaysia. Artic. IOSR J. Bus. Manag. 23(2), 59–62 (2021) 4. Malaysian Courier, Express, and Parcel (CEP) Market | 2022 - 27 | Size, Share (2022). https://www.mordorintelligence.com/industry-reports/malaysia-courier-exp ress-and-parcel-cep-market. Accessed 24 Jul 2022 5. KPMG: E-commerce retai l l ogi sti cs i n I ndi a Driving the change (2018)

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6. Lee, I., Lee, K.: The Internet of Things (IoT): applications, investments, and challenges for enterprises. Bus. Horiz. 58(4), 431–440 (2015) 7. Gulcid, A.: Multi-stakeholder perspective of courier service quality in B2C e-commerce (2021) 8. Rajendran, S., Wahab, S.N., Rajendran, S.D., Teknologi, U., Selangor, M.C.: Investigating last-mile delivery options on online shoppers experience and repurchase intention. Int. J. Electron. Mark. Retail. 13(2), 224–241 (2022) 9. Allen, J., et al.: Understanding the impact of e-commerce on last-mile light goods vehicle activity in urban areas: the case of London. Transp. Res. Part D Transp. Environ. 61, 325–338 (2018) 10. T. Consultancy Services: Three Focus Areas for the Logistics Industry in a New Low-Touch World 11. Siew, W.Y.: National Internet Of Things (IoT) Strategic Roadmap (2019) 12. Tiwari, S.: A review of internet of things application in Malaysia. Borneo J. Sci. Technol. 4(1), 70–79 (2022) 13. Costs slowing down adoption of IoT (2022). https://www.thesundaily.my/local/costs-slo wing-down-adoption-of-iot-FH8382513. Accessed 25 Jul 2022 14. Industry, G.: Industry 4.0: building the digital enterprise Metals key findings (2016) 15. Shaar, E.M.A.L., Khattab, S.A., Alkaied, R.N., Manna, A.Q.: The effect of top management support on innovation: the mediating role of synergy between organizational structure and information technology. Int. Rev. Manag. Bus. Res. 4(2), 499–513 (2015) 16. Geissbauer, R., Schrauf, S., Koch, V., Kuge, S.: Industry 4.0 - opportunities and challenges of the industrial internet. Strateg. Former. Booz Company, PwC 13, 1–51 (2014) 17. Obaidat, M.A., Obeidat, S., Holst, J., Al Hayajneh, A., Brown, J.: A Comprehensive and Systematic Survey on the Internet of Things: Security and Privacy Challenges, Security Frameworks, Enabling Technologies, Threats, Vulnerabilities and Countermeasures 18. Ghobakhloo, M., Hong, T.S., Sabouri, M.S., Zulkifli, N.: Strategies for successful information technology adoption in small and medium-sized enterprises. Inf. 3(1), 36–67 (2012) 19. Pradhan, R.P., Mallik, G., Bagchi, T.P.: Information communication technology (ICT) infrastructure and economic growth: a causality evinced by cross-country panel data. IIMB Manag. Rev. 30(1), 91–103 (2018) 20. Achimugu, P., Oluwagbemi, O., Oluwaranti, A., Afolabi, B.: Adoption of information & communication technologies in developing countries: an impact analysis. JITI J. Inf. Technol. Impact 9(1), 37–46 (2009) 21. MITI Malaysia: Industry 4WRD: National Policy on Industry 4.0 (2018) 22. Parasuraman, A., Colby, C.L.: An updated and streamlined technology readiness index: TRI 2.0. J. Serv. Res. 18(1), 59–74 (2015) 23. Magotra, I., Sharma, J., Sharma, S.K.: Adoption of self-service technologies among banking customers: a revisit. Int. J. Appl. Manage. Technol. 18(1) (2019). https://doi.org/10.5590/ IJAMT.2019.18.1.05

Blockchain Technology in Malaysian Estate Distribution: A Systematic Review Syahirah Balqis Anuar1(B) , Fatin Afiqah Md Azmi2 , and Syaza Nur Syazwana Sidek1 1 Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia, Johor Bahru,

Johor, Malaysia [email protected] 2 Centre for Real Estate Studies (CRES), Institute for Smart Infrastructure and Innovative Construction (ISIIC), Universiti Teknologi Malaysia, Johor Bahru, Johor, Malaysia [email protected]

Abstract. In Malaysia, the process of claiming an estate is quite complicated. It is managed by multiple agencies and a dual legal system, which is costly and timeconsuming. Blockchain technology is therefore being considered a good database for consolidating all documents and agencies involved in the estate distribution procedure into a single platform. Even though blockchain technology is becoming increasingly popular, research on how it may be used in the distribution of estate is still lacking. In response to this, we conducted a systematic evaluation on the application of blockchain technology to be implemented in estate distribution in Malaysia. In this paper, we conduct a systematic review to identify, extract, and analyze all relevant publications relating to inheritance within 12 years, between 2010 and 2022 in online databases. This analysis is limited to the papers based on the relevancy of the paper on the subject being studied in respect of blockchain in estate distribution. Of those, 17 were assessed for eligibility, and the remaining three were found useful for this study. A systematic review was conducted to examine the factors causing arrears in the claiming process, blockchain characteristics that may be implemented in estate distribution, applicability of blockchain decision and future implementation. Previous research has investigated and analyzed the use of blockchain in inheritance, but much of it focuses on the wills of the deceased. There are still gap in study for Muslim estate distribution with the procedure included faraid distribution approach. This paper highlights the roles and features of blockchain technology and will assist academics in understanding blockchain technology, especially inheritance-related. Finally, the corresponding solutions and ideas are summarized, and the future development of blockchain technology in estate distribution is analyzed and judged. Keywords: Estate distribution · Inheritance · Blockchain technology · Systematic review

1 Introduction Inheritance is an asset left by the deceased to be inherited by family members or parties entitled to receive it. The right to inherit includes tangible and intangible material. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (Eds.): ICETIS 2022, LNNS 584, pp. 352–365, 2023. https://doi.org/10.1007/978-3-031-25274-7_29

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Tangible material types can be tangible and personal such as land, underground, cars, etc. The amount of unclaimed or frozen inheritance in Malaysia shows an increasing trend every year and has become an ongoing issue to this day. Estate distribution arrears have increased to 72,348 cases, worth RM12.6 billion in 2022 from the previous year. If the solution to the problem of estate management is not taken seriously, it is not impossible that the case of freezing the property will become more serious. If the estate is abandoned and not developed, it can lead to losses for Muslims themselves because the value of the unclaimed property reaching billions of ringgit can be used as a source for solving the issue of poverty in the Malaysian community. The longer this is left, the harder it will be to resolve. Neglect in the inheritance management and distribution of the inheritance became one of the country’s main issues until it received the attention of the highest leaders and was brought into discussion in the Malaysian Parliament (Shafie et al. 2016). Blockchain is the underpinning technology that has created the most popular cryptocurrency, bitcoins. The heart of blockchains’ promise resides in the unique qualities of a distributed database and how they might improve transparency, security, and efficiency. One of its technological benefits is that it permits reliable transactions without a centralized management system, even if there are untrusted players in the network. Shaheen et al. (2021) stated that there are four primary headers present in each block: (1) Previous Hash: previous block location is determined by this hash address, (2) Transaction Details: information and details of verified transactions, (3) Nonce: a cryptography arbitrary number to distinguish the hash address for the block, (4) Hash Address of the Block: the preceding headers are transmitted through a hashing method that provides the unique hash address or the hash of the block. Blocks cannot be modified or changed owing to the decentralized nature and the structure of Blockchain. Once a transaction is recorded in a block, it cannot be altered or changed. Thus, blockchain technology is the perfect solution for estate distribution. This article comprises six sections: 1) An overview of estate distribution and blockchain technology. 2) Methodology. 3) Factors Causing Arrears in The Claiming Process 4) Critical Issues of the Estate Distribution Chain to Solves. 5) Applicability of Blockchain Decision 6) Result and Discussion.

2 Methodology According to Baron et al. (2014), stability, repeatability, and precision are the foundations of good literature. Previous literature reviews were analyzed and summarized carefully. A comprehensive review was done to evaluate the factors which influence the inheritance claiming process and the implementation of blockchain in inheritance. It is a well-established approach that recommends a straightforward procedure that heavily depends on the researcher’s discretion and judgment (Phillips et al. 2017). Keele (2007) described a systematic literature review as a method for locating, analyzing, evaluating, and interpreting relevant research on a particular issue. In general, it seeks to synthesize the current and previous literatures in terms of searching for a particular keyword and finding the gaps for suggesting more research and offering a new framework (Azham et al. 2015). Waddington et al. (2014) also emphasized that the goal of this systematic review is to undertake a thorough and systematic search followed by a comprehensive and unbiased synthesis of the existing research findings.

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This review involves studies on the topic published within 12 years, between 2010 and 2022 in online databases. This study used twelve primary sources of data as follows: (a) (b) (c) (d) (e) (f) (g) (h) (i) (j)

Web of Science, Scopus, IEEExplore Digital Library, Google Scholar, SpringerLink, Jstor, Science Direct, Wiley, MyCite or MyJurnal, and Emerald.

The keywords used in searching the above-mentioned database include ‘blockchain and estate distribution’, ‘blockchain and inheritance’, ‘will and blockchain’ and ‘smart contract and inheritance’.

3 Factors Causing Arrears in the Claiming Process The issues in the distribution of Islamic inheritance in Malaysia resulting in arrears of inheritance are not new. Problems in the distribution of inheritance can arise in various situations. Multiple issues cause the ineffectiveness of the existing inheritance claim process. It can be categorized into several factors that focus on the main issues. This paper will focus on problems arising from the failure of the estate management system. 3.1 Lengthy and Costly in Inheritance Procedure The most influential factor causing arrears in the claiming process is lengthy and costly procedure (Azmi and Mohammad 2015a, b). Nabilah Syifaa and Nur Baizura (2019), in their study, have done a survey on 60 heirs that registered in JKPTG in Perak Tengah, as respondents, which covers three Land Office which is in Batu Gajah, Ipoh, and Seri Iskandar. This research was carried out using a questionnaire instrument to identify the challenges heirs faced during the inheritance claiming process. The findings of the study show that the majority of 59% of the respondents agreed the inheritance’s fees are too high. Meanwhile, 47% of the heirs said they have high-cost transportation problems. There are various costs associated with any transaction or application to claim the estate of the deceased; this is one of the major factors that makes the beneficiary reluctant to continue the necessary procedure, especially when the value of the claim is relatively small after the distribution. The estate distribution process fee is calculated based on the total value of the inheritance. The higher the property’s value, the more the payment that the heir must pay. Therefore, most heirs believe that the claim is not worthwhile and will be a burden, as the net value paid to them will be less than the value that must be released. According to Abdul Rahman and Hassan (2019), one of the sources of the problem is the empowerment of two courts under substantive law and required procedures. In addition, the settlement of intestate death cases that lead to distribution by faraid law might cause years-long delays in the settlement term (Samori et al. 2016; Ghul et al. 2015).

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3.2 Complicated Jurisdiction in Inheritance Procedure The second factor is the inheritance procedure which, to be told as a complicated process and too many jurisdictions (Azmi and Mohammad 2015a, b) to go through, confuses the public in dealing with the matter. The heirs need to go through the procedures because the authorities want to ensure that all the inheritance belonging to the deceased is distributed fairly to those entitled to receive it and avoid fraud (Abu Bakar et al. 2020). However, it can affect the heirs. Remy Rony & Mohd Shukor (2019) research shows that 53% of the heirs agree that claiming inheritance procedure is too complicated. According to scholars, the existence of various institutions that have authority in the process of managing inheritance makes the existing system or law of management and administration of Islamic inheritance ineffective because it is unclear (Muhammud & Syazari 2022). Rahman (2018) states that the existence of various agencies makes the heirs who wish to make a claim application confused. The existing management and administration system of Muslim estates is divided into four main powers, namely Amanah Raya Berhad, Department of Director General of Lands and Mines, Syariah Court, and High Court. The distribution divided into the jurisdiction of two courts and two administrative agencies is an implication of inheritance classification, namely small and large inheritance (Md Azmi & Sabit Mohammad 2011). This also affects those who live in remote areas who are less exposed to office procedures. The complicated inheritance application process causes confusion and difficulty for them, resulting in less motivation to continue (Mohamad Al-Bakri 2011). 3.3 Untraceable Documents of Inheritance The third factor affecting the arrears of inheritance claims is the failure of heirs to submit complete documents to make property claims due to untraceable documents (Abu Bakar et al. 2020; Muhammud & Syazari 2022). Among the documents that need to be submitted for the procedure are death certificates, marriage documents, divorce papers, change of religion letters, family identification cards, heirs, faraid certificates issued by the Syariah Court, as well as documents of the deceased’s assets that include all his property including letters related to land grants, bank books, unit trusts, salaries and others (Zulkafli & Ahmad 2016; Muhammad Saifullah 2020). Wan Hasan (2010) opined that the loss of those documents is caused by the delay of the heirs in making claims resulting in overlapping deaths among the heirs. The failure of the heirs to submit the documents makes the inheritance distribution process difficult (Zulkafli & Ahmad 2016) (Fig. 1).

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Fig. 1. Conceptual framework of the effectiveness of inheritance’s claiming process

4 Critical Issues of the Estate Distribution Chain to Solves As a result of the controversy in the property administration system in Malaysia, causing unease in the community, an integrated system must be introduced. This is to achieve a good inheritance management and administration system that can prevent the community from struggling with the same problems. Blockchain technology is a system that can give pleasure to all parties, including management and beneficiaries. With the increasing dominance of this technology in the global market, this system is expected to provide a high level of governance and be cost-effective compared to existing traditional methods (Pilkington 2016; Noja et al. 2021). 4.1 Enhance Transactional Transparency Some records that consist of documents required in the matter of inheritance distribution that is filed manually cannot be verified as valid. Therefore, the existence of a system that can record all transactions and documents in real-time is able to provide great benefits to the administration and management system. Of course, the information is only disclosed to certain parties. In their study related to wills, Chen et al. (2021) introduced blockchain technology with the characteristics of decentralized, open, anonymous, and times of interruption, which simultaneously can maintain the integrity of the content of the will. Therefore, the heirs consisting of the applicant, heirs, and family members of the applicant, can verify the documents and compare the will signature information at any time. Since the signature revealed in the will requires the applicant to use their private key, no one, including the court and illegal parties, can freely use the applicant’s private key to sign documents in the smart contract. Therefore, verification of whether it is forged cannot be ensured by checking whether the signatures on the will match. This can be practiced in the estate administration system that involves various documents from agencies; the agency removes the estate applicant’s doubts about the document’s validity and the property’s distribution. 4.2 Integrated Property Development System The estate’s management and administration is a process involving various jurisdictions and agencies from the beginning of the application process to the completion of the

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distribution. Therefore, a network platform is needed to bring together all the parties involved in one space so that the distribution process can be accelerated compared to the existing process that requires the heirs to deal with the parties separately. Blockchain technology has centralized characteristics. The centralized data storage room only gives permission to some parties to be in the room. Alharthi (2021), in his study, introduced a sharia-compliant WAQF Chain model that is able to integrate all operations into one platform while helping to monitor and manage the establishment and development of inheritance endowments. This can facilitate all the organizations involved to achieve holistic and contextual development results. 4.3 Eradication of Corruption Blockchain technology in estate management facilitates transactions digitally without borders and is free from the control of individuals or agencies that may intend to change any document in the estate. This technology can also store records in electronic codes and links in a chain structure while simultaneously making it difficult to manipulate the data (Salarzehi et al. 2010). In addition, it also creates trust through existing mechanisms where trust does not exist. For example, the ledger in the smart contract is copied between different nodes, simultaneously eliminating the possibility of falsifying records (Alharti 2021). Chen et al. (2021) stated that this blockchain technology can ensure that beneficiaries who receive benefits will not experience unfair arbitration. This can create trust between parties who do not know each other without needing the help of a trusted third party to intervene, allowing anyone to do transactions without the help of an intermediary. 4.4 Immutability Blockchain technology also has the characteristics of immutability that it cannot be altered once the records are generated into the block and uploaded into the chain. This is because other block validations will not recognize and accept the modified information. In the event of a modification, the hash of the value block will fail to verify it. Therefore, all data that has been recorded cannot be tampered with (Chen et al. 2021).

5 Applicability of Blockchain Decision Among the benefits of using blockchain technology is improving the supply chain by enabling faster delivery of data or products in addition to saving costs, increasing the marketability of a product, improving coordination between all parties involved in transactions, and helping access financing. Many sectors are looking for ways to integrate Blockchain technology into their infrastructures. Therefore, Nodehi et al. (2022) have proposed a decision flow chart that helps the industry evaluate and select the right approach to help solve the problem. In deciding to apply a new system to the affairs of a sector, the first step that needs to be taken into account is how many parties will be involved in writing their input in the ledger that is shared in the database. If only one party is involved, traditional databases

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can be more efficient. Nevertheless, blockchain technology is a suitable platform if it requires several parties to do business with each other faster. The property distribution in the estate’s management involves many parties, including the court, land office, financial institution, beneficiaries, and others. This process requires some parties to share data and documents to continue the distribution procedure. Therefore, blockchain technology can integrate all the agencies and documents involved throughout the process into a single platform. The next step is to ensure the need for trust between the parties involved in the transaction. Blockchain technology is not more useful if several parties are involved in writing a shared database, but there is complete trust in each other. If the parties think otherwise, blockchain technology equipped with The Elliptic Curve Digital Signature Algorithm (ECDSA), which is used to encrypt private data through a secured channel, can be an option. The documents used in the inheritance distribution process are private and confidential information. The next thing in determining whether blockchain is a suitable option is whether the data to be entered needs to be modified. If traditionally, the parties involved in the platform need temporary modifiability of shared documents and a uniform set of rules for similar cases, blockchain can be used. Any party cannot modify the documents shared in the distribution of inheritance. It is to get a smooth business and high trust value. In selecting the chain network system that must be used, if the transaction requires the miners/validators in the network that are known and trusted nodes to verify the transaction, they use permissioned blockchain networks. Otherwise, the network will be blocked without permission, and any node can be set as a validator based on the required

Fig. 2. Applicability of blockchain decision diagram. Source. Nodehi et al. (2022)

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conditions. Blockchain in the distribution of inheritance uses a hybrid blockchain, a combination of both types of networks. A single entity manages it, but with oversight provided by a public blockchain, required for certain transaction validations. The hybrid blockchain architecture is distinguished because it is not publicly accessible yet retains the benefits of blockchain technology, such as integrity, transparency, and security. It is entirely customizable regarding who can join the blockchain network and whose transactions are made public, which is the heirs in the case of estate distribution (Fig. 2).

6 Result and Discussion There were 408 papers returned by the query from the searched databases and five papers from other sources. After removing the duplicate, the remaining 402 articles were reviewed in repositories for a more thorough review using a systematic review process to assess eligibility. The eligibility of the papers was chosen based on the relevancy of the paper on the subject being studied in respect of blockchain in estate distribution. Of those, 17 were assessed for eligibility, and the remaining three were found useful for this study (Refer Fig. 3).

Fig. 3. PRISMA diagram

In 2010, Lee et al. proposed a new electronic will system based on government public keys. Chen et al. (2012) then proposed an online will preservation system based on a secret sharing mechanism, which combines a public key and symmetric key system to prevent family disputes caused by inheritance and distribution. Through online hosting, you can reduce costs and increase efficiency; privacy is protected by powerful security mechanisms that can resist attacks, improve paper defects, and meet various security requirements and other advantages. In 2017, Sreehari et al. proposed the concept of

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saving wills in the blockchain through smart contracts. Blockchain technology to draft a will can be tamper-proof, safe, and transparent. Additionally, it improves the speed of the probate and solves many annoying issues in the current will system. However, this does not mean that the electronic wills stored in a secure deposit box are completely safe and reliable; on the other hand, whether the testator can test a will is not easy to accurately identify in the complex environment of the Internet. Generally, it is not easy to effectively guarantee and verify the authenticity of the content of an electronic will. Therefore, Chen et al. (2021) proposed a traceable online will system based on blockchain and smart contract technology to solve the current validity, privacy, and security protection issues that arise when producing a will. Kim and Huh (2020) suggested more transparent algorithms, blockchain shading, and intelligent country functions. The blockchain deep neural network (DNN) is used to develop architectures, and deep neural network-based units are constructed with an artificial neural network (ANN) base (Table 1). 6.1 Smart Contract Smart contracts are a significant development in blockchain technology (Ream et al. 2016). It was proposed as a computerized transaction protocol that implements an agreement’s contractual terms. The contractual clauses that have been authorized are transformed into executable computer programmes. Similarly, the logical linkages between contractual phrases have been retained in programmes as logical flows. Each contract statement’s execution is recorded as an immutable transaction in the blockchain. Smart contracts ensure proper access management and contract enforcement. Specifically, developers can provide access authorization for each contract function. As soon as any condition in a smart contract is met, the triggered statement will perform the related function in a predetermined manner. For instance, Alice and Bob agree on the punishment for contract violation. If Bob violates the contract, the relevant penalty (as mentioned in the contract) will be deducted (paid) from his deposit. This function would assist the automated transfer of property ownership from the deceased to the beneficiaries. 6.2 InterPlanetary File System (IPFS) The InterPlanetary File System (IPFS) is a peer-to-peer distributed file system that links all computing devices to the same file system. IPFS is similar to the Web in certain aspects, but it can also be viewed as a single BitTorrent swarm exchanging objects within a single Git repository. IPFS provides a content-addressed block storage paradigm with high throughput and content-addressed hyper connections. This creates a generalized Merkle DAG, a data structure upon which versioned file systems, blockchains, and even a Permanent Web can be constructed. IPFS combines a distributed hash table, a block exchange with incentives, and a self-certifying namespace. There is no single point of failure in IPFS, and nodes are not required to trust one another. IPFS and blockchain are perfect pairings. IPFS enables the addressing of vast volumes of data and the incorporation of an immutable, permanent IPFS hash into a blockchain transaction. This timestamps and secures your material without requiring you to post it on the blockchain. A cryptographic hash is a unique digital fingerprint assigned to each file. IPFS eliminates

Blockchain model

Smart Will

Traceable Online Will System

No.

1.

2.

Chen et al. (2021)

Sreehari et al. (2017)

Author(s), year

· Smart Contract · Elliptic Curve Digital Signature Algorithm (ECDSA) · ERC(Ethereum Request for Comments)-20

· Smart Contract · InterPlanetary File System (IPFS) · Ethereum

Features

(continued)

· The research model used three main characteristics of blockchain decentralization, non-modification, and publicly verifiable chains, allowing data to be decentralized without relying on other regulatory agencies and hardware facilities, with data being stored in a message block · The proposed scheme can meet the requirements of information security, including ensuring data integrity, and that information is irreversible, non-reproducible, and tamperproof

· Using Smart Will for drafting the will ensures that the will gets probated faster and there is no need for any legal com-plications or extra time for reference that can benefit the beneficiaries · This model makes the will tamper-proof, secure, transparent, and increases the speed of probation without dealing with the tribulations caused by the current system

Result and discussion

Table 1. A systematic review on blockchain technology in inheritance

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Blockchain model

Legacy Heritage Inheritance Blockchain

No.

3.

Kim & Huh (2020)

Author(s), year · Resilient Distributed Dataset (RDD) · Convolutional Neural Networks (CNN) algorithm

Features

Table 1. (continued)

· The research is dealt with an artificial intelligence system, which was used in various verification works regarding identification of wills, verification, and unequal wills of inheritance · The model is for solving problems using legacy inheritance blockchain research architecture which provides integrity, public verification, transparency and has characteristics such as ash acidity

Result and discussion

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redundancy across the network and keeps track of every file’s version history. Each network node maintains only the essential data and a small amount of indexing information that aids in determining who stores what. IPNS is a decentralized naming system that allows any file to be identified by a human-readable name (Sreehari et al. 2017). 6.3 Elliptic Curve Digital Signature Algorithm (ECDSA) The Elliptic Curve Digital Signature Algorithm (ECDSA) is the elliptic curve equivalent of the Digital Signature Algorithm (DSA), which encrypts private data via a protected channel. Typically, it is utilized in the current blockchain technology system. Since it looks more sophisticated than standard discrete logarithm (DL) systems, the strengthper-key-bit is significantly higher. Elliptic curve cryptosystems (ECC), which utilized fewer parameters than discrete logarithm (DL) systems, obtained the benefit in terms of speed and smaller keys and certificates, which might be crucial in contexts with limited processing power, storage space, bandwidth, or power consumption (Johnson et al. 2001). Once a transaction has been sent, it is broadcast to all neighboring nodes in a peer-topeer (P2P) network where all peers have equal rights. The nodes in the context of estate distribution relate to the involved agencies and administrative organizations. Once other nodes have received the transaction, the sender’s public key is used to authenticate the transaction’s authenticity in accordance with predetermined block validation rules. If the transaction is genuine, it will be broadcast until all nodes have received and validated it. If not, the item will be eliminated throughout this procedure. Only valid transactions may be stored in the blockchain network’s new block (S. Shi et al. 2020). 6.4 ERC(Ethereum Request for Comments)-20 The Ethereum Request for Comments (ERC)-20 is a scripting standard for the Ethereum blockchain. This technical standard describes the rules and actions an Ethereum token and smart contract must follow and the implementation procedures required. This feature has emerged as the technical standard utilized by all smart contracts on the Ethereum blockchain for token implementation. It provides a set of rules that all tokens based on Ethereum must adhere to. ERC-20 tokens are value-bearing blockchain-based assets that may be sent and received (Chen et al. 2021). 6.5 Resilient Distributed Dataset (RDD) A resilient distributed dataset is a read-only collection that is kept and partitioned across a cluster of numerous machines. Typical Spark programmes are input into one or more RDDs, followed by a sequence of transformations, and output as target RDD sets. The term “elastic” indicates that when a partition is lost, Spark can recompute and automatically restore the RDD processing. MR-like Spark apps, jobs, phases, and tasks use the job approach. It consists of a single MapReduce task with one map, and one reduce. Spark is responsible for all non-circular directed graphs. When Spark is executed, the stage is partitioned into numerous jobs, each running in parallel on a distributed RDD partition in the cluster, similar to a MapReduce task. Always executed within an application

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context (expressed by a Spark context object) that supports RDD and shared variables. The RDDs of a Job executed inside the same application are accessible by other RDDs cached (Kim & Huh 2020).

7 Conclusion In a nutshell, among various studies, however, there is no significant relevant research on blockchain in Muslims estate distribution. Among them, blockchain can be solely employed in resolving the problems related to wills for non-Muslims. However, most of the benefits from the blockchain model described may be implemented in the Muslim estate distribution method. Using a smart contract can replace the traditional way of administrating inheritance property. Its implementation can cut costs and eliminate the presence of mediators or intermediaries that help the inheritance property distribution process. InterPlanetary File System (IPFS) may also work well in estate distribution procedures as no servers are necessary; the participants can transfer their data or documents without expense. Furthermore, it encrypts the material. The Elliptic Curve Digital Signature Algorithm (ECDSA) may deliver a high level of security and non-repudiation. ERC (Ethereum Request for Comments)-20 is a standard protocol to maintain and follow, which can give a smooth procedure for the estate distribution process. Combining all these qualities may give a solid blockchain system with all the functions necessary for the estate distribution procedure. This idea will produce an effective and efficient inheritance claiming process in the estate distribution that is recommended for future research.

References Abu Bakar, A., Mohamad Rapi, M.A.A., Sallehuddin, M.R.: Property Inheritance management: issues unclaimed property. Webology 17(2) (2020) Alharthi, W.J.: Using Blockchain in WAQF, Wills and Inheritance Solutions in the Islamic System (2021) Azmi, F.A.M., Mohamad, M.T.S.: A proposal for a single tribunal of estates distribution in Malaysia. Jurnal Teknologi 75(10), 7–16 (2015a) Azmi, F.A.M., Mohamad, M.T.S.: The possible conflicts of property rights affecting the distribution of estates Muslims. In: Proceedings of the 26th International Business Information Management Association Conference - Innovation Management and Sustainable Economic Competitive Advantage: From Regional Development to Global Growth, IBIMA 2015, pp. 4035–4048 (2015b) Chen, C.L., Lee, C.C., Tseng, Y.M., Chou, T.T.: A private online system for executing wills based on a secret sharing mechanism. Secur. Commun. Netw. 2012(5), 725–737 (2012) Chen, C.-L., Lin, C.-Y., Chiang, M.-L., Deng, Y.-Y., Chen, P., Chiu, Y.-J.: A traceable online will system based on blockchain and smart contract technology. Symmetry 13, 466 (2021). https:// doi.org/10.3390/sym13030466 Ream, J., Chu, Y., Schatsky, D.: Upgrading Blockchains: Smart Contract Use Cases in Industry. Deloitte Press 2016 (2016) Kim, S.K., Huh, J.H.: Neuron blockchain algorithm for legal problems in inheritance of legacy. Electronics 9, 1595 (2020). https://doi.org/10.3390/electronics9101595

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How to Become King? Insights from the Importance Performance Map Analysis of User-Based Authenticity F.-E. Ouboutaib(B) , A. Aitheda, and S. Mekkaoui Research Team in Marketing Management and Territorial Communication, School of Commerce and Management, Agadir, Morocco [email protected]

Abstract. Although understanding user authenticity in digital context is gaining increasing importance in behavior field, scant research has examined and extended knowledge by using the Importance Performance Map Analysis (IPMA). This paper fills this gap. First, it presents the digital content marketing and its relationship with the quest of authenticity by consumer. Next, it examines the importance and performance of factors in predicting the user behavior. The constructs of trust and attachment display significant levels of importance in predicting user behavior. These findings can show the insights that managers require to better enunciate digital content strategies. Managerial effort should consider this reality in order to increase online community. Keywords: IPMA · Authenticity · Content marketing · Digital

1 Introduction Digital offers opportunities to share, communicate, take competitive advantage, and to be a king. After more than 23 years of this well-known word from Gate, content is still being a very important key for a successful digital strategy [1]. The managerial challenge is clear. Digital data has become a source and a condition for the competitiveness of the brand [2–5]. Content is a competitive advantage influencing brand positioning. The balance of marketing strategy is more designed on the digital practices than the traditional ones; Digital Space has predicted that before 2025, online marketing will likely overtake the offline markets [2]. Furthermore, a Content Marketing Institute’s survey noted that 86% of business-to-consumer firms and 91% of business-to-business enterprises have embraced content marketing strategy. In this vein, digital content marketing is likely to have ongoing influence on business practices in the future [2]. The web has transformed the ways of living, doing business, and consuming. Brands are interacting with consumers and consumer is interacting with consumers. Content has gradually evolved from a tool to communicate to that of marketing intelligence and superior performance source [6]. It is a key element in the inbound marketing approach [7, 8], so being able to use it effectively and efficiently is a solution among others to © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (Eds.): ICETIS 2022, LNNS 584, pp. 366–376, 2023. https://doi.org/10.1007/978-3-031-25274-7_30

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manage its existence and to succeed [1, 5, 9]. Digital Content has been defined as a marketing tool that influences the consumer’s attachment, trust, and engagement by focusing on the user’s appreciation [7]. In today’s era, digitalization has taken a huge significance where everything is becoming digital. Surveys have noted that the global digital content marketing revenue has jumped from $87.2b in 2009 to $144.8b in 2014, and to $313.4b in 2019 [7]. The latest statistics underline that consumers are passing an average of 6 h and 42 min [9]; as well, in 2017, the Statista Research Department has emphasized that 70% of consumers need to learn about products by digital content [7]. Despite the opportunities offered by the web, the central challenge is to determine which content meets the consumer expectations. In particular, an understanding of how this tool can encourage the consumer engagement is the base of digital marketing strategy. Scholars have underlined that this issue is related to the brand’s ability to achieve a long-term relationship that complements its marketing strategy [6]. Moreover, they have underlined that research in consumer behavior has now gradually stressed a significant craze of authenticity [10–13] that affects the purchasing behavior [10, 14, 15]. What persists ambiguous is the influence of determinants of perceived authenticity on consumer behavior, especially in the digital era. Remarkably, authenticity, as a driving forces of online consumer behavior [12, 16], has not been amply examined [17, 18]. To continue the same, it is evident that human behavior is defined to be influenced by social groups [12]. Particularly, studies have reported that social interactions are getting influenced by the digital technologies which is being impacted business and online communities [7, 9, 12]. Closer to the focus of this paper, it brings a methodological contribution based on the authenticity’s literature and our previous research. It is, in our best knowledge, the first research that discussed the performance/importance of authenticity dimensions and digital content marketing, especially it responses on the following question: what is the most relevant factor for managerial action? It contributes by discussing the relevant performance factors of digital content. The assessment involved the measurement model and the importance-performance map analysis [19]. The paper used the SmartPls version 3.3.2 to operate this approach.

2 Conceptual Model Development 2.1 Digital Content Marketing Digital social networks are increasingly connecting their users who live a significant virtual existence. Sharing on these canals is changing the way we are living and consuming [20]. Smartness is becoming a key condition for prosperity and economic growth [21]. In this sense, digital content is emerging as a key resource and a condition for business competitiveness in an online world. According to [1], the most important question is to define how companies can benefit from this content and not whether it is a king or not. Investment in the digital transition is not only evaluated on the basis of return on investment, but also on the risk of not investing. Researches have underlined that less digitally mature businesses are more unstable and going to take considerable risk in the future [2, 22].

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Talking to clients and prospects is one of the oldest and most fundamental practices in marketing. Whereas yesterday the consumer would have to contact the brand directly to get information, now knowledge is just a click away! The web has been putting the power and strength back in the hands of its users. Content marketing allows companies to track and maintain a direct relationship with the audience. It is a key element in the inbound marketing approach [7, 8], so being able to use it effectively and efficiently is the major challenge for the digital marketer [1]. This technique is based on artificial intelligence [5] and the optimization engine [3] to boost the user’s experience on the web. It mainly refers to the content created and shared on the net by the consumer himself [1]; it has been described as the hugely essential tool to shape customer attachment and engagement [7]. Prior research on online content marketing has accentuated its value on brand experience and loyalty for consumers [4]. Essentially, this tactic has strengthened the business ability to explore the competitive strategy opportunities [3, 23]. The shared content aims to share information on a particular topic in order to influence knowledge, share values, and promote socialization between brand and consumer [4, 5, 7, 24]. In this thinking, scholars have approved the link between user content and purchase intention [1] because it affects the credibility and trust of the consumer towards the brand. According to [7], the philosophy of pure advertising resides in an effort to influence the consumer to buy, while that of content is rooted in the development of the consumer’s appreciation; by generating content that encounters the user needs, brand seems to fascinate a qualified consumer [25]. It encourages conversation between brand and consumer [26] and plays a component role in business development strategies [27]. 2.2 Authenticity and Digital Content Marketing Marketing research agrees that authenticity is an important criterion for the success of products and brands [10, 11, 14, 16, 28–32]. Authenticity is not only linked to the authentic intention of the producer [10, 16], but it is also a constructed and subjective reality linked to the consumer [16, 32, 33]. Empirical data underline that it refers to origin of the producer and the product [34, 35], consumer symbolism [36], place [11, 34], and the sense of tradition and unicity [15, 37, 38]. It explains the brand love that motivate consumers to attach to it [13]. As there is no consensus on the definition of authenticity, a polymorphic concept, it refers to a variety of dimensions and characteristics. Remarkably, according to [10] being perceived as authentic is a condition of success for the producer. In the same line of thinking, [39] points out that as the audience expands, the perception of authenticity becomes more and more complicated. Scholars have defined authenticity by inauthenticity [40, 41]. There is good explanation to suppose that this might be the case. Reviews concluded that contentment is relatively linked to the authenticity, whereas anxiety feels correlated to inauthenticity [41]. In this vein, [40] notes that inauthenticity also signifies exploiting things for selfish justifications. Nowadays’ online era, the huge interactions among businesses and consumers progressively take place via online media and devices. An assortment of tools give the prowess to design, share, and review the content in digital platforms. In this shift, the

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role of different manners and marketers to expose and share authenticity have been discussed in several research. [18] have underlined that the authenticity of the producer is the most element emerging from the online review. Notably, according to [17] classical communication approach can lead to less effectiveness as the digital content has led to the explosion of data sharing, as there has been a shift toward the online recommendation consideration. In this line of thinking, consumer talks to consumers. If the brand is perceived as authentic, consumer takes the initiative to share his opinion on the web [12], it is a key channel of sharing and socialization [4]. The latest statistics underline that consumers are passing an average of 6 h and 42 min [9] and social media platforms are the second-hugest business in digital advertising with the revenue of $153 billion in 2021 [42].

3 Research Design and Methodology 3.1 Data Collecting and Instrument The model shows the direct links between the variables issued from the literature and the target variable (Fig. 1). The survey carried out in Morocco and it utilized in person collection of paper questionnaires. Participation was voluntary under the condition of being a consumer.

Fig. 1. User behavior model based on authenticity’s dimension

The consumer was asked to state his/her level of agreement with a series of statements based on a seven-point Likert scale. All items are issued from the literature [15, 34, 36, 43–45] (Table 4) and constructs were measured through a multi-item scale to ensure the content validity. In addition, the questionnaire solicited information on purchasing frequency, and other data to provide a detailed view of the behavior and socio-demographic characteristics of the user. Following the fieldwork, a screening approach was executed to select the valid responses. To ensure that the respondent is a consumer, the questionnaire contains questions on the type of product usually consumed and the monthly budget for purchases. All the scales of measurement are issued from the literature and adapted to the research’s

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context [15, 34, 36, 43–45]. The questionnaire focuses on the consumption of Moroccan local products. In this context, we define user as a consumer of this product and utilize the web [46]. The respondents (n = 300, convenience sample, 132 women, and 168 men) satisfy the following criteria: 1-use of social Medias to interact and get information; and 2-being familiar with the net. 3.2 Data Analysis The data analyses used the IPMA. It is very important in a managerial sense, it allows detecting the constructs that have a less important performance, but that possess a higher level of importance [47]. It is very close to our vision, we argue that this purpose is very helpful for digital managers in order to create and share digital content. According to [19], IPMA gives opportunity to use variable data in order to obtain more insight and prosperous conclusions. The paper was applied it in conformity with its general principles and criteria [19, 48, 49]. The assessment involved the measurement model and the importance-performance map analysis [19]. SmartPLS version 3.3.2 was utilized to operate this approach.

4 Results and Discussion 4.1 Measurement Model According to [48], the assessment of the measurement model is based on the evaluation of indicators loadings, internal consistency, convergent validity, and discriminant validity. Cronbach’s alpha and composite reliability typically represent the reliability and should both be equal to or superior than 0.70 [47, 48]. As a result, the reliability measures have been confirmed. Table 1 shows that the Cronbach’s alpha of all constructs is above 0.70 expected for intention construct (0.696*) which is acceptable according to literature [49]. Results underline that all indicator loading are above 0.7 and the average variance extract of all construct are above 0.6 which is a very recommended by literature in order to comply with the established threshold values [47]. For the discriminant validity evaluation, the Heterotrait-Monotrait ratio (HTMT) of correlations should be examined. From Table 2, results underline that the HTMT values were adequate. [48] suggested adequate discriminant validity when the square root of the AVE is larger than the corresponding correlations.

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Table 1. Results of measurement model assessment Construct

Item

Loading

Alpha

CR

AVE

Orgn

Orgn1/Orgn2/Orgn3 Orgn4

0.914/0.898/0.933 0.923

0.937

0.950

0.841

Trd

Trd_1/Trd_2/Trd 3

0.946/0.961/0.949

0.948

0.967

0.906

Sym

Sym_1/Sym_2/Sym_3 Sym_4

0.792/0.898/0.929 0.913

0.906

0.935

0.783

intent

D_int1/D_int 2/Dint3

0.752/0.745/0.866

0.696*

0.831

0.623

Attct

Att_1/Att_2/Att_3

0.905/0.936/0.912

Trt

Cn_1/Cn_2/Cn_3

0.921/0.922/0.927

0.914

0.946

0.853

Table 2. Results of HTMT Dint D_int

Ogn

Symb

Trdt

Tst

Att

0.789

Ogn

0.580

0.917

Symb

0.425

0.536

0.885

Tdn

0.514

0.755

0.656

0.952

Tst

0.633

0.547

0.425

0.533

0.923

Att

0.421

0.396

0.528

0.506

0.449

0.918

4.2 IPMA Analysis Findings note that Trust and Origin have a high importance. Furthermore, Origin and Tradition show a high performance than Trust. Moreover, the attachment construct exhibits the third level of importance on user behavior with a less performance value. Additionally, Symbolism and Tradition constructs display the lowliest values on importance (Table 3). Table 3. Importance and performance of constructs Importance Origin Symbolism

Performance

0.255

68.073

0.040

48.972

−0.011

64.568

Trust_

0.373

55.266

attachment

0.090

49.633

Tradition

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Fig. 2. IPMA user-behavior Performance Main construct

Table 4. Measurement of constructs Origin

Refined from [34, 35]

Symbolism

Refined from [15, 36]

Tradition

Refined from [15]

Attachment

Adapted from [44]

User behavior

Refined from [45]

5 Conclusion The research focuses on the question, “How to be a king?” More clearly, it sheds light on the most important construct in the prediction of user behavior. The answer to this question is very relevant for digital content marketing. Literature has highlighted that the aim of digital content marketing is to develop the interactivity between brands and users [2, 7, 9, 23]. In this vein, managers should considerate it in the formulation of the content to share on the web. IPMA Matrix (Fig. 2) shows an unbalanced amount of importance and performance among constructs. The IPMA matrix revealed that the trust is the most important construct to focus on. However, it still requires effort on its performing. This result is in line with the research of [7]. They have stressed that digital content should establish a trusting relationship between the brand and user. It is an ongoing socialization process [4, 5, 7, 24]. This result pushes managers to invest in a relationship of trust in order to engage users, as the digital marketing strategy should consider what really motivates people to use the internet [1, 6].

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The origin construct also shows a high importance in the target construct. Nevertheless, its performance displays a peak level. This finding leads us to recommend maintaining this level without investing too much. It seems interesting to direct the effort towards considering what effectively motivates the user. We can explain that by the context of this research. This result is consistent with the research of [10]. It has underlined that the emphasis for an established producer is thus not to parade its authenticity but to prevent being perceived as inauthentic. Evidently, the sense of place is very linked to the authenticity [11], but this latter is not limited only to the objective definition. In this sense, the competitive advantage of digital content lies in the different positioning of what is shared to attract more engagement and develop user appreciation as the recent literature in digital content marketing has stressed [4, 6]. The attachment construct has shown the third level of importance and meagre performance. For digital content, this means that digital marketing strategy should create and share content that develops its performance. When the attachment is boosting positively, user can participate, share, and create content in online communities. This key role is also stressed in other contexts [12]. Research have supported that the social interactions are being influenced by digital technologies [12], demonstrating that the digital community is a component of social exchange dynamics. In this era, companies should be attentive toward the presence of online communities. Using IPMA, this paper, also, underlined the huge significance of trust and attachment in predicting user behavior. The paper contains some limitations that occurred beyond the authors’ control. The first aspect concerned the constructs mobilized in the research model. Future papers could investigate the role of others relevant construct in digital behavior. The second aspect concerned the research’s context. As the web has removed the different geographical boundaries, the study of the importance and performance of different cultures could bring even more insights that are interesting. It is very important to investigate other brands and users from different cultures. Future research should also attempt to apply the findings of this research to other types of media, as some disparities could possibly be predicted. Live streaming come to mind as one of the most forms of media that are recently employed in the context of digital marketing [50]. In spite of the current developments in digital content marketing, authors propose that research should investigate the IPMA on other targets construct like attachment and trust, as survival of the online kingdom requires a trusting relationship and an attachment with users. Acknowledgments. Authors thank very much all the respondents who showed a great kindness and interest for scientific research.

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The Impact of Organ Donation Information Dissemination on Social Media Towards Registration of Organ Donors: A Moderating Role of Family Discussion Faerozh Madli1(B) , Stephen Laison Sondoh Jr.1 , Andreas Totu1 , Sharifah Nurafizah Syed Annuar2 , Yuzainy Janin1 , Rudy Ansar1 , Yusman Yacob4 , and Tat-Huei Cham3 1 Faculty of Business, Economics and Accountancy, Universiti Malaysia Sabah, Sabah Kota

Kinabalu, Malaysia {faerozhmadli,jude,andreast,zayy,rudyansar}@ums.edu.my 2 Faculty of Business and Management, Universiti Teknologi MARA Sabah Branch, Kota Kinabalu, Malaysia [email protected] 3 UCSI Graduate Business School, UCSI University, Kuala Lumpur, Malaysia 4 Faculty of Business and Management, Universiti Teknologi MARA Sarawak Branch, Mukah, Malaysia [email protected] Abstract. Recent literature on organ donation suggests that the dissemination of organ donation information on social media should be emphasised to increase the registration of organ donors. Therefore, based on the Information Adoption Model, the current study investigated the impact of disseminating organ donation information on social media towards the intention to register as organ donors. Due to the importance of family discussion, this study also examined the moderating of family discussion in registering as organ donors. This study involved samples of undergraduate university students from public universities in Malaysia. The data obtained were analysed using SmartPLS. The result revealed that family discussion moderates the relationship between information adoption and intention to register as organ donors. The results also suggest that information adoption positively contributes to the intention to register as organ donors. The current research provides strong evidence to formulate a sound marketing strategy associated with organ donation. Keywords: Organ donation · Social media · Malaysia · Marketing communication · Social marketing · Information adoption model

1 Introduction The scarcity of organ donors is a major problem worldwide [1]. This issue has led to the development of strategic initiatives by combining many parties, such as the government to increase the rate of organ donor registration [2]. The initiatives to increase organ donor pledgers have been primarily propelled by the Ministry of Health Malaysia [3, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (Eds.): ICETIS 2022, LNNS 584, pp. 377–391, 2023. https://doi.org/10.1007/978-3-031-25274-7_31

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4]. However, the number of organ donors in Malaysia is still very low, at approximately 1.3% of the population [5]. Moreover, Fig. 1 shows the tabulation of fluctuation of organ donation registration from 2019 until 2021. In other words, the figure shows Malaysia’s inconsistent registration pattern for organ donors. Several previous studies in Malaysia suggested the importance of promoting organ donation information to encourage and increase the registration of organ donors [6, 7]. On the other hand, several studies found that family discussion is an important determiner of individuals’ intention to register as organ donors [8, 9]. Besides that, the importance of family discussion was proven to affect people’s scope of information or knowledge about organ donation [8, 10]. Studies also found that the media significantly affects information adoption in the organ donation context [11]. However, some individuals are unwilling to register as organ donors due to the lack of family support [11]. This is supported by [12], who stated that family discussion strongly influences a potential donor’s intention. Similarly, family discussion is considered an important factor to potential organ donors as they will discuss registering as organ donors with their family members and seek approval from them [13]. In the Asian context, family plays a massive role in people’s lives. Family decisions and consent are important in matters such as organ donation [10]. Thus, [14] suggested a need to understand the influence of family discussion on the intention to register as an organ donor.

Statistic of Organ Pledgers in Malaysia 10000

7690 5697

5000

1786

0 2019

2020

2021

Number of Pledgers

Fig. 1. Statistic of organ donation registration in Malaysia from 2019 until 2021 Source: National Transplant Resources Centre (2022)

[15] suggested that the public health organisation should utilise social media because it is an effective tool to increase the awareness of the target population through delivering health-related information. Youths are the target population which is considered valuable because they are the largest group of social media users. They also consider social media as the main resource for information on various topics, including health-related [16]. Considering the impact of social media in disseminating information, the Ministry of Health Malaysia (MOH) has used social media to promote and disseminate information regarding organ donation to the public [17]. The information about health campaigns individuals receive through social media influences them to demonstrate the behavioural intention to change their lifestyle [18]. The informative nature of social media also influences individuals to accept the information as useful [19]. However, [20] mentioned that it is a major challenge for organisations to predict individual acceptance of the information they receive on social media platforms. [21] highlighted that while social

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media is useful for sharing information about organ donation, much work has to be done to develop an effective approach to reach out to the youths. This study intends to investigate the role of social media and the moderating role of family discussion on an individual’s intention to register as an organ donor. The discussion in this paper starts by reviewing the topic of organ donation and the role of social media in the context of health-related information, specifically in the context of organ donation. The next section discusses the adoption of information and the intention to register as organ donors. The paper concludes with a discussion on the value of information dissemination and the contribution of family discussion to one’s decision to sign up as an organ donor.

2 Literature Review 2.1 Information Adoption Information adoption is the personal process by which a person consciously engages with and uses information [22]. Information adoption also refers to the degree to which people adopt information they deem meaningful after evaluating its veracity [23]. In the context of the current study, information adoption is the individual internationalisation process where the information exposed to the individual online will be transferred as the internalised knowledge of the individual. Concerning information on the online environment, several researchers highlighted some important variables that play an important role in the online information world in society. Various scholars have discussed the importance of information quality for spreading health information online [24, 25]. Information quality has been rated high since it impacts how well individuals accept health information [25]. Several researchers have also discussed the significant role of quality health information on societal acceptance [25, 26]. Information credibility has been identified as a highly relevant criterion for information acceptance [27]. In this regard, people will be more inclined to trust the information they obtained from a credible source. A past social media study discovered that the utility of online health information is positively impacted on information usefulness by its reliability [28]. In the meantime, the value of visual information is deeply ingrained in human nature, making it a significant component of the marketing plan [29]. The public’s attention is crucially drawn to visual information in this study’s context. Furthermore, the literature on health information has reported a strong correlation between information credibility and usefulness in increasing consumer awareness [28]. Information sharing is stated as an important element in various aspects ranging from aspects of business and community [30]. In the current study, information sharing refers to sharing information associated with organ donation from different parties to others on social media platforms. One of the key factors contributing to the success of information sharing was the truthfulness generated from the collectiveness of opinion from the community in social media and the perceived normative [31]. In addition, accessibility which refer to the ease of people can acquire information on social media sites and significantly influences the public’s acceptance of information [32]. Social media’s advent in the context of organ donation has improved communication and allowed instant access to information about organ donation (including statistics and information about actual patient stories). This subsequently improved the attitude regarding organ donation [33].

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In the health industry, people change their behaviour due to health concerns and take matters related to their health seriously. This drives them to use online health information to fulfil their needs towards information [34]. In addition, [32] suggested that the promotional campaign should be shaped to fit the target community for fulfilling their needs for information regarding organ donation and influence the community to register as organ donors. Internet source (social media) is the major channel utilised by people to fulfil their information needs. It affects them to accept the information as useful [35]. In addition, the individual attitude has the highest impact on acceptance and usage of the information from the Internet [36]. [37] revealed that a positive attitude towards health information presented through digital media positively affects the perceived usefulness and allows the public to make decisions on their health matters. Nevertheless, the linear and positive relationship between the predictors and information usefulness is not always observed. As there are so many online digital health information options, people are less likely to believe that the information is of high quality and as a result, they are more likely to be reluctant to accept or use it [37]. It was argued that there are emerging issues in the relationship between predictors of information usefulness, which subsequently influence the significant effects of these variables on information usefulness. These predictors include information quality, information credibility, visual information, information sharing, information accessibility, information needs, and attitude towards information. This study argues that these factors have a more significant effect on information adoption than information usefulness. Due to the vital role of information adoption in the communication process, information adoption reflects the degree to which people will accept perceived information as meaningful [22]. Hence, the current study proposes significant, positive relationships between these variables and information adoption. 2.2 Intention to Register as Organ Donors In general, intention refers to preparation to engage in the actual behaviour [38]. Intention also refers to how one behaves according to their intentions; this component is impacted by their ability, their need to engage in such behaviour, and the environmental circumstances that enable them to do so [39]. Thus, intention, in the current study context, refers to the desire to sign up as an organ donor. Likewise, the intention to register as organ donors depends on how the media presents the information to dispel misunderstandings about organ donation, which will eventually increase people’s intentions [13]. [40] discussed that information adoption and individual intention are significantly correlated, with respondents using computers as intermediaries to get information about organ donation. A study in digital marketing, [41] empirically examined purchase intention in the context of social media and found that the adoption of information positively impacts intention. On the other hand, this study examined the impact of information adoption on the intention to register as organ donors. 2.3 Family Discussion Organ donation should be viewed as a family process rather than as an individual process or a solely personal choice [42]. In Asia, the family institution is described as extremely

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central in daily lives, and family discussion is the major predictor of their decision to register as organ donors [43]. In Malaysia, the Human Tissue Act 1974 stipulated the importance of family consent in organ donation matters [44]. Family support is also an important antecedent for younger people in their intention to register as organ donors [8]. Moreover, the Asian culture showed that the youth or younger generations always seek input and support from their family on many topics, including organ donation [45]. [45] discussed also the importance of information about organ donation and consulting with family members in the individual process of registering as organ donors. Information adoption positively correlates with the intention to register as organ donors [40]. On the other hand, [46] found no positive relationship between information adoption and intention to register as organ donors, while [47] revealed that individuals still show a positive intention to register as organ donors even though they have little information on organ donation. These contradicting findings reflect the need to include a moderating variable in the relationship between information adoption and intention for organ donor registration. [45] emphasised the significance of family discussion during the process of registering as an organ donor and the value of information on organ donation. In this regard, the family discussion is discussed as a moderator in deciding to become an organ donor which act as degree of confirmation [48]. Therefore, the present studies propose to examine the family discussion as a moderator on the relationship between information adoption and intention to register as organ donors. 2.4 Hypotheses Guided by the Information Adoption Model by [49], the current study examines how social media may affect how youths, notably Malaysian public university undergraduate students to adopt organ donation information. The current study forms several recommendations in the context of information characteristics that could persuade people to adopt information which affects their desire to register as an organ donor. The following hypotheses are proposed: H1: Information quality has a positive effect on information adoption. H2: Information credibility has a positive effect on information adoption. H3: Visual information has a positive effect on information adoption. H4: Information sharing has a positive effect on information adoption. H5: The accessibility of information has a positive effect on information adoption. H6: The need for information has a positive effect on information adoption. H7: The attitude towards information has a positive effect on information adoption. H8: The information adoption positively affects the intention to register as organ donors. H9: The positive relationship between information adoption and intention to register as organ donors will be stronger for those with high family discussions (Fig. 2).

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F. Madli et al. Information Quality Information Credibility Visual Information Information Sharing Accessibility of Information

H1 H2 H3

Information Adoption

H4

Intention to Register as Organ Donors

H8

H5

H9

H6 H7

Family Discussion

Needs of Information Attitude towards Information

Fig. 2. Conceptual framework

3 Research Methodology There are several types of ways to investigate the research phenomenon. However, the current research uses correlational research. The current study employs the correlational study to test the relationship between the predictor variables towards information adoption and subsequently influence on intention to register as organ donors. The predictors or exogenous variables towards information adoption include information quality and credibility. In addition, the current study sample will be the public university students in Malaysia. According to the recommendation by [50], the acceptable sample size is at least five participants for each construct. Thus, based on the recommendation from previous studies, the total number of questionnaires is 65 items covering all variables. The appropriate sample size for this study is 325 (65 × 5 = 325). Therefore, the minimum sample size of this research is 325 public university students. The purposive sampling technique is suitable for current study because it aligned with the study’s objective, where the sample is chosen based on the characteristic of the population. Besides, as this research is quantitatively based, the measurement used is adapted from previous literature. The questionnaire survey was chosen because it is the best method to collect data and ensure a valid response to research questions. The items for all variables were adapted from previous studies.

4 Findings 4.1 Demographics The profile of respondents for the current study consists of 438 respondents. The total sample shows that most of the respondents are female, representing 69.9% of the sample.

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132 respondents are male, representing 30.1% (132) of the total sample. Meanwhile, age distribution shows that the respondents in this research fall into two groups, mostly in age 18–23 which contains 341 respondents, and the second group is 24–29, which contains 97 respondents. In the category of stream of study, most of the respondents from the science stream accumulate 83.3% (365), while the non-science stream shows around 16.7% (73). 4.2 Measurement Model Evaluation Two types of analysis were performed: convergent validity and the second is discriminant validity in order to assess the measurement model. The convergent validity employs examining the loadings of the items, average variance extracted (AVE), and the composite reliability [51]. The result shows that all variables have the values of composite reliability (CR) higher than 0.7 and average variance extracted (AVE) values higher than 0.5. These indicators are based on the suggestion of [52]. The criterion in [53] has been criticised as insufficient in assessing the discriminant validity. [54] recommended assessing the discriminant validity through the heterotraitmonotrait ratio of correlations (HTMT). Thus, this study performed the heterotraitmonotrait ratio of correlations (HTMT) to assess the discriminant validity. Table 1 indicates that all values are less than 0.85 and less than 0.90 and fulfil the requirements suggested by [55] and [56]. In this regard, discriminant validity based on the heterotrait-monotrait ratio of correlations (HTMT) is acceptable or achieved. Table 1. Discriminant validity: heterotrait-monotrait ratio of correlations (HTMT) AI Accessibility Information Attitude Towards Information Family Discussion Information Adoption Information Credibility Information Quality Information Sharing Intention to Register as Organ Donor Needs of Information Visual Information

ATI

FD

IA

IC

IQ

IS

INT

NI

VI

0.809 0.223

0.116

0.819

0.812

0.212

0.693

0.678

0.119

0.634

0.681

0.658

0.157

0.653

0.794

0.750

0.764

0.065

0.745

0.713

0.776

0.415

0.459

0.130

0.550

0.293

0.324

0.437

0.644

0.573

0.324

0.704

0.453

0.417

0.376

0.293

0.711

0.719

0.113

0.634

0.693

0.737

0.775

0.386

0.294

4.3 Hypothesis Testing Results Table 2 show the result of the hypothesis testing of current research. Following the rules suggested by previous researchers in the context of PLS-SEM, the second step

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that should be employed is hypothesis testing and assessing the significance value of path coefficients [57]. The 5000 sub-sample of bootstrapping was employed for current research by following the guidelines from [58]. There are have 8 hypotheses testing for a direct relationship. The result indicates that 5 out of 8 are supported, which is the t-values exceed or at least 1.645 at a .0.05 level of significant [52]. In detail, the result for H4 (β = 0.187, p < 0.01) and H5 (β = 0.182, p < 0.01) indicate that information sharing and accessibility of information have a positive and significant impact on information adoption, as expected. Similarly, the result of H6 (β = 0.256, p < 0.01) and H7 (β = 0.258, p < 0.01) indicate that the need of information and attitude towards information have a positive and significant impact on information adoption. Meanwhile, the H8 (β = 0.472, p < 0.01) indicate that the information adoption have a positive and significant impact on intention to register as organ donors as expected, explaining 24.5 per cent of the variance. Therefore, H4, H5, H6, H7, and H8 are supported. However, the result for H1 (β = 0.047, p > 0.05) and H2 (β = 0.008, p > 0.05) indicate that the information quality and information credibility do not have a positive and significant impact on information adoption. The result for H3 (β = 0.044, p > 0.05) also shows that visual information does not has a positive and significant impact on information adoption. Thus, H1, H2, and H3 are not supported. Table 2. Hypothesis testing Relationship

Std. beta

Std. error

t-value

P values

Results

H1

Information quality -> Information adoption

0.047

0.051

0.918

0.179

H2

Information credibility -> Information adoption

0.008

0.048

0.166

H3

Visual information -> Information adoption

0.044

0.057

H4

Information sharing -> Information adoption

0.187

H5

Accessibility of information -> Information adoption

0.182

r2

f2

0.05

0.95

VIF

Not supported

0.002

−0.038

0.130

2.403

0.434

Not supported

0

−0.070

0.091

2.326

0.769

0.221

Not supported

0.002

−0.049

0.139

2.274

0.059

3.164

0.001

Supported

0.036

0.089

0.283

2.477

0.064

2.835

0.002

Supported

0.033

0.076

0.288

2.584

0.610

(continued)

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Table 2. (continued) Relationship

Std. beta

Std. error

t-value

P values

Results

H6

Needs of information -> Information adoption

0.256

0.041

6.204

0

H7

Attitude towards information -> Information adoption

0.258

0.060

4.317

H8

Information 0.472 adoption -> Intention to register as organ donors

0.052

9.155

r2

f2

0.05

0.95

VIF

Supported

0.110

0.187

0.323

1.52

0

Supported

0.070

0.165

0.363

2.462

0

Supported

0.308

0.380

0.552

1.006

0.245

4.4 Moderator Analysis In addition, the moderation effect in the research model should be measured when the objective is to test whether the influence of the construct exists on the relationship between exogenous construct and endogenous construct [59]. Therefore, to analyse the effect of moderator in research, there are several ways to analyse it as suggested by the previous researcher, which is: 1) indicator approach, 2) two-stage approach, and 3) orthogonalisation approach [60]. However, in the current study context, the interaction term used is the two-stage approach. This approach runs the moderator analysis by estimating the score of latent variables. Hence, as shown in Table 3, the moderation effect for family discussion shows a significant moderation effect on the relationship between information adoption and intention to register as organ donors (t-value = 9.415, p = 0.048). The positive impact on the relationship between information adoption and intention to register as organ donors become stronger for individuals with high family discussion compared to individuals with low family discussion. Thus, based on the explanation, the H9 in current research is supported. Table 3. Moderating effects: family discussion

H9

Hypothesis

Standard beta

Standard error

t-values

Decision

Family*adoption -> Intention to Register as organ donors

0.476

0.051

9.415

Supported

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5 Discussion The study’s findings demonstrate that the youth or respondents are perceived the predictor have a positive impact towards information adoption, but it does not for information quality, information credibility and visual information. In details, this finding implies that adopting information positively correlates with information sharing, needs of information and attitude towards information. On the other hand, information adoption is not significantly influenced by information quality, credibility, or visual content. Correspondingly, in the context of information quality, the result of the current study is not supported, indicating that information quality does not have a significant positive impact on information adoption. This findings parallels with [61], which found no relationship between information quality and acceptance of information on online platforms because technology is already at an advanced stage of innovation, which has led to a major improvement in information quality. In other words, people now see information on the Internet as being of high quality due to the advancement of technology. The current study’s findings also demonstrate that the adoption of information is neither positively nor significantly influenced by the information credibility. These findings are supported by [62], which argues that most individuals perceive that all information sources on the Internet are equally reliable or credible. This argument shows that the predictor of information credibility has little to no influence on an individual’s decision to adopt the information about organ donation presented on social media platforms. Besides that, the current study also examines the relationship between visual information and information adoption. The result shows that the visual information has no significant positive impact on information adoption. [63] found that the role of visual information is at par in communication, which means the text and visual have the same impact on people to accept the information. Based on the consistency of the current study finding and previous studies, the current study concludes that visual information has no significant impact on information adoption on social media that is associated with the organ donation context. Besides that, information sharing is one of the important factors in the communication process through social media platforms [64–68]. In context of current study, the result shows that information sharing has a significant positive impact on information adoption. This result is consistent with [67], which discusses that information sharing significantly affects young people’s acceptance of information on social media because the informative and interactive presentation element attracts them. In addition, the result of the current study revealed that accessibility of information has a significant positive impact on information adoption. The current study results show that information needs positive significant correlate with information adoption. This result is consistent with previous studies conducted by [69]. Further, [69] conducted research in the context of social media and found that this platform offers a significant source of information for people to better understand the health-related topic and drives them to change to positive behaviour. In other words, social media can offer an abundance of information that can fulfil the needs of people in the context of information related. Besides that, the current study found that the attitude toward information significantly positively impacts information adoption. In the context of programmes implemented by the government agencies, the attitude towards information on the online medium is observed to give a substantial positive role towards the

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positive outcome as it makes society support that government during crisis-related [70]. In this light, this implies the attitude toward organ donation information on social media has a significant positive impact on the adoption of information. The result indicates that information adoption positively impacts the intention to register as organ donors. People adopt the information on social media due to how useful and helpful it is for them [71, 72]. Further, in previous studies on context health information by using social media as a medium for communication, youths’ adoption of health information from social media could significantly affect their intention for behavioural changes [71]. Meanwhile, for the result of the moderator effect of family discussion, the current study’s findings show that the positive impact of information adoption is strengthening the relationship with intention to register as organ donors for both groups, which is high family discussion and low family discussion. However, the high family discussion among individuals is more adopt the information about organ donation on social media platforms than low family discussion. The possible reason behind this different level of intention to register as organ donors is the motivation to adopt information on social media platforms. The family factor is stated as the important factor to trigger the motivation for youth generation [73]. In case of low family discussion, the individual is experience low motivation to adopt the information, which weakens the relationship between information adoption and intention to register as organ donors. Meanwhile, for high family discussion, the individual is experiencing the high motivation to adopt the information, which resulted in strengthening the relationship between information adoption and intention to register as organ donors. Regarding its theoretical implication, the current study extended the IAM model by bringing several predictors toward information adoption and subsequently influencing the intention to register as organ donors, such as visual information and information sharing. Therefore, the result of the current study proves that this extended model holds a significant relationship in Malaysia, particularly in the organ donation context. Moreover, the extended or conceptual model is pivotal in the objective to make significant support for any worthwhile theory or model, and for the case of the current study is Information Adoption Model. Meanwhile, the current study’s findings also provide several managerial implications for non-profit organisations, charitable organisations, and relevant authorities. The managerial implication of the current study is particularly for the party or organisation involved with organ donation. Based on the compelling data presented in the current study, marketing managers can more clearly understand the context of encouraging organ donation through social media communication. More specifically, it helps to identify the elements determining individuals’ intention to register as organ donors, which will guide managers or practitioners to formulate a sound marketing strategy to increase organ donor registration.

6 Conclusion Nowadays, based on the trend of organ donation registration in Malaysia, an organisation needs to make a sound strategy to increase the registration of organ donors. In this regard, promoting organ donation information on social media appears promising to increase organ donors’ registration. Therefore, the current study was executed to examine the

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impact of organ donation information disseminated through social media platforms. The current success is to obtain 438 samples among youth or university students in Malaysia who offer their perspectives on organ donation information on social media. The result shows that adopting organ donation information on social media positively impacts their intention to register as organ donors. Regarding the predictors of the current study towards information adoption, there might be other potential variables or predictors in the context of the peripheral or central route that play a part in forming an individual to adopt the information. Additionally, future research should be carried out on different target populations, such as adults and different cultures, to better comprehend the intention to register as organ donors. In addition, it would be interesting to examine the moderating roles that might influence registering as organ donors, such as personal values, religious beliefs, and technological advancement.

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Does Electronic Word-of-Mouth Still Contribute to Boosting Purchase Intention? Understanding the Role of Gender as a Moderator Boon-Liat Cheng1

, Tat-Huei Cham2 , Yuan Feng Cai3 and Michael M. Dent1

, Anuja Chalke1(B)

,

1 Sunway University Business School, Sunway University, Selangor, Malaysia

{boonliatc,michaelmd}@sunway.edu.my, [email protected] 2 UCSI Graduate Business School, UCSI University, Kuala Lumpur, Malaysia 3 Faculty of Business and Technology, Stamford International University, Bangkok, Thailand [email protected]

Abstract. Digital marketing has accelerated e-WOM (electronic word-of-mouth) growth and enabled openness in communication. E-WOM remains a widely accepted medium to achieve a competitive advantage, especially since consumer trust in organisations and marketing initiatives declines rapidly. This study examines the impact of normative influence, informative influence and trust on Generation Y’s e-WOM adoption. The impact of e-WOM on purchase intention; and the moderating role of gender are also assessed. 406 respondents completed selfadministered questionnaires. AMOS and SPSS were used to test the hypothesised relationships. Results indicate that trust and informative influence have an impact on e-WOM, and e-WOM has an effect on purchase intention. Gender does not exert a moderating effect between e-WOM and purchase intention. The findings will assist marketeers to gain a clearer understanding of factors impacting e-WOM adoption and design more effective online marketing campaigns to boost purchase intention. Keywords: e-WOM · Informational influence · Normative influence · Purchase intention · Trust

1 Introduction The current commercial scenario is characterised by a rapid decline in consumer trust towards businesses and advertising initiatives [1, 2]. The importance of WOM (wordof-mouth) is also widely acknowledged in the digital medium, where information is rapidly disseminated online and consumers participate in providing, seeking and sharing opinions [3–5]. e-WOM is defined as positive or negative statements about a product, brand or service shared by current or past customers; which is attainable by an extensive base of internet users worldwide through blogs, social networking platforms, forums, virtual communities and review sites [6]. There is a widespread consensus that e-WOM © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (Eds.): ICETIS 2022, LNNS 584, pp. 392–403, 2023. https://doi.org/10.1007/978-3-031-25274-7_32

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can positively impact consumer attitudes and purchase decisions [7]. Individuals rely on e-WOM rather the sponsored marketing content whilst making purchase decisions; endorsing the efficacy of WOM in shaping consumer behavior [8–10]. The purpose of e-WOM, its valence and volume, its impact on consumer responses, and the efficacy of e-WOM have received significant attention in the recent past [7, 10]. There has been minimal research on the mechanism between e-WOM engagement and purchasing intent [11]. [12] claim that prior research on e-WOM communication is rather dispersed and inconclusive, with attention being paid to a variety of factors, including message qualities, source trustworthiness, social impacts, situational features, etc. As an avenue for further research, [13] suggest that e-WOM can be examined in relation to social, cultural, demographic, and personality factors as these may affect the efficacy of e-WOM in shaping consumer responses. Existing interpersonal communication theories, according to [14], may not adequately capture the dynamics of e-WOM communication because individuals may portray numerous roles, including those of transmitters, seekers, and providers of reviews. Limited attention has been allocated to assess potential antecedents of e-WOM from the message receiver’s perspective in the social network context [14]. Extant literature acknowledges that the same e-WOM content may resonate differently with different information receivers based on their individual experiences, tendencies and worldviews [15, 16]. Substantial research has focused on message related or communicator-specific factors impacting e-WOM adoption. Further investigation into receiver related aspects such as normative and informational influence, trust and gender will be beneficial to determine their significance in Gen-Y consumers’ e-WOM adoption. This is especially relevant in the post-pandemic global marketplace, wherein consumers gain confidence to make informed purchase decisions by referring to e-WOM disseminated on social network platforms and review websites [17]. This study aims to address a knowledge gap by examining the role of certain receiverrelated social elements in e-WOM adoption for Gen-Y consumers, and their potential impact on purchase intention. The conceptual model of this study suggests that the main components impacting e-WOM are interpersonal influence (both normative and informational) and trust. In the direct correlation between e-WOM engagement and purchase intention, gender is considered as a potential moderator. This study seeks answers to these specific research questions: RQ1: Which interpersonal influence components contribute to e-WOM for Gen-Y consumers? RQ2: Does a positive relationship exist between trust and e-WOM? RQ2: Does gender exert a significant moderating effect on the relationship between e-WOM and purchase intention? This research focuses on online consumer review sites like TripAdvisor, Amazon, Agoda, and Google Reviews since an increasing number of customers are relying on peer reviews to obtain product information. Findings from this study will provide marketers with relevant information about social factors impacting Generation Y’s e-WOM behaviour and its role in purchase intent formation.

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2 Literature Review and Hypotheses Development 2.1 Electronic Word-of-Mouth (e-WOM) WOM is defined as the consumers’ behaviour of exchanging marketing information with others, and it has played an increasingly important role in influencing consumers’ purchasing choices since the mid-20th century [15, 16]. For example, word-of-mouth (WOM) has been shown to change how people think about both products and brands. This can lead to changes in consumers’ judgments, value assessments, and plans to buy [17–20]. From the point of view of online marketing, e-WOM (Electronic Word of Mouth, also called Word of Mouse) is a digital version of traditional word-of-mouth marketing, and it is described as “any positive or negative statement made by potential, actual, or former customers about a product or company, which is made available to a multitude of people and institutions via the Internet” [21]. E-WOM outperforms traditional WOM in terms of distance, time, and expenses [22]. With the Internet, consumers can share their thoughts and experiences freely about a product with people all over the world at any time and for almost no cost. Therefore, e-WOM is getting more important for marketers because user comments or user-generated material may be analysed for valuable consumer information, including members’ likes, dislikes, wants, behaviours, or worries about products/brands, that ultimately affect consumers’ purchase intentions [3]. 2.2 Interpersonal Influence Interpersonal influence is one of the key social cues that affect e-WOM engagement [23]. According to the Dual-process theory by [24], influence between people can be divided into two categories: informational influence and normative influence. Informational influence occurs as a result of the acceptance of third-party information as proof of reality. It is based on the receiver’s self-verification of the obtained information. Some of the important influencing factors are content, source, and receiver [25]. On the other hand, normative influence occurs when the norms or expectations of others are imbedded into the group’s or community’s choice preferences. It is based on the opinions of others rather than the receiver’s own self-judgment. In general, consumers who have been exposed to informational influence are more likely to seek advice from an informed audience. Consequently, they are more likely to use online social media and engage in e-WOM. Alternatively, consumers who are more vulnerable to normative pressures choose items and brands based on their loved one’s approval to acquire social acceptance [23]. In short, e-WOM’s social impact may be explained by a consumer’s receptivity to interpersonal influence [3, 23, 25]. Hence, we propose that: H1: Consumers’ susceptibility to normative interpersonal influence is positively related to their e-WOM engagement in product review websites. H2: Consumers’ susceptibility to informational interpersonal influence is positively related to their e-WOM engagement in product review websites.

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2.3 Trust According to past literature, users’ willingness to follow the advice of their peers online is influenced in part by their level of confidence in the information they get [26–28]. Confidence in the ability of others to react in accordance with expectations and in a mutually beneficial manner is a key component of trust. In the contemporary online marketing environment, it has become a crucial means of establishing customer trust [2], which in turn facilitates e-WOM. The link between trust and e-WOM engagement has been established. For example, [29] indicate that consumers’ perceived trust toward sources can positively influence their e-WOM adoption. The authors explain that high source trustworthiness reduces information elaboration efforts and thus increases eWOM engagement. In this study’s focus, we use the online reviewers as the source of information based on an interpersonal perspective. By following the same logic, we predict that when consumers form trust towards the reviewers, they are more likely to accept the information provided by the reviewers. Accordingly, we propose that: H3: Consumers’ perceived trust on source is significantly related to e-WOM engagement in product review websites. 2.4 Purchase Intention A person’s purchase intention is a measure of how likely or unlikely it is that he or she will buy something [4, 30, 31]. Potential customers form their opinions about a product or brand based on what other people say about its usefulness, quality, and price. Today, online advice has become so important to how people plan to act that e-WOM is seen as a key source for consumers trying to decide what to buy, and the positive impact of e-WOM on purchase intention has been well documented [32]. A recent meta-analytic review, [33] concluded that consumers are more rational than emotional when forming their purchase intentions, and thus they tend to rely more on external factors such as e-WOM to make the decision. In this study, we predict that, based on rational consideration, online product reviews can assist consumers in reducing potential purchase uncertainty. Consequently, they are more likely to form purchase intentions. Thus, we hypothesise that: H4: The e-WOM engagement is positively related to consumer’s purchase intention. 2.5 The Moderating Role of Gender The impact of gender on shopping behaviour has long been a topic of interest to researchers. Previous studies have found gender differences in terms of attitudes toward e-WOM and shopping behaviour [34], likely due to their differences in perceiving the risk associated with the behaviour. Specifically, women tend to be more risk averse than men, given the critical role they play in taking responsibility for their family [35]. The findings are supported by Social Role Theory, which suggests that gender differences in behaviour result from social norms that guide the appropriateness of behaviour for men and women [36]. In support of previous findings, a recent study reveals that the impact of

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e-WOM on intention is about 1.3 times higher in men than in women [6]. Accordingly, we predict that: H5: Gender moderates the relationship between e-WOM engagement and purchase intention. Based on [23]’s empirical study, a theoretical framework (Fig. 1) was set up for this research, with trust, normative impact, and informational influence as the main factors that lead to e-WOM engagement and, in turn, consumers’ purchase intentions.

Fig. 1. Research model

3 Research Methodology A quantitative research approach was employed. Purposive sampling was adopted to collect a sample of 450 Gen Y respondents who fall between the ages of 26 and 41 years old as of 2022; and who have had online purchase experience in the past six months. Google forms were used to collect the data. The survey link was shared with respondents who met the screening criteria through social network platforms like Facebook and WhatsApp. Participants were asked to indicate their agreeableness to statements aiming to capture their opinions and perceptions, based on their most recent online purchase experience. Gen Y was selected for this study as they are seen as more tech-savvy and are regular users of e-WOM [37]. Moreover, Gen Y makes up a significant part of the world’s population, and they tend have more money to spend than other generations [38]. A self-administered questionnaire that included demographic questions and several scales for each variable was used to gather the data. To gather the necessary data, existing scales with established reliability and validity were used. These variables’ measurement items were modified from previously published literature [23, 39, 40]. A seven-point Likert scale (ranging from “1 = Strongly Disagree” to “7 = Strongly Agree”) was

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applied to each variable. Prior to answering the questionnaire, two screening questions were included to make sure that the respondents belonged to the Gen Y demographic and had prior experience reading internet reviews. The data were analysed using SPSS and AMOS software in accordance with the research objectives. Grounded upon the suggestion by [8, 15], the common method variance (CMV) in the present study was addressed using Harman’s single factor test. The results of the test indicated that the variance that explained the first factor is 27.4%, which fulfilled the recommended cutoff value (i.e.,