Digital Economy for Customer Benefit and Business Fairness: Proceedings SCBTII 2019 9780367477226, 9781003036173

Table of contents :
Cover
Half Title
Title Page
Copyright Page
Table of contents
Preface
Scientific committee
Organizing committee
Acknowledgements
Android-based fiqh consultation application development
The effect of satisfaction using digital innovation results towards
employees’ work ethics and productivity at PT. GSM
Retail in the digital era: Case study of a modern store and a smart
store
The role of customer online review in the buying decision process
of a digital tourism product: A conceptual framework
The role of status consciousness in determining the millennial
generation shopping style
Effect of brand image and brand personalit
y on brand loyalty
with brand trust as a mediator
Analyzing e-commerce customer experience using text mining
: Case
study of Paperlust.Co
Measuring supply chain performance in the fabric industry
in Cigondewah
Analysis of e-payment service quality in Bandung
The effect of social media communication on brand awareness
and perceived quality of Indihome
External and internal factors of mobile games adoption
in Indonesia
Paradoxes of healthcare in G
oa
What constitutes
loyalty in e-tailing?
The effect of social media communication on brand awareness
and perceived quality
Defining lifestyle, consumer culture, and postmodernism
in industry 4.0
Influencer marketing: Brand awareness and purchase intention
on YouTube
Strategy formulation to increase the number of citations using
concept mapping
The application of supply chain practice
s for the jeans industry
in Cihampelas
The application of storytelling in public relations strategy: The case
of a Hi-tech company
Transformational leadership in Shariah banking: Case study
millennial employee in Bank Syariah Mandiri
Entrepreneurship training and development programs:
Entrepreneurs’ perceptions
How coworking space impacts innovation: A literature review
Environment changes and effects to the fashion business
Food and beverages subsector companies valuation, for 2018
projection
Analysis of the influence of compensation and transformational
leadership style on employee performance in PT. Finnet Indonesia
Analysis of efficiency in telecommunication technology companies

in Eastern and South East Asia using analysis data envelopment
method
Indonesia financial sector stock prediction using long short-term
memory network algorithm and modeling (study of banking in
August 2018 LQ45 Index)
The effect of financial technology, interest rate, and exchange rate
towards money supply: An evidence from Indonesia
Bankruptcy prediction using Altman and Zavgren model
in property and real estate registered in Indonesia stock exchange
period 2014-2018
The effect of the world markets on Indonesian stock markets post
USA and China trade wars
Social network analysis in digital marketing company business
ecosystem
How social media impact digital entrepreneurial intention among
private university students in Bandung city (Telkom University,
Widyatama University, and Parahyangan Catholic University)
The relevance of entrepreneurship learnin
g processes towards
technopreneur competencies within higher education institutions
Efficiency measurement of metal and mineral mining sector
companies listed on the Indonesia stock exchange (IDX): Data
envelopment analysis approach
The effect of return on equity, earning per share, and price earning ratio on stock price (case study of plastic and packaging subsector companies listed on the Indonesia stock exchange 2012–2016)
Author Index

Citation preview

DIGITAL ECONOMY FOR CUSTOMER BENEFIT AND BUSINESS FAIRNESS Edited by Grisna Anggadwita and Erni Martini

DIGITAL ECONOMY FOR CUSTOMER BENEFIT AND BUSINESS FAIRNESS

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON SUSTAINABLE COLLABORATION IN BUSINESS, INFORMATION AND INNOVATION (SCBTII 2019), BANDUNG, INDONESIA, OCTOBER 9—10, 2019

Digital Economy for Customer Benefit and Business Fairness

Edited by Grisna Anggadwita & Erni Martini Telkom University, Bandung, Indonesia

Routledge is an imprint of the Taylor & Francis Group, an informa business © 2020 Taylor & Francis Group, London, UK Typeset by Integra Software Services Pvt. Ltd., Pondicherry, India All rights reserved. No part of this publication or the information contained herein may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, by photocopying, recording or otherwise, without written prior permission from the publisher. Although all care is taken to ensure integrity and the quality of this publication and the information herein, no responsibility is assumed by the publishers nor the author for any damage to the property or persons as a result of operation or use of this publication and/or the information contained herein. Library of Congress Cataloging-in-Publication Data Applied for Published by: CRC Press/Balkema Schipholweg 107C, 2316XC Leiden, The Netherlands e-mail: [email protected] www.crcpress.com – www.taylorandfrancis.com ISBN: 978-0-367-47722-6 (Hbk) ISBN: 978-1-003-03617-3 (eBook) DOI: https://doi.org/10.1201/9781003036173

Digital Economy for Customer Benefit and Business Fairness – Anggadwita & Martini (eds) © 2020 Taylor & Francis Group, London, ISBN 978-0-367-47722-6

Table of contents

Preface

ix

Scientific committee

xi

Organizing committee

xiii

Acknowledgements

xv

Android-based fiqh consultation application development N.M.G. Fitri, R. Andreswari & M.A. Hasibuan

1

The effect of satisfaction using digital innovation results towards employees’ work ethics and productivity at PT. GSM R. Limoa & R. Wahyuningtyas

8

Retail in the digital era: Case study of a modern store and a smart store A.I. Munandar & S. Khoriyah The role of customer online review in the buying decision process of a digital tourism product: A conceptual framework A. Permatasari, S.A. Mapuasari, E. Yuliana & N.F. Ahmad

14

20

The role of status consciousness in determining the millennial generation shopping style A. Arisman & D. Risana

26

Effect of brand image and brand personality on brand loyalty with brand trust as a mediator M.Y. Faridian Wirayat & I. Rachmawati

32

Analyzing e-commerce customer experience using text mining: Case study of Paperlust.Co A. Alamsyah, D.P. Ramadhani, M.A.A. Saputra & A. Amran

40

Measuring supply chain performance in the fabric industry in Cigondewah Rr.R.F. Hutami, S.G. Hidayat & S. Dharmoputro

46

Analysis of e-payment service quality in Bandung E. Azis, M.A. Akbar & M.M.A. Rohandi

52

The effect of social media communication on brand awareness and perceived quality of Indihome Indrawati & W. Ardhana

57

External and internal factors of mobile games adoption in Indonesia Indrawati, M.R. Gaffar & S.K.B. Pillai

64

Paradoxes of healthcare in Goa D. Gaunekar, S.K.B Pillai, J. Castanha & Indrawati

71

What constitutes brand loyalty in e-tailing? M. Carrasco, N. Talkar, S.K.B. Pillai, J. Castanha & Indrawati

78

The effect of social media communication on brand awareness and perceived quality Indrawati, W. Ardhana & J. Castanha

85

v

Defining lifestyle, consumer culture, and postmodernism in industry 4.0 S.O. Emovwodo, L. Andriamalala, B.A. Rizki & K.A. Suwito

91

Influencer marketing: Brand awareness and purchase intention on YouTube R.E. Rahman & R.D. Astuti

95

Strategy formulation to increase the number of citations using concept mapping N. Eva & R. Gadang

100

The application of supply chain practices for the jeans industry in Cihampelas M.R. Farhan, S. Dharmoputra & Rr. R.F. Hutami

104

The application of storytelling in public relations strategy: The case of a Hi-tech company J.A. Taufiq & M.T Amir

108

Transformational leadership in Shariah banking: Case study millennial employee in Bank Syariah Mandiri D. Irawan & A.I. Munandar

115

Entrepreneurship training and development programs: Entrepreneurs’ perceptions K. Omar, M.A.S.A. Halim, Y.M. Yusoff, R. Wahyuningtyas, S.D. Sya’diah & A. Mulyana

120

How coworking space impacts innovation: A literature review M.T. Amir

126

Environment changes and effects to the fashion business R. Martiniatin & A. Ghina

131

Food and beverages subsector companies valuation, for 2018 projection D. Isnaini & D. Rahadian

141

Analysis of the influence of compensation and transformational leadership style on employee performance in PT. Finnet Indonesia M.T. Hafiz & F.P. Sary

147

Analysis of efficiency in telecommunication technology companies in Eastern and South East Asia using analysis data envelopment method Suhartoko & P.M. Sitorus

153

Indonesia financial sector stock prediction using long short-term memory network algorithm and modeling (Study of Banking in August 2018 LQ45 Index) M. Pantagama & B. Rikumahu

159

The effect of financial technology, interest rate, and exchange rate towards money supply: An evidence from Indonesia K.M. Hati & A. Krisnawati

165

Bankruptcy prediction using Altman and Zavgren model in property and real estate registered in Indonesia stock exchange period 2014-2018 T.T. Gustyana & A. Sipahutar

171

The effect of the world markets on Indonesian stock markets post USA and China trade wars W. Aminah & W. Utama

179

Social network analysis in digital marketing company business ecosystem A.R. Putra & S. Noviaristanti

vi

184

How social media impact digital entrepreneurial intention among private university students in Bandung city (Telkom University, Widyatama University, and Parahyangan Catholic University) E. Yuliana, S.A. Sakinah & A. Permatasari

188

The relevance of entrepreneurship learning processes towards technopreneur competencies within higher education institutions M. Rosyadhi & A. Ghina

194

Efficiency measurement of metal and mineral mining sector companies listed on the Indonesia stock exchange (IDX): Data envelopment analysis approach W. Widodo & D. Rahadian

201

The effect of return on equity, earning per share, and price earning ratio on stock price (case study of plastic and packaging subsector companies listed on the Indonesia stock exchange 2012–2016) H. Hendratno & S. Agustina Author index

206

212

vii

Digital Economy for Customer Benefit and Business Fairness – Anggadwita & Martini (eds) © 2020 Taylor & Francis Group, London, ISBN 978-0-367-47722-6

Preface

Sustainable Collaboration in Business, Information and Innovation (SCBTII) is an international conference held by the School of Economics and Business, Telkom University, Bandung, Indonesia. This conference brings together academics, professionals, entrepreneurs, researchers, learners, and other related groups from around the world who have special interest in theories and practices in the field of digital economy for global competitiveness. These conferences provide opportunities for both presenters and participants to exchange new ideas and experiences, establish research relations, and find global partners for future collaboration. Considering that technology and industry 4.0, at present, are still leading in making trends and opportunities in global businesses, the implication of industry 4.0 makes competition in the business world even more attractive, yet fierce. At the SCBTII 2019 conference, the topics that carried more emphasis were exploring opportunities, challenges, and the sustainability of the digital economy for customer benefit and business fairness. Opportunities and challenges for business development in industry 4.0 are getting stronger but also provide businesses the ability to compete globally. Some factors that must be considered in winning global competition are the customers benefits and business fairness to achieve sustainability in facing the challenges of today's digital economy. Industry 4.0 has main characteristics such as strong robots, machine autonomy, internet of things (IoT), and artificial intelligence (Pagac, 2015). The dissemination of industry 4.0 knowledge is becoming increasingly important. Employees must have comprehensive technical skills to move from current operational tasks to more strategic ones in the future, or digital processes require staff with coding skills (Tozkwitalska and Slavik, 2018). Industry 4.0 provides the idea that there is a growing digitalization of the entire cost chain and the inter-connection results of people, gadgets, and systems through the exchange of real-time facts. In addition, Shamim et al (2016) and Slavik (2015) added the elements needed to implement industry 4.0 and highly skilled people. However, in fact, the focus is more on technical aspects rather than the role of people (Mohelska and Sokolova, 2018). SCBTII 2019 invited experts to discuss issues related to studies that can improve the ability of organizations to explore opportunities to make business more sustainable in the face of competition in the digital economy. We invited experts from academia and experienced practitioners from Indonesia, Australia and Malaysia. The speakers of this conference were: Tommy Wong, chairman of Indonesia Learning Network, founder of Billioneire Mindset Indonesia and Owner of Victorindo Group; Vanessa ratten, Associate Professor in Entrepreneurship and Innovation in the Department of Management, La Trobe Business School at La Trobe University, Melbourne, Australia; Riyanarto Sarno, Professor in software engineering at ITS Surabaya; Indrawati, Associate Professor in marketing from School of Economics and Business, Telkom University: and, Azlan Amran, Professor in Corporate Sustainability, Universiti Sains Malaysia (USM). The theme of SCBTII 2019 was: “Exploring opportunities, challenges, and sustainability of digital economy for customer benefits & business fairness”. We believe that the papers in this proceeding will provide excellent references to academics and practitioners. Conference Chair Sri Widiyanesti, Ph.D. ix

Digital Economy for Customer Benefit and Business Fairness – Anggadwita & Martini (eds) © 2020 Taylor & Francis Group, London, ISBN 978-0-367-47722-6

Scientific committee

Yudi Fernando, Ph.D. Universiti Malaysia Pahang Dr. Yuvaraj Ganesan Universiti Sains Malaysia Prof. Naili Farida Universitas Diponegoro Prof. Sam’un Jaja Raharja Universitas Padjadjaran Dr. Astrie Krisnawati, S.Sos., M.Si.M. Telkom University Dr. Dadan Rahadian, S.T., M.M. Telkom University Dr. Farida Titik Kristanti, S.E., M.Si. Telkom University Dr. Gadang Ramantoko Telkom University Dr. Majidah, S.E.,M.Si. Telkom University Dr. Palti Mt. Sitorus, Drs., M.M. Telkom University Dr. Riko Hendrawan, S.E., M.M., ACP., CSCP., QIA. Telkom Universit

xi

Digital Economy for Customer Benefit and Business Fairness – Anggadwita & Martini (eds) © 2020 Taylor & Francis Group, London, ISBN 978-0-367-47722-6

Organizing committee Conference Chair Sri Widiyanesti, S.T., M.M. Co-Conference Chair Puspita Wulansari, S.P., M.M. Members Elvira Azis, S.E., M.T. Nike Mandasari, S.Si. Husna Rahmi, S.Sos., M.Ikom. Kharisma Ellyana, S.M.B. Muhammad Azhari, S.E., M.B.A. Ardan Gani Asalam, S.E., M.Ak. Khairunnisa, S.E., M.M. Mediany Kriseka Putri, S.K.G, M.B.A. Puspita Kencanasari, S.Kom., M.Ti. Dedik Nur Triyanto, S.E., M.Acc. Sisca Eka Fitria, S.T., M.M. Dini Wahjoe Hapsari, S.E., M.Si., Ak. Wulandari Ayungningtyas, S.Ikom., M.M. Grisna Anggadwita, S.T., M.S.M. Indira Rahmawati, S.T., M.S.M. Erni Martini, S.Sos., M.M. Ratih Hendayani, S.T., M.M. Andrieta Shintia Dewi, S.Pd., M.M. Ir. Tri Djatmiko, M.M. Nensi Damayanti, S.S. Hani Gita Ayuningtias, S.Psi., M.M. Sri Rahayu, S.E., M.Ak., Ak. Dr. Adhi Prasetio, S.T., M.M. Asep Sudrajat, S.Kom. Riefvan Achmad Masrury, S.Si., M.B.A. Tieka Trikartika Gustyana, S.E., M.M. Indra Gunawan, S.Kom. Harrys Sudarmadji, S.M.B. Setiadi, S.Kom.

xiii

Digital Economy for Customer Benefit and Business Fairness – Anggadwita & Martini (eds) © 2020 Taylor & Francis Group, London, ISBN 978-0-367-47722-6

Acknowledgements

SCBTII 2019 was supported by the School of Economics and Business, Telkom University. We would like to express our gratitude to Associate Professor Vanessa Ratten (La Trobe University) as keynote speaker, Professor Riyanarto Sarno (Institute of Technology Surabaya), Associate Professor Indrawati (Telkom University), and Tommy Wong for their contribution as invited speaker. We also want to thank the companies Mandiri Bank, PT Telkom Indonesia, Finnet, Mitratel, Telkom Akses, and Telkom Infra for their financial support for our conference. Finally, also thanks to our co-hosting universities: Nurtanio University and Galuh University.

xv

Digital Economy for Customer Benefit and Business Fairness – Anggadwita & Martini (eds) © 2020 Taylor & Francis Group, London, ISBN 978-0-367-47722-6

Android-based fiqh consultation application development Nyimas Marissa Gita Fitri, Rachmadita Andreswari & Muhammad Azani Hasibuan School of Industrial and System Engineering, Telkom University, Bandung, Indonesia

ABSTRACT: Knowledge about fiqh can guide a person to carry out all his or her activities and routines as required by Islam. Based on the authors’ survey, 75.8% of 231 respondents had experienced problems in understanding fiqh knowledge such as the implementation of worship and Islamic laws. As many as 90% people stated that it was important for them to find solutions to their problems. This study aimed to solve this problem by developing an Android-based fiqh constultation application. The Iterative Incremental method was used to develop the application g. Fiqh consultation application is expected to become a solution for people who experience difficulties in understanding fiqh knowledge. The results of the study show that the fiqh consultation application that has been built is functioning properly and runs according to design. Keywords:

Android, Consultation, Development, Fiqh, Iterative Incremental

1 INTRODUCTION Mobile application development refers to the process of developing software for mobile devices such as smartphones. Mobile applications are software that runs on mobile devices (Baktha, 2017). By using a mobile application, users are provided various features that make it possible to meet all needs such as ease of communication, entertainment, shopping, and accessing various information. Mobile applications are easy to use, inexpensive, and can be run on most phones. They can be classified into three main types: native applications, hybrid mobile applications, and web-based applications. Native applications are built for use on cer­ tain platforms, such as iOS and Android, whereas mobile web applications consist of specially developed web pages that take into account the characteristics of mobile devices. Hybrid mobile applications are built with HTML-based applications and then installed and wrapped in native apps (Cruz, 2016). Android is a mobile operating system developed by Google based on a modified version of the Linux kernel and other open-source software (Li, 2016). Android is designed primarily for touchscreen mobile devices such as smartphones and tablets. To develop Android applica­ tions, you can use the Java Libraries that have been provided by the Google Android SDK (Mukherjee, 2015). In the fiqh consultation application development, Kotlin is used as the programming lan­ guage. Itruns on the Java Virtual Machine and can be compiled to JavaScript source code. Kotlin was first developed by JetBrains programmers based in Russia in 2010 for personal use until in 2011 it was published as open source under the Apache license. Then in 2017, Google IO Conformance announced Kotlin as the official Android programming language (Panchal, 2017). Islamic law (Fiqh) is the core of Islamic discipline related to various aspects, including wor­ ship ('ibadah), transactions (mu'amalah), inheritance (mirath), crime (jinayah), justice (qada'), marriage (munakahat), international affairs (fiqh al-dawlah), politics (siyasa), and others. Knowledge of fiqh can guide a person to conduct all activities and routines as required by Islam. In this case, Imam Abu Hanifa defined fiqh as “the ability of oneself to know what to 1

have and what is needed from it” (Muhammad, 2013). Given the importance of fiqh in Muslim life, a lack of sufficient understanding of fiqh is a crucial issue and it is important to quickly find reliable answers and truths so as not to have doubts in acting and living life; then we must ensure that knowledge about fiqh is publicly conveyed and can be achieved by all Muslims. Technology and science are not new in Islam, as technology has been adopted and used in different ways by Muslim scholars in the early history of Islam. Technological progress can be an efficient tool for disseminating knowledge and increasing the level of understanding of fiqh and fatwa among Muslims (Muhammad, 2013). Based on a survey conducted by the authors on October 1, 2018, as many as 75.8% of 231 respondents admitted having experienced doubts about the procedures or law in carrying out worship. As many as 90% of 231 respondents stated that it was important for them to find solutions to their problems. Based on the results of the survey, the researchers developed an Android-based fiqh consultation application. The main feature of this fiqh consultation application is the direct question-and-answer feature in private (chat) with an ustadz (religious expert) about the problems faced so that users obtain correct, appropriate, and accurate answers. Users can also choose religious teachers or experts who will be asked questions according to the desired educational background or scientific field. The Iterative Incremental method is used in fiqh consultation application development and was used in this research. This a methodology relies on building software applications at one step or one loop at a time until all requirements are met (Despa, 2014). To assess the functionality in this fiqh consultation application, testing needs to be carried out. The authors used black box testing, also referred to as functional testing, which is based on information from specifications derived from the user’s perspective. In black box testing, participants do not need to have knowledge of programming languages and testing is carried out to ascertain ambiguity and needs of user requirements (Nidhra, 2012). Thus, black box testing is used for validation, such as whether we are building the right software. It is usually used to find functions that are incorrect or missing or interface errors (Mustaqbal, 2015).

2 RELATED WORKS The development of the Android-based fiqh consultation application is based on many concepts through many research processes. There is some literature related to the development of this application, especially regarding the application of fiqh, Android application development, and consultation applications. Flora et al. ( 2014) significantly proved the successful use of the Agile approach in developing mobile applications. The research was carried out via a survey through a mobile developers forum with 130 responses. The results of the survey stated that 86% of respondents believed Agile methods and practices are suitable for the development of mobile applications. Kusumadewi et al. (2018) successfully developed ABFI using technology and information to facilitate study and understanding of various schools (mazhab) of fiqh. Moham­ med et al. (2017) developed a real-time chat application on the Android platform that allows users or patients to communicate and consult with a doctor. Iqbal et al. (2017) developed a nutritional consultation application using the waterfall method, and evaluation results using functional testing showed that online nutrition consultations can overcome nutritional service problems that are more often encountered in conventional consultations so that nutrition ser­ vices play an active role in supporting healthy lifestyles and efforts to prevent diseases.

3 RESEARCH METHODOLOGY The Iterative Incremental method allows the project team to manage digital projects more effectively by reducing excess resources dedicated to managing the project. By using an itera­ tive ongoing process and a short design time frame, the project team will quickly be able to adapt the project to a rapidly developing environment (Salve, 2018). The systematic research used in this study is shown in Figure 1. 2

Figure 1.

Systematic research.

4 RESULTS AND DISCUSSION In this systematic research, the development of the fiqh consultation application was carried out using the Iterative Incremental method, which is divided into four activities: planning, analysis and design, development, and testing. The development of the fiqh consultation application was carried out with three iterations, and the process is described in the subsec­ tions that follow. 4.1 Planning In the planning activity, researchers will focused on identifying the functional and nonfunc­ tional requirements of the system and the non-functional requirements of the system based on the requirements obtained from the design. The following in Table 1 provides. is an analysis of the functional requirements of the system. 4.2 Analysis and design In the analysis and design activity, researchers focused on the analysis and design of the system to support the implementation stages of application development. The use case dia­ gram in the development of the fiqh consultation application in the second iteration can be seen in Figure 2. The use case diagram in Figure 2 illustrates some system functionalities in the consult­ ation requirements. In this consultation requirement, there are users and Firebase as actors and users have functionality that sees the ustadz list and ustadz details, send mes­ sages, and views the message history. Firebase has the functionality to access an ustadz list and ustadz details.

3

Table 1. Functional requirements. REQ-ID

Requirements

Description

REQ-01 REQ-02

Sign Up Sign In

REQ-03

Reset Password

REQ-04

Edit Profile

REQ-05 REQ-06

Sign Out Ustadz Fragment

REQ-07

Ustadz Detail

REQ-08

Message Fragment

REQ-09

Chat

Account registration process so that users get access rights The process of accessing the application using an account that has been registered The process of resetting a password if the user forgets the password entered during registration The process of changing user account information to update data in the application The process of breaking access rights from the application The process of accessing the list of ustadz available for consultation The process of seeing details of ustadz such as the place and date of birth, educational history, scientific fields and mazhab The process of the user accessing the history of consultation chats that have been carried out with ustadz The process of sending a message to the ustadz for a fiqh consultation

Figure 2.

Use case diagram.

4

4.3 Development In the development activity, the researchers focused on implementing application devel­ opment by writing the application program code. The development is based on the requirements as well as analysis and design of applications that have been made previ­ ously. The following are some of the results of the implementation that has been car­ ried out. Figure 3 shows the result of the implementation of the sign-in page in the fiqh con­ sultation application for user login. Figure 4 shows the implementation of the home page in the fiqh consultation application that contains the ustadz who is online, selected article sliders, article categories, and fiqh info article. Figure 5 shows the implementation of the ustadz page in the fiqh consultation application. This page dis­ plays a list of all the ustadz, and there is also a search function for the ustadz and the “tanya ustadz” function. Figure 6 shows the implementation of user chat room pages with the ustadz.

Figure 3.

Sign-in page.

Figure 5.

Ustadz page.

Figure 4.

Home page.

Figure 6.

5

Chat page.

Table 2. Black box testing. ID

Functionality Expected output

PF-01 Sign Up PF-02 Sign In PF-03 Sign Out PF-04 Access Ustadz List PF-05 Access Ustadz Detail PF-06 Access Message History PF-07 Chat

Account successfully registered, then the system displays a successful notification and home page. The system displays a home page and notification user logged successfully. The user session ends and the system displays the sign-in page. The system displays a list of available ustadz.

Output

Status

Sign Up success

Successful

Sign In success

Successful

Sign Out success Successful

Access Ustadz List success The system displays ustadzes’ detailed information. Access Ustadz Detail success

Successful

The system displays a history list of messages.

Successful

The system displays a chat page and messages successfully sent.

Access Message History success Chat success

Successful

Successful

4.4 Testing In the testing activity, the application testing will be done using black box testing. The test is carried out to test the functionality of the fiqh consultation application by working on test cases and ensuring the application performs functions in accordance with the expected output. Black box testing that has been carried out as shown in Table 2 is a test of the functionality that exists in the fiqh consultation application. Based on the results of these functional tests, all functions in this fiqh consultation application run well and display the output as expected from the system requirements.

5 CONCLUSIONS Based on the results of research that has been done, the fiqh application was successfully developed by meeting the design requirements using the Iterative Incremental software devel­ opment method. The main feature provided in the fiqh consultation application is the Tanya Ustadz feature that allows users to consult directly with an ustadz via personal chat. The results using black box testing methods indicate that the fiqh consultations application has been built successfully and is functioning properly and running in accordance with the design. REFERENCES Baktha, K. 2017. Mobile Application Development: All the Steps and Guidelines for Successful Creation of Mobile App: Case Study. International Journal of Computer Science and Mobile Computing 6(9): 15–20. Cruz, A. M. Rd. & Paiva, S. 2016. Modern Software Engineering Methodologies for Mobile and Cloud Environments. Hershey: IGI Global. Despa, M. L. 2014. Comparative Study on Software Development Methodologies. Database Systems Journal 5(3): 37–56. Flora, H. K., Chande, S. V., & Wang, X. 2014. Adopting an Agile Approach for the Development of Mobile Applications. International Journal of Computer Applications 94(17): 43–50. Iqbal, M. & Husin. 2017. Perancangan dan Implementasi Konsultasi Gizi Online Berbasis Web. Seminar Nasional Hasil Penelitian (Jember: Ristekdikti): 117. Kusumadewi, N., Abdai, M. H. M., & Pratama, R. 2018. ABFI (Aplikasi Belajar Fikih Ikhtilaf) Pengem­ bangan Aplikasi Fiqih Perbandingan Mazhab Berbasis Android. Jurnal Islam Nusantara 2(1): 90.

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Li, Y., Lu, X., & Fang, J. 2016. Android System Security Vulnerability and Response Measures. In 2nd Information Technology and Mechatronics Engineering Conference (ITOEC 2016). Beijing, China: Atlantis Press. Mohammed, M. A., Bright, A. S. K., & Apostolic, C. 2017. Mobile-Based Medical Health Application Medichat App. International Journal of Scientific & Technology Research 6(5): 70. Muhammad, M. R. B. & Muhammad, M. B. 2003. Using Information and Communication Technology (ICT) to Disseminate the Understanding of Islamic Jurisprudence (Fiqh) and Juridical Opinion (Fatwa): A View of a Technologist. Kuala Lumpur: Malaysian Institute of Islamic Understand­ ing. 1–28. Mukherjee, S., Prakash, J., & Kumar, D. 2015. Android Application Development & Its Security. Inter­ national Journal of Computer Science and Mobile Computing 4(3): 714–719. Mustaqbal, M. S., Firdaus, R. F., & Rahmadi, H. 2015. Pengujian Aplikasi Menggunakan Black Box Testing Boundary Value Analysis. Jurnal Ilmiah Teknologi Informasi Terapan I(3): 31. Nidhra, S., & Dondeti, J. 2012. Black Box and White Box Testing Techniques – A Literature Review. International Journal of Embedded Systems and Applications (IJESA) 2(2): 29–50. Panchal, R. K. & Patel, A. K. 2017. A Comparative Study: Java Vs Kotlin Programming in Android. International Journal of Innovative Trends in Engineering & Research 2(9): 4–10. Salve, S. M., Samreen, S. N., & Khatri-Valmik, N.2018. A Comparative Study on Software Development Life Cycle Models. International Research Journal of Engineering and Technology 5(2): 696.

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Digital Economy for Customer Benefit and Business Fairness – Anggadwita & Martini (eds) © 2020 Taylor & Francis Group, London, ISBN 978-0-367-47722-6

The effect of satisfaction using digital innovation results towards employees’ work ethics and productivity at PT. GSM Ronald Limoa & Ratri Wahyuningtyas Telkom University, Bandung, West Java, Indonesia

ABSTRACT: PT. GSM makes various technological innovations including those to improve employees’ work ethic and productivity. This research analyzed the effect of satisfac­ tion of using digital innovation on the PT. GSM’s employees’ work ethic and productivity, the effect of work ethic on productivity, and the effect of satisfaction of using digital innov­ ation on productivity with employees’ work ethic as an intervening variable. This relational descriptive quantitative research used three variables namely satisfaction of using digital innovation (independent), employees’ performance (dependent), and employees’ work ethic (intervening). Data were collected by surveying 500 PT. GSM employees who had worked for at least 1.5 years. Results showed that satisfaction of using digital innovation had a positive and significant effect on the productivity of PT. GSM employees, work ethics affected their productivity, and employees’ work ethic mediated the effect of satisfaction of using digital innovation on productivity.

1 INTRODUCTION Established in 1995, PT. GSM is currently the largest cellular operator in Indonesia with 178 million customers. It continues to carry out digital innovations, both externally for its cus­ tomers and internally for its employees. This research focuses on PT. GSM’s digital innov­ ations for internal needs. Through ICT Network Management, PT. GSM implemented a number of digital innovations including: (i) HCM (Human Capital Management) Portal, a website application facilitating the employees in their activities and needs; (ii) Moana, a mobile application for the employee attendance; (iii) My Happy Works, an internal social media application for sharing and promoting internal office movements that are pleasant and easy; (iv) E-Click, a one-stop application for all services in HCM. Based on an internal survey, the company’s internal digital innovation was relatively successful. PT. GSM’s management breakthrough on digital innovation is its attention to diverging organizational design and digital innovation. Hoffman (2018) argued that it is not easy to diverge between the dynamic organizational design and digital innovation because digital innovation develops non-linearly, reflexively, and not centralized, unlike conventional man­ agement logic. However, Hoffman (2018) stated that little attention is given to the role of organizational design aimed at appropriate structures to accommodate digital innovation management. Ciriello et al. (2018) revealed that digital innovation can take the form of prod­ ucts, platforms, processes, implementations, and their exploitation and exploration. It is found that PT. GSM’s digital innovation form is not in products or production for customers; but in the internal platform. A number of studies have found that digital innovation has a positive effect on work ethic (Attaran & Attaran 2019; Martin & Thi 2015), there is a relationship between digital innov­ ation and work productivity (Brown 2011), and there is a relationship between work ethic and employees’ productivity (Ahmadie et al. 2017; Sunanda 2018). Based on this background, this research focuses on the effect of satisfaction of using the digital innovation on work ethics and productivity of PT. GSM employees. This research 8

finds out whether (i) satisfaction with digital innovation affects work ethics; (ii) satisfaction with digital innovation affects productivity; (iii) work ethic affects the employees; and (iv) sat­ isfaction with digital innovation affects productivity with employees’ work ethics as an inter­ vening variable.

2 LITERATURE REVIEW 2.1 Digital innovation Digital innovation has three key characteristics: convergence, generativity, and distributed nature (Hoffman, 2018). Convergence is the synergy of products and services that previously served different markets and purposes. Digital convergence integrates and implements digital technology into non-digital artifacts, making all types of products and services more flexible or ‘smart’ that can ultimately change the scope and meaning of existing products (Tilson & Lyytinen 2010; Pon et al. 2015). Generativity is the overall capacity of technology to produce change driven by a large, diverse, and uncoordinated audience because of its open modular architecture (Hoffman, 2018). Digital innovation has a distributed nature as the sociotechnical process covers the business ecosystem. Currently, digital innovation occurs in a cooperative, competitive and cooperative setting (Hoffman, 2018). Regarding the effect of digital innovation on employees’ work ethic and productivity, Mokolensang (2013) tested 16 variables driving employees’ productivity. He found five vari­ ables that significantly affected employees’ productivity: work environment, improvement, work support, employee monitoring, and overtime assignments. Fallahi et al. (2010) men­ tioned wages, fixed capital per employee, export orientation, R&D activities, and labor educa­ tion affected employees’ productivity. Brown (2011) found that innovation greatly contributed to employees’ productivity. Innovation in digital technology is believed to be related with employees’ work ethics and productivity, which are fundamental in organiza­ tional management. 2.2 Work ethic Ahmadie et al. (2017) define work ethic as an individual’s strength of judgment that makes the person aware of his/her good and bad actions and it helps him/her choose appropriate behavior. Ethical behavior and employment relations are significant for prod­ uctivity and organizational performance (Sunanda 2018). Sharma & Rai (2015) developed a work ethic measurement in three dimensions. First, work as a central life interest, which refers to how important work is in a person’s life by representing selfidentification. Second, moral attitude to work represented by an individual’s belief in moral and fair principles, such as hard work. Third, intrinsic work motivation driven by pleasure and challenges in the work itself. Sharma & Rai (2015) describe these dimen­ sions in 10 indicators developed into 10 statements. 2.3 Productivity Productivity illustrates the relationship between capital, land, and energy used to produce results. Goshu et al. (2017) suggest an alternative approach in measuring employees’ product­ ivity to detect problems easily and realistically, is compatible with modern systems and man­ agement tools, and has the potential to adapt to various companies types. Three dimensions proposed by Goshu et al. (2017) in measuring employees’ productivity are refined and used in this research. First, representing the company’s productivity, meaning that the personal level productivity must follow the direction of company’s productivity. Second, setting the priority of the problem and solution, by focusing on the priority of the problem, solution, and poten­ tial improvements. Third, completing results, including cost effectiveness, minimal error rates, accuracy of resource allocation, and linkages to previous work results. 9

3 RESEARCH HYPOTHESIS Based on the results of the previous studies, the following hypotheses were proposed: 1. Hypothesis 1: Satisfaction of using digital innovation results has a positive effect on employ­ ees’ work ethics. 2. Hypothesis 2: Satisfaction of using digital innovation results has a positive effect on employees’ work productivity. 3. Hypothesis 3: Employee’s work ethic has a positive effect on employees’ work productivity. 4. Hypothesis 4: Satisfaction of using digital innovation results influences employees’ product­ ivity with employees’ work ethic as an intervening variable.

4 RESEARCH METHODOLOGY This research employs causal quantitative method (Cooper & Schindler, 2014). The variable operations were based on Hoffmann (2018) for Satisfaction on Digital Innovation (X); Sharma & Rai (2015) for Work Ethic (Y); and Ghosu et al., (2018) for Productivity (Z). The instrument used was questionnaires with 5-point Likert Scale distributed to the sample of 500 employees of PT. GSM, including staff (band-1), supervisor (band-2), manager (band-3), gen­ eral manager (band-4), vice president (band-5), and senior vice president (band-6). Structural Equation Modeling (SEM), in this case Partial Least Square (PLS)–SEM, was the analysis tool.

5 RESULTS AND DISCUSSION 5.1 Description of variables The average (mean) of respondents’ perceptions of the three variables namely satisfaction of digital innovation, work ethics, and productivity were 4.04, 4.05, and 3.87 respectively. 5.2 CFA model SEM analysis in this research used a two-stage approach. The first stage was the measurement of variables by confirmatory factor analysis (CFA) techniques to obtain a fit construct. The second stage was to measure or test the structure of a fit SEM model, by testing the good­ ness of fit (GOF) index (Hair et al. 2014). After testing the CFA model, a significance test of the dimensions and indicators that reflect the construct was carried out, and also the construct val­ idity test. All relationships of latent variables with latent variables were greater than 1.96. Like­ wise, all indicators have shown significance > 1.96. Then, most of the P value was 0.05, namely the relationship of X (Digital Innovation) with W (Productivity) (0.065), but the CR of X with W had a significance of > 1.96. In this case only one (CR or P) was significant, which means it did not have to be both (Hair et al. 2014). Furthermore, the Goodness of Fit (GOF) indicators had not shown a high GOF. However, there are a number of indicators that met GOF, such as AIC, CAIC, and PNFI. One indicator showed poor fit (RMSEA), and two indicators were nearly fit (GFI, and AGFI). Therefore, the model was still acceptable even with a moderate level of acceptance. 5.3 Hypothesis testing Hypothesis testing was performed using t-value with a significance level of 0.05. The t-value in AMOS 22.00 is the critical ratio (CR) value in the regression weights (group number 1­ Default model) of the fit model. If the CR value ≥ 1.967 or the probability value (P) ≤ 0.05, then H0 is rejected, which means Ha is accepted (Hair et al. 2014). The CFA model regression weights values are shown in Table 1. 10

Table 1.

Regression weights.

Regression Constructions

Estimate

S.E.

C.R.

P

1.698 0.101 3.708

0.687 0.051 1.295

2.470 1.981 2.863

0.014 0.048 0.004

2.952 2.463 2.402 2.951 2.492 7.055 7.546 2.280 2.598 4.268

0.003 0.014 0.016 0.003 0.013 *** *** 0.023 0.009 ***

Relationship between Latent Variables Y←X W ← Y (**) W←X

Relationship of Observable Variables (indicators) with Latent Variables X5 ← X X4 ← X X3 ← X X1 ← X W2 ← W W4 ← W W6 ← W Y4 ← Y Y3 ← Y Y1 ← Y

6.061 1.706 1.512 6.528 0.192 0.647 0.658 0.189 0.214 0.662

2.053 0.693 0.630 2.212 0.077 0.092 0.087 0.083 0.082 0.155

Source: Processed from research results (2019) (**) Taken from the CFA Model-2.

The results of testing the entire hypothesis proposed in this study are as follows. Hypothesis 1 states that satisfaction of using digital innovation (X) had a positive effect on employees’ work ethic (Y). Table 1 shows that the t-value or CR was 3.470 ≥ 1.967 or the P value was 0.014 ≤ 0.05. This means that H0 was rejected. Hypothesis 2 states that satisfaction of using digital innovation (X) had a positive effect on employees’ work productivity (W). Table 2 shows that the t-value or CR was 2.863 ≥ 1.967 or the P value was 0.004 ≤ 0.05. This means that H0 was rejected. Hypothesis 3 states that employees’ work ethic (Y) had a positive effect on employees’ work productivity (W). Table 2 shows that the t-value or CR was 1.981 ≥ 1.967 or P value of 0.048 ≤ 0.05. This means that H0 was rejected. Hypothesis 4 states that satisfaction of using the results of digital innovation (X) affected employees’ prod­ uctivity (W) and employees’ work ethic (Y) as an intervening variable. The analysis used path analysis by looking at the direct effect, indirect effect, and the total effect. The path analysis in this research construct is formulated in the following equation. W ¼ pWX þ ðpYX x pWY Þ þ error

ð1Þ

where pWX is the effect of satisfaction using digital innovation (X) effect on employees’ prod­ uctivity (W) = 3.71; pYX is the effect of Satisfaction using digital innovation (X) effect on employees’ work ethic (Y) = 1.70; pWY is the effect of satisfaction using the results of digital innovation (X) has an effect on (Y) = 0.12 Total influence W ¼ 3:71WX þ ð1:70YX x 0:12WYÞ þ error 0:05 ¼ 3:71 þ ð1:70 x 0:12Þ þ 0:05 ¼ 3:71 þ 0:204 þ 0:05 errors ¼ 3:914

ð2Þ

It is evident that the coefficient of indirect effect satisfaction using the results of digital innov­ ation (X) influenced employees’ productivity (W) through the mediating/intervening variable of work ethic (Y), which was 3.914. It was greater than the coefficient of direct influence satis­ faction using innovation results digital (X) on employees’ productivity W (3.71). This proves that the mediating/intervening variable, namely the employees’ work ethic (Y) contributed to

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Table 2. Technical efficiency scores of ICT companies. Firm

2013

2014

2015

2016

2016

Average

AIS Axiata China Mobile China Telecom China Unicom Chunghwa Digi.com FarEasTone Globe Telecom HKT Indosat Jasmine KT Corp LG U+ Link Net M1 Maxis NTT PLDT Samart Telcoms Singtel SK Telecom Smartfren Smartone Starhub Taiwan Mobile Telkom Thaicom Time dotCom XL Axiata

0.9514 0.9559 0.9748 0.9709 0.9396 0.9423 0.9196 0.8561 0.9025 0.9450 0.9140 0.6170 0.9626 0.9089 0.4468 0.7146 0.9668 0.9760 0.9669 0.5531 0.9646 0.9640 0.4080 0.7253 0.7894 0.7699 0.9821 0.6242 0.3850 0.8838

0.9496 0.9519 0.9764 0.9662 0.9513 0.9454 0.9258 0.8402 0.9264 0.9591 0.9228 0.6188 0.9584 0.8837 0.5463 0.7287 0.9704 0.9699 0.9656 0.5129 0.9589 0.9619 0.4482 0.6832 0.7976 0.7293 0.9829 0.6894 0.3942 0.8702

0.9580 0.9461 0.9759 0.9566 0.9179 0.9624 0.9163 0.8464 0.9263 0.9622 0.9355 0.5500 0.9697 0.8778 0.5947 0.7350 0.9698 0.9702 0.9265 0.4607 0.9616 0.9649 0.4724 0.4297 0.7869 0.8529 0.9831 0.6990 0.3886 0.8835

0.9207 0.9501 0.9767 0.9552 0.9424 0.9618 0.9303 0.8628 0.9357 0.9688 0.9475 0.5601 0.9700 0.9046 0.6668 0.7220 0.9745 0.9619 0.9340 0.4756 0.9558 0.9623 0.4778 0.9572 0.7835 0.8854 0.9850 0.6414 0.4122 0.8760

0.9484 0.9594 0.9773 0.9604 0.9635 0.9551 0.9240 0.8730 0.9297 0.9719 0.9492 0.6011 0.9704 0.9154 0.6974 0.7174 0.8942 0.9623 0.9381 0.5276 0.9512 0.9633 0.5477 0.6434 0.7836 0.8552 0.9849 0.6309 0.4507 0.8615

0.9456 0.9527 0.9762 0.9619 0.9429 0.9534 0.9232 0.8557 0.9241 0.9614 0.9338 0.5894 0.9662 0.8981 0.5904 0.7235 0.9551 0.9681 0.9462 0.5060 0.9584 0.9633 0.4708 0.6878 0.7882 0.8186 0.9836 0.6570 0.4062 0.8750

Mean

0.8294

0.8329

0.8260

0.8486

0.8436

0.8361

the effect of satisfaction of using digital innovation (X) on employees’ productivity (W). This means that hypothesis 4 was proven.

6 SUGGESTION The results showed that satisfaction of using digital innovation is a predictor for employees’ work ethics and productivity, since it had a positive effect on employees’ work ethic and prod­ uctivity even in the absence of intervening variable. The results also showed that the employ­ ees’ work ethic had a positive effect on employees’ work productivity. These results are in accordance with Ahmadie et al. (2017) and Sunanda’s research (2018). Moreover, satisfaction using digital innovation results had a positive effect on employees’ productivity through employees’ work ethics. These results direct to some suggestions in academic context. First, at least one exogenous variable should be added to compare the influence power of digital innov­ ation with other exogenous variables. Second, academics should do more research on digital innovation as it has high relevance with the industrial era 4.0. Practically, digital innovation must continue being company’s main programs as it improves work ethic and productivity. Some of the company’s concrete steps that can be car­ ried out are (i) determining the innovation priority; (ii) receiving other companies’ innovations to be integrated, linked and developed, and (iii) implementing digital technology into non­

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digital artifacts. These steps will result in a more flexible company’s products/services, which are able to change products’ scope and meaning. In addition, the work ethic of individual employees and team-work (collegial) must be improved by detecting this early in the recruitment. Work ethic must be prepared institution­ ally through company compliance and corporate culture, since this is related to the main life interest, individual’s moral and intrinsic motivation. REFERENCES Ahmadiee, A., Sori, L., & Iman, M. 2017. Surveying The Relationship Between Work Ethics And Employee’s Productivity. Kuwait Chapter of Arabian Journal of Business and Management Review, 6(7): 23–26. Attaran, M., Attaran, S. 2019. The Need for Digital Workplace: Increasing Workforce Productivity in the Information Age. International Journal of Enterprise Information Systems, 15(1): 1–35. Brown, F., Guzman, A. 2014. Innovation and Productivity Across Mexican Manufacturing Firms. Jour­ nal of Technology Management & Innovation, 9(4): 36–52. Ciriello, R. F., Schwabe, G., & Richter, A. 2018. Digital Innovation. Business and Information Systems Engineering. Retrieved from https://link.springer.com/article/10.1007%2Fs12599-018-0559-8. Cooper, D. R., & Schindler, P. M. 2014. Business Research Methods. Twelfth Edition. New York: McGraw-Hill. Fallahi, F., Sojoodi, S., & Aslaninia, N. M. 2010. Determinant of Labor Productivity in Manufacturing Firms of Iran: Emphasizing on Labor Education and Training. MPRA Paper No. 27699: 1–18, Retrieved from http://mpra.ub.uni-muenchen.de/27699. Gosshu, Y. Y., Kitaw, D., & Matebu, A. 2017. Development of Productivity Measurement and Analysis Framework for Manufacturing Companies. Journal of Optimization in Industrial Engineering, 22: 1–13. Hair, J. H., & Hult, G. T. M., Ringle, C. M., Sarsdtedt, M. 2014. A Premier Partial Least Squares Struc­ tural Equation Modeling (PLS-SEM). Thousand Oaks: SAGE Publication. Hoffman, D. 2018. Shaping Wellsprings of Innovation: towards Organizational Design Configurations for Digital Innovation Management. Proceeding at Twenty-Six European Conference on Information Systems (ECIS 2018), Portsmouth, UK, 2018: 1–14. Martin, L., & Thi, T. U.Ng. 2015. The Relationship between Innovation and Productivity Based on R &D and ICT Use: An Empirical Analysis of Firms in Luxembourg. Revue Economique, 66 (6): 1105–1130. Mokolensang, P. M. 2013. The Determinants of Employee Productivity in Regional Office of Bank Negara Indonesia Manado. Jurnal EMBA, 1(4): 1533–1543. Njuri, E., Okech, T. C. 2016. Determinants of Employee Productivity in Kenya’s Private Limited Com­ panies in the Manufacturing Sector. International Journal of Economic, Commerce and Management, 4(10): 661–676. Nylen and Holmstrom 2015. Digital Innovation Strategy: A Framework for Diagnosing and Improving Digital Product and Service Innovation. Business Horizon, 58: 57–67. Pon, B., Seppala; & Kenney, M. 2014. One Ring to Unite Them All: Convergence, the Smartphone, and the Cloud. Journal Compet Trade. DOI. 10.1007/s10842-014-0189-x. Sharma, B. R., & Rai, S. 2015. A Study to Develop an Instrument to Measure Work Ethic. Global Busi­ ness Review, 16(2): 1–14. Sunanda, K. 2018. Impact of Work Place Ethics on Employee and Organization Productivity. Inter­ national Journal of Management (IJM), 9(1): 22–28. Tilson, D., Lyytinen, K. 2010. Desperately Seeking the Infrastructure in IS Research: Conceptualization of “Digital Convergence” as Co-evolution pf Social and Technical Infrastructure. Proceedings of the 43rd Hawaii International Conference of Sydney Sciences-2010: 1–10.

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Digital Economy for Customer Benefit and Business Fairness – Anggadwita & Martini (eds) © 2020 Taylor & Francis Group, London, ISBN 978-0-367-47722-6

Retail in the digital era: Case study of a modern store and a smart store Adis Imam Munandar & Siti Khoriyah School of strategic and Global Studies University of Indonesia, Jakarta, Indonesia

ABSTRACT: The digital age has started to penetrate the retail industry in Indonesia. This study aimed to compare and analyze the technology and customer perceptions in modern stores and smart stores. Qualitative research methods were utilized through observations, indepth interviews, and surveys. The results showed that artificial intelligence, face recognition, big data, radio-frequency identification, cameras, and smartphones were used in smart stores. Modern stores need to improve in terms of technology, while smart stores need to educate their customers.

1 INTRODUCTION Reports of social media management companies have been releasing patterns of digital behavior and online shopping in Indonesian society, which continues to increase (Hootsuite, 2019). Indonesia’s population is the fourth largest in the world, with an average age of 28 years, that has become an urban middle class that enjoys technology and is free to access the internet (Hootsuite, 2019). Most Indonesians have two or more smartphones (Azali, 2017). The lucrative image of Indonesian customers has become a powerful magnet for technology-based electronic trading giant companies to do business in Indonesia and invest billions of US dollars to enter Indonesia’s large retail market. The United States and China have been racing to pioneer cashier shops since 2015. The number of Amazon shops going to the Amazon electronic trading company has now reached dozens of branches distributed in major cities in the United States (Cheng, 2019). The electronic trading company Alibaba has also established a grocery store plus restaurants called Hema that are spreading in major cities and the largest cities in China (Laubscher, 2018). Conventional retail shops abroad support technology because they pose tough challenges: increasingly smart and critical customers and globalization that attracts more players to the competition arena and intensifies competition among retail entrepreneurs (Grewal et al., 2017). The tight retail business competition of each company generates continuous adjustments and efficiencies. Companies that desire to continue to grow require solid and innovative business operations (von Briel, 2018). The results of other studies showed that the retail industry in the United States suffered losses due to theft and human negligence of up to US$60 billion in 2015, which was usually worse in the holiday season (Leinbach-Reyhle, 2015). Retail companies were helped by the digital age in reaching their customers. Understanding customers can be a mystery and companies must gather more precise information about customers to put all the pieces of the puzzle together as a whole (Evans & Kitchin, 2018). Information is needed about customer interest in products, customer preferences in purchasing through internet or smartphone sites, past customer interactions with stores, transaction history in stores, etc. (Balaji & Roy, 2017). Modern retail studies in the digital age have been conducted quite often (Child et al., 2015; Peltola et al., 2015; Verhoef et al., 2015; Evans & Kitchin, 2018). Among them were studies on technology adoption in retail (Narayan et al., 2015), self-service technology in retail 14

(Kaushik & Rahman, 2015; Lee, 2015; Demoulin & Djelassi, 2016), studies on smart retail (Priporas et al., 2017), and a review of the Amazon Go smart shop (Polacco & Backes, 2018). The previous studies were more concentrated on applied technology in retail. There were only limited studies on selected retailers (Goodman & Remaud, 2015; Kim et al., 2016) and the retail shopping experience (Yakhlef, 2015). This study is more focused on efforts to compare technology and buying experience in the case studies of modern and smart stores that have different unique features.

2 METHOD A descriptive qualitative research method was used in the case study of modern (minimarket) and smart stores (JD.ID). The research was carried out by conducting store observations, customer interviews, and surveys. The smart store was located on the third floor of PIK Avenue shopping center, Jl. Pantai Indah Kapuk Boulevard, Penjaringan, North Jakarta while the modern store (Alfamart minimarket) was in a location around the PIK area. Observation and in-depth interviews were organized to compare the technology of modern stores and smart stores. The survey was conducted with closed questions and open questions were structured around payment transactions, facilities, products, services, prices, and technology used. The respondents answered 20 questions measured on a Likert scale to explore the buying experience.

3 RESULTS AND DISCUSSION The research used the Alfamart minimarket, which has many branches in Indonesia, to represent the modern store. These outlets generally sell a variety of food products, drinks, and other necessities at competitive prices. The differences in the use of technology between the smart and the modern stores are shown in Table 1. The results also examine the perceptions of modern store and smart store customers. The findings regarding the perceptions of customers who have shopped at smart stores and modern stores are shown in Figure 1. Customer perception of the speed of calculating the smart cash register in seconds is considered successful in providing a unique shopping experience in the smart store. Today’s customers acknowledge that the big names of smart stores guarantee the security of credit card data and GO-PAY data on smartphones. In addition, digital payments are more practical because bills automatically go directly to privately owned smartphones. Customers do not need to swipe credit cards and sign payment in front of the store’s other visitors. In modern stores, cashiers are slower than smart cash machines because they scan items, swipe debit or credit cards into EDC machines, count cash, and return change. The customer opinion that appeared most frequently was that the level of security of transactions with credit cards and debit cards was better now as long as they did not forget to change their PIN regularly and periodically. Various payment options through credit card, debit card, GOPAY application, OVO application, and cash are allowing customers to choose their preferred payment method. Cashier staff can be relied on despite occasional mistakes, for example, errors in entering the price of goods that causes long queues, especially during rush hour and busy days. The majority of customers currently remain comfortable in interactions with cashiers such as discussing discounts, amounts on loyalty cards, promotion of upcoming items, and others. Customer opinion about smart stores reflects an appreciation of the nuances of the minimalist and all-white shop, as it looks very clean. In addition, arranging food, beverages, clothing, home supplies, and cosmetics into separate sections makes it easy for customers to find the products they want. The atmosphere of modern retail shops is more comfortable because the customers have been regular customers for years and have more familiarity with the stores. 15

Smart store

1. Take a photo with your smartphone until the photo is recorded; a green check mark appears. 2. Payment without PIN worth IDR 10,000. The system refunds in 7–14 business days. 3. Fill in the payment method column by credit card. 4. Proof of authenticity of credit card ownership 5. Confirmation of activation of payment without PIN The technologies are smartphonesand Android or iOS and OTP operating systems.

1. The customer places a smartphone containing QR code identification information on the scanner while pointing his or her face toward the camera at the shop door that is connected to the face recognition system. The technologies are a scanner door that reads QR codes, face recognition, and cameras.

1. Radio-frequency identification (RFID) label installed on each product. Each RFID label contains a chip and antenna that contains wireless sensors to track the movements and traffic of people in the store with a high degree of accuracy. 2. Valuable information about customer spending habits in real time, for example, which products customers are interested in and buy, or which products are not selling well. The technologies are cameras, RFID labels, and sensors.

Registration

Entering the store

Shopping

Differences in the adoption of technology in smart stores and modern stores.

Stages

Table 1.

1. Bar code label with the scanner. Bar codes are read by a scanner that measures the light that reflects and interprets the bar code into numbers and letters that are passed on to the computer. This assists the store operations team in managing a large inventory, so that potential errors can be minimized. The technologies are bar codes and scanning devices.

1. Customers pass through electronic article surveillance at the entrance and exit of a store to prevent theft of goods and count the number of people entering and exiting. The technologies are electronic article surveillance.

1. Registration in modern retail stores still uses paper forms that are filled in by customers. 2. Then customer data are submitted into the computer by customer service personnel. 3. Store A has implemented an innovation in the form of digital registration with the LINE instant messaging application to encourage more customers to register as members of Store A. The technologies are computers and smartphones.

Modern store (minimarket)

Payment

1. Make automatic payments without a cashier or through self-checkout. 2. The smart cash register attempts to verify the faces of customers who enter the box and identify the items in the shopping basket; then the machine counts all the items for about 5 seconds. The duration of the count depends on the amount of goods purchased by customers. 3. Proof of payment is automatically stored on customer smartphones. 4. The exit door is open after the payment process is completed; customers can then go to the receptionist to pack the goods. The technologies are sensors, computer with vision or image recognition, RFID label scanners, and cameras.

1. The cashier operates the point of sales machine equipped with a card swipe machine or electronic data capture. 2. The bar code scanner is a smart cash register. 3. Payment methods available are credit cards, debit cards, cash, and digital money. The card swipe machine is provided by the bank. The technologies are cashier point-of-sale machines, card swipe machines, bar code scanners, and computers.

Figure 1.

Differences in perception in retail stores.

At the smart store, the customer service team has served customers kindly and patiently, especially those who came for the first time and tried to adapt to new technology. In addition, customers are quite satisfied with the prices of goods, which are lower than the market prices in almost all categories of goods. Customers questioned the source of profit because technology investment is highly expensive, so low prices are considered a gimmick to lure new customers. In smart stores, prices of goods are competitive when compared to other modern retail stores. Apart from that, employees are available to help if there are difficulties.

4 CONCLUSION Digitalization allows companies to improve the quality and speed of service by reducing errors due to fatigue and losses due to unsold goods, so store performance increases. Smart stores have utilized an advanced technology while modern stores employ technology that remains simple and semimanual (half manual, half-automatic). Consumer acceptance of the smart store is the same as for the modern store. Smart stores will replace modern stores and eliminate some types of labor, so stakeholders need to provide new skills (re-skilling) and additional skills (up-skilling) to the retail workforce to match the changes in the digital age. REFERENCES Azali, K. 2017. Indonesia’s Divided Digital Economy. Perspective 70: 1–12. http://setkab.go.id/inilahperpres-no-74Balaji, M. S. & Roy, S. K. 2017. Value Co-creation with Internet of Things Technology in the Retail Industry. Journal of Marketing Management 33(1–2): 7–31. doi: 10.1080/0267257X.2016.1217914. Cheng, A. 2019. Why Amazon Go May Soon Change the Way We Shop. Forbes. https://www.forbes.com/ sites/andriacheng/2019/01/13/why-amazon-go-may-soon-change-the-way-we-want-to-shop/ #12f5e41f6709 (accessed June 5, 2019). Child, P., Kilroy, T., & Naylor, J. 2015. Modern Grocery and the Emerging-Market Customer: A Complicated Courtship. McKinsey and Company. http://www.mckinsey.com/industries/retail/

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our-insights/modern-grocery-and-the-emergingmarket-consumer-a-complicated-courtship (Accessed June 7, 2019). Demoulin, N. T. M. & Djelassi, S. 2016. An Integrated Model of Self-Service Technology (SST) Usage in a Retail Context. International Journal of Retail and Distribution Management 44(5): 540–559. doi: 10.1108/IJRDM-08-2015-0122. Evans, L. & Kitchin, R. .2018. A Smart Place to Work? Big Data Systems, Labour, Control and Modern Retail Stores. New Technology, Work and Employment 33(1): 44–57. doi: 10.1111/ntwe.12107. Goodman, S. & Remaud, H. 2015. Store Choice: How Understanding Customer Choice of “Where” to Shop May Assist the Small Retailer. Journal of Retailing and Customer Services 23: 118–124. doi: 10.1016/j.jretconser.2014.12.008. Grewal, D., Roggeveen, A. L., & Nordfält, J. 2017. The Future of Retailing. Journal of Retailing. doi: 10.1016/j.jretai.2016.12.008. Hootsuite (2019) Digital 2019 Indonesia. Vancouver. JD.ID 2019. JD.ID Visi & Misi. https://www.jd.id/help/question-24.html (accessed June 5, 2019). Kaushik, A. K. & Rahman, Z. 2015. An Alternative Model of Self-Service Retail Technology Adoption. Journal of Services Marketing 29(5): 406–420. doi: 10.1108/JSM-08-2014-0276. Kim, S., et al. 2016. Customer Emotions and Their Triggers in Luxury Retail: Understanding the Effects of Customer Emotions Before and After Entering a Luxury Shop. Journal of Business Research69(12): 5809–5818. doi: 10.1016/j.jbusres.2016.04.178. Laubscher, H. 2018. A Visit to Hema in Beijing Change My View of Grocery Shopping. Forbes. https:// www.forbes.com/sites/hendriklaubscher/2018/10/10/a-visit-to-hema-in-beijing-changed-my-view-of-gro cery-shopping/#61eafee33c54 (accessed June 5, 2019). Lee, H. J. 2015. Customer-to-Store Employee and Customer-to-Self-Service Technology (SST) Interactions in a Retail Setting. International Journal of Retail and Distribution Management 43(8): 676–692. doi: 10.1108/IJRDM-04-2014-0049. Leinbach-Reyhle, N. 2015. New Report Identifies US Retailers Lose $60 Billion a Year, Employee Theft Top Concern, Forbes. https://www.forbes.com/sites/nicoleleinbachreyhle/2015/10/07/new-report-identi fies-us-retailers-lose-60-billion-a-year-employee-theft-top-concern/#20ba0d0180eb (accessed June 5, 2019). Levy, M. & Weitz, B. A. 2012. Retailing Management. 8th edition. New York: McGraw-Hill Irwin. Narayan, V., Rao, V. R., & Sudhir, K. 2015. Early Adoption of Modern Grocery Retail in an Emerging Market: Evidence from India. Marketing Science34(6): 825–842. doi: 10.1287/mksc.2015.0940. Peltola, S., Vainio, H., & Nieminen, M. 2015. Key Factors in Developing Omnichannel Customer Experience with Finnish Retailers. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). New York: Springer Science+Business Media, pp. 335–346. doi: 10.1007/978-3-319-20895-4_31. Polacco, A. & Backes, K. 2018. The Amazon Go Concept: Implications, Applications, and Sustainability. Journal of Business & Management 24(1), pp.79–92. doi: 10.6347/JBM.201803_24(1).0004. Priporas, C. V., Stylos, N., & Fotiadis, A. K. 2017. Generation Z Customers’ Expectations of Interactions in Smart Retailing: A Future Agenda. Computers in Human Behavior 77: 374–381. doi: 10.1016/j.chb.2017.01.058. Verhoef, P. C., Kannan, P. K., & Inman, J. J. 2015. From Multi-Channel Retailing to Omni-Channel Retailing. Introduction to the Special Issue on Multi-Channel Retailing. Journal of Retailing. 91(2): 174–181. doi: 10.1016/j.jretai.2015.02.005. von Briel, F. 2018. The Future of Omnichannel Retail: A Four-Stage Delphi Study. Technological Forecasting and Social Change 132: 217–229. doi: 10.1016/j.techfore.2018.02.004. Yakhlef, A. 2015. Customer Experience Within Retail Environments: An Embodied, Spatial Approach. Marketing Theory 15(4): 545–564. doi: 10.1177/1470593115569016.

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Digital Economy for Customer Benefit and Business Fairness – Anggadwita & Martini (eds) © 2020 Taylor & Francis Group, London, ISBN 978-0-367-47722-6

The role of customer online review in the buying decision process of a digital tourism product: A conceptual framework A. Permatasari & S.A. Mapuasari Faculty of Business, President University, Bekasi, Indonesia

E. Yuliana Faculty of Economics and Business, Telkom University, Bandung, Indonesia

N.F. Ahmad School of Business and Management, Bandung Institute of Technology, Bandung, Indonesia

ABSTRACT: In marketing travel, the customer online review (COR) plays an important role in validating information about products or services that consumers want and leads to buying interest. Therefore, the role of COR in the buying decision-making process becomes very important. This study aimed to develop a conceptual framework of the role of CORs in building consumer confidence and buying interest in purchasing travel products online in Indonesia. This study used an archival method to review and discuss the role of COR in the consumer buying process. Research data were obtained from literature studies and empirical data through in-depth interviews with customers of online travel agency services. The results showed there are seven hypotheses constructed to explore the COR roles in consumer purchase interest in e-com­ merce tourism products. The novelty of this study is construction of an online tourism market­ ing model that is capable of exploring real consumer experience and knowledge in Indonesia.

1 INTRODUCTION The trend of digital tourism is becoming more popular every day, thus transforming society into a hyper-connected culture. According to Celdrán-Bernabeu et al. (2018), “Now, the digital tourist consumes and generates experiences thanks to co-creation processes which are sup­ ported by social networks, apps, inspiring videos, forums, online sales platforms or blogs, etc.” Therefore, hundreds of websites and applications are being created and distributed every year to meet the increasing demand for the digital tourism market (Hudson & Thal, 2013). This makes online travel companies an important representative in fostering a new economy. E-commerce tourism or online travel agent is a solution for travelers/tourists to buy prod­ ucts and services by effectively using a computer network. Online travel agents (OTAs) have become a popular means for travelers or tourists to organize their trips. One of the biggest challenges for the sustainability of OTAs is managing their services through customer online reviews (CORs). CORs can direct tourists to become interested in buying tourism products online. CORs also can benefit e-commerce tourism to market and sell tourism products online. Therefore, CORs have a very important role in anticipating shifts in consumer pur­ chasing interest in choosing travel products through OTAs. This study explored aspects of CORs in building customer online trust (COT) and purchase intentions in experiential buying behavior of OTAs. The objective is to increase an understanding of online traveler buying behavior and the decision-making process in buying the products. The novelty of this study is in defining the conceptual framework regarding the roles of CORs and COTs in consumer purchase intention in digital tourism products.

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2 LITERATURE REVIEW 2.1 Digital tourism in Indonesia Digital tourism industry has introduced a new dimension of purchasing tourism products in the world. Celdrán-Bernabeu (2018) also introduced the concept of smart tourism. This study explores digital tourism purchase behavior (Hudson, 1999; Hudson & Thal, 2013). In under­ standing traveler or tourist behavior, there are many fundamental influences on a person’s decision to purchase a product or brand (Mautinho, 1987; Hudson, 1999; Hudson & Thal, 2013). The stimulus is processed in itself, according to product characteristics and experience, before finally a purchase decision is taken. Usually travelers search for online shopping and positive brand reputation (Wang & Hu, 2009; Hudson & Thal, 2013). Based on experiences (Wang & Hu, 2009), travelers can provide reviews by commenting through likes or dislikes on services provided by OTAs, such as travel tickets, tour packages, hotel, tour guide services, travel maps, accommodation costs, and more. Therefore, the COR gains the awareness of travelers to buy the product (Park et al., 2007). 2.2 Consumer buying decisions E-commerce companies are already aware of the importance of understanding customer atti­ tudes and their buying behavior. Decision-making can be defined as the determination of a series of activities to achieve the desired results (Permatasari & Kuswadi, 2017). This research used four variables from previous research on online consumer behavior by Li et al. (2007) and Park et al. (2007): e-commerce knowledge, perceived reputation, perceived risk, and perceived ease of use. In digital business, customer experiential learning is very important, as stated by Park et al. (2007), Lee et al. (2011), and Trenz and Berger (2013). The role of CORs provided by e-commerce companies can connect customers to view and develop an interest in buying online products. According to Wen (2009), a decision is a definite answer to a question about what should be done in connection with planning decisions. At the end of the process, a consumer can form basic decisions that lead to a complete good plan or other (new) plan.

3 RESEARCH METHOD This study used archival research methods to review previous research and conduct literature reviews (Saunders & Lewis, 2012). The analysis was carried out by reviewing the literature inthe context of the discussion of e-commerce tourism. The aim of the study is to produce a conceptual framework. The results capture the phenomenon of the COR and its role in the decision-making process in purchasing digital tourism products. This study has seven initial hypotheses about the role of customer online reviews in driving consumer confidence and interest in buying tourism products.

4 RESULTS AND DISCUSSION 4.1 Purchase intention Portions of this research were based on articles by Ling et al. (2011), Lee et al. (2011) and Kim and Lennon (2013). Those researchers define consumer purchase intention as a customer’s plan to buy goods or services. For Ramadani et al. (2014), purchase intention or interest in buying a consumer starts with analyzing information about a particular brand or product before deciding to buy a brand or product. In addition, purchase intention is influenced by the experience, preferences, and environment of consumers in gathering information, evaluat­ ing all possible alternatives, and making purchasing decisions (Wang & Hu, 2009; Lee et al., 2011). Therefore, when consumers take an interest in buying something or a particular 21

product or brand, they have gone through certain evaluations of several possible alternatives (Ling et al., 2011; Permatasari & Kuswadi, 2017). 4.2 E-commerce knowledge E-commerce knowledge is information possessed during the e-commerce buying process. According to Li et al. (2007), the growth of the internet has become one of the most abundant sources of consumer information. Knowledge is an important resource for consumers and companies today, and companies want to mobilize these resources to improve their processes, products, services, and demand (Ratten, 2012; Ramadani et al., 2014). Therefore, it takes big steps to ensure that e-commerce companies use the right techniques to manage the informa­ tion at hand. E-commerce knowledge means the customers have some idea, or know about the e-commerce existence in the business area (Permatasari & Kuswadi, 2017). Therefore, based on literature studies and background, the following hypothesis is proposed: H1: E-commerce knowledge has a positive influence on consumer online trust in buying online travel products. 4.3 Perceived reputation Perceived reputation or a good company reputation is positively related to online shopping trust. The process of building a positive company reputation is not easy. Consumer trust can increase significantly when a company is considered to have a good reputation (Permatasari & Kartikowati, 2018). A positive reputation is perceived as the level where people believe in the honesty and attention of the company to its customers. Research shows that there is a positive relationship between company reputation and consumers’ online shopping trust (Casalo et al., 2007; Kim & Lennon, 2013; Broutsou & Fitsilis, 2014). Consumers’ perceived reputation of a company had a positive impact on their trust and interest in shopping online, especially ini­ tial trust in the company (Casalo et al., 2007; Broutsou & Fitsilis, 2014). Therefore, based on literature studies and background, following hypothesis is proposed: H2: Perceived reputation has a positive influence on consumer trust in buying online travel products. 4.4 Perceived risk Consumers are satisfied with products and services that do not have the potential to harm them. In other words, perceived risk refers to uncertainty for consumers regarding whether the transaction can produce benefits or losses (Permatasari & Kuswadi, 2017). Consumer con­ fidence in evaluating product quality and making decisions to buy products will reduce per­ ceived risk (Kim & Lennon, 2013). Increasing consumer risk perception can reduce the perceived value of shopping activities on e-commerce sites (Ling et al., 2011). Most consumers feel insecure because of the perceived risk of making online payments, so they prefer to place orders through traditional channels where they can chat with sellers (Jang et al., 2013; Perma­ tasari and Kuswadi, 2017). Therefore, based on literature studies and background, the follow­ ing hypothesis is proposed: H3: Perceived risk has a positive influence on consumers’ online trust in buying online travel products. 4.5 Perceived ease of use Consumers pursued the benefits of using online transactions such as ease of use and faster service. Previous research by Ramadani et al. (2014) and Permatasari and Kartikowati (2018) defined the ease of use of information systems to be free from the pressures physical and mental efforts. E-commerce companies continue to innovate to provide ease of use for an 22

online shopping portal, and the easier use of an online shopping portal will increase consumer buying interest (Li et al., 2007; Gangguly et al., 2010). Ling et al. (2011) justify their argument by stating that the higher the ease of use of an information system, the higher the usefulness of the information system for its users. Therefore, based on literature studies and background, the following hypothesis is proposed: H4: Perceived ease of use has a positive influence on consumer online trust in buying online travel products. 4.6 Customer online trust Customer online trust (COT) is very important in doing online transactions (Lee et al., 2011). Online products and services usually cannot be immediately verified and there is still a lack of rules and habits for managing e-commerce (Koufaris, 2004; Chen & Barnes, 2007). In the transaction process, companies need to build and maintain trust between buyers and sellers (Ling et al., 2011). The relationship between risk and trust is reciprocal: risk creates opportun­ ities for trust, which leads to risk taking. The higher the risk faced by consumers, the lower their trust in the company (Gangguly et al., 2010). Therefore, based on literature studies and background, the following hypotheses are proposed: H5: Customer online trust has a positive influence on customer online review (CORs) in buying online travel products. H6: Customer online trust has a positive influence on purchase intentions in buying online travel products. 4.7 Customer online review Customer online reviews (CORs) facilitate consumers’ searching process to find products that best suit their needs. Companies need to more actively developCOR as a promotional and marketing strategy to make consumers more aware of their positive products, services, and brands that leads to increasing sales. CORs are a powerful source of information in the con­ sumer market. CORs display opinions expressed online by consumers about products or ser­ vices (Trenz & Berger, 2013). Consumer reviews play an important role in product evaluation and provide comprehensive information about various valence attributes of a product (Park et al., 2007). CORs can also influence consumer trust in the initial buying interest formed by consumers. It can be concluded that COR has become a trendy phenomenon, and an increas­ ing number of current consumers are now looking for peer consumer reviews before making a purchasing decision (Lee et al., 2011). Therefore, based on literature studies and back­ ground, the following hypothesis is proposed: H7: The customer online review (COR) has a positive influence on the purchase intention for online travel products. 4.8 Conceptual framework and discussion The conceptual framework developed by previous researchers is shown in Figure 1. There are four factors that influence customer online trust: e-commerce knowledge, perceived reputa­ tion, perceived risk, and perceived ease of use. E-commerce knowledge and perceived reputa­ tion in the first and second hypotheses are proposed to have a significant effect on building customer confidence in buying online tourism products. According to Ratten (2012), the growth of the internet has become one of the most abundant sources of consumer informa­ tion, and the use of the internet by consumers to search for information and their channel choices has made it relevant research on the factors that lead to intention in buying to a final purchase. The perceived risk and perceived ease of use variables are predicted to have a significant effect on online trust (Ganguly et al., 2010; Ling et al., 2011). The ease of use of websites and online tools will add value to consumer trust and make consumers have a more 23

Figure 1.

E-tourism commerce conceptual framework.

positive attitude toward products or services. The relationship between online trust and con­ sumer interest in buying online tourism products is proposed to have a positive connection. Gangguly et al. (2010) and Permatasari and Kartikowati (2018) stated that COTdoes not dir­ ectly influence consumer buying interest, but rather significantly influences when COR becomes the variable mediator.

5 CONCLUSION This study defined seven hypotheses to test in developing a model of COR in the tourism busi­ ness. This study recommended testing the model using Sequential Equation Modeling (SEM) (Hair et al., 2010). The model will accommodate companies in developing a COR strategy using customer experiential learning to convince travelers. Responses or comments posted will provide references to potential new customers and get responses in the form of feedback from service providers. Hence, customer experience makes it possible for OTAs to increase trust and interest in purchasing the offered tourism products. ACKNOWLEDGMENTS This research was supported and funded by the Ministry of Research, Technology and Higher Education Republic of Indonesia (RISTEKDIKTI). We thank our partners from the Univer­ sity Research Center of the President who have provided insights and expertise that are very helpful in this research. REFERENCES Broutsou, A. & Fitsilis, P. 2012. Online Trust: The Influence of Perceived Company’s Reputation on Consumers’ Trust and the Effects of Trust on Intention for Online Transactions. Journal of Service Science and Management 5(04): 365. Casalo, L. V., Flavián, C., & Guinalíu, M. 2007. The Influence of Satisfaction, Perceived Reputation and Trust on a Consumer’s Commitment to a Website. Journal of Marketing Communications 13(1): 1–17. Celdrán-Bernabeu, M. A., Mazón, J. N., Ivars-Baidal, J. A., & Vera-Rebollo, J. F. 2018. Smart Tourism: A Study on Systematic Mapping. Cuadernos de Turismo, N° (41): 655–658. Chen, Y. H. & Barnes, S. 2007. Initial Trust and Online Buyer Behaviour. Industrial Management & Data Systems 107(1): 21–36.

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Hudson, S. 1999. Consumer Behavior Related to Tourism. Consumer Behavior in Travel and Tour­ ism 7–32. Hudson, S. & Thal, K. 2013. The Impact of Social Media on The Consumer Decision Process: Implica­ tions for Tourism Marketing. Journal of Travel & Tourism Marketing 30(1–2), 156–160. Jang, Y. T., Chang, S. E., & Chen, P. A. 2015. Exploring Social Networking Sites for Facilitating Multi-channel Retailing. Multimedia Tools and Applications 74(1): 159–178. Kim, J. & Lennon, S. J. 2013. Effects of Reputation and Website Quality on Online Consumers’ Emo­ tion, Perceived Risk and Purchase Intention: Based on the Stimulus-Organism-Response Model. Jour­ nal of Research in Interactive Marketing 7(1): 33–56. Koufaris, M. & Hampton-Sosa, W. 2004. The Development of Initial Trust in an Online Company by New Customers. Information & Management 41(3): 377–397. Lee, J., Park, D. H., & Han, I. 2011. The Different Effects of Online Consumer Reviews on Consumers’ Purchase Intentions Depending on Trust in Online Shopping Malls: An Advertising Perspective. Inter­ net Research 21(2): 187–206. Li, R., Kim, J., & Park, J. 2007. The Effects of Internet Shoppers’ Trust on Their Purchasing Intention in China. JISTEM-Journal of Information Systems and Technology Management 4(3): 269–286. Ling, K. C., Daud, D. B., Piew, T. H., Keoy, K. H., & Hassan, P. 2011. Perceived Risk, Perceived Tech­ nology, Online Trust for the Online Purchase Intention in Malaysia. International Journal of Business and Management 6(6): 167. Malhotra, N. 2012. Review of Marketing Research. Singapore; RMR. Moutinho, L. 1987. Consumer Behaviour in Tourism. European Journal of Marketing 21(10): 5–44. Park, D. H., Lee, J., & Han, I. 2007. The Effect of Online Consumer Reviews on Consumer Purchasing Intention: The Moderating Role of Involvement. International Journal of Electronic Commerce 11(4): 125–148. Permatasari, A. & Kartikowati, M. 2018. The Influence of Website Design on Customer Online Trust and Perceived Risk Towards Purchase Intention: A Case of O2O Commerce in Indonesia. Inter­ national Journal of Business and Globalisation 21(1): 74–86. Permatasari, A. & Kuswadi, E. 2017. The Impact of Social Media on Consumers’ Purchase Intention: A Study of Ecommerce Sites in Jakarta, Indonesia. Review of Integrative Business and Economics Research 6(S1): 321–335. Ramadani, V., Demiri, A., & Saiti-Demiri, S. 2014. Social Media Channels: The Factors That Influence the Behavioural Intention of Customers. International Journal of Business and Globalisation 12(3): 297–314. Ratten, V. 2012. Social Cognitive Theory in Technological Innovation. International Journal of Electronic Finance 6(1): 1–12. Saunders, M. N. & Lewis, P. 2012. Doing Research in Business & Management: An Essential Guide to Planning Your Project. Upper Saddle River, NJ: Pearson. Trenz, M. & Berger, B. 2013. Analyzing Online Customer Reviews: An Interdisciplinary Literature Review and Research Agenda. ECIS 2013 Completed Research. Paper 83. http://aisel.aisnet.org/ ecis2013_cr/83 Wang, H. & Hu, Z. 2009. Online Trust between Inexperienced Consumers and Experienced Consumers: An Empirical Study. In Second International Conference on Future Information Technology and Man­ agement Engineering, Hong Kong, November 13–14, 2009, pp. 167–170. Wen, I. 2009. Factors Affecting the Online Travel Buying Decision: A Review. International Journal of Contemporary Hospitality Management 21(6): 752–765.

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Digital Economy for Customer Benefit and Business Fairness – Anggadwita & Martini (eds) © 2020 Taylor & Francis Group, London, ISBN 978-0-367-47722-6

The role of status consciousness in determining the millennial generation shopping style A. Arisman & D. Risana Perjuangan University, Tasikmalaya, Indonesia

ABSTRACT: The purpose of this study was to determine how the influence of status con­ sciousness on brand status selection and brand preferences affects millennial generation shopping style in the city of Tasikmalaya. This study used survey methods to obtain infor­ mation on status consciousness, brand status, brand preference, and shopping style in the millennial generation, as well as causality relationships between variables through statis­ tical calculations. Data collection used purposive sampling techniques and consisted ofquestionnaires to 148 respondents who are productive millennials. Through calculations using Structural Equation Model analysis, the results of this study indicate that status con­ sciousness is proven to be an antecedent of brand status and brand preferences. Brand preferences also have a significant influence on the formation of millennial shopping styles. However, brand status has not been proven to have an influence on millennial shopping styles. The novel value of this study is in placing brand status and brand preference as mediating variables.

1

INTRODUCTION

The development of the Muslim fashion industry in every country that has a Muslim popula­ tion seems to have been greeted with high enthusiasm among young people, which in this case can be called the millennial generation. The trend in Muslim fashion seems to be toward showing status in society, which has resulted in changes in consumption practices of this mil­ lennial generation in the category of fashion products. There are two main issues related to consumption practices that can be of concern, namely the individual’s desire for a certain status and the purchase or consumption of a certain brand status (O’Cass & Siahitri, 2014). This problem is also globally relevant, as the desire for status and high consumption of brand status seems to be increasing in developing Asian countries, especially in Indonesia. Status consciousness and a desire to consume certain brands place Indonesia in a key position both commercially and theoretically in relation to the development of the fashion industry and the status of certain brands. Growth in consumption of status can be seen in the generation of con­ sumers who adhere to the pattern of consumerism that is occurring in Indonesia today (O’Cass & Choy, 2008). McKinsey (2012) suggested that most consumers have an emo­ tional bond with the use of products that reflect their image and status. Emotional ties and feelings of status greatly influence the buying decisions of many consumers in Indonesia, especially in the younger generation. In some studies, status consciousness has been known to be an important driving force in consumption. Consumers tend to get status through the consumption of certain products that are considered to have high status (O’Cass & McEwan, 2004). Although research on con­ sumption status and the fashion industry continues to develop, there are still gaps in research on consumption status and the role of products. A gap that requires study is the interaction between status consciousness, perceived brand status, and brand preference, and how this

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interaction affects shopping style for a Muslim fashion brand in developing countries such as Indonesia (O’Cass & Choy, 2008; Eastman & Eastman, 2011). Based on the foregoing explanation, status consciousness is a driving force in con­ sumption patterns, especially for millennials who wish to be recognized for their status according to today’s lifestyle. Millennial generation consumers tend to get status through the consumption of certain products that are considered to reflect the status they want to express. Therefore, it is necessary to examine the interaction of status consciousness with perceived brand status and brand preference, and how this interaction affects con­ sumers’ willingness to pay premium prices for a Muslim fashion brand. This research was conducted among millennials in the city of Tasikmalaya because it is one of the big­ gest Muslim fashion producing cities in Indonesia and is considered to offer several alternative Muslim fashion brands.

2 LITERATURE REVIEW 2.1 Brand status and status consciousness Status consciousness is seen as a motivation for obtaining social prestige from product owner­ ship that communicates one’s status. In societies that have a high level of consumerism, the symbolic meaning of an object is seen as more important than the object itself. In other words, individuals consume their status symbol and not its object (Trentmann, 2004). Status conscious consumersplace a high priority on the ownership of goods-laden status. However, the focus of ownership has increasingly shifted from products to brands (O’Cass & Choy, 2008; Phau & Cheong, 2009). There is a difference in consumers’ perceptions of the status of various brands, and it will be more clearly seen in consumers who have status consciousness compared to consumers who have no or little status consciousness. Consumers who are aware of the status will look for brands with expressive or symbolic characteristics to show their uniqueness (Clark et al., 2006), self-confidence (Husic & Cicic, 2009), or even how classy they are (Phau & Leng, 2008). This is very relevant to what is happening in Indonesia, where there has been a shift in con­ sumer behavior in the increasingly consumptive millennial generation and also in the availabil­ ity of brands that can reflect ownership of certain statuses. Thus, status consciousness is the main determinant of brand status. H1: Status consciousness has a significant effect on brand status. 2.2 Brand preference Brand preference is one form of consumer appreciation of the brand (Kotler, 2009: 50), while according to Lou and Lee (2009: 49), brand preference is a condition in which consumers like the brand because the brand is pleasing. This preference is formed from the perception of a product or service brand that is closely related to consumers’ assessment of satisfaction or dissatisfaction with a particular brand (Tjiptono, 2007: 58). Consumer preferences are known that are predicted directly or indirectly by consumption values, which include social values such as symbolic consumption values and personal values such as sensitivity to prestige (Mulyanegara & Tsarenko, 2009). This happens especially in younger consumer groups or in the millennial generation by adopting Western consumption behavior (Bennett & Bryant, 2010). Consumption has increasingly shifted toward the demon­ stration of social position especially through branded items such as fashion products. In this case, consumers who have status consciousness will focus more on brand names as a tool to express their own status (Han et al., 2010). Thus, status consciousness is the main determinant of brand preference. H2: Status consciousness has a significant effect on brand preferences.

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2.3 Shopping style Research aimed at understanding the way consumers shop has been around for more than half a century, with early work developing typologies of shopping styles. More typology was developed because researchers used various approaches and different contexts (Jarratt, 1996). However, it has been agreed that some buyers exhibit a stable style and shopping behavior, which may conflict with that of other buyers (Bakewell & Mitchell, 2003). Consumer decisionmaking style has been defined as “a mental orientation that characterizes the consumer’s approach to making choices and has cognitive and affective characteristics.” In line with the concept of personality in psychology, individuals are considered to have stable and enduring consumption personalities (Sproles & Kendall, 1986). In general, strong brands with high brand equity may have the ability to set premium prices or high prices (Goldsmith et al., 2010: Han et al., 2010). Higher prices are very important for brand status because status seekers are motivated to impress others through consuming status that is embedded in a brand (Husic & Cicic, 2009). Rating attributes that reflect status can include superior quality, high prices, exclusivity, stand­ ards of excellence, and associations with wealth or success (O’Cass & McEwan, 2004). The assessment is based largely on consumers’ assessment of brand value and material value, so consumers are willing to pay the symbolic benefit or expressive value of the brand compared to its functional value (Netemeyer et al., 2004). Researchers have stated that status seekers are willing to pay for products deemed to convey status and they are less price sensitive (Eastman & Eastman, 2011; O’Cass & Siahitri, 2014;). As a result, the brand acts as a tool to enhance the status that will potentially influence the shop­ ping style of the consumer. Recognizing the increasing growth of brand-oriented millen­ nial consumers and increasing middle-class income in Indonesia, millennials will be willing to buy to fulfill the brand status they like and desire. H3: Brand status has a significant effect on shopping style. H4: Brand preference has a significant effect on shopping style.

3 METHODOLOGY The object of this research is status consciousness, brand status, brand preference, and shop­ ping style for Muslim fashion products by the millennial generation in the city of Tasikma­ laya. A survey research method was used, with a questionnaire as the main data collection tool. This questionnaire was distributed to 148 respondents who were productive millennials. The sampling technique used was purposive -sampling with the determination of the require­ ments, that is, people who have made purchases of special Muslim fashion brand products such as Zoya, Rabbani, etc. A Likert scale with a magnitude of five scales, both positive and negative, was used for the measurement of the respondents’ answers. To determine the causal­ ity relationship between research variables, Structural Equation Model (SEM) analysis was used.

4 RESULTS AND DISCUSSION The results are based on research conducted on samples taken from as many as 148 respond­ ents consisting of millennials in Indonesia, especially those in the city of Tasikmalaya, with the profile of respondents viewed from several aspects, including gender, age, education, employment, and income. The results of data collection on the characteristics of respondents by age of millennials are quantitatively dominated by respondents aged between 22 and 26 years, reaching 60.8%; those with an undergraduate level of education or more reaching 82.4%; those who have jobs as private employees reaching 45.3%; and those with an income of less than 5 million rupiah per month reaching 82.57%.

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Based on the results of SEM analysis, it is known that the critical ratio value for normality test­ ing gets a score that does not exceed a cut-off value of 2.58. Testing the measurement model of variable status consciousness, brand status, and shopping style produces several loading factor values that do not meet the criteria because they are below the minimum value of 0.6. Only meas­ urements of brand preference variables passed the test. Therefore, the measurement of variables that do not meet the criteria of brand status, and the following were removed from the analysis process: interested in brand status in status consciousness variables; distinctive, sophisticated, and high esteem in brand status variables; and price-conscious, confused by overchoice, and habitual in the shopping style variable. From Table 1 it can be seen that the remaining loading factor (Lf) for the measurement of each variable is used to measure the validity, reliability, and variance extracted as one of the assumptions that must be met. The Lf value is the confirmation value for testing validity. Reliabil­ ity testing based on the remaining Lf values results in construct reliability and variance extracted values greater than 0.7. Based on the results of SEM analysis that has been carried out for testing the causality model between research variables,the goodness of fit index value obtained was chi square = 309.444, the minimum discrepancy function/degree of freedom (CMIN/DF) = 2.677, goodness of fit index (GFI) = 0.853, tucker lewis index (TLI) = 0.954, comparative fit index (CFI) = 0.964, and root mean square error of approximation (RMSEA) = 0.089. Therefore test­ ing of this research model is still acceptable because the TLI and CFI values meet the minimum criteria for model eligibility even though the value of other criteria is at marginal fit.

Table 1. Measurement of research variables. Variable

Indicators

Loading factor (Lf)

Status consciousness

• • • • • • • • •

Brands are status symbols Brands are prestigious Brands reflect wealth Brands reflect achievement Brands reflect success Status brands are important Interested in status brands Concerned about brand status Like status brands

.919 .937 .908 .912 .911 .447 .896 .498 .931

Brand status

• • • • • • • •

Prestigious Status Distinctive Sophisticated High esteem Success Wealth Exclusive

.912 .895 .432 .476 .348 .933 .922 .915

Brand preference

• Only look for branded fashion clothing • Prefer to buy imported fashion clothing • Care a lot about brand origin

.905 .918 .934

Shopping style

• • • • • • • •

.866 .868 .904 .919 .449 .910 .411 .364

Perfectionist Brand conscious Novelty Recreational Price conscious Impulsive Confused by overchoice Habitual

29

Figure 1.

The result of SEM analysis.

Based on the results of SEM analysis that has been done to test the model and the hypoth­ esis of the causality relationship between research variables, it was found that status con­ sciousness is proven to be an antecedent of brand status and brand preference, with a p-value (0.000) less than the sig-α value of 0.05. The brand status cannot be proven to have an influ­ ence on shopping style and produces a p-value of 0.691, but brand preference is proven to have a significant effect in determining millennial shopping style in the city of Tasikmalaya, with a p-value of 0.015.

5 CONCLUSION Status consciousness of the millennial generation will determine the brand status of Muslim fashion products on the market; when a brand is deemed to be in accordance with the status sought by the millennial generation, it will have a good brand status. In addition, high status consciousness will also be a key factor in selecting certain product brands. The better the status consciousness, the better the brand status and brand preferences. Brand status cannot be used as a reference for the formation of a millennial shopping style; what determines the shopping style of a millennial is based on brand preferences. The better the brand preference, the more it will determine the shopping style of this millennial generation. This study has limitations in terms of developing variable concepts that focus only on spe­ cific brands that are closely related to social status. This research also focused only on the millennial generation. A suggestion for further research is to add the concept of antecedents to shopping style by considering aspects other than social, for example, by developing con­ cepts with a cultural approach, adoption of technology, or other approaches that are con­ sidered still relevant. REFERENCES Apelbaum, E., Gerstner, E., & Naik, P. A. 2003. The Effects of Expert Quality Evaluations versus Brand Name on Price Premiums. Journal of Product & Brand Management 12(3): 154–165. Bennett, J. & Bryant, S. 2010. A Cross-Cultural Initiative: Engaging China’s Middle Class. International Trade and Finance Association Conference Papers, p. 1. Chen-Yu, H.J. & Kincade, D. H. 2001. Effects of Product Image at Three Stages of the Consumer Deci­ sion Process for Apparel Products: Alternative Evaluation, Purchase and Postpurchase. Journal of Fashion Marketing and Management 5(1): 29–43. Clark, R. A., Zboja, J. J., & Goldsmith, R. E. 2006. Status Consumption and Role-Relaxed Consump­ tion: A Tale of Two Retail Consumers. Journal of Retailing and Consumer Services 14(1): 45–59.

30

Del Río, A. B., Vazquez, R., & Iglesias, V. 2001. The Effects of Brand Associations on Consumer Response. Journal of Consumer Marketing 18(5): 410–425. Eastman, J. K.& Eastman, K. L. 2011. Perceptions of Status Consumption and the Economy. Journal of Business and Economics Research 9(7): 9–19. Eastman, J. K., Goldsmith, R. E., & Flynn, L. R. 1999. Status Consumption in Consumer Behavior: Scale Development and Validation. Journal of Marketing Theory and Practice 7(3): 41–52. Goldsmith, R. E., Flynn, L. R., & Kim, D. 2010. Status Consumption and Price Sensitivity. Journal of Marketing Theory and Practice 18(4): 323–338. Hair, J. F., et al. 2010. Multivariate Data Analysis, 7th edition. Upper Saddle River, NJ: Prentice-Hall. Han, Y. J., Nunes, J. C., & Drèze, X. 2010. Signalling Status with Luxury Goods: The Role of Brand Prominence. Journal of Marketing 74(4): 15–30. Husic, M. & Cicic, M. 2009. Luxury Consumption Factors. Journal of Fashion Marketing and Manage­ ment 13(2): 231–245. Kelan, E. & Lehnert, M. 2009. The Millennial Generation: Generation Y and the Opportunities for a Globalised, Networked Educational System. Beyond Current Horizons (https://www.researchgate.net/pub lication/255588891_The_Millennial_Generation_Generation_Y_and_the_Opportunities_for_a_Globali sed_Networked_Educational_System) Kotler, P., Keller, D., & Lane, K. 2009. Manajemen Pemasaran Jilid 1, 13th edition, translated by B. Sabran, MM. Jakarta: Penerbit Erlangga. Mandhachitara, R. & Piamphongsan, T. 2011. Professional Women’s Variety-Seeking Behavior in Fash­ ion Clothing: New York City and London. Academy of Marketing Studies Journal 15(1): 23–43. McKinsey Global Institute. 2012. Meet the 2020 Chinese Consumer. www.mckinseychina.com/wp-content/ uploads/2012/03/mckinsey-meet-the-2020-consumer.pdf Michaelidou, N. & Dibb, S. 2006. Product Involvement: An Application in Clothing. Journal of Consumer Behaviour 5(5): 442–453. Mulyanegara, R. C. & Tsarenko, Y. 2009. Predicting Brand Preferences: An Examination of the Predictive Power of Consumer Personality and Values in the Australian Fashion Market. Journal of Fashion Market­ ing and Management 13(3): 358–371. Netemeyer, R. G., Krishnan, B., Pullig, C., et al. 2004. Developing and Validating Measures of Facets of Customer-Based Brand Equity. Journal of Business Research 57(2): 209–224. O’Cass, A. & Choy, E. 2008. Studying Chinese Generation Y Consumers’ Involvement in Fashion Cloth­ ing and Perceived Brand Status. The Journal of Product and Brand Management 17(5): 341–352. O’Cass, A. & Frost, H. 2002. Status Brands: Examining the Effects of Non-Product-Related Brand Associ­ ations on Status and Conspicuous Consumption. The Journal of Product and Brand Management 11(2): 67–86. O’Cass, A. & McEwan, H. 2004. Exploring Consumer Status and Conspicuous Consumption. Journal of Consumer Behaviour 4(1): 25–39. O’Cass, A. & Siahtiri, V. 2014. Are Young Adult Chinese Status and Fashion Clothing Brand Conscious? Journal of Fashion Marketing and Management 18(3): 284–300. Phau, I. & Cheong, E. 2009. How Young Adult Consumers Evaluate Diffusion Brands: Effects of Brand Loyalty and Status Consumption. Journal of International Consumer Marketing 21(2): 109–123. Phau, I. & Leng, Y. S. 2008. Attitudes Toward Domestic and Foreign Luxury Brand Apparel: A Comparison Between Status and Non-Status Seeking Teenagers. Journal of Fashion Marketing and Management 12(1): 68–89. Rajagopal. 2010. Conational Drivers Influencing Brand Preference Among Consumers. Journal of Trans­ national Management 15(2): 186–211. Sethuraman, R. & Cole, C. 1997. Why Do Consumers Pay More for National Brands than for Store Brands? Cambridge: Marketing Science Institute. Skuras, D. & Vakrou, A. 2001. Consumers’ Willingness to Pay for Origin Labelled Wine: A Greek Case Study. British Food Journal 104(11): 898–912. Stafford, D. & Griffis, H. 2008. A Review of Millennial Generation Characteristics and Military Work­ force Implications. Millennial Behaviors and Demographics. Alexandria, VA: CAN. Sugiama, G. 2008. Metode Riset Bisnis Dan Manajemen. Bandung: Guardaya Intimarta. Suliyanto. 2007. Pelatihan Analisis Data. Laboratorium Manajemen Fakultas Ekonomi Kampus Unsoed Grendeng: Purwokerto. Tjiptono, Fandy. 2007. Strategi Pemasaran, 2nd edition. Yogyakarta: Penerbit Andi. Trentmann, F. 2004. Beyond Consumerism: New Historical Perspectives on Consumption. Journal of Contemporary History, Vol. 39 No. 3, pp. 373–401. Umar, Husain. 2002. Riset Pemasaran dan Perilaku Konsumen. Jakarta: PT Gramedia Pustaka Utama.

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Digital Economy for Customer Benefit and Business Fairness – Anggadwita & Martini (eds) © 2020 Taylor & Francis Group, London, ISBN 978-0-367-47722-6

Effect of brand image and brand personality on brand loyalty with brand trust as a mediator Mohamed Yusuf Faridian Wirayat Faculty of Economics and Business, Telkom University, Bandung, Indonesia

Indira Rachmawati Faculty of Economics and Business, Telkom University, Bandung, Indonesia Graduate School of Business, Universiti Sains Malaysia, Malaysia

ABSTRACT: PT Toyota Astra Motor (TAM) has a variety of products in several segments in Indonesia. However the status of TAM as the largest wholesales holder in several GAI­ KINDO categories has been declining because other competitors also offer their products in the same segment. The aim of this study was to find out the effect of brand image and brand per­ sonality on brand loyalty with brand trust as a mediator in Indonesia. The subject of this research were people who have useds and will reuse PT TAM products. Data analysis in this study used smartpls 3.0 Structural Eqaution Modelling (PLS-SEM). The results of this research show that the aspect that must be improved based on the Importance and Performance Matrix Analysis is brand trust. Brand trust occurs when the company promises to provide quality prod­ ucts to consumers and sucessfully fulfills those promises.

1 BACKGROUND OF THE STUDY PT Toyota Astra Motor was established on April 12, 1971 in Indonesia. At the time, TAM was run as an importer and distributor of Toyota products. The products offered by TAM covers a variety of segments, such as city car, low–cost green car (LCGC), hatchback, multi­ purpose vehicle (MPV), sport utility vehicle (SUV), sedan, sportscar, hybrid, and some com­ mercial trucks. In addition, PT Toyota Motor Manufacturing Indonesia (TMMIN) which assembles Toyota products, has also become an exporter and supplier of spareparts for the product line up.In 2017, Indonesian market share was dominated by Japanese brands such as Toyota, Daihatsu, Honda, Suzuki, and Mitsubishi. Toyota got the highest sales from Japanese brands in Indonesia, with 34.40% or 371,332 units out of 1.079.000 vehicle units sold in Indonesia. The highest sales of Toyota products are from LMPV Toyota Avanza with 116,311 units; LCGC Toyota Calya with 73,326; and Medium MPV Toyota Kijang Innova, with 61,775 units sold in 2017. Moreover, in 2018 PT Toyota Astra Motor became Trademark Holding Sold Agent (ATPM), which has the highest number of wholesales according to the Associ­ ation of Indonesia Automotive Industries (GAIKINDO) data publication. The top 5 out of 30 ATPM wholesales in Indonesia in 2018 were 352,141 Toyota units, 202,738 Daihatsu units, 162,170 Honda units, 142,881 Mitsubishi Motors units, and 118,014 Suzuki units. In 2015, 2016, and 2017 Toyota became the ATPM with the highest marketshare in Indo­ nesia. Although in these years Toyota had the highest marketshare, the number of sales did not increase every year. In 2015 to 2016 Toyota’s sales increased from 321,818 units to 389,242 units.

32

Even though Toyota always had the highest sales in Indonesia, customer growth did not always increase from 2013 to 2017. In 2013 there was an increase of 0.071% in cus­ tomer growth. It then decreased by 0.21% in 2014 and continued to fall in 2015 by 0.05%. After that, customer growth rose by 0.185% in 2016 but was followed by a decline of 0.026% in 2017. PT TAM offers a variety of products in different segments in Indonesia. Some of the prod­ ucts can increase the wholesales of Toyota. However, the number of products in several seg­ ments in GAIKINDO decreased. This happened because other brands offered products that were competing directly with the products that Toyota offered. Thus, in several segments such as MPV, Mitsubishi Motors offered Mitsubishi Xpander, which competed with Toyota Avanza. Suzuki also offered Baleno and Ignis, which competed with Toyota Yaris and Toyota Agya. Lastly, Nissan offered Nissan Terra, which competed with the SUV segments from Toyota that is Fortuner. 1.1 Problem statement Consumer brand image for TAM can affect brand loyalty of the company because brand image of the company can differentiate Toyota from others. Brand personality can affect brand trust, in this case the consumer who chooses Toyota because of consumer trust and their perception of personality towards Toyota. Brand trust could also affect brand loyalty because consumers will consider whether or not Toyota met their needs and expectations 1.2 Research question Do brand image and brand personality affect brand loyalty through brand trust as a mediator? 1.3 Purpose of the study The purpose of the study was to find out the effect of brand image and brand personality on brand loyalty through brand trust as a mediator. 1.4 Hyphoteses New products that compete with ATPM in the automotive industry make the competition tighter. Competition will lead ATPM to introduce various developments to attract consumers’ interest. Users can have different experiences with each product. This research was conducted based on the phenomenon described in the earlier in the background section, and therefore aimed want to find out the effect of brand image and brand personality on brand loyalty with brand trust as a mediator. This study was replicated from that of Mabkhot (2017), whose article was chosen because the framework and the variables fit this research. The variables used in this study are brand image, brand personality, brand trust, and brand loyalty. This research purposes seven hyphoteses, as follows: H1: There is a positive significant relationship between brand image (BI) and brand loy­ alty (BL). H2: There is a positive significant relationship between brand personality (BP) and brand loyalty (BL). H3: There is a positive significant relationship between brand image (BI) and brand trust (BT). H4: There is a positive significant relationship between brand personality (BP) and brand trust (BT). H5: There is a positive significant relationship between brand trust (BT) and brand loy­ alty (BL). 33

H6: Brand trust (BT) has a mediating effect on brand image (BI) and brand loy­ alty (BL). H7: Brand trust (BT) has a mediating effect on brand personality (BP) and brand loy­ alty (BL).

2 LITERATURE REVIEW There are four theoriesy used in this research. The first is brand image, Ferrinadewi (2008) stated that brand image is a perception of a brand that is a reflection of a consumers memory of his or her associations with the brand. Brand image is a concept created by a consumer’s subjectivity and emotion. The second is brand personality, Ferrinadewi (2008) defined brand personality as a consumer’s emotional response to the brand that is different from the response to other brands. Therefore, brand personality must be created in order to get a response that’s different from the response to other brands. Making a brand come alive can occur when marketers create product variants based on personalities that match market advice. The third is brand trust, in business, trust between companies (buyer–seller) can deter­ mine the indicator which is related to performance such as information exchange, problem fixing, satisfaction with the results, and greater motivation in evaluating the results of the deci­ sions (Ferrinadewi,2008). The fourth is brand loyalty, Aaker (2017) believes that brand loyalty cannot happen without making a purchase and having experiences. Brand loyalty is the basis of brand equity, which is created by many factors, mainly the user experience. However, loy­ alty is influenced by the dimensions of brand equity, such as awareness, association, or impression of quality.

3 RESEARCH METHODOLOGY This research used a quantitative method with a conclusive purpose. Data retrieval was done within one period. The data were then being processed and analyzed and conclusions drawned. There were 39 indicator items that were spread through Google Forms for obtaining secondary data. Nonprobability sampling with incidental sampling was used, which means that those who satisfied the research criteria would be considered as the sample. The criteriona for the sample is that the participant has used and will reuse the products from PT Toyota Astra Motor in Indonesia. The data were analyzed via The Partial Least Squares Structure Equation Modeling (PLS-SEM) with smartPLS 3.0 software. The total number of respond­ ents for this research is 400; 241 men and 159 women responded to the questionnaire.

4 RESULT AND DISCUSSION 4.1 Convergent validity and reliability Figure 1 presents the boostrapping result from the data that have been calculated with struc­ tural equation modeling (SEM) with smartPLS 3.0 software. Based on Table 1, it can be seen that the outer loading 37 out of 43 items complied with the rule of thumb in convergent valid­ ity with outer loading > 0.70 and average variance extracted > 0.50. The Cronbach’s alpha and composite reliability must be > 0.70. 4.2 Discriminant validity Table 2 presents Fornell Larcker Criterion in which the variables have to be highly correlated from one to another. In this research there is one variable that does not match the criteria which is, brand trust, so the researchers used the Heterotrai–Monotrait (HTMT) test. If the

34

Figure 1.

Boostrapping results.

Table 1. Convergent validity and reliability. Loading factor

Cross loading

Notes

Image 1

0.873

0.873

Valid

Image 3

0.904

0.904

Valid

Image 5

0.879

0.879

Valid

Personality 2

0.729

0.729

Valid

Personality 3

0.757

0.757

Valid

Personality 4

0.774

0.774

Valid

Personality 5

0.791

0.791

Valid

Personality 6

0.766

0.766

Valid

Personality 7

0.779

0.779

Valid

Personality 8

0.755

0.755

Valid

Personality 9

0.730

0.730

Valid

Personality 10

0.765

0.765

Valid

Personality 11

0.721

0.721

Valid

Personality 12

0.734

0.734

Valid

Personality 13

0.793

0.793

Valid

Composite reliability

Cronbach’s alpha

AVE Notes

0.916

0.862

0.784 Reliable

0.958

0.952

0.573 Reliable

(Continued )

35

Table 1. (Continued ) Loading factor

Cross loading

Notes

Personality 15

0.743

0.743

Valid

Trust 1

0.790

0.791

Valid

Trust 3

0.844

0.844

Valid

Trust 4

0.771

0.770

Valid

Trust 5

0.816

0.817

Valid

Trust 6

0.796

0.795

Valid

Trust 7

0.806

0.806

Valid

Trust 8

0.775

0.774

Valid

Trust 9

0.772

0.773

Valid

Loyalty 1

0.836

0.706

Valid

Loyalty 2

0.711

0.552

Valid

Loyalty 3

0.832

0.689

Valid

Loyalty 4

0.782

0.637

Valid

Loyalty 7

0.800

0.708

Valid

Loyalty 8

0.786

0.677

Valid

Loyalty 9

0.796

0.663

Valid

Loyalty 10

0.774

0.675

Valid

Loyalty 11

0.795

0.650

Valid

Loyalty 15

0.833

0.674

Valid

Loyalty 16

0.807

0.642

Valid

Loyalty 17

0.815

0.656

Valid

Loyalty 18

0.794

0.621

Valid

Composite reliability

Cronbach’s alpha

AVE Notes

0.946

0.938

0.634 Reliable

0.933

0.917

0.636 Reliable

Source: Processed by the writer, 2018.

HTMT score is < 0.90 it can be concluded that the variable is valid, and it can be seen that because all of the scores are < 0.90 it can be conluded as valid. Based on the results from Table 4 it can be concluded that R2 has a moderate score. If the score is above 0.75 it is considered strong; above 0.50 moderate; and above 0.25, weak. Moro­ ver, the criteria for Q2 are that Q2 > 0 indicates that the model has predictive relevance, whereas Q2 < 0 indicates that the model does not have predictive relevance. As for the goodness of fit (GoF) criteria if GoF > 0.36, then the results of this research show a good/fit model. 36

Table 2.

Fornell Larcker criterion.

BI BL BP BT

BI

BL

BP

BT

0.885 0.603 0.687 0.653

0.758 0.749 0.826

0.757 0.749

0.796

BT

Source: Processed by the writer, 2018.

Table 3.

Heterotrait–Monotrait.

BI BL BP BT

BI

BL

BP

0.662 0.764 0.733

0.787 0.882

0.804

Source: Processed by the writer, 2018.

Table 4. Value of R2, Q2, GoF.

Brand image Brand personality Brand loyalty Brand trust

R2

Q2

Goodness of fit

0,719 0,596

0,425 0,352

0.5351

Source: Processed by the writer, 2018.

From Table 5 it can be concluded that: H1: There is no positive and significant relationship between brand image and brand loyalty. H2: There is a positive and significant relationship between brand personality and brand loyalty. H3: There is a positive and significant relationship between brand image and brand trust. H4: There is a positive and significant relationship between brand personality and brand trust. H5: There is a positive and significant relationship between brand trust and brand loyalty. H6: Brand trust has a mediating effect between brand image and brand loyalty. H7: Brand trust has a mediating effect between brand personality and brand loyalty. Table 5. Hyphotesis testing. Hyphoteses H1 H2 H3 H4 H5 H6 H7

Path BI ➔ BL BP ➔ BL BI ➔ BT BP ➔ BT BT ➔ BL BI ➔ BT ➔ BL BP ➔ BT ➔ BL

Path coefficient

t-statistic

t-table

Decision

0.008 0.293 0.262 0.569 0.601 0.158 0.342

0.184 4.630 5.196 12.003 9.483 4.795 7.670

1.64 1.64 1.64 1.64 1.64 1.96 1.96

Not-supported Supported Supported Supported Supported Supported Supported

Source: Processed by the writer, 2018.

37

Figure 2.

Importance and peformance matrix analysis.

4.3 Importance and performance matrix analysis Figure 2 Shows that Importance and Performance Matrix Analysis can conclude that brand trust is a priority to the company that must be developed further. Ferrinadewi (2008) argues that brand trust is an ability of a brand to be trusted, which is based on consumer confidence that the product is able to fulfill the value that has been promised and has good intentions based on consumer confidence that the brand is able to prioritize the interest of consumers. 5 CONCLUSION AND SUGGESTIONS Conclusions of this research are, H1 is rejected because the t-statistics for brand image is < 1.64, which means that it does not satisfy the criteria. Hence, there is no positive and signifi­ cant relationship between brand image and brand loyalty. H2 shows that there is a positive and significant relationship between brand personality and brand loyalty, which means that sincerity, excitement, competence, sophistication, and ruggedness can increase brand loyalty, as consumers will buy products that fit them. H3 shows that there is a positive and significant relationship between brand image and brand trust. This result shows that perception of the brand is a reflection of consumer memory associated with the brand, so that if it is good then it will increase the consumer’s brand trust. H4 shows that there is a positive and significant relationship between brand personality and brand trust and that brand personality can describe one’s thoughts and feelings towards one­ self. Therefore, consumers assume that buying products from Toyota is an expression of their personality and then they will trust Toyota because it fits their personality. H5 shows that there is a positive and significant relationship between brand trust and brand loyalty, which means that before a consumer could can have brand loyalty, the consumer needs to first have a good experience with the brand. Furthermore, H6 shows that brand trust became a variable that has a mediating effect between brand image and brand loyalty. Therefore a good percep­ tion on the part of consumers will make them trust the brand and a good experience will make them loyal to the brand. Lastly, H7 shows that brand trust has a mediating effect between brand personality and brand loyalty. It means that consumer will trust Toyota because they assume that Toyota is an expression of their personality and their experience using the prod­ ucts makes them loyal to Toyota. Suggestions for further researchers are to add variables that can affect brand trust and brand loyalty. According to Zehir (2011) brand trust and brand loyalty are very important for the automotive industry, and can be increased by adding the variables brand communication and product/service quality. REFERENCES Aaker, D. A. 2017. Ekuitas Merek. Jakarta: Spektrum.

Akib, S. 2016. Soal Penjualan, Toyota Juara di 2015 dan Optimis di 2016. Retrieved from otospirit.com:

https://www.otospirit.com/soal-penjualan-toyota-juara-di-2015-dan-optimis-di-2016/46195

38

CNN. 2019. Penjualan Mobil 2018 Tembus Target, Toyota Tergencet. Retrieved from CNN Indonesia: https://www.cnnindonesia.com/teknologi/20190118125005-384-361893/penjualan-mobil-2018-tembus­ target-toyota-tergencet Ferrinadewi, E. 2008. Merek dan Psikologi Konsumen. Yogyakarta: Graha Ilmu.

GAIKINDO. 2018. Wholesales - Retail Sales - Production - Export Import. Jakarta: GAIKINDO.

Ghozali, I. & Latan, H. 2015. Partial Least Squares, Konsep Teknik dan Aplikasi dengan mengguna­ kan SmartPLS3.0 untuk penelitian empiris. Semarang: Badan Penerbit Universitas Diponegoro Semarang. Hair, J., Hult, G., RIngle, M., & Sartedt, M. 2017. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Thousand Oaks, CA: SAGE. Handayani, R. 2017. Toyota Indonesia Capai Penjualan 389.242 Unit pada 2016. Retrieved from repub­ lika.co.id: https://www.republika.co.id/berita/otomotif/mobil/17/01/06/ojc9df359-toyota-indonesia­ capai-penjualan-389242-unit-pada–2016 Henseler, J. D. 2014. Common Beliefs and Reality About Partial Least Squares: Comments on Rönkkö & Evermann 2013. Organizational Research Methods 17(2): 182–209. Henseler, J., Ringle, C., & Sarstedt, M. 2015. A New Criterion For Assessing Discriminant Validity in Variance-Based Structural Equation Modelling. Journal of the Academy of Marketing Research, 43(1). 115–135. Latan, H., & Noonan, R. 2017. Partial Least Squares Path Modeling. Springer. Mabkhot, H. A. 2017. The Influence of Brand Image and Brand Personality on Brand Loyalty: Mediat­ ing by Brand Trust: An Empirical Study. Jurnal Pengurusan 50, 71˗82. Sugiyono, P. 2017. Metode Penelitian Bisnis. Bandung: Alfabeta. Sujarweni, V. 2014. Metodologi Penelitian. Yogyakarta: Pustaka Baru Press. Sujarweni, V. W. 2015. Metodologi Penelitian Bisnis & Ekonomi. Yogyakarta: Pustaka Baru Press. Toyota. 2018. Coroporate Information. Diambil kembali dari Toyota Astra: https://www.toyota.astra.co.id/ corporate-information/profile Zehir, C., Sahin, A., Kitapci, H., & Ozsahin, M. 2011. The Effect of Brand Communication and Service Quality in Building Brand Loyalty Through Brand Trust; The Empirical Research on Global Brands. Procedia Sciences and Behavioral Sciences 24: 1218–1231.

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Digital Economy for Customer Benefit and Business Fairness – Anggadwita & Martini (eds) © 2020 Taylor & Francis Group, London, ISBN 978-0-367-47722-6

Analyzing e-commerce customer experience using text mining: Case study of Paperlust.Co A. Alamsyah & D.P. Ramadhani Telkom University, Bandung, Indonesia

M.A.A. Saputra Institut Teknologi Bandung, Bandung, Indonesia

A. Amran University Sains Malaysia, Penang, Malaysia

ABSTRACT: The rapid development of information technology brought major changes to business activities. Traditionally, the transaction process has been done directly. As a result of the internetpresence, online platforms known as e-commerce began to facilitate the trans­ action. Furthermore, the online platform also facilitates customers in expressing their opin­ ions and complaints, one of which is through online chats with customer service. The emergence of e-commerce shifts the business focus from only goods and services to customer information and an intelligence orientation. E-commerce companies are forced to under­ stand customer perception including identifying customer satisfaction and disappointment issues related to their service. Customer chat data contain the information needed to under­ stand this customer perception. In this study, we analyzed the e-commerce customer experi­ ence through customer chat data. We propose two methods to mine the text: sentiment analysis and topic modeling. Sentiment analysis aims to identify the customer satisfaction level, while topic modeling aims to extract the important customer issues within each senti­ ment class. We use Paperlust.Co, the invitation and card designs e-commerce company based in Australia, as a case study. This research provides an evaluation and valuable infor­ mation related to customer experience in the e-commerce industry. We discovered that Paperlust.Co customers feel satisfied with the company service. Operational problems such as site failure were the major issues in the customer opinions and complaints. This insight might help e-commerce companies to deliver more value that fits well with their customers’ needs.

1 INTRODUCTION The application of e-commerce changed the habits of customers who initially ordered and bought conventionally to become online purchasers. The implementation of e-commerce also changes the fulfillment of customer needs, and the key to the success of the company lies not only in the quality of products/services but also in efforts to satisfy the needs of its customers and then provide good and pleasant service to ensure they become loyal custom­ ers. To get it all, every company needs to recognize its customers’ desires and needs. Infor­ mation about the customer can help the company make major decisions about reorganizing a business, service offerings, marketing, and other strategies. Customer information can tell the company exactly what a customer wants and needs or provide an aggregate view of cus­ tomers’ feelings about a specific area of company business (Anderson & Kerr, 2012). Technological advancements have enabled firms to manage customer relationships more efficiently and to create better customer experiences (Peppers & Rogers, 2017). In line with 40

these goals, Paperlust.Co team wished to analyze their customer experience by understand­ ing customer perceptions about their service and identify the issues that satisfy their custom­ ers and those that disappoint them. This research took places on the Paperlust.Co live chat platform, which is a customer ser­ vice platform with the highest chat intensity. As the data that are collected from Paperlust’s live chat platform are unstructured, we proposed two methods of text mining: sentiment analysis and topic modeling. Sentiment analysis aims to identify the customer satisfaction level, while topic modeling aims to extract the important customer issues within each senti­ ment class.

2 LITERATURE REVIEW 2.1 Customer experience Customer experience is one of the important keys in marketing. Customer experience is an internal and subjective response that consumers must direct or indirect contact with a company. Organizations need to manage their customer experiences and relationships effectively to remain competitive. Technological advancements have enabled firms to manage customer relationships more efficiently and to create better customer experiences (Buttle & Groeger, 2017). 2.2 Text mining Text mining is an interdisciplinary field that draws on information retrieval, data mining, machine learning, statistics, and computational linguistics. A substantial portion of the infor­ mation is stored as text such as news articles, technical papers, books, digital libraries, email messages, blogs, and web pages (Han et al., 2012). In this research, we used two text mining methods: sentiment analysis and topic modeling. 2.3 Sentiment analysis Sentiment analysis is also called opinion mining. This method examines the opinions, senti­ ments, evaluations, acceptance, attitudes, and emotions of people regarding entities such as products, services, organizations, individuals, issues, events, topics, and other attributes that represent a considerable problem. Analysis of sentiment and opinion mining focuses on opinions that elicit positive or negative sentiment responses (Liu, 2012). This method is also able to measure the quality of service of the company and specifically can also meas­ ure public sentiment on each performance feature of the existing company. 2.4 Topic modeling Topic modeling is the process of identifying the underlying semantic structure of a document with the use of a hierarchical Bayesian analysis on the collection of documents (Blei et al., 2003). A topic model is a statistical model used to identify the latent topics that occur in a collection of text documents and is therefore often considered to be a text-mining tool for the discovery of hidden semantic structures in a text body.

3 METHODOLOGY We break down the research into four stages: data collection, preprocessing, sentiment ana­ lysis, a topic modeling applied on the result of sentiment analysis, and result interpretation. The research workflow can be seen in Figure 1. 41

Figure 1.

Research workflow.

3.1 Data collection The data used in this research are all chat data that received by the live chat platform on the Paperlust.Co website from January 1, 2018 to December 21, 2018. Data were properly collected from Paperlust’s internal server by appropriate permission and access from the company. There were 17,387 items of chat data received in the data collection period. 3.2 Data preprocessing Data preprocessing is an important part of data mining, particularly in text mining and text classification. Preprocessing needs to be done to produce high-quality data before the classifi­ cation process. The preprocessing task is to clean up and restructure data so that data can be analyzed in the next process in text mining (Hofmann & Chisholm, 2016). Figure 2 shows the preprocessing step done in this research. Tokenization is the process of chopping text into small elements and meaningful words. Stemming is the process of normalizing the text into a standard language, and the words in the text document will be modified following the grammar context. In other words, stemming is a normalizing process transformed into standard language and grammatically correct. Stopwords are a process to remove words that often appear in text documents and have a low value and are not useful (Hofmann & Chisholm, 2016). 3.3 Sentiment analysis To classify text in the sentiment dimension, we use the machine learning principle with the Naïve Bayes Algorithm. We classify the data by a ratio of 70% for training data and 30% for testing data. For the training data, the text must be labeled to a sentiment class. 3.4 Topic modeling To model the topic, we separate the sentiment analysis result into three separate files (positive, neutral, and negative) respectively to capture the whole topic in the dataset.

Figure 2.

Text data preprocessing step.

42

4 RESULTS AND ANALYSIS 4.1 Sentiment analysis The result of sentiment analysis shows that the chat received is more likely to have a neutral sentiment, in which case the percentage of positive sentiment is larger than that of the negative sentiment, as we can see in Figure 3. 4.2 Topic modeling For topic modeling, we extracted the most dominant topic for each sentiment class (positive, neutral, and negative). The light blue color in Figure 4 represents the overall term frequency, while the red color represents estimated term frequency within the selected topic in the docu­ ment; for example, in Figure 4 the word “email” has both a red and a blue color, which means the word “email” occurs not only in these individual topics.

Figure 3.

Sentiment analysis result.

Figure 4.

Topic modeling in positive sentiment.

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As shown in Figure 4, the most dominant topic in the positive sentiment document is the customer asking the customer support team for help in updating the information by email and checking that everything is fine. The most dominant topic in the neutral sentiment, as shown in Figure 5, is the customer’s desire to make a change and add his or her guest name in his or her invitation design, check the font size, and make sure the date are correct. In the last, as shown in Figure 6, the most dominant topic in the negative sentiment docu­ ment concerns the customer thinking he or she just wants to make sure about the order and needs the sample design because he or she is worried about paying.

Figure 5.

Topic modeling in neutral sentiment.

Figure 6.

Topic modeling in negative sentiment.

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5 CONCLUSION In this research, we have successfully implemented the text mining method to properly classify and summarize thousands of chats in the Paperlust.Co live chat platform to analyze the com­ pany’s customer experience. The Naïve Bayes Algorithm used in this research to classify text in the form of sentiment is suitable. The Latent Dirichlet Allocation Algorithm also proved its capability in extracting in-depth insight into the topics discussed in the large-scale dataset. Based on the results of both analyses, most Paperlust.Co’s customers are satisfied with the service provided, especially by the customer support team. However, the Paperlust.Co team should give attention to ensuring the company’s reliability and convince customers to make a purchase. For future research, we plan to add more data to enrich the training data, so the result will be more accurate, and expand the text classification model to multilabel classification because one customer chat contains more than one sentiment or dimension. REFERENCES Anderson, K. & Kerr, C. 2012. Customer Relationship Management. New York: McGraw-Hill. Blei, D. M., Ng, A. Y., & Jordan, M. I. 2003. Latent Dirichlet Allocation. Journal of Machine Learning Research 3: 993–1022. Buttle, F. & Groeger, L. 2017. Who Says What to Whom in What Channel? A Rules Theoretic Perspec­ tive on Word-of-Mouth Marketing. Journal of Marketing Management 33(13–14): 1035–1059. Han, J., Pei, J., & Kamber, M. 2011. Data Mining: Concepts and Techniques. Philadelphia: Elsevier. Hofmann, M. & Chisholm, A. (Eds.). 2016. Text Mining and Visualization: Case Studies Using OpenSource Tools, Vol. 40. Boca Raton, FL: CRC Press. Liu, B. 2012. Sentiment Analysis and Opinion Mining. Morgan and Claypool Publishers. Peppers, D. & Rogers, M. 2016. Managing Customer Experience and Relationships: A Strategic Frame­ work. Hoboken, NJ: John Wiley & Sons.

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Digital Economy for Customer Benefit and Business Fairness – Anggadwita & Martini (eds) © 2020 Taylor & Francis Group, London, ISBN 978-0-367-47722-6

Measuring supply chain performance in the fabric industry in Cigondewah Rr.R.F. Hutami, S.G. Hidayat & S. Dharmoputro Telkom University, Indonesia

ABSTRACT: Cigondewah is one of the most well-known industrial centers in Bandung that is popular for its fabric industry. Business activities in Cigondewah have an important role in the lives of workers and the development of the regional economy. Although the business activity in Cigondewah is still promising, competition in the future cannot be avoided. The advancement of technology and also the ASEAN Economic Community (AEC) have resulted in even tighter competition. Most small to medium sized (SME) industries are facing the same challenge, which are related to human resources, production, packaging technology, product standardization, branding, promotion and marketing, capital access support, and lack of public/domestic market education about MEA 2015. In the production area, several problems also exist, such as availability of raw materials, price of materials, and also limited number of suppliers; as a result, entrepreneur in Cigondewah are highly dependent on their suppliers. There are many possible ways to solve this problem; one of them is by analyzing the Supply Chain Management Practice (SCMP) in the Cigondewah Industrial Center. Previous research showed that higher application and improvement in SCMP would directly lead to an increase in supply chain performance and overall company performance (SCPM). Supply Chain Per­ formance Measurement (SCPM) consists of seven variables: supply chain management flexi­ bility, supply chain management integration, responsiveness to customers, efficiency, quality, product innovation, market performance, and partnership quality. This research involved 30 respondents who represented their businesses. Some of the respondents were the owners or people who were in charge in their business. They were selected using the convenience method. Using descriptive statistics, the information on how supply chain was being imple­ mented in Cigondewah could be obtained. The results showed there are no dominant variables among these seven variables. All of the variables linked to the SCPM are in good agreement with the criteria.

1 BACKGROUND The enactment of the ASEAN Economic Community (AEC) in 2015 has broughtopportu­ nities and challenges for the economy in Indonesia. Every country belonging to AEC, includ­ ing Indonesia, will experience free market flows in the form of goods, services, investments, and labor between all members. This condition will lead to competition. Indonesia already developed some strategies to face this competition; however, it is not yet able to overcome all the problems, for example, problems related to human resources, product standardization, branding, marketing, difficult to get good raw materials and qualified suppliers, and other issues. In Indonesia, small and medium sized enterprises (SMEs) are divided based on location of raw of materials, capital, number of manpower, location selection, individual productivity, and also specific classification. Specific classification in SME is divided by scale, such as micro-enterprises, small businesses, and large enteprises. According to the deputy of financing from the Ministry of Cooperatives and SME, there are 58 million units of micro business, 59,260 units of small business, and 4,987 units of large business (Walfajri, 2019). 46

Table 1.

Industrial center in Bandung.

Region

Industry

Cibaduyut Cihampelas Suci Binong jati Cigondewah Cibuntu Sukamulya

Shoe Jeans T-shirt Knit fabric Fabric Tofu dan Tempe Doll

Types of business in Indonesia vary, such as food processing, textiles, leather goods, wood processing, paper processing, pharmaceutical chemistry, rubber processing, steel or metal pro­ cessing, equipment, and mining. These businesses are widespread in all provinces in Indonesia. Bandung, which is one of the cities in West Java province, has several small and medium busi­ nesses with various products, as shown in Table 1. These SMEs are also part of industrial centers. In this study, we only focus on the fabric industry in Cigondewah. Cigondewah is a textile industry area that developed in the early 1990s in the south Bandung, originating from resi­ dential areas and over time developing into the present industrial area (Budi, 2013). The existence of business activities in Cigondewah has an important role in the lives of workers and the development of the regional economy. Therefore, some effort must be made so that Cigondewah continues to evolve. One of the strategies is to pay attention to supply chain activities. Several research studies related to the implementation of the supply chain in small industries continues to be done. One of them is research conducted by Eeya et al. (2010) in Uganda that discusses how SMEs in Uganda have poor product quality as well as slow delivery. According to Eeya et al. (2010), SMEs in some developing countries face a variety of challenges, one of which is related to the performance of the supply chain. In this study, our objective is measuring Supply Chain Performance in the fabric industry in Cigondewah.

2 THEORETICAL 2.1 Supply chain and supply chain management There are some differences between researchers when they explained basic theory. Several researchers started with the supply chain definition and others used the supply chain manage­ ment approach as their basic theory. The definition of supply chain management according to Heizer et al. (2017) is the integra­ tion of activities to obtain materials and services, turning them into intermediate goods and finished goods, and sending the goods to the consumer. According to Lyson and Farringto (2006), supply chain management is an organizational network consisting of upstream to downstream parts with different activities and processes that make products and services valu­ able. These downstream and upstream parts are connected with the chain and different magni­ tudes exist between strategic and systematic consequences of handling downstream components (Tripathi et al., 2018). According to Azevedo (2013), the supply chain is a network that can deliver the right prod­ ucts and services to consumers on time along with specific requirements. In general, these two definitions had the same goal of delivering products or services to customers. The difference is that supply chain management includes activities in an input being processed to become an output, whereas the second definition only focused on the media used to deliver the products and services. In this article, supply chain management concepts are considered preferable. 47

2.2 Supply chain performance Supply chain performance is important because today chain–chain competition is slowly taking over competition between firms. In other words, a company’s strengths are seen from its supply chain and not on the basis of the company as an individual entity (Eyaa et al., 2010). Supply chain performance is assessed in terms of quality, cost, and timeliness with rele­ vant information available; for example, when accurate information about the request level is provided, the company can project how much production is desired and produce the goods on time, so it is possible to deliver the goods on time and eliminate the bullwhip effect, and ultim­ ately create a supply chain with good performance. In addition, measuring supply chain per­ formance will help a firm to see the real effect of external risk (Quang & Yoshinori; 2019). Performance measurements in supply chain analysis are classified into three categories. Each category has several indicators. First, financial performance measures total cost, distri­ bution cost, manufacturing cost, inventory cost, return on investment, total revenue, and profit. Second, operational performance measures customer response time, manufacturing lead time, product quality, and product availability. Finally, overall supply chain performance measures customer satisfaction and supply chain flexibility (Palang & Tippayawong, 2019). Other studies also found that the availability and access of supply information can be indica­ tors to measure performance in the supply chain (Tripathi et al., 2019). Most companies failed to measure supply chain performance because they see the supply chain from their own perspective. The supply chain must be able to integrate and synchronize the objectives of all members, so they are seen as a whole and can be managed thoroughly (Chan & Qi, 2003).

3 RESEARCH METHODOLOGY The data collection in this study was carried out using a quantitative approach. A question­ naire was used to collect data from entrepreneurs or owners of small and medium-sized indus­ trial businesses around Cigondewah. Based on the information from the coordinator of Cigondewah, 133 small and medium-sized industrial business units have been registered. Using convenience as the sampling method, we gathered 30 respondents who owned or man­ aged business. Supply Chain Performance Measurement (SCPM) variables used in this study are based on the study of Gawankar et al. (2015). The SCPM is divided into two variables: traditional measures and relationship measures. Traditional measures consist of seven dimensions: supply chain flexibility, supply chain integration, responsiveness to customers, efficiency, quality, product innovation, and market performance. Relationship measures have two dimension, which are partnership quality and supplier performance. Respondents must respond to every question in the questionnaire by choosing an answer from a four (4)-point Likert scale: 4 – strongly agree, 3 – agree, 2 – disagree, and 1 – strongly disagree. Validity and reliability tests were carried out using 30 samples from the question­ naire that had been filled out by businesses people in the Cigondewah fabric industry and pro­ cessed using IBM SPSS statistics software 23. The results showed that all the variables are valid and reliable. To evaluate supply chain performance in the fabric industry in Cigonde­ wah, we used descriptive statistics to determine performance from each variable with the help of MS Excel software.

4 ANALYSIS 4.1 Characteristics of respondents The results of data processing show that the majority of respondents were male. From the variations of age, it is known that there were 2 respondents of age between 21 and25 years, 18 ofage between 26 and 30, 4 of age between 31 and 35, 3 ofage between 36 and 40, and 3 of age 48

above 40 years. It can therefore be concluded that the respondents are predominantly of ages between 26 and 30 years. According to their role in business in Cigondewah, 69.9% are resellers, 25.45% are craftsmen, 3.6% are distributors, and the rest are suppliers. Also, they were asked if they knew about the supply chain. All respondents answered No. However, when the activities in the supply chain, were explained, they said that some of activities were already being implemented. It means that their knowledge about the supply chain still can be improved. 4.2 Traditional measures The first variable in the Supply Chain Performance Measurement (SCPM) is traditional measures. It means that the performance of the supply chain is focused on financial measures, which is a historically oriented approach (Gawankar et al., 2015). Using descriptive statistics, the total score of each dimension of SCPM will be categorized to match the range as follows: range between 0% and 25% means very poor, range between 25% and 50% means poor, range between 50% and 75% means good, and range between 75% and 100% is very good. 4.2.1 Supply chain flexibility We asked four questions related to supply chain flexibility to know the ability of the supply chain to handle unstandardized orders including the number of features, sizes, and color; abil­ ity to adjust capacity when the customer requests changes; ability to respond to market need; and ability to provide a low-cost product. The result of the total score was 64.9%, which means that supply chain flexibility among entrepreneurs or owners of small and medium-sized industrial businesses around Cigondewah can be categorized as Good. 4.2.2 Supply chain integration There are two questions representing this dimension: communication and coordination among the supply chain members and integration of information systems. The result of the total score was 65.3%, which means that supply chain integration among entrepre­ neurs or owners of small and medium-sized industrial businesses around Cigondewah can be categorized as Good. The highest score was for indicators of communication between suppliers, consumers, and distributors and the lowest score was for integration of system information. 4.2.3 Responsiveness to customers There are two questions representing this dimension: timeliness in fulfilling orders and con­ sumer response time. The result of the total score was 65%, which means that the supply chain in fulfilling orders and response time to consumers was Good. In this case, the ability to fulfill consumer orders on time was more dominant. 4.2.4 Efficiency There are two questions representing this dimension: business value in transactions and total operating expenses. The result of the total score was 68.3%, which means that efficiency among entrepreneurs or owners of small and medium-sized industrial businesses around Cigondewah can be categorized as Good. 4.2.5 Quality There are two questions representing this dimension: ability to provide high-quality products and competitiveness in quality. The result of the total score was 67.1%, which means that the quality of products offered by entrepreneurs or owners of small and medium-sized industrial businesses around Cigondewah can be categorized as Good. The highest score was for indica­ tors of competitiveness in quality and this result was relevant to the achievement of Indonesia as one of top five shoe producers in the world. 49

4.2.6 Product innovation The ability of entrepreneurs or owners of small and medium-sized industrial businesses around Cigondewah to create innovation to increase consumer interest can be categorized as Good because they achieved 65.8% of the total score. 4.2.7 Market performance Related to the market performance in the supply chain, we want to know how the products are spread, whether only in Cigondewah or also outside Cigondewah. We also asked if the sales were increasing every year. The results for both questions were categorized as Good, with a total score 65.8%. 4.3 Relationship measures The second variable in SCPM is relationship measures. According to Gawankar et al. (2015), relationship measures are more focused on measuring nonfinancial indicators; some authors also view these as soft measures. Relationship Measures consists of two dimensions: partnership quality and supplier per­ formance. Partnership quality was measured by identifying several indicators such as relations with suppliers, mutually beneficial relationship, and willingness to share risk. Results from the questionnaire show that the relationship between entrepreneurs and their suppliers are Good, with a total score 65.4%. Several questions related to supplier performance in Cigondewah were asked, such as deliv­ ery of materials on time, providing highly reliable materials, and providing materials at low cost. The results of all the indicators have an average total score 68.7%, which is categorized as Good.

5 CONCLUSION AND SUGGESTIONS Business activity in Cigondewah is dominated by resellers. The resellers obtained the products from factories, which already used technology in producing the fabric. Most entrepreneurs including sellers and craftsmen lack knowledge about Supply Chain Management and min­ imal information about the benefits of implementing a supply chain. The results show that the relationship between the supply chain members in Cigondewah was based on financial interest. The total score percentages show that, although both trad­ itional and relationship measures were in the same range, the total score for traditional meas­ ures was more dominant. Among traditional measures, efficiency had the highest percentage, which indicates that entrepreneurs in Cigondewah know how to minimize their operating expenses. Although the flexibility among supply chain members is also good, they need to improve their ability in fulfilling customer requests for unstandardized orders. The total scores of relationship measures were also categorized as Good. Its means that there are good relationships among supply chain members and they also have qualified sup­ pliers. Although all the variables and dimensions are in the same category, namely Good, this research needs to be explored to understand the relationship between Supply Chain Perform­ ance and knowledge about the supply chain concept. This research has a limitation, and to improve future research it would be better to involve more respondents. REFERENCES Azevedo, S., Carvalho, H., & Cruz-Machado, V. 2013. Using Interpretive Structural Modelling to Iden­ tify and Rank Performance Measures: An Application in the Automotive Supply Chain. Baltic Journal of Management 8(2): 208–230. Budi, A. 2013. Sentra Kain Murah Cigondewah. https://bedanews.com/2013/06/20/sentra-kain-murah­ cigondewah/(accessed October 15, 2019).

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Chan, F. T. & Qi, H. J. 2003. Feasibility of Performance Measurement System for Supply Chain: A Process-Based Approach and Measures. Integrated Manufacturing Systems 14(3): 179–190. Eyaa, S., Ntayi, J. M., & Namagembe, S. 2010. Collaborative Relationships and SME Supply Chain Performance. World Journal of Entrepreneurship, Management and Sustainable Development 6(3): 233–245. Gawankar, S. A., Kamble, S., & Raut, R. 2017. An Investigation of the Relationship Between Supply Chain Management Practices (SCMP) on Supply Chain Performance Measurement (SCPM) of Indian Retail Chain Using SEM. Benchmarking 24(1): 257–295. Heizer, J., Render, B., & Munson, C. 2017. Operations Management: Sustainability and Supply Chain Management, 12th editio. Hoboken, NJ: Pearson. Lysons, K. & Farrington, B. 2006. Purchasing and Supply Chain Management. Upper Saddle River, NJ: Pearson Education. Palang, D. & Tippayawong, K. Y. 2018. Performance Evaluation of Tourism Supply Chain Manage­ ment: The Case of Thailand. Business Process Management Journal 25(6): 1193–1207. Quang, H. T. & Hara, Y. 2019. The Push Effect of Risks on Supply Chain Performance: Service-Oriented Firms. Business Process Management Journal 25(7): 1734–1758. Tripathi, S., Rangarajan, K., & Talukder, B. 2019. Segmental Differences in Pharmaceutical Industry and Its Impact on Supply Chain Performance. International Journal of Pharmaceutical and Healthcare Marketing 13(4): 516–540. Walfajri, M. 2019. Jumlah pelaku UMKM di 2018 diprediksi mencapai 58,97 juta orang.https://keuan gan.kontan.co.id/news/jumlah-pelaku-umkm-di-2018-diprediksi-mencapai-5897-juta-orang (accessed October 30, 2019).

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Digital Economy for Customer Benefit and Business Fairness – Anggadwita & Martini (eds) © 2020 Taylor & Francis Group, London, ISBN 978-0-367-47722-6

Analysis of e-payment service quality in Bandung E. Azis & M.A. Akbar Telkom University, Bandung, West Java, Indonesia

M.M.A. Rohandi Bandung Islamic University, Bandung, West Java, Indonesia

ABSTRACT: Current developments in technology and information processing have led to changes in payment systems, making it easier for consumers to carry out various transaction activities. As data show a trend of declining corporate profits in retail businesses, companies must find ways to gain alternative revenue by utilizing e-payment services. The purpose of this study was to analyze and compare of e-payment Service Quality at Indomaret and Alfamart in Bandung. The method used was comparative quantitative research, and the population comprised users of e-payment services in Indomaret and Alfamart. A purposive sampling technique was used, with nonprobability sampling and a Bernoulli method with a 5% error rate. Service Quality (ServQual) tools used as measures of quality were Reliability, Respon­ siveness, Assurance, Empathy, and Tangibles. Data analysis used a Wilcoxon test. The results showed that there were significant differences between the quality of Indomaret and Alfamart e-payment services, With Indomaret service quality better than that of Alfamart.

1 INTRODUCTION Technological developments have led to changes in the -payment system, whichincludes a set of rules, institutions, and mechanisms used to carry out the transfer of funds in economic activities (Bank Indonesia, 2019a). The facilities provided by banks for noncash payment systems include ATMs, debit cards, and electronic money (Rahman, 2018). The online payment system, known as e-payment, is a payment made electronically (Trihasta & Fajaryanti, 2008) that uses the internet as an intermediary tool to make transactions (Bank Indonesia, 2019b). Every company must provide the best service in order to gain customer satisfaction, although there are some problems such as slow service, complicated queues, waiting times, and others (Foster, 2008). Competing companies usually will highlight their products’ advan­ tages and use a variety of strategies with good sales techniques. One important feature in the sales strategy is the quality of service to consumers. Service is one important factor that needs to be considered by the company in order to provide satisfac­ tion to customers. Service is any action or performance that one party can offer to another, is basically intangible, and does not result in any ownership (Kotler & Keller, 2016). According to Harman (2017), services are activities that do not have physical substance, cannot be touched, and cannot be seen by eye that are given from one party to another party. In a different viewpoint, according to Tjiptono and Chandra (2005), services are activities, bene­ fits, or satisfaction offered for sale. In recent years various retail business companies have sprung up. The emergence of minimarkets in the past few years has accelerated growth in modern society. Indomaret is a company with assets of Rp. 22 trillion while Alfamart is a retail company with assets of Rp. 21.5 trillion in the third quarter of 2017 (Rahmayanti, 2018). In various places in Indonesia we can see the competition between Indomaret and Alfamart side by side or face to face. They

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have a business strategy that is not different; the two compete with each other not only in price and service but also in the number of outlets owned (Ryza, 2015). Based on the financial statements of the third quarter of 2017, Alfamart posted an income of IDR 45.6 trillion but experienced a significant decrease in net profit, reaching 85.84% in the third quarter of 2017. Indomaret has a situation similar to that of Alfamart. Indomaret experi­ enced a decrease in net profit of up to 95% to Rp. 60 billion with an income position of Rp. 47 trillion (Rahmayanti, 2018). PT. Sumber Alfaria Trijaya Tbk, which manages Alfamart outlets, has payment services using the e-payment system, as does PT. Indomarco Prismatama, which manages Indomaret outlets. Indomaret also has an e-payment service called i-payment. With the online payment service Indomaret and Alfamart can serve all forms of payment needed. Indomaret and Alfamart provide a total of 107 services, ranging from shopping services, electricity token pur­ chases, credit purchases, tickets, and others. According to Kotler and Keller (2016), quality is the overall features and characteristics of a product or service that have the ability to meet the needs and expectations of consumers. Goetsch and Davis (2014) define quality as “dynamic conditions related to products, services, human resources, processes and environments that meet or exceed expectations”. It can be inter­ preted that quality is an ability of a product to meet customer satisfaction needs. To measure the level of performance of retail companies in the consumer’s perspective, a company needs to be considered according to the level of service quality provided. Lewis and Booms in Tjiptono (2017) stated that Service Quality is a measure of how well the level of service provided is able to match customer expectations. The main factors that influence the quality of service are the service expected by the customer (Expected Service) and the percep­ tion of the service (Perceived Service) (Parasuraman in Tjiptono, 2012). Service quality has several dimensions; it is called Service Quality (ServQual) according to Parasuraman et al. in Kotler and Keller (2016). The five dimensions are Reliability, Respon­ siveness, Assurance, Empathy, and Tangibles.The purpose of this study was to determine the quality of service in Indomaret and Alfamart and the differences in the services they offer in Bandung (Apriyani & Sunarti, 2017; Wahab, 2017).

2 RESEARCH METHOD This study used comparative (Divayana, Ari and Rahanatha, 2018) quantitative research (Sugiyono, 2018) to provide information about the reasons behind consumer behavior (Sujar­ weni, 2015) to choose the best place or dealer, because in this research we compare Indomaret and Alfamart. Descriptive analysis is used to describe the condition of research variables (Sugiyono, 2018; Widodo, 2017). The populations in this study are Indomaret and Alfamart consumers in Bandung, whose exact number is unknown. Determination of the number of samples is done by using nonprob­ ability sampling, with the purposive sampling method (Indrawati, 2015) and the Bernoulli method (Zikmund, 2010), with a 5% error rate to obtain a total sample of 385. This study used primary data obtained directly by distributing questionnaires and second­ ary data obtained through third-party media such as books, articles, and others. In this study, researchers used a comparative hypothesis, as follows: H1: There is a difference between the quality of e-payment services at Indomaret and Alfamart.

3 RESULTS AND DISCUSSION A previous study conducted by Umiyati and Suyoto (2014) showed that there was a difference between service satisfaction in Indomaret and Alfamart in Dukuhwaluh in general, as this study was limited to the implementation of e-payment in Bandung. 53

This research conducted two stages of analysis: descriptive analysis and t-test analysis. The questionnaire used in this study contained 11 statements on an ordinal scale and was distrib­ uted after validity and reliability testing was conducted. In this descriptive analysis, respondent data are grouped by gender, age, and income. The results show that 61.82% of respondents are male, 63.38% of respondents are aged between 21 and 30 years, and 32.47% of respondents have an income of less than IDR 1 million. Descrip­ tive data analysis uses a continuum data line that divides the assessment criteria into five areas ranging from very bad to very good. Based on the respondent result on service quality of e-payment at Indomaret, the dimen­ sions of Reliability have an average score 77.7% and are in the Good category, with the smal­ lest value contained in the statement that the payment process via e-payment at Indomaret did not err, at 75.9%. The Responsiveness dimensions have an average score 74.2% and are in the Good category, with the smallest value contained in the statement that no pending trans­ action occurred in payments with e-payment at Indomaret, at 72.9%. The dimensions of Assurance have an average score of 80.3% and are in the Good category, with the smallest value being a statement of trust with the e-payment payment system at Indomaret, at 79.8%. Empathy dimensions have an average score of 74.8% and are in the Good category, with the smallest value contained in the statement that all needs can be met with the existence of e-pay­ ment at Indomaret, at 74.2%. Tangible dimensions have an average score of 85.8% and are in the Very Good category. The overall average score of Servqual in Indomaret is 78.6%, in the Good category. Based on the respondents’ results on service quality of e-payment at Alfamart, the dimensions of Reliability have an average score 76.7% and are in the Good category, with the smallest value contained in the statement that the payment process via e-payment at Alfamart did not err, at 74.8%. Responsiveness dimensions have an average score of 72.9% and are in the Good category, with the smallest value contained in the statement that no pending transaction occurred in payments with e-payment at Alfamart, at 71.7%. Assurance dimensions have an average score of 78.4% and are in the Good category, with the smallest value being a statement of trust with the e-payment payment system at Alfamart at 77.3%. Empathy dimensions have an average score of 74.2% and are in the Good category, with the smallest value contained in the statement that all needs can be met with the existence of e-payment at Alfamart, at 74%. Tangible dimensions have average score 81,1% and are in the category of Very Good. The over­ all average score of Servqual in Alfamart is 76.7% and is in the Good category.

Table 1. Recapitulation table of e-payment of service quality at Indomaret and Alfamart. Variable dimensions

Indomaret (%)

Alfamart (%)

Reliability Responsiveness Assurance Empathy Tangible Total Average

77.7% 74.2% 80.3% 74.8% 85.8% 11.865 78.6%

76.7% 72.9% 78.4% 74.2% 81.1% 11.661 76.7%

To prove that the e-payment service quality data at Indomaret and Alfamart differ or do not differ significantly, a similarity test of the two initial states is performed using the t-test method. If the t-test for comparison of data obtained is normal, then the test uses an inde­ pendent t-test (parametric). If the normality test results are not normal then the Wilcoxon (nonparametric) test is used. This technique is used to test the significance of the comparative hypothesis of two samples that correlate if the data are in ordinal (tiered) form. 54

Table 2. Wilcoxon test for ServQual e-payment in Alfamart and ServQual e-payment in Indomaret. Test Statisticsa −2.290b

0.022

Z Asymp. Sig. (2-tailed)

Source: Data processing Azis, Akbar and Rohandi, 2019. a Wilcoxon signed ranks test. b Based on positive ranks.

This research used spss 23 tools to make it easier to do calculations; the normality test results obtained Asymp. Sig values. (2-tailed) of 0.000 in e-payment service quality data at Indomaret and 0.000 in e-payment service quality data at Alfamart. Based on the results, the two data groups are smaller than alpha (Asymp.Sig. < 0.05), and it can be concluded that the data qual­ ity of e-payment services at Indomaret and Alfamart is not normally distributed. Therefore testing for e-payment service quality data at Indomaret and Alfamart will be conducted using the Wilcoxon-ranked test method. The Wilcoxon test gives an Asymp. Sig. (2-tailed) value of 0.022. Because the p-value is smaller than α (0.022 < 0.05), there is a difference between the quality of e-payment services at Indomaret and Alfamart.

4 CONCLUSION Based on the analysis and discussion, there are several findings, as follows: First, the results of descriptive analysis of overall data respondents indicate the service quality of e-payment at Indo­ maret in Bandung is in the Good category, with an average rating of 78.6%. Second, the results of descriptive analysis of overall data respondents indicate the service quality of e-payment at Alfamart in Bandung is in the Good category, with an average rating of 76.7%. Third, the comparative analysis shows the Asymp. Sig. (2-tailed) result is 0.022, which is smaller than α (0.022 < 0.05). It was concluded that there was a significant difference between the service quality of e-payment at Indomaret and Alfamart in Bandung. Based on the aforementioned findings it can be concluded that Indomaret is better than Alfamart in all aspects of service quality. Using storytelling as one of the communications strategies may involve GOJEK’s intention to be perceived as a creative and innovative com­ pany. It has used the storytelling technique in order to touch the audience’s emotions through one of its programs called Life at GOJEK. REFERENCES Apriyani, D. A. & Sunarti, S. 2017. Pengaruh Kualitas Pelayanan Terhadap Kepuasan Konsumen (Survei Pada Konsumen the Little a Coffee Shop Sidoarjo). Jurnal Administrasi Bisnis 51(2):1–7. Bank Indonesia. 2019a. Sistem Pembayaran. www.bi.go.id: https://www.bi.go.id/id/sistem-pembayaran/ Contents/Default.aspx Bank Indonesia. 2019b. Uang Elektronik. www.bi.go.id: https://www.bi.go.id/id/edukasi-perlindungan­ konsumen/edukasi/produk-dan-jasa-sp/uang-elektronik/Pages/default.aspx Divayana, D., Ari, K., & Rahanatha, G. B. 2018. Studi Komparatif Pengaruh Kualitas Pelayanan terha­ dap Kepuasan Nasabah pada PT. Bpr. Mertha Sedana dan PT. Bpr. Mas Giri Wangi. E-Jurnal Mana­ jemen Universitas Udayana 7(4):2134–2163. Foster, B. 2008. Manajemen Ritel. Bandung: Alfabeta. Goetsch, D. L. & Davis, S. B. 2014. Quality Management for Organizational Excellence. Upper Saddle River, NJ: Pearson.

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Indrawati, P. D. 2015. Metode Penelitian Manajemen dan Bisnis Konvergensi Teknologi Komunikasi dan Informasi. Bandung: PT. Refika Aditama. Kotler, P., Keller, K. L., Armstrong, G., Armstrong, G., & Keller, K. 2016. Marketing Management, 15th global edition. London: Pearson Educationn Limited. Malau, H. 2017. Manajemen Pemasaran Teori dan Aplikasi Pemasaran Era Tradisional Sampai Era Mod­ ernisasi Global, cetakan pertama. Penerbit: Alfabeta, Bandung. Rahman, A. 2018. Uang Elektronik di Indonesia Rp. 11,5 Triliun. bisnis.com: http://finansial.bisnis.com/ read/20180217/90/739792/uang-elektronik-di-indonesia-rp115-triliun Rahmayanti, E. 2018. Adu Kuat Alfamart dan A, Kinerja Siapa Lebih Unggul? Bareska: https://www. bareksa.com/id/text/2018/03/07/adu-kuat-alfamart-dan-indomaret-kinerja-siapa-lebih-unggul/18617/ news Ryza, P. 2015. Persaingan Indomaret dan Alfamart Merembet ke Kancah Digital. DailySocialid: https:// dailysocial.id/post/indomaret-alfamart-digital Sugiyono, P. D. 2018. Metode Penelitian Kuantitatif. Bandung: Penerbit Alfabeta. Sujarweni, V. W. 2015. Metodologi Penelitian Bisnis & Ekonomi. Yogyakarta: Pustaka baru Press. Tjiptono, F. 2017. Service Management: Mewujudkan Layanan Prima Edisi 2. Yogyakarta: Andi. Tjiptono, F. & Chandra, G. 2005. Service, Quality & Satisfaction. Yogyakarta: Andi Offset. Trihasta, D. & Fajaryanti, J. 2008. E-payment System. Proceeding KOMMIT 2008. Umiyati, U. & Suyoto, S. 2014. Analisis Perbandingan Kepuasan Konsumen Atas Pelayanan Minimar­ ket Indomaret Dan Alfamart Di Wilayah Dukuhwaluh (a Vomparative Analysis of Costumer Statifac­ tion on the Service of Indomaret and Alfamart Minimarket in Dukuhwaluh). Media Ekonomi Universitas Muhammadiyah Purwokerto 14(2):64–76. Wahab, W. 2017. Pengaruh Kualitas Pelayanan terhadap Kepuasan Nasabah Industri Perbankan Syar­ iah di Kota Pekanbaru. Maqdis: Jurnal Kajian Ekonomi Islam 2(1):51–66. Widodo. 2017. Metodologi Penelitian. Jakarta: Rajawali Press. Zikmund, W. G., Carr, J. C., Babin, B., & Griffin, M. 2010. Business Research Methods. Scarborough, ON, Canada: Nelson Education.

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Digital Economy for Customer Benefit and Business Fairness – Anggadwita & Martini (eds) © 2020 Taylor & Francis Group, London, ISBN 978-0-367-47722-6

The effect of social media communication on brand awareness and perceived quality of Indihome Indrawati & Welly Ardhana Faculty of Economics and Business, Telkom University, Indonesia

ABSTRACT: The purpose of this study was to determine the effect of firm-created commu­ nication and user-generated communication in social media against brand awareness and per­ ceived quality of Indihome. This study was conducted using a sample of 400 respondents who are Indihome and Twitter users through an online questionnaire. The test used Structural Equation Modeling. The results showed that the proposed model is valid and reliable. From the hypothesis studied, firm-created communication significantly influences brand awareness (R2 = 18.9%) and user-generated communication in social media significantly influences brand awareness (R2 = 13.8%). Both simultaneously also significantly affect brand awareness (R2 = 32.8%). In addition, the results of this study indicate that brand awareness significantly influences perceived quality (R2 = 34.0%). In addition, this study proves that the model used successfully measures the influence set on research objectives.

1 INTRODUCTION In order to provide the best service for its customers, PT Telkom (Telkom) has utilized Twitter to interact with customers about its products and services. Telkom expects its presence in social media can affect the desire of customers to use its products. Telkom must make good use of its existence by continuously delivering information related to the company and its the vision and mission to the general public. Social media utilization by the company is in line with the high usage of these media by users when connected to the internet. According to survey results of the Association of Inter­ net Service Providers Indonesia (APJII), 87.40% of internet users in Indonesia use social media when accessing the internet. Some previous research found that the use of social media by this company provides many benefits. Companies can build relationships with customers in a timely and low-cost manner (Kaplan & Haenlein, 2010), talking to customers and vice versa and customers talking to other customers (Mangold & Faulds, 2009). Lastly, social media is useful to increase brand recognition and brand loyalty (Zai, 2015). According to Schivinski and Dabrowski (2015), communication through social media affects brand equity, in particular brand awareness, brand loyalty, and perceived quality. Meanwhile, although Telkom has used social media to communicate, its influence on brand awareness and perceived quality of Indihome is not yet understood, either firm-created communication or user-generated communication.

2 LITERATURE REVIEW 2.1 Social media marketing According to Zai (2015), social media marketing is a process of getting traffic or attention through social media sites. Generally this activity focuses on creating content that attracts attention and leads its readers to share the various social media that follow. According to 57

Tuten (as cited in Ismail, 2017) social media marketing is a broad category of advertising spending, including advertising using social network, virtual worlds, user-generated product reviews, blogger endorsement, RSS feeds of content and social news sites, podcasts, games, and consumer-generated advertising. Companies can take advantage of this mechanism to disseminate information about their products from one consumer to another through social media. Increased company informa­ tion can grow brand awareness and increase customer service. According to Gordhamer (2009), in social media marketing companies must change their business from hard selling to an effort to build connections with consumers as much as possible. In addition, companies should put forward small actions rather than big campaigns because with these actions they are more able to reach more consumers and achieve goals in a short time. According to Lar­ oche et al. as cited in Putter (2017), brand loyalty becomes an increasing focus when compan­ ies consider the use of social media. 2.2 Firm-created social media communication Firm-created social media communication refers to the messages and content posted by firms on their official social media (Kumar et al., 2016: 3). According to Schivinski and Dabrowski (2013: 19), there are four indicators that can be used to measure firm-created social media communication: Satisfaction of Social Media Account, Expectation of Social Media Account, Attractiveness of Social Media Account, and Performance of Social Media Account. Furthermore, social media offers a new way for companies and customers to connect with each other. According to Brodie et al. (2013), corporate marketing managers expect their social media communications to engage with loyal customers and influence consumers’ per­ ceptions of products, disseminate information, and learn from and about their social media audiences. Contrary to the traditional source of communication created by companies, according to Kaplan and Haenlein (2010) communication in social media has been recognized as a mass phenomenon with a wide demographic appeal. The popularity of the implementation of social media communications among companies can be explained through the dissemination of information via the internet (Li & Bernoff, 2011) and greater capacity to reach the general public than traditional media (Keller, 2009). In addition, internet users are switching from traditional media and are increasing the use of social media to seek information and opinions relating to brands and products (Mangold & Faulds, 2009; Bambauer-Sachse & Mangold, 2011). 2.3 User-generated social media communication As a result of changes brought about by Web 2.0 technology, users have become more active where previously service providers supported only application users as collaborators in con­ tent creation. According to Berthon et al. (2012) Web 2.0 allows the creation of content by users on the internet. In addition, according to Kaplan and Haenlein (as cited in Ismail, 2017), Web 2.0 also allows exchange of content. According to Mangold and Faulds (2009), users can also discuss the content they get and forward to other users. Thus information is spread through content obtained from the user to other users. The wide spread of information has resulted in behavioral changes, where people prefer to find information about products online compared to through traditional media such as television, radio, and magazines (Man­ gold and Faulds, 2009). According to Daugherty et al. (2008), user-generated content is media content created or produced by the general public rather than by a company and is distributed over the internet. According to the Organisation for Economic Co-operation and Development (2007), content defined as user-generated content must meet three criteria: first, the content is published on the website or social network accessible to the public or a group of people; second, the content contains creative elements; and third, the content is not made by professionals. 58

2.4 Brand awareness According to Aaker (1991), brand awareness is the ability of consumers to recognize or remember the brand in the category. In line with the definition, according to Kotler and Keller (2012) brand awareness is the ability of consumers to recognize or remember the brand in the category, in sufficient detail to make a purchase. From the consumer side, this brand awareness develops starting from the level of no awareness of the existence of the brand to loyalty to one brand only. 2.5 Perceived quality According to Aaker (1991) perceived quality is the perception of the customer toward the over­ all quality or superiority of a product or service. According to Bhuian (1997) perceived quality is the assessment of the consistency of product specifications or evaluation of the added value of a product. According to Garvin (1983) perceived quality is defined based on user recogni­ tion. According to Zeithaml (1988) perceived quality is the consumer’s assessment of the accu­ mulation of product benefits and subjective assessment of product quality. Owing to the perception and valuation of each customer, perceived quality cannot be determined objectively. 3 HYPOTHESIS DESIGN In this study the authors use the dependent variable (Y) based on perceived quality of Indi­ home and associate it with perceived quality in the studies of Schivinski (2013) and Shojaee and Azreen bin Azman (2013). As for the independent variable (X1) the authors linked social media communications produced by Telkom with firm-created communication and for the independent variable (X2) the authors linked Indihome user-generated social media communi­ cations with user-generated communication according to the previous studies of Bruhn et al. (2012), Schivinski (2013), and Schivinski and Dąbrowski (2013). For intervening variables the authors associate brand awareness with brand awareness in the research of Schivinski (2013). The model uses three moderation variables—age, gender, and education—with the aim of testing the moderation effect of social media communication on brand awareness. In previous studies these three moderating variables had not been used. The hypotheses of this present study are therefore as following: H1: Firm-created communication has a positive effect on brand awareness. H1a: The influence of firm-created communication on brand awareness is moderated by age. H1b: The influence of firm-created communication on brand awareness is moderated by gender. H1c: The influence of firm-created communication on brand awareness is moderated by education. H2: User-generated communication has a positive effect on brand awareness. H2a: The influence of user-generated communication on brand awareness is moderated by age. H2b: The influence of user-generated communication on brand awareness is moderated by gender. H2c: The influence of user-generated communication on brand awareness is moderated by education. H3: Brand awareness has a positive impact on perceived quality.

4 HYPOTHESIS DESIGN The variables in this study consist of two independent variables, one intervening variable and one dependent variable. In order to fulfill content validity, the definitions of variables and items used to measure the variables in this study were adopted from the previous research of 59

Schivinski and Dabrowski (2015) and Schivinski (2013) such as firm-created communication satisfaction (FC1), firm-created communication expectation (FC2), firm-created communica­ tion performance (FC3), firm-created communication performance among competitors (FC4), user-generated communication satisfaction (UGC1), user-generated communication expect­ ation (UGC2), user-generated communication performance (UGC3), user-generated commu­ nication performance among competitors (UGC4), easy brand recognition (BA1), brand symbol or logo recognition (BA2), brand recall (BA3), brand recognition among competitors (BA4), product quality (PQ1), product reliability (PQ2), and brand worthiness (PQ3). The unobserved or latent variables that are not directly measured are firm-created commu­ nication, user-generated communication, brand awareness or brand association, brand loy­ alty, and perceived quality. Nominal data are used for generic or screening questions and interval data for more specific questions using a 5-point Likert scale. In addition, primary data were obtained from the distribution of questionnaires, while secondary data were taken from textbooks, articles, and journals from internet, and several other websites that support the research. A purposive sampling method was used in which the researcher intentionally chose a certain sample member because the sample is representative or can provide information to answer the research problem (Indrawati, 2015). This study investigated the opinion of Twitter users of Indihome’s brand, so the selected sample is Twitter users who are aware of the brand Indihome. The data analysis method consisted of descriptive analysis and statistical test analysis used to answer the purpose of research. According to Sugiyono (2010), descriptive statistics are statistics used to analyze data by describing the data that have been collected as they are with­ out intending to make inferences. Statistical analysis is done by using partial least squares (PLS). PLS has several evaluations of existing structural models and measurement models. The evaluation of the measurement model tested convergent validity, discriminant validity, composite reliability, and average variance extracted, while in the evaluation of the structural model, the R-squared (R2) test and path coefficient estimation test are used.

5 RESULTS Based on an analysis of the characteristics of 400 respondents, the majority of respondents live in Bandung, as many as 223 people or 55.75%, while the minority of respondents live in Jogyakarta and Semarang, with 5 people each, or 1.25%. The statistical test of 400 respondents’ data obtained was done with the help of the SmartPLS 3.0 application program. The first stage assesses the criterion of convergent validity with a good indicator constraint if its loading factor is greater than 0.70, while a loading factor of 0.50 to 0.60 can be maintained for a model that is still in the development stage (Ghozali, 2014: 39).This convergent validity found that the whole loading factor has a value above 0.50. Thus it can be concluded that the construct has good convergent validity. The next stage assesses Cronbach’s Alpha, Composite Reliability, and Average Variance Extracted (AVE) criteria. Each construct is said to be reliable if it has Cronbach’s Alpha and Composite Reliability greater than 0.70, while the AVE value is expected to be greater than 0.50 (Ghozali, 2014: 40). Based on Table 1, all constructs have Cronbach;s Alpha and Com­ posite Reliability values greater than 0.70, and the entire construct has an AVE value greater than 0.50. Table 1. Cronbach’s alpha, composite reliability, and AVE results. Hypotheses

Parameter

Original sample (O)

t-statistics (st. dev.)

Decision

R2

H1 H2 H3

FC->BA UGC->BA BA->PQ

0.341 0.255 0.583

3.902 2.927 14.110

Accepted Accepted Accepted

0.328

60

0.340

The next stage is the inner model test, which is the analysis of the results of the rela­ tionship between constructs. The relationship between constructs can be said to be sig­ nificant if it has a t-statistics score greater than 1.96. The result of the inner model test can be seen in Table 2. From Table 1 it can be seen that the variables of Firm-Created Communication (FC) and User-Generated Communication (UGC) have an influence simultaneously with the Brand Awareness (BA) variable of 0.328 or 32.8%, while the rest of the 67.2% is influ­ enced by other variables not observed in this study. Brand Awareness (BA) variables have an influence on Perceived Quality (PQ) of 0.340 or 34%, while the rest of the 66% influenced by other variables was not observed in this study. The result also shows that the influence of FC and UGC on brand awareness is categorized as Weak because R2 < 0.33, while the influence of brand awareness to perceived quality was in the Moderate category because R2 > 0.33. The next step is testing the effect of the moderator variables age, gender, and education using the Chin formula (2000) with the provision that there is significant difference if the t-value of the formula result is greater than 1.96. The results of the test calculation can be seen in Table 2. Based on Table 2, it is found that the result of calculation t-value is less than 1.96 so that H1 is rejected. It means the variables age, gender, and education are not moderators because the influence of the variables firm-created communication and user-generated communication on brand awareness is not different. The final result of this research model can be seen in Figure 1.

Table 2.

Inner model test results.

Hypotheses

Figure 1.

Parameter

t-value

Decision

Variable moderator: Age H1a FC->BA H2a UGC->BA

0.21 0.17

Rejected Rejected

Variable moderator: Gender H1b FC->BA H2b UGC->BA

0.06 0.99

Rejected Rejected

Variable moderator: Education H1c FC->BA H2c UGC->BA

0.17 0.2

Rejected Rejected

Research framework.

61

6 CONCLUSION This research found that Telkom firm-created communication affects brand awareness of Indihome significantly simultaneously with user-generated communication, while brand awareness of Indihome significantly affects the perceived quality. This finding is consistent with previous research by Schivinski (2013), Shojaee and Azreen bin Azman (2013), and Schi­ vinski and Dabrowski (2015). The simultaneous effect of firm-created communication and user-generated communication on brand awareness is in the Weak category (R2 = 0.328). The influence of Brand awareness of Indihome to Perceived Quality is in the Moderate category (R2 = 0.34). On the other hand, the moderator variables age, gender, and education have no effect either on firm-created communication or user-generated communication because the result of the Chin formula shows there is no significant differentiation between each group of data (young/ old, male/female, lower/higher education). In addition, this research proved that the research model can be used to study the influence of social media communication on brand awareness and perceived quality of a product. Therefore the model can be used to conduct the same research in companies and other prod­ ucts. Furthermore, social network data analytics can be used to investigate consumer attitudes on Twitter (Indrawati & Alamsyah, 2017) toward Indihome brand awareness for a deeper study on the effect social media communication on brand awareness. REFERENCES Aaker, D. A. 1991. Managing Brand Equity: Capitalizing on the Value of a Brand Name. New York: The Free Press. Bambauer-Sachse, S. & Mangold, S. 2011. Brand Equity Dilution Through Negative Online Wordof-mouth Communication. Journal of Retailing and Consumer Services 18(1):38–45. Berthon, P. R., Pitt, L. F., Plangger, K., & Shapiro, D. 2012. Marketing Meets Web 2.0, Social Media, and Creative Consumers: Implications for International Marketing Strategy. Business Horizons (55):261–271. Brodie, R. J., Ilic, A., & Hollebeek, L. 2013. Consumer Engagement in a Virtual Brand Community: An Exploratory Analysis. Journal of Business Research 66(8):105–114. Bruhn, M., Schoenmueller, V., & Schafer, D. B. 2012. Are Social Media Replacing Traditional Media in Terms of Brand Equity Creation? Management Research Review 35(9):770–790. Chauhan, K. & Pillai, A. 2013. Role of Content Strategy in Social Media Brand Communities: A Case of Higher Education Institutes in India. Journal of Product & Brand Management 22(1):40–51. Gordhamer, S. 2009. 4 Ways Social Media is Changing Business. Mashable Asia: http://mashable.com/ 2009/09/22/Social-Media-Business/ Henseler, J. & Fassott, G. 2010. Testing Moderating Effects in PLS Path Models: An Illustration of Available Procedures. In V. E. Vinzi, W. W. Chin, J. Henseler, & H. Wang (eds.), Handbook of Partial Least Squares Concepts, Methods, and Application (pp. 713–735). Berlin: Springer. Indrawati, P.D., 2015. Metode Penelitian Manajemen dan Bisnis Konvergensi Teknologi Komunikasi dan Informasi. Bandung: PT Refika Aditama. Indrawati. 2017. Perilaku Konsumen Individu dalam Mengadopsi Layanan Berbasis Teknologi Informasi & Komunikasi. Bandung: Refika Aditama. Ismail, A. R. 2017. The Influence of Perceived Social Media Marketing Activities on Brand Loyalty: The mediation Effect of Brand and Value Consciousness. Asia Pacific Journal of Marketing and Logistics 29(1):129–144. Kaplan, A. M. & Haenlein, M. 2010. Users of the World, unite! The Challenges and Opportunities of Social Media. Business Horizons, 53, 59–68. Kemp, S. 2017. Digital in Southeast Asia in 2017. WeAreSocial Blog: http://wearesocial.com/blog/2017/ 02/digital-southeast-asia–2017 Kotler, P. & Keller, K. L. 2012. Marketing Management. Upper Saddle River, NJ: Prentice Hall. Kumar, A., et al. 2016. From Social to Sale: The Effects of Firm-Generated Content in Social Media on Customer Behavior. Journal of Marketing 80(1):7–25. doi: 10.1509/jm.14.0249. Li, C. & Bernoff, J. 2011. Groundswell: Winning in a World Transformed by Social Technologies. Boston: Harvard Business Review Press.

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Digital Economy for Customer Benefit and Business Fairness – Anggadwita & Martini (eds) © 2020 Taylor & Francis Group, London, ISBN 978-0-367-47722-6

External and internal factors of mobile games adoption in Indonesia Indrawati & M.R. Gaffar Faculty of Economics and Business, Telkom University, Bandung, Indonesia

S.K.B. Pillai Goa Business School, Goa University, Goa, India

ABSTRACT: Developments in information and communication technology have led to a growth in mobile usage, with mobile games being played by most people. Mobile games have become the biggest contributor to the revenue of the iOS and Google play applications in Indonesia. Gamers mostly play the games developed by foreign industries rather than those developed by local domestic industries, as a result of which in Indonesia usage of local mobile games is very low. Data were collected from 400 respondents to identify the factors that affect users’ behavior in choosing mobile games. The data were analyzed using partial least squares. The results revealed that economic value and social influence factors affect the usage intention of mobile gamers. The identified factors predict 66% of consumer behaviour towards the use of mobile devices for playing games.

1 INTRODUCTION The number of smartphone users has been increasing over time as a result of advances in information and communication technology. There were 27.4 million smartphone users in Indonesia in 2013, which increased to 70.22 million in the 2018, and it is estimated that in 2022 it will increase to 89.86 million users (Statista, 2019a). Smartphone offers many uses, including playing games, as a result of which gamers have shifted from a fixed connection of online games to a mobile connection. The game industry in Indonesia has experienced positive growth over the years, as it has contributed to the total value of USD 1.4 billion in the ASEAN market and has an annual growth rate of 5.8%. User penetration was 15.9% in the year 2019 and is expected to hit 22.1% by 2023 (Statista, 2019b). Even though Indonesia has huge revenue growth potential from the gaming industry, local game developers did not get any benefits, as most gamers tend to use and play foreign games (Bhaskoro, 2014). Understanding consumer behavior toward usage of mobile games in Indonesia is needed in order to identify the factors that motivate customers to play foreign as compared to local games, which will be beneficial for the local game developers in Indonesia in competing with the foreign game developers. Several studies on adoption of mobile games have been conducted over the years in different countries, namely China (Liu & Li, 2011; Zhou, 2013); Taiwan (Liang & Yeh, 2011); and in the United States, Spain, and the Czech Republic (Okazaki et al., 2008). To the authors’ knowledge no study has been done in Indonesia to study consumer intentions to adopt mobile games.

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2 LITERATURE REVIEW 2.1 Background Increasing use of smartphones over the years made it possible for game developers to develop particular gaming applications so that people can play the games by using their smartphones. Several studies have been conducted over the years to study consumer behavior with respect to mobile gaming adoption by using the Technology Acceptance Model (TAM). This model was developed by Fred D. Davis in1989 and has two factors that influence behavior with regard to technology adoption, namely Perceived Ease of Use (PEoU) and Perceived Usefulness (PU). Further, TAM 2 was developed by adding a new construct (Venkatesh et al., 2000). To understand consumer behavior with respect to mobile games, many studies were conducted using TAM as a basic model. Okazaki et al. (2008) identified that perceived convenience is the strongest determinant of the attitude and attitude further influences intention of gamers to play mobile games. Liang and Yeh (2011) developed and measured a mobile gaming adoption model having a construct of Playfulness, Ease of Use, Attitude, Subjective Norm, and Continuance Intention and revealed that Continuance Intention was mostly influenced by Attitude. Moreover, mannerism in mobile gaming is mostly influenced by Playfulness. Liu and Li (2011) explored the impact of Use Context and found that it has a significant impact on Perceived Ease of Use, Attitude, Behavioral Intention, Perceived Enjoyment, and Cognitive Concentration. Furthermore, they also proved that Attitude affected Behavioral Intention of the mobile gamers. 2.2 Content quality Content Quality (CTQ) reflects the attractiveness of content, timelines of content, and personalized content. The very existence of any game is based on the ability to differentiate it from other games in order to attract people to play the particular game. Similarly, periodic updates and personalization of games will provide an exciting experience to gamers. Studies showed that content quality will significantly influence the flow; where challenges and content quality of games is good, gamers will continuously play the games (Zhou 2013). 2.3 Visual appeal Visual Appeal (VA) is a minimum requirement that a user wants from games that will also provide entertainment for gamers in order so they will have a good user experience. Earlier research on the visual aesthetics of the computer interface indicates that the pleasure experienced by users was mostly influenced by Visual Appeal (Liang & Yeh, 2011). 2.4 Flow Flow reflects the sensation that people feel when they do things with total involvement (Zhou, 2013). When the challenge exceeds abilities, users will feel anxious. Conversely, when the ability exceeds the challenge, then the user will get tired of the game. While both challenges and abilities are below the threshold, the user will be apathetic. Only when the challenges and abilities exceed the threshold value will the user feel the Flow. Cognitive Concentration (CC) and Perceived Enjoyment (PE) are two dimensions of the Flow (Liu & Li, 2011). Perceived Enjoyment is the extent to which an activity is perceived to be enjoyable in its own right, and this property is separate from any beneficial performance consequences that may be anticipated, while Cognitive Concentration is the effort required by users during their mobile game involvement, and mobile users can normally perform multiple tasks at the same time (Davis et al., 1992). 2.5 Social influence Social Influence is defined as the effect of other important people’s opinions on individual users (Zhou 2013). It is related to the subjective norm variable of the Theory of Planned Behavior 65

(TPB). At the time friends as well as colleagues who are close or important to users recommend users play the mobile game, the users probably follow their suggestion and play the game even if the user has not formed a positive attitude toward it. The Unified Theory of Acceptance and Use of Technology (UTAUT) argues that Social Influence is a significant determinant of adoption by users (Venkatesh et al., 2003). Existing research also describes Social Influence on Behavioral Intentions in the context of mobile data and instant messaging services (Zhou, 2003). 2.6 Economic value Economic Value represents the perceived affordability of online shopping, in that users can afford a reasonable investment of time and money (Mathwick et al., 2001). It is well defined as the trade-offbetween the investment of time and money to play mobile games with the value obtained by playing mobile games (Okazaki et al., 2008). The investment of money that must be spent in order to be able to play games includes mobile internet package subscription fee, the cost of downloading mobile games, and costs incurred for the transactions in the game.

3 METHODOLOGY In order to understand the gamers’ behavior toward adoption of mobile games, taking model of Zhou (2013) as a base, a personal interview was conducted among 25 respondents from various backgrounds (government employee, bank employee, college student, teacher) to obtain insight from the respondents regarding their behavior toward mobile game adoption. Based on the results of the interview, the model shown in Figure 1 was proposed to predict consumer behavior toward mobile game in Indonesia by separating Flow variables into Perceived Enjoyment and Cognitive Concentration. The Flow variable reflects that users enjoy and concentrate on the game. All respondents who play mobile games enjoy playing them, but some of cannot concentrate on the game even though they enjoy playing it. The Visual Appeal construct was added to the model based on interview results, as most of the respondents agreed that visual appeal is an important factor for them in choosing mobile games. The study also replaced Usage Cost with Economic Value, as it can represent the trade-off between cost (time and money) and value that user can get as compared to Usage Cost. Hence, the proposed model has five independent variables, namely Content Quality (CTQ), Perceived Ease of Use (PEoU), Visual Appeal (VA), Social Influence (SI), and Economic Value (EV); three intervening variables, namely Perceived Enjoyment (PE), Cognitive Concentration (CC), and Usage Intention (UI); and one dependent variable which, is Use Behavior (UB). The proposed model is shown in Figure 1, which indicates the schematic relation among variables. The hypotheses shown in Table 1 were developed.

Figure 1.

Proposed research model.

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Table 1. H1: H2: H3: H4: H5: H6: H7: H8: H9: H10: H11:

Hypotheses of the study.

PEoU positively influences PE. PEoU positively influences CC. CQ positively influences PE. CQ positively influences CC. VA positively influences PE. VA positively influences CC. SI positively influences UI. PE positively influences UI. CC positively influences UI. EV positively influences UI. UI positively influences UB.

The model contains nine constructs that were adapted from earlier research through which a structured questionnaire was developed and distributed among the respondents. A 5-point Likert scale extending from strongly disagree (1) to strongly agree (5) was used. Data collected from 400 valid respondents were analysed using SmartPLS.

4 ANALYSIS AND DISCUSSION The demographic characteristics of the respondents were analyzed using descriptive statistics, where the majority of the respondents were male (61%), in the age group of 10–25 years (52.50%), having a bachelor’s degree (71.75%), and working as employees (57.25%) in the major city of Banding (29.60%) in Indonesia. To check the reliability and validity of data confirmatory factor analysis was done, with the results shown in Table 2. A validity test was done using convergent validity, which is represented by a Loading Factor (LF) and Average Variance Extracted (AVE). The model is valid if each item has a value of LF above 0.5 (Latan, 2012) and AVE above 0.5 (Liang & Yeh, 2011). A reliability test can be done using Cronbach’s Alpha and Table 2. Results of confirmatory analysis. Variables/items

LF

Perceived Ease of Use (PEoU) 1. It is easy for me to learn mobile games. 2. Skillfully using the mobile game is easy for me. 3. I find mobile games easy to use. 4. It takes me too much time to learn to play mobile games. 5. I think learning to play mobile games is very simple.

0.76 0.82 0.84 0.79 0.67

Content Quality (CTQ) 1. This mobile game provides up-to-date content. 2. This mobile game provides attractive content. 3. This mobile game provides content related to my needs. 4. Mobile games contain original ideas. 5. Eager to take risk to play mobile games as they are innovative.

0.57 0.79 0.75 0.82 0.74

Visual Appeal (VA) 1. Displays of mobile games are aesthetically appealing. 2. I usually find the design of mobile games visually attractive. 3. Mobile game display layout is visually comforting. 4. It is fun watching the colors and pictures in mobile games. 5. The graphical elements in mobile games are attractive. 6. The graphical illustrations in mobile games are visually appealing.

AVE

CR

CA

0.61

0.89

0.84

0.55

0.86

0.79

0.60

0.90

0.87

0.74 0.79 0.79 0.71 0.87 0.74 (Continued )

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Table 2. (Continued ) Variables/items

LF

Social Influence (SI) 1. People who influence my deeds think I should play mobile games. 2. People who are essential to me think I should play mobile games. 3. People who play mobile games have more regards.

0.85 0.90 0.68

Cognitive Concentration (CC) 1. While playing mobile games I fully concentrate on them. 2. While playing, I am intensely absorbed in the mobile games. 3. While playing, I am deeply engrossed in the mobile games. Perceived Enjoyment (PE) 1. While playing this mobile game, I find a lot of pleasure. 2. I think the process of playing mobile game would be pleasant. 3. I think playing mobile games would bring me pleasure. 4. I enjoy playing mobile games. 5. Mobile games make me happy. Economic Value (EV) 1. The price of mobile game is reasonably considered. 2. The mobile game has a good value for the money. 3. At the current price, mobile games provide a good value. 4. The cost of mobile games is reasonable. 5. The cost of mobile games is sensible. 6. Overall, I am pleased with the cost of mobile games. Usage Intention (UI) 1. Given the chance, I plan to use this mobile game. 2. I have plans to use this mobile game. 3. I will play mobile games more frequently in the future. 4. I will try transformed types of mobile games in the future. 5. I would prefer playing mobile games than any other games. Use Behavior (UB) 1. My routine is fixed to play mobile games. 2. I regularly play mobile games. 3. I play mobile games more often. 4. I choose to play mobile games more than any other media games.

0.56 0.77

AVE

CR

CA

0.69

0.87

0.77

0.79

0.61

0.74 0.73 0.59

0.88

0.83

0.71

0.94 0.77

0.92

0.67

0.91

0.87

0.94

0.91

0.78 0.78 0.77 0.77 0.76

0.86 0.75 0.89 0.90 0.85 0.86 0.80 0.88 0.76 0.77 0.78 0.93 0.93 0.90 0.77

Source: User compilation based on primary data.

Composite Reliability indicator. The model is valid if each item has a value of Composite Reliability above 0.7 (Zhou, 2013) and Cronbach’s Alpha above 0.6 (Choe & Schumacher, 2015). Figure 2 reveals the result of path coefficient and t-statistics value. A one-tailed test was used to measure the relations between constructs. Thus, t-statistics of each relation must be above 1.65 in order to prove the hypotheses. In Figure 2, t-statistics are represented by the number in parentheses at each construct’s relations. All hypotheses were supported where PEoU, CQ, and VA affect both CC and PE, which affect UI, SI, and EV and also influence UI which further affects UB. Among all factors that affect Usage Intention, Economic Value has the largest effect (path coefficient = 0.30). The second factor is Social Influence (path coefficient = 0.28). This suggests that price is still sensitive for Indonesian mobile gamers. Indonesian mobile gamers prefer playing free games to playing paid games. Indonesian mobile gamers are also affected by the social environment and are most affected by people around who have an influence on their behavior. Therefore, local game makers must consider those two factors and make them the highest priority in developing mobile games. The proposed research model can predict a Usage Intention of 60% (R2 = 0.60) and Use Behavior up to 66% on adoption of mobile games (R2 = 0.66). This indicates that the model has a good prediction. 68

Figure 2.

Path analyses of mobile games adoption model.

The fitness of the model was also calculated using the Goodness of Fit (GoF) index obtained as the geometric mean of the average communality index and the average R2 value as suggested by Peng and Lai (2012). The GoF index is bounded between 0 and 1 and it is a descriptive index, which means that there is no inference-based threshold to judge the statistical significance of their values. As a rule of thumb, a value of the relative GoF equal to or higher than 0.90 indicates that the model is good (Vinzi et al., 2010). The GoF value of the study was 0.54, which is acceptable, as it is more than 0.5.

5 CONCLUSION Indonesia has good potential for mobile gaming markets. But the revenue generated from the local game makers is very low, as most of gamers plays the games developed by foreign players. Thus it is essential to study consumer behavior on mobile gaming adoption in Indonesia. It was found that usage intention of gamers to play a game is highly influenced by the Economic Value. Even Social Influence, Cognitive Concentration, and Perceived Enjoyment influence the usage intention to a high extent. Thus, local game developers must focus on these factors and try to develop their games in more innovative ways compared to other developers; they will thereby be in a position to compete with foreign game manufacturers. Local game makers should make all games as a freemium so as to attract more gamers to play the games developed by them. Making innovative and educative games will be a good way for them to survive in the long term. They must also try to introduce some more realistic games with good graphics and also provide some vouchers for the winners, as it will motivate them to play the games continuously. This will generate more revenue to the business and also attract an audience to play the mobile games developed by local game developers. REFERENCES Bhaskoro, A. T. 2014. Industri Game Indonesia Harus dapat Beradaptasi dengan Cepat. http://dailyso cial.net/post/nxtcon-2014-industri-game-indonesia-harus-dapat-beradaptasi-dengan-cepat/. (Accessed January 8, 2015). Choe, P. & Schumacher, D. 2015. Influence of Different Types of Vibration on Technical Acceptance of a Mobile Game Aiming for Hedonic Satisfaction. International Journal of Human-Computer Interaction 31:33–43. Davis, F. D., Bagozzi, R. & Warshaw, P. 1992. Extrinsic and Intrinsic Motivation to Use Computers in the Workplace. Journal of Applied Social Psychology 22(14):1111–1132. Latan, H. 2012. Structural Equation Modeling Konsep dan Aplikasi Menggunakan Program LISREL 8.80. Bandung: Alfabeta.

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Liang, T. P. & Yeh, Y. H. 2011. Effect of Use Contexts on the Continuous Use of Mobile Services: The Case of Mobile Games. Pers Ubiquit Comput 15:187–196. Liu, Y. & Li, H. 2011. Exploring the Impact of Use Context on Mobile Hedonic Services Adoption: An Empirical Study on Mobile Gaming in China. Computers in Human Behavior 27:809–898. Mathwick, C., Malhotra, N., & Rigdon, E. 2001. Experiential Value: Conceptualization, Measurement and Application in the Catalogue and Internet Shopping Environment. Journal of Retailing 77(1):39–56. Okazaki, S., Skapa, R., & Grande, I. 2008. Capturing Global Youth: Mobile Gaming in the U.S., Spain, and the Czech Republic. Journal of Computer-Mediated Communication 13:827–855. Peng, D. X. & Lai, F. 2012. Using Partial Least Squares in Operations Management Research: A Practical Guideline and Summary of Past Research. Journal of Operations Management 30:467–480. Statista. 2019a. Smartphone Users in Indonesia. https://www.statista.com/statistics/266729/smartphoneusers-in-indonesia/(accessed November 20, 2019). Statista 2019b. Mobile games in Indonesia. https://www.statista.com/outlook/211/120/mobile-games/indo nesia#market-globalRevenue Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. 2003. User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly 27(3):425–478. Venkatesh, V., Viswanath, & Davis, F. D. 2000. A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science 46(2):186–204. Vinzi, V. E., Chin, W. W., Henseler, J., & Wang, H. 2010. Handbook of Partial Least Squares: Concept, Methods and Applications. New York: Springer Science+Business Media. Zhou, T. 2013. Understanding the Effect of Flow on User Adoption of Mobile Games. Pers Ubiquit Comput 17:741–748.

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Digital Economy for Customer Benefit and Business Fairness – Anggadwita & Martini (eds) © 2020 Taylor & Francis Group, London, ISBN 978-0-367-47722-6

Paradoxes of healthcare in Goa D. Gaunekar, S.K.B Pillai & J. Castanha Goa Business School, Goa University, Goa, India

Indrawati Faculty of Economics and Business, Telkom University, Bandung, Indonesia

ABSTRACT: The study aimed to assess the satisfaction level of patients with respect to health­ care services provided by public and private hospitals. The result of the Importance Performance Analysis (IPA) revealed that in general, when both public and private hospitals are considered, the level of satisfaction among patients is very low. Public sector healthcare completely lacks over­ all service quality whereas the private sector shows some signs of satisfaction. This being the case, because no other options are available, patients keep visiting both public as well as private sector hospitals for their ailments, making it a paradox. Concerned hospital authorities and also man­ agement must focus on improving service quality so as to ensure that the overall image of the healthcare sector in Goa improves in the coming years, which may be considered as an Unique Selling Proposition (USB) for making Goa a health tourism destination.

1 INTRODUCTION This article begins with few quotes by people who considered the health of an individual as the basis for a happy life. “Health is wealth” is a common saying and good health is the feeling of mental, physical, and social well-being by any person and refers not only to freedom from diseases (Health Is Wealth Quotes, 2019). “Health is the greatest gift, contentment is the greatest wealth, faithfulness the best relation­ ship” (Buddha).

“It is health that is real wealth and no pieces of gold and silver” (Mahatma Gandhi).

“The first wealth is health” (Ralph Waldo Emerson).

“The greatest wealth is health” (Virgil).

“Early to bed and early to rise, makes a man health wealthy and wise” (Benjamin Franklin).

From these few notable quotes from ancient and modern times, one can see that it is well known around the world that “Health of an individual is the best wealth one can have,” an axiom practiced by people around the world from time immemorial, and is still applicable in the present technological era. This being the case, there are still many parts in the world that do not have proper healthcare services, with people suffering because they cannot get medicine on time, do not get proper treatment due to lack of availability of technology and human resources, and also children are still suffering from malnutrition. Technological advancement has led to huge investment in research and development, which leads to introduction of new medicines and improved treatment processes with upgraded equipment that increases treatment cost. India, the second most populated country in the world, is still at a developing stage with limited resources, still trying to adapt to technological changes, but having problems with healthcare facilities. The healthcare sector in India is still not on par with international stand­ ards. In India, public sector hospitals depend on government funding, which sometimes results in limited funds for expenditure on healthcare facilities and a failure to provide better services, 71

as the cost of equipment and maintenance costs are high, making the hospitals beyond the pub­ lic’s reach. This situation is exploited by the private sector, which can earn greater revenue and are better at providing services that are beyond the reach of people in the lower income cat­ egory. The Government of India has allowed foreign players to merge with domestic players to strengthen the healthcare sector so as to provide better treatment. In keeping with these points, the present study is an attempt to identify the satisfaction level of patients when it comes to service quality of healthcare in public and private sector hospitals. To the authors’ knowledge, no similar study has been carried out in the state of Goa. Healthcare is one of the crucial and essential aspects of an economy, making the present work unique in trying to fill the existing research gap that may be useful for various stakeholders, viz., academic institutions, govern­ ment, private hospitals, entrepreneurs, medical students, and also the general public.

2 LITERATURE REVIEW Whether healthcare services are provided by public or private players, patients visit any of these service providers for all their ailments, and the success of any such healthcare service depends on how well the patients are treated and their level of satisfaction. Hence the import­ ance of understanding two important aspects of marketing comes into the picture, namely (1) who the patients are and (2) whether they are satisfied. 2.1 Demographic profiles and preferred hospital Many studies have been conducted focusing mainly on the sociodemographic profiles of patients and their perceptions while selecting a particular hospital. Gender was found to be a decisive criterion when selecting a hospital; namely, females prefer private rather than public hospitals (Cinaroglu, 2014). Location also influences choices, with patients preferring nearby hospitals rather than those farther away (Dranove et al., 1993; Escarce & Kanika, 2009). Popu­ lation growth was found to be one of the factors influencing the increase in the number of pri­ vate hospitals, which normally provide better service than the public hospitals (Escarce & Kanika, 2009; Singh & Shah, 2011). Hence it is said that better service quality is available at the private hospitals (Alrubaiee & Alkaaida, 2011), and therefore private hospitals have a vital role in any society. Patients choose a hospital according to what matters most to them, which may be location, cost consideration, infrastructure availability, suggestion from friends, referrals by doctors, etc., and there is a need for customization of healthcare services to meet the increasing demand (Minviellea et al., 2014). There are different patterns of planning and utilization of healthcare facilities for different socioeconomic population groups. Thus, in this study an attempt was made to identify the demographic profiles of patients in the state of Goa and to see which type of hospital is preferred by the patients with respect to their socioeconomic status Hence following hypothesis was developed: H1: The demographic profiles of the patients and their choice of hospital do not have any relationship.

2.2 Level of patient satisfaction If the expectations are equal to or less than the actual experience, one can see patients’ delight or satisfaction; otherwise there is dissatisfaction. Happy and satisfied patients become loyal and act as agents of growth for the hospitals by referring their families, relatives, and also friends. Healthcare service quality is one of the crucial factors determining satisfaction level, both in public and private hospitals (Irfan & Ijaz, 2011; Yousapronpaiboon & Johnson, 2013; Karekar et al., 2015). A patient’s age plays an important role, with elder patients found to be more satisfied than younger ones when it comes to the level of communication between nurs­ ing assistants and patients (Chavesa & Santosa, 2016). Since healthcare services are intangible, 72

perceptions and expectations of the patients need to be measured, which is very difficult in the present highly competitive healthcare sector (Cronin & Taylor, 1992; Duggirala et al., 2008), and healthcare service providers need to ensure that patients’ expectations are met completely (Parasuraman et al., 1985; Zeithaml & Berry, 1988; Zeithaml et al., 1993; Kalajaa et al., 2016) so that their satisfaction level increases and leads to brand loyalty (Sharma, 2017). An attempt is made to assess the satisfaction level of patients with respect to quality of the healthcare ser­ vices provided by public and private hospitals in Goa. Hence the following hypothesis was formulated: H2: Patients are satisfied with the quality of healthcare services provided by public and private hospitals.

3 METHODOLOGY To assess how the demographic variables of the patients influence the preference for the type of hospital and their level of satisfaction, a structured questionnaire was administered to 300 patients based on purposive sampling, of which 245 questionnaires were found to be complete (126 patients visiting public hospitals and 119 patients using private hospitals), with a response rate of 81.67%. The composition of respondents included students, businessmen, employees of the private sector as well as government sector, and also housewives. The ques­ tionnaire consisted of two parts, the first being the demographic profile (gender, age, marital status, occupation, income, and location) and the preference of hospital. A chi-square test was used to find out if there is any association between the hospitals preferred by the respondents and their demographic characteristics. The second part dealt with the satisfaction level of respondents toward the quality of services offered by the hospitals. A total of 29 statements were identified that were divided into four constructs: service provided (10 statements), infra­ structure availability (7 statements), attitude of staff (8 statements), and experience (4 state­ ments), where respondents were asked to identify what was their expectation before visiting the hospital and what they actually experienced after visiting the hospital on a 5-point Likert’s scale: 1 for strongly disagree and 5 for strongly agree. Importance Performance Analysis allows measurement of the gap between the expectations and the resulting experience to assess whether patients are satisfied with the quality of services provided by the hospitals.

4 ANALYSIS AND DISCUSSION 4.1 Who are the patients and what is their choice of hospital? Demographic profiles of the respondents showed that male patients dominated (52.4%) the gender category in the case of public hospitals whereas female patients dominated (59.7%) in the case of private hospital (Cinaroglu, 2014). With respect to patients age, youngsters (below 30 years of age) dominated, both in public (54.8%) and private (62.2%) hospitals (Cinaroglu, 2014). This resulted in the occupation category showing the majority falling under students (34.1% public and 53.8% private hospitals). The majority of the patients came under the cat­ egory of unmarried in both public (56.3%) and private (68.1%) hospitals. The result also revealed that gender, age, and marital status do not have any association with respect to the patient’s choice of hospitals. When it came to occupation (χ2 = 20.799*), income (χ2 = 8.129*), and location (χ2 = 18.652*), there was a significant association with the choice of hospitals. Lower income patients prefer public hospitals (31.7%) and higher income patients prefer private sector hos­ pitals (35.3%), mainly because of the differential cost of treatment between public and private sector hospitals. With respect to location, the majority of patients from North Goa prefer public sector hospitals because the government medical college is located in North Goa. Except with respect to gender, patients of both public and private sector hospitals show 73

similar characteristics, but three variables, namely, occupation, income, and location, resulted in significant association with the choice of hospitals, hence H1 that is, “The demographic profiles of the patients and their choice of hospital do not have any relationship” is accepted with an exception for occupation, income, and location. 4.2 Are the patients happy? Using Importance Performance Analysis (IPA), both the original (Martilla & James, 1977) and modified (Abalo et al., 2007; Chen, 2014) versions, an attempt is made to see the satisfaction level of patients with respect to their choice of hospitals. The four categories A, B, C, and D of satisfac­ tion levels (refer to Table 1) are named as follows: A is termed “Concentrate here,” where the Table 1. Importance performance analysis. Public

Private

Variables

O

M

O

M

Services provided Quality of food Waiting room Beds of patients Room environment Hygiene of equipment Information provided Sanitation facility Medicine availability Emergency services Hospital policies

C C B A B C C A B C

A* A* A* A* A A* A* A* A* A*

C C D C B C C C B C

B A* B B* B* A* A* A* A* A*

Experience Experience with doctor Physical environment Overall environment Treatment process

B B B B

A* A* A* A*

B B B B

A* B* A* B*

Infrastructure availability Hospital location Treatment cost Facilities Washroom Equipment Water supply Water supply

B C B C B B B

A A A A* A* A A

A C B B B B

A* B B* A* A* B* A*

Attitude of staff Doctors’ treatment Nurses/ward staff Supporting staff Staff knowledge Staff punctuality Staff communication Helpful nature of staff Friendliness of staff

B A C B C B B B

A* A* A* A* A* A* A* A*

B B D A C B B B

A* A* B* A* A* A* A* A*

Source: Compiled from the primary data analysis result. * Significant at 0.05.

A = Concentrate here; B = Keep up the good work; C = Low priority; D = Possible overkill; O = Original IPA result; M = Modified diagonal IPA result.

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level of satisfaction is rated below average and urgent corrective action is required. B termed “Keep up the good work,” where the satisfaction level is above average and the status quo must be retained. C termed “Low priority,” where the level of satisfaction is below average and needs improvement. D is termed “Possible Overkill,” where the level of satisfaction is above average, and there is no need for any improvements in quality and hence no need for further investments (D’Mello et al., 2016). If any of the variables fall under “A” in the original (O) IPA and the modified (M) IPA, it signifies customer dissatisfaction; hence those variables need to be carefully looked into and serious strategic initiatives need to be planned and implemented with a proper increase in resource mobilization for improving the satisfaction level among patients. In other words, any variable indicated with an A shows complete dissatisfaction. Based on the 29 variables newly developed and represented in the form of statements coming under the four constructs as mentioned in the Methodology section, patients indicated their pref­ erences. The reliability statistics (Cronbach alpha) revealed that all four constructs for both public and private hospitals fell within the acceptable limits, namely for public hospitals, services pro­ vided (α = 0.954), infrastructure availability (α = 0.869), attitude of employees (α = 0.966), and experience (α = 0.920). For private hospitals, reliability statistics revealed similar values for ser­ vices provided (α = 0.925), infrastructure availability (α = 0.896), attitude of employees (α = 0.944), and experience (α = 0.895). With respect to public hospitals (refer to Table 1), the resulting original IPA (shown under Public O) shows that patients are not satisfied with only three variables (marked with A), two related to services provided (patients room environment and medicine availability) and one related to attitude of employees (nurses/ward staff work). As the original IPA is inappropriate to measure satisfaction level, the modified IPA (shown under Private M) is considered and it was found that all 29 variables coming under the four constructs are under A, indicating a complete absence of satisfaction among the patients, which is very serious for the health and safety of the general public. Of the 29, 23 variables show statistical significance that the satisfaction level of the patients is very low. Authorities, especially the government, must consider this as critical and ensure that serious efforts are made to improve the facilities available at the public sector hospitals and also ensure that professional training is imparted to the employees so that patients will be treated with proper care in the future. Private hospitals also show that the satisfaction level of patients is not that high (refer to Table 1). The original IPA (shown under Private O) resulted in four variables as unsatisfac­ tory (marked with A), one from services provided (medicines availability), two from infra­ structure availability (hospital location and clean washroom), and one from attitude of employees (staff knowledge). The modified IPA (shown under Private M) showed that of the 29 variables from four constructs, only 19 fall under A, showing dissatisfaction by the patients, which is quite more impressive than for the public sector hospitals. Though 10 vari­ ables show patient satisfaction, 26 variables show statistical significance indicating that the majority of the variables need to be carefully checked and controlled by the management of private sector hospitals for the purpose of safety and security of patients coming for treat­ ments for their ailments. Thus in the case of public as well as private sector hospitals, level of satisfaction is found to be very low, as the majority of the variables are statistically significant with respect to the gap between expectation and experience of patients (23 out of 29 in public hospital and 26 out of 29 in private hospital): hence, H2, “Patients are satisfied with the quality of healthcare services provided by public and private hospitals” is rejected and it can be concluded that there is com­ plete absence of patient satisfaction when it comes to the quality of healthcare services pro­ vided by public as well and private hospitals in the state of Goa. This finding is similar to some of the earlier findings of customer satisfaction level in the state of Goa with respect to the entertainment industry (Castanha et al., 2017), banking sector (Dsouza et al., 2018), and also telecommunication industry (Gaonkar et al., 2018). All three studies revealed that the majority of the variables were falling under a level of dissatisfaction; hence both the original and modified IPA indicated complete dissatisfaction among the customers. This is a clear indication that the level of satisfaction of Goan customers is very low and none of the 75

stakeholders who are supposed to protect customers are taking any initiatives for improving the service quality; hence customers were forced to use the services in spite of the poor quality, which is considered a paradox in Goa.

5 CONCLUSION, LIMITATIONS, AND FURTHER RESEARCH DIRECTION The more patients are treated well, the higher will be the satisfaction level. The reputation of hospitals depends largely on their patients’ satisfaction level, followed by role of specialized equipment, qualified staff (doctors, nurses, technical and administrative), and the infrastruc­ ture and location of the hospitals. Patients generally select a hospital (public or private) mainly based on their past experiences or references from people they know. The findings of this study make it clear that patients’ choices of hospitals are influenced by their occupation, income, and location. Gender and age also influence the choices. This study clearly indicates that patients are unhappy with the type of service provided by the hospitals; thus an urgent need arises to improve healthcare service quality and maintain the professional culture, but as of now the situation remains pathetic, making healthcare a paradox in Goa. Results may be further verified with a random sample in the future. A comparative study between countries might provide insights in identifying future strategies for improving service quality. The results may also provide some insights into how and in what way differential quality services as well as differential pricing strategy are followed by these service providers in different countries. REFERENCES Abalo, J., Varela, J., & Manzano, V. 2007. Importance Values for Importance-Performance Analysis: A Formula for Spreading Out Values Derived from Preference Ranking. Journal of Business Research 60(2):115–121. Alrubaiee, L. & Alkaaida, F. 2011. The Mediating Effect of Patients Satisfactions of Healthcare Quality Patient Trust Relationship. International Journal of Marketing Studies 3(1):103–127. Castanha, J., Subhash, K. B., Chang, L., & Ganef, J. P. 2017. Importance–Performance Analysis of Entertainment Industry. In Marketing Management: Innovations & Strategies (pp. 15–45). Goa, India: Broadway Publishing House. Chavesa, C. & Santosa, M. 2016. Patient Satisfaction in Relation to Nursing Care at Home. ProcediaSocial and Behavioral Sciences 217:1124–1132. Chen, K. 2014. Improving Importance-Performance Analysis: The Role of the Zone of Tolerance and Competitor Performance. The Case of Taiwan’s Hot Spring Hotels. Tourism Management 40:260–272. Cinaroglu, S. 2014. Patients Perception of Reputation and Image-Private and Public Hospitals. African Journal of Marketing Management 6(2):12–16. Cronin, J. & Taylor, S. A. 1992. Measuring Service Quality a Re-Examination and Extension. Journal of Marketing 56(3):55–68. D’Mello, C., Kamat, K., Scaglione, M., Weiermair, K., Ganef, P., & Subhash, K. B. 2016. Assessing Tourist Infrastructure in Goa: A Gap Analysis. Anais Brasileiros De Estudos Turisticos (Brazilian Annals of Tourism) 6(1):50–65. Dranove. D., White, W. D., & Wu, L. 1993. Segmentation in Local Hospital Markets. Medical Care 31 (1):52–64. Dsouza, R. S., Subhash, K. B., Chen, R. F., & Weiermair, K. 2018. Service Quality and Customer Satisfaction: An Empirical Analysis of Banking Sector in Goa. International Journal of Banking, Risk and Insurance 6(2):1–22. Duggirala, M., Rajendran, C., & Anantharaman, R. 2008. Patient-Perceived Dimensions of Total Quality Service in Healthcare. Benchmarking 15(5):560–583. Escarce, J. J. & Kanika, K. 2009. Do Patients Bypass Rural Hospitals Determinants of Inpatient Hospital Choice in Rural California. Journal of Health Care for the Poor and Underserved 20(3):625–644. Gaonkar, D. N., Castanha, J., Subhash, K. B., Chang, L. & Chen, R. 2018. Importance Performance Analysis of Telecommunication Industry. Proceedings of the 6th International Seminar & Conference on Learning Organisation – ISCLO 2018 (pp. 29–43). Telkom University Bandung, Indonesia.

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Health is Wealth Quotes 2019. Quotes on Health is Wealth. https://www.indiacelebrating.com/quotes/ health-is-wealth-quotes/ (accessed November 9, 2019). Irfan, S. M. & Ijaz, A. 2011. Comparison of Service Quality between Private and Public Hospitals: Empirical Evidences from Pakistan. Journal of Quality and Technology Management 4(1):1–22. Kalajaa, R., Myshketab, R., & Scalerac, F. 2016. Service Quality Assessment in Health Care Sector the Case of Durres Public Hospital. Procedia-Social and Behavioral Sciences 235:557–565. Karekar, P., Tiwari, A., & Agrawal, S. 2015. Comparison of Service Quality between Private and Government Hospitals: Empirical Evidences from Yavatmal City Maharashtra. International Journal of Advance Research in Computer Science and Management Studies 3(6):39–43. Martilla, J. A. & James, C. 1977. Importance-Performance Analysis. Journal of Marketing 41(1):77–79. Minviellea, E., Waellib, M., Sicottec, C. & Kimberly, J. R. 2014. Managing Customization in Health Care: A Framework Derived from the Services Sector Literature. Health Policy 117:216–227. Parasuraman, A., Zeithaml, V. A., & Berry, L. L. 1985. A Conceptual Model of Service Quality and Its Implications for Future Research. Journal of Marketing 49(4):41–50. Sharma, V. 2017. Patient Satisfaction and Brand Loyalty in Health-Care Organizations in India. Journal of Asia Business Studies 11(1):73–87. Singh, R. G. & Shah, M. K. 2011. Customers Preference for Selecting Private Hospital: A Study in Manipur. Management Convergence 2(2):41–50. Yousapronpaiboon, K. & Johnson, W. 2013. Comparison of Service Quality between Private and Public Hospitals in Thailand. International Journal of Business and Social Science 4(11):176–184. Zeithaml, V. A. & Berry, L. L. 1988. SERVQUAL: A Multiple-Item Scale for Measuring Customer Perceptions of Service Quality. Journal of Retailing 6(41):12–40. Zeithaml, V. A., Berry, L. L., & Parasuraman, A. 1993. The Nature and Determinants of Customer Expectations of Service. Journal of the Academy of Marketing Science 12(1):1–12.

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Digital Economy for Customer Benefit and Business Fairness – Anggadwita & Martini (eds) © 2020 Taylor & Francis Group, London, ISBN 978-0-367-47722-6

What constitutes brand loyalty in e-tailing? M. Carrasco, N. Talkar, S.K.B. Pillai & J. Castanha Goa Business School, Goa University, Goa, India

Indrawati Faculty of Economics and Business, Telkom University, Bandung, Indonesia

ABSTRACT: Bringing customers close to e-web stores using appropriate technology leads to brand loyalty, but only if the factors influencing brand loyalty in e-tail industry are known. The present study identified nine factors responsible for brand loyalty: quality of product, quality of service, variety, time, offers, discounts, return policy, price, and delivery charges. Further, mean analysis revealed that demographic variables and brand loyalty factors do have relationships, namely, age has a significant impact on quality service and return policy; educa­ tion has a significant impact on quality product; income has a significant impact on quality product, quality service, and time duration; and occupation has a significant impact on quality service, time duration, and price. Thus focusing more and improving on these brand loyalty factors can build a strong brand loyalty among the existing customers and will also attract the potential customers, which will enhance the competitive advantage over other e-web stores.

1 INTRODUCTION In the present technological era, e-markets are becoming more aggressive and competitive towards improving the brand loyalty so as to maintain existing customers as well as to attract potential customers. Many new e-tail enterprises are coming up with new strategies and pol­ icies in order to attract customers. Every e-tailing organization now realizes the significance of retaining its existing customers by building a long-term relationship. They must not only try to attract new customers but also have to maintain a good relationship with the existing cus­ tomers, thereby creating brand loyalty among the customers, because preserving a long-term relationship with an organization by evaluating the organization’s core product and services (Yen & Gwinner, 2003) ensures its very survival and success. The increasing use of the internet by the younger generation in India is creating large opportunities for online retailers. India (461 million users) is the second largest online market, behind China (765 million users) but ahead of the United States (244 million users), which is expected to increase to 635.8 million during the next 2 years (IAMAI, 2017; Statistica, 2019; Wikipedia, 2019a). People are busy in their day-to-day lives and have to keep time for every activity. When it comes to manual shopping, much time is wasted in traveling, visiting, and selecting particular products and services. A solution for saving time is e-web stores, which came to the rescue of people as they do not have to travel, can comfortably sit at home or at the workplace, and place an order. This has led to an exponential growth in e-tailing in India and the need of the hour is to study whether customers’ purchase decisions are influenced by e-web store brand and whether they will remain loyal to that particular e-web store over a period of time. A question arises regarding how technology can reduce the distance between consumers and the e-tailing industry; hence the present study attempted to measure the factors influencing brand loyalty in the e-tailing industry.

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2 LITERATURE REVIEW Customers may demonstrate loyalty to brands, activities, services, product categories, or stores or a positive attitude may be shown by customers toward a brand that may sometimes lead to a connection with it (Uncles et al., 2003). There are three conditions to brand loyalty: first, the customer has a positive attitude toward a brand; second, the customer has a commitment to the brand; and third, the customer intends to continue purchasing from the same brand in the future (Mowen & Minor, 1998). Some of the general indicators of brand loyalty shown by customers includes saying positive things, recommending other parties, encouraging friends and relatives to buy the product of particular brand, considering the brand as the first choice to buy services, and doing more business with the same brand (Grem­ ler & Brown, 1996). Brand loyalty is the willingness of a person to continue buying the same products and services in the present as well as in the future instead of shifting to other brands. Customers tend to be loyal when their expectations are met; hence the aim of every business must be to ensure complete customer satisfaction to secure a continued association between the customer and the business. 2.1 E-tailing and demographic profiling E-tailing is a shortened form of electronic retail, or e-shopping or online store. An online store may also be called as e-web-store, e-shop, e-store, internet shop, web-shop, web-store, online store, online storefront, and virtual store (Wikipedia, 2019b). Growth and development of technology helped businesses in adopting e-commerce in order to sell their products and ser­ vices to their customers in a better and faster way. Most of the goods and services are now available online, through various e-tailers, providing a variety of choices to the customers. When it comes to e-web stores, wide options are available to customers, namely players like Amazon, Myntra, Flipkart, Jabong, Brand factory, etc. which allow men, and women, to shop for fashion and lifestyles, and also allows purchase of any products from A to Z including shoes, clothing, electronics, and accessories, for which one need only the app on their smartphone. All e-web stores have their own terms and conditions and benefits to offer to their customers, thus becoming the favorites of each customer. In online shopping, e-loyalty is defined as a customer’s favorable attitude and commitment toward the online retailer that results in repeat purchase behavior (Anderson & Srinivasan, 2003). The gender of customers was found to have some effect on online shopping, with men find­ ing online shopping more pleasing than women did. Most of the customers who prefer online shopping do have proficiency in the English language (Basahih, 2013). A significant relation­ ship between attitudes toward online retail shopping, gender of online shoppers, and purchase intention for men was also identified. Women prefer offline shopping whereas online shopping is oriented toward men (Davis et al., 2012). Age, education, and also income influence tech­ nology adoption, as early adopters of online shopping are likely to be well educated, with higher household incomes (Chen et al., 2002). Every customer has his or her own preferences when it comes to liking or disliking a certain product and brand. Studying the e-web stores preferred by the customers will help the e-tailers to customize their services depending on cus­ tomers’ demographic profiles. Knowing the customer preferences with respect to their online shopping also provides much needed information to the e-tailers in developing effective mar­ keting strategy. 2.2 What constitutes brand loyalty for e-web stores? Brand is one of many factors that may influence customers’ purchasing behavior because of the uniqueness and specialty of the product. Store brand can be determined as private label, own label, or retail brand (Huang & Huddleston, 2009). A good brand name or symbol should be able to deliver product image to its customers and has value added in it, which should also be identifiable (Palumbo & Herbig, 2000). This being the case, for some categories 79

of goods and services, brand name is more important in online shopping than in traditional shopping environment but depends on what attribute information is available (Degeratu et al., 2000). E-tailing brand loyalty is lower when it comes to online grocery shopping, where online shoppers select from a smaller set of brands, thereby remaining loyal to a smaller number of brands (Andrews & Currim, 2004). During the purchasing stage, product assortment, post-sale services, and information quality seem to be the most important points to help consumers decide what product they should select, or what seller they should buy from (Koo et al., 2008). Post-purchase behavior is also crucial in online purchasing. Consumers sometimes have a problem or concern about the product, or they might want to change or return the product that they have bought. Thus, return and exchange services become more important at this stage (Liang & Lai, 2002). In the present study an attempt was made to identify the influencing factors that will affect the brand loyalty for e-web stores and also studied whether demographic variables have any influence on those identified factors. Hence the following hypothesis is developed. H1: Demographic variables do not have an impact on those factors influencing brand loy­ alty for e-web stores.

3 METHODOLOGY To know the demographic variables of e-web store consumers, their online shopping prefer­ ences, and also influencing factors of brand loyalty, a structured questionnaire was adminis­ tered during August 2017 to March 2018 among 250 respondents based on convenience sampling, of which 200 usable questionnaires were received back with a response rate of 80%. There were two sections, the first section being the demographic profiles and online shopping preferences. The second section included 32 statements, for assessing factors influencing brand loyalty for e-web stores, where customers were asked to rate the extent to which they strongly disagree (1) to strongly agree (5) on 5-point Likert scale. Exploratory Factor Analysis (EFA) was used to identify the various factors influencing the brand loyalty for e-web stores. Subsequently with the help of a means test, t-test, and F-test, we tried to find out if there is any association between factors influencing the brand loyalty factor for e-web stores and cus­ tomers’ demographic variables.

4 ANALYSIS AND DISCUSSION 4.1 Who are the e-tail customers and what are their preferences? The composition of the respondents shows that the majority (66%) are women (Davis et al., 2012). When it comes to age, young people dominated (87% of women and 88.5% of men) below the age of 30 years, which is an indication that they are more involved in online shopping than their older counterparts, because technology adoption is faster and easier among younger people. Level of education is also high, as 66.6% of the men and 77.9% of the women have at least a college degree (Chen et al., 2002; Basahih, 2013). With respect to marital status, the majority of respondents are unmarried, of which 78.3% are men and 54% are women, which also indi­ cates the purchasing power available with the younger people because of a low dependency ratio among the Indian population. Many studies show that young people have a high ten­ dency to become early adopters of online shopping. The majority of the respondents fall under the lower income category (58% are men and 62.6% are women). One of the limitations of the study is that with respect to occupation, the majority of the respondents are students (53.6% are men and 67.2% are women), which may be taken care of in subsequent studies in the future. With respect to annual spending of respondents on online shopping, the majority of men and women spent between 75 and 140 USD.

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When it comes to preferences of customers (see Table 1) with respect to the e-web store, Amazon ranks first (39.5%) followed by Flipkart (35%) and Myntra (18%), with the remain­ ing ones not having any significant share. The main reason for doing online shopping was found to be the savings in time (26.8%) from a busy schedule, the types of discounts (24.8%) offered during various festival times, the variety of items (23.4%) to choose from, and the convenience (18.2%) of doing online shopping anytime from anywhere. These are the main reasons considered as influencing factors for making an online shopper loyal to any e-web store. Most of the respondents were of the opinion that they are influenced by the frequent advertisements (40%) provided by the e-web stores, based on customers’ past buying behavior using data mining through deep learning of the artificial intelligence. This is in a way beneficial to the customers as they can see numerous price discounts and quan­ tities being offered through the advertisements that are not available in traditional shop­ ping. But there were many reasons for dissatisfaction pointed out by customers, mainly the quality (32.6%), product delay (18.7%), out of stock (18.2%), no match (12.3%), and high price (10.2%). The first three are very crucial and need to be tackled properly by all the e-web stores, because quality can be assessed or verified only once the product reaches the customer’s hands, but not all products have a return policy. Only those suppliers who can deliver quality products on time need to be identified and promoted for the benefit of customers, which leads to the ultimate aim of generating brand loyalty.

Table 1. E-tail customer preference (multiple responses). Particulars

Response (No. and %)

Preferred E-web Stores Amazon Myntra Flipkart Snapdeal Jabong Others

79 36 70 06 05 04

39.5 18.0 35.0 03.0 02.5 02.0

Reason For Preference Time saving Convenient More variety Discounts Others

119 81 104 110 30

26.8 18.2 23.4 24.8 06.8

Purchase Influenced by Friends Relatives Advertisements Others

55 34 80 31

27.5 17.0 40.0 15.5

Dissatisfied because of Product delay No match Out of stock High price No variety Quality Others

35 23 34 19 01 61 14

18.7 12.3 18.2 10.2 00.5 32.6 07.5

Source: Analysed and compiled based on primary data.

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4.2 What constitutes brand loyalty and the influence of demographic variables? The EFA for the 32 variables resulted in nine constructs/factors responsible for brand loyalty of e-web stores, namely (F1) quality product (α = 0.804), (F2) quality services (α = 0.801), (F3) var­ iety (α = 0.760), (F4) time duration (α = 0.672), (F5) offers (α = 0.675), (F6) discounts (α = 0.652), (F7) return policy (α = 0.676), (F8) price (α = 0.644), and (F9) delivery charges (α = 0.551). All nine factors were found to have acceptable reliability statistics individually and the overall reliabil­ ity was found to be α = 0.845. The Kaiser-Mayer-Olkin (KMO) test of sample adequacy was found to be 0.753 and the total variance explained was 63.71%; hence further mean analysis was carried out to see if there are any linkages between the demographic variables and the nine identi­ fied influencing factors for brand loyalty. The result of the means test (see Table 2) reveals that the demographic variables education and income do have a significant influence on (F1), quality of the product. This indicates that highly educated as well as high-income customers consider quality when it comes to deciding whether to have brand loyalty toward e-web stores or not. When it comes to (F2), quality of services, three demographic variables show significance: age, income, and occupation. Those falling below 30 years of age who are students from high-income families tend to be loyal toward services provided by the e-web stores. There is no effect of (F3), variety, on any of the demographic variables, which shows that whether e-web stores provide variety goods and services or not, the loyalty of customers is not influenced. When it comes to the delivery time, (F4) time duration, income and occupation do have an impact, which indicates that students falling under a low-income category consider time duration as an influencing factor for becoming loyal to the e-web store, as they want to satisfy their postponed needs at the earliest possible time. With respect to (F5), offers and (F6) discounts, there was no significant influence shown by any of the demographic variables. This also indicates that though customers tend to buy more based on the special offers and discounts, they never consider this as a deciding factor for becoming loyal to any of the e-web store; they keep on shifting between as long as they are satisfied with the best offers and discounts because these offers and discounts keep on chan­ ging between e-web stores from time to time during different festivals. In the case of (F7), return policy, age is a determining factor of becoming loyal, as younger people tend to be more loyal toward those e-web stores that provide an easy return policy. When it comes to (F8), price, occupation does have an influence, because students with low income tend to buy from those e-web stores providing value for money offers. Finally, when it comes to (F9), delivery charges, none of the demographic variables shows any influence, as this aspect is considered as not significant as long as the product is delivered on time and has the standard quality at an affordable price. The delivery charges are considered as lower than the time and money spent on buying from traditional stores. Based on these findings, it can be concluded that the formulated hypothesis H1: “Demo­ graphic variables do not have an impact on those factors influencing brand loyalty for e-web stores” is rejected, as only gender and marital status showed an insignificant result, whereas

Table 2. Mean analysis (t-test and F-test: p-values of nine factors are shown). Demographic variables

F1

F2

F3

F4

F5

F6

F7

F8

F9

Gender Age Education Marital status Income Occupation

0.92 0.35 0.04* 0.93 0.01* 0.26

0.16 0.01* 0.56 0.10 0.03* 0.03*

0.07 0.27 0.35 0.29 0.59 0.35

0.35 0.59 0.19 0.29 0.00* 0.00*

0.66 0.42 0.21 0.15 0.40 0.81

0.07 0.65 0.84 0.55 0.38 0.36

0.31 0.05* 0.07 0.62 0.62 0.15

0.81 0.86 0.09 0.27 0.28 0.05*

0.74 0.69 0.99 0.31 0.32 0.44

Source: Analysed and compiled based on primary data. * Significant at 0.05.

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age, education, income, and occupation showed a significant influence on the factors leading to brand loyalty for e-web stores. From the preceding discussion it is clear that demographic variables influence the behavior of customers when it comes to brand loyalty. The management of e-web stores must focus on understanding who the customers are and what exactly they consider important when it comes to buying products and services online. If the managements of e-web stores are able to find answers to these two basic questions of marketing, they will be in a position to proactively decide about positioning their e-web stores strategically, which in turn will result in attracting more and more customers and finally lead to building a good brand image resulting in brand loyalty in the near future.

5 CONCLUSION Initial results revealed that the three e-web stores most trusted by customers are Amazon, Flipkart, and Myntra. The reasons for doing online shopping were mainly time savings, dis­ count offers, variety of products available, and convenience, which are in line with earlier studies. Nine factors were identified that influence brand loyalty for e-web stores. Moreover, for the majority of the factors there exist significant differences across demographic variables. Thus, it becomes very important for all e-web stores to improve their services in order to retain customers. Owing to developments in technology it is easy to customize the services provided by the e-web stores. Hence in order to build a strong brand loyalty among their cus­ tomers, e-web stores have to consider all the aforementioned factors and customize their ser­ vices based on the demographic variables and needs of their customers. The delivery charges should be reduced and the return policy should be simplified, thereby building strong brand loyalty among existing customers.

6 LIMITATIONS AND DIRECTIONS FOR FURTHER RESEARCH A future study based on a random sample including a greater number of multistakeholders might give a confirming picture. An intercountry, interregional study may also provide useful tips for comparative analysis to know the different marketing strategies followed by the e-web stores, influencing factors of brand loyalty, as well as satisfaction levels among customers. This provides better insights into how and in what way customers behave when it comes to e-web stores. This might provide much needed inputs for understanding levels of customer satisfaction/dissatisfaction between countries and also knowing about the preferred brand. One can also extend the study by assessing the UTAUT 2 model in the e-tailing industry for understanding customer behavior when it comes to technology adoption. REFERENCES Anderson, R. E. & Srinivasan, S. 2003. Satisfaction and e-Loyalty: A Contingency Framework. Psych­ ology and Marketing 20(2):123–138. Andrews, R. L. & Currim, I. S. 2004. Behavioral Differences between Consumers Attracted to Shopping Online vs. Traditional Supermarkets: Implications for Enterprise Design and Strategy. International Journal of Internet Marketing and Advertising 1(1):38–60. Basahih, E. O. 2013. An Explorative Analysis of Electronic Retailing Customer Adoptions in the Context of Saudi Arabia. University of Ottawa. https://ruor.uottawa.ca/bitstream/10393/26120/1/Basahih_E man_2013_Thesis.pdf (accessed April 27, 2019). Chen, L. D., Gillenson, M. L., & Sherrell, D. L. 2002. Enticing Online Consumers: An Extended Tech­ nology Acceptance Perspective. Information and Management 39(8):705–719. Davis, R., Lang, B., & Diego, J. S. 2012. Does Gender Mediate Online Shopping Attitudes and Purchase Intentions? Marketing Academy Conference Proceedings. http://pandora.nla.gov.au/pan/25410/20140311­ 1105/anzmac.org/conference/2012/papers/081ANZMACFINAL.pdf (accessed April 27, 2019).

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Degeratu, A., Rangaswamy, A., & Wu, J. 2000. Consumer Choice Behavior in Online and Traditional Supermarkets: The Effects of Brand Name, Price, and Other Search Attributes. International Journal of Research in Marketing 17(1):55–78. Gremler, D. D. & Brown, S. W. 1996. Service Loyalty Its Nature, Importance, and Implication. In Edvardsson, B., Brown, S. W., Johnston, R. & Scheuing, E. E. (Eds), Proceedings American Marketing Association 171–180. Huang, Y. & Huddleston, P. 2009. Retailer Premium Own-Brands Creating Customer Loyalty through Own-Brand Products Advantage. International Journal of Retail & Distribution Management 37 (11):975–992. IAMAI. 2017. Mobile Internet Report 2017. Internet and Mobile Association of India. https://cms.iamai. in/Content/ResearchPapers/2b08cce4-e571-4cfe-9f8b-86435a12ed17.pdf (accessed April 30, 2019). Koo, D. M., Kim, J. J., & Lee, S. H. 2008. Personal Values as Underlying Motives of Shopping Online. Asia Pacific Journal of Marketing and Logistics 20(2):156–173. Liang, T. P. & Lai, H. J. 2002. Effect of Store Design on Consumer Purchases: An Empirical Study of Online Bookstores. Information & Management 39(6):431–444. Mowen, J. C. & Minor, M. 1998. Consumer Behaviour, 5th edition. Upper Saddle River, NJ: Prentice Hall. Palumbo, F. & Herbig, P. 2000. The Multicultural Context of Brand Loyalty. European Journal of Innov­ ation Management 3(3):116–124. Statistica, 2019. Internet Usage in India – Statistics & Facts. https://www.statista.com/topics/2157/inter net-usage-in-india/(accessed April 30, 2019). Uncles, M. D., Dowling, G. R., & Hammond, K. 2003. Customer Loyalty and Customer Loyalty Programs. Journal of Consumer Marketing 20(4):294–316. Wikipedia, 2019a. List of Countries by Number of Internet Users. https://en.wikipedia.org/wiki/List_of_ countries_by_number_of_Internet_users (accessed April 30, 2019). Wikipedia, 2019b. Online Shopping. https://en.wikipedia.org/wiki/Online_shopping (accessed September 18, 2019). Yen, H. J. B. & Gwinner, K. P. 2003. Internet Retail Customer Loyalty the Mediating Role of Relational Benefits. International Journal of Service Management 14(5):483–500.

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Digital Economy for Customer Benefit and Business Fairness – Anggadwita & Martini (eds) © 2020 Taylor & Francis Group, London, ISBN 978-0-367-47722-6

The effect of social media communication on brand awareness and perceived quality Indrawati & W. Ardhana Faculty of Economics and Business, Telkom University, Bandung, Indonesia

J. Castanha Goa Business School, Goa University, India

ABSTRACT: The present study aimed to examine the effect of social media communication on brand awareness. The data were collected from 400 valid respondents in Indonesia by using a purposive sampling technique. The data were analyzed using Smart Partial Least Squares software. The result revealed that firm-created social media communication and usergenerated social media communication have a significant influence on brand awareness. Fur­ ther, brand awareness has a positive and significant impact on perceived quality. The results also indicated that the demographic variables of age, gender, and education do not moderate the effect of social media communication on brand awareness. Thus by focusing more on social media communication, a company not only can enhance the awareness of its products and services but can also improvise on the quality by using the feedback provided by the cus­ tomers, thereby building a strong brand loyalty among existing and potential customers.

1 INTRODUCTION Social media facilitates the creation and sharing of ideas, information, and other forms of expression via networks, as a result of which its usage is increasing every day. There are many platforms in social media that help users to interact with each other, including Facebook, Twitter, YouTube, Instagram, Linkdln, Pinterest, etc. According to the Association of Inter­ net Service Providers of Indonesia or Asosiasi Penyelenggara Jasa Internet Indonesia (APJII), 87.40% of internet users use social media in Indonesia (Kajian, 2014). Today even companies have started using social media, as it provides many benefits. Compan­ ies using social media can build strong relationships with their customers at low cost (Kaplan & Haenlein, 2010) by interacting with their customers; even customers can interact with other cus­ tomers (Mangold & Faulds, 2009). They can thereby increase brand recognition and build brand loyalty and perceived quality (Schivinski & Dabrowski, 2015; Zai, 2015). In Indonesia, many years ago PT Telkom (Telkom) started using Twitter to interact with customers about its products and services. Thus, there arises a need to study the influence of social media communi­ cation on brand awareness and perceived quality. This article investigates the impact of firmcreated and user-generated content on the social networking site Twitter on brand awareness and also on perceived quality with respect to Indihome (one of Telkom’s products). 2 LITERATURE REVIEW

2.1 Social media marketing Social media marketing focuses on creating a content that attracts attention of many users through social media sites and enables them to share the information among their friends, 85

families, and relatives. It attracts huge traffic if the content used on social media sites is visu­ ally pleasing (Zai, 2015). Companies can take advantage of this to advertise and provide infor­ mation about their products, which will increase brand awareness among various customers. Companies must try to change their business from hard selling into an effort to build connec­ tions with customers as much as possible (Gordhamer, 2009). In addition, they should put for­ ward small actions rather than big campaigns, as these will help them to reach more customers and achieve goals in a short time and also at effective cost. 2.2 Firm-created social media communication In most internet applications, Web 2.0 technology is being used that helps users to find informa­ tion about an online product very easily. One of these is social media. Social networks through online media are understood as the diversity of digital information sources created, initiated, streamed, and consumed by internet users as a form of providing insight to other users about the product, brand, service, and its issues (Chauhan & Pillai, 2013). Today companies have realized that by using social media communication they can build a two-way relationship with their cus­ tomers and also encourage them to interact and provide valuable feedback (Li & Bernoff, 2011). Social media offers many new ways for companies and customers to connect and to interact with each other. Thus, corporate marketing managers expect firm-created social media communi­ cations to interact with loyal customers, influence consumers’ perceptions of products, provide information, and get their feedback (Brodie et al., 2013). Today’s customers are using more social media to seek information and opinions relating to brands and products; thus companies are also switching from traditional media to social media (Garvin, 1983; Mangold & Faulds, 2009; Kaplan & Haenlein, 2010; Bambauer-Sachse & Mangold, 2011), as it has greater capacity to reach the general public (Keller, 2009; Li & Bernoff, 2011). 2.3 User-generated social media communication User-generated content is media content created or produced by the general public and distributed over the internet (Daugherty et al., 2008). Today’s social media make use of Web 2.0 technology, which allows users to create the content (Berthon et al., 2012), exchange the content they created (Kaplan & Haenlein, 2010; Kotler & Keller, 2012), and also discuss and forward the content to other users (Mangold & Faulds, 2009). User-generated content must meet three criteria: first, the content is published on the website or social network accessible to the public or a group of people; second, the content contains creative elements; and third, the content is not made by pro­ fessionals. Thus, by using social media communication users can share their experiences about the product, rate the product, review the product, and also get other information about the product. Hence consumers promote the product indirectly through their positive or negative reviews. 2.4 Brand awareness Brand awareness is the ability of consumers to recognize or remember the brand in different situations (Aaker, 1996). Brand name is the most important element in brand awareness that plays an important role in purchase intention, as customers tend to purchase a well-known and familiar product. Purchase decision will be affected through brand association, and when the product owns a positive brand image, it will help in marketing activities (Keller, 1993). Thus, marketing managers can use social media to promote their brand name and awareness among customers, as it has positive impact on purchase decisions. 2.5 Perceived quality Perceived quality is the perception of the customers toward the overall quality or superiority of a product or service (Aaker, 1996) and judgment on the consistency of a product specifica­ tion or an evaluation of the added value of a product (Bhuian, 1997). In order to judge the 86

quality of the product consumers will use their previous experience, education level, perceived risk, purchase purpose, knowledge, and feelings to evaluate product benefits, technology, dur­ ability, and reliability while purchasing a product. Thus, a company can showcase all the product features on social media, thereby enabling customers to judge the product positively; it will also influence their purchase decision. 2.6 Research model and hypothesis design Brand awareness has a significant and positive influence on brand loyalty and perceived qual­ ity (Chi et al., 2009). Social media also has a significant impact on brand loyalty. Thus, in this study, based on literature review, two independent variables were identified: firm-created social media communication (FCSMC) and user-generated social media communication (UGSMC) (Bruhn et al., 2012; Schivinski, 2013, Schivinski & Dabrowski, 2015). Perceived Quality (PQ) was the dependent variable (Bruhn et al., 2012; Schivinski, 2013), while the inter­ vening variable was Brand Awareness (BA) (Schivinski, 2013). The research model is shown in Figure 1. The model uses three moderation variables—age, gender, and education—that were not studied in the previous research. For the study three main hypothesis and six sub-hypotheses were developed as shown in Table 1.

Figure 1.

Research model.

Table 1. H1 H1a H1b H1c H2 H2a H2b H2c H3

Hypotheses of the study. FCSMC has a positive impact on BA.

The influence of FCSMC on BA is moderated by age.

The influence of FCSMC on BA is moderated by gender.

The influence of FCSMC on BA is moderated by education.

UGSMC has a positive impact on BA.

The influence of UGSMC on BA is moderated by age.

The influence of UGSMC on BA is moderated by gender.

The influence of UGSMC on BA is moderated by education.

BA has a positive impact on PQ.

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3 METHODOLOGY In order to study the effect of social media on brand awareness and perceived quality, a structured questionnaire was developed and distributed among 400 Twitter users by using purposive sampling. The questionnaire was divided into two sections; the first dealt with the demographic profile and the second section with the factors influencing social media. Fif­ teen statements were identified that were divided into four constructs: firm-created social media communication (four statements), user-generated social media communication (four statements), brand awareness (four statements), and perceived quality (three statements), where respondents were asked to rate each statement using a 5-point Likert scale, with 1 for strongly disagree and 5 for strongly agree. In order to analyze the data, descriptive statistics, Confirmatory Factor Analysis (CFA), and SEM-PLS techniques were used.

4 ANALYSIS AND DISCUSSION The composition of the respondents shows that the majority are men (55.25%). With respect to age, young people dominated (63.75%), which clearly shows that more young people use social media as compared to older people. With respect to education, the majority of the respondents had higher education (73.75%). The data were initially tested for validity and reliability using Cronbach’s Alpha (CA) and Composite Reliability (CR), and Convergent Validity (CV) was also tested with the help of Average Variance Extracted (AVE) shown in Table 2, which clearly indicates that all 15 statements of the four constructs are valid and reliable, as the CA and CR values are greater than 0.70 and the AVE value is greater than 0.05 (Ghozali, 2014; Indrawati, 2015, 2017). Table 3 shows the result of one latent variable influencing another latent variable; based on the structured model and using the bootstrap procedure the path coefficients and t-test values are obtained. The relationship between constructs is said to be significant if the t-value is greater than 1.64 (one-tailed test). Hence, the formulated hypotheses (H1, H2, and H3) are accepted and it can be said that firm-created social media communication and user-generated social media communication have positive and significant impacts on brand awareness and brand awareness significantly influences perceived quality (Schivinski, 2013; Shojaee & Azman, 2013; Schivinski & Dabrowski, 2015). The inner model test result gave an R2 value of firm-created social media communication and user-generated social media communication as 32%, which indicates that this model has weak predicting ability of factors affecting brand awareness. The R2 value of brand awareness

Table 2. CA, CR, and AVE. Construct

CA

CR

AVE

FCSMC UGSMC BA PQ

0.92 0.94 0.86 0.89

0.94 0.95 0.91 0.93

0.80 0.85 0.71 0.82

Table 3. Path, coefficients, t-value, and hypothesis status. Path

Coefficients

t-value

Hypothesis

FCSMC ➔ BA UGSMC ➔ BA BA ➔ PQ

0.341 0.255 0.583

3.902 2.927 14.110

H1 Accepted H2 Accepted H3 Accepted

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Table 4. Moderation effect and hypothesis status. t-value Path

Age

Gender

Education

Hypothesis

FCSMC ➔ BA UGSMC ➔ BA

0.21 0.17

0.06 0.99

0.17 0.20

H1a,b,c Rejected H2a,b,c Rejected

on perceived quality is moderate (34%). To test the sub-hypotheses of the effects of three mod­ erating variables—age, gender, and Education—the Chin formula was used, which indicates that the t-value must be equal or greater than 1.96. Table 4 indicates that for all three moder­ ating variables the t-value is less than 1.96; thus we reject all sub-hypotheses. The variables age, gender, and education don’t moderate the influence of the variables of firm-created social media communication and user-generated social media communication on brand awareness.

5 CONCLUSION The use of social media is increasing day by day. Whenever a person wants to share anything or express his or her view on any subject, social media is used. As any information may go viral over social media, companies have also started using social media to promote their prod­ ucts, provide information, and also interact with customers. Hence the present study aimed to assess the influence of social media communication on brand awareness and on perceived quality. The results revealed that there exists a positive and significant impact of firm-created social media communication and user-generated social media communication on brand awareness and brand awareness also has a significant impact on perceived quality. While, moderating variables age, gender, and education have no impact in firm-created social media communication and user-generated social media communication on brand awareness. The study has a few limitations that can provide guidelines for future research. This study focused only on one social media, i.e., Twitter, so one can undertake a study by considering all social network sites, which will give better insight. Moreover, only two factors influencing brand awareness were taken into consideration that explain only 34% of variance. Hence, one can study the remaining unobserved factors that will influence brand awareness. Companies can use social media communication in order to provide information to their customers and also to interact with them, as studies show social media do play an important role in any business. Thus, marketing managers can take advantage of the same in order to provide product awareness and also to build brand loyalty among their existing and potential customers. REFERENCES Aaker, D. A. 1996. Measuring Brand Equity Across Products and Markets. California Management Review 38(3): 102–120. Bambauer-Sachse, S. & Mangold, S. 2011. Brand Equity Dilution Through Negative Online Word-of­ mouth Communication. Journal of Retailing and Consumer Services 18(1):38–45. Berthon, P. R., Pitt, L. F., Plangger, K., & Shapiro, D. 2012. Marketing Meets Web 2.0, Social Media, and Creative Consumers Implications for International Marketing Strategy. Business Horizons 55:261–271. Bhuian, S. 1997. Marketing Cues and Perceived Quality Perceptions of Saudi Consumers toward Products of the US, Japan, Germany, Italy, UK, and France. Journal of Quality Management 2(2):217–235. Brodie, R. J., Ilic, A., & Hollebeek, L. 2013. Consumer Engagement in a Virtual Brand Community: An Exploratory Analysis. Journal of Business Research 66(8):105–114.

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Bruhn, M., Schoenmueller, V., & Schafer, D. B. 2012. Are Social Media Replacing Traditional Media in Terms of Brand Equity Creation? Management Research Review 35(9):770–790. Chauhan, K. & Pillai, A. 2013. Role of Content Strategy in Social Media Brand Communities a Case of Higher Education Institutes in India. Journal of Product & Brand Management 22(1):40–51. Chi, H. K., Yeh, H. R., & Yang, Y. T. 2009. The Impact of Brand Awareness on Consumer Purchase Intention: The Mediating Effect of Perceived Quality and Brand Loyalty. The Journal of International Management Studies 4(1):135–144. Daugherty, T., Eastin, M. S., & Bright, L. 2008. Exploring Consumer Motivations for Creating User-Generated Content. Journal of Interactive Advertising Vol 8 No. 2:16–25. Garvin, D. 1983. Quality on the Line. Harvard Business Review 12:65–73. Ghozali, I. 2014. Analisis Multivariate Lanjutan dengan Program SPSSS. Semarang BP Universitas Diponegoro. Gordhamer, S. 2009. 4 Ways Social Media Is Changing Business. Mashable Asia: http://mashable.com/ 2009/09/22/Social-Media-Business/ Indrawati, 2015. Metode Penelitian Manajemen dan Bisnis Konvergensi Teknologi Komunikasi dan Informasi Bandung. Indrawati. 2017. Perilaku Konsumen Individu dalam Mengadopsi Layanan Berbasis Teknologi Infor­ masi & Komunikasi Bandung Refika Aditama. Kaplan, A. M. & Haenlein, M. 2010. Users of the World, Unite! The Challenges and Opportunities of Social Media Business Horizon 53:59–68. Keller, K. L. 1993. Conceptualizing Measuring and Managing Customer-Based Brand Equity. Journal of Marketing 57(1):1–22. Keller, K. L. 2009. Building Strong Brands in a Modern Marketing Communications Environment. Jour­ nal of Marketing Communications 15:139–155. Kotler, P., & Keller, K. L. 2012. Marketing Management. Prentice Hall. Li, C. & Bernoff, J. 2011. Groundswell Winning in a World Transformed by Social Technologies. Boston, M.A: Harvard Business Review Press. Mangold, G. & Faulds, D. J. 2009. Social Media: The New Hybrid Element of the Promotion Mix. Busi­ ness Horizons 52:357–365. Pusat Kajian Komunikasi Universitas Indonesia & APJII. 2014. Profil pengguna internet Indonesia 2014. Schivinski, B. 2013. Effects of Social Media Communication on Brand Equity and Brand Purchase Intention. PhD Interdisciplinary Journal (2):157–162. Schivinski, B. & Dabrowski, D. 2015. The Impact of Brand Communication on Brand Equity through Facebook. Journal of Research in Interactive Marketing 9(1):31–53. Shojaee, S. & Azman, A. 2013. An Evaluation of Factors Affecting Brand Awareness in the Context of Social Media in Malaysia. Asian Social Science 9(17):72–78. Zai, R. Y. 2015. Social Media: A New Trend in e-Marketing. Business Dimensions 2:27–32.

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Digital Economy for Customer Benefit and Business Fairness – Anggadwita & Martini (eds) © 2020 Taylor & Francis Group, London, ISBN 978-0-367-47722-6

Defining lifestyle, consumer culture, and postmodernism in industry 4.0 S.O. Emovwodo, L. Andriamalala, B.A. Rizki & K.A. Suwito Department of Media and Communication, Airlangga University, Surabaya, Indonesia

ABSTRACT: In this age, lifestyle, within the context of consumer culture, places a premium on individuality, self-expression, and a stylistic self-consciousness that is expressed in fashion styles, leisure pastimes, eating or drinking preference, etc., especially among the middle class (petite bourgeoisie). For this class, a commodity ordinarily meant to be consumed has become a symbol signifying taste, status, and luxury. This study aimed to highlight the impact of industry 4.0 in defining identity, lifestyle, and consumer culture. The study confirmed an unprecedented rise of a new petite bourgeoisie in today’s postmodern society, as well as the consumption of commodities as a sign. It concludes that industry 4.0 shares the same charac­ teristics with postmodernism, which is an attempt by the new petite bourgeoisie to change the whole game using technology to disruptively challenge traditional institutions, distinctions, and hierarchies; acknowledge polyculturalism and the popular; and celebrate differences.

1 INTRODUCTION The First Industrial Revolution took place in Britain around the late 1700s with the use of steam to advance factories, railways, steamships, and agricultural methods. The Second Industrial Revolution, recorded around the first two decades of the 20th century, was birthed through the discovery of electricity and distribution of the same. This led to mass production of consumer goods, televisions, cars, and other everyday essentials. The Third Industrial Revolution, around the 1950s and 70s, saw an increased use in technology and digital systems such as internet, computers, etc. With the coming of the Fourth Industrial Revolution, referred to as the melding of different technologies, there is a culmination of technologies from previous revolutions, with speed as the distinguishing factor (Canon, 2016; Davis, 2016). According to Palmer (2014), postmodernism is “best understood as a questioning of the ideas and values associated with a form of modernism that believes in progress and innova­ tion. . .postmodernism does not designate any one style of art or culture. On the contrary, it is often associated with pluralism and an abandonment of conventional ideas of originality and authorship in favour of a pastiche of ‘dead’ styles.” For Featherstone (2007) postmodernism cuts across a broad range of artistic, intellectual and academic fields such as music, fiction, film, drama, photography, literary theory and criticism, philosophy, anthropology sociology, and geography. Thereby confirming that postmodernism has sufficient appeal to interest a larger middle-class audience. Having confirmed postmodernism’s far reaching effect, one would agree that it would impact on the lifestyle of members of today’s modern society. While the term “lifestyle” is trending and fashionable, it has a more restricted sociological meaning in reference to the distinctive style of life of specific status groups. However, when used in the confines of contemporary consumer culture it refers to individuality, self-expression, and a stylistic self-consciousness. One’s body, clothes, speech, leisure pastimes, eating and drinking preferences, home, car, choice of holidays, etc. are to be regarded as indicators of the individuality of taste and sense of style of the owner or consumer (Weber, 1968; Sobel, 1982; Rojek, 1985 in Featherstone 2007).

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For a start, an understanding of the three phrases that highlight the tendencies within con­ sumer culture as pointed out by Stuart and Elizabeth Ewen (1992) is important. They are: “Today there is no fashion: there are only fashions.” “No rules, only choices.” “Everyone can be anyone.” These three phrases simply imply “a movement towards a society without fixed status groups in which the adoption of styles of life (manifest in choice of clothes, leisure activ­ ities, consumer goods, bodily dispositions) which are fixed to specific groups have been sur­ passed” (Featherstone, 2007: 81). From the foregoing, we can see the recent trends within postmodern consumer culture are based upon a profusion of information and proliferation of images that cannot be ultimately stabilized, or classed, and suggesting an irrelevance of social divisions and ultimately the end of “class.” 2 LITERATURE REVIEW Some researchers have come to see consumer culture as one focused on materialism; hence material goods and their production, exchange and consumption are to be understood within a cultural matrix, together with the “embedded economy” drawing attention to the cultural preconditions of economic life (Sahlins, 1974, 1976; Douglas & Isherwood, 1980; Leiss, 1983; Elwert, 1984). In this principle of consumer culture, the commodity or material goods is seen as a symbol that communicates or expresses the users’ lifestyle, status, class, and taste. This signals a movement away from just the satisfaction or utility to be derived from the use of such a commodity. The movement away from regarding goods merely as utilities having a use-value and an exchange-value is best understood with Baudrillard’s theorization of the commodity-sign. To explain this, Baudrillard introduces the notion of “symbolic exchange” or “sign exchange.” Sign exchange is a process in which goods are exchanged as commodities, but with the new elem­ ent of symbolic value, or status, they provide the consumer. Symbolic value presents a new explanatory tool in the discussion of value and is designed to augment the explanatory power of use value and exchange value in a consumption-oriented society (Koch & Elmore, 2006). Sign value emerges as the new key term for analyzing value in a consumer society, one in which the fetishism of commodities is complemented by a new fetishism attached only to the symbolic value of objects. The commodity ordinarily meant to be consumed has therefore now become a sign signifying taste and class luxury, exotica, beauty, and romance, making it incredibly difficult to decipher the original use or function of mundane, everyday consumer goods. For Baudrillard (1983) electronic mass media plays the key role in pushing the commodity as sign narrative. Con­ sumer culture for Baudrillard is effectively a postmodern culture, a depthless culture in which all values have become transvalued and art has triumphed over reality (Featherstone, 2007).

3 METHODOLOGY Purposive sampling was conducted for the interviews. Four consumers of products and ser­ vices in this contemporary society, the mall, were interviewed. To maintain the confidentiality of respondents, the researchers decided to use coding for the key respondents. Interviews were conducted for approximately 25 minutes and they were done as a group, as they were a group of friends all having dinner together. The interview was conducted on April 13, 2019 at Food Court, Galaxy Mall. Further details about the key informants are as follows: 1. 2. 3. 4.

KT, female, 24 years old WK, male, 28 years old LC, female, 16 years old YD, male, 28 years old

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From the questions, the researchers wanted to find out how the respondents make meaning of shopping and eating in the mall as a lifestyle. Researchers were also trying to discover how consumption patterns can be used as a distinction of their identity as new petite bourgeoisie. Observations were carried out at Galaxy Mall, Surabaya. Observations were done twice: on Thursday night, a weekday (April 11, 2019), at 8:00 p.m. and Saturday night (weekend), at 6:00 p.m.

4 DISCUSSION Research results discovered the following discussion from the interviews and observations: 1. The consumers of products and services in contemporary society conduct a little research by reading reviews online, especially on social media such as Instagram, Facebook, or Google Map to determine where to go in order to get the best service or product. This is a characteristic of the new petite bourgeoisie that desires to learn and remain ever curious. 2. Another important note is their consumption pattern. They don’t just consume food or drinks, but also consume information. They read reviews online, checking for the latest promos and ads being offered by restaurants and other business owners such as OVO and GoFood, among others, to get the best deal and quality available at the cheapest rate. They stand distinct from others who probably just consume without recourse to research, thus giving them a higher status. 3. Information consumption includes consideration of hygiene and aesthetics of the environment/place for shopping and dining. Distribution of this information is a way to improve the status of both contributor (online reviewer) and the recipient. The contributor is con­ sidered as knowledgeable and earns a large online following for knowing where to get the best deal at cheap rates, thus increasing his or her symbolic status. The recipient who has followed the recommendations will derive satisfaction and can also become a reviewer. 4. Another consumption pattern is the need to update social media handles with pictures after having visited a new place or tried something new. Initially, contributors don’t consider it important to keep updating their social media handles, but as time goes by, they do so in order to get some level of psychological gratification. 5. Also, a consumer pattern indicative of petite bourgeois is more value for less price. In the past consumers had no problem eating street food and dining at local cafes but now, as a result of exposure and information, they can get what they refer to as more “hygienic” and healthy food at much discounted prices using online payment methods such as gopay, OVO, T-cash, etc. 6. Consumers strive to spread the idea of what they know about such aspects as hygiene and aesthetics to a wider audience via social media by sharing their own experiences.

5 CONCLUSION According to research result and analysis, it is clear that industry 4.0 shares the same charac­ teristics with postmodernism. If a society is postmodern, it must prioritize the consumption of resources in everyday life. In this view, mass media advertising and market dynamics lead us to a constant search for new fashions, new styles, new sensations, and new experiences, which has led to an unprecedented rise of the new middle class. Industry 4.0 and the postmodern era share similar features, as seen in the wide use of technology: a profusion of information and the proliferation of images difficult to categorize or class, thus suggesting an irrelevance of social divisions and ultimately the end of “class.” People are “condemned” to live in a postmodern society, so to enjoy this era, we should learn to live with each other even though we have different lifestyles or identities. That should be a cogent reason to maintain peace and harmony. Postmodernism is the attempt by the new petite bourgeoisie to subvert the whole game by collapsing traditional distinctions and 93

hierarchies, acknowledging polyculturalism and the popular as they fit in with global circum­ stances, and celebrating differences. REFERENCES Baudrillard, J. 1983. Simulations. Semiotext(e) Inc. Cannon, T. 2016. How Will the Fourth Industrial Revolution Affect Us? https://www.redbrickresearch. com/2016/05/26/how-will- the-fourth-industrial revolution- affect-us/(accessed January 17, 2019). Davis, N. 2016. What Is the Fourth Industrial Revolution? https://www.weforum.org/agenda/2016/01/ what-is-the-fourth-industrial-revolution/(accessed January 17, 2019). Douglas, M. & Isherwood, B. (1980) The World of Goods. Harmondsworth: Penguin. Elwert, G. 1984. Markets, Venality and Moral Economy. Mimeo; in conference on Civilizations and The­ ories of Civilizing Processes: Comparative Perspective, University of Bielefeld. Ewen, S. & Ewen, E. 1992. Channels of Desire: Mass Images and the Shaping of American Consciousness. Minneapolis: University of Minnesota Press. Featherstone, M. 2007. Consumer Culture and Postmodernism, 2nd edition. Los Angeles, London, New Delhi, Singapore: Sage Publications. Koch, A. M. & Elmore, R. 2006. Simulation and Symbolic Exchange. Jean Baudrillard’s Augmentation of Marx’s Theory of Value. Politics & Policy 34(3):556–575. Leiss, W. 1983. The Icons of the Marketplace. Journal of Theory, Culture & Society, 1(3):10–21. Palmer, D. 2019. Explainer: What Is Postmodernism? The Conversation. https://theconversation.com/ explainer-what-is-postmodernism-20791 (accessed April 13, 2019). Rojek, C. 1985. Capitalism and Leisure Theory. London: Tavistock. Sahlins, M. 1978. Culture and Practical Reason. Chicago: University of Chicago Press. Sahlins, M. D. 1984. Stone Age Economics. London: Tavistock. Sobel, E. 1982. Lifestyle. New York: Academic Press. Weber, M. 1968. Economy and Society, Vol 3. New York: Bedminster Press.

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Digital Economy for Customer Benefit and Business Fairness – Anggadwita & Martini (eds) © 2020 Taylor & Francis Group, London, ISBN 978-0-367-47722-6

Influencer marketing: Brand awareness and purchase intention on YouTube R.E. Rahman & R.D. Astuti Master of Management, Faculty of Business and Economics, University of Indonesia, Jakarta, Indonesia

ABSTRACT: According to a research conducted by WeAreSocial in 2019, YouTube is the most active social media platform used by Indonesian, for one of its benefits as a media for marketing various kinds of products. People who have big impact on social media are usually called social media influencer. This research focuses at the impact and role of perceived trust in mediating social media influencer value (SMIV) model, which consists of advertising value and source credibility towards brand awareness and purchase intention, with a product that is categorized as high involvement product. The method used in this research is quantitative research by conducting survey process. The data obtained will be analyzed with Structural Equation Model (SEM) method and the result shows that perceived trust did not give any mediating impact. However, SMIV model has some direct effect towards brand awareness and purchase intention. Further results are also discussed in this research.

1 INTRODUCTION A research conducted by WeAreSocial in 2019 shows that most of Indonesian internet users streams contents by watching videos online, with YouTube as the most favorite contentstreaming platform. This phenomenon made tons of social media influencers to enter the plat­ form massively, because YouTube can be a place for them to make money, by using a strategy called influencer marketing. A research conducted by an influencer marketing platform named SociaBuzz titled “The State of Influencer Marketing 2018 in Indonesia” showed that on a scale of 1 to 10, the effect­ iveness of using influencer in achieving business goals is scored 8. Based on the findings above, this research presents an integrated model created by Lou & Yuan (2019) named social media influencer value (SMIV). SMIV model is aiming to account influencer’s advertising value and source credibility towards brand awareness and purchase intention, with perceived trust in branded content as a mediating variable.

2 LITERATURE REVIEW 2.1 Advertising value and source credibility Ducoffee (1996) explained that advertising value is the evaluation of an advertisement’s ability to convey information, functions, and attractiveness offered. Informative value and entertainment value are the factors that affects the advertising value. Informative value refers to the ability of advertisements to provide information that aims to increase consumer purchasing satisfaction, while entertainment value refers to assumptions made in the use and gratification of advertising. Source credibility refers to the ability of the source to persuade the audience (Munnukka et al., 2010), There are four factors that describe source credibility which is expertise, trustworthiness, attractiveness, and similarity. Expertise refers to competence, including a person’s knowledge or skills in making certain claims related to a particular subject or topic. Trustworthiness refers to 95

perceptions of information about source that are honest, sincere, or true. Attractiveness refers to source’s likeability. Similarity refers to the mutual feeling the audiences felt towards the source of information. 2.2 Perceived trust in branded content According to Hudson & Hudson (2006), branded content is described as a content that has is focused on combining entertainment with brands, using ways that did not make the consumers think that they are actually seeing an advertisement. In the online world, trust is defined as form­ ing believe between two parties. In this case, it’s the influencers and their followers (Aljazzaf, Perry, & Capretz, 2010). Therefore, perceived trust in branded content refers to the perception of trust that arises when assessing a branded content (D’Alessandro, Giradi, & Tiangsoongnern, 2012). 2.3 Brand awareness and purchase intention Aaker (1991) describes brand awareness as the ability of consumers to recognize and create a recalling memory of brands in different kinds of situations they’re in. When a consumer is going to buy a product and able to remember the chosen brand, that consumer is already aware of the brand (Padhy & Sawlikar, 2018). Purchase intention is a decision-making process of purchasing goods or services, based on con­ sumers’ experience in learning, choosing, using, or even getting rid of a product (Kotler & Keller, 2009), that includes the possibility of the consumers willing to buy a particular product/service, presumed that it has overtaken actual purchase decision process (de Magistris & Gracia, 2008).

3 METHOD 3.1 Model and hypothesis This research applies a research model conducted by Lou & Yuan (2019) in explaining the role of influencers to affect consumers through social media, and to see whether perceived trust in branded content could or could not mediate the relationship between influencer mar­ keting that consists of advertising value and source credibility towards brand awareness and purchase intention. There are 5 hypotheses that have been developed in this research. H1: Influencer marketing, which consists of a) advertising value and b) source credibility will positively influence con­ sumers’ brand awareness. H2: Influencer marketing, which consists of a) advertising value and b) source credibility will positively influence purchase intention. H3: Perceived trust in branded content has mediating effect on a) advertising value and b) source credibility towards brand awareness. H4: Perceived trust in branded content has mediating effect on a) advertis­ ing value and b) source credibility towards purchase intention. H5: Brand awareness will posi­ tively influence purchase intention. 3.2 Object and subject YouTube is the main scope of this research, with Chandra Liow as the research subject, because he’s doing influencer marketing and have an achievement by being awarded as Top YouTube Channel at the Influence Asia 2017 event. The research object is a branded video content in Chandra’s channel, named “JANGAN PLAGIAT UNBOXING”. The video con­ tains a promotion of Asus ROG laptop that is categorized as a high involvement product. A high involvement product generally has long buying process that involves complex behav­ ior. In addition, this research will find whether a branded content from an influencer with lots of followers could gain more positive effect towards brand awareness and purchase intention or not (De Veirman, Cauberghe, & Hudders, 2017). 96

3.3 Sample, procedure, and measurement The sample in this research are Indonesians with an age range of 18 years old and older, is using YouTube, knows who Chandra Liow is, have seen the research object, and knows about influencer marketing. In order to achieve the research objectives, screening questions is pre­ sented before respondents could participate further. There are 320 respondents that is eligible to fill the main questions. The measurement of this research is using Likert and Semantic Differential Scale, with the main questions extracted from various sources. This research captured influencer’s advertising value and credibility, with items extracted from Voss, Spangenberg, & Grohmann (2003) that were anchored by 6-point Semantic Differential Scale and Munnukka, Uusitalo, & Toivonen (2016) that were anchored by 6-point Likert Scale. Then, this research measured trust in influen­ cer marketing by extracting items from Wu & Lin (2017) that were anchored by 6-point Seman­ tic Differential Scale. To capture brand awareness and purchase intention, items from Yoo, Donthu, & Lee (2000) and Yuan & Jang (2008) have been extracted and anchored by 6-point Likert Scale. This research adopted a structural equation modeling (SEM) path approach to estimate the relationship that has been hypothesized and to explain causalities between constructs. This research also conducted a mediation test, in order to analyze whether perceived trust in branded content could fully, partially, or even cannot mediate relationship between influencer marketing with brand awareness and purchase intention using causal step method that have been explained by Baron & Kenny (1986).

4 RESULTS 4.1 SEM path modeling

Figure 1.

SEM path model.

4.2 Measurement validation and reliability This research uses IBM AMOS 24.0 to perform validation and reliability measurement, along with the structural modeling. The results of reliability analysis showed that Cron­ bach’s Alpha and composite reliability (CR) values were above .70 for all of the latent constructs, indicating reliable measurement instruments. All of the latent constructs’ aver­ age variance extracted (AVE) values were above .50. So, all of the constructs are con­ sidered as valid and reliable. 4.3 Hypothesis testing A goodness of fit test is needed before testing all the hypotheses. Measurement indicators for goodness of fit test is based on CMIN/DF, GFI, and RMSEA. The result shows that research 97

model is acceptable because it shows a good-fit model. The result of the hypothesis test shows that H1a is accepted (t = 2,548 & p = 0,011), but H1b is rejected (t = 1,223 & p = 0,221). Then, H2a is rejected (t = -0,682 & p = 0,495) and H2b is accepted (t = 2,269 & p = 0,023). H3a-b (t = 1,684 & p = 0,092) and H4a-b (t = -0,062 & p = 0,950) are all rejected. Lastly, H5 is accepted (t = 10,547 & p = < 0,01).

5 CONCLUSION Perceived trust in branded content has no mediating effect on influencer marketing towards brand awareness and purchase intention. However, influencer marketing has a positive and significant effect towards some constructs in brand awareness and purchase intention. Lastly, brand awareness has a positive and significant effect on purchase intention, and also can fully mediate the relationship between advertising value in influencer marketing towards purchase intention. 5.1 Managerial implications Influencer marketing can be considered as an effective marketing method. However, before conducting influencer marketing strategy, the brand need to create contents that can be seen as a helpful and useful content. The brand also need to promote the chosen influencer(s) by posting the contents made by the chosen influencer(s) through the brand’s official web and social media to increase the influencer’s interest. Lastly, the brand should make consumers remember their existence and can distinguish them among the other brands. One of the example is the brand can create a personal touch with consumers through social media or sponsoring various activities. 5.2 Limitations and further research There are some limitations in this research. First, respondents who can fill out the research survey are limited to people who knows Chandra Liow and this can lead to bias in the research data. Second, there is only one influencer and one social media platform used as the research object. Lastly, only high involvement product used in this research. A few suggestions that can be done in order to do further research are: not limiting respond­ ents’ knowledge towards influencers, use more than one influencer and social media platform, and also make a comparison between high involvement product and low involvement product. REFERENCES Aaker, D.A. (1991). Marketing Research. Hoboken: John Wiley & Sons. Aljazzaf, Z. M., Perry, M., & Capretz, M. A. (2010, September). Online trust: Definition and principles. In 2010 Fifth International Multi-conference on Computing in the Global Information Technology (pp. 163–168). IEEE. Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psycho­ logical research: Conceptual, strategic, and statistical considerations. Journal of personality and social psychology, 51(6), 1173. D’Alessandro, S., Girardi, A., & Tiangsoongnern, L. (2012). Perceived risk and trust as antecedents of online purchasing behavior in the USA gemstone industry. Asia Pacific Journal of Marketing and Logistics, 24(3), 433–460. De Magistris, T., & Gracia, A. (2008). The decision to buy organic food products in Southern Italy. Brit­ ish food journal, 110(9), 929–947. De Veirman, M., Cauberghe, V., & Hudders, L. (2017). Marketing through Instagram influencers: the impact of number of followers and product divergence on brand attitude. International Journal of Advertising, 36(5), 798–828.

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Ducoffe, R. H. (1996). Advertising value and advertising on the web-Blog@ management. Journal of advertising research, 36(5), 21–32. Hudson, S., & Hudson, D. (2006). Branded entertainment: a new advertising technique or product place­ ment in disguise?. Journal of Marketing Management, 22(5-6), 489–504. Kotler, P. & Keller, K.L. (2009). Marketing Management. Upper Saddle River: Pearson Prentice Hall. Lou, C., & Yuan, S. (2019). Influencer marketing: How message value and credibility affect consumer trust of branded content on social media. Journal of Interactive Advertising, 19(1), 58–73. Munnukka, J., Uusitalo, O., & Toivonen, H. (2016). Credibility of a peer endorser and advertising effectiveness. Journal of Consumer Marketing, 33(3), 182–192. Padhy, S., & Sawlikar, R. (2018). The Role of Brand Equity And Brand Awareness on Consumers’ Pur­ chase Intention. International Journal of Business and Management Invention, 7(1), 12–16. Voss, K. E., Spangenberg, E. R., & Grohmann, B. (2003). Measuring the hedonic and utilitarian dimen­ sions of consumer attitude. Journal of marketing research, 40(3), 310–320. Wu, T. Y., & Lin, C. A. (2017). Predicting the effects of eWOM and online brand messaging: Source trust, bandwagon effect and innovation adoption factors. Telematics and Informatics, 34(2), 470–480. Yoo, B., Donthu, N., & Lee, S. (2000). An examination of selected marketing mix elements and brand equity. Journal of the academy of marketing science, 28(2), 195–211. Yuan, J., & Jang, S. (2008). The effects of quality and satisfaction on awareness and behavioral inten­ tions: Exploring the role of a wine festival. Journal of Travel Research, 46(3), 279–288.

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Strategy formulation to increase the number of citations using concept mapping N. Eva & R. Gadang School of Economics and Business, Telkom University, Bandung, Indonesia

ABSTRACT: This study aims to formulate Telkom University strategy to enter the QS world university ranking registration in 2017. The study concerns Scopus indexed number of citations as one of the QS World Ranking parameters. This study analyzes the strategies that have been taken to increase the number of citations. This study also explores information about the factors that may influence the increase of the number of citations which will serve as the basis in formulating alternative strategies to increase the number of citations. Data col­ lection is conducted through unstructured and semi-structured interviews. Data processing is done employing Concept Mapping, using python based “Orange” data mining software. The finding reveals 4 strategy clusters where cluster 2, the most influential strategy cluster, includes the strategy to improve lecturer quality, and the quality of research content, expand academic networks, and publish in reputable publications. The study found that Concept Mapping tech­ nique is extremely useful to deal with this kind of strategic study.

1 INTRODUCTION Albatch, as a higher education expert from Boston College USA, said that there are two things that can be the reasons why university ranking is very popular today. From government and society point of view, university ranking can represent the accountability of a higher edu­ cation institution. Meanwhile, from university point of view ranking can be a magnet to attract students and also outstanding lecturers. Ranking can indicate the prestige of a university and that is why institution ranking is very popular nowadays. There are a lot of techniques and institutions that provide university ranking. In Indo­ nesia, at least there are four popular techniques, namely: webometric, 4ICU, QS Rank­ ing, and THE ranking. Each of them has its own methodology to provide the rank of university. Indonesia’s ministry of research, technology, and higher education made a policy that QS World University Ranking would be the official and main reference of University ranking in Indonesia. They support higher education in Indonesia to be involved. Mr. Mohamad Nasir also said that there were three higher education institu­ tions in Indonesia that were targeted to get world top 500 in QS ranking. Based on widely important goal (WIG) of Telkom Foundation, the long term goal of Telkom Uni­ versity is to be an international research university. Based on the policy of Indonesia and WIG of Telkom Foundation, Telkom University should have made an attempt to register in QS World Ranking in 2017. There are six parameters in QS World Ranking. Academic reputation has the biggest point, 40%. From the preliminary data analysis, it is found that there is a correlation between cit­ ation and academic reputation. The data showed that it is positively significant of about 80%. It means that citation can affect academic reputation. Based on the portrait of publication in Telkom University in 2017, we have known that there is a huge gap among, especially from the big three (ITB, UI, UGM). Telkom University had 797 publications while ITB had 7852 publications; UI had 7201 publica­ tions and UGM with 4884 publications. As indicated above, we have two major 100

questions. One of them is to formulate strategy to improve citation. The answer towards this question will be taken as an aspect in formulating the new strategy to improve cit­ ation. The objective of this study is formulating alternative strategy to increase the number of citations in Telkom University.

2 LITERATURE REVIEW Citation is a reference to a document given by other earlier documents (Pattah, 2013). Mean­ while, according to Sulistyo-Basuki in Margono (1999), citation is a work referenced and used in blibiography in an article or book. Andriani (2002) states that underlying the existence of citation is the relationship between a work (citing) and another work (cited). Citation is a foundation in the dissemination of science and the main methods of measuring the quality of research, as well as the form of knowledge investment (Silvello, 2018). Based on the above understanding it can be said that citation is a communication system that exists in research ecosystem. According to Wang and Soergel in Andriani (2003), criteria is a filter applied by an individ­ ual in making a decision to cite. Some of the criteria for considering a document to be cited are: a. Topic. The contents of the document should relate to the research being studied by the researcher who will be in the process b. Discipline or subject area. Researchers will likely be looking at documents in the same dis­ cipline as the research being done. c. Pioneering. The document should contain substantial information because a number of documents may contain techniques, methods, or theories used overtime. d. Journal name and document form. e. Author. Documents written by the experts in the related field will be perceive as essential documents by the researcher who will cite. f. Novelty. A document is cited as it contains new or unknown information. g. Publisher. The reputation of the publisher can guarantee the quality of the issue. h. Recency. Updates relate to publishing time. Nicolaisen (2013) explains that the common components of the citation theory consist of a culture of research and writing culture. The culture of research is divided into the problem studied, the theories referred to solve the problem, and the method used while the culture of writing will lead to the citing habits. A combination of these components can produce a research document that is eligible to cite.

3 METHODOLOGY Based on the literature study that has been done, the research will be focused on the formula­ tion stage of functional level strategy that emphasizes the optimization of resources owned by the organization (Sedamaryanti, 2014). This study used concept mapping technique as the research methodology. Concept mapping can be considered as a structured methodology for organizing ideas a group or an organization has, in accommodating diverse groups of stake­ holders and helping them rapidly form a common framework that can be used for planning. Referring to the formulation analysis framework/formulation, then at the input stage, the researchers tried to collect internal and external factors that affect the increase of lecturer’s writing citation with resource based view approach. However, based on Nicolaisen (2013) on the common component of citation theory, they consist of culture in research, culture in the making, and cross citation habit, and according to Ibrahim et al. (2013) the main thing to con­ sider in an effort to improve the number of citations is improving the quality and the sensitiv­ ity. Therefore, the research model of this study may then be described as in the following figure. 101

Figure 1.

Research model.

3.1 Data The study is a verbatim analysis based on interviews with informants. The discussion of the study will be explained in the next sub chapter while the comprehensive form of the transcript of the interview is provided in the attachment section. The explanation on the results of the data analysis conducted by the researchers contains some alternative strategies that can be considered to be implemented as an effort to increase the number of citations.

4 RESULT AND DISCUSSION After collecting the data, the researchers conducted a more in-depth search related to the influence of each factor and strategy which according to the study resource can be applied to improve the citation of lecturer of Telkom University. After that stage weight was given to all factors to determine their influence towards citation. After the weighting, clustering process is conducted with hierarchical clustering method and the result is as follows. a. b. c. d. e. f. g. h. i. j. k. l. m.

Facility Lecturer Quality Research Budget Research Content Writing Skill Leadership Internal Policy Academic Network Reputable Publication Cross Citation Culture Repository Government Policy Technology 102

C

F1

F2

F3

F4

F5

F6

F7

F8

F9

F Value

Sum

Technology Cross Citation Culture Government Policy

C 5 5

14 2 1

1 1 1

1 2 1

1 2 1

1 2 1

1 1 1

1 1 1

1 3 1

4 19 13

15

47

Reputable Publication Lecturer Quality Research Content Academic Network

C2 5 4 4

3 4 5 5

4 4 4 4

4 5 5 4

5 5 5 3

5 5 5 4

5 5 5 4

4 5 5 5

5 5 4 5

5 43 42 38

40

163

Writing Skill Repository Facility

C3 3 3

4 4 3

3 4 5

4 1 3

2 4 3

5 3 4

5 4 4

4 5 3

4 4 4

5 32 32

36

100

Leadership Internal Policy Research Budget

C4 4 4

5 1 1

1 1 4

3 2 2

2 4 4

3 5 4

4 1 4

2 3 4

3 5 3

4 26 30

27

83

5 CONCLUSION The finding reveals 4 strategy clusters to improve the number of citations. The cluster sequence ranging from the most influential to the less influential are cluster 2, 3, 4, and 1. Cluster 2, being the most influential, is strategy to improve lecturer quality, improve the qual­ ity of research content, expand academic networks, and publish in reputable publications. The researchers find that Concept Mapping technique is extremely useful to deal with this kind of strategy study. REFERENCES Andriani, J. (2002). Studi kualitatif mengenai alasan menyitir dokumen: kasus pada lima mahasiswa pro­ gram pascasarjana IPB. Jurnal Perpustakaan Pertanian, 11(2), 29–40. Andriani, J. (2003). Studi Kualitatif Mengenai Kriteria Menyitir Dokumen: Kasus Pada Beberapa Mahasiswa Program Pascasarjana IPB. Jurnal Perpustakaan Pertanian, Vol.12 (1),10–19. Ibrahim, G. M., Carter Snead III, O., Rutka, J. T., & Lozano, A. M. (2012). The most cited works in epilepsy: Trends in the “Citation Classics”. Epilepsia, 53(5), 765–770. Margono, T. (1999). Studi Tingkat Relevansi Penyitiran pada Penerbitan Artikel Bidang Perpustakaan Dokumentasi, dan Informasi. Jurnal Kepustakawanan dan Masyarakat Membaca, 15(1), 9–16. Nicolaisen, J. (2003). The social act of citing: Towards new horizons in citation theory. Proceedings of the American Society for Information Science and Technology, 40(1), 12–20. Pattah, Sitti Husaebah. (2013). Pemanfaatan Kajian Bibliometrik Sebagai Metode Evaluasi dan Kajian Dalam Ilmu Perpustakaan dan Informasi. Khizanah Al-Hikmah, Vol 1(1), 47–57. QS Ranking Methodoly 2017: Methodology. (2017). Methodology. Retrieved from https://www.topuni versities.com/qs-world-university-rankings/methodology, accessed on 26 December 2017. Sedamaryanti. (2014). Manajemen Strategi. Bandung: Refika Aditama. Silvello, G. (2018). Theory and practice of data citation. Journal of the Association for Information Sci­ ence and Technology, 69(1), 6–20.

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The application of supply chain practices for the jeans industry in Cihampelas Muhammad Ramzi Farhan, Soeparwoto Dharmoputra & Rr. Rieka F. Hutami Faculty of Economics and Business,Telkom University, Bandung, Indonesia

ABSTRACT: Through this research, measurement of business actors’ assessment of supply chain management practice variables will be made consisting of nine dimensions, namely: supply chain characteristics, level of information sharing, customer relationship, supply chain integration, quality of information sharing, JIT capabilities, inclusion in strategic decision making, involvement in product quality and development, and mutual trustworthiness. The data collection method was carried out through the distribution of sixty questionnaires to jeans business people in Cihampelas and interviews with three people as supporting data from the questionnaires that were distributed. Data processing was performed using descriptive statistical analysis and Miles and Huberman analysis. The results showed that customer rela­ tionships was the most important of the nine dimensions, while the just-in-time (JIT) capabilities and supply chain integration dimensions were the two least important positions.

1 INTRODUCTION Bandung City has an increasing number of tourist visits every year (Open Data Bandung City, 2017). In the city of Bandung there are also several economic centers that play a role in improving the city’s economy, such as: Cibaduyut, Cihampelas, Cigondewah, Suci, Binong Jati, Cibuntu, and Sukajadi. In accordance with Bandung City’s economic development pol­ icies, in the 2014–2018 RPJMD of the City of Bandung the fourth mission was to build a strong, developed, and judicial economy. One of the goals is to develop Bandung’s excellent MSME products through the strategy of developing and strengthening Bandung’s potential industrial centers. Based on the Decree of the Mayor of Bandung No.530/Kep.295­ DisKUKM.PERINDAG/2009 dated March 3, 2009, which until now has not been changed, the City of Bandung has seven industrial centers that become business centers as well as inter­ national class tourist attractions.

2 LITERATURE REVIEW Heizer and Rander (2014) define supply chain management as the management of activities in the context of obtaining raw materials and turning these into processed goods or semifinished goods and finished goods and then sending the product to consumers through a distribution system. These activities include traditional purchasing functions plus other important activities related to suppliers and distributors (Heizer & Rander, 2004). Banerjee’s (2015) research has defined SCM practices as a series of activities carried out in an organiza­ tion to promote effective supply chain management. In his 2015 journal article entitled “Retail Supply Chain Management Practices in India: A Business Intelligence Perspective,: Banerjee stated that there are nine dimensions in supply chain practices, and, according to him, those nine dimensions are the basis of industry practice:

104

1. 2. 3. 4. 5. 6. 7. 8. 9.

Supply chain charateristics, Level of information sharing, Customer relationship, Supply chain integration, Quality of information sharing, JIT capabilities, Involvement in product quality and development Inclusion in strategic decision making, and Mutual trustworthiness

And here are descriptions of each of the nine dimensions of the supply chain according to Banerjee in his journal: 1. Supply Chain Characteristics: Various important characteristics in the supply chain of each retail company are useful for gaining a competitive advantage. 2. Level of Information Sharing: Sharing information in terms of the urgency and adequacy of the information. 3. Customer Relationship: Proactively fostering reciprocal relationships with customers, so as to be able to conduct a backflow of information that is important to produce a customer-centered supply chain. 4. Supply Chain Integration: Cohesiveness between organizations is reflected in their coordinating efforts. 5. Quality of Information Sharing: Completeness and accuracy of information sharing among trading partners. 6. JIT Capabilities: Management of company inventory through developing zero inventory capabilities. 7. Inclusion in Product Quality and Development: Full joint efforts in the supply chain to achieve goals. 8. Involvement in Product Quality and Development: Joint efforts towards selling quality prod­ ucts and making ongoing efforts to develop products to be in line with emerging customer needs. 9. Mutual Trustworthiness: Availability to share important information with trading partners that symbolizes trust.

3 RESEARCH METHOD The method used in this study is a combination/mix method, which combines quantitative methods and qualitative methods, with the aim of obtaining more comprehensive, objective, reliable, and valid data (Sugiyono, 2011). The quantitative method in this study was carried out by distributing questionnaires to sixty jeans industry entrepreneurs in Cihampelas, then the data was processed using descrip­ tive statistical calculations to get a conclusion; the qualitative method used in this study was by conducting interviews with three speakers to strengthen the results obtained from the quan­ titative method. Based on the objectives, this research belongs to the descriptive research type, which is the method used to describe the characteristics of a group. It can also be said that descriptive research is research that seeks to describe an actual problem or event that is hap­ pening at the present time (Indrawati, 2015: 116). This study aims to describe the condition of supply chain management that has been carried out in the Cihampelas jeans industry. This type of research belongs to the correlational research type, namely research conducted to describe important variables related to a research problem (Indrawati, 2015: 117). In this study, the variable used, supply chain management practices, was derived from the results of research conducted by Mohua Banerjee (2015) related to the current conditions that occur in the Cihampe­ las jeans industry. The implementation of this research uses a cross section, namely data collection was carried out only once and then the data was processed and analyzed, and conclusions were 105

Figure 1.

Supply chain practices dimension.

reaached (Indrawati, 2015: 118). In accordance with his understanding, in this study, quantitative and qualitative data retrieval is only done once and then the data obtained is processed so that it can produce conclusions. The variable used in this study is supply chain management practices with its total of nine dimensions in accordance with Banerjee’s 2015 theory: namely, supply chain characteristics, level of information sharing, customer relationship, supply chain integration, quality of infor­ mation sharing, JIT capabilities, inclusion in strategic decision making, involvement in prod­ uct quality and development, and mutual trustworthiness.

4 ANALYSIS AND RESULTS The results obtained through the distribution of questionnaires to sixty respondents and also interviews with three sources showed that the dimension of customer relationship was ranked first among the nine dimensions, supported by the results of interviews with three sources who argued that the relationship with customers was the main thing because the business actors in Cihampelas Street all have a seller background. Next, here is the order based on the percent­ ages of the nine dimensions studied: 1. 2. 3. 4. 5. 6. 7. 8. 9.

Customer relationship 80.6% Involvement in product quality and development 79% Quality of information sharing 78.5% Inclusion in strategic decision making 77.5% Mutual trustworthiness 77% Level of information sharing 76.85% Supply chain characteristics 76.5% Just-in-time capabilities 75.33% Supply chain integration 74.4%

Dimensions of supply chain integration are in the last place with a percentage value of 74.4% but still in the good category. From the interviews with the three speakers, two speakers said that they did not prioritize their promotion or marketing aspects, on the grounds that the Cihampelas companies themselves are already well-known so they no longer need to make more effort to promote their products. In accordance with what is in the Mohua Banerjee (2015) journal, the promotion or marketing process is a statement point contained in the supply chain characteristics dimension, which is ranked seventh with a percentage of 76.5%. 106

While the last two dimensions are just-in-time capabilities and supply chain integration with percentages of 75.33% and 74.4% respectively, it does not mean that business actors do not pay attention to these two dimension points. These percentages are still in the good category, which means that business actors have applied it, it’s just that these two dimensions have not been maximized in their application so they are in the last two dimensions which have the lowest percentage.

5 CONCLUSIONS The results of the study can be concluded as follows: 1. Business actors in the jeans industry in Cihampelas have actually carried out supply chain management, starting from the stage of purchasing from the factory to the seller who can sell their products to customers, but most of them do not know that all the activities they do are called supply chain management. 2. The most dominant dimensions of supply chain practices are customer relationship and involvement in product quality and development, which means that business actors who all have the role of sellers in their business prioritize relationships with customers and also choose good quality suppliers to be invited to collaborate as business partners in the supply chain.

REFERENCES Banerjee, M., & Mishra, M. (2017). Retail supply chain management practices in India: A business intel­ ligence perspective. Journal of Retailing and Consumer Services, 34, 248–259. Heizer, J., & Render, B. (2014). Operations Management Sustainability and Supply Chain Management. Upper Saddle River, N.J: Prentice Hall. Indrawati. (2015). Metode Penelitian Manajemen dan Bisnis Konvergensi Teknologi Komunikasi dan Infor­ masi. Bandung: PT Refika Aditama. Open Data Kota Bandung. (2017). Data Kunjungan Wisatawan Luar Kota Bandung yang Berkunjung ke Kota Bandung Selama Tahun 2010–2015. [Online]. http://data.bandung.go.id/dataset/data-kunjun gan-wisatawan-ke-kota-bandung-tahun-2010-2015/resource/3f080688-e5a6-4cb3-99fe-920c2149129c [25 Januari 2019]. Sugiyono. (2016). Metode Penelitian Kuantitatif, Kualitatif, dan R&D. Bandung: Alfabeta.

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The application of storytelling in public relations strategy: The case of a Hi-tech company J.A Taufiq Department of Communication Science, Universitas Bakrie, Jakarta, Indonesia

M.T Amir Department of Management, Universitas Bakrie, Jakarta, Indonesia

ABSTRACT: Storytelling now becomes an increasingly popular tool in public relations. It con­ sists of several features that maximize the functions of public relations such as managing relation­ ships, and communicate as well as instill corporate values internally and externally. This study evaluates the application of storytelling in public relations practices of a fast-growing technology company in Indonesia, GOJEK. Content analysis and utilizing the concept of rhetorical principles in storytelling, as well as master plots of storytelling, are conducted. A story about an employee creating impact for the drivers by creating an easier way of transactional withdrawal is examined. Theoretical and managerial implications are discussed.

1 INTRODUCTION Storytelling has been widely known as an impactful approach in organizational commu­ nication, including in public relations (PR) (Heath, 2006). Stories have been a part of human activity for multiple years, and have the power to inform, persuade, elicit emo­ tional responses, build support for coalitions and initiatives, and build civil society. Storyt elling is a valuable medium for companies to communicate with their externals as it can bridge the barriers within through engagement at a personal level. Storytelling is useful because it can merge and coordinate with the stories that are communicated. In the life of PR, storytelling has been considered a favorable approach and demanded by professionals (Kent, 2015). PR functions have a critical role to send messages and information of the organization to the both external and internal audience. PR is all about managing relationships, crafting stra­ tegic stories, conveying expertise, and solving organizational problems through strategic com­ munications (Seitel, 2017). The many functions of public relations include writing, counseling, stakeholder and shareholder relations, social media, interface and crisis communication. These functions can be communicated effectively by using storytelling because the organiza­ tion’s goals, mission, values, or any informational and persuasive material could be conveyed and more easily understood by the audience. In time, these advantages would facilitate PR professionals in creating trustworthy PR content. Trustworthy PR content depends largely on subtle rhetorical principles that could stimulate identification, empathy, and memorable situations and experiences (Kent, 2015). Although the imperative of storytelling in PR is widely accepted in many areas of studies such as science, politics, and various others, the application in technological companies, especially in an Indo­ nesia setting, is lacking. One growing technological company in Indonesia, GOJEK, has applied this method in several of its campaigns and videos (Kent, 2015). GOJEK is a technological company that offer more than 20 services for everyday challenges, based on what they call superApps. To continue growing as a leading tech company across Southeast Asia, the company uses an array of integrated communication 108

strategies for internal and public engagement. New innovative media and channels that reflect important trends emerging in the field, including digital media, management and co-creation of meaning, as well as storytelling, are used to build internal and public loyalty. This study evaluates the application of storytelling in PR practices of a fast-growing tech­ nology company in Indonesia, GOJEK. It begins with an overview of the storytelling concept, mainly in PR. Next, the study provides a brief overview of rhetorical principles of storytelling, including some models of master plots. After this, one of the practices in the study subject, along with the discussion of the application, especially the using of plot of the story and how it creates compelling messages, will be presented. The plot of quest, adventure, and discovery are identified. The managerial implication of these findings discussed. 2 LITERATURE REVIEW 2.1 The power of storytelling in communication Storytelling has become part of many social communication aspects, ranging from chil­ dren stories to religious services to local myths (Kent, 2015), and has some key features that made it a compelling communication tool. Woodside (2010) proposes that storytell­ ing makes it easier to memorize as humans tend to categorize, store, and retrieve infor­ mation in the form of stories. Audiences could easily access remembered information as stories comes with many indicators that create implicit or explicit awareness and emo­ tional connection in their minds. Storytelling is a social phenomenon that has the ability to transend age groups, genders, and cultures and is crucial to all nations, societies, and cultures from the earliest recording of communication (Denning, 2006). The structure, fullness, and whole purpose of storytelling has passed through all kinds of levels in soci­ ety. The practice of telling stories has also helped advertisers to create brand loyalty, making it a tool of advertising and marketing (Woodside, 2010). The advantages of storytelling often involve the memory of the audience. A substantial amount of information stored in and retrieved from memory is episodic—stories that include inciting inci­ dents, experiences, outcomes/evaluations, and summaries/nuances of person-to-person and person-and-brand relationships within specific contexts (Fournier, 1998). Studies also found that retrieving, reliving, or repeat watching of stories results in what Aristotle (Hiltunen, 2002) refers to as “proper pleasure”—a catharsis—that relates usefully to the work of Holt (2004). Watching, retrieving, and telling stories enables the learner (sometimes with the assistance of the trainer but not necessarily the protagonist) to experience one or more archetypal myths. In branding litera­ ture, the marketer knows that specific brands and products often play pivotal roles in enabling consumers to achieve the proper pleasure that results in a consumer mentally and/or physically enacting a specific archetype—and reliving the experience by periodically retelling a given story. The storytelling also involves the feature of clarity. Individuals seek clarity, to make sense of prior conversations, events, and outcomes from others and themselves by telling stories. The concept of sense-making, as the statement from Weick’s term “How do I know what I think until I hear what I say?” (Maitlis & Sonenshein 2010) partly summarizes this proposition. 2.2 The components of a good story A story is divided in three parts: a beginning, a middle, and an end. To be completely a good story also requires the addition of a fair plot, how we tell the story, identification of characters that connect with the audience, a climax, and a conclusion or a continuation. A compelling story should have four important components in order to be compelling: employment, narra­ tive, identification, and a form (Kent, 2015). First, a good story means you have to decide the plot or what kind of story you want to tell. An array of events, combined to be retold in a plot (White, 1973), creates an explanation of what kind of story is being told. Storytellers back in the day took the events of people’s 109

everyday experience and formed explanations through greater events. This process still applies to PR today, where a skilled communicator must narrate a story that echoes meanings for the intended audience. Storytellers need to understand that specific audiences require different plots or genres, which vary with the situation. The second component is the narrative perspectives that create meaning. This is a natural ability to take action from the words of a story, as it was also a part of our everyday lived experience. Public relations professionals must share stories that create positive moral impact and contextual organizational actions. As a communicator, using narratives will be a helpful way to persuade and identify our audience. Narration can be used as a tool to win over people who are hard to shift their opinion and pursue an individual if composed accordingly. Third, it is important to that the target audience identify with the characters of a story. According to Kent (2015), identification is divided into three types. Identification by sym­ pathy is used with empathetic language, creating a sense through similar emotions. Identifica­ tion by antithesis as a common rhetorical strategy used to make explicit references accepted by the audience. Lastly, identification by unawareness, is to generate hidden identification using concepts and principles. According to Fisher’s phrase (1985), all stories must resonate with the audience. If they don’t, the story falls short. Fourth, a good story is strengthened by good form. Form influences how we make sense of information and remember it over time. Listening to the same music over and over again, or watching the same movies numerous times, creates a memory we rarely forget. Memorizing random information happens because their form stays in shape, even in the event of changing times. People recognize a good story in their compelling plot and form. 2.3 Master plots in the story Although some stories may contain several different plot types, every narrative plot is unique. The master plots in the stories that organizations created serve independently to build connec­ tion and identification (Kent, 2015). Storytellers need to create credible characters, employ interesting plotlines, and be compassionate to the audience. The aim of using master plots by a public relations practitioner to compile a great story for his or her organization is to be aware of how plots are used and point them out in multiple ways in the same story. The crucial part of learning the master plots is not how the plots are being labeled but acknowledging the many possibilities of using them. There are twenty master plots commonly used for storytellers but there are five plots that are used the most for professional communicators: quest, adventure, rivalry, the underdog, and wretched excess. A quest is a search for a person, place, or thing, tangible or intangible (Kent, 2015). A quest story can be used by an organization as an opportunity to expand or change for its crisis management situation. In an adventure story, the main focus is the journey, and story comes with an adventurer ready to start exploration, a setting, and a scene. Rivalry can be a competitive story of rivals against each other who have the same goal. The underdog, the outcast, struggles against the big corporation; lastly a wretched excess story is usually when changes strike in the daily routines of a group of people and some­ thing big, good, or bad happens in the end to resolve the excess. 2.4 Narrative techniques in public relations The key to a good story is focused on imagining the plot in terms of its narrative rather than depending on its images. Unlike marketing and advertising which drive the audience to visual content, public relations sends a deeper message through storytelling (Kent, 2015). Every type of master plot has potential to be used for PR. These narrative techniques are informative and are applicable in different types of messages. The master plots discussed by Kent can be used in social media, crisis management, branding, annual reports, public speeches, etc. The 110

various ways of using the narrative master plots help professional communicators to easily connect with clients or organizations. In conclusion, master plots are beneficial as tools to create and promote a compelling story. 3 METHOD The purpose of this study is to evaluate the use of storytelling to attract followers or consumers in public relations. This study uses a qualitative approach and descriptive single case study. This method is chosen to intensively explore how an entrepreneurship initiative is driven by certain practices. This is parallel with Yin’s (2009) suggestion where this kind of case study can be conducted to describe a phenomenon and real-life context, although the external validity of case studies is relatively limited (Yin, 2009). The purpose of this method is not to generalize the specific results but to explore more new ideas and develop the theory from the results. The case used is a video about Arlinda Juwitasari as GOJEK’s product manager, posted in several social media platforms such as LinkedIn and Youtube. The story describes how she always wondered how a startup could be associated with a vast number of GOJEK drivers. She wanted to make a priority of the driver partners by facilitating a more comfortable and efficient system of fund withdrawals. In this two-minute video, Arlinda shares her story on why she wanted to make this system easier for GOJEK’s most important partners. The footage has been presented in a program called Life at GOJEK in various social media platforms. It all shares the same content but with different types of media such as videos, articles, and short clips, sharing the daily lives behind-the-scenes of how the people of GOJEK create ideas and making social impacts. To identify the specific components of a good storytelling strategy and its master plot, con­ tent analysis was adopted. Content analysis is defined as a research method for the subjective interpretation of the content of text data through the systematic classification process of coding and identifying themes or patterns (Hsieh & Shannon, 2005: 1278). It focuses on the characteristics of language of communication with attention to the content or contextual meaning. Several studies of social media have used this approach (Lin & Peña, 2011; Shen & Bissell, 2013). Selected components and master plots were analyzed with a prior analysis framework built from previous theory using Kent’s concept (2015). Data coding and classifi­ cation of text are developed for further analysis. 4 RESULTS 4.1 Brief profile of GOJEK in Indonesia Nadiem Makarim, the CEO of GOJEK, came up with an idea to built an online trans­ port made easier for ojek-users on a daily basis. He identified a problem where ojek drivers actually spent more time waiting for orders than picking up customers, despite the jammed streets of Jakarta. He thought that it would be efficient if there was an easier way for drivers to do their job and people to ride through the daily traffic. In 2011, GOJEK was born. GOJEK is an online transportation platform based in Indo­ nesia with currently 22 other services provided such as food delivery, logistics, financial, and many more. GOJEK stands with three important pillars: speed, innovation and social impact. GOJEK is available through the App store and Google Play and has been downloaded by more than 8 million users. In 2015, GOJEK has rapidly grown and has hired up to 10,000 drivers spread across Indonesia, operating in big cities like Jakarta, Bandung, Bali, and Surabaya, and plans to grow more in other cities. This fintech startup, as the first unicorn company in Indonesia, has a clear voice of its nationalist communication, advertising slogans such as “#AnakBangsaBisa” and “Karya Anak Bangsa.” 111

Using story as one of its communications strategies may involve GOJEK’s intention to be perceived as a creative and innovative company. It has used storytelling techinuqes in order to touch the audience’s emotions through one of its program called Life at GOJEK. 4.2 Life at GOJEK programs The case analyzed is part of the Life at GOJEK program using various social media platforms. It all shares the same content but with different types of media such as videos, articles, and short clips, sharing the daily lives behind-the-scenes on how the people of GOJEK create ideas and making social impacts. On its YouTube platform, Life at GOJEK is divided into several themes: Go-Learn, Meet the Series, Go Make Impact, Go Talk about It, and GO-Tech. As a big company that needs to keep up with a fast-paced environment, there are always new challenges every day. By involving thou­ sands of people to make it work, Life at GOJEK introduces the people of GOJEK and shows how they identify a small problem and turn it into a solution to change the lives of millions in Indonesia. One of Life at GOJEK’s videos tells a story how GOJEK as a product is built by its customers, especially their GOJEK drivers. GOJEK drivers, as partners, play an important role to provide the services and to persevere in this business, or by listing facts, using either the style tag List summary signs or the style tag List number signs. 4.3 Scale at impact with life at GOJEK Arlinda narrates on how she never knew or tried to commute using GOJEK before she was asked to work there. She had a trial of using the transport service and started to interact with its drivers. The drivers she met always had a story to tell, whether about buying new things for their children or how their job has made it possible for them to fund their lives. One day, one of the drivers came into their headquarters and claimed that he needed to withdraw money to pay his daughter’s hospital bills. The process of the normal withdrawals requires a longer time, but there would be emergency cases like these. Arlinda and her team came up with a solution: a new feature called instant with­ drawals, to make it easier for drivers to withdraw money. The biggest challenge was to educate several drivers who never had a bank account before to use this feature. Now, withdrawing money is no hassle and can be done in seconds. They tested this feature with several drivers and the response was astonishing. The drivers felt proud to be part­ ner of a company that has big intentions of helping their lives. This made Arlinda senti­ mental and she felt that the drivers’ happiness was something that money couldn’t buy. Today, by enabling on-demand cash withdrawal, GOJEK had improved the welfare of 1 million drivers. Though this simple move made a big change, there were troubleshoot­ ing moments the team had to face. This made Arlinda realize that being part of the country’s biggest e-commerce, there will always be another problem to solve, but it pushed her to keep challenging herself to come up with new solutions and to grow a bigger impact. 4.4 Discussions Arlinda’s video closely follows the four components that make a story compelling and one main masterplots as suggested by Kent (2015). First, a compelling story must have meaning. Arlinda’s story has the purpose of telling its audience that being part of GOJEK means living by its three pillars, one of which is social impact. GOJEK aims to show that their people not only want to create a product but also change the lives of others and have a positive impact. The audience might be moved by Arlinda’s emotional state as it potentially resonates with real-life experiences. This indicates the type of emplotment GOJEK wanted to achieve. The second component is what idea is pursued from the narrated story, as words have the ability to influence the actions of other people (Kent, 2015). “Simple things that may mean 112

little to us mean a lot to them,” as Arlinda said, shows that the GOJEK team prioritize their partners’ lives rather than just selling their product. As the audience will feel touched by the spirit of GOJEK’s team, this creates an important moral issue where the organization is taking action. This is powerfully persuasive for the audi­ ence as it displays the organization’s cause to help people’s lives. This narrative expression shows that GOJEK wanted its audience to know their concern for their partners needs, which persuades them that the company is having a positive impact on others. The third component shown is that the story can be identified by antithesis, where the story­ teller makes explicit references to something shared in common with the audience. In the video, Arlinda describes what she felt when she heard one of the drivers required money immediately but could not get it. That showed she understood that it was difficult to deal with the situation. Her sympathetic expression shows as she tells her story: she started to feel emotional and shed a few tears when she recalled the GOJEK drivers being thankful for the new instant withdrawal features. The fourth component of a compelling story is a structured form. In this story, GOJEK has created a strong form to make it a meaningful context. The story flows from how Arlinda started in GOJEK and talks about her journey as part of the team. She tells of everyday problems faced by her team and the company’s partners, and how it made her grasp the thought of changing people’s lives and making an impact. People will instantly recognize the true aim of GOJEK as a company is to help their partners and inspire others to solve more problems with technology. Other than having a good structure of employment, identification, and form, a compelling story needs to be categorized through the master plots. According to Kent (2015), professional communicators must understand the possibilities of a good story to use it as a tool for an organization’s communication strategy. GOJEK’s type of story is a quest, where the story­ teller’s intention is to illustrate the search for a solution to help its partners withdraw money through an easier process. Arlinda begins the story by telling the struggle of a driver in need of money, which brings the audience to question, “Will he make it?” In the next act of the story, Arlinda comes up with a solution of creating a new feature of instant money withdrawals for drivers. It was a helpful result for the problem. Shifting to the third act, Arlinda felt moved by the reaction of her work to complete her quest, to fulfill the drivers’ needs. This leads to new goals to never stop challenging herself to solve more problems and to remember that creating products at GOJEK directly impacts lives. The quest plot is an opportunity for an organization to evolve or transform. This story helps GOJEK to communicate its pillars by telling a story of how their team solves a problem and creates a solution with a big impact, in the hopes of evolving into a company with immense influence for the better. The table below summarize above find­ ings and discussion.

Table 1. The summary of findings and discussion. The component of compelling story

Based on the chosen case study

Employment

GOJEK’s purpose to show its three important pillars through Arlinda’s story.

Narration

GOJEK’s powerful persuasion through its emotional narrative expression in the story.

Identification

The story can be identified by antithesis but on some narrative level, it can also be identified by sympathy.

Form

The story’s form flows from a meaningful context.

Master plots

GOJEK’s story is a quest type of master plot, where Arlinda as the story­ teller shows her journey in the search of a solution.

113

5 CONCLUSION Using storytelling content to foster shared interests with an audience offers organizations an array of benefits as convincing communication tools. Storytelling can generate interest, and build the likeability and credibility of the content communicated. This study evaluates story­ telling strategy application; such as the choosing of rhetorical aspects and master plotting in a PR communication. The case in this study explains that GOJEK uses storytelling as a tool to communicate its important pillars. Their aim is to be perceived as a company that respects its partners, and to continue to bring positive social impact to society. In this story, GOJEK has all four components that create a compelling story. It has meaningful employment, narra­ tive, identification, and form. In this case, the master plot is dominantly a quest plot, where the narrative leads us to the search of finding a solution. The reasons may be that GOJEK’s purpose as a company is to have as positive an impact as possible. This study is not only for the community but also for the socioeconomic state and ecosystem of their driving partners. This study only involves one communication strategy, while GOJEK uses many other strat­ egies as well. Other research might add some more insights on how GOJEK uses its creativity to market its brand and establish its ideas. REFERENCES Denning, S. (2006). Effective Storytelling: Strategic Business Narrative Techniques. Strategy & Leader­ ship, 34(1): 42–48. Fisher, W. R. (1985). The Narrative Paradigm: An Elaboration. Communications Monographs, 52(4): 347–367. Fournier, S. (1998). Consumers and Their Brands: Developing Relationship Theory in Consumer Research. Journal of Consumer Research, 24(4): 343–373. Heath, R. L. (2006). Onward into More Fog: Thoughts on Public Relations’ Research Directions. Jour­ nal of Public Relations Research, 18(2): 93–114. Hiltunen, A. (2002). Aristotle in Holliwood: The Anatomy of Successful Story-Telling. Bristol, UK: Intellect Book. Holt, D. B. (2004). How Brands Become Icons: The Principles of Cultural Branding. Harvard Business Press. Hsieh, H. F., & Shannon, S. E. (2005). Three Approaches to Qualitative Content Analysis. Qualitative Health Research, 15(9): 1277–1288. Kent, M. L. (2015). The Power of AStorytelling in Public Relations: Introducing the 20 Master Plots. Public Relations Review, 41(4): 480–489. Lin, J. S., & Peña, J. (2011). Are You Following Me? A Content Analysis of TV Networks’ Brand Com­ munication on Twitter. Journal of Interactive Advertising, 12(1): 17–29. Maitlis, S., & Sonenshein, S. (2010). Sensemaking in Crisis and Change: Inspiration and Insights from Weick (1988). Journal of Management Studies, 47(3): 551–580. Seitel, F. P. (2011). The Practice of Public Relations. Pearson. Shen, B., & Bissell, K. (2013). Social Media, Social Me: A Content Analysis of Beauty Companies’ Use of Facebook in Marketing and Branding. Journal of Promotion Management, 19(5): 629–651. Tesch, R. (1990) Qualitative research: Analysis types and software tools. Falmer, New York. White, H. (2014). Metahistory: The Historical Imagination in Nineteenth-Century Europe. JHU Press. Woodside, A. G. (2010). Brand-Consumer Storytelling Theory and Research: Introduction to a Psychology & Marketing Special Issue. Psychology & Marketing, 27(6): 531–540. Yin, R. K. (2009). Case Study Research: Design and Methods (4 ed.). Los Angeles, CA: Sage.

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Transformational leadership in Shariah banking: Case study millennial employee in Bank Syariah Mandiri Dani Irawan & Adis Imam Munandar School of Strategic and Global Studies, University of Indonesia, Jakarta, Indonesia

ABSTRACT: Transformational leadership emphasizes idealized influence, inspirational motivation, intellectual stimulation, and individual consideration. Islamic banks apply reli­ gious values in their activities. The purpose of this study is to analyze the sharia values in transformational leadership in millennial employees. The study case of this research is on the Bank Syariah Mandiri (BSM), specifically on the millennial generation in the regional of Jakarta. The research used explanatory research with a qualitative approach. The primary data was in-depth interviews and observation. The results showed directing mentoring, coach­ ing, and supervision applied to millennial employees. Morning prayer activities, Wednesday afternoon prayers, daily briefings, Friday morning dhikr, personal mentoring, outbound, team building, and casual chatting on the sidelines of work attempt to facilitate changes in values and culture in the BSM millennial generation. BSM leaders include Islamic values in transformational leadership.

1 INTRODUCTION The millennial generation dominates the demographic of employees in a company. The character­ istics possessed by each generation are very different. Millennials, for example, pay attention to their personal career, believe that they can achieve anything, seek recognition from others, and often demand quick feedback (Sack, 2006). On the negative side, millennials can easily lose con­ centration when they do many tasks (Rubinstein et al. 2001). One of companies with the most employees included in the millennial generation category is PT Bank Syariah Mandiri (BSM). Based on data, in 2017, the number of BSM employees reached 15,659 with the majority of BSM employees aged under 35 years (www.syariahmandiri.co.id, 2018). BSM is the largest Islamic bank in Indonesia: in 2018, the total assets of BSM were IDR 98.34 trillion, and they were able to record a profit of IDR 605 billion or an increase of 65.74 percent (yoy) when compared to the previous year of IDR 365 billion. BSM’s main vision is to be “Leading and Modern Sharia Bank,” which is focused in the vision for employees, namely “BSM is a bank that provides oppor­ tunities for housing and professional careers.” As an Islamic bank, BSM runs its business and organizational activities based on Islamic values, including in the leadership process that empowers the potential of the millennial generation, because the millennial generation has unique characteristics that are different from other generations. BSM leaders internalize Islamic values to millennial employees through a transformational leadership style. Many discussions and studies related to transformational leadership have been carried out including by Hai-Jiang Wang, Evangelia Demerouti, and Pascale Le Blanc (2017), who dis­ cussed the relationship between transformational leadership and job crafting; Isabel Buil, Eva Martinez, and Jorge Matute (2019), who focused their research on mechanisms and boundar­ ies that explain the relationship between transformational leadership and frontline employee performance; and Thi Thu Nguyen, Lokman Mia, Lanita Winata, and Vincent K. Chong (2017) who examined the effects of transformational leadership on management control sys­ tems on managerial performance. In this study, the authors aim to analyze the values of Islam (sharia) in transformational leadership in BSM millennial employees. 115

2 METHOD The authors used a qualitative approach to understanding social phenomena that occur and it was carried out using various methods such as interviews, observations, and review of docu­ ments (Moleong, 2002). The social phenomenon that the researchers analyze is the application of Islamic values (sharia) in transformational leadership for millennial employees at BSM. Data collection methods used in this research are literature study, observation at the BSM Branch Office in Jakarta, and interviews with seven BSM Branch Managers in Jakarta area.

3 RESULT The millennial generation is the generation born between 1982 and 2002 (Ali & Lilik Pur­ wandi, 2017). This means that people aged 19 to 41 years are included in the millennial gener­ ation. The age group of 19 years to 41 years is the age group of workers. Therefore, the largest composition of the workforce at present is dominated by millennial generation. Leadership is a process whereby individuals influence groups of individuals to achieve common goals (Northouse, 2016). Leadership has many characteristics or styles; one of them is transformational leadership style. Transformational leaders motivate others to do more than usual and even more than they initially think of (Bass & Riggio, 2006). This means that transformational leaders must be able to convince their members that they have more abilities that can be optimized to do something more than what they could be doing. This is an advan­ tage of transformational leaders: they are able to understand the state of their members and motivate them to always optimize their abilities. Transformational leadership represents the behaviors of leaders to inspire their followers to perform at a higher-than-expected level (Resick et al., 2009). BSM leaders carried out a transformational leadership style process by internalizing the company’s culture to each employee. The company culture in BSM is sum­ marized in the word “ETHIC” which stands for Excellence, Teamwork, Humanity, Integrity, and Customer Focus.

These five company values are applied by every employee. The main task to ensure that the company values have been applied by each employee is the responsibility of a leader as the employee’s direct supervisor. Each leader has their own style in presenting organizational values to each employee. The most dominant style in BSM is the transformational leadership style. Transformational leadership transforms the personal values of followers to support the vision and goals of the organization by fostering an environment where relationships can be formed and by establishing a climate of trust in which visions can be shared (Stone et al., 2004). ETHIC represents values built within the organization that are transformed by each employee aimed to create an organizational culture that is relevant to the current situation. 116

These values are always updated according to changing times. The leader influences employees to implement ETHIC in the company environment, and it is even expected that ETHIC become the values shared by every BSM employees in any situation. Transformational leaders not only affect their individual followers (Newman et al., 2018; Ng, 2017), but can also shape their organizational culture (Bass & Avolio, 1993) by highlighting a need for change to create a new shared vision and building commitment among their followers to this vision (Dunne et al, 2016; Hartog et al., 1997). Transformational leadership is able to create the situation of an organization working together where organizational goals are very well understood by every employee, so that employees can carry out their duties excellently. The atmosphere of friendship or humanity creates the pattern of good cooperation; as well as being financial service providers, BSM employees must uphold integrity and focus on customers to serve optimally. The dynamics of transformational leadership involve followers having a strong personal identification with the leader, a shared vision for the future, and the ability to work collectively for the benefit of the group (Kelloway et al., 2003). Transformational leadership has four dimensions (Avolio et al., 2009): charismatic leader­ ship, or idealized influence, inspirational motivation, intellectual stimulation, and individual­ ized consideration. 3.1 Charismatic leadership, or idealized influence Transformational leaders are role models; they are respected and admired by their followers. Followers identify with leaders and want to emulate them. Leaders have a clear vision and sense of purpose and they are willing to take risks (Bass, 1998), come to work on time, discip­ line to the working rules, and provide be enthusiastic about the task. To find out the employ­ ee’s needs, leaders should hold formal and informal discussions at every opportunity. Every morning briefing discusses the work plan for the next week and the work on that day, and morning prayers which become routine are used to find out what deficiencies and support management needs. If the needs are personal, the leader asks that question by discussing it privately with its employee. After getting information from employees, the leader tries to fulfill those needs. For example, if employees need additional tools from the head office, then the leader will coordin­ ate with related sections. However, if the needs are personal, such as lack of work motivation so that employees were seen as not enthusiastic and losing enthusiasm, then the leader pro­ vides motivation and inspiration through the knowledge he has. In addition, the leader pre­ sents religious values, such as work is a way to seek God’sblessing, work is a process of endeavor as a human being, work is destiny and the obligation as someone to take responsibil­ ity for the future of his family. Later, the leader evaluates the employee’s changes in performance and enthusiasm for work. If the results are in line with expectations, the leader gives a sincere compliment and appreciation, such as a charter or food package. If employees have an opinion for organiza­ tional improvement, it can be communicated through open forum such as morning prayer and morning briefing or it can also be communicate directly to the leader. After consideration of his opinion, it can be developed into a project, and in appreciation, the employee is appointed to be responsible for the proposed project. 3.2 Inspirational motivation Transformational leaders behave in ways that motivate others, generate enthusiasm, and chal­ lenge people. These leaders clearly communicate expectations and demonstrate a commitment to goals and a shared vision. Such leaders tend to be able to articulate, in an exciting and com­ pelling manner, a vision of the future that the followers are able to accept and strive towards (Bass, 1998). For example, when employees need motivation to remain enthusiastic at work, then leaders explain values about excellent and hard work, and that Islam teaches about 117

excellent work and working hard. The philosophy of work in Islam itself is worship, so the leader gives an understanding that work is a form of worship, especially work in Islamic banks, which aim to fight for the Islamic economy. The leader said that the employees worked well, while expecting a reward in the form of a reward from God. In addition, when employees find it difficult to build a good team, the teamwork values will be explained by the leader in accordance with Islamic values so that the employees understand the importance of teamwork, especially reinforced by the Islamic values related to teamwork. The leader explains that helping is a virtue in Islam and includes the command of Allah, so that employees get a spiritual impetus to be able to form a solid team. 3.3 Intellectual stimulation Transformational leaders actively solicit new ideas and new ways of doing things. They stimu­ late others to be creative and they never publicly correct or criticize others (Bass, 1998). Lead­ ers see the results of assigning tasks to employees, and when employees complete tasks well and according to the expectations of the leader, they will be given rewards such as praise or even a prize. The leader gives praise for maintaining the values that have been held by employ­ ees. Results that exceed expectations are the best results. Employees who can do satisfactory results, work together with teammates, adjust, remain full of integrity, and focus on the cus­ tomer as a service provider are employee models that every leader expected. Therefore, leaders form employees by giving awards in the form of Umrah to motivate employees to be more engaged with the company. In addition, praise for the employee’s work will be influential because the employees feel they are valued. 3.4 Individualized consideration The leader listens the opinions of employees on every occasion, such as morning prayer activ­ ities, Wednesday afternoon prayers, daily briefings, Friday morning dhikr, personal mentor­ ing, outbound, team building, and casual chat on the sidelines of work aimed at facilitating change (facilitate to transform) in the value and culture in BSM millennial generation. The result of listening is understanding that if something new is included then a project team will be formed with the employee who proposes the change as the head of the project team because millennial employees are more happy with changes and challenges and get bored quickly on a repetitive job. When the leader knows that there is a boredom in his employees, the leader will arrange an outing with his employees to refresh work atmosphere. The individualized consideration component of transformational leadership also underscores the necessity of altruism if leadership is to be anything more than authoritarian control (Kanungo & Mendonca, 1996). Leaders give employees the freedom to think about something new and to be creative as long as it is still in accordance with company rules. Millennial employ­ ees are highly creative. The leader must be able to take advantage of the employee’s situation. In a company, there are ineveitably employees who feel neglected. The leader knows by lis­ tening the employee’s opinion and observing the results of the employee’s performance by coaching, mentoring, and direct supervision of the employee. Besides that, they are also motivated by Islamic values regarding the spirit of life, responsibility, and progress in life. As today must be better than yesterday, we must utilize this gift of life for something that is rewarded, including to work seriously, and also, as a Muslim, if given a mandate must be responsible until the mandate is completed. The great desire of millennial employees who tend to want something instantaneously must be accompanied by the supervision of the leader so that the employee is more tenacious and determined to achieve it. Millennial employees want a faster grade and position upgrade. The expectation is directed by the leader into something positive. The leader explained that if they were able to complete work excellently, demonstrated good teamwork, were able to adapt amidst other employees in a humane manner, had integrity, and paid attention to the 118

customer, then the reward awaited them. The leader provides an understanding of Islamic patience values that God is with those who are patient.

4 DISCUSSION AND CONCLUSIONS BSM leaders showed how direct mentoring, coaching, and supervision were applied to millen­ nial employees. They transformed sharia values, summarized in ETHIC, to millennials through various activities including morning prayer activities, Wednesday afternoon prayers, daily briefings, Friday morning dhikr, personal mentoring, outbound, team building, and casual chatting on the sidelines of work to facilitate changes in values and culture in the BSM millennial generation. REFERENCES Avolio, B. J., Walumbwa, F. O., & Weber, T. J. 2009. Leadership: Current Theories, Research, and Future Directions. Annual Review of Psychology, 60, 421–49. Bass, B. 1998. Transformational Leadership: Industry, Military, and Educational Impact. Mahwah, NJ: Erlbaum Associates. Bass, B. M., & Avolio, B. J. 1993. Transformational Leadership and Organizational Culture. Public Administration Quarterly, 17(1): 112–121. Bass, B. M., & Riggio, R. E. 2006. Transformational Leadership (2nd Ed.). Mahwah, NJ: Lawrence Erl­ baum Associates. ISBN 0–8058–4761–8. (e-book). Buil, I., Martinez, E., & Matute, J. 2019. Transformational Leadership and Employee Performance: The Role of Identification, Engagement and Proactive Personality. International Journal of Hospitality Management, 77, 64–75. Dunne, T. C., Aaron, J. R., McDowell, W. C., Urban, D. J., & Geho, P. R. 2016. The Impact of Leader­ ship on Small Business Innovativeness. Journal of Business Research, 69(11): 4876–4881. Kanungo, R. N., & Mendonca, M. 1996. Ethical Dimensions in Leadership. Sage, Beverly Hills, CA. Kelloway, E. K., Barling, J., & Helleur, J. 2000. Enhancing Transformational Leadership: The Roles of Training and Feedback. The Leadership and Organization Development Journal, 21, 145–149. Moleong, L. J. 2002. Metodologi Penelitian Kualitiatif (Edisi Revisi). Bandung: PT Remaja Rosdakarya. Newman, A., Herman, H. M., Schwarz, G., & Nielsen, I. 2018. The Effects of Employees’ Creative SelfEfficacy on Innovative Behavior: The Role of Entrepreneurial Leadership. Journal of Business Research, 89(Aug): 1–9. Nguyen, T. T., Mia, L., Winata, L., & Chong, V. K. 2017. Effect of Transformational-Leadership Style and Management Control System on Managerial Performance. Journal of Business Research, 70, 202–213. Resick, C. J., Whitman, D. S., Weingarden, S. M., & Hiller, N. J. 2009. The Bright-Side and the Dark-Side of CEO Personality: Examining Core Self-Evaluations, Narcissism, Transformational Leadership, and Strategic Influence. Journal of Applied Psychology, 94(6): 1365–1381. Sacks, D. 2006. Scenes from the Culture Clash. Fast Company, 102, 72–77. Stone, A. G., Robert F. Russell, R. F., & Patterson, K. 2004. Transformational Versus Servant Leader­ ship: A Difference in Leader Focus. Leadership & Organization Development Journal, 25(4): 349–361. Wang, H.-J., Demerouti, E., & Le Blanc, P.. 2017. Transformational Leadership, Adaptability, And Job Crafting: The Moderating Role Of Organizational Identification. Journal of Vocational Behavior, 100, 185–195.

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Entrepreneurship training and development programs: Entrepreneurs’ perceptions K. Omar, M.A.S.A. Halim & Y.M. Yusoff University Malaysia Terengganu, Malaysia

R. Wahyuningtyas Telkom University, Bandung, Indonesia

S.D. Sya’diah & A. Mulyana Universitas Padjadjaran, Bandung, Indonesia

ABSTRACT: In Malaysia, the number of entrepreneurs in Small and Medium Enterprises (SMEs) is increasing from year to year and this gives a good signal to the country. To ensure that Malaysian entrepreneurs keep on improving, the government has embarked on develop­ ing capacity-building initiatives through various ministries and agencies to promote and nur­ ture entrepreneurs by actively providing various kinds of entrepreneurial training and development programs. However, there is a difference in perception of the entrepreneurial programs offered. Therefore, this study aims to examine the perception towards differences of entrepreneurial practices and entrepreneurial development programs among entrepreneurs in East-Coast and West-Coast Peninsular Malaysia. This study collected data through a questionnaire survey. There were 500 sets of questionnaire distributed to the SDSI entrepre­ neurs, however, 466 sets were returned and usable for the data analysis. The results indicate that there is no significant difference in perception of entrepreneurial skills training among the SDSI entrepreneurs between East-Coast and West-Coast Peninsular Malaysia with the signifi­ cant value of 0.186 (t = –1.324; p > 0.05). Interestingly, it was found that region is not a significant factor contributing to the effectiveness of entrepreneurial skills training and prac­ tices among respondents. There is also no significant difference in business financing practice between these two regions. The results also show that there is a significant difference on per­ ception of ICT technology training among the SDSI entrepreneurs in East-Coast and WestCoast Peninsular Malaysia with the significant value 0.030 (t = 2.179; p < 0.05). It is hoped these results will provide additional information to the training providers especially those who have been entrusted by the Malaysian government to train the entrepreneurs and bring them to better positions in this competitive business world.

1 INTRODUCTION Starting 2008, campaigns to venture into businesses were conducted aggressively in some states to increase awareness among low-income rural women, especially in Kelantan and Terengganu, the two states in Peninsular Malaysia with the highest poverty rates and lowest levels of human development. New and existing entrepreneurs are encouraged to get involved in SMEs to ensure the sustainability of their businesses. Recognizing the importance of SMEs in raising national economic rates, various efforts have been made by the government to grow SMEs internationally, including providing entrepreneurship development programs. Entrepreneurship Development Program (EDP) is a program that helps entrepreneurs to develop their entrepreneurial abilities, particularly skills that are required to run a business successfully. 120

The active entrepreneurship development programs in Malaysia include entrepreneurial skills training, access to markets, business networking, and information and communications technology (ICT). One of the initiatives undertaken by the government in the effort to increase business performance among rural people is through SMEs entrepreneurship development programs under the One District One Industry Program or known as ‘Satu Daerah Satu Industri’ (SDSI) program. SDSI is one of the programs that has been planned in detail by the government to enhance the role of SMEs throughout the country. The SDSI Program has been adapted from the One Village One Product (OVOP) program in Japan and the One Tambun One Product (OTOP) program in Thailand. Apart from a training and development program, entrepreneurs also need financial support for their business to remain and grow and become profitable. The financial needs of a business will vary according to the type and size of the business. The Malaysian government, through its respective agencies, has tried playing banker to those SME entrepreneurs. There­ fore, this study aims to examine perceptions of entrepreneurial training development pro­ grams and practices, as well as business financing programs, among SDSI entrepreneurs in East-Coast and West-Coast Peninsular Malaysia.

2 LITERATURE REVIEW Good training and development programs help businesses to stay viable and grow profits. The volatile business environment always requires a new set of skills. Today, the emergence of new technologies has made the need for training and development unavoidable. 2.1

Entrepreneurial development program

Entrepreneurship development can be defined as programs of activities to enhance the know­ ledge, skill, behavior, and attitudes of individuals or groups set to assume the role of entrepre­ neurs. All countries around the world are very concerned about the development of entrepreneurship from various segments of the population such as minorities, women, people with disabilities, and rural people as one way to stimulate their country’s economic develop­ ment (Osemeke, 2012). The Malaysian Government has implemented a number of entrepre­ neurial policies and programs for those who are keen to venture into this field. Various entrepreneurial development programs, training, and advisory services have been provided by the Malaysian Government in order to help the entrepreneurs in Malaysia (Statistics Depart­ ment, 2009). According to Chell (2013), entrepreneurial skill incorporates behavior that can be strategic, tactical, and personal. Specific skills such as internal control, risk-taking, innovation, changeorientation, diligence, and leadership distinguish an entrepreneur from a manager (Hisrich, 2005). Hisrich (2005) added that entrepreneurial training can be divided into several classes, including technical skills, business management training, and private entrepreneurial training. Social norms and culture are important factors to the development of entrepreneurial activ­ ities in Malaysia. A study by Fouda (2002) found that a stronger community mentality among people of West Cameroon could be a motivating factor to entrepreneurship development in the Cameroonian context. Therefore, it can be hypothesized that; H1: There would be a significant difference in perception of entrepreneurial skills training among SDSI entrepreneurs in East-Coast and West-Coast Peninsular Malaysia. 2.2 Information and communications technology Information and communications technology, or better known by the term ICT, is believed to be the best medium in today’s business activities. This is because ICT can have an impact on the mega economy if the ICT technology is used efficiently and effectively to improve business performance. ICT is a medium that can contribute towards economic growth of a country 121

(Kriz & Qureshi, 2009). Besides that, ICT among villagers is able to change their socioeco­ nomic situation (Soriano, 2007). With the sophistication of today’s technology, entrepreneurs should no be longer faced with obstacles to securing information that can ease their business activities. Therefore, it can be hypothesized that: H2: There would be a significant difference in perception on ICT technology among SDSI entrepreneurs in East-Coast and West-Coast Peninsular Malaysia. 2.3 Business financing Entrepreneurship is a human energy or quality that is responsible for the creation of work by using production factors such as capital, materials, labor, and the ability to perform such activities as business opportunities, business ventures, and mobilizing economic resources to accelerate the growth of the economic process (Aziz et al., 2010). Up to today, the government has helped many low- and medium-income groups in Malaysia to engage and groom in entre­ preneurship. The provision of business start-up capital by the government, equipment assist­ ance, and active entrepreneurship counseling programs was carried out to open up the minds of people to involve in business and improve their economy (Anne, 2008). Social factors may be equally important as availability of loans, technical assistance, physical facilities, as well as information (Gnyawali & Fogel, 1994). Thus, it was then hypothesized that; H3: There would be a significant difference in perception on business financing among SDSI entrepreneurs in East-Coast and West-Coast Peninsular Malaysia.

3 METHODOLOGY 3.1 Population and sample size The population of this study encompasses of 1,431 entrepreneurs registered under the SDSI Program in Peninsular Malaysia. Based on Krejcie & Morgan (1970) and Glenn (1992) table, the sample size of this study then was determined to be 306 respondents. 3.2 Instruments and measurements The data for this study was collected through a questionnaire survey that asked questions related to the entrepreneurial development program, business performance, and respondents’ background and business profile. The seven-point Likert Scale is used to measure the extent of respondents’ views of the developed variables.

4 FINDINGS AND DISCUSION Based on the statistics provided by the Prime Minister’s Department in 2018, there are 1,431 SDSI entrepreneurs in Peninsular Malaysia; 514 are SDSI entrepreneurs in East Coast and 917 in West Coast. 4.1 Response rate There were 500 sets of questionnaires distributed to the SDSI entrepreneurs in East-Coast and West-Coast Peninsular Malaysia, however 466 sets of questionnaire (93.2% response rate) were returned and usable to proceed with further analysis in this study.

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4.2 Descriptive respondent’s profile As for demographic profile of SDSI entrepreneurs in East-Coast Peninsular Malaysia, 124 (53.9%) of the total respondents are male entrepreneurs, while the remaining 106 respondents (46.1%) are female entrepreneurs. The analysis found that respondents aged from 40 to 49 were the largest group involving 91 entrepreneurs (39.6%), while only 3 entrepreneurs (1.3%) out of 230 respondents are youth entrepreneurs aged below 30 years old. Most of SDSI entre­ preneurs in East-Coast Malaysia were educated in secondary school (30.9%); 28.7% obtained certificates; and 12.6% and 11.7% were diploma holders and degree holders respectively. In terms of the level of entrepreneurs’ satisfaction towards SDSI program that has been devel­ oped by the government, 63% were satisfied with the SDSI programs provided by the govern­ ment, 32.6% were neutral, and 4.3% were not satisfied. Meanwhile, analysis of the demographic profile of SDSI entrepreneurs in West-Coast Pen­ insular Malaysia reveals that 111 (47.0%) of the total respondents are male entrepreneurs, while the remaining 125 respondents (53.0%) are female entrepreneurs. The analysis found that respondents aged between 40 and 49 were the majority involving 87 entrepreneurs (36.9%). In terms of education, the majority possessed certificates (35.2%). In terms of their satisfaction towards entrepreneurship programs provided to them, 131 respondents (55.5%) were satisfied with SDSI Program, 99 entrepreneurs (41.9%) were neutral, and 6 entrepreneurs (2.5%) were not satisfied with the SDSI programs. 4.3 T-test analysis Table 1 shows that there is no significant difference in perception of entrepreneurial training and development among the SDSI entrepreneurs in East-Coast and West-Coast Peninsular Malaysia with the significant value 0.186 (t = –1.324; p > 0.05). The result indicates that region is not a vital factor contributing to significant difference on entrepreneurial training practices among SDSI entrepreneurs in East-Coast and West-Coast Peninsular Malaysia. Thus, this study rejected the result of a study by Fouda (2002) that stated region was a motivating factor that influenced the perception of entrepreneurship training and develop­ ment. Interestingly, the results reveal that the SDSI entrepreneurs in East-Coast Region tend to practice entrepreneurial skills that they learned during the training into their business activ­ ity slightly more with a mean value of 5.4822, as compared to SDSI entrepreneurs in WestCoast Region with a mean value of 5.3576. As shown in Table 2, the results indicate that SDSI entrepreneurs in West-Coast Region tend to practice ICT technology in their business activity more with the a mean value of 4.8387, as compared to East-Coast Region with a mean value of 4.5984. The results also show that there is a significant difference in perception of ICT technology training among SDSI entrepreneurs in East-Coast and West-Coast Peninsular Malaysia with the significant value 0.030 (t = 2.179; p < 0.05). The result proved that region is a crucial factor that contributed to different perception on ICT technology training and practices among SDSI entrepreneurs in East-Coast and West-Coast Peninsular Malaysia. This difference in perception between EastCoast and West-Coast Region verified that the practice of ICT technology is influenced by the ICT exposure and practice by society in those respective regions.

Table 1. Perception of entrepreneurial skills training.

Entrepreneurial skills training

Region

Mean

Std. Deviation

EastCoast

5.4822

1.11848

West­ Coast

5.3576

0.90402

123

t

Sig.

–1.324

0.186

Result H1 - Not accepted

Table 2. Perception of ICT technology training.

ICT technology

Region

Mean

Std. Deviation

EastCoast

4.5984

1.36176

West­ Coast

4.8387

t

Sig

2.179

0.030

Result H2 - Accepted

0.99433

Table 3. Perception on business financing.

Business financing

Region

Mean

Std. Deviation

EastCoast

4.7565

0.98676

WestCoast

4.7578

t

Sig.

0.014

0.989

Result H3 - Not accepted

0.93563

Besides training, access to financial assistance is also crucial for entrepreneurs to stay and grow in their business. Table 3 shows a very close mean value between East-Coast and WestCoast Region on business financing practice among the SDSI entrepreneurs with 4.7565 and 4.7578 respectively. Thus, it is clearly shown that there is no significant difference in business financing practice between these two regions since the significant value is 0.989 (t = 0.014; p > 0.05). The result highlights that region is not a prominent factor that contributed to significant difference on business financing practices among SDSI entrepreneurs in East-Coast and WestCoast Peninsular Malaysia. Thus, the finding by Gnyawali and Fogel (1994) was rejected.

5 CONCLUSION From the results, it can be concluded that majority of SDSI entrepreneurs, whether in EastCoast or West-Coast Peninsular Malaysia, are satisfied with the training and development pro­ grams that have been provided to them by government agencies. In fact, region is not the important factor that influences their perception on training and development programs. How­ ever, ICT training maybe a bit different because it is known to the public that people in WestCoast Region are much more exposed to ICT than people in East-Coast and that is the reason why there is a different perception on the ICT training and practices Region, as revealed by the SDSI respondents who participated in this study. In terms of financial assistance, respondents from both regions reveal that there is no difference in financial training and practice, which also supports the theory that the government gives the fair treatment to all SDSI entrepreneurs. ACKNOWLEGEMENT This research was supported by the Malaysian Ministry of Higher Education (FRGS). REFERENCES Aziz, R. A., Mahmood, R., Abdullah, M. H., & Tajudin, A. (2010). The mediating effects of entrepreneur­ ial orientation on the relationship between leadership styles and performance of SMEs in Malaysia (Doc­ toral dissertation, Universiti Utara Malaysia).

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Anne, S. (2008). Universities: An Entrepreneurs’ Ecosystem. Universiti Sains Malaysia. Dewan Bahasa dan Pustaka. Fourth Edition. Kuala Lumpur. Chell, E. (2013). Review of Skill and the Entrepreneurial Process. International Journal of Entrepreneurial Behavior & Research, 19(1): 6–31. Glenn, E. N. (1992). From Servitude to Service Work: Historical Continuities in the Racial Division of Paid Reproductive Labor. Signs: Journal of Women in Culture and Society, 18(1): 1–43. Gnyawali, D. R., & Fogel, D. S. (1994). Environments for Entrepreneurship Development: Key Dimen­ sions and Research Implications. Entrepreneurship Theory and Practice, 18(4): 43–62. Fouda, O. M. (2002). La perception de l‟ esprit d‟ entreprise chez les jeunes des différentsgroupes ethni­ ques camerounais (The perception of entrepreneurship among young people from different ethnic group in Cameroon). Actes Deuxième congrès de l’entrepreneuriat, champ de l’entrepreneuriat et dyna­ mique des sociétés. Hisrich, R. D. (2005). Entrepreneurship Education and Research. Grundungsforschung and grundung­ slehre, 17–94. Krejcie, R. V., & Morgan, D. W. (1970). Determining Sample Size for Research Activities. Educational and Psychological Measurement, 30(3), 607–610. Kriz, K., & Qureshi, S. (2009). The Role of Policy in the Relationship between ICT Adoption and Eco­ nomic Development: A Comparative Analysis of Singapore and Malaysia. Academia Education. Osemeke, M. (2012). Entrepreneurial Development and Interventionist Agencies in Nigeria. International Journal of Business and Social Science, 3(8): 255–265. Soriano, C. R. R. (2007). Exploring the ICT and Rural Poverty Reduction Link: Community Telecenters and Rural Livelihoods in Wu’an, China. The Electronic Journal of Information Systems in Developing Countries, 32(1): 1–15. Statistics Department. (2009). Malaysia: Labor market report Q1 2009.

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Digital Economy for Customer Benefit and Business Fairness – Anggadwita & Martini (eds) © 2020 Taylor & Francis Group, London, ISBN 978-0-367-47722-6

How coworking space impacts innovation: A literature review M.T. Amir Department of Management, Universitas Bakrie, Jakarta, Indonesia

ABSTRACT: The global trend of coworking spaces also occurs in Indonesia, and its impact on promoting a new climate for developing innovation is critical. However, how specifically the coworking spaces’ promises promote innovative behavior of individual tenants is relatively unknown. This study reviews the extant literature on coworking spaces and evaluates the roles of the space manager on creating positivity, interaction, and the process of innovation (gener­ ating, promoting, and implementing ideas) of the tenants. It explores the combination of posi­ tive organizational scholarship, social interaction, and innovation concepts, to describe the strategies of the managers, and the potential experiences of the tenants. This study contributes to the domain of creativity and innovation research, especially in the context of individual innovation. At the practical level, the study is expected to provide insights and applied recom­ mendations for coworking spaces managers to benefit tenants and gain loyalty from them. The findings may inform further research in the area of innovation and the future of work.

1 INTRODUCTION Since Brad Nueberg introduced the concept in 2005 (Bostman & Rogers 2010), the popularity of coworking space (CWS) as an alternative work spatial continues to rise and its types and definitions are developing. Spinuzzi (2012) conceptualizes CWS as an “open-plan environment in which people work together with other unaffiliated professionals for a fee.” Suggesting a collaboration environ­ ment, Capdevila (2015) defined CWS as localized spaces where independent professionals work-sharing resources are open to sharing their knowledge with the rest of the community. Interaction is one of the requirements in developing creativity, and how the role of CWS in supporting the process of professional innovation becomes the interest of scholars. The value offered by CWS seems relevant in promoting innovation processes; in idea generation, promo­ tion, as well as implementation. In their study of dynamics of creativity in a creative city, Grandadam et al. (2013) suggested that the interaction of diverse people has a significant role in generating new ideas, concepts, or skills. Bostman & Rogers (2010) and Spinuzzi (2012) emphasized the collaboration benefits of CWS in creating a conducive environment for innovation. While growing in creative cosmopolitan cities, like San Francisco, NewYork, London, and Amsterdam (Gandini, 2015), the phenomenon of coworking space also occurs in Jakarta, the capital city of Indonesia. Despite this promising development, coworking spaces managers still have various challenges and problems. For instance, coworking spaces in Indonesia need a different business model than their counterpart in advanced industrial countries due to the different market characteristics (Low, 2017). Although there is a trend to and phenomenon of the growing CWS, it is not fully under­ stood how coworking spaces can be effective in fostering innovation and what role of the CWS managers could play. Also, there is still little academic research, particularly in the Indo­ nesian context, dedicated to the domain of innovative behavior of CWS users. This study examines literature to check the link between positivity and the interactions that CWS involve in tenants innovativeness. What kind of strategies do CWS normally take and how 126

do the tenants perceive them, or what kind of experiences do they have. The study will contribute to the domain of creativity and innovation research, especially in the context of individual innov­ ation. At the practical level, the study is expected to provide insights and applied recommenda­ tions for coworking spaces managers in benefitting tenants and gaining loyalty from them.

2 THE NATURE OF COWORKING SPACE Coworking spaces, as one form of the continuum that includes working from the traditional home office, can be viewed from two aspects: First, as a work facility, where it is defined as open-plan offices where independent knowledge workers work, that normally used by a mobile, independent professional; and second, as Water-lynch et al. (2016) suggest, it also involves professional work and its interaction mechanism; that is, “working alone together” where collaboration occurrs with different backgrounds of professionals and specific strategies of the office manager. Kristensen (2004), for instance, found that workspace supported different stages of a creative process. Examining an interdepartmental project team as a case study, the study found that cre­ ativity could take place depending on the confinements of workspaces. The limitation was found to allow certain cognitive processes but at the same time also restricts others. This, in turn, induces emotions that will facilitate or reduce the enhancement of creativity. Samani et al. (2014) suggest an organization needs a flexible workplace, in this kind of environment, to support creativity. This study draws on the work of Raitis et al. (2017), where positivity involves positive attributes in the organizational context. For the purpose of this study, positivity is defined as ambiance or setting that produces positive perception. For instance, it may create attractiveness, ambiance, and atmosphere in joyful, attractive or eye-catching, unique or distinctive physical facilities in the cow­ orking space that potentially create one of the positive emotions. In the creativity literature, some studies have indicated the link of creativity and workspace arrangement. Fuzi et al. (2014) and Kristensen (2004) found the need to develop a diverse office environment that provides a degree of comfort in order that collaboration is facilitated to promote creative work.

3 POSITIVITY IN INNOVATION This research is quantitative research with an explanatory study. Data was collected using ques­ tionnaires distributed in the form of Google docs and filled out by 106 respondents. This research uses quantitative method and Bernoulli sampling. With (α) =10%, the minimum number of respondents is 97. This research uses Bernoulli sampling because the size of the existing population cannot be known with certainty (Indrawati, 2015). This research uses a Likert scale. Innovation is often viewed as a new idea or concept of how to organize a solution to a problem (de Jong & den Hartog, 2007). Following the dynamic of competition and technology, labor mobility, and distributed knowledge across organizations, the innovation setting has changed. Increasingly, innovation emerges at the crossroads of knowledge territories. Organizations now recognize the need for external capabilities, rather than relying solely on internal innovation cap­ ability. According to Enkel et al. (2009), external actors have become an increasingly crucial part of companies’ innovation capability, including from the environment of coworking space. The assumption that innovativeness can be increased can be drawn from the study of posi­ tivity and innovation, where innovation is seen as a process. In this perspective, innovation consists of stages explaining the process of generating, promoting, and implementing useful ideas (Carmeli et al., 2006). Each process involves certain challenges (Amir, 2014), where a certain work climate may have a role in facilitating the innovation process. Some studies seek the link between psychological aspects of creative or innovative behav­ iour. Sweetman et al. (2010), for instance, found that the element of psychological capital is associated with creative performance. It specifically emphasizes that the idea-generation phase of creativity needs psychological resources such as hope or optimism. Figure 1 depicts the connection between positivity, interaction, and innovativeness. The next section discusses these aspects in more detail. 127

Figure 1.

Coworking space facilitating innovative behaviour.

3.1 Positivity in generating ideas In positive psychology, Seligman (2012) also predicts that optimists incline to be imaginative and propose new ideas easily as they are visionary. Positivity may facilitate a person’s creative self-efficacy, and creative performance is found related (Tierney & Farmer, 2002). Coworking space can be expected to stimulate prerequisite conditions for idea-generating. The link between the positive emotion resulting from the CSW environment and the creativ­ ity produced for its members can also be drawn from the opportunity recognition theory. Positive emotion may play an important role in the process of noticing, interpreting, and evaluating the potential of opportunities, since it enhances cognitive performance and broad­ ens the scope of attention (Fredrickson & Branigan, 2005; Isen, 2002), and increases the real­ ization or implementation of opportunity. 3.2 Positivity in promoting and implementing ideas In the promoting and implementing stage, innovators need aspects that buffer their emotion when they face challenges, such as persuading others. Positive emotion that results from a positive climate may provide innovators with the energy for this purpose, as they need to persuade others reiteratively. When they have negative feedback, such as rejecting ideas or avoiding discussion, positive emotion helps them to be persistent (Amir, 2016). Consistent with this, Cohn et al. (2009) found positive emotion facilitates innovators as a resource for adaptation in altered situations. Innovators also face challenges when many parties need con­ vincing. Positive emotion provides confidence and a positive outlook, helping innovators to maintain their effort and find alternatives if one approach fails.

4 SOCIAL INTERACTION IN INNOVATION Botsman and Rogers (2010) note that social interaction contributes to innovation. Hence, it is important that managers of coworking spaces supporting the formation and nurturing of social capital. Social interaction increases the chance of knowledge exchange between different people at coworking spaces. To realize the interaction need for members, management of coworking spaces conduct sev­ eral strategies. For instance, management could promote interaction by coordinating and con­ necting the member through several tools. Members recognize that appropriate connection is one of their purposes in choosing a coworking space. The connecting role of management can lead to a reduction of the time needed to find appropriate connections and to an increase in the chance of the connections being valuable (Hering & Phillips 2005). One of the popular strategies in promoting interaction in coworking space is by the mixing of tenants (Cabral & Van Winden, 2016). Identifying and mixing specific characteristics of tenants will help them to increase the chance to interrelate, cooperate, and benefit from each 128

other’s attendance. Another strategy often used to encourage interaction is using social net­ working tools, like social events, awareness creating, and social network sites.

5 CONCLUSION AND RECOMMENDATION The review above discussed the possibility of coworking spaces managers using strategies that lead to positivity and interaction, that in turn facilitate the innovativeness of the tenants. Cow­ orking spaces offer an alternative workplace for various professions and its advantages are found to build individual and team innovativeness. Creating positivity in the form of arran­ ging comfortable ambiance is foundational to increasing tenants’ creativity. The newness and uniqueness of the physical arrangement in the CWS is also another crucial form of positivity. In forging these interactions, establishing the right communication strategies and attractive, as well as relevant, events have a significant role. In later phases of innovation, CWS needs to be more active in designing specific strategies in recruiting tenants, including cooperating with relevant organizations that may become access points for tenants to realize their innovation. This study contributed to innovation research by understanding the role of positivity and interaction. The details of how each stage of innovation is impacted by those factors may be a further interesting area to be explored. REFERENCES Amir, M. T. (2014). The Role of Resilience in Individual Innovation. https://ro.ecu.edu.au/theses/873 Bostman, R., & Rogers, R. (2010). What’s Mine Is Yours: The Rise of Collaborative Consumption. New York: Harper Collins. Cabral, V., & Van Winden, W. (2016). Coworking: An Analysis of Coworking Strategies for Interaction and Innovation. International Journal of Knowledge-Based Development, 7(4): 357. Capdevila, I. (2015). Co-working Spaces and the Localised Dynamics of Innovation in Barcelona. Inter­ national Journal of Innovation Management, 19(03): Carmeli, A., Meitar, R., & Weisberg, J. (2006). Self-Leadership Skills and Innovative Behavior at Work. International journal of manpower, 27(1): 75–90. Cohn, M. A., Fredrickson, B. L., Brown, S. L., Mikels, J. A., & Conway, A. M. (2009). Happiness Unpacked: Positive Emotions Increase Life Satisfaction by Building Resilience. Emotion, 9(3): 361. Enkel, E., Gassmann, O., & Chesbrough, H. (2009). Open R&D and Open Innovation: Exploring the Phenomenon. R&d Management, 39(4): 311–316. Fredrickson, B. L., & Branigan, C. (2005). Positive Emotions Broaden the Scope of Attention and Thought-Action Repertoires. Cognition & Emotion, 19(3): 313–332. Fuzi, A., Clifton, N., & Loudon, G. (2014). New In-House Organizational Spaces that Support Creativ­ ity and Innovation: The Co-Working Space. R & D Management Conference 2014, Stuttgart. Gandini, A. (2015). The Rise of Coworking Spaces: A Literature Review. ephemera, 15(1): 193–205. Grandadam, D., Cohendet, P., & Simon, L. (2013). Places, Spaces and the Dynamics of Creativity: The Video Game Industry in Montreal. Regional Studies, 47(10): 1701–1714. Hering, D., & Phillips, J. (2005). Innovation Roles: The People You Need for Successful Innovation. White Paper, NetCentrics Corporation. Indrawati, P. D. (2015). Metode Penelitian Manajemen dan Bisnis Konvergensi Teknologi Komunikasi dan Informasi. Bandung: PT. Refika Aditama. Isen, A. M. (2002). Missing in Action in the AIM: Positive Affect’s Facilitation of cognitive Flexibility, Innovation, And Problem Solving. Psychological Inquiry, 13(1): 57–65. De Jong, J. P., & Den Hartog, D. N. (2007). How Leaders Influence Employees’ Innovative Behaviour. European Journal of Innovation Management, 10(1): 41–64. Kristensen, T. (2004). The Physical Context of Creativity. Creativity and Innovation Management, 13(2): 89–96. Low, L. 2017. Connecting Creative Communities: Creative Hubs in Malaysia,Thailand, Indonesia & the Philippines. creativeconomy.britishcouncil.org.

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Raitis, J., Harikkala-Laihinen, R., Hassett, M., & Nummela, N. (2017). Finding Positivity during a Major Organizational Change: In Search of Triggers of Employees’ Positive Perceptions and Feel­ ings. In Emotions and Identity (pp. 3–16). Emerald Publishing Limited. Samani, S. A., Rasid, S. Z. B. A., & bt Sofian, S. (2014). A Workplace to Support Creativity. Industrial Engineering & Management Systems, 13(4): 414–420. Seligman, M. E. (2011). Flourish: A Visionary New Understanding of Happiness and Well-Being. Policy, 27(3): 60–61. Spinuzzi, C. (2012). Working Alone Together: Coworking as Emergent Collaborative Activity. Journal of Business and Technical Communication, 26(4): 399–441. Sweetman, D., & Luthans, F. (2010). The Power of Positive Psychology: Psychological Capital and Work Engagement. Work Engagement: A Handbook of Essential Theory and Research, 54–68. Tierney, P., & Farmer, S. M. (2002). Creative Self-Efficacy: Its Potential Antecedents and Relationship to Creative Performance. Academy of Management Journal, 45(6): 1137–1148. Waters-Lynch, J., Potts, J., Butcher, T., Dodson, J., & Hurley, J. (2016). Coworking: A Transdisciplinary Overview. SSRN 2712217.

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Digital Economy for Customer Benefit and Business Fairness – Anggadwita & Martini (eds) © 2020 Taylor & Francis Group, London, ISBN 978-0-367-47722-6

Environment changes and effects to the fashion business R. Martiniatin & A. Ghina Telkom University, Bandung, Indonesia

ABSTRACT: Environment changes also happen in the fashion business. Fashion entrepre­ neurs, as the owners of fashion MSMEs, should develop plans and strategies so that they can deal with change. The purpose of this study is to identify the changing fashion business environment in Bandung, the impact of the changes, and how entrepreneurs respond to it. The interviewees involved were fashion entrepreneurs as the owners of MSMEs (three people), and stakeholders including the government sector, associations, communities, media, and academics (five people). Entrepreneur owners of UMKM were interviewed about the environment, and characteristics and behavior relevant to running their MSME. Stakeholders were interviewed about the environment, characteristics, and behavior based on their observations of MSMEs under their guidance. The data collection in this study util­ ized qualitative data by using an interview protocol to identify changes in the fashion busi­ ness environment. The findings of this research is that there are changes in the fashion business environment, including changes in fashion trends, changes in production scale, changes in marketing strategy, and macro-economic changes such as inflation and regional minimum wage increases. Thus it can be seen that the entrepreneur must be able to respond quickly to the various possibilities of a changing business environment and always have a carefully planned set of strategic steps for the company.

1 INTRODUCTION Entrepreneurial activities generally occur intentionally. Entrepreneurs intend to pursue opportunities, enter new markets, and offer new products. Intentions capture motiv­ ational factors that can influence behavior: motivational factors are an indication of how hard the entrepreneurs try and how extensive their efforts in planning and executing entrepreneurial behavior are. In general, the stronger the intention to use these behaviors, the better the performance will be. Individuals have strong intentions to have a business when they feel the business is feasible and they desire to carry out the business (desir­ able). In order to have an understanding of feasibility and desirablity, individuals should first describe the factors that influence their intention, one of which is the environment (Hisrich et al., 2010). Entering the 21st century, there has been a big change in the busi­ ness environment as this is the era of globalization in business, market, technology and information, and quality management so that the organization and management of many companies experienced change in their search for competitive advantage. Such changes may have an impact on corporate strategy (Kuswanto, 2013). Data from Badan Pusat Statistik in 2014 showed that West Java has 355,327 MSMEs in the fashion sector (BPS, 2019), and Bandung, as the capital of West Java, is projected to become Fashion City (Bandung Fashion City, 2019). The source of this research is a fashion entrepreneur who has a nonbusiness education background and has been in business for more than five years. The purpose of this study is to identify the changing fashion business environment in Ban­ dung, the impact of the change, and what entrepreneurs should do to respond to it.

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2 LITERATURE REVIEW The owners of MSMEs, here referred to as entrepreneurs, are individuals who undertake entre­ preneurial activities, namely, a process of creating more value at the expense of time and energy and assuming the financial, psychological, and social risks that will accompany it, which will result in financial rewards, personal satisfaction, and independence (Hisrich et al., 2010). Kasmir (2006) mentioned that entrepreneurs are courageous people who take risks opening a business, courage being seen as to be independent and dare to start a business without fear or anxiety even in uncertain conditions. In his mind, an entrepreneur always seeks, utilizes, and creates business opportunities that can provide benefits. Based on the Law of the Republic of Indonesia Number 20 of 2008 on MSMEs, it is explained that: 1. Micro enterprise is a productive business owned by an individual and/or individual busi­ ness entity fulfilling the criteria of micro business as regulated in this Law. 2. A small business is a stand-alone productive economic enterprise undertaken by an individ­ ual or business entity that is neither a subsidiary nor a branch of a company owned, con­ trolled, or part, either directly or indirectly, of a medium-sized or large-scale business that meets the criteria small business as defined in this Law. 3. An intermediate enterprise is a stand-alone productive economic enterprise, carried out by an individual or business entity not owned by an individual or a business entity which is a subsidiary or branch of a company owned, controlled, or otherwise part, either directly or indirectly, of a small business or a large business with a net sum or an annual sale as stipulated in this Law. Entrepreneurs, then, undergo an entrepreneurial process, which involves searching for, evaluating, and developing opportunities to create new ventures. There are formal or informal mechanisms for identifying business opportunities. Most entrepreneurs use informal sources to get ideas, such as by being sensitive to comments and complaints about events, or learning of opportunities from friends and coworkers. Once business opportunities are identified, the evaluation process begins. The basis of the process of identification and evaluation is to understand the factors that create opportunities: technology, market changes, competition, or changes in government regulations (Hisrich et al., 2010). Some literature is used as a reference in identifying the stakeholders of MSMEs in Indo­ nesia. Based on the Organization for Economic Cooperation and Development (OECD) report 2016 and the Global Entrepreneurship Monitoring (GEM) Report for Indonesia, 2014, the stakeholders are: 1. 2. 3. 4. 5.

Public Sector : Government Private Sector : NGO, Associate, Media Financial Sector : Financial Institution Academic Community

Government, as a stakeholder of MSMEs, is a regulator or policy maker that regulates all aspects of MSMEs’ activities as contained in the Law of the Republic of Indonesia Number 20 Year 2008 starting from the classification of the type of micro, small, or medium enterprises based on net asset value of the company excluding land and buildings, and arranging matters related to financing, partnerships, and so forth. NGOs, Associations, and Communities have the function of being party to mentoring the government program and also providing assistance to small businesses through the holding of entrepreneurship seminars and training (GEM, 2014). Academics, as stakeholders, are responsible for generating the latest concepts and theories rele­ vant to the business required by entrepreneurs to achieve sustainable competitive advantage. Aca­ demics conduct research and development as a form of dedication to the community, including by providing mentoring and coaching to build small businesses around the campus. As proof of participation in improving MSMEs, academics today have started entering the curriculum of entrepreneurship in their education (GEM, 2014). 132

Financial institutions have an interest in inviting small businesses to become business partners. In addition, Bank Indonesia, as a central bank, has a long-term program to develop small-scale businesses through the real sector of the Technical Assistance program. Nonbank financial institu­ tions such as Pegadaian also focus on helping small businesses by providing soft loans. Microfi­ nance institutions in the form of cooperatives and BMTs have a market share of small businesses in remote villages. State-owned enterprises also have programs to assist small business capital through the Partnership and Community Development Program (GEM, 2014). Unique small business stakeholders include mass media. Information is conveyed through reviews, news, opinions of progress, and progress of small businesses. Media should be opti­ mized for the branding of products and business of every MSME (GEM, 2014). The involvement of stakeholders also has a role in creating an environment that can affect entrepreneurship development. MSMEs stakeholders consist of government, NGOs, associ­ ate, community, academics, financial institutions, and media (GEM, 2014). The basis of the process of identification and evaluation of an opportunity is to understand that the environment that supports the creation of the opportunity itself is technology, market changes, competition, or changes in government regulations (Hisrih et al., 2010). The research framework in this study is described in Figure 1 below.

Figure 1.

Research framework.

3 METHODOLOGY Because the field of entrepreneurship research is still in the development stage, the research undertaken in this field should emphasize interpretation and understanding, not rational pre­ dictions or explanations (Dana 2004). Characteristics of the study are described in Table 1. The most appropriate paradigm used is interpretivism or a qualitative approach – a way to gain insight by improving our overall comprehension. Qualitative research explores the richness, depth, and complexity of a phenomenon, and is broadly defined as all types of research that yield findings not through statistical procedures or other quantification processes (Wahyuni, 2012). This research belongs to qualitative research, defined as a research approach involving data analysis in the form of description and wherein the data is to be quantified. Quantifying quali­ tative data is done by coding or categorizing. This type of research attempts to transform the object of research into a form that can be presented, such as field notes, interviews, conversa­ tions, photographs, recordings, and memos (Indrawati, 2015).

Table 1. Characteristics of the study. Characteristics Paradigm Methodology Method Purpose Researcher’s involvement Unit of analysis Execution time

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Interpretivism Qualitative Case Study Descriptive Interfere with data Unit of analysis Cross sectional

This study used qualitative data as the data collection method, using the interview protocol to identify environment changes in the fashion business to understand the meaning behind the existing data (Sugiyono, 2014). Based on its purpose, this research can be categorized as descriptive research because this research is described or itself describes phenomenon that happened in the real world. Qualitative research does not use the population, because qualitative research is based on particular cases that exist in specific social situations; likewise, the results of this research will not be applied to the population, but rather in social situations that have similarities with the social situation in the case studied. In this study, researchers observe in-depth a patterned activity (activity) of a group of people (actors) in a particular place (Sugiyono, 2014). In this study, the operational variables outlined were based on the framework in Figure 1 and then transformed into an interview protocol in the form of questions to be asked of informants in order to identify changes in the business environment experienced by fashion entrepreneurs. Interviews were also conducted with some entrepreneurial experts representing stakeholders in order to identify changes in the business environment and what entrepreneurs do, based on the stakeholders’ observations. The interview protocol is shown in Table 2. Data collection techniques were based on the purposive sampling method with criteria determined by researchers. Table 3 below shows the people interviewed. This research used as data source questions derived from the interview results that the researcher used for general guidance of the interview, where the questions asked were not standard (scripted) words, but were modified (adapted) during the interview. The reason for choosing this method is because the researcher wanted to dig deep (in-depth interview) about the phenomenon to be studied (Dana, 2004). In addition, this method is intended to provide more opportunity for resource persons to speak and potentially to direct research into novel and unexpected areas of research (Neergaard et al., 2007).

Table 2. Interview protocol. No. Variables

Interview Protocol

Expected Answers

What would you do if there are major changes such as related to government regulations, changes in exchange rates and interest rate? (Q1) Do you think the business envir­ onment is changing? (Q2) Does the change have an impact on your business? (Q3)

Interviewees will explain what they have done to face up to major changes.

Qs for MSMEs 1

Environment changes and pol­ itical situation will signifi­ cantly affect the entrepreneurial process [1]

2 3

4

What do you do to respond to the impact of the change? Does it have obstacles? How to over­ come these obstacles? Explain. (Q4)

Interviewees will explain the business change. Interviewees explain that the changes sometimes have an impact to their business. Interviewees describe the efforts made to respond to change, and how to overcome various obs­ tacles that occur.

Qs for Stakeholders 1

What is the environment busi­ ness change? (Q1)

Interviewees describe the envir­ onment business change.

2

What is the impact of changes to their business (Q2)

Interviewees describe the impact of environment business change.

3

How should entrepreneurs respond environment business change (Q3)

Interviewees describe how entre­ preneurs should respond to the environment business change.

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Table 3.

Interviewees criteria.

Category

Interviewees

Entrepreneurs

1. Nazmi Indonesia 2. Load Bag 3. Acidwerk Kids 1. Government 2. Association 3. Community 4. Academics

Stakeholders

This research was conducted by testing the source triangulation or checking the results of interviews against existing documents, as well as checking answers from several different sources that have consistently said the same thing. Triangulation was also obtained from selected sources so as to reach the saturation level of information. In addition, this research also implemented triangulation theory by using some perspective or theory to interpret the data (theory/perspective triangulation). This research used the analytical technique of the Miles and Huberman model where data ana­ lysis in qualitative research is done at the time the data collection takes place, and after completion of data collection is done in a certain period. Analysis of the answers that would be discussed during the interview was done before the interview process took place. If the answer made by the resource person at the interview was not satisfactory, then the research question could be revisited up to a certain, predetermined stage to obtain data that was considered credible. The Miles and Huberman model, in Sugiyono (2014), emphasizes analysis activity conducted interactively and continuously until the data is saturated. Activities in data analysis include data reduction, display data, and conclusion drawing/verification. Before going into data reduction, researchers first per­ form anticipatory data reduction. Anticipatory data reduction is when the researcher decides which conceptual model to use, the object to be studied, the research question to be used, and the data collection method to be used (Sugiyono, 2014). This model will be shown at Figure 2 below.

Figure 2.

Miles and Huberman model.

4 RESULT AND DISCUSSION This article will describe the interview data that has been written in the form of coding pre­ sented in the research results. Based on the coding, then, an explanation of identification related to the environment changes of the fashion business from MSMEs owner-as­ entrepreneur perspectives and from stakeholder perspectives will follow.

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4.1 Interviewees characteristics In this study there are two groups of interviewees, namely: a. Entrepreneur In this group, there are three entrepreneurs from the field of fashion with nonbusiness educa­ tion backgrounds who have been in business for more than five years as resource persons. They were interviewed to get a clear picture about environment business changes from their perspectives. They are shown in Table 4. Table 4.

Entrepreneur group.

Code

Interviewees

Occupation

N1 N2 N3

Aninda Nazmi Mulki Nazmulhakim Adhy Perdana

Owner of Nazmi Indonesia Owner of Load Bag Owner of Acidwerk

b. Stakeholders

In this group, four speakers were interviewed, representing the government sector, association,

community, and academia, to get an idea about environmental business changes from stake­ holder perspectives based on their expertise. They are shown in Table 5 below.

Table 5. Stakeholders group. Code

Interviewees

Occupation

N4 N5 N6 N7

Lina Auliana Shantika Feby Arhemsyah Muchdi Citra Leorista Sisca Eka

Kamar Dagang Indonesia; as government Cofounder Kicks; as association Bandung Fashion Society; as community Lecturer; as academics

4.2 Results The results of the interviews were converted into text verbatim so that the written text con­ veyed was exactly the same as the spoken. The results of the verbatim interviews were then grouped into two groups: data derived from interviews with entrepreneurs (N1, N2, and N3), and data derived from stakeholder sources (N4, N5, N6, and N7). Then each verbatim result underwent a process of reduction, whereby data that was important and directly related to the research wascoded, while non-essential data was eliminated. Then the reduced data was col­ lected in a table for subsequent triangulation between interviews with entrepreneurs and the results of interviews with stakeholders. Based on interviews with N1 to N3, environment changes will be identified at Table 6 below.

Table 6. Environment changes from entrepreneurs’ perceptions. Questions N1

N2

N3

Conclusion

Q1

No action is taken Environment change is about increasing or

No action is taken

No action is taken

Environment change is about marketing stra­ tegically, from offline

1. Design style 2. Production scale 3. Marketing strategy

Q2

No action is taken Environment change is about

(Continued )

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Table 6. (Continued ) Questions N1 the style of fash­ ion design Changes occur in accordance with taste

Q3

Q4

Never experienced a significant change in the business environment

N2

N3

Conclusion

decreasing production scale Changes will have an impact on costs, but can still be handled by revenue Immediately responds to changes caused by consumer demand

marketing to online marketing Shop closures, downsiz- 1. Changes in design style ing employees 2. Increased production costs 3. Increase in product prices 4. Offline store closing 5. Downsizing organizations Must quickly find Must respond immediately a solution when and find a solution impacted by environmental changes

Based on interviews with N4 to N7, environment changes will be identified at Table 7 below.

Table 7. Environment changes from stakeholders’ perceptions. Qs

N4

N5

N6

N7

Q1

a. Small changes: fashion trend b. Big changes: inflation, regional minimum wage rise

Trend changes, and change the offline sales system to online

Q2

a. Employee No direct downsizing impact b. Cost efficiency c. Raise prices d. Bankruptcy

Q3

Immediately Response can respond to the be done impact of business gradually environment changes

Changes occur a. Added scale of 1. Changes in fashion trends due to the production 2. Change in onslaught of b. Strategic production scale cheap goods from changes, such 3. Changes in China; change as marketing marketing from offline strategies strategy business to online 4. Macroeconomic changes Adjust with the 1. Changes in Create new times, and adjust design style marketing to customer needs 2. Increased strategies; more production costs often socialize 3. Increase in domestic product prices products. Close 4. Offline store the store and closing become fully 5. Streamlining the online organization 6. Cost efficiency 7. Develop new marketing strategies 8. Bankruptcy Must respond Immediately Immediately immediately and respond to the respond to the find a solution impact of business impact of environment business changes environment changes

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Conclusion

4.3 Discussion Actions taken if there are changes in government regulations, exchange rates, or interest rate­ sThe N1, N2, and N3 informants unanimously state that no actions were taken because changes to government regulations, exchange rates, or interest rates do not significantly affect the business because, according to N1, N2, and N3, 99% of consumers are local Indonesian. However, if there is a change in the exchange rate resulting in an increase in raw-material prices, then one consequence will be that the entrepreneur must make adjustments to the sell­ ing price of the product. A. Changes in the business environment There are differences of opinion on this subject: N1 mentions that changes in the business environment usually occur in the form of a change in clothing model, where if the fashion trend is happening in accordance with the taste of the time, it will be adjusted in the next product. N2 mentions that the business environment can change depending on consumer demand, because N2 is on the bottom line in a clothing production process. For example, if at any time, there are consumers who place orders in large quantities and with short deadlines, then N2 will cooperate with partners to complete the order. N3 says that business changes have occured since the beginning, with emerging online stores that forced him to change the business from offline to online sales. The study continued with stakeholder interviews. N4 of Kadin separated business environ­ ment changes into two categories: minor changes and major changes. Small changes include a change of fashion trend, while major changes include the occurrence of inflation or an increase in the regional minimum wage that cause raw material prices to rise. N5, as a stakeholder of the association aspect, explained that what is considered as a change of fashion business environment is the change of trend and change of the sales system from offline to online, which requires the entrepreneur to arrange a new marketing strategy and of course will cause changes in the company. N6, as a stakeholder of the community aspect, stated that there has been a change as a result of the onslaught of goods from China, where the price on products is very cheap so it is not possible for local businesses to compete. In addition, advances in information and communi­ cation technology have lead to a change from running a business offline to being online. Meanwhile, N7, as an academic, stated that the addition of scale of production could cause changes in the company; for example, if the production process is done in a place that is too small and the production process must move to a larger place. Changes in marketing from offline to online also lead to a change of marketing strategy. Based on these explanations, it appears that every resource person interviewed has a different view on what causes the change of the fashion business environment. Based on these condi­ tions, it can be concluded that changes in the business environment can occur as a result of: a. Changes in fashion trends b. Changes in production scale c. Changes in marketing strategy d. Macro-economic changes such as inflation and regional minimum wage increases B. The impact of changes in the business environment The existence of both small changes and major changes in the business environment, will cer­ tainly have an impact on the business. The following will explain what impacts occur as a result of business environment changes: N1 states that a change in fashion trends may be a change in design style if the trend is in accordance with the taste of the designer. If the changes is seen as not appropriate, then the design style will remain in accordance with the initial taste of the designer. 138

N2 states that, as a result of changes in orders, companies not infrequently have to make adjustments to product prices. If there is an addition to the order and it must be done in a short time, then N2 will see additional working hours, which will automatically increase the production costs and lead to increased product prices. N3 tells of the impact of changes in the business environment that it faces, as do others, as a result of sales changes from offline to be online. N3 must close a store that previously existed in RE Martadinata Street and moved to his residence in Sumber Sari Complex. Shop closure is a step toward cost efficiency, including reduced store rental costs and store overhead costs. In addition, as a result of closing the store, the N3 must streamline the number of employees and cause unemployment. To explore more information on this subject, stakeholders were interviewed and the results are as follows: N4 states that an impact of business environment changes that often happens is entrepreneurs streamline the organization as a form of cost efficiency, raising the price of the product until bankrupt. N5, as founder of Kicks, states that, although there are changes, based on N5 observations, the fashion business players who joined in Kicks do not too feel the negative impact of changes in the business environment. N6, as stakeholder of the community aspect, explained that the impact of business environ­ ment change is that entrepreneurs have to arrange new marketing strategy to make sales happen and promote domestic product more frequently. One of them is routinely doing fash­ ion shows so that the local society still prefers domestic products, easily beating goods from China. Another effect known to N6 is that there are entrepreneurs who have had to shut down their offline stores and turn to online stores exclusively. N7, an academic informant, explains that the impact of business environment changes is that entrepreneurs must be able to make various adjustments in keeping with the development of the era and must be able to make adjustments in accordance with the needs of consumers. Thus, the business environment changes have various impacts, described by both entrepreneur and stakeholder resource persons, and can be summarized into several points, including: 1. Changes in design style 2. Increased production costs 3. Increase in product prices 4. Offline store closing 5. Streamlining the organization 6. Cost Efficiency 7. Develop a new Marketing Strategy 8. Bancrupty C. Response to the impact of business environment changes Related to this, if the entrepreneurs feel the impact of the business environment changes according to the explanation of N2, N3, N4, N6, and N7, then entrepreneurs should immedi­ ately respond and find solutions to allow their business to proceed. N5 basically stated the same thing but added that the response can be done gradually. N1 does not argue because, until now, N1 has never experienced a significant change in the business environment.

5 CONCLUSION AND RECOMMENDATION This study aims to map the behavior of MSMEs in responding to environmental changes, where the environmental changes have a significant impact on the survival of the company because, based on the results of interviews with the entrepreneurs, the effect of environmental 139

changes could result in bankruptcy. To deal with changes in the environment, entrepreneurs must be able to respond quickly and always have a plan regarding strategic steps for the company. In running a business, entrepreneurs must develop business plans and business strategies that are mature and developed for short-term and long-term planning. Because in planning and strategy all business-related issues are included, this should start from the company’s vision and mission, and include environmental and market analysis, business description, pro­ duction plans, marketing plans, organizational planning, and financial projections. In addition, business planning is useful for knowing what is needed in running a business, making entrepreneurs focus on the business and helping to formulate strategies to deal with competitors, determine the most suitable business, learn how to get revenue from the business, find out how much it will cost to run the business, and understand the profitability of the business. Stakeholders have a role in creating an environment that can influence the development of entrepreneurship and must make more of a effort to implement programs aimed at improving the quality of MSMEs, such as training, seminars, and management guidance. Suggestions for further research include in-depth and focused research on the role of stake­ holders so that they can synergize between them, so that the synergy can encourage the growth of quality MSMEs, especially in fashion. REFERENCES Badan Pusat Statistik. (2019). http://www.bps.go.id.

Bandung Fashion City (2019). http://www.insidebandung.com/2014/06/bandung-kota-fashion.html.

Dana, L. (2004). Handbook of Research on International Entrepreneurship. Glouchestershire: Edward

Elgard Publishing Limited. GEM Indonesia. (2014). Entrepreneurship in Indonesia; Conditions and Opportunities for Growth and Sustainability. Global Entrepreneurship Monitor 2015/2016 Report. Hisrich, R. D., Peters, M. P., & Shepherd, D. A. (2010). Entrepreneurship. McGraw Hill. Indrawati. (2015). Metode Penelitian Manajemen dan Bisnis, Konvergensi Teknologi Komunikasi dan Infor­ masi. Bandung: PT Refika Aditama, pp.206 Kasmir, S. M. (2006). Kewirausahaan. Jakarta: PT Raja Grafindo Persada, pp. 16. Kuswanto, H. (2013, April). Dampak perubahan lingkungan bisnis terhadap perusahaan, organisasi, manajemen strategi dan akuntansi manajemen”. Neergaard, H., Ulhøi, & John, P. (2007). Handbook of Qualitative Research Methods in Entrepreneurship. Gloucestershire: Edward Elgar Publishing Limited, pp. 83. Organisation for Economic Co-operation and Development (OECD). (2016). Active with Indonesia. OECD. Sugiyono. (2014). Memahami Penelitian Kualitatif. Bandung: Alfabeta, pp. 91–92. Wahyuni, S. (2012). Qualitative Research Method: Researech and Practice. Jakarta: Penerbit Salemba Empat, pp. 17.

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Digital Economy for Customer Benefit and Business Fairness – Anggadwita & Martini (eds) © 2020 Taylor & Francis Group, London, ISBN 978-0-367-47722-6

Food and beverages subsector companies valuation, for 2018 projection D. Isnaini & D. Rahadian Telkom University, Bandung, Indonesia

ABSTRACT: The objective of this research is to forecast the fairness of stock price of food­ and-beverages subsector companies listed in the Indonesian Stock Exchange. This research uses the Discounted Cash Flow (DCF) method with the Free Cash Flow to Firm (FCFF) approach and Calculate Relative Valuation method with Price to Book Value (PBV) and Price to Earn­ ings Ratio (PER) approaches. This research uses three scenarios, namely pessimistic, moderate, and optimistic. For the needs of the next five years’ projections, from 2018 to 2022, data pro­ cessed from historical financial performance data from the period 2013 to 2017 is used. The results, using the DCF method in the pessimistic scenario, showed that INDF, ICBP, and MYOR stock prices are overvalued; using the DCF in the moderate and optimistic scenario showed that INDF, ICBP, and MYOR stock price are undervalued. Furthermore, using the Relative Valuation method with the PER approach, at the pessimistic scenario INDF and ICBP are at fair value but MYOR is overvalued; in the moderate and optimistic scenario, INDF was fair valued, and ICBP and MYOR are overvalued. Using PBV at pesimistic, moderate, and optimistic scenarios, all the result for INDF, ICBP and MYOR are undervalued.

1 INTRODUCTION The food and beverage industry contributed 34.33% to the GDP (Gross Domestic Product) of the Indonesian national non–oil-and-gas manufacturing industry last year, compared to other sectors (Bisnis.com, 2018). The Ministry of Industry recorded the food and beverage industry was able to grow up to 8.67% in the second quarter of 2018. This performance exceeded national economic growth. The food and beverage industry also contributed greatly to the value of investment throughout the first semester of 2018 because it contributed 47.50% or fulfilled Rp21.9 trillion for domestic investment (PMDN). As for foreign investment (PMA), the food industry paid 10.41% (USD 586 million). Neaxie et al.’s research (2017) explained that at this time the interest of the Indonesian people to invest in the capital market in the form of shares began to increase along with posi­ tive economic growth and the development of information technology. Stocks are included in one financial instrument that has the characteristics of high risk, high return. Stock prices fluc­ tuate due to various factors and information circulating on the exchange. People who want to invest in the capital market, need to be aware that stock prices do not always show a positive trend, as shown in Figure 1 below. Damodaran (2012) defines the intrinsic value of a stock as the current value of the cash flows that will be obtained over the life of the company. The process of determining the rea­ sonable price for a stock is known as stock valuation. It classified into three methods, namely discounted cash flow valuation, relative valuation, and contingent claim valuation. Dis­ counted cash flow (DCF) valuation is an approach that connects the value of an asset with the present value of expected future cash flows on the asset. Relative Valuation (RV) is an approach that estimates the value of an asset by comparing several assets related to common variables, such as income, cash flows, book value, and sales. Contingent claim valuation is an approach that uses option pricing models to measure the value of an asset that has the same 141

Figure 1. The chart of stock price growth of IHSG, INDF, ICBP, and MYOR from Jan 2013 to Jun 2018. Source: idx.co.id

characteristics. In this study, the authors use the DCF method due to its ability to capture the potential of future growth. Also, the RV method is chosen because it can facilitate or assist the author to determine analyses.

2 LITERATURE REVIEW 2.1 Value of the firm According to Sujoko & Soebiantoro (2007), value of the firm is defined as investors’ percep­ tions of the level of success of the company in managing its current resources, which is reflected in share prices in the coming year. The value of the company can be seen from its stock price. Stock prices are formed at the request and offer of investors, so that the stock prices can be used as company value (Wijaya & Wijaya, 2013). It is very important for com­ panies to maximize the value of the company, because maximizing the value of the company also means maximizing the company’s main objectives. Increasing the value of the company is an achievement in accordance with the wishes of its owners (stake holders), because with the increase in company value, the welfare of the owners will also increase. 2.2 Valuation To determine a fair share price in a company, it is necessary to forecast the company’s earnings and dividends (Damodaran, 2012). Forecasting requires a fundamental analysis of a company. Fundamental analysis is a method of evaluating securities in measuring intrinsic value such as income and dividends by examining economic, financial, and other qualitative and quantitative factors. Valuation is an important tool in analyzing fundamental analysis. According to Damo­ daran (2012), in general, there are three approaches to valuation, namely as follows: 1) Discounted cash flow (DCF) valuation is an approach that connects the value of an asset with the present value of the expected future cash flow of the asset. 2) Relative valuation is an approach that estimates the value of an asset by comparing several assets related to common variables, such as income, cash flows, book value, and sales. 3) Contingent claim valuation is an approach that uses option pricing models to measure the value of an asset that has the same characteristics. 142

2.3 Discounted cash flow and free cash flow to firm Djaja (2017) states that discounted cash flow is a valuation model taking into account invest­ ment opportunities whose current company value is calculated using the company’s present value of cash flow. According to Damodaran (2002), free cash flow to firm (FCFF) is cash flow available to all claim holders, both for creditors and shareholders, both common stock­ holders and preferred stockholders. This method calculates the intrinsic value of a company by discounting FCFF with weighted average of capital (WACC). 2.4 Valuation relative through the approach of price earnings ratio and price to book value The relative valuation method is used to estimate the value of a stock by comparing the price of a stock that has almost the same business characteristics, paying attention to income, book value, or sales (Brown & Reilly, 2011). According to Damodaran (2002) this method aims to value assets by comparing similar assets in the market. Some relative valuation models are valuations with multiple price to equity (P/E), and price to book value (PBV) comparisons. To compare the PBV and PER of a stock, it is better to use shares from similar companies in the same industry. PBV and PER of a stock, it is better to use shares from similar companies in the same industry. PER ¼ Po : Estimated EPS

ð1Þ

PBV ¼ Po : BV

ð2Þ

Where Po, Price of Stock 2.5 Framework Assumptions and projections of company conditions form the basis of this valuation, using historical data from 2013–2017 as a basis for projections. Then projections are made to deter­ mine future cash flows and calculate their present value. Stock valuation uses one of the two existing valuation methods, using discounted cash flow with the free cash flow to equity approach and the relative valuation model. Using these methods requires assumptions and projections to determine the condition of the company to get free cash flow in the future and then calculate the present value. Assumptions and projections need to be adjusted to a particular scenario because the future is something that is uncertain. There are several scen­ arios that are determined and viewed based on environmental data and facts. So on this basis this study uses three scenarios of optimistic conditions (above industrial growth), moderate (most likely conditions), and pessimistic (under industrial growth)

Figure 2.

Framework.

143

3 RESEARCH METHODOLOGY 3.1 Type of research data collection, sources, population, and sample data The type of research used is verification research with quantitative methods that aim to explain the phenomena that exist by using valuable numbers to get the intrinsic value of com­ pany shares engaged in the food and beverages subsector using the DCF method, FCFF approach, and RV with the PER technical approach and PBV. The data source in this study uses secondary data, that is, five-year historical data in the form of annual reports and finan­ cial reports that are the object of research sourced from idx.com, the investment world, and the official website of the research object. The population taken is all shares of food and bev­ erages subsector companies on the IDX, while the sample data uses purpose sampling tech­ niques, namely food and beverage subsector companies listed on the JCI index that still have active transactions until 2018, namely PT Indofood Sukses Makmur, Tbk (INDF); PT. Indo­ food CBP Sukses Makmur, Tbk (ICBP); and PT. Mayora Indah, Tbk (MYOR). 3.2 Data analysis technique In this study, the technical analysis of the data that will be used are as follows: 1. Calculation of Share Value Using the DCF Method with the FCFF Approach: The first stage assumptions need to be established before carrying out further calculations. This aims to avoid any estimation errors that will result in the calculation results becoming biased. The steps for analysis by the author are as follows: a. Analyze the global macroeconomy to determine the condition of the global economy and examine its correlation to the company’s stock price. b. Analyze the macroeconomic analysis of Indonesia to determine the condition of the econ­ omy in Indonesia and examine its correlation to the company’s stock price. c. Analyze the food and beverages industry in Indonesia. d. Collect and classify historical data of each company. e. Calculate the revenue projection assumption. All data is calculated on average and the per­ centage is used as reference data to make projections for the next five years, namely 2018–2022. f. Calculate EBIT projections for the next five years, namely 2018–2022. g. Calculate cash flow with DCF (FCFF) and terminal value. After that a projection is made to the accounts that form the EBIT (i.e., sales, costs), then the accounts are entered into the FCFF forming financial accounts (EBIT, depreciation and amortization, company tax, capital expenditure, working capital difference) as in formula: FCFF ¼ ðEBIT ð1 - Tax RateÞþDepreciation - Capex - CWC

ð3Þ

If the projected value of free cash flow until 2022 and cash flow at the terminal value (con­ stant future growth) has been obtained, then the value needs to be discounted using a discount rate that has been calculated previously. Cash flow needs to be discounted by dividing FCFF at one plus WACC rank year projection using formula (4) in accordance with the projection year (first year = 1, second year = 2, and so on) to get the present value of projected future cash flows, with a discount on the terminal value using formula (4) using the rank in the last projection year. TV ¼ FCFF n þ 1=ðWACC - gnÞ

ð4Þ

h. Project cost of capital (cost of capital) or WACC. Because FCFF in the future must be discounted at a discount rate using WACC, we need to calculate the costs of forming WACC, namely debt costs and capital costs using formula (5) and formula (6). Then WACC is calculated using formula (7). 144

i. Calculate the value of firm (company value).

After making a discount on the FCFF and the terminal value then adding up the company

value.

WACC ¼

CoD ¼ ixð1 - tÞ

ð5Þ

CoE ¼ Rf þ βðRm - Rf Þ

ð6Þ

Equity Debt x CoE þ x CoD Debt þ Equity Debt þ Equity

ð7Þ

j. Calculate the company’s Equity Value (EV) The value of equity is obtained by means of the value of the company that has been calcu­ lated in the previous section reduced by the amount of debt held, reduced by the ownership of a minority company, and added to the amount of cash held. 2. Calculation of Share Value Using the Relative Valuation Model a. Calculate P/ER The intrinsic value of shares in each company is calculated using the P/ER approach using formula (8). Calculate the estimated cash dividend per share. The next step is to calculate the accumulated dividends received during the year specified. Po ¼ Estimasi EPS x PER

ð8Þ

b. Calculate P/BV Calculating the fair value of each company using the PBV approach using formula (9); it is better to use the PBV ratio of shares from similar companies in the same industry. Furthermore, each of these similar companies is calculated using last year’s PBV and is averaged. Then the average result of the PBV ratio. PBV ¼ Po : BV

ð9Þ

4 RESULT AND DISCUSSIONS Table 1. Valuation result. Method/Approach Scenario

DCF/FCFF

RV-PER

Intrinsic Stock Price Jan, 2 2018 Evaluation Company Value

Pessimistic INDF ICBP MYOR Moderat INDF ICBP MYOR Optimistic INDF ICBP MYOR

7,406 6,377 1,994 7,936 9,334 2,192 9,210 12,275 2,304

7,550 9,100 2,120 7,550 9,100 2,120 7,550 9,100 2,120

Overvalued Overvalued Overvalued Undervalued Undervalued Undervalued Undervalued Undervalued Undervalued

145

RV-PBV

Result Evaluation Result Evaluation 15.65 30.73 30.73 16.71 31.65 33.65 19.25 41.41 35.36

Fairvalued Overvalued Overvalued Fairvalued Overvalued Overvalued Fairvalued Overvalued Overvalued

0.15 0.30 0.25 0.16 0.43 0.28 0.19 0.57 0.29

Undervalued Undervalued Undervalued Undervalued Undervalued Undervalued Undervalued Undervalued Undervalued

Based on observations of the five-year data per quarter, the intrinsic value at the pessimis­ tic scenario of INDF, ICBP, and MYOR in using the DCF method showed overvalued condi­ tions; in using the RV method with PER approach for INDF and ICBP showed fair valued but MYOR showed overvalued conditions; and using the RV method PBV approach for INDF, ICBP, and MYOR showed undervalued conditions. The intrinsic value at the moder­ ate scenario of INDF, ICBP, and MYOR using the DCF method showed undervalued condi­ tions; using the RV method with PER approach for INDF showed fair valued conditions but ICBP and MYOR at overvalued conditions; and PBV approach of INDF, ICBP, and MYOR showed undervalued conditions. The intrinsic value at optimistic scenario of INDF, ICBP, and MYOR using the DCF method showed undervalued conditions; using the RV method with PER approach for INDF showed fair valued conditions but ICBP and MYOR at over­ valued conditions; and PBV approach of INDF, ICBP and MYOR showed undervalued conditions.

5 CONCLUSION The authors recommend buying shares of INDF as a long-term investment because the com­ pany’s performance for the last three years is experiencing positive revenue growth, measured in the growth ratio of 6.06%. This figure is above the industry average of 5.62%. Also for ICBP, the authors suggest buying shares. In the last five years, the ICBP growth shows fluctuating numbers but it is still above the industry average, so it needs to be further investigated related to future improvements. The positive side of the company is that there are a number of companies’ businesses carrying out operational cost efficiency, and in the last three years it has tended to decrease which can increase the company’s EBITDA percentage. For MYOR, in the last five years’ performance, company growth shows fluctuating figures and the results are far above the industry average; after further investigation, there is a large contribution of exports that affects the increase in revenue. REFERENCES Damodaran, A. 2012. Investment Valuation: Tools and Techniques for Determining the Vvalue of Any Asset (Vol. 666). Canada: John Wiley & Sons. Djaja, I. (2018). All About Corporate Valuation (Edisi Kedua). Jakarta: Elex Media Komputindo. Neaxie, L. V., & Hendrawan, R. (2017). Stock Valuations in Telecommunication Firms: Evidence from Indonesia Stock Exchange. Journal of Economic & Management Perspectives, 11(3): 455–455. Reilly, F. K., & Brown, K. C. (2011). Investment analysis and portfolio management. Cengage Learning. Sujoko, K., & Soebiantoro, U. (2007). Effect of Shareholding Structure, Leverage, Internal and External Factors Against company value. Journal of Management and Enterpreneurship, 9(1), 41–48. Wijaya, A. P. W. A. P., & Wijaya, A. P. (2013). Analisis Rasio Keuangan dalam Merencanakan Pertum­ buhan Laba: Perspektif Teori Signal. Kajian Ilmiah Mahasiswa Manajemen, 2(2).

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Digital Economy for Customer Benefit and Business Fairness – Anggadwita & Martini (eds) © 2020 Taylor & Francis Group, London, ISBN 978-0-367-47722-6

Analysis of the influence of compensation and transformational leadership style on employee performance in PT. Finnet Indonesia Muhammad Thoriq Hafiz & Fetty Poerwita Sary Faculty of Economic and Business, Telkom University, Bandung, Indonesia

ABSTRACT: Research aims to analyze the influence of compensation and transformational leadership on the performance of employees of PT. Finnet Indonesia in 2018. This research data is derived from the respondent’s response, as well as other research results relevant to the object studied, in this case the primary data of PT. Finnet Indonesia. The method used is a quantitative descriptive study. The unit is 155 employees who work at PT. Finnet Indonesia. Data was collected using a questionnaire with 37 items. The analysis technique uses the ana­ lysis path, and the data was processed by using SPSS application version 2.0 for Windows. The results of the study used multiple regression analyses that showed that the compensation and transformational leadership style partially affected the performance implementation of the employees.

1 INTRODUCTION Development of the business world today in Indonesia and globally has progressed considerably. In the current free-market era, all companies are required to grow to be able to compete in the business world both in Indonesia and in the world. So that every company can work optimally, effectively, and efficiently, a company’s performance must continue to increase and continue to compete in the current era of globalization. One of the things companies need to be aware of in order to improve performance is the management of human resources within the company. Performance is the result of work in quality and quantity, achieved by the employees in carrying out their duties in accordance with the responsibilities given (Mangkunegara, 2009:67) As seen in Table 1, the performance target rises annually and almost every year the target can be achieved. If viewed from the prespective of the performance realization of 2018, there is a decline in employee performance by not achieving its performance target. According to GM Human Resource PT. Finnet Indonesia, “in 2018 a decline occurred in the performance of employees who have an impact on the achievement of the company target.” The decline in employee performance might be because there is a reduction in incen­ tives received by employees of PT. Finnet Indonesia. Another thing that is likely to have a negative impact on employee performance is the replacement of the President director of PT. Finnet Indonesia in March 2018. The new director has no close relationship with the employees and does not understand employees’ needs. Employees performance is not separated from the compensation they receive and the con­ duct of its leaders. Compensation is the remuneration provided to the employees. Employees who have a good performance record will certainly get a service response, for example, a raise of salary or other benefits. Some research suggests that leadership styles are influential on the performance of employees, as said by Krisna et al. (2015:4) Transformational leadership is, in principle, motivating subordinates to do better: in other words, increased trust or subordinate confidence will affect performance enhancement. On the phenomenon of these factors, by criticing the real conditions in the field, there is an allegation that compensation and transformational leadership style can have a significant influence on the performance of PT. Finnet Indonesia. This refers to the decline in employee 147

Table 1. Performance realization of employees PT. Finnet Indonesia for three years (in trillion rRupiah). Year

Performance Targets

Performance Realization

Performance Percentage

2016 2017 2018

8 10 15.4

9 13 13.2

112.5 % 130 % 85.7 %

Source: Data PT. Finnet Indonesia, February 2019

performance in the year 2018 where there was a reduction of incentives and the replacement of the President director of PT. Finnet Indonesia. Based on the background outlined above, the authors examined the relationship between compensation and leadership style and the company’s performance titled: “Analysis of the influence of compensation and transformational leadership style to employee performance” with case studies at PT. Finnet Indonesia.

2 LITERATURE REVIEW 2.1 Human resource management According to Dessler in Wardhana (2014:4), “Human resource management is the process of pro­ curement, training, assessment, and compensation to employees, and the attention of their work­ ing relationships, the provision of occupational health and safety guarantees, and a sense of justice.” Edison et al. (2017:10) explains that human resource management is a management that focuses on maximizing employee capability through strategic measures in order to improve the performance of staff/employees in light of organizational objectives. Robbins (2008:29) defines performance as a result achieved by the employees in the works according to certain criteria that apply to a job. Simamora (2014:42) defines compensation as including financial rewards and services as well as benefits received by employees as part of personnel relationships. Compensation is what employees receive in exchange for their contributions to the company. Bass (in Northouse, 2013:179) states that transformational leadership motivates followers to do with: 1) Increase the level of follower understanding of usability and value and the team objectives are detailed and ideal. 2) Make followers exceed your own interests for the sake of your team or organization. 3) Move followers to meet higher level needs. According to James MacGregor (in Djohan, 2016:14), the behavior of transformational leadership and their subordinates can improve motivation and moral work.

Figure 1.

Research framework.

148

3 RESEARCH METHODOLOGY This study uses quantitative methods with a type of description and verifiable research. Quan­ titative methods were chosen because the study formulated specific problems recognisable from the beginning using structured research design, proven validity, and rehabilitation. Descriptive research was used to test the corresponding study subject. This research has a number of respondents – as many as 155 employees (N = N – 2 = 153) – and α value of 0.05 then obtained the value of R table is 0.157. And all the items of the correl­ ation R count on each variable with SPSS calculations greater than the R table. The research statement is declared valid if the research statement item has a value of corrected item-total correlation (rcount) greater than the rtable in this study of 0.157. The validity test on this study uses SPSS for Windows version 25 software. The reliability level of each research variable can be seen from the numerical value of the reliability coefficient. Measuring instrument can be said to be reliable if it has an value of Cronbach > 0.7. Reliability test is using SPSS for Windows version 25 software. The reliability test result of variable compensation and transformational and performance leadership style shows the value of Cronbach α > 0.7 so the variables in this study can is said to be reliable. 4 RESULTS AND DISCUSSION The results of a descriptive analysis show that compensation has the highest percentage value of 80.5% and the lowest of 77.8%, and an average of 79.4%. This indicates that the compensa­ tion is given correctly to employees PT. Finnet Indonesia, and it is an indication that the com­ pensation is good. The results of the descriptive analysis show that the transformational leadership style has the highest percentage value of 79.1%, the lowest of 76.4%, and the aver­ age of 78.3%. This demonstrates the uniformity of the transformational leadership style in the lead of employees PT. Finnet Indonesia, and it is an indication that the transformational lead­ ership style is good. The results of a descriptive analysis show that employee performance has the highest per­ centage value of 81.9%, the lowest of 79.2% and the average of 80.6%. This calculation is taken from a valid statement in this variable. This calculation shows the uniformity of the behavior of employees of PT. Finnet Indonesia, and it is an indication that the performance of employees in accordance with the expectations of the company is good. Test correlation in this research was conducted to determine the relationship between two or more independent variables for dependent variables simultaneously and to know the size of relationships between the research variables, knowing the relationship between two or more independent variables against dependent variables simultaneously and knowing the magnitude of the relationship between the research variables and the fol­ lowing results in Table 2.

Table 2. Result of Pearson inter-cariable correlation analysis. Compensation

Style of Leadership

Performance

Compensation

Pearson Correlation Sig (2-Failed)N

1 155

0.154 0.056 155

0.574** 0.000 155

Leadership Style

Pearson Correlation Sig (2-Failed)N

0.154 0.056 155

1 155

0.336** 0.000 155

Performance

Pearson Correlation Sig (2-Failed)N

0.544** 0.000 155

0.336** 0.000 155

1 155

149

Correlation test results as stated in Table 4.4 to determine whether there is a relationship between compensation and employee performance and leadership style and the employee per­ formance. Based on the results, the test proved that there was a positive and significant rela­ tionship between compensation and the leadership style with performance. This is demonstrated with the value of the coefficient of coleration of compensation with employee performance of 0.574 and the value of the coleration of leadership styles with employees per­ formance of 0.336. The significance value (2-tailed) of 0.000 < 0.05 indicates that the relation­ ship created between compensation and performance and leadership style and performance is significant. According to Table 3, the value of the constants (a) of = 13.773 has been the value of the regression coefficient for X1 (B1) of 0.575, and the value of regression coefficient for X2 (B2) of 0.242. So it can be in the form of multiple linear regression equations as follows: Y ¼ 13:773 þ 0:575 X1 þ 0:242 X2 The values A, B1 and B2 in the above equation can be interpreted as follows: a) Y = 13.737 means if the compensation (X1) and the transformational leadership style (X2) is worth 0 then employee performance will be worth 13.737 units. b) X1 = 0.575 means if the compensation (X1) is increased by one unit, while the transform­ ational leadership style (X2) is constant then employee performance will be worth 0.575 units. The coefficient of positive value means there is a positive relationship between employee performance compensation. The better the value of compensation, the better employee performance. c) X2 = 0.242 means if the transformational leadership force (X2) is increased by one unit, while the compensation (X1) is constant then employee performance will be worth 0.242 units. The coefficient of positive value means there is a positive relationship between the transformational leadership force and employee performance. The better the value of trans­ formational leadership style, then the better the employee performance. Refering to Table 3, based on the results of the t-test, the value of the compensation was calculated at 8.350. Then this value will be compared to the t table. With α of 0.05 and df = 155, results is 1.975. It is revealed that the calculated value for the X1 of 8.350 is greater than the t table of 8.350 > 1.975, which means that compensation has a partially significant effect on employee performance. Referring to Table 3 then obtained the value of t to calculate the transformational leader­ ship style of3.967. Then this value will be compared to t table. With α of 0.05 and df = 155, for two-sided testing result is 1.975. It is revealed that the calculated t value for X2 is 3.967 greater than t table which is 3.967 > 1.975, which means that the transformational leadership style has a partially significant effect on employee performance. Based on test f results in Table 4, then the value of f is calculated at 48.911. Then this value will be compared to the f table. With α of 0.05, DF1 = 2 and DF2 = 152, a known f table

Table 3.

Results of multiple regression equations. Untandardized Coefficient

Standardized Coefficient

Model

B

Std Error

Beta

t

Sig

(Constant) Compensation Leadership Style

13.773 0.575 0.242

4.025 0.069 0.061

0.534 0.234

3.422 8.350 3.967

0.001 0.000 0.000

150

Table 4. Test result F. Model

Sum of Squares

df

Mean Square

f

Sig.

Regression 1 Residual Total

2520.430 3909.957 6430.387

2 152 154

1260.215 25.723

48.991

0.000b

Description: n = 155; α = 0.05; FTabel = 3.06

Table 5. Model 1

Coefficient of determination. R a

0.626

R2 Square

Adjusted R2

Std. Error of the Estimate

0.392

0.384

5.072

amounted to 3.06. From these values, it is known the value f count (48.991) > f table (3.06), underlies a significant simultaneous influence of the compensation (X1) and the transform­ ational leadership style (X2) against the employee’s performance (Y). This study uses multiple linear regression analyses, so as to find the value of a determiniation coefficient: the value used is Adjusted R2. Based on the output of SPSS for Windows software version 25, the Adjusted R2 value of 0.384 or 38.4% is known. This shows that the compensation and transformational leadership style have a simultant effect on the employees’ performance of 38.4%. The remaining 61.6% is the influence of other variables that are not researched.

5 CONCLUSION AND SUGGESTION Based on the analysis of data that has been done, the conclusions obtained are as follows: 1. There is a relationship between employee compensation and performance. The result of a descriptive analysis shows that 79.4% of compensation affects employee performance at PT. Finnet Indonesia. Indicators are researched, namely the principal salary, incentives, benefits, and facilities. 2. There is a relationship between the transformational leadership style and employee per­ formance. The result of a descriptive analysis shows that a 78.3% transformational leader­ ship force affects the employee’s performance at PT. Finnet Indonesia. The indicators are researched, namely ideal influence, inspiring motivation, intellectual stimulation, and per­ sonal considerations. 3. Based on the results of the data process, it can be seen that the average percentage for the employee performance variable is 80.6%. Employee performance in PT. Finnet Indonesia belongs to the “good” category, with the following indicators researched: quality, quantity, timeliness, effectiveness, independence, and work commitment. 4. Based on the results of research conducted by researchers, compensation and transform­ ational leadership style influence the performance of employees. From the correlation ana­ lysis, results show the relationship between the compensation and the performance of employees resulted in a value of 0.574 which demonstrates the correlation relationship between the compensation and the employee’s performance. 5. The test coefficient of determination, the compensation and transformational leadership style have redistributed influence by 38.4%, while the remainder is 61.6% due to other vari­ ables that are not researched. The conclusion of the entire test is that compensation and transformational leadership style influence the performance of employees.

151

Based on results from calculation of the descriptive analysis on the variable compensation, sub values of the salary variable principal yields the lowest value of 77.8%, which shows that the employee of PT. Finnet is less satisfied with the basic salary that has been given. Things could be improved by adding employee transport money, given the increase in transport costs experienced by employees because of the increase in fuel and the increase of public transport tariffs, so the daily needs of employees can be fulfilled. If viewed from the results of a descriptive analysis of a variable in the transformational lead­ ership style sub-value of personal considerations results in the lowest value of 76.4% which shows that the leader in PT. Finnet is not paying sufficient attention to subordinate advice. This can be improved by creating employee forums regularly so that employees feel cared for and heard. PT. Finnet Indonesia is one of the subsidiaries of Telkom Group located in South Jakarta, which is crawling forward. Therefore, the next research is expected to consider adding other variables that can affect the performance of employees of PT. Finnet Indonesia. Because compensation and transformational leadership style are influential in improving the performance of employees of PT. Finnet Indonesia, the recommendation is that the same research be undertaken at other Telkom Group facilities, so that Telkom Group can increase employee performance together to achieve the company’s vision and mission. REFERENCES Dessler, G. 2011. Human Resource Management. (12th ed.). New Jersey: Pearson Pentice Hall.

Edison et al. 2017. Manajemen Sumber Daya Manusia. Cetakan 1, Bandung: Henry,Simamora. 2014.

Manajemen Sumber Daya Manusia. Edisi Ketiga. Yogyakarta. STIE YKPN. Kasmir. 2016. Analisis Laporan Keuangan. Jakarta: Raja Grafindo Persada. Krisna et al. 2017. Hipospadia Bagaimana Karakteristiknya di Indonesia. Mangkunegara, A. 2009. Manajemen Sumber Daya Manusia. Bandung: PT. Remaja Rosdakarya. Northouse, P. G. 2013. Leadership: Theory and Practice (6th ed.). California: Sage. Robbins, Stephen P. 2008. Perilaku Organisasi. Index. Jakarta. Syarif, Djohan. 2003. Strategi Pembinaan dan Pengembangan SDM Perguruan Tinggi Dalam mening­ katkan Mutu Pendidikan Tinggi (Studi Kasus di Perguruan Tingg di Jakarta). Jurnal Ekonomi STEI, Nomor 1, tahun XII, Januari-Maret. [4 maret 2016].

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Digital Economy for Customer Benefit and Business Fairness – Anggadwita & Martini (eds) © 2020 Taylor & Francis Group, London, ISBN 978-0-367-47722-6

Analysis of efficiency in telecommunication technology companies in Eastern and South East Asia using analysis data envelopment method Suhartoko & Palti Marulitua Sitorus Telkom University, Bandung, West Java, Indonesia

ABSTRACT: This study aims at determining, analyzing and comparing the efficiency values of 25 telecommunications companies in East and Southeast Asia that listed in the top 300 valuable brands in 2018. Data was processed using the MaxDEA-7 software to obtain the value of company efficiency. A correlation analysis was carried out to produce the correlation coefficient between input output variables and efficiency values. The analysis results then were compared between each company and per each group of countries. The results revealed that the telecommunications company, PT NTT (Japan), was a company with the highest effi­ ciency value, which was most influenced by capex, revenue and subscriber variables, while PT FarEasTone (Taiwan) had the lowest efficiency value with most contributed by capex and subscriber variables. In country categories, Japan had the highest efficiency values with the most influential variables being capex, revenue and subscriber, and Singapore had the lowest efficiency value.

1 INTRODUCTION The objects of this study are companies engaged in telecommunications technology operated in the Eastern and Southeast Asian region and listed in the Top 300 Most Valuable Brands category in the year 2018 issued by Brand Finance (www.brandfi nance.com). Brand Finance is an independent consulting agency engaged in the busi­ ness strategy and brand assessment of companies in the world, especially telecommuni­ cations companies. In the present digitalization era, the culture and lifestyle of people have also changed, especially among youth and millennial generation. They are a very large market segment for data services (such as social media applications, vlogs, online games, etc.). Innovative digital products that are able to meet their needs are promis­ ing future market potentials. On the other hand, the growth opportunities in the digital era also pose challenges for players in the telecommunications technology industry. In attempting to fulfill the needs of customers, telecommunications technology companies must spend substantial investment costs in providing high speed and broad reach service, both from com­ pany’s investment and operational costs. Figure 1. depicts the comparison of variables in the financial statements of telecom­ munications companies in East and Southeast Asia in the period 2013-2017, which showed a positive and significant growth trend in total revenue. However, the growth in average numbers of Earning Before Interest, Tax, Depreciation, and Amortization (EBITDA) margins and Net Profit Margin (NPM) from these companies were not in line with revenue growth.

153

Figure 1. Asia.

Revenue, EBITDA margin & NPM telecommunications companies in East and Southeast

2 STUDY LITERATURE AND METHODS The company efficiency is measured to evaluate company performance, in which its performance is based on measurements of the activities carried out by the organization with a variety of analysis, for example: cost per unit, profit per unit, value of satisfac­ tion per unit, and so on. Data Envelopment Analysis (DEA) is a non-parametric method in operations research and economics used to measure the production effi­ ciency of the Decision-Making Unit (DMU) where the production process involves sev­ eral input factors and output factors (Charnes et al, 1978). Avkiran (1999) defines DEA as a technique to measure the relative efficiency of various organizational units that can uncover the exact relationship between diverse inputs and outputs, which pre­ viously could not be accommodated through traditional ratio analysis. In general, there are two models used in the DEA method, namely the CCR (1978) model and the BCC (1984) model. The CCR model, or also called Constant Return to Scale (CRS), assumes that the ratio of input and output additions is the same. On the other hand, the BCC model is the result of the development of the CCR model proposed by Banker, Charnes, and Cooper. This model assumes the ratio of the addition of input and output is not the same (variable return to scale). This study measured the efficiency of an output-oriented approach from the telecom­ munications companies using the DEA method of the BCC model due to the condition of the telecommunications technology industry in the study period (2013-2018) that was still developing and producers/companies were expected to maintain or increase their input and output. The formulation in measuring efficiency with the DEA method is as follows. s P

Maxðh0 Þ ¼

r¼1 m P i¼1

154

ur yr0 vi xi0

Where: s P

ur yrj

� 1; j ¼ 1; K; n; ur ; vi � 0; r ¼ 1; K; s; i ¼ 1; K; m

r¼1 m

P

vi xij

i¼1

The DEA method in measuring efficiency can process many inputs and many outputs, and it is simple as it does not require the assumption of a functional relationship between the input and output variables. Inter-DMU (Decision Making Unit) can be compared directly (homoge­ neous). However, the DEA method has limitation on sample selection, and relative measure­ ment of efficiency between DMUs, and the statistical hypothesis testing is difficult due to the non-parametric measurements.

3 RESULT 3.1 Average efficiency value per company After obtaining input (capital expenditure, operating expense, personnel expense, & total assets) and output (subscribers, revenue) variable data, then the value of efficiency with the DEA (Data Envelopment Analysis) method was calculated using MaxDEA-7 soft­ ware. The configuration using BCC/VRS (Variable Return to Scale) model which is output oriented. The efficiency value of 1 indicated that the company was efficient. The best conditions occurred in 2013 in which there were 11 companies with an efficiency value of 1 (49%). While the worst conditions occurred in 2015 where there were only two companies with an efficiency

Table 1. The Efficiency value of telecommunications companies in east and southeast Asia. No

Company

Year 2013

Year 2014

Year 2015

Year 2016

Year 2017

Average

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

AIS Axiata China-Mobile China-Telecom China-Unicom Chunghwa Digi.com FarEasTone Globe-Telecom HKT Indosat KT-Crop LG-U+ M1 Maxis NTT PLDT Singtel SK-Telecom Smartfren Smartone Starhub Taiwan-Mobile TelKom XL-Axiata

1 0.9121 1.0000 1.0000 0.9487 0.9305 1.0000 0.7810 0.8531 0.8560 0.9696 0.9510 1.0000 1.0000 1.0000 1.0000 0.9982 0.8023 0.9304 1.0000 0.9717 0.8355 0.9580 1.0000 1.0000

0.9945 0.8812 1.0000 0.9963 0.9585 0.9442 0.9999 0.7617 0.8489 0.9098 0.9422 0.9359 0.9587 0.9794 1.0000 0.9910 0.9040 0.7922 0.9347 1.0000 0.8077 0.8324 1.0000 1.0000 0.9402

0.9741 0.8855 0.9974 0.9606 0.9035 1.0000 0.9676 0.7761 0.8532 0.9368 0.9542 0.9908 0.9246 0.9210 0.9771 1.0000 0.7702 0.8001 0.9546 0.9931 0.8223 0.8035 0.8049 0.9806 0.9741

0.9004 0.8839 0.9930 09535 0.8839 1.0000 0.9867 0.7970 0.8727 0.9645 0.9949 0.9936 0.9639 0.9345 1.0000 1.0000 0.7845 0.7956 0.9430 0.8146 1.0000 0.7987 0.8552 1.0000 0.9088

0.9413 0.8996 1.0000 0.9741 1.0000 0.9706 0.9995 0.8067 0.8455 0.9859 1.0000 1.0000 1.0000 0.9167 0.8077 1.0000 0.8568 0.7893 0.9504 0.8341 0.6379 0.7712 0.8029 1.0000 0.8826

0.962061 0.892451 0.998078 0.976913 0.938921 0.969049 0.990732 0.784499 0.854681 0.930582 0.972192 0.974263 0.969420 0.950332 0.956947 0.998196 0.862728 0.795890 0.942628 0.928371 0.847917 0.808254 0.884194 0.996113 0.941135

155

value of 1. NTT (Japan) and Telkom (Indonesia) were companies that were able to achieve an efficiency value of 1 in 4 times. In addition, NTT (Japan) obtained the highest achievement, while FarEasTone (Taiwan) obtained the worst achievement. The analysis of the correlation between each input-output variable (capex, opex, personal expense, total assets, revenue and subscriber) with the value of each efficiency each telecom­ munications company was conducted to find out what factors influence the efficiency value of each company. Table 2 presents the value of the correlation coefficient between output variables and the efficiency value. Positive coefficient values indicated a direct relationship between variables and efficiency values, while negative values indicated an inverse relationship. The value close to 1 (positive or negative) meant the stronger influence of variable values on the efficiency value. 3.2 Comparison of telecommunications company efficiency per country The further analysis was carried out by grouping 25 telecommunications companies in each country. The objects of this study were categorized into 10 countries namely China, Hong Kong, Indonesia, Japan, South Korea, Malaysia, the Philippines, Singapore, Taiwan and Thailand. Table 3 presents that Japan (in this case is only represented by NTT telecommunications companies) had the highest average efficiency value compared to the average value of telecom­ munications companies in other Eastern and Southeast Asian countries that was equal to 0.9983. The correlation coefficient between input and output variables and the value of com­ pany efficiency in a group of countries can be analyzed as follows: (1) The companies in the Philippines, Taiwan and Thailand had a very strong correlation coefficient on the

Table 2. Correlation coefficient of efficiency value with input output variables. Company

Capex

Opex

PE

Tot Asset

Revenue

Subs

AIS Axiata China-Mobile China-Telecom China-Unicom Chunghwa Digi.com FarEasTone Globe-Telecom HKT Indosat KT-Crop LG-U+ M1 Maxis NTT PLDT Singtel SK-Telecom Smartfren Smartone Starhub Taiwan-Mobile TelKom XL-Axiata

-0.95494 -0.2059 0.125617 -0.93947 -0.61056 -0.97257 0.218885 -0.98719 0.172866 -0.728222 -0.17356 -0.41744 -0.21113 -0.65531 -0.43242 -0.45741 -0.99789 -0.90363 -0.926 -0.3916 -0.40535 -0.00749 0.381442 -0.06095 -0.02942

-0.96678 -0.1358 -0.2716 -0.6449 -0.0484 -0.8906 -0.4759 -0.4415 0.1661 0.8009 0.7515 -0.7499 0.6375 -0.8057 -0.9711 0.2439 -0.8644 -0.7494 0.4095 -0.9111 0.2268 -0.8820 0.3821 0.0349 -0.3011

-0.7310 0.0164 -0.3636 -0.7522 0.4129 0.8707 -0.8418 0.6672 -0.0864 0.7474 0.7675 -0.4424 -0.0140 -0.9094 -0.8975 0.2530 -0.0437 -0.8516 0.9242 -0.9734 0.2843 -0.5420 -0.9627 -0.0576 -0.9363

-0.9210 -0.3060 -0.5246 -0.8790 -0.6599 0.7380 -0.1009 0.4357 0.1779 0.9137 -0.7897 -0.9211 -0.5685 -0.8337 -0.3824 0.3304 -0.9154 -0.6349 0.6040 -0.8553 -0.5618 -0.8407 -0.5188 -0.0695 -0.4922

-0.5469 0.0101 0.3135 -0.5903 0.2134 0.8050 -0.2937 -0.2738 0.1543 0.8967 0.8194 0.0078 0.8220 -0.6703 -0.9813 0.4103 0.0816 -0.7704 0.6928 -0.8351 0.7876 -0.4911 -0.8067 0.0442 -0.2413

0.2526 0.0552 -0.2710 -0.4767 0.5518 -0.0129 -0.8275 -0.6751 0.3909 0.7947 0.8213 0.8757 0.0259 0.1075 0.8464 0.3928 0.5957 -0.6815 0.6922 0.2505 -0.7735 -0.2256 -0.0166 0.1355 0.1680

156

Efficiency

Table 3. Efficiency value of companies per country. Country

Year 2013

Year 2014

Year 2015

Year 2016

Year 2017

Average

China Hongkong Indoneisa Japan Malaysia Philippines Singapore South Korea Taiwan Thailand Average

1 1 1 1 1 0.995811 0.862303 0.997499 0.921974 1 0.9777587

1 1.0000 1.0000 0.9915 0.9909 1.0000 0.8508 0.9624 0.9442 1.0000 0.9740

0.9915 0.9021 0.9884 1.0000 0.9838 0.8499 0.8490 1.0000 0.9803 1.0000 1.9545

0.9885 1.0000 1.0000 1.0000 0.9886 0.8671 0.8475 0.9900 1.0000 0.9363 0.9618

1.0000 0.9879 1.0000 1.0000 0.9341 0.8821 0.8371 1.0000 0.9773 0.9759 0.9594

0.9960 0.9780 0.9977 0.9983 0.9795 0.9190 0.8493 0.9900 0.9648 0.9824 0.9655

Table 4. Pearson correlation coefficient between input output variables and efficiency values of each Country. Negara

Capex

Opex(excl.PE)

Personnel Expense

Total Assests

Subscribers

Revenue

China Hongkong Indoneisa Japan Malaysia Philippines Singapore South Korea Taiwan Thailand

-0.516219 -0.489533 0.265288 -0.457409 -0.603833 -0.957708 -0.546233 -0.588498 -0.935796 -0.891155

-0.130756 -0.836006 0.007652 0.243865 -0.978723 0.941036 -0.774865 -0.113654 0.024033 -0.884914

-0.336656 -0.543385 -0.033250 0.252972 -0.925726 -0.685319 -0.990731 -0.250196 0.949958 -0.526376

-0.604718 -0.223330 -0.168209 0.330392 -0.653603 -0.775016 -0.877301 0.248250 0.822283 -0.798983

-0.046374 -0.283209 0.313349 0.392761 0.871771 -0.885990 -0.145905 0.255401 -0.018098 0.068151

-0.159950 -0.206395 0.031920 0.410303 -0.932699 -0.919327 -0.722341 0.460105 0.882405 -0.303279

Capex variable; (2) Hong Kong, Malaysia, the Philippines, Singapore and Thailand had very strong correlation coefficients between the Opex variables, and the value of effi­ ciency; (3) Malaysia, Singapore and Taiwan had very strong correlation coefficients between personal expense variables to the value of efficiency; (4) The Philippines, Singa­ pore, Taiwan and Thailand had a very strong correlation coefficient between the total asset variable and the efficiency value; (5) Malaysia, the Philippines and Taiwan had a very strong correlation coefficient between efficiency and revenue variables. Malaysia and the Philippines had a very strong correlation coefficient with revenue variables; (6) China had a strong correlation coefficient to the variable total assets and capex; (7) Indonesia was more influenced by subscriber variables with the correlation coefficient level in the criteria being quite strong; and (8) Japan had a level of correlation coefficient that is strong enough to the variable capex, revenue and subscribers.

4 CONCLUSIONS Based on the aforementioned findings and discussion, the conclusions are: 1. The relationship between input variables (capex, opex, personal expense and total assets) on the efficiency value of telecommunications companies in East and Southeast Asia had a strong correlation coefficient with an inversely proportional pattern. This means that effi­ ciency in terms of cost was needed to increase the value of company efficiency.

157

2. The relationship between output (revenue) variables and the value of efficiency in some tele­ communications companies in East and Southeast Asia had a strong correlation coefficient but some are inversely proportional, meaning that several companies are able to increase the value of efficiency, because the cost required is greater. From the subscriber variable, the strong influ­ ence is directly proportional to the efficiency value of some companies, while other companies the efficiency value is not strongly influenced by the output variables. 3. From 25 telecommunications companies, it was found that NTT (Japan) was the most efficient company with an efficiency value of 0.9983, followed by China Mobile from China with an efficiency value of 0.9981, and Telkom with an efficiency value of 0.9961. The top three com­ panies had the strongest efficiency values. While the three companies with the lowest efficiency values were Starhub (Singapore), SingTel (Singapore) and FarEasTone (Taiwan). 4. From the grouping of companies into 10 countries, telecommunications companies in Japan had the highest efficiency values of 0.9983. Meanwhile, companies in Singapore had the lowest average efficiency (0.8493). The efficiency value of companies in Japan was quite strongly influenced by capex and revenue variables, while the efficiency value for companies in Singapore was strongly influenced by capex and revenue variables, and very strongly influenced by opex, personal expense and total asset variables.

REFERENCES Al-Farisi, A.S, Hendrawan, R, (2010). “Measuring Efficiency as Intermediation Approach Between Con­ ventional and Sharia Bank in Indonesia”. Journal of Finance and Banking, Vol 14, No.3 Septem­ ber 2010. 501–508. Avkiran, N. K. 1999. The Evidence on Efficiency Gains: The Role of Mergers and The Benefits to The Public. Journal of Banking and Finance, 23, 991–1013. Charnes, A., Cooper, W.W., & Rhodes, E. 1978. Measuring The Efficiency of Decision Making Units. European Journal of Operational Research 2 (1978) 429–444. Iamratanakul, S.N. (2003). Efficiency in the Telecommunications Industry: An International Comparison using DEA. [online] https://www.researchgate.net/publication/266894331 [07 November 2018]. Kang, C. C. (2007). Measuring the Production and Cost Efficiency in Telecommunication Industry: The Taiwan Case. Proceedings of the Eastern Asia Society for Transportation Studies, Vol 6. Masson, S., Jain, R., Ganesh, N.M., and George, S.A. (2016). Operational Efficiency and Service Delivery Performance: A Comparative Analysis of Indian Telecom Service Providers. Benchmarking: An Inter­ national Journal. 23 (4). 893–915. Ross, S. A., Westerfield, R. W., and Jaffe, J. F., (2013). Corporate Finance 10th Edition. New York. McGraw-Hill. Sekaran, U., Bougie, R., (2016). Research Methods for Business: A Skill-Building Approach Seventh Edi­ tion. West Sussex. John Wiley & Sons Ltd. Suleiman, M.S., Hemed, N.S., and Wei, J. (2017). Evaluation of Telecommunication Companies Using Data Envelopment Analysis: Toward Efficiency of Mobile Telephone in Tanzania. International Journal of e-Education, e-Business, e-Management and e-Learning. 8 (3).

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Digital Economy for Customer Benefit and Business Fairness – Anggadwita & Martini (eds) © 2020 Taylor & Francis Group, London, ISBN 978-0-367-47722-6

Indonesia financial sector stock prediction using long short-term memory network algorithm and modeling (study of banking in August 2018 LQ45 Index) M. Pantagama & B. Rikumahu School of Economics and Business Telkom University, Bandung, Indonesia

ABSTRACT: Stock investors expect optimal returns on investing in the stock market, so they need ways to be able to predict the stock price that will be their portfolio. The stock price movement that is time-series, can be predicted by various theories in financial mathematics. Deep learning applies mathematical algorithms and modeling to create many neural networks for various needs and solutions, one of which can predict the movement of time-series values. The financial sector leads the LQ45 index in Indonesia Stock Exchange market; this paper will make stock price predictions in a year using the Long Short-Term Memory (LSTM) Network Algorithm and Modeling. We use stock price data for ten years from 2009 to 2018, where stock daily closing prices are used for learning data. The goal is to predict the price of stock daily closing of the financial sector list on LQ45 index for year 2018 by using a history of nine years back and then measuring the accuracy of stock price predictions using LSTM Network.

1 INTRODUCTION The Indonesia Stock Exchange (IDX) periodically produces LQ45 semiannual reports about Company Performance: at the time this research was compiled, the latest report was released in August 2018. Performance of the LQ45 index, in which the banking subsector consists of BBCA, BBRI, BBRI, BBTN, BJBR, and BMRI, dominated the index with a total market capitalization of 33.92%, or more than one-third of the index’s capitalization. The movement of the closing prices of the six banks’ daily shares can be seen in Figure 1. Stock price volatility is much influenced by fundamental information in the form of salient issues in society compared to technical information relating to price and sales volume in a time series (a series of variables arranged by time) (Murphy, 1999). By utilizing past histor­ ical data, it will be possible to predict future data using time-series analysis. Artificial Intelligence (AI) is a thriving field with many practical applications and active research topics (Manurung et al., 2018). Time-series predictions that can use Machine Learn­ ing with supervised learning using regression is a very popular method to make stock price predictions where each method has its advantages and disadvantages depending of the type of data being processed (Konar & Bhattacharya, 2017). Neural Network, the Long Short-Term Memory Network model has the advantage of updating better equations and back propagation. The LSTM network consists of many LSTM cells that are connected and perform efficiently in the learning process (Haykin, 2010). The study objective was to build a deep learning model to predict stock price of the Indonesian banking subsector in LQ45 by using the Long Short-Term Memory Network and then conduct accuracy testing by comparing the results of stock price predictions with actual stock price data.

159

Figure 1.

Daily closing stock price of banking subsector at LQ45.

2 THEORY AND METHODOLOGY 2.1 Stock prediction Stock prices on the market fluctuate in such a way that stock prices are difficult to estimate without using the right analytical tools. To predict stock prices, several analyses can be used as follows: 1. Fundamental analysis: Focuses on key data in financial statements to calculate whether stock prices have been accurately appreciated (Kodrat & Indonanjaya, 2010). 2. Technical analysis: The term “market action” includes three sources of information that can be obtained by price, volume, and open interest; the term “forecasting future price trends” refers to prices and returns that can be used to predict prices in the future (Murphy, 1999). The methods that can use are Moving Average (MA) and Autoregressive Integrated Moving Average (ARIMA). 3. Machine Learning: Refes to computer science that gives computers the ability to learn. We can use supervised learning with regression method to predict stock prices from data his­ tory with various types of learning (Konar & Bhattacharya, 2017).

2.2 Long sort-term memory network Hochreiter & Schmidhuber (1997) introduce Long Short-Term Memory (LSTM) Network as a type of Recurrent Neural Network (RNN) architecture that repeats. LSTM Networks enable learning of long-term dependency and they work very well in a variety of problems. Patterson & Gibson (2017) state that on the Neural Network, the Long Short-Term Memory Network model has the advantage of updating better equations and better backpro­ pagation. The LSTM network consists of many LSTM cells that are connected, perform well, and have efficient ways in the learning process. 2.3 Research methodology From Figure 2, the method of data processing is divided into two parts according to function, namely: 1. Data Sources function provides data. The process consists of: a. URL-based API as a source of daily stock price data b. Data from LQ45 financial sector shares are stored in a database c. Preprocessing data on various variances is simplified after a statistical rule, called data transformation d. Data transformation performed by extracting the data needed to analyze the data neatly arranged according to the pattern

160

Figure 2.

Research flowchart.

2. Data Analytics functions to analyze and test to get stock price predictions in the following ways: a. Technical analysis methods with Moving Average in this study will use Exponential Moving Average (EMA), Autoregressive Integrated Moving Average (ARIMA), and Long Short-Term Memory (LSTM) b. Data testing is performed for prediction accuracy by calculating Root Mean Square Error (RMSE) sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

n P

RMSE = 1n ðfi - yiÞ2 where fi projection value and yi actual value i¼1

c. Prediction results are the material for research evaluation

3 RESULT AND DISCUSSION 3.1 Normalization data As shown in Figure 1, the closing price of the six banking issuers has a striking price differ­ ence. In order to facilitate the analysis, normalization is done by using a scalar from 0 to 100. The results of normalization can be seen in the Figure 3 below.

Figure 3.

Daily closing stock price of banking subsector at LQ45 with normalized 0–100.

161

3.2 Research result EMA equation is rewritten as follows: EMA ¼ Price ðtÞ x k þ EMA ðt - 1Þ x ð1 - kÞ; where k ¼ 2=ðN þ 1Þ by using N = 20 or also called windows time 20 days which is close to a month period of workday time. The RMSE values and final price predictions are shown in Table 1. In the analysis of ARIMA models (p, d, q), the order p, d, and q is used to predict stock prices between 0 to 3. Programming carried out using python simulates the combination between p, d, and q to get the smallest MSRE. As for the results of the modeling, price predic­ tions can be made with the following equation: Price ðtÞ ¼ ¢1: Price ðt - 1Þ þ μ þ ε ðtÞ þ Y1:ε ðt - 1Þ In this study, calculations using EMA and ARIMA, which are fixed prediction calculations, will be used as a reference to better look for errors. Analysis of the ARIMA model has a smaller RMSE compared to EMA, and LSTM learning results must have a smaller RMSE than the ARIMA to measure accuracy. The LSTM model for conducting learning requires parameters that must be set and then tested to find the errors (RMSE) as a precision test. The appropriate machine learning LSTM results will get a small error value but sometimes get a larger error value. From several considerations and experiments conducted in order to get a fit model for all input data, this study uses the LSTM parameters, among others: 1. 2. 3. 4. 5.

Number of hidden layers : 2 pieces Number of unit cells per layer : 120 units Drop out : not used Data Window : 20 periods Iteration (epoch) : 50 times

From python programming and using hard and Tensor flow libraries to create LSTM machine learning models, a summary is obtained based on the RMSE value of each iteration (epoch) of the cell units. Each cell unit stores learning memory and Table 1. RMSE values and final price predictions using the EMA model. Windows Code

Test RMSE(%) Begin Last Predict Delta Gain

BBCA BBNI BBRI BBTN BJBR BMRI

20 20 20 20 20 20

261 261 261 261 261 261

1,530 2,479 2,491 3,664 1,432 2,269

21,900 9,900 3,640 3,570 2,400 8,000

26,000 8,800 3,660 2,540 2,050 7,375

25923 8718 3641 2578 2033 7333

–77 –81 –19 38 –17 –42

4,023 –1,182 1 –992 –367 –667

Table 2. RMSE values and final price predictions using the ARIMA model. Code

ARIMA

Observ

Test

RMSE(%)

Begin

Last

Predict

Delta

Gain

BBCA BBNI BBRI BBTN BJBR BMRI

(0,1,2) (1,0,0) (0,1,2) (1,0,0) (1,0,0) (1,1,1)

2,203 2,206 2,207 1,970 1,835 2,207

261 261 261 261 261 261

1.311 1.900 1.934 2.730 1.175 1.831

21,900 9,900 3,640 3,570 2,400 8,000

26,000 8,800 3,660 2,540 2,050 7,375

26,017 8,796 3,656 2,539 2,048 7,376

17 –4 –4 –1 –2 1

4,117 –1,104 16 –1,031 –352 –624

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Table 3. RMSE values and final price predictions using the LSTM model. Code

Epoch

Train

Test

RMSE(%)

Begin

Last

Predict

Delta

Gain

BBCA BBNI BBRI BBTN BJBR BMRI

14 21 8 9 3 10

2,203 2,206 2,207 1,970 1,835 2,207

261 261 261 261 261 261

1.306 1.892 1.918 2.717 1.164 1.825

21,900 9,900 3,640 3,570 2,400 8,000

26,000 8,800 3,660 2,540 2,050 7,375

25,977 8,800 3,640 2,531 2,050 7,377

-23 0 -20 -9 0 2

4,077 -1,100 -0 -1,039 -350 -623

Table 4. RMSE performance of EMA, ARIMA, and LSTM. Method

BBCA(%)

BBNI(%)

BBRI(%)

BBTN(%)

BJBR(%)

BMRI(%)

EMA ARIMA LSTM

1.530 1.311 1.306

2.479 1.900 1.892

2.491 1.934 1.918

3.664 2.730 2.717

1.432 1.175 1.164

2.269 1.831 1.825

predicts stock prices over the training period (2018 period). In stock price prediction research what is used as a reference is the learning outcome with the smallest RMSE value. Out of 50 iterations performed, the RMSE results with the best iteration are shown in the Table 3. 3.3 Performance comparison of EMA, ARIMA, and LSTM From the results of EMA and ARIMA calculations, as well as machine learning out­ comes using the LSTM model, we can compare the performance of each model as shown Table 4. From RMSE performance, LSTM is better than ARIMA, let alone EMA. The pur­ pose of this study is to compare the accuracy of the predicted value with the actual value with the RMSE indicator that can be proven. So, the purpose of this research is quantitatively and with verification achieved, although the accuracy between ARIMA and LSTM is very small, but it can still be optimized with deeper learning with various parameters that can be tested on LSTM. Prediction of stock prices for the testing period during the 2018 for the EMA, ARIMA, and LSTM models can be seen in the following pictures.

4 CONCLUSION AND SUGGESTION 4.1 Conclusions From the results of processing and analysis on financial sector stock data that includes the LQ45 index on the Jakarta Stock Exchange for the period August 2018, the following conclu­ sions are reached: 1. Technical analysis in predicting stock prices by using time-series prediction analysis can be done by machine learning by using the Long Short-Term Memory (LSTM) model which is part of deep learning by providing better prediction results. 2. The results of errors with RMSE parameters below 3% or having accuracy above 97% are good results. Deviation of stock prices affects the accuracy of predictions; sample data that has a large standard deviation has a large error as well.

163

Figure 4.

Closing stock price predictions using EMA, ARIMA, LSTM.

4.2 Suggestions From these conclusions, the following are suggestions for further research: 1. The learning outcomes of this study are limited by parameters and the time taken. Better results will be given when applying machine learning analysis with longitudinal research. 2. The practical thing to be able to provide optimal results, is the need to provide input from fundamental analysis to machine learning with an appropriate Neural Network model. 3. In 2018, BCA shares still provide profits that can be maintained by investors, while the shares of other banks managed by the government have decreased, indicating that good government policies for managing state banks are needed.

REFERENCES Haykin, S. (2010). Neural Networks and Learning Machines, 3/E. Pearson Education India. Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8): 1735–1780. Kodrat, D. S., & dan Indonanjaya, K. (2010). Manajemen Investasi. Bandung: Alfabeta. Konar, A., & Bhattacharya, D. (2017). Time-Series Prediction and Applications. Springer International Publishing. Manurung, A. H., Budiharto, W., & Prabowo H. (2018). Algorithm and Modeling of Stock Prices Fore­ casting Based on Long Short-Term Memory (LSTM). International Journal of Innovative Computing Information and Control (ICIC). 12, (12: tentative), December 2018. Murphy, J. J. (1999). Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications. Penguin. Patterson, J., & Gibson, A. (2017). Deep Learning: A Practitioner’s Approach. USA: O’Reilly Media Inc.

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The effect of financial technology, interest rate, and exchange rate towards money supply: An evidence from Indonesia Kadina Mutiara Hati & Astrie Krisnawati Faculty of Economic and Business, Telkom University, Indonesia

ABSTRACT: Money supply is one of the important indicators in the formulation of monetary policy. Money supply in Indonesia has increased from 2008 to 2018. Money supply must be regu­ lated, in order that the impact of inflation that occurs is not so high. This study aims to find out the result of the effect of financial technology, interest rate, and exchange rate on money supply in Indonesia. The number of research samples is 11 data of money supply. The data was obtained by applying the purposive sampling technique. Data analysis method in this research is multiple linear regression. The results of this study indicate that partially financial technology has a significant effect on money supply, the interest rate and the exchange rate have no significant effect on money supply in Indonesia. Furthermore, financial technology, interest rate, and exchange rate simultaneously have a significant effect on money supply in Indonesia.

1 INTRODUCTION Money supply is one of the important indicators in the formulation of monetary policy. Money supply is the responsibility of the monetary system (Central Bank, Commercial Bank, and Rural Credit Bank/BPR) to the domestic private sector (not including the central government and non­ residents). Budhi (2001) states that money supply will affect the value of money implemented at the price and product level. If the money supply is greater than the production of goods and ser­ vices, it will have an impact on increasing prices, at the same time, this means the value of money decreases. Ramadhani and Widyo (2019) state that the circulation rate of money as one of the economic features is the main factor that fully defines the current economic situation and has a direct impact on the inflation rate. Money supply and inflation rate are the main determinant of the high level of economic growth that is able to create jobs, reduce poverty, increase per capita income and living standards that lead to economic development (Phibian, 2010). As shown in Table 1, the amount of money supply has increased from 1998 to 2018. Money supply must be regulated, in order that the impact of inflation that occurs is not so high. Money supply can be effected by several factors such as digital payments, exchange rate, and interest rate. Aprileven (2015) and Luwihadi (2017) state that interest rate and exchange rate can affect money supply. Hariani (2014) states that gross domestic product, interest rate on Indonesian bank certificates (SBI), and gross fixed capital formation have an effect on money supply. Istanto and Syarief (2015) state that digital technology has a significant posi­ tive effect to money supply. Ramadhani and Widyo (2019) state that digital payments and exchange rates can affect money supply. Istanto and Syarief (2015) state that digital transactions or transcations through financial tech­ nology has a negative and significant effect on money supply. Based on Table 1. in 1999. 2002. 2004, and 2005 financial technology (fintech) remained constant. but money supply increased. Aprileven (2015) states that interest rate has a negative and significant effect to money supply. Based on Table 1. in 2005. 2008, and 2013 interest rate increased. but money supply also increased.

165

Table 1.

The money supply.

Years

The Money Supply

Financial Technology

Interest Rate

Exchange Rate

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

577381 646205 747028 786741 849401.0833 903986.6667 970838.1667 1092068.833 1260444.667 1461509.917 1697269.25 1971663.5 2216640.569 2571212.773 3046428.308 3465705.273 3868128.991 4357519.483 4698476.657 5163295.281 5518336.633

1 1 2 5 5 8 8 8 11 17 20 21 24 32 41 63 82 111 144 158 159

0.493241667 0.231275 0.116658681 0.166158333 0.149475 0.099275 0.074458333 0.13 0.0975 0.8 0.0925 0.071458333 0.065 0.065833333 0.057708333 0.064791667 0.075416667 0.075208333 0.06 0.045625 0.051041667

8025 7100 9595 88228.16667 9261.166667 8571.166667 9030.416667 9750.583333 9141.25 9142.416667 9771.666667 10356.16667 9078.25 8773.25 9418.583333 10562.66667 11884.5 13457.58333 13329.83333 13398.16667 14267.33333

Luwihadi (2017). and Ramadhani and Widyo (2019) state that exchange rate has a positive and significant effect on money supply. Based on Table 1. in 2006. 2007. and 2011 exchange rate decreased. But money supply also increased. Based on this background, the formulation of the problem in this research is whether fintech, interest rate, and exchange rate affect money supply partially and simultaneously. The purpose of this research is to determine the effect of fintech, interest rates, and exchange rates to money supply partially and simultaneously.

2 LITERATURE REVIEW 2.1 Money supply Money supply is the total value of money in the public. Bank Indonesia defines money supply in the narrow sense (M1) and in the broad sense (M2). Rahardja and Manurung (2008) state that money supply in the narrow sense (M1) is the amount of money supply consisting of cur­ rency and demand deposits, while money supply in a broad sense (M2) is M1 plus time deposits. M1 ¼ C þ D Information: M1 = Money supply in the narrow sense C = Currency D = Demand deposit M2 ¼ M1 þ TD

166

Information: M2 = Money supply in a broad sense TD = (Time deposit) 2.2 Financial technology Bank Indonesia (2017) states that financial technology is the use of financial system technol­ ogy that produces new products, services, technology, and/or business models and can have an impact on monetary stability, financial system stability, efficiency, smoothness, security, and reliability of payment systems. According to Narayan and Sahmina (2018), the growth of fintech companies is calculated using the cumulative growth of fintech companies every year. Fintech cumulative ¼ ðFintech start - upst1 Þ þ ðFintech start - upst1 þ Fintech start - upst2 Þ þ : . . .

2.3 Interest rate Amarasinghe’s (2015) financial theory explains that interest rates are a measure of time value of money and are one of the determinants of stock prices. Kasmir (2010) states that interest rate is the interest given to debtor or customers at the price that must be paid to the Bank. The amount of interest rates is determined by each central bank in each country (Pardede et al, 2016). The interest rate in this research uses the BI Rate. 2.4 Exchange rate Exchange rate is stated by Mankiw (2007:128) as the unit rate level of the population of the two countries to trade with each other. Triyono (2008) states that the exchange between two different currencies is a comparison of values between the two currencies. In this research, the rate used is the price of the US Dollar and the currency of Indonesia is Rupiah. 2.5 Hypothesis Based on the framework, the research hypothesis was that fintech, interest rate, and exchange rate affect money supply partially and simultaneously. Detailed research hypothesis are below: H1: Financial Technology (Fintech) has a significant affect to money supply in Indonesia. H2: Interest rate has a significant affect to money supply in Indonesia. H3: Exchange rate has a significant affect to money supply in Indonesia. H4: Financial Technology (Fintech), interest rate, and exchange rate simultaneously have a significant effect to money supply in Indonesia.

3 METHODOLOGY The population of this research is the M1 and M2 concept of Indonesian money supply recorded from 1960. The number of research samples is 11 data of money supply recorded at the Ministry of Trade Republic of Indonesia. The data was obtained by purposive sampling technique. Data analysis method in this research is multiple linear regression.

167

4 RESULT AND DISCUSSION 4.1 Classical assumption test a. Normality test: In this research, the authors used the normality test with the KolmogorovSmirnov test and the results obtained as in Figure 1 below. The Asymp value. Sig (2-tailed) or significance value in this research is 0.150; Sujarweni (2015) states that if the significance value is greater than 0.05 then the data is normally distributed. b. Heteroscedasticity test: Ghozali (2011) states that the results of the glejser test showed that no heteroscedasticity of the SPSS calculation had a significance probability value above a 5% confidence level. A significance level in this research above 0.05, then there is no het­ eroscedasticity in this study. c. Autocorrelation test: Durbin-Watson value in this research is obtained of 0.779; this value will be compared with the Durbin-Watson table value using 5% significance, with a sample size of 11 (n) and number of independent variables 3 (k). The table value DL = 1.9280, DU = 0.5948, 4-DU = 3.4052, 4-DL = 2.072. By looking at the value of Durbin-Watson that is between DU and 4-DU, it can be concluded from the data in the study that there is no autocorrelation. d. Multicollinearity test: Multicollinearity test is used to determine whether the regression model found a correlation between independent variables; if there is a correlation, then there is a problem called multicollinearity (multico). Look at the eigenvalue and condition index. If the eigenvalue is more than 0.01 and or the condition index is less than 30, it can be concluded that multicollinearity symptoms do not occur in the regression model. The results of this study, the eigenvalue is 0.02> 0.01 even though collinearity diagnostics 56.616 were more than 30.

4.2 Regression analysis The regression equation for the effect of fintech, interest rate, and exchange rate on money supply can be formulated as follows: MS ¼ 2690204:982 þ 20276:475Fintech - 116648:197IR þ 31:295ER The intercept or constant coefficient of 2690204.982 shows that if the value of fintech, interest rates, and exchange rate is constant (0), then the money supply value of 2,690,204.982. In addition, if there is an increase in fintech of 1%, there will be an increase in money supply growth of 2,027,645.5%, if every 1% increase in interest rate, money supply will increase by 11,664,819.7%, and if there is a 1% increase in exchange rate, then money supply will an increase of 31,295.5% assuming other variables are con­ sidered fixed. 4.3 Hypothesis test The t-test is used to see the effect of the variable X (independent) on the variable Y (dependent). partially by seeing which hypotheses are accepted.

Figure 1.

T-test.

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a. Effect of fintech to Money Supply: The statistical t value for the fintech variable is 2.396 and value of t table 2.3060, which indicates that the value of t statistics is greater than t table, then statistically at the 0.05 significance level, Fintech has a significant effect on money supply. Fintech has a positive and significant effect on money supply in the public. If Fintech experiences an increase, money supply will increase. The increase in money supply is affected by increased lending. Istanto and Syarief (2015) state that digital transactions or transactions through digital technology have a significant positive effect on money supply. b. Effect of interest rate on money supply: The statistical t value for the interest rate variabel is 0.154 and value of t table 2.3060, which indicates that the value of t statistics is smaller than t table, then statistically at the 0.05 significance level, interest rate has no significant effect on money supply. Interest rate has a negative and not significant effect to money supply in the public. Hariani (2014) states that interest rate has a negative and not signifi­ cant effect on money supply. The direction of the negative effect indicates that if interest rates increase, money supply will decrease. Budhi (2001) states that if the government sets the interest rates high, people are expected to be cautious in using money or level of con­ sumption will be reduced, thus reducing money supply. c. The effect of exchange rate to money supply: The statistical t value for the exchange rate variable is –0.881 and value of t table 2.3060, which indicates that the value of t statistics is smaller than t table, then statistically at the 0.05 significance level, exchange rate has no significant effect to money supply. Exchange rate has a positive and not significant effect on money supply in the public. Budhi (2001) states that exchange rate has a positive and not significant effect on money supply. The direction of the positive influence means that if the rupiah exchange rate decreases (depreciates), then money supply in the community will also decrease. When the rupiah weakens, there will be a tendency for people to exchange their dollars, so that the person will get a bigger profit. This is resulting in an increase in money supply. This can be done by raising the interest rate of savings (saving rate), this strategy will certainly be an attraction for people to save, so that the money in the public is drawn again into the hands of banks. The f test is used to see the effect of the X variable (independent) on the Y variable (dependent) simultaneously by seeing which hypotheses are accepted. Effect of fintech, interest rate, and exchange rate on money supply simultaneously: The stat­ istical f value of this study is 58.57 and value of f table is 4.46, which indicates that the statis­ tical f value is greater than f table, and statistically at the 0.05 significance level, fintech, interest rates, and exchange rates simultaneously have a significant effect to money supply. Lintangsari et al. (2018) states that transactions using digital payments can increase money supply. The increase in noncash payments has a substitution effect and efficiency. Syarifuddin et al. (2009) state that the substitution effect results in a decrease in currency demand and an increase in M1 and M2. Budhi (2001) states that interest rate have a negative effect to money supply: if interest rates increase, money supply decreases. Money supply can be affected by exchange rate. Ramadhani and Widyo (2019) states that exchange rate has a positive effect on money supply. Fintech, interest rates, and exchange rates simultaneously have a significant effect on money supply. If the government wants to reduce money supply, the government should restrict the growth of financial technology in the business of lending consumption, but improve financial health technology such as payment, personal finance, and wealth manage­ ment, insurtech etc. The government must also increase interest rates and the value of the rupiah simultaneously.

Figure 2.

F-test.

169

5 CONCLUSION Based on the results of multiple linear regression analysis and hypothesis testing, the conclu­ sions obtained from this research are as follows: 1. Financial technology (Fintech) has a positive and significant effect to money supply. 2. Interest rate has a negative and not significant effect to money supply. 3. Exchange rate has a positive and not significant effect to money supply. Financial technol­ ogy (Fintech), interest rates, and exchange rates simultaneously have a significant effect on money supply

REFERENCES Amarasinghe, AAMD. 2015. Dynamic Relationship between Interest Rate and Stock Price: Empirical Evidence from Colombo Stock Exchange. International Journal of Business and Social Science, 6 (4):92–97. Aprileven, Hendra Putra. 2015. Pengaruh Faktor-faktor Ekonomi Terhadap Inflasi di Indonesia yang Memediasi Oleh Jumlah Uang Beredar. Economics Development Analysis Journal.4(1). Bank Indonesia. 2017. Financial Programming and Policies (FPP). Jakarta: Bank Indonesia. Budhi, Made Kembar Sri. 2001. Faktor-faktor Yang Mempengaruhi Jumlah Uang Beredar. Buletin Studi Ekonomi, (6) 1: h:1–5. Ghozali, I. 2011. Aplikasi Analisis Multivariate dengan Program IBM SPSS 20. Semarang: Badan Pener­ bit Universitas Diponegoro. Hariani, Prawidya RS. 2014. Faktor-Faktor Yang Mempengaruhi Jumlah Uang Beredar Di Indonesia Per­ iode 1990–2010. JURNAL EKONOMIKAWAN 14(2). Istanto, Lasondy S and Syarief Fauzie. 2015. Analisisi Dampak Pembayaran Non Tunai Terhadap Jumlah Uang Beredar Di Indonesia.Jurnal Ekonomi dan Kuangan, 2(10). Kasmir. 2010. Manajemen Perbankan.Edisi Ketiga. Cetakan Keenam. Jakarta: PT. Raja Grafindo Persada. Lintangsari, Nastiti Ninda, Nisaulfathona Hidayati, Yeni Purnamasari, Hilda Carolina, dan Wiangga Febranto. 2018. Analisis Pengaruh Instrumen Pembayaran Non-Tunai Terhadap Stabilitas Sistem Keuangan Di Indonesia. Jurnal Dinamika Ekonomi Pembangunan, 1(1). Lodha, S.L. 2013. A Review of Empirical Studies on Money Supply at Abroad and in India. Journal of Economics and Sustainable Development, 4(13): 158–169. Luwihadi, Ni Luh Gede and Sudarsana Arka. 2017. Determinan Jumlah Uang Beredar dan Tingkat Inflasi di Indonesia Periode 1984-2014. Jurnal Ekonomi Pembangunan, 6(4). Mankiw, G. 2007. Makroekonomi. Edisi keenam. Jakarta: Penerbit Erlangga. Narayan, Seema dan Sahmina. 2018. Has Fintech Influenced Indonesia’s Exchange Rate and Inflation?. Bulletin of Monetary Economics and Bankin, 21(2): 190–202. Pardede, Noel, Raden Rustam Hidayat, and Sri Sulasmiyati. 2016. Pengaruh Harga Minyak Mentah Dunia, Inflasi, Suku Bunga (Central Bank Rate), dan Nilai Tukar (Kurs) terhadap Indeks Harga Saham Sektor Pertambangan di ASEAN (Studi pada Indonesia, Singapura dan Thailand Periode Juli 2013-Desember 2015). Jurnal Administrasi Bisnis, 39(1). Phibian, N. O. 2010. The Quantity Theory of Money: Evidence from Nigeria. Central Bank of Nigeria (CBN) Economic and Financial Review, 48(2),91–107. Rahardja Prathama, Manurung Mandala. 2008. Pengantar Ilmu Ekonomi (Mikroekonomi dan Makroe­ konomi) Edisi Ketiga. Lembaga Penerbit Fakultas Ekonomi Universitas Indonesia. Ramadhani, Rizal dan Widyo Nugroho. 2019. Analysis of The Effect of Exchange Rates, E-money, and Interest Rates on The Amount of Money Supply and Its Implication on The Inflation Level in Indo­ nesia Period 2012-2017. International Journal of Accounting & Finance (IJAFAP). 2(1). Sujarweni, V. W. 2015. SPSS untuk Penelitian. Yogyakarta: Pustaka Baru Press. Syarifuddin, Ferry, Ahmad Hidayat, dan Tarsidin. 2009. Dampak Peningkatan Pembayaran Non-Tunai Terhadap Perekonomian Dan Implikasinya Terhadap Pengendalian Moneter Di Indonesia. Buletin Ekonomi Moneter dan Perbankan. Triyono. 2008. Analisis Perubahan Kurs Rupiah Terhadap Dollar Amerika. Jurnal Ekonomi Pembangu­ nan.9(2), 156–167.

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Bankruptcy prediction using Altman and Zavgren model in property and real estate registered in Indonesia stock exchange period 2014-2018 T.T. Gustyana & A. Sipahutar Telkom University, Bandung, Indonesia

ABSTRACT: Property and real estate have a direct contribution to the national economy. In Recent years property’s demand has decreased, it caused property developers complaining about the sluggish of property industry and choosing to go out of business. Some companies suffered losses that if left uninterrupted, would result in Bankruptcy. The aims of this study is to analyse bankruptcy prediction of property and real estate companies using Altman and Zavgren models and then compare the two models by analysing the level of accuracy of each bankruptcy prediction model. The data used in this study are secondary data in the form of financial statements of each properties and real estate companies listed on the Indonesia stock exchange period 2014-2018. The results of this study indicate that Altman Z”-Score model is more accurate than Zavgren model.

1 INTRODUCTION The property industry has a strategic position to play a direct role in the national economy. This sector is believed to be able to become a benchmark of economic growth in the future. The role given by the property sector includes contributing to the Indonesian economy through industry; property can also invite potential new investors who come to Indonesia (Hartomo, 2017). GDP growth in the property and real estate sector has fluctuated but not significantly. According to data from Central Bureau of Statistics shows contribution data of property industry toward GDP from 2013 to 2017 where in 2016 it was 2.82% decreased in 2017 by 2.79% (BPS, 2018). The growth in the property sector has been moving slowly over the past few years, causing property developers who are members of the Indonesian Real Estate (REI) to complain about the sluggishness of the property industry. The sluggishness of the property sector is also accompanied by licensing issues which are also considered to still be homework because the process takes a lot of time. A number of property developers chose to go bankruptcy because they did not have a strategy to survive. DPD REI West Java recorded 40% of the total 490 developers stopped operating that means 196 property developers went bankrupt (Alexander, 2018). In addition to West Java, Real Estate Indonesia (REI) in Central Java recognizes that home sales continue to fall, which has occurred in the past three years. The number of houses and apartments that sold in Central Java in 2016 was 11,500 units. That number continues to decline and in 2017 only reached 8,900 units, until September 2018 only 5,600 units were sold (Prabawati, 2018). Demand for property has declined in the last two years, 2016 and 2017. This is caused by rules for granting property loans and decrease of society’s purchasing ability. The increase in living expenses causing people to prefer saving money or investing in stocks or other banking products that are considered to be more profitable than property sector (Murdaningsih, 2017).

171

Figure 1.

Growth of property demand.

The decline in property sales in the past few years has certainly had an impact on companies in the property sector. It also has an impact on the net profit/loss of the company’s finances, if the company’s performance is decreases continually that will give negative impact on the com­ pany, such as bankruptcy. In obtaining maximum profits, various risks will arise; one of the risks is bankruptcy. Bankruptcy is a situation where the company’s operating cash flow is inadequate to pay off its obligations (Rya & Gustyana. 2018) . Financial distress is a condition in which a company experiences negative net operating income for several years, besides that financial distress is a stage of decline in financial conditions experienced by a company before bankruptcy or liquidation (Putra et al. 2016).

2 LITERATURE REVIEW 2.1 Financial statements Financial statements provide a visual representation of the company that used to describe the business to investors and other people outside the company as well as to company employees. Thus, we can think of the company’s financial statements and various terms used to describe the company and its operations as a business language (Keown et al. 2018). 2.2 Financial statements analysis Financial statement analysis is the application of analytical tools and techniques to general financial statements and related data to obtain estimates and conclusions that are useful for business analysis (Subramanyam. 2014). 2.3 Financial performance Performance is the result of an evaluation of the work that has been done; the results of the work are compared with criteria that have been set together. Measurement of financial per­ formance can be done by using financial statements as a basis for measuring performance (Sujarweni. 2017).

172

2.4 Bankruptcy Bankruptcy is usually interpreted as a failure of the company in carrying out the company’s operations to generate profits. Financial distress is the stage of decline in financial conditions experienced by a company that occurred before the bankruptcy or liquidation (Putra et al. 2016). 2.5 Bankruptcy prediction model a. Altman Z”-Score Model The Z-Score analysis was first put forward by Edward I Altman in 1968. Altman produced a formula called the Z-Score which uses multiple discriminant analysis (MDA) (Altman & Hotchkiss, 2006) . Formula Z”-Score is the latest formula that is very flexible because it can be used for various types of business fields of the company, both those that go public or not, and suitable for use in developing countries. The formula for calculating the Altman model: Z” ¼ 6; 56ðX1 Þ þ 3; 26ðX2 Þ þ 6; 72ðX3 Þ þ 1; 05ðX4 Þ

ð1Þ

Where: Z” = bankruptcy index X1 = working capital/total assets X2 = retained earnings/total assets X3 = earnings before interest and tax/total assets X4 = market value of ordinary shares and preferred shares/book value of total debt The cut-off values for the Z value can explain whether the company will fail or not in the future. By division into 3 categories: a. If the value of Z”> 2.6, including companies that are not bankrupt or safe zone. b. If the value is 1.1 1.96), so that it can be assumed that when there is a change in prior knowledge it will have an impact on selfefficacy. The results also show a positive direction, which means when the company increases prior knowledge, self-efficacy will increase as well, and vice versa. Table 1 shows that selfefficacy has a significant effect on digital entrepreneurial intention with a path coefficient of

Table 1. T-value, standardized coefficient value, and R-square value. Structural Equations

Influence

η1 = (γ11 × ξ1) + (γ12 × η2) + (γ13 × ξ2) + (γ14 × ξ3) +ζ1

ξ1 towards η1 η2 towards η1 ξ 3 towards η1 ξ4 towards η1 ξ1 towards η2

η2 = (γ21 × ξ1) + ζ1 Source: Research processed data result

191

t-value 6.833 4.216 –3.052 –0.953 9.886

SC 0.533 0.280 –0.155 –0.048 0.617

R2 0.611

0.381

0.280 with t-values greater than critical values (4.216 >1.96). Meanwhile, prior knowledge moderated by social media has no significant effect on digital entrepreneurial intention since the path coefficient is –0.155 with t-values less than critical values (–3.052 < –1.96), so it can be assumed that when there is a change in social media interacting with prior knowledge, it will not have an impact on digital entrepreneurial intention. Social media does not have a significant effect on moderating self-efficacy towards digital entrepreneurial intention. With a path coefficient of –0.048 with t-values smaller than critical values (–0.953 > –1.96), it can be assumed that when changes occur in social media interaction with self-efficacy it will not have an impact on digital entrepreneurial intention. Social media as moderating variable shows a negative direction that means when the individual interacts with social media and prior knowledge then digital entrepreneurial intention will decrease and vice versa. The measure­ ment of the validity of the SEM model in this study uses first order confirmation analysis (First Order CFA), where a variable is said to have good validity on the construct or latent variable if the standardized loading factor is greater than or equal to the critical value of 0.50 or 1.96. Digital entrepreneurial intention, social media, prior knowledge, and self-efficacy variables have a standard factor load value greater than 0.50. Then it can be concluded that all indicators have good measurement validity, although, on loading factor standards and con­ struct reliability, the overall latent constructs have good reliability except for self-efficacy because the coefficient construct reliability (CR) is less than 0.70.

5 CONCLUSION AND SUGGESTION Study results explain that the existence of prior knowledge and self-efficacy, combined with social media usage, do not positively affect a student’s intention to become a digital entrepreneur. Less optimal use of social media, nonsupportive social media contact, little exposure to entrepreneurship knowledge, or many reasons of which the author may be unaware might explain why social media usage does not influence an individual’s interest in digital entrepreneurship. Self-efficacy and social media have high-level value percentage compared to other variables. Prior knowledge regarding entrepreneurship of students was quite high, however this variable has the lowest value percentage compared to selfefficacy, social media, and digital entrepreneurial intention. The authors concluded that there is high self-confidence among private universities students and this would provide them more opportunities in choosing digital entrepreneurship as a future career. Under­ graduate college students might already be aware of how digital technology changed the way creation is practiced and needed in every sector, thus, technology has become part of life. Digital technology simplifies people’s work with its benefits, for instance, helping entrepreneur’s business with marketing activity, reaching customers, and knowing target the market through social interaction. Future researchers might continue in a wider area of research, such as students from differ­ ent major facilities or faculties: for example, engineering students could be a suitable object since the potential to becoming an entrepreneur comes from various educational backgrounds. Likewise, future research might examine millennials from different age groups in Indonesia as the study object in order to get a more generalized study result. Afterwards, the social media variable should not be a moderator variable in future research since it has shown a negative result on digital entrepreneurial intention. Social media might not work as a moderator but there is a possibility that social media creates a positive impact on digital entrepreneurial intention as an independent variable. Even technology infrastructure is constantly advancing and developing society, and there is a big challenge in digital entrepreneurship literature. It is assumed studies on digital entrepreneurship, specifically using intention as a topic is still rare to find. The authors hope this research stimulates further research to continue to follow the same quantitative approach with different research specification objects in order to enrich the relevant academic articles of digital entrepreneurship. Research on entrepreneurship in the digital sphere is very important for new entrepreneurs to study and should be included in the agenda in the near future. 192

REFERENCES Cho, H., So, J., & Lee, J. 2009. Personal, Social, and Cultural Corralates of Self-Efficacy & Beliefs Among South Korean College Smokers. Health Communication, 24(4): 337–345. Farani, A. Y., Karimi, S., & Motaghed, M. 2017. The Role of Entrepreneurial Knowledge as a Competence in Shaping Iranian Students’ Career Intentions to Start a New Digital Business. Euro­ pean Journal of Training and Development, 41(1): 83–100. Liñán, F. 2004. Intention-Based Models of Entrepreneurship Education. Piccolla Impresa/Small Business, 3, 11–35. Lope Pihie, Z., & Bagheri, A. 2013. Self-Efficacy and Entrepreneurial Intention: The Mediation Effect of Self-Regulation. Vocations and Learning. Porter, M., Anderson, B., & Nhotsavang, M. 2015. Anti-Social Media: Executive Twitter Engagement and Attitudes about Media Credibility. J. Commun. Management. 19, 270–287. Potosky, D., & Ramakrishna, V. 2002. The Moderating Role of Updating Climate Perceptions in Rela­ tionship between Goal Orientation, Self-Efficacy and Job Performance, 15, 275–297. Matthews, R., Hall, K. R., & Matthews, L. (2017). Application of New Theory in Entrepreneurship: Social Cognition. Sambamurthy, V, Bharadwaj, A., & Grover, V. 2003. Shaping Agility through Digital Options: Recon­ ceptualizing the Role of Information Technology in Contemporary Firms. MIS Quarterly, 27(2): 237–263. Shane, S., & Venkataraman, S. 2000. The Promise of Entrepreneurship as a Field Entrepreneurship. Academy of Management Review, 25(1): 217–226. Shaver, K. G., & Scott, L. R. 1991. Person, Process, Choice: the Psychology of NewVenture Creation. Entrepreneurship Theory and Practice, 16(2): 23–45. Tierney, P., & Farmer, S. M. 2002. Creative Self-Efficacy: Its Potential Antecedents and Relationship to Creative Performance. Academy of Management Journal, 45, 1137–1148. Turban, E., Leidner, D., McLean, E., & Wetherbe, J. 2008. Information Technology for Management: Transforming Organizations in the Digital Economy. (6th ed.). WileyPLUS, Hoboken, NJ. Wilson, F., Kickul, J., & Marlino, D. 2007. Gender, Entrepreneurial Self-Efficacy, and Entrepreneurial Career Intentions: Implications for Entrepreneurship Education. Entrepreneurship Theory and Prac­ tice, 31(3): 387–406. Wood, R., & Bandura, A. 1989. Social Cognitive Theory of Organizational Management. Academy of Management Review, 14, 361–384.

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Digital Economy for Customer Benefit and Business Fairness – Anggadwita & Martini (eds) © 2020 Taylor & Francis Group, London, ISBN 978-0-367-47722-6

The relevance of entrepreneurship learning processes towards technopreneur competencies within higher education institutions M. Rosyadhi & A. Ghina Telkom University, Bandung, Indonesia

ABSTRACT: Technopreneurs have been recognized as one of the important factors driving a country’s economy in this technological era, wherein it is necessary to develop technopre­ neurs’ expertise in university to gain early advantages. This study using descriptive research to analyze the learning process that has been given to students of the undergraduate program of information system of Telkom University specializes in technopreneurship toward the devel­ opment of their personal entrepreneurial competencies (PECs). The results of in-depth inter­ views from this study found that the learning process was quite good enough although isn’t optimal yet. Furthermore, the average PEC level of the students has only reached the moder­ ate level. The competency with the highest score is information seeking, while the lowest is assertiveness. Overall, it can be concluded that there is room for improvement in various aspects involved in the learning process.

1 INTRODUCTION Entrepreneurship has a very important role in promoting economic growth, especially through the establishment of new business and employment (GEM, 2012). Previous research has exam­ ined whether entrepreneurship has a direct relationship with economic growth (Portela et al., 2012). McClelland (1967) stated, “A country can be prosperous only if there is an entrepre­ neur at least 2% of the population.” By the end of 2017, Indonesia Ministry of Cooperatives and SMEs targeted entrepreneurial ratios to reach 4% of the total population in Indonesia, because the current entrepreneurship ratio of 3.1% is still lower compared to other countries such as Malaysia 5%, China 10%, Singapore 7%, Japan 11%, and USA 12%. The number of Indonesia’s entrepreneurs who run their business as technology based is less than 1% (Mohamad Nasir, 2017). The low number of technopreneurs (technological entrepre­ neurs) can actually be resolved if science and technology are made as the key factor. With them, a country can optimize the utilization of its resources effectively and efficiently, which eventually will contribute significantly to the country’s economy, only if followed by support­ ive policies and regulations among key stakeholders. Creating a technopreneur is closely related to the world of education, especially higher educa­ tion. The conceptual model of technopreneur proposed by Prodan (2007) shows that one of the major stakeholders involved in forming a technopreneur is the University. Telkom University is one of the few universities that has incorporated entrepreneurship learning as a compulsory basic in their education system: the Information System Study Program of Telkom University provides a technopreneurship specialization course. However, currently, most of the students only create their venture/startup to finish the task and don’t take serious action to establish and grow their venture/startup. This resulted in efforts to evaluate the current learning process, including curriculum, teaching methods, learning outcomes of each subject related to the devel­ opment of technopreneur students, and competencies level (see Table 1 for research questions and objectives).

194

Table 1. Research question and research objective. Research Question

Research Objective

a. How is the learning process of technopreneurship related to aspects of curriculum, teaching methods, and learning outcomes applied for Undergraduate Program of Information System of Telkom University?

a. Evaluate the learning process of technopreneurship related aspects of curriculum, teaching methods, and learning outcomes applied for Undergraduate Program of Information System of Telkom University.

b. What is the level of Personal Entrepreneurial Competencies (PECs) owned by the students who were to specialize in technopreneurship?

b. Measure the level of Personal Entrepreneurial Competencies (PECs) owned by the students who were to specialize in technopreneurship.

c. How is the relationship between the learning processes of technopreneurship and the development of PECs on the students specialize in technopreneurship?

c. Evaluate the correlation between learning process of technopreneurship and the development of PECs on the students specialize in technopreneurship

2 LITERATURE REVIEW Entrepreneurial education (EE) covers all activities aimed at encouraging the entrepreneurial mindsets, attitudes, and skills needed to become entrepreneurs, and includes other aspects such as idea generation, start-up, growth, and innovation (Fayolle, 2009). In EE, there are six aspects to consider (Mwasalwiba, 2010), namely: definition and object­ ive, type of program offered and target participants, material content, teaching methods, activities undertaken by the community, and indicators for evaluation and resulting impact. There are 16 teaching methods (Mwasalwiba, 2010; Pittaway & Edwards, 2012; Mariana, 2017) that have been sorted and included in the most important category, namely: lecture, case study, presentation, video, games & competition, simulation, workshop, discussion, guest lecturer, study visity, collaborative project, business funding, exhibition, variative assessment aspect, variation assessors and variative assessment composition. EE has been said to play a vital role in developing students’ entrepreneurial competencies (Izquierdo et al., 2005; Ghina et al., 2017). One of the earliest psychological studies on entre­ preneurship was done by McClelland (1967). The purpose of his research was to identify and analyze the psychological factors that produce the personality of an entrepreneur, which then lead him to concentrate on “entrepreneurial competence” and propose that proactivity, initia­ tive, assertiveness, a strong achievement orientation and commitment to others are the compe­ tencies possessed by successful entrepreneurs (McClelland, 1967). McClelland’s study proposed 13 entrepreneurial competencies together, referred to as personal entrepreneurial competencies/PECs (McClelland, 1985). The 13 submitted PECs were initiative, sees and acts on opportunities, persistence, information seeking, concern for high quality of work, commit­ ment to work contract, efficiency orientation, systematic planning, problem solving, selfconfidence, assertiveness, persuasion, and use of influence strategies. One of the studies related to technopreneurial competence was conducted by Malach-Pines et al. (2005), whose research comparing high-tech entrepreneurs in three different countries – Israel, the United States and Hungary – found that initiatives were the hallmark of success in businesses involving high tech. Within this context, further research is required for comprehensive evaluation and understanding of EE toward the development of PECs. This research was focused on inputs (curriculum), process (teaching methods), and outputs (competencies) involving lecturers and students.

195

3 CONCEPTUAL FRAMEWORK There are seven stakeholders proposed by Prodan (2007) in his technopreneurship framework: technopreneur, university, corporation, capital, market/customer, government, and consultant. University should include institutional goals that specify what creates a technopreneur. Even fur­ ther, university should establish learning processes that support the development of technopre­ neur’s competencies. This study focus on the development of competencies generated through the learning process which are related to the 13 variables of PECs by McClelland et al. (1987). This research evaluated the relevance of learning processes in technopreneurship specializa­ tion toward the development of PECs, which lead to answer the research questions. The con­ ceptual framework of learning process toward PECs development can be seen in Figure 1.

4 RESEARCH METHOD The research methodology was conducted by using the qualitative research process of O’Don­ nell and Cummins (1999), shown in Figure 2. The qualitative method was used to gain understanding from the interviewees’ point of view about the learning process and to obtain a reliable score of the competencies. Qualitative research doesn’t use the term “population,” but does use Spradley’s “social situation” so the researcher can observe a patterned activity from a group of actors in a scope of place (Sugiyono, 2013). First, place is the object of research where there is a patterned activity/social interaction that is being studied, which is in this research is at Undergraduate Program of Information System of Telkom University. Second, activity is a patterned activity/social interaction explored in research, which means the subjects were studied in learning processes that related to the technopreneurship specialization. The subjects are entrepreneurship, business process modeling, business process engineering, analysis and design of information systems, information system project management, technopreneurship 1, technopreneurship 2 and technopreneurship 3. Third, actor is a group of people who perform a patterned activity/social interaction in a particular place. The results can’t be generalized to the population but are expected to be applied to other social situations that are similar to the social situation under study (Sugiyono, 2014). Data analysis and data interpretation was divided into two parts. First is the learning pro­ cess, which includes curriculum, to identify the relationship between the subjects in the devel­ opment of students’ technopreneurship competencies; teaching method, to evaluate the methods used in the delivery of the related subjects to the students of technopreneurship; and learning outcomes, to evaluate the implementation of the courses that has been applied. Second is to evaluate the relationship between the learning process towards the development of PECs of technopreneurship students.

Figure 1.

The conceptual framework of learning process toward PECs development.

196

Figure 2.

The conceptual framework of learning process toward PECs development.

Figure 3.

Research objects.

5 FINDINGS AND DISCUSSION 5.1 Learning process In the learning process, we identified three key factors: curriculum, teaching method, and learning outcomes. This research tried to identify 16 teaching methods (Figure 4) linked to the previous studies about EE by Mwasalwiba (2010), Pittaway & Edwards (2012), and Mariana (2017). The results are in Figure 4: 1. Subject; Entrepreneurship. Learning Outcomes; 88% of the learning outcomes had been achieved by the interviewees. Teaching Methods; a,c,d,e,h,i,m,n,p. 2. Subject; Business Process Modeling. Learning Outcomes; 76% of the learning outcomes had been achieved by the interviewees. Teaching Methods; a,c,j,n,p. 3. Subject; Business Process Engineering. Learning Outcomes; 100% of the learning outcomes had been perfectly achieved by the interviewees. Teaching Methods; a,b,c,j,n,p. 4. Subject; Analysis and Design of Information System. Learning Outcomes; 96% of the learning outcomes had been achieved by the interviewees. Teaching Methods; a,b,c,h,j,n,p. 5. Subject; Information System Project Management. Learning Outcomes; Only 50% of the learning outcomes had been achieved by the interviewees. Teaching Methods; a,b,c,h,j,n,p. 6. Subject; Technopreneurship 1. Learning Outcomes; 83% of the learning outcomes had been achieved by the interviewees. Teaching Methods; a,b,c,f,h,i,j,n,p. 7. Subject; Technopreneurship 2. Learning Outcomes; 93% of the learning outcomes had been achieved by the interviewees. Teaching Methods; a,b,c,f,h,i,j,n,p. 8. Subject; Technopreneurship 3. Learning Outcomes; Only 53% of the learning outcomes had been achieved by the interviewees. Teaching Methods; a,b,c,f,h,i,j,n,p. 197

Figure 4.

Teaching methods.

5.2 Personal entrepreneurial competencies (PECs) There were 13 PECs evaluated on the students on a scale (0–1: very low, 2: low, 3: medium, 4: high, 5: very high) as Table 2 shows. Maximum score per PECs was 65 point, but, as shown in the Table 2, the highest score only reached 42 points and the lowest only reached 30 points. The highest competencies were information seeking and commitment to work contract with 22 points each, while the lowest was assertiveness with 11 points. 5.3 Relation of learning outcomes in subjects to pecs All of the components of the expected PECs to be owned by students are already within the learning outcomes. Out of the 13 PECs that are evaluated, the top 3 competencies included in all of the 52 Learning Outcomes (LO) are ‘d’ (50 LO), ‘j’ (45 LO), and ‘h’ (31 LO), while the bottom three are ‘k’ (12 LO), ‘l’ (12 LO), and ‘m’ (14 LO). The relationships of each learning outcome to each subject toward PECs (graph can be seen in Figure 5) are as follows: 1. Entrepreneurship, within 7 LO, all of the 13 PECs expected are already contained in them. PECs ‘b’, ‘d’, and ‘h’ are the most developed, while ‘l’ and ‘m’ are least developed. The ratio of LO relationship toward PECs in this subject is 44%, 1 LO in this subject could develop 5–6 PECs (44%) from total of 13 PECs. 2. Business Process Modeling, within 11 LO, only ‘d’, ‘h’, and ‘j’ can be developed. The ratio of LO relationship toward PECs in this subject is 16%, 1 LO in this subject could develop 2–3 PECs (16%) from total of 13 PECs.

Table 2. Personal entrepreneurial competencies (PECs). PECs

S1

S2

S3

S4

S5

S6

Total

a. Initiative b. Seeks and acts on opportunities c. Persistence d. Information seeking e. Concern for high quality of work f. Commitment to work contract g. Efficiency orientation h. Systematic planning i. Problem solving j. Self-confidence k. Assertiveness l. Persuasion m. Use of influence strategies

3 3 3 3 3 4 2 3 2 3 1 3 3

4 4 3 3 3 4 3 3 3 3 3 3 3

4 3 3 4 3 4 3 4 2 4 1 4 3

2 4 3 4 2 3 4 3 3 3 2 2 2

4 2 2 4 4 4 2 2 2 2 2 3 2

2 3 2 4 2 3 2 2 2 2 2 2 2

19 19 16 22 17 22 16 17 14 17 11 17 15

Total

36

42

42

37

35

30

* S: student

198

Figure 5.

Subject’s learning outcomes toward PECs.

3. Business Process Engineering, within 5 LO, ‘d’ and ‘j’ are the most developed, while ‘g’ is least developed. The ratio of LO relationship toward PECs in this subject is 32%, 1 LO in this subject could develop 4–5 PECs (32%) from total of 13 PECs. 4. Analysis and Design of Information System, within 9 LO, ‘d’ and ‘j’ are the most devel­ oped, while ‘b’ and ‘m’ are least developed. The ratio of LO relationship toward PECs in this subject is 47%, 1 LO in this subject could develop 6–7 PECs (47%) from total of 13 PECs. 5. Project Management Information System, within 5 LO, ‘d’ and ‘j’ are the most developed in this subject. The ratio of LO relationship toward PECs in this subject is 37%, 1 LO this subject could develop 4–5 PECs (37%) from total of 13 PECs. 6. Technopreneurship 1, within 5 LO, ‘d’ and ‘j’ are developed by all of the LO in this subject. The ratio of LO relationship toward PECs in this subject is 49%, 1 LO in this subject could develop 6–7 PECs (49%) from total of 13 PECs. 7. Technopreneurship 2, within 5 LO, ‘d’ and ‘j’ are developed by all of the LO. The ratio of LO relationship toward PECs in this subject is up to 75%, 1 LO in this subject could develop 9–10 PECs (75%) from total of 13 PECs. 8. Technopreneurship 3, within 5 LO, the ratio of LO relationship toward PECs in this sub­ ject is up to 89%, 1 LO in this subject could develop 11–12 PECs (89%) from total of 13 PECs.

6 CONCLUSIONS In learning process there were three aspects that became objects of attention. First is curriculum, which given the Undergraduate Program of Information System of Telkom University specializes in technopreneurship have been appropriate and supportive to pro­ duce graduates who have the expertise of being a technopreneur. However, there is still room for improvement by involving or combining more advanced material from the business aspect with existing materials. Second is that learning outcomes (LO) from each subject studied in this research are not optimal yet: from the total of 52 LO from 8 sub­ jects, total relationship ratio of LO toward PECs is less than 45%. The top three compe­ tencies that should have been developed were information seeking, self-confidence, and systematic planning, while the bottom three are assertiveness, persuasion, and use of influence strategy. Last but not least is teaching methods applied for the curriculum to achieve the 52 LO are still ineffective because mostly done by conventional teaching 199

methods. Out of the total 16 teaching methods expected to be identified from the 8 sub­ jects, only 13 teaching methods were identified. Out of the 13 teaching methods that were identified, only 7 teaching methods have been applied to more than 50% of the subjects. Six other teaching methods appeared only in 1–3 subjects or less than 40% of the subjects. The overall level of personal entrepreneurial competencies (PECs) is still at the middle level. Competencies that are ranked the top three in sequence are information seeking, commitment to work contract, and initiative, while competencies that are ranked the bottom three in sequence are assertiveness, problem solving, and use of influence strategies. Only with the establishment of a suitable curriculum, followed by effective teaching methods, can the goals of learning outcomes in forming technopreneur competencies be formed properly. Otherwise, if the curriculum is unsuitable or ineffective teaching methods were used then the pur­ pose of the learning outcomes will fail and the student wouldn’t have the corresponding compe­ tencies to become a technopreneur. The foregoing research findings provide insights for either academics or practitioners within higher education institutions as a guideline for attaining an effective entrepreneurial education specifically in technopreneurship. Naturally, further research may be conducted as an explanatory study through cross-case analysis. Longitudinal studies may also be needed to evaluate each competency’s effectiveness to determine which competency to develop first for the students to become successful entrepreneurs. REFERENCES Fayolle, A. (2009). Entrepreneurship Education in Europe: Trends and Challenges. Universities, Innovation and Entrepreneurship: Good Practice Workshop. GEM. (2012). GEM 2012 Global Report. Global Entrepreneurship Research Association, London Business School, United Kingdom. Ghina, A., Simatupang, T. M., & Gustomo, A. (2017). The Relevancy of Graduates’ Competencies to The Effectiveness of Entrepreneurship Education: A Case Study at SBM ITB – Indonesia. Journal of Entrepreneurship Education, 20(1); 1–24. Izquierdo, E., & Buelens, M. (2011). Competing Models of Entrepreneurial Intentions: The Influence of Entrepreneurial Self-Efficacy and Attitudes. International Journal of Entrepreneurship and Small Business, 13(1): 75–91. Malach-Pines, A., Levy, H., Utasi, A., & Hill, T. L. (2005). Entrepreneurs as Cultural Heroes: A Cross-Cultural, Interdisciplinary Perspective. Journal of Managerial Psychology, 20(6): 541–555. Mariana. (2017). Analisis Proses Pembelajaran Kewirausahaan Di Perguruan Tinggi (Studi Kasus Di Program Studi Manajemen Bisnis Telekomunikasi Dan Informatika - Universitas Telkom). Tesis Magister Manajemen pada Telkom University Bandung: diterbitkan. McClelland, D. (1967). The Achieving Society. Princeton, NY: Van Nostrand. McClelland, D. C. (1985). How Motives, Skills, and Values Determine What People Do. American Psychologist, 40(7): 812. McClelland D., Mansfield, R. S., Spencer, L. M., & Santiago, J. Jr. (1987). The United States Agency for International Development Washington, D.C. Report. pdf.usaid.gov/pdf_docs/Pdaav866.pdf. Mwasalwiba, E. S. (2010). Entrepreneurship Education: A Review of its Objectives, Teaching Methods, and Impact Indicators. Education + Training, 52(1): 20–47. O’Donnell, A., & Cummins, D. (1999). The Use of Qualitative Methods to Research Networking in SMEs. Qualitative Market Research: An International Journal, 2(2): 82–91. Pittaway, L., & Edwards, C. (2012). Assessment: Examining Practice in Entrepreneurship Education. Education+ Training, 54(8/9): 778–800. Portela, M., Rozas, E. V., Neira, I., & Viera, E. (2012). Entrepreneurship and Economic Growth: Macroeco­ nomic Analysis and Effects of Social Capital in the EU. In Entrepreneurship – Born, Made and Educated, T. B. Helmchen, (pp. 249–264). www.intechopen.com/download/books/books,_isbn/978-953-51-0210–6. Prodan, I. (2007). Technological Entrepreneurship: Technology Transfer from Academia to New Firms. http://alexandria.tue.nl/openaccess/Metis213321.pdf. Sugiyono. (2013). Metode Penelitian Bisnis. Bandung: Alfabeta. Sugiyono. (2014). Memahami Penelitian Kualitatif. Bandung: Alfabeta.

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Efficiency measurement of metal and mineral mining sector companies listed on the Indonesia stock exchange (IDX): Data envelopment analysis approach W. Widodo & D. Rahadian Telkom University, Bandung, West Java, Indonesia

ABSTRACT: This research aims at measuring and analyzing the technical efficiency of mining companies in the metal and mineral mining sub-sectors listed on the Indonesia Stock Exchange (IDX) in the 2012–2017 periods. The method used in this research on efficiency is a non-parametric Data Envelopment Analysis (DEA) method with an output-oriented BCC/ VRS model. Based on the results of the research, it was found that the average efficiency score of metal and mineral mining companies was at a fairly efficient level with a score of 0.71. How­ ever, the individual efficiency scores showed that, from three selected companies, three compan­ ies still scored below 0.6 while the other three companies scored above 0.8. Furthermore, when compared based on the category of company ownership, it was found that the average effi­ ciency level of state-owned companies was higher than private companies. However, when viewed individually, the efficiency score of one private company, i.e. INCO, was at an efficient level and with insignificant score difference with the state-owned companies TINS and ANTM.

1 INTRODUCTION According to the data reported by the Extractive Industries Transparency Initiative (EITI) in 2016, the revenue received by the government from Mineral and Coal (Minerba—Mineral dan Batubara) mining companies was Rp52.09 trillion, or equivalent to 3.35% of total state rev­ enue. This revenue decreased from the previous years, which were Rp62.48 trillion (4.14% of total state revenue) in 2015, Rp155.15 trillion (10.01% of total state revenue) in 2014, and Rp125.57 trillion (8.73% of total state revenue) in 2013. The downward trend is in line with the implementation of Law No. 4 of 2009 on Mineral and Coal Resources, which limits the export of raw minerals as of January 2014. As predicted, since the implementation of this Law, there has been a decline in the production and revenue of metal mining companies, which contributed to the decline in state revenue. Based on the two phenom­ ena above, there is a concern with the efficiency level of Minerba companies. This is because the efficiency level of the companies certainly has an influence on company productivity. Hosseinzadeh et al. (2016), Tsolas (2010), Zhao et al. (2011), Reddy et al. (2013), Honglan, Ruyun and Xiaona (2014) and Hosseinzadeh et al. (2018), also have done similar study related to mining companies efficiency recently. With using Data Envelopment Analysis (DEA) approach, those studies performed research in mining companies in several countries such as Australia, United States, India and China. From those studies, it was found that there are some differences in factors that influence the level of company efficiency. 2 LITERATURE REVIEW There are two approaches to measuring the efficiency of an organization, namely the Paramet­ ric Frontier and Non-parametric Frontier approaches. The Parametric Frontier Approach is divided into three approached, including the Stochastic Frontier Approach (SFA), Thick 201

Frontier Approach (TFA), and Distribution Free Approach (DFA). Meanwhile, the Non­ parametric approach is divided into two approaches, namely Data Envelopment Analysis (DEA) and Free Disposal Hull (FDH) (Berger & Humphrey, 1997). Taking into account the research subject, the advantages of managing many variables, and the research methods used in previous studies, this research used the method of Data Envelopment Analysis (DEA), which was developed by Charnes et al. (1978). The DEA method has been widely used to measure the efficiency in many management and engineering fields, such as CEO performance, transportation service performance, energy efficiency, hotel management efficiency, etc. The selection of input and output variables is very important in the DEA method (Zhu, 2016). 3 METHODOLOGY 3.1 Data and variable The research subjects were selected under several considerations; the period was limited to the 2012–2017 periods only to get more company samples; the research subjects include compan­ ies listed on the Indonesia Stock Exchange; and the companies had a continuous operation and generate revenue in the 2012–2017 period. With that in mind, six companies were selected as the research subject. Based on the previous studies conducted in various industries, including mining and other (construction, zakat institution, and banking) industries, and by following the production approach in selecting input and output variables to be used in research (Hosseinzadeh et al., 2016), the variables chosen to be used in this research are personnel expense (Tsolas, 2010; Zhao et al., 2011; Reddy et al., 2013; Hosseinzadeh et al. 2018; Ascarya & Yumanita, 2007), operating expense (Zhao et al., 2011; Hosseinzadeh et al., 2018; Rusydiana, 2013; Hosseinza­ deh et al., 2016), and total fixed asset (Samal et al., 2005; Zhao et al., 2011; Hosseinzadeh et al., 2016) as the input variables and operating revenue (Hosseinzadeh et al., 2018; Hossein­ zadeh et al., 2016; Zhao et al., 2011) as the output variable. 3.2 Data envelopment analysis model This research used DEA with Variable Return to Scale (VRS) model, which was developed by Banker et al. (1984). VRS model has been chosen in this study because the nature of the industry is still quite developing.The basic DEA and VRS models are presented by Equation 1 and 2 below. PP μk yk0 Efficiency of DMU0 ¼ PK¼1 m i¼1 Vi Xi0

ð1Þ

Where: m = different inputs and k = different outputs xij = sum of inputi consumed by DMUj and ykj = sum of outputk produced by DMUj max

Xp

s:t Pp k¼1

μK yKj -

k¼1

Xm

Pm i¼1

i¼1

μKyK0 -u0 Vi Xi0 ¼ 1

Vi Xij -u0 � 0

μk � ε;Vi � ε

202

j ¼ 1; . . . ; n k¼ 1; . . . ; p i¼ 1; . . . ; m

ð2Þ

4 FINDINGS AND DISCUSSION 4.1 Descriptive analysis of input and output variables Based on the analysis of the growth of Personnel Expense, Operating Expense, Fixed Asset, and Revenue variables of the research subject, the following growth data was obtained. Table 1. Descriptive statistics of metal and mineral mining companies (2012-2017). Variable Category

Variable

N

Max

Min

Mean

Skewness

CAGR

Output

Revenue* Personnel Expense* OPEX* Fixed Asset*

36 36 36 36

12,909,589 472,698 1,259,629 22,115,945

13,903 2,605 7,904 51

5,349,400 144,283 472,697 5,906,748

0.141 0.552 0.328 1.205

1.64%

9.03%

2.59%

11.91%

Input

* in millions of Rupiah

Companies with negative growth (decrease) of all input variables were CITA and CKRA. The biggest decrease occurred in the Fixed Asset variable of CKRA with a decrease of 65.03%. The fluctuating value of the input variables was generally caused by the company’s financial strategy and performance. Additionally, companies with negative revenue growth (decrease) of the output variable were CITA and INCO. CITA was a company with the high­ est revenue decline of 22.6%. The fluctuating growth of the Revenue variable was not only influenced by the company’s production factors, but also by external factors, such as the implementation of the Minerba Law of 2014. 4.2 Technical efficiency analysis By using the 64-bit MaxDEA Basic (www.maxdea.com) software, the efficiency scores of the 36 DMUs that have been collected was calculated. From this calculation, the following effi­ ciency score of each DMU was obtained. Table 2. Technical efficiency score of metal and mineral mining companies. Company

2012

2013

2014

2015

2016

2017

Average

ANTM TINS CITA CKRA INCO PSAB

1 1 0.86709 0.29365 1 0.79879

1 0.82571 1 0.65038 0.96633 0.18828

0.82477 0.98835 0.10750 0.25808 1 0.60150

0.86678 0.97450 0.01122 0.23174 1 0.45746

0.74522 0.94707 0.35996 1 0.60747 0.33882

1 1 0.54339 1 0.66055 0.30601

0.90613 0.95594 0.48153 0.57231 0.87239 0.44848

Mean

0.82659

0.77178

0.63003

0.59028

0.66642

0.75166

0.70613

Based on the results of the calculation, it was found that the average technical efficiency score of metal and mineral mining companies was at a fairly efficient level with a score of 0.71. The company with the highest average technical efficiency score was PT Timah Tbk (TINS) with an efficiency score of 0.96, while the company with the lowest average efficiency score was PT J Resource Asia Pasifik Tbk (PSAB) with a score of 0.45. Based on the grouping of BUMN (state-owned) companies and non-BUMN (private­ owned) companies, it was found that the average efficiency of BUMN companies was higher than non-BUMN companies. The average technical efficiency score of BUMN companies was 0.93, while the average technical efficiency score of non-BUMN companies was 0.59.

203

Table 3. Technical efficiency score based on BUMN & non-BUMN companies grouping. Company

2012

2013

2014

2015

2016

2017

Average

BUMN Non-BUMN

1 0.73988

0.91286 0.70125

0.90656 0.49177

0.92064 0.42510

0.84615 0.57656

1 0.62749

0.93103 0.59368

5 CONCLUSIONS AND RECOMMENDATIONS The results of the research showed that the average efficiency score of metal and mineral mining companies was at a fairly efficient level with a score of 0.71. However, the individual efficiency scores showed that, from six selected companies, three companies still scored below 0.6 while the other three companies scored above 0.8. Therefore, it can be concluded that, although on average industry the efficiency score was at an efficient level, three companies were still at an inefficient level. For companies that were already at an efficient level or those with scores in the range of 0.8 to 0.9, there are still rooms for improvements to increase their efficiency level. In addition to that, the correlation analysis found correlations between the input and output variables and the efficiency scores. In order to analyze the correlation, Pear­ son Correlation coefficient has been used in this study, and it was found that, in all companies only the output variable was positively correlated with the efficiency scores they obtained. Based on the grouping of BUMN companies with non-BUMN companies, it was found that the average efficiency of BUMN companies was higher than non-BUMN companies. However, when viewed individually, INCO, which was included in the category of nonBUMN companies, had a high efficiency score, which was 0.872. INCO was also included in the company that had the second most DMU (after TINS), which was used as a benchmark 21 times by other DMUs. When viewed based on the efficiency value, it can be concluded that INCO can be considered as the company with the highest efficiency score, along with TINS and ANTM, while the other three non-BUMN companies were still in an inefficient condition. REFERENCES Ascarya & Yumanita, D. (2007, April). Comparing the Efficiency of Islamic Banks in Malaysia and Indonesia. In IIUM International Conference on Islamic Banking and Finance (IICiBF): Research and Developement between Ideals and Realities. IIUM–Kuala Lumpur (pp. 23–25). Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management science, 30(9), 1078–1092. Berger, A. N., & Humphrey, D. B. (1997). Efficiency of financial institutions: International survey and directions for future research. European journal of operational research, 98(2), 175–212. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429–444. Hosseinzadeh, A., Smyth, R., Valadkhani, A., & Le, V. (2016). Analyzing the efficiency performance of major Australian mining companies using bootstrap data envelopment analysis. Economic Modelling, 57, 26–35. Hosseinzadeh, A., Smyth, R., Valadkhani, A., & Moradi, A. (2018). What determines the efficiency of Australian mining companies?. Australian Journal of Agricultural and Resource Economics, 62(1), 121–138. Reddy, G. T., Sudhakar, K., & Krishna, S. J. (2013). Bench marking of coal mines using data envelop­ ment analysis. International Journal of Advanced Trends in Computer Science and Engineering, 2(1), 159–164. Rusydiana, A. S. (2013). Mengukur Tingkat Efisiensi dengan Data Envelopment Analysis. Bogor: SMART Publishing. Samal, A.R., Mohanty, M.K., & Sharma, M.C. (2005). Technical efficiency Analysis in Illinois Coal Mining Sector. Taylor & Francis Group, London, ISBN 04 1537 4499.

204

Tsolas, I. E. (2011). Performance assessment of mining operations using nonparametric production ana­ lysis: A bootstrapping approach in DEA. Resources Policy, 36(2), 159–167. Zhao, X., Li, L., & Zhang, X. S. (2011, September). Analysis of operating efficiency of Chinese Coal Mining industry. In 2011 IEEE 18th International Conference on Industrial Engineering and Engineer­ ing Management (pp. 889–893). IEEE. Zhu, J., (2016). Data Envelopment Analysis: A Handbook of Empirical Studies and Applications. New York. Spinger Science+Business Media.

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Digital Economy for Customer Benefit and Business Fairness – Anggadwita & Martini (eds) © 2020 Taylor & Francis Group, London, ISBN 978-0-367-47722-6

The effect of return on equity, earning per share, and price earning ratio on stock price (case study of plastic and packaging subsector companies listed on the Indonesia stock exchange 2012–2016) H. Hendratno & S. Agustina Telkom University, Bandung, Indonesia

ABSTRACT: This study aims to look at the variables Return on Equity (ROE), Earning Per Share (EPS), and Price Earning Ratio (PER) to share prices in the plastic and packaging subsector in 2012–2016. The data collection method is done through secondary data from the official website of the Indonesia Stock Exchange (IDX), namely idx.co.id. The sample is ten companies using purposive sampling method. Data were analyzed using panel data regression with a significance level of 5%. Based on the results of data processing, this study shows that partially Return On Equity and Earning Per Share do not have a significant effect on stock prices, while Price Earning Ratio has a significant effect on stock prices. Simultaneously ROE, EPS, and PER have a significant effect on stock prices. Independent variable are explained by the dependent variable by 12.2%, while 87.8% is explained by other variables outside the study.

1 INTRODUCTION Investments made by the public in the capital market continue to increase each year. This is due to the efforts of the Indonesian government to facilitate investors who want to invest in Indonesia by improving a positive investment climate, among other factors, such as stable eco­ nomic conditions, conducive political conditions, and the potential natural resources owned by Indonesia. Various sectors and subsectors have been listed on the Indonesia Stock Exchange, one of which is the plastic and packaging subsector, although in 2012 and 2015 the performance of the plastic and packaging subsector experienced a decline, due to the scarcity of plastic-making raw materials and the decline in demand (Wijaya, 2012). According to Yumia and Khairunnisa (2015), if the company has a high profit, that will be taken by investors as information that the company is in good condition. There­ fore, it will increase investor interest in investment and the stock price in a company will also rise. If there is a decrease in net income, it will lead to a decreasement in stock prices. Stock prices usually reflect the performance of the company itself: the better the performance of a company, the higher the price of shares offered (Darmadji & Fakhru­ din, 2011: 102). Stock prices of plastic and packaging subsector companies from 2012 to 2016 tended to experience fluctuations in accordance with the performance of the plastic and packaging subsector companies. For a few years, there were ups and downs in net profit: this is not in line with stock prices. For example, net profit of the company with the issuer code AKPI has decreased, but the share price of the company has actually increased; in contrast, in 2014, PT Berlina Tbk. (BRNA)’s net profit decreased but stock prices actually rose in that year. There is another ratio that needs to be considered by investors before investing in compan­ ies other than ROE, namely Earning Per Share (EPS). According to Ratih et al. (2013), EPS is a ratio that is used to see the income or profits to be obtained by shareholders based on shares owned. Ali et al. (2015), in his study, stated the value of a company’s EPS reflects the 206

company’s ability to generate profits, thus it is important for investors to know the EPS value of the company. Based on the background mentioned above, this study sets the title “The Effect of Return on Equity (ROE), Earning Per Share (EPS), and Price Earning Ratio (PER) on Stock Prices in Plastic and Packaging SubSector Companies Listed on the Exchange Indonesian Stock Exchange (IDX) 2012–2016.”

2 THEORETICAL FRAMEWORK 2.1 Investment Investment is an activity carried out by investors by placing a number of funds in the company with the aim of obtaining profits in the future (Tandelilin, 2017: 2). 2.2 Capital market The capital market is a meeting place for parties with excess funds and those who lack funds by trading long-term securities (validity period of more than one year) (Tandelilin, 2017: 25). 2.3 Shares Shares are a sign of a person’s participation or ownership in a certain company (Halim, 2015: 6). The same opinion was expressed by Tandelilin (2017: 31) in his book explaining that shares are certificates that show ownership of a company. 2.4 Stock prices Stock prices are prices that occur on a stock exchange at a certain time. Stock prices can change up or down veru quickly, in a matter of minutes or seconds (Darmadji and Fakhrud­ din, 2011: 102). 2.5 Return on Equity (ROE) Return on Equity (ROE) is a financial ratio that illustrates the extent to which companies are generating profits that can be obtained by shareholders. ROE is also a ratio used to show the ability of a company to generate net income using its own capital. ROE is formulated as fol­ lows (Tandelilin, 2010: 372):

ROE ¼

net profit x100 Equity

ð1Þ

2.6 Earning Per Shares (EPS) According to Fahmi (2012: 97) Earning Per Share (EPS) is a profit given to investors based on each share owned. The EPS formula, according to Tandelilin (2017: 376), is as follows:

EPS ¼

Net Profit number of shares outstanding

207

ð2Þ

2.7 Price Earning Ratio (PER) Sharma (2011) explains that Price Earning Ratio (PER) is to show the relationship between the market price of a company’s stock with earnings per share. According to Darmadji and Fakhruddin (2011: 156), PER describes the market’s appreciation of the company’s ability to generate profits. PER, according to Darmadji and Fakhruddin (2011: 156), can be formulated as follows: price per share ð3Þ PER ¼ EPS 2.8 Framework

Figure 1.

Framework of study (source: Yumia and Khairunnisa, 2015).

3 METHOD 3.1 Operational variable Table 1. Operational variables. No. Variable 1. 2.

Return on Equity (X1) Earning Per Share (X2)

3.

Price Earning Ratio (X3)

4.

Stock price (Y)

Operational Definition

Indicator

ROE is a ratio to measure the level of return on equity after net income after tax. EPS is one of the ratios to measure the profits given to investors from each share held. PER is a ratio to measure the amount of rupiah that investors are willing to pay. The price formed in the stock trading market.

ROE ¼

Source: (Data processed, 2018).

208

net profit Equity x100

Scale Ratio

profit EPS ¼ number ofnet shares outstanding Ratio

PER ¼

price per share EPS

Closing stock price

Ratio Ratio

3.2 Population and sample This study uses a purposive sampling method in determining research samples. The criteria used to select samples are companies listed on the Indonesia Stock Exchange and companies whose financial statements are complete. 3.3 The relationship between independent variables with the dependent variable Company performance can be seen in the level of financial ratios and the impact on the value of the company which is reflected in the stock price. Some ratios that can be used are Return on Equity, Earning Per Share, and Price Earning Ratio. ROE, EPS, and PER have a significant and positive influence on stock prices (Ratih et al., 2013). ROE, EPS, DER (Debt to Equity Ratio), and PER ratios can significantly influence stock prices, some researchers use ROE, EPS, and PER ratios as independent variables and stock price as the dependent variable.

4 RESULTS AND DISCUSSION 4.1 Population and sample The average value of Return On Equity (ROE) is –56.91% with a standard deviation of 354.037%. The average Earning Per Share (EPS) value is Rp12.10 with a standard deviation of Rp25.96. The average value of Price Earning Ratio (PER) is 53.37x with a standard devi­ ation of 350.736. The standard deviation value that is greater than the average value indicates the amount of data deviation, which means that the ROE, EPS, and PER variables fluctuate quite high. The average value of the stock price is Rp355.18 with a smaller standard deviation of Rp290.189, which means a small data deviation. 4.2 Selection of panel data regression estimation method This study uses panel data, which is a combination of data research conducted in cross section and time series. Panel data regression analysis is divided into three models; namely, common effect model, fixed effect model, and random effect model, which must first be estimated using the chow test, the Hausman test, and the Lagrange multiplier test (Basuki and Prawoto, 2016: 276). Chow test is a test used to choose the most common effect or fixed effect model. If the cross section probability value F > significance level is 5%, then H0 is accepted and the common effect is the model used. But if the cross section probability value F < significance level is 5%, then H0 is rejected and the right model to use is the fixed effect model. Based on the results of the chow test, it can be seen that the probability of cross section F is 0.0000 < 0.05. Based on the provisions in decision-making, it can be concluded that H0 is rejected and the right model to use is the fixed effect model. The next step is to test the fixed effect model with the random effect model using the Hausman test. Hausman test is a testing technique used to see which model is the most appropriate to use, whether a fixed effect model or a random effect model. In the Hausman test, if the value of chi-square statistics> critical value of chi-square statistics (chi-square 5%, df 3), then use a fixed effect model, which means that H0 is rejected. If the chi-square statistic value chi-square critical value, it is better to use a random effect model. And if the statistical value < critical value of chi-square then use the common effect model: [ 2P 2 ] xe n:T T P 2 - 1 2ðT - 1Þ e

ð4Þ

Note: n = Total of company T P 2 = Total of period P xe2 = Average number of residual squares e = Total squared residuals ] [ 10 x 5 52 ð480761; 3Þ -1 LMcount ¼ 3174164 2ð5 - 1Þ 2 [ ] 12:019:032; 5 ¼ 6; 25 -1 3174164 2

[ ] 50 25ð480761; 3Þ ¼ -1 8 3174164 2

¼ 6; 25½3; 79]2 LMcount ¼ 89; 75

¼ 6; 25 x 14; 36

Based on the calculation of the Lagrange multiplier test, the LMcount is 89.75 > of the critical value of chi-square (chi-square 5%, df = 3) which is 7.815. In accordance with the provisions, if LMcount > critical value of chi-square then the right model to use is the random effect model. Based on the results of tests that have been done, it can be concluded that the panel data regression model used in this study is a random effect model.The panel data regression equa­ tion is as follows: Stock price ¼ 347; 1552 þ 0:008990 ROE þ 0:780392 EPS þ 0:140138 PER þ e Partial test (t-test) aims to determine the regression coefficient independently of the dependent variable, whether it has a significant or not significant effect. The decision-making provisions in the t-test are that if Tcount ≤ Ttable, then H0 is accepted, which means it does not have a significant effect. But if Tcount > Ttable, then H0 is rejected, which means there is a significant influence. Df = 46 (n–k = 50–4 = 46), the significance level is 0.05. So Ttable = 1.67866. 1. Return On Equity (ROE) has a t value of 0.182329 and Ttable 1.67866, so Tcount ≤ Ttable, then H0 is accepted, which means that ROE does not have a significant effect on stock prices, but has a potential effect on stock prices. 2. Earning Per Share (EPS) has a value of Tcount 0.873833 and Ttable 1.67866, so Tcount ≤ Ttable, then H0 is accepted, which means that EPS does not have a significant effect on stock prices, but has a potential effect on stock prices. 3. Price Earning Ratio (PER) has a t value of 2.899305 and Ttable 1.67866, so Tcount > Ttable, then H0 is rejected, which means that PER has a significant effect on stock prices and has a potential effect on stock prices. Simultaneous test (f-test) aims to determine the regression coefficient together on the dependent variable, whether it has a significant effect or not. The decision-making provisions in the simultan­ eous test are if Fcount ≤ Ftable, then H0 is accepted which means it does not have a significant effect. Meanwhile, if Fcount > Ftable, then H0 is rejected, which means there is a significant influ­ ence. Df1 = k–1 = 4–1 = 3. Df2 = n–k = 50–4 = 46, the significance level of 0.05. So Ftable = 2.81. Based on data processing, the result obtained that Fcount 3.274343 > Ftable 2.81, then H0 is rejected, which means ROE, EPS, and PER together have a significant effect on stock prices.

210

The coefficient of determination uses Adjusted R Square, because in this study using more than two independent variables in seeing the effect of independent variables on the dependent variable. The acquisition of Adjusted R Square is 0.122226 or 12.2226%. This shows that the independent variables consisting of ROE, EPS, and PER are only able to explain the depend­ ent variable that is the stock price of 12.2226%, while the remaining 87.77774% is explained by other variables outside the variables studied. This research found that, although partial test independent variables ROE, EPS, and PER have no effect on the dependent variable, simultaneously (simultaneously) they have a significant effect on stock prices.

5 CONCLUSION AND SUGGESTION In conclusion, Return on Equity (ROE) does not have a significant effect but has a positive effect on stock prices. Earning Per Share (EPS) does not have a significant effect but has a positive influence on stock prices. Price Earning Ratio (PER) has a significant effect and has a positive effect on stock prices. Simultaneously, ROE, EPS, and PER together have a significant effect on stock prices on plastic and packaging subsector companies listed on the Indonesia Stock Exchange in 2012–2016. For future researchers with the same topic, it is suggested to add or use other variables and use a longer observation period. The company is expected to strengthen its performance fundamentals so that the ratios used to view stock prices such as Return On Equity, Earning Per Share, and Price Earning Ratio are maintained, because this simultaneously influences stock prices. For potential investors and investors who want to invest in a company in the capital market, the results of this study can be treated as an additional reference. REFERENCES Ali, A., Sharif, I., & Jan, A. F. 2015. Effect of Dividend Policy on Stock Prices. Business & Management Studies 3(1): 56–87. Asmirantho, E., & Somantri, O. K. 2017. The Effect of Financial Performance on Stock Price at Pharma­ ceutical Sub-Sector Company Listed in Indonesia Stock Exchange. Jurnal Ilmiah Akuntansi Fakultas Ekonomi, 94–107. Basuki, A. T., & Prawoto, N. 2016. Analisis Regresi Dalam Penelitian Ekonomi Dan Bisnis. Jakarta: PT Rajagrafindo Persada. Darmadji, T., & Fakhruddin, H. M. 2011. Pasar Modal di Indonesia. Jakarta: Selemba Empat. Dissanayake, T., & Biyiri, E. 2017. The Impact of Internal Factor on Share Price: Reference to Hotel Industry in Colombo Stock Exchange. Business and Management Research Journal, 33–37. Fahmi, I. 2012. Manajemen Investasi. Jakarta: Salemba Empat. Halim, A. 2015. Analisis Investasi di Aset Keuangan. Jakarta: Mitra Wacana Media. Modi, S., & Pathak, B. V. 2014. A Study on Value Relevance of Accounting Information in India Stock Market: the Case of Auto Sector. The International Journal of Business & Management, 166–180. Ratih, D., Suryadi, & E. P., A. 2013. Pengaruh EPS, PER, DER, ROE terhadap Harga Saham pada Perusahaan Sektor Pertambangan yang Terdaftar di Bursa Efek Indonesia (BEI) Tahun 2010–2012. Diponegoro Journal of Social and Politic, 1–12. Sharma, S. 2011. Determinants Of Equity Share Price In India. Journal of Arts, Science, & Commerce, 51–60. Suparningsih, B. 2017. Effect of Debt to Equity Ratio, Price Earning Ratio, Net Profit Margin, Return On Investment, Earning Per Share in Influence Exchange Rates and Indonesia Interest Rates (SBI) Share Price in Textile and Garment Industry Indonesia Stock Exchange. International Journal of Multidisciplinary Research and Development, 28–62. Tandelilin, E. 2017. Pasar Modal Manajemen Portofolio dan Investasi. Yogyakarta: PT Kanisius.

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Digital Economy for Customer Benefit and Business Fairness – Anggadwita & Martini (eds) © 2020 Taylor & Francis Group, London, ISBN 978-0-367-47722-6

Author Index

Agustina, S. 206

Ahmad, N.F. 20

Akbar, M.A. 52

Alamsyah, A. 40

Aminah, W. 179

Amir, M.T 108, 126

Amran, A. 40

Andreswari, R. 1

Andriamalala, L. 91

Ardhana, W. 57, 85

Arisman, A. 26

Astuti, R.D. 95

Azis, E. 52

Hasibuan, M.A. 1

Hati, K.M. 165

Hendratno, H. 206

Hidayat, S.G. 46

Hutami, Rr.R.F. 46, 104

Carrasco, M. 78

Castanha, J. 71, 78, 85

Limoa, R. 8

Dharmoputra, S. 104

Emovwodo, S.O. 91

Eva, N. 100

Indrawati, 57, 64, 71, 78,

85

Irawan, D. 115

Isnaini, D. 141

Khoriyah, S. 14

Krisnawati, A. 165

Mapuasari, S.A. 20

Martiniatin, R. 131

Mulyana, A. 120

Munandar, A.I. 14, 115

Rahman, R.E. 95

Ramadhani, D.P. 40

Ramzi Farhan, M. 104

Rikumahu, B. 159

Risana, D. 26

Rizki, B.A. 91

Rohandi, M.M.A. 52

Rosyadhi, M. 194

Sakinah, S.A. 188

Saputra, M.A.A. 40

Sary, F.P. 147

Sipahutar, A. 171

Sitorus, P.M. 153

Suhartoko 153

Suwito, K.A. 91

Sya’diah, S.D. 120

Talkar, N. 78

Taufiq, J.A 108

Noviaristanti, S. 184

Utama, W. 179

Omar, K. 120

Wahyuningtyas, R. 8, 120

Widodo, W. 201

Wirayat, M.Y.F. 32

Fitri, N.M.G. 1

Gadang, R. 100

Gaffar, M.R. 64

Gaunekar, D. 71

Ghina, A. 131, 194

Gustyana, T.T. 171

Pantagama, M. 159

Permatasari, A. 20, 188

Pillai, S.K.B 64, 71, 78

Putra, A.R. 184

Hafiz, M.T. 147

Halim, M.A.S.A. 120

Rachmawati, I. 32

Rahadian, D. 141, 201

212

Yuliana, E. 20, 188

Yusoff, Y.M. 120