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Ambidextrous Organizations In The Big Data Era: The Role Of Information Systems
 3030365832,  9783030365837,  9783030365844

Table of contents :
Contents......Page 5
List of Figures......Page 8
List of Tables......Page 9
1 The Modern Organization, Data, and Better Performances......Page 10
References......Page 13
2 Setting the Stage: BDA, Dynamic Capabilities and Ambidexterity, What We Know So Far?......Page 14
2.1 Big Data: A Revolution Arrived......Page 15
2.2 What Is Big Data? Towards a Consensual Definition......Page 17
2.3 BDA, Dynamic Capabilities, Ambidexterity and Performance......Page 21
2.4 Exploring Existing Literature on Big Data, Dynamic Capabilities and Ambidexterity: Defining the Research Methods......Page 25
2.5 Results of Bibliometric Analysis......Page 29
2.6 Systematic Review of Literature......Page 31
2.6.1 Blue Cluster: Big Data, Dynamic Capabilities, Supply Chain Management and Performance......Page 32
2.6.2 Red Cluster: Big Data, Dynamic Capabilities, Production Processes, and Manufacturing......Page 37
2.6.3 Green Cluster: Big Data, Dynamic Capabilities, and Strategy Making......Page 38
2.6.4 Yellow Cluster: Knowledge Exploitation Through BDA Systems......Page 39
2.6.5 Purple Cluster: BDA and Performance Related Outcomes......Page 40
2.7 What’s Next?......Page 41
References......Page 42
3 From Big Data to Performance: The Importance of Ambidexterity, Agility and BDA Integration in Business Processes—A Theory-Based Framework......Page 48
3.1 The Integration of BDA in Modern Organizations: Perspectives About Information Systems, Ambidexterity, and Agility......Page 49
3.1.1 The Relevance of Agility and Ambidexterity in Modern Organizations......Page 50
3.2.1 The Ambidextrous Organization and Agility......Page 52
3.2.2 Big Data, Business Process Management Systems and Ambidextrous Organizations’ Agility......Page 58
3.3 A Conceptual Framework on Big Data, Business Process Management Systems, and Agility......Page 61
3.4.1 Theoretical Implications for Researchers......Page 65
3.4.2 Preliminary Suggestions for Managers......Page 67
References......Page 70
4 Agility Through BDA and Ambidexterity: Some Empirical Evidence from Managers’ Experiences......Page 77
4.1 The Need for Empirical Evidence......Page 78
4.2 BDA Capabilities, Ambidexterity, Agility, and Organizational Performance......Page 79
4.3.1 Organizational Ambidexterity and BDA Capabilities......Page 83
4.3.2 The Relationship Between Organizational Performances, Agility and, Ambidexterity......Page 85
4.3.3 Proposed Model......Page 87
4.4.1 Sampling......Page 88
4.4.2 Measures......Page 90
4.4.3 Statistical Analysis and Hypotheses Testing......Page 91
4.5 Discussion and Managerial Implications......Page 92
4.6 Conclusions, Limitations, and Suggestions for Future Research......Page 96
References......Page 97
5 Conclusion......Page 101
References......Page 103
Index......Page 104

Citation preview

Ambidextrous Organizations in the Big Data Era The Role of Information Systems

Riccardo Rialti Giacomo Marzi

Ambidextrous Organizations in the Big Data Era

Riccardo Rialti · Giacomo Marzi

Ambidextrous Organizations in the Big Data Era The Role of Information Systems

Riccardo Rialti Department of Economics and Management University of Florence Florence, Italy

Giacomo Marzi Lincoln International Business School University of Lincoln Lincoln, Lincolnshire, UK

ISBN 978-3-030-36584-4  (eBook) ISBN 978-3-030-36583-7 https://doi.org/10.1007/978-3-030-36584-4 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Cover illustration: © Melisa Hasan This Palgrave Pivot imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Contents

1 The Modern Organization, Data, and Better Performances 1 References 4 2 Setting the Stage: BDA, Dynamic Capabilities and Ambidexterity, What We Know So Far? 5 2.1 Big Data: A Revolution Arrived 6 2.2 What Is Big Data? Towards a Consensual Definition 8 2.3 BDA, Dynamic Capabilities, Ambidexterity and Performance 12 2.4 Exploring Existing Literature on Big Data, Dynamic Capabilities and Ambidexterity: Defining the Research Methods 16 2.5 Results of Bibliometric Analysis 20 2.6 Systematic Review of Literature 22 2.6.1 Blue Cluster: Big Data, Dynamic Capabilities, Supply Chain Management and Performance 23 2.6.2 Red Cluster: Big Data, Dynamic Capabilities, Production Processes, and Manufacturing 28 v

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Contents

2.6.3 Green Cluster: Big Data, Dynamic 29 Capabilities, and Strategy Making 2.6.4 Yellow Cluster: Knowledge Exploitation 30 Through BDA Systems 2.6.5 Purple Cluster: BDA and Performance 31 Related Outcomes 2.7 What’s Next? 32 References 33 3 From Big Data to Performance: The Importance of Ambidexterity, Agility and BDA Integration in Business Processes—A Theory-Based Framework 39 3.1 The Integration of BDA in Modern Organizations: Perspectives About Information Systems, 40 Ambidexterity, and Agility 3.1.1 The Relevance of Agility and Ambidexterity 41 in Modern Organizations 3.2 The Ambidextrous Organization in Big Data Era 43 43 3.2.1 The Ambidextrous Organization and Agility 3.2.2 Big Data, Business Process Management Systems and Ambidextrous Organizations’ Agility 49 3.3 A Conceptual Framework on Big Data, Business 52 Process Management Systems, and Agility 3.4 Insights Emerging from the Developed Framework 56 56 3.4.1 Theoretical Implications for Researchers 3.4.2 Preliminary Suggestions for Managers 58 61 3.5 Going a Bit Further, the Need for Empirical Evidence References 61 4 Agility Through BDA and Ambidexterity: Some Empirical Evidence from Managers’ Experiences 69 70 4.1 The Need for Empirical Evidence 4.2 BDA Capabilities, Ambidexterity, Agility, 71 and Organizational Performance

Contents     vii

4.3 Validating the Theory and the Main Hypothesis 75 by Empirical Investigation 4.3.1 Organizational Ambidexterity 75 and BDA Capabilities 4.3.2 The Relationship Between Organizational 77 Performances, Agility and, Ambidexterity 4.3.3 Proposed Model 79 4.4 Methods 80 4.4.1 Sampling 80 4.4.2 Measures 82 83 4.4.3 Statistical Analysis and Hypotheses Testing 4.5 Discussion and Managerial Implications 84 4.6 Conclusions, Limitations, and Suggestions for Future Research 88 References 89 5 Conclusion 93 References 95 Index 97

List of Figures

Fig. 2.1 Manuscripts’ temporal distribution Fig. 2.2 VOSviewer result Fig. 3.1 Market capitalizing agility and operational adjustment agility and their role in shaping an agile organization (Source Authors’ elaboration) Fig. 3.2 The importance of BDA capable BPMS for ambidextrous organizations’ agility Fig. 4.1 Research model

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List of Tables

Table 2.1 Principal outlets for publications on big data and ambidexterity Table 2.2 Most prolific authors Table 2.3 Most relevant institutions Table 2.4 Country of origin of the manuscripts Table 3.1 Agile organization principles and ambidextrous organization paradigm Table 4.1 Sample characteristics

21 23 24 26 46 82

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1 The Modern Organization, Data, and Better Performances

Abstract Modern organizations could nowadays benefit from the existence of more information than ever. Such new information has arisen in the form of big data, which are large and unstructured datasets formed by digital data. Thanks to these datasets, an organization could potentially obtain competitive advantages and improve its performance. However, such positive effects do not come without a burden, which is represented by the need for a radical transformation. Moving from these premises, the objective of this book is to empirically explore the role of contemporary Information Systems (IS) in this transformation and its effects on performance. Keywords Big data · Big data era · Information Systems · Digital transformation · Performances “Big Data” is a current buzz word, often with different meanings and more often without a clear understating of what big data is. In this book, we try to propose a wide spectrum overview of what “Big Data” means for organizations and their managers through the work done by the authors in this field. © The Author(s) 2020 R. Rialti and G. Marzi, Ambidextrous Organizations in the Big Data Era, https://doi.org/10.1007/978-3-030-36584-4_1

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This book is the result of past researches, ongoing research projects that are published (or submitted for publishing) in leading academic journals, and maturated expertise gained over the years by the authors. The book is a comprehensive review, and it gives several results and insights that are the outcome of researches and studies in a format that is easy to understand by managers, practitioners, students, and other non-technical/academic audiences. The empirical results presented in the book are, therefore, based on real-world data and validated with the maximum academic rigor required by scientific publications. Moving from this premise, an attempt is made to understand how an organization can benefit from the use of big data? In order to receive a benefit from the huge amount of information (big data), the first step is to control and regulate the flow of information. Effective management of the information inflow in an organization could only be achieved by an efficient Information System (IS), which is capable of organizing, storing and delivering the right information at the right time. It is important to perceive that to receive benefits from big data; Information Systems should be controlled and not be controlled by big data. However, which benefits an organization could receive from big data is determined by several factors. The management of this huge amount of information has a cost, therefore, the benefits must be clear. One of the major benefits that an organization could receive from an effective big data management is the ability to develop an ambidextrous approach to the market, which is the topic of the present book. An organization could be defined ambidextrous when it is able to exploit the current market opportunities while at the same time exploring new opportunities. The currently exploited opportunities were the explored opportunities of yesterday and the explored opportunities of today will become the opportunities to be exploited tomorrow (Kriz, Voola, & Yuksel, 2014). In order to be able to pursue this unceasing process of renovations, the organization needs a constant and well-managed flow of information, the big data. In order to accomplish the objective of the book, a top-down approach has been followed from the academic literature to the voice of managers and practitioners.

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In Chapter 2, the concept of Big Data, Dynamic Capabilities, and Ambidexterity is introduced by presenting the latest researches available on these topics. While doing so, it is also shown how big data is strongly connected with the strategies followed by the organization and how its role has been constantly recognized as a key success factor for modern organizations. Chapter 3 explores the theoretical and managerial connection between big data, ambidexterity, and an organization’s agility. This chapter highlights an empirical process to find how and to what extent big data can influence an organization’s characteristics. In particular, the market and operation agility have been considered for this purpose. It has been shown that the correct use of big data can positively influence these two crucial aspects of the organization (Constantiou & Kallinikos, 2015). In Chapter 4, a purely empirical approach is presented, where it is shown how managers can reach strategic agility through the use of big data. The empirical validation and confirmation of the concepts and best practices as presented in the two previous chapters are also explained. It also offers some additional insight into managerial practices in implementing big data (Dezi, Santoro, Gabteni, & Pellicelli, 2018; Mardi, Arief, Furinto, & Kumaradjaja, 2018). Chapter 4 explores the microlinkages that exist between Big Data Analytics (BDA) capabilities to get agility through BDA and ambidexterity. Some empirical evidence from managers’ experiences of large European organizations is analyzed using multiple regressions approach to test the developed model. The analysis of results indicates that BDA capabilities play a significant role in an organization’s strategic agility. The present book ends with inferences and a summary of the managerial perspective on how big data can influence the organization’s strategy, operational capability, and performance. Finally, if a reader wants to delve into the academic literature behind the present book, the use of references given at the end of each chapter and cited throughout will permit a deeper exploration of the topics presented in the book for further research and applications.

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Authors hope that the audience of this book can find the present text interesting and helpful in developing an efficient and effective managerial practice of big data in their everyday activities.

References Constantiou, I. D., & Kallinikos, J. (2015). New games, new rules: Big data and the changing context of strategy. Journal of Information Technology, 30 (1), 44–57. Dezi, L., Santoro, G., Gabteni, H., & Pellicelli, A. C. (2018). The role of big data in shaping ambidextrous business process management: Case studies from the service industry. Business Process Management Journal, 24 (5), 1163– 1175. Kriz, A., Voola, R., & Yuksel, U. (2014). The dynamic capability of ambidexterity in hypercompetition: Qualitative insights. Journal of Strategic Marketing, 22(4), 287–299. Mardi, M., Arief, M., Furinto, A., & Kumaradjaja, R. (2018). Sustaining organizational performance through organizational ambidexterity by adapting social technology. Journal of the Knowledge Economy, 9 (3), 1049–1066.

2 Setting the Stage: BDA, Dynamic Capabilities and Ambidexterity, What We Know So Far?

Abstract There are several manuscripts available which use dynamic capabilities as their primary theoretical approach to study the effects of big data on organizations. However, systematization is still missing with all these manuscripts. For this purpose, 217 manuscripts were extracted from the database. Clarivate Analytics Web of Science Core Collection and a bibliometric analysis was implemented. This analysis was combined with a review of the literature. After analysis, manuscripts on big data and dynamic capabilities were divided into five clusters. These clusters were identified as manuscripts on strategy making, big data and supply chain management, manufacturing, performance, and knowledge exploitation through Big Data Analytics (BDA). The literature review helped the authors to understand the contents of these clusters. Keywords Bibliometric analysis · Big data · Big Data Analytics · Dynamic capabilities · Performance · Literature review

© The Author(s) 2020 R. Rialti and G. Marzi, Ambidextrous Organizations in the Big Data Era, https://doi.org/10.1007/978-3-030-36584-4_2

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2.1

Big Data: A Revolution Arrived

In the current century, ‘Big data’ has emerged as one of the most used buzzwords in managerial literature. A surge has occurred in the number of published researches about big data within the management and marketing domain (Wamba & Mishra, 2017). As it is possible to imagine, most of these researches target to improve the existing knowledge about how big data can be turned into money and improve an organizations’ functioning. As a result, the present chapter explores and further comments on the findings presented by the extensive literature research proposed by Rialti, Marzi, Ciappei, and Busso (2019). From this perspective, it has commonly been observed how big data and Big Data Analytics (BDA) has dramatically affected the ways of conducting business (McAfee & Brynjolfsson, 2012). Managers of today have large amounts of information at their disposal and, therefore, are more informed about the status of internal operations, processes involving supply chain, the performance of the workforce, and the behavioral patterns of the consumer (Hofacker, Malthouse, & Sultan, 2016). In fact, in the digitalized economy, it has been observed how any organization could potentially collect large amounts of data that was unthinkable just a few years ago. Along with the ability to collect data, decision making processes by the management has evolved, and the managers are now capable of making the decision about implementing the best-suited strategy based on the newly acquired information (Mardi, Arief, Furinto, & Kumaradjaja, 2018). Due to the emerging potential of big data, the need for systems to decodify these extremely large datasets became evident (Labrinidis & Jagadish, 2012). Indeed, it has become apparent that these systems have the capability to transform unstructured datasets into information in the form of insights that can impact management decisions and organizational performances. Big data and BDA capable systems are also related to improved organization performances in terms of agility, flexibility, dynamism, and ambidexterity. This is particularly important when considering that such systems may be adaptable to different kinds of data and, hence, may provide a continuous flow of information to ensure the continuation of a process, even amidst difficulties (Xuemie, 2017). It

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has also become apparent that these systems could assist organizations in identifying and exploiting new opportunities. Here, the importance of dynamism emerges, and the organization becomes capable of scanning the environment continuously to obtain a competitive edge. As a result of correctly utilizing big data, they can generate value (Rialti, Marzi, Silic, & Ciappei, 2018). Since the availability of BDA capable Information Systems (IS) and procedures have been deemed to be linked with organizational agility, flexibility, and dynamism, therefore, scholars have recently begun to use literature pertaining to dynamic capabilities as a theoretical lens through which they interpret the effects of big data on organizations. BDA capable IS can be used in different situations; and during any environmental turbulent situation, it can even give organizations an advantage over the competition (Lee, Kao, & Yang, 2014). The use of BDA capable information system is normally associated with usual processes and routines that may be useful in solving different problems that are data-related. Several other theoretical approaches have also been used by scholars to underpin the topic, for example, existing research has explored big data focusing on the information value theory, resource-based view, technology acceptance model, or the dynamic capabilities. The literature on the topic is still fragmentary. Particularly, the literature on the topic of big data and the impact of big data on management and dynamic capabilities need further systematization (Xuemie, 2017). It is, therefore, clear that there is a need to address this specific literature gap; which is demonstrative of the requirement of recording and organizing the existing literature (Ardito, Scuotto, Del Giudice, & Petruzzelli, 2018). For this, we need to perform a bibliometric analysis and literature review. This chapter is organized as follows. The following section focuses on the importance of big data and BDA capable systems for the organizations, and how the dynamic capabilities contribute to an organization in this stream of literature. The next section describes the methodological procedure. In the fifth section, bibliometric results are discussed which are followed by the literature review in the next section. Finally, the suggestions for future research are presented by the author to provide a guide to the reader.

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2.2

What Is Big Data? Towards a Consensual Definition

According to McAfee and Brynjolfsson’s seminal research (2012, p. 5), “smart leaders across industries will see using big data for what it is: a management revolution”. Today, the enormity of the impact that the big data has in the world of management is clear to all researchers who are associated with big data. For any organization, information is recognized as one of the essential value-creating factors. Because of the characteristics of big data, the extent to which value can be created by using the information has simply reached to an unmatched level. Big data is different from conventional large datasets in terms of seven parameters acknowledged as the ‘Seven Vs of Big Data’: Volume, Velocity, Variety, Veracity, Value, Variability, and Visualization (Mishra, Luo, Jiang, Papadopoulos, & Dubey, 2017). The seven parameters of big data are defined below. 1. Volume: This characteristic refers to the sheer dimensions of this typology of datasets. Indeed, big data’s dimensions frequently exceed terabyte (Mishra et al., 2017). Such a phenomenon is a direct consequence of the fact that worldwide computers, mobile devices, and machines generate data that exceeds 2.5 exabytes per day. Coherently, companies such as Walmart or Amazon have more data from consumer transactions than ever-imagined just 10 years ago. 2. Velocity: Velocity parameter in terms of big data is the “rate at which data is generated and the speed at which it should be analyzed and acted upon” (Gandomi & Haider, 2015, p. 138). In the current digital era, businesses such as social networks are collecting data at every instant of their operations. Facebook, for example, collects about 136,000 photos, 510,000 comments, and 290,000 updates every minute; which is a large amount of data to be managed in real-time. 3. Variety: This is a trait associated with the “heterogeneous sources of big data” (i.e. sensors embedded in machines, consumers’ activities on social media, B2C or B2B digital interactions, etc.) and the consequent assorted formats that the files composing big data may assume”

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(Rialti et al., 2018, p. 1098). Considering the Facebook example again, all this collected data may be in different formats like .jpg format, .doc format .mp3, and .mp4 format, etc. The problem is then related to merging all the formats in order to create files that may be analyzed using a single analytical tool. Veracity: Veracity is a characteristic associated with the required degree of trustworthiness that must be possessed by the sources of big data (Mishra et al., 2017). As it is easy to imagine, unreliable data generate unreliable results and unreliable decisions. Thus, it is necessary to consider only data whose origin could be traced and assessed periodically. Value: It is a characteristic related to the ultimate economic value of big data. This is the value that might be generated after any organization uses processes and technologies to analyze the collected big data (Xuemie, 2017). Obviously, in the massive amount of data, there is always the need to identify the ones that could be used to generate strategic decision. Variability: This trait suggests that there might be variations in flow rate of data, processing, and data sources (Wamba & Mishra, 2017). As the data depends on the tool used to generate it, it may be possible to comprehend how the quantity may vary with time. Visualization: It concerns with the possibility for data analysts to get visual insights as an output of big data analysis (Liao et al., 2018). This characteristic, albeit often neglected by scholars, is fundamental as there is no use of a large dataset if it cannot be transformed into sharable information.

Moving ahead from these premises, big data could then be defined as any huge dataset of unstructured data, that could not be analyzed using traditional database tools and methods, and that originate from reliable sources and intrinsically contain information that could be transformed to give strategic insights that help in making decisions. Considering the characteristics of big data, organizations face a difficult challenge while trying to extract any insight from these datasets. Big data is identified as large and complex datasets because of which traditional database management software cannot be used for processing big

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data (Manyika et al., 2011). For collecting, storing, and analyzing big data, tools are required that are based on artificial intelligence paradigms such as data lakes, NoSQL data models, and schema-less data storageretrieval. It is, therefore, necessary for organizations to define proper BDA processes for the defined stages. These stages are data acquisition, cleansing, integration, modeling, and interpretation. According to Prescott (2014), to gain any competitive advantage and economic value form collected information, it is important that the organizations develop systems and procedures that can not only collect data but also remove any unworthy components (messages without any useful content or spam messages). The implementation of the identified processes has its own challenges. Frequently, to accept BDA capable systems or processes, the whole organization’s culture needs to be changed (Rialti et al., 2018). The resistance to change may come from managers and employees. Because of the fear of change, they may oppose the implementation of automatic processes that are able to complement human intervention in the process of decision making. It is observed that when managers’ and employees’ mindsets become familiarized with big data and BDA processes, the whole organization could be characterized by a culture assisting the use of big data in the business. Once these systems are in place, overcoming all potential difficulties, the changes are likely to give positive outcomes (Akter, Wamba, Gunasekaran, Dubey, & Childe, 2016). More accurate decisions may be made by managers based on the insights they gather from BDA capable IS. Specifically, the implementation of BDA provides the following benefits. a. Information from big data offers managers the possibility of knowing their consumers better than ever. It is possible to predict individual consumer’s behavior and propose tailored offerings in terms of prices (Erevelles, Fukawa, & Swayne, 2016). b. Big data can dramatically improve organizations’ internal operational efficiency. On the one hand, BDA may prove extremely useful for controlling business processes (Del Giudice, 2016). Indeed, BDA capable IS for business process management (or BDA capable BPMS)

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may allow managers to identify bottlenecks in the production processes, inefficiencies in the usage of machinery and causes for wastage of resources. On the other hand, BDA capable systems have also been linked to better workforce utilization as they permit managers to monitor the performance of each employee. c. BDA capable systems may also positively impact an organization’s ability to pursue collaborations with partners. Particularly, BDA capable systems may improve knowledge flow and facilitate sharing between partners as BDA systems are frequently built around jointlydeveloped database architectures (Vera-Baquero, Colomo-Palacios, & Molloy, 2016). d. BDA capable IS may play a role in fostering the organizational capability of identifying and seizing new opportunities. Because of the newly extractable information, BDA capable systems can improve organizational exploitation and exploration capabilities and, consequently, ambidexterity (Rialti et al., 2018). In short, big data is progressively influencing organizations’ competitiveness. Better performance of organizations is found to be positively linked with the big data impact and the effect of BDA capable systems on organizations, according to the relevant literature (Gunasekaran, Yusuf, Adeleye, & Papadopoulos, 2018). Particularly, organizations may see an improvement over the time in the performance metrics which include supply chain efficiency, workforce utilization, and production processes efficiency and economic performance metrics with the utilization of big data and BDA (McAfee & Brynjolfsson, 2012). Nevertheless, all the positive impacts of big data are dependent on the acceptance of big data by organizations’ managers (Teece, 2009). Thus, organizational processes, which include allocation of resources, organization, and utilization, play a vital role in the ability of an organization to obtain the benefits that can be derived from big data (Côrte-Real, Oliveira, & Ruivo, 2017). In this perception, pertinent literature pointed out that for a modern organization it is important not only to implement BDA systems

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and processes but also to develop the so-called organizational BDA capabilities (Wamba et al., 2017). Organizational BDA skills are an overall ensemble of capabilities that comprise BDA infrastructure flexibility (the BDA capable IS and processes that were previously explained), BDA management skills and BDA personnel skills. Indeed, the three of them are fundamental for any organization trying to face big data advent. If the BDA infrastructures, which include all the technical IS that are capable of collecting, storing, processing, and analyzing big data, are too unyielding, and are unable to self-adapt to different kinds of data, they cannot make sure that there is any data flow in any situation. BDA management skills are fundamental to the selection and the implementation of the correct BDA infrastructure and to extract the right information from the datasets. On the one hand, managers need to have enough skills to decide which technical solution is the best for their organization, and on the other hand, they need to be skilled enough in BDA to understand the information extracted from the datasets and to make the right decisions according to the new insights. The personnel should be skilled in BDA as personal skills are related to the lower rejection of BDA within the organization—less resistance to new IS implementation—and better functioning of the BDA infrastructure. Additionally, as employees are often the ones doing the analysis of the data, they need adequate skills, both, to maintain the infrastructure working and to identify the right data to be analyzed. From this perspective, the studies investigating the importance of big data for organizations often focus on dynamic capabilities (Wamba et al., 2017). In fact, the development of such an intertwined system of capabilities is necessarily related to the organizational capability to use existing elements in a different way to adapt to new situations.

2.3

BDA, Dynamic Capabilities, Ambidexterity and Performance

Teece, Pisano, and Shuen in 1997 initially coined the concept of dynamic capabilities. As per their seminal manuscript, the organization’s ability to adapt a resource base to make it work in different situations

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gives the crux of the concept of dynamic capabilities (Eisenhardt & Martin, 2000). In short, the expression ‘dynamic capabilities’ may be reassumed by the organization’s degree of ability to adapt sufficiently and in time to any change in the environment by modifying external or internal resources and processes, depends on existing competencies (Teece, Pisano, & Shuen, 1997). While dynamic capabilities definitions link changes to organizational improvisation, dynamic capabilities may “actually consist of identifiable and specific routines” (Eisenhardt & Martin, 2000, p. 1107). Indeed, the best practices within an organization may get diffused by some organizational routines and processes. Complete organizational routines can be split into smaller routines or small processes, that can be considered as the ‘bricks’ for forming the complete routine or process (Eisenhardt & Martin, 2000). In the case of a mutated environment, these bricks may be assembled differently to create a new routine or process so that the organization can survive and succeed. This phenomenon signifies the importance of knowledge and expertise accumulation within an organization and highlights the fact that this knowledge base is useful in multiple situations. Dynamic capabilities are, therefore, related to reusable routines or processes. Theoretical perspectives that unpack the effects of big data and the BDA system’s availability in an organization have frequently been adopted by scholars as dynamic capabilities, in the big data era (Erevelles et al., 2016). Big data is indeed a resource related to information that requires routines and processes to be turned into meaningful insights. For increasing the efficiency of big data analysis, previous expertise of analysts and managers can play a key role. The knowledge spanning the organization about BDA processes and BDA capable IS is indeed fundamental to the extraction of insights from big data. Coherently, a certain degree of routinization may also be an advantage to process big data. The smaller routines that represent the bricks may be useful in solving different big data-related problems (Braganza, Brooks, Nepelski, Ali, & Moro, 2017). Building on these findings, it has been observed that the presence of existing adaptable routines and processes to analyze big data may increase organizational flexibility and agility (Chen, Chiang, & Storey, 2012), a phenomenon related to the huge amount of information that

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BDA systems can process and provide to managers in real-time. The consequence of this is that the organization may better identify and exploit the opportunities arising in the market, hence, imbibing the characteristics of an ambidextrous organization (Rialti et al., 2018). Thus, while traditional information systems have emerged almost as a factor fostering organizational rigidity, BDA capable IS have the potential to enhance organizational dynamism by increasing organizational agility, flexibility, and opportunity exploitation potential. The characteristics of big data and BDA systems and processes coupled with the possibility to adapt different routines (bricks) to different kinds of data. Hence, the adaptation capability of BDA systems and processes to modify themselves for different kinds of data may allow the organization to thrive in different situations and to survive eventual environmental shocks. In this way, in line with the basic assumptions of dynamic capabilities research (Teece, 2009), it is evident that this adaptation capability may positively influence organization performance (Wamba et al., 2017). Moving on from these premises, researchers have explored the effect of big data and BDA capable processes on systems using dynamic capabilities as the main theoretical lens. For example, researchers have utilized dynamic capabilities for interpreting the means in which BDA capable processes can create competitive advantages. To overcome rivals, the BDA processes are based on re-applicable routines, which can generate new information (Braganza et al., 2017). Hence, BDA processes are equivalent to continuous knowledge generation and diffusion processes. This way, these processes give analysts and managers the ability to recognize good opportunities and they can reject opportunities that are not profitable. Secondly, to study the effect of big data on marketing strategies, a theoretical approach to analyze the dynamic and adaptive capabilities of BDA was used (Khan & Vorley, 2017). This phenomenon has been associated with the BDA processes’ and systems’ potential to unpack the consumers’ behavioral patterns and, therefore, to promote the creation of marketing strategies that are customized (Erevelles et al., 2016). Thirdly, the organizational and financial performance may be influenced by both, process-oriented dynamic capabilities and BDA (Wamba et al.,

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2017). Within an organization, the diffusion of BDA processes and systems is influenced by the ability of processes to adapt to changing situations. Finally, another study reported that a competitive advantage can be generated based on the ability of BDA processes and systems to adapt to different kinds of data and environments that are continually evolving (Sivarajah, Kamal, Irani, & Weerakkody, 2017). With this theoretical perspective, it is possible to imagine and to understand in-depth how the overall three specific BDA capabilities are related to organizational performances. Indeed, these three capabilities are on their own related to routines that are necessary to transform big data into value. In respect of the first capability, BDA infrastructures (which are the ensemble of information systems capable to collect, store, process and analyze big data) should be self-adaptable to different kinds of data. This capability related to systems is indeed fundamental to ensure that technologies will be able to process different data flows and formats in any situation (Labrinidis & Jagadish, 2012; Rialti et al., 2018). Thus, BDA infrastructure flexibility (or capability) may generate routines concerning the management of information flows and to deal with different problems. Next, BDA managerial capabilities are fundamental in the selection and the implementation of the BDA infrastructure that is right, and in the identification of the right information extracted from the datasets. Managers should, in fact, have enough competence to decide which technical solution is the best for their organization. Similarly, they need to have enough data analytic skills to take the right decisions according to the newly available data. Such kind of capabilities could influence significantly how managers behave in respect of data related concerns and affect the strategy they select to challenge new information. Yet, they could improve managerial skills concerning the identification of the most valuable information among data at disposition. Insights are frequently hidden among data that may seem worthless, and individual BDA capabilities of managers may influence looks of information and the suggestion these figures may provide to employees. The personnel, finally, should be skilled in BDA too. Personnel’s BDA capabilities are related to lower rejection of BDA within the organization,

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less resistance to new IS implementation and better functioning of the BDA infrastructure. Additionally, since employees are often the ones conducting the analysis of the data, they need adequate skills to identify the right data to be analyzed (Wamba et al., 2017). Only if employees are skilled enough, they may report proper insights to managers. For these reasons, the dynamic capabilities are also useful guidance to unpack how BDA is related to organizational ambidexterity. In particular, it is observed that ambidexterity is considered as one of the dynamic capabilities of an organization. Ambidexterity, indeed, is the organizational capability to use existing capacities to explore the environment and exploit opportunities available. The pertinent literature shows that the structure of published research regarding big data needs to be properly studied (Braganza et al., 2017; Bresciani, Ferraris, & Del Giudice, 2017). Specifically, a better understanding is required about how literature about big data and dynamic capabilities is organized. It is observed that the bibliometric method is used by a maximum number of manuscripts to investigate other streams of literature that are related to big data (Ardito et al., 2018) and very few of them have focussed on this particular topic.

2.4

Exploring Existing Literature on Big Data, Dynamic Capabilities and Ambidexterity: Defining the Research Methods

Before the theoretical and empirical explorations of the relationship between big data, BDA, dynamic capabilities and ambidexterity, it is necessary to analyze the existing literature on the topic. In this sense, two methodologies have been selected: bibliometric method and the systematic literature review. Comprehensive maps have been generated by using a bibliometric method. These maps give the knowledge structure of a given stream of literature. However, while studying a field of research that is still developing, it is important to implement a literature analysis accurately. For this

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purpose, Caputo, Marzi, Pellegrini, and Rialti (2018) used a combination of techniques, bibliometric analysis, and review of the literature. The first step included a bibliometric analysis. The results of this analysis were collected and the literature was reviewed. The basis of the bibliometric analysis is the technique of ‘visualization of similarities’ (VOS) (Van Eck & Waltman, 2010). The authors followed the method recommended by Tranfield, Denyer, and Smart (2013) for the review of the literature. The complete process of two methodologies consists of six steps. These six steps are described in detail below. a. Step 1: The Query. The first step is associated with searching the database, Clarivate Analytics Web of Science Core Collection, for the specified research query. The most valuable and high-impact data has been collected in this database and it is very useful for bibliometric studies because of its high reliability. In detail, the search for the query process is fundamental, as the query is an ensemble of words used to identify a set of papers in international online repositories. The procedure of selection of a research query begins with a review of literature of the manuscripts focussed on using BDA for management and considering the primary underlying theory as dynamic capabilities for grasping all of the terms that are utilized for explaining the phenomena that needs to be analyzed (i.e. Akter et al., 2016; Wamba et al., 2017). After multiple iterations targeting to outline an expansive research query, the following query was selected: TS = ((“big data*” OR “big data analytics*”) AND (“dynamic capabilities*” OR “ambidexterity” OR performance*) AND (organization* OR firm* OR business* OR enterprise*)) This query will identify most of the existing research on the topic. Indeed, specific words such as “big data” or “big data analytics” allow to collect the papers on such topics in relation to the theoretical streams of the research because of use of words such as “dynamic capabilities”, “ambidexterity” and “performance”. Yet, the inclusion of words like, “organization”, “firm” and “business” allows the inclusions of papers coming from the business and management field only as business organizations are the main object of the research.

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In the titles, abstracts, and keywords, a full search of the chosen terms is performed by the ‘TS’ operator. The search can be limited by document type, such as “articles, books, book chapters, book reviews, early access articles, and editorial material”. The timespan of search is also defined such as a ten-year cross-section—namely 2008 to 2018— can be considered as timespan. In order to be sure about the inclusion of the most significant papers, the query is iterated on the three most relevant databases, EBSCO, Scopus and Web of Science (WOS). The searches on all the relevant databases may offer similar results in terms of the number of papers. The analysis using WOS database is considered the most upto-date on extremely recent literature in management. For the example query and the above conditions, 423 entries are obtained. b. Step 2: The Inclusion Criteria. The second step is focused on defining the criteria of inclusion of the documents to be utilized in the study, followed by the analysis and selection of each document manually. In detail, the inclusion criteria are used by a researcher to clean the dataset identified in Step 1. Hence, the author decides which paper is included for review based on the presence of the inclusion criteria in the paper itself. Using two inclusion criteria, the first of which was the most accepted definition of big data as “datasets whose size is beyond the ability of a typical database software tools to capture, store, manage and analyze” (Manyika et al., 2011, p. 1). The authors then excluded manuscripts without a research perspective focused on dynamic capabilities and manuscripts that did not belong to management-related literature. This led to the dataset being reduced from 423 to 217 entries. c. Step 3: Collaborative Analysis of the Papers. The third step consists of critically analyzing the manuscripts that were selected in the previous step and working to derive an effective knowledge of BDA and how it is associated with dynamic capabilities and organizations’ performances (Wamba et al., 2017). In this perspective, during this phase, it becomes possible for the users to be safe about the content of an included research and to critically explore the perspectives considered by original authors.

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d. Step 4: Bibliometric Indicators Analysis. The fourth step is regarded as the preliminary segment of the bibliometric analysis. Specifically, the research volume is analyzed using the activity indicators which reflect how the literature evolved quantitatively over time. Particularly, the number of papers for a year and the most prolific authors are identified. In addition, an analysis concerning the most institutions and the countries their authors (and related manuscripts) come from has also been performed with the help of the analytics functions of VOSviewer 1.6.5. e. Step 5: Bibliometric Analysis. The fifth step involves proper bibliometric analysis. For this purpose, as suggested by Van Eck and Waltman (2010), to aggregate the manuscripts, an aggregation mechanism is utilized by using the VOSviewer 1.6.5 algorithm with bibliographic coupling. When two papers cite a common third paper in their references, it is indicative of bibliographic coupling. These two papers can then be referred to as bibliographically coupled. The output generated by the VOSviewer is in the form of a map in which the relatedness of the terms can be inferred by the distance between the terms. A strong association between the terms is reflected by a smaller distance (Van Eck & Waltman, 2010). In addition to this, the knowledge base diversity is highlighted by the cluster analysis in an aggregate way: if the manuscripts are in the same cluster, then based on their shared references they are a group that is strongly linked together. Thereby, based on similarity, a stream of research is represented in a cluster. The manuscripts are presented in a useful way on the map generated by VOSviewer which optimizes their visualization; thus, there is no meaning of the axes of the map (Van Eck & Waltman, 2010). f. Step 6: Systematic Literature Review. This last step includes the process of literature review (Tranfield, Denyer, & Smart, 2013) and is based on the results generated from the VOS aggregation. The clustering results generated by VOSviewer were used and the most influential manuscripts inside the displayed clusters are analyzed to highlight their main areas of interest. In detail, the focus during this phase is on the exploration of the linkages existing between papers moving from their contents and the perspectives used by previous researches. Similarly, the evolution of literature is observed.

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2.5

Results of Bibliometric Analysis

In this section, the results of the bibliometric analysis are presented and analyzed. The distribution of manuscripts over the years is presented in Fig. 2.1. As seen in Fig. 2.1, most of the manuscripts that are selected are published within the last 5 years. Only 2 manuscripts were published in 2012 and before this, there were no publications. Although since the previous decade, the significance of big data and BDA for management has been researched extensively, there have been explorations into this field with a focus on the theoretical principle of dynamic capabilities only during the last five years. Based on the pattern observed by the number of manuscripts that are published each year, this area has still not reached maturity. Indeed, every year, the number of manuscripts published on this topic is increasing. In regards to the principal outlets selected by authors on the topic, as shown in Table 2.1, Journal of Knowledge Management and Journal of Business Research present the most elevated number of manuscripts (8 and 7, respectively). Such a phenomenon is obviously related to the interest of these two particular journals toward the multifaceted emerging importance of Big Data. The other journals dealing with the topic mostly 2018

47

2017

94

2016

30

2015

23 14

2014 2013

7

2012

2 0

20

40

Fig. 2.1 Manuscripts’ temporal distribution

60

80

100

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Table 2.1 Principal outlets for publications on big data and ambidexterity Journal

Number of papers

Journal of Knowledge Management Journal of Business Research International Journal of Production Economics International Journal of Operations & Production Management International Journal of Production Research Journal of Cleaner Production Business Process Management Journal Decision Support Systems Business Horizons Industrial Management & Data Systems Management Decision MIS Quarterly Production Planning & Control Sustainability Accounting Auditing & Accountability Journal Asia-Pacific Journal of Operational Research Business & Information Systems Engineering Computer Standards & Interfaces Computers & Industrial Engineering Creativity and Innovation Management European Journal of Marketing Expert Systems with Applications Interfaces Journal of Business Logistics Journal of Corporate Accounting and Finance Journal of Intelligence Studies in Business Journal of Management Information Systems Scientometrics

8 7 6 5 5 5 4 4 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2

deal with the operation management area. Indeed, one of the principal effects of the implementation of BDA is the evolution of traditional operation management procedures. Finally, as it is possible to observe, counterintuitively, several journals concerning environmental concerns (i.e. Sustainability and Journal of Cleaner Production) are present on the list. This fact is related to scholars’ attention at the impact of BDA in reducing organization impact on the environment. The most prolific authors, instead, are the pioneers of this field of research. As an example, Stephen Childe, Angappa Gunasekaran,

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Rameshwar Dubey, Samuel Fosso Wamba, and Shahriar Akter are the most prolific authors on these topics. As a matter of fact, they were among the first authors to deal with the topics related to the implementation of BDA within modern organizations. Moreover, they were among the first authors to use dynamic capabilities as a theoretical lens to observe how BDA could change the way organizations compete. The results are shown in Table 2.2. To what concerns the most prolific institutions and the most prolific countries, results are shown in Tables 2.3 and 2.4 respectively. In Fig. 2.2, the VOS analysis results are presented for the manuscripts that are most influential and only the first author’s surname is included. From the above analysis of the 217 manuscripts, five clusters can be identified. As set in the query, the selected manuscripts in the clusters have used dynamic capabilities as a theoretical principle. In detail, the analysis of 5 clusters shows: a cluster (the blue one) containing papers about Big Data, Dynamic Capabilities, Supply Chain Management, and Performance; a cluster (the red one) including papers about Big Data, Dynamic Capabilities, Production Processes, and Manufacturing; another cluster (the green one) is about Big Data, Dynamic Capabilities, and Strategy Making; and the last cluster (the yellow one) dealing with Knowledge Exploitation Through BDA Systems, and, finally, a cluster (the purple one) about BDA and performance. The contents of the clusters are explored in-depth in the next section.

2.6

Systematic Review of Literature

Comprehensibly with another research which contains both, a bibliometric analysis and a literature review (Wamba & Mishra, 2017), the authors followed a similar approach, resulting in the analysis of the ten most influential manuscripts belonging to each identified cluster (Caputo et al., 2018; Rialti et al., 2019). In this perspective, the selected papers were then characterized by the highest scores in terms of normalized citations. These manuscripts, in addition, also get selected as they were, in most of the cases, the ones most closely connected to others, according to VOSviewer 1.6.5.

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Table 2.2 Most prolific authors Author

Number of papers

Childe, Stephen J. Dubey, Rameshwar Akter, Shahriar Gunasekaran, Angappa Hazen, Benjamin T. Wamba, Samuel Fosso Boone, Christopher A. Cao, Guangming Colomo-Palacios, Ricardo Duan, Yanqing Molloy, Owen Tan, Kim Hua Vera-Baquero, Alejandro Zhan, Yuanzhu Arnaboldi, Michela Buxmann, Peter Chongwatpol, Jongsawas Del Giudice, Manlio Erickson, G. Scott Ezell, Jeremy D. Ji, Guojun Kowalczyk, Martin Kwon, Ohbyung Ma, Jian Marco, Manuel Mate, Alejandro Papadopoulos, Thanos Peral, Jesus Ren, Steven Ji-Fan Rothberg, Helen N. Skipper, Joseph B. Stantchev, Vladimir Wang, Yichuan

5 5 4 4 4 4 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

2.6.1 Blue Cluster: Big Data, Dynamic Capabilities, Supply Chain Management and Performance This cluster includes manuscripts dealing with how big data affects supply chain management, related dynamic capabilities, and performance. BDA capable systems and processes may significantly affect supply chain

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Table 2.3 Most relevant institutions Institution

Number of papers

Politecn. Milan Univ. Nottingham Auburn Univ. Univ. Carlos III Madrid Univ. Plymouth Univ. Wollongong City Univ. Hong Kong Constituent Symbiosis Int. Univ. Coventry Univ. Erasmus Univ. Georgia Southern Univ. Indian Inst. Technol. Delhi. Iowa State Univ. Massey Univ. Natl. Univ. Ireland. Natl. Univ. Singapore Neoma Business School Ostfold Univ. Coll. Tsinghua Univ. Univ. Bedfordshire Univ. Manchester Aarhus Univ. Air Force Inst Technol Arizona State Univ. Brunel Univ. London Chinese Acad. Sci. Ewha Womans Univ. George Washington Univ. Heriot Watt Univ. Indiana Univ. Ithaca Coll. Kyung Hee Univ. Lunghwa Univ. Sci. & Technol. Marist Coll. McMaster Univ. Natl. Inst. Dev. Adm Newcastle Univ. NYU Ohio State Univ. Old Dominion Univ.

6 5 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 (continued)

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Table 2.3 (continued) Institution

Number of papers

Renmin Univ. China Rutgers State Univ. Srh hsch Berlin Tech. Univ. Darmstadt Texas Christian Univ. Toulouse Business Sch. Univ. Alicante Univ. Arkansas Univ. Calif. San Diego Univ. Cambridge Univ. Chinese Acad. Sci. Univ. Deusto Univ. Liverpool Univ. Massachusetts Univ. Massachusetts Dartmouth Univ. St. Gallen Univ. Tennessee Univ. Texas Austin Xiamen Univ.

2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

management (Gunasekaran et al., 2018). The routinization of BDA processes may have an important role in making sure that these processes are able to adapt to different situations. Hence, there may be an increase in organizational agility, dynamism, and flexibility because of BDA processes specifically identifying problems and opportunities in supply chain management. According to a similar perspective, increased sustainability has also been associated with the improvement of the supply chain management ability of BDA processes. This phenomenon is associated with the fact that as efficiency increases in the management of supply chains, the quantity of waste may be reduced (Papadopoulos et al., 2017). Similarly, BDA processes may influence supply chain management, more precisely, how the application of BDA enhances and speeds up managerial decision processes about procurement (Lamba & Singh, 2017). These findings have also been supported by the empirical exploration by Ghasemaghaei, Ebrahimi, and Hassanein (2017).

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Table 2.4 Country of origin of the manuscripts Country

Number of papers

USA England Peoples R. China India Australia France Germany Italy Spain Ireland Netherlands South Korea Canada Norway Taiwan Singapore Belgium Denmark Finland New Zealand Sweden Switzerland Bahrain Brazil Greece Portugal Scotland South Africa Thailand UAE

70 35 29 13 12 10 10 10 9 6 6 6 5 5 5 4 3 3 3 3 3 3 2 2 2 2 2 2 2 2

It emerged that, to a certain degree, the impact of BDA on supply chain management, may positively affect stock replenishment and product availability. For example, the ways in which BDA systems based on networks of sensors may increase the efficiency of retail chains have been examined by Li and Wang (2017) and by Wu and Lin (2018). The organization can, thus, dynamically re-organize processes according to emerging needs (Stefanovic, 2015). Increased supply chain and stock replenishment efficiency may, as a by-product increase organizational performances. Specifically, Martin,

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Fig. 2.2 VOSviewer result

Borah, and Palmatier (2017) and Money and Cohen (2018) have concluded that the ability to forecast resource usage may improve performance. In fact, when a business is capable to increase the accuracy of predicted resources’ consumption, it may be capable to achieve significant savings in costs. In respect of the relationship of this phenomenon with the notion of dynamic capabilities, it is possible to observe how the more the predictions become a routine in operative practices the more they will become fast and accurate.

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2.6.2 Red Cluster: Big Data, Dynamic Capabilities, Production Processes, and Manufacturing The second cluster contains manuscripts on big data and production processes related to dynamic capabilities. BDA capable IS or processes may offer managers an opportunity to monitor production processes (Chen, Mao, & Liu, 2014). From this perspective, it emerges that with BDA methodologies, managers may control any part of the production process associated with less resource wastage (Sivarajah et al., 2017). Similarly, Tan assessed how BDA may foster innovation in all material processes, from supply chain to production. According to Wu, managers may get the ability to make informed choices on strategies based on the large quantities of data that can be provided by BDA. BDA adoption was also studied by Zudor for finding a way to make strong collaborations amongst partners belonging to the same production chain. The architecture of BDA capable IS may encourage data sharing which is related to the efficiency of the production process between partners. Finally, BDA capable IS may ensure continuous control over the quality of the production Kibira, Morris, and Kumaraguru (2016). Additionally, some manuscripts included in this cluster outline how BDA capable systems may improve the efficiency of processes involving products or services (Erickson & Rothberg 2017; Moeuf, Pellerin, Lamouri, Tamayo-Giraldo, & Barbaray, 2018) and show that big data competencies are fundamental in making this possible (Wang & Cotton, 2018). In detail, it has been observed how BDA in most of the modern businesses could decide in an easier way to prioritize the production of a batch of products with respect to machinery’ capacity. Algorithms for BDA tools reasoning are, sometimes, more accurate than the human mind, in the identification of production priorities, production slots and in the daily division of work. In addition, compared to the human mind, artificial intelligence is unbiased, as decisions taken by computers are based only on objective data. All these findings are consistent with existing and emerging literature on Industry 4.0. In conclusion, automation

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could help managers to increase the efficiency of the business and generate greater income and, BDA capable IS could integrate with automatic machines by embedding their AI with the right information to take autonomous decisions.

2.6.3 Green Cluster: Big Data, Dynamic Capabilities, and Strategy Making Scholars have substantially focused on the impact of big data on strategy making processes because of the information that big data can contain. BDA can provide managers with opportunities to know more about their consumers and insights arising from BDA capable IS can offer realtime actionable knowledge (Prescott, 2014). BDA capable IS can also enhance strategic decision-making processes related to marketing (Erevelles et al., 2016). It has been shown that information about consumers can allow managers to react dynamically to evolving consumer’s preferences. The integration of BDA related routines or processes in decision making has been also seen to influence organizational adaptive capabilities. Such a phenomenon, however, isn’t typical only of organizations producing goods, but can also be seen in services providers, to better deal with the requirements of their consumers and to effectively monitor the utilization of resources. (Wang, Kung, & Byrd, 2018). The manuscripts in the cluster show how managers can better allocate resources to respond to critical situations. BDA may allow managers to respond to changes concerning day-to-day operations (Melnyk, Flynn, & Awaysheh, 2018). This phenomenon is related to the knowledge flow generated by BDA capable IS, which may change the managers’ way of thinking and acting. Additionally, such systems may allow knowledge to be distributed across the whole organization (Wang, Zhu, Song, Hou, & Zhang, 2018). Manuscripts belonging to this cluster deal also with the importance of the alignment between BDA capabilities of organizations and their expected outcomes. As stressed by Akter et al. (2016), if big data systems and processes are aligned with managerial objectives, these can offer proper information required to managers for formulating satisfactory strategies. In these instances,

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organizations may create value from big data (Bedeley, Ghoshal, Iyer, & Bhadury, 2018; Mamonov & Triantoro, 2018). BDA could, thus, influence how managers decide to behave and plan the strategic paths to be followed by an organization. This is because BDA managers may potentially decide using accurate information regarding patterns concerning both consumers and competitors.

2.6.4 Yellow Cluster: Knowledge Exploitation Through BDA Systems The fourth cluster includes manuscripts about the importance of BDA capable IS and processes for knowledge generation and its exploitation. In this perspective, He stressed how organizations that aim to utilize the informative potential of big data should adopt developed processes capable to extract the best contents from big data. Yet, it emerges that the systems to collect and analyze the data should be integrated with the systems capable to diffuse the knowledge within the organization. This is necessary to make knowledge flows arrive at the right decision-maker. These findings are also confirmed by Xie, Kwok, and Wang (2017) for big data in the hospitality sector. Indeed, it emerged that when the right information arrived at the right hotels, it may gain competitive advantages. Therefore, managers may give better responses to consumers’ requests (Pournarakis, Sotiropoulos, & Giaglis, 2017). Similarly, Arnaboldi, Azzone, and Sidorova (2017) evaluated how big data may be advantageous to firms only if the insights may be used by the right manager to deal with the equilibrium between the organization and the surrounding environment. Other most influential manuscripts deal with very closely related topics. Lee (2017), for example, has emphasized that the major challenge of this era of big data is how the required information can be extracted from big data and how to exploit this new knowledge. Maklan, Peppard, and Klaus (2015) explored how knowledge generated from big data may improve marketing strategies. Then, Tirunillai and Tellis (2014) investigated the techniques to extract knowledge from big data.

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2.6.5 Purple Cluster: BDA and Performance Related Outcomes Superior performance is the target of most managers and managers are usually more than eager to follow any possible path to improve the performance of their organization. In this perspective, BDA capable IS and processes may represent a fundamental tool to improve the performance of organizations (Chen et al., 2013). As emphasized by Gani, Siddiqa, Shamshirband, and Hanum (2016) and Kowalczyk and Buxmann (2015), organizational capabilities may be influenced by BDA which can be used for identifying and exploiting opportunities that exist in the external environment. On the one hand, BDA capable IS may help the organization in scanning the surrounding environment for additional information, while on the other hand, managers have at their disposition the insights to extract new information extracted from BDA, and for formulating the strategies required for capturing and exploiting the new emerging opportunity. This is linked to the real-time insights that may be mined from big data and their accuracy. At present, managers have access to the perceptions and the ideas of consumers as they are freely and easily available on the internet and they may be analyzed with the help of proper tools (Christensen, Nørskov, Frederiksen, & Scholderer, 2017). The alignment between BDA capable IS and processes by using knowledge management tools can result in the diffusion of these insights within the organization. This may help in making strategic managerial decisions that are supported by accurate information. An organization may improve its performance by reducing costs or increasing revenues (Centobelli, Cerchione, & Esposito, 2018). The manuscript by Vera-Baquero et al. (2016) confirms that BDA is associated with better performance of an organization as it facilitates better monitoring of internal processes by the manager. BDA managers may get familiarized with the performance of the processes, identify bottlenecks or problems, and may find solutions to the problems. Thus, managers may accomplish an improvement in the performance of the organization by increasing the individual processes’ performance (Ramanathan, Philpott, Duan, & Cao, 2017).

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As it is possible to observe from Fig. 2.2, such a cluster is also intertwined with the majority of other ones. In fact, several papers from the purple cluster are positioned amidst the ones of the yellow, blue and red clusters. In this perspective, it is then possible to assess that literature exploring the impact of BDA on organizational performances is also related to other streams like the ones exploring the role of BDA in improving supply chain efficiency and in process management. These observations suggest that the effect of BDA on performances exists where these capabilities, processes, and technologies are capable to influence organizational aspects. BDA alone then may not have any positive effect if the organization is not willing to change accordingly.

2.7

What’s Next?

The existing knowledge is systematized after the review of important manuscripts on big data and dynamic capabilities. Firstly, it is established that there are five clusters in all the research on dynamic capabilities (Akter et al., 2016; Wamba & Mishra, 2017). Then, assessment is done to find how BDA can increase dynamic capabilities related to the adaptation of operation processes and supply chain strategies to mutated environments (Gunasekaran et al., 2018). It was also observed that BDA is frequently linked to superior performances (Prescott, 2014). Indeed, as BDA capable IS and processes may adapt to changing data and situations, they can affect organizational dynamic capabilities by providing insights on future strategies that need to be implemented. Consequently, organizations may re-organize processes and re-allocate resources, which may affect performance positively (Wamba et al., 2017). From the example studied, according to Akter et al. (2016), the alignment between the capabilities of big data and manager’s expectations regarding the implementation of BDA should be monitored by the manager. It is also found that managers may not find the insights needed for developing new strategies if the big data capabilities and objectives of the organization are not aligned. What clearly emerged from the example is that big data could influence performance with the help of newly available information and

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become instrumental in the development of new routines to exploit it. Yet, extant literature still must clarify how BDA could become a dynamic capability, and how it could influence ambidexterity. Moving from these premises, the next chapters will first try to outline the mechanisms concerning how BDA could influence organizational routines from a theoretical standpoint.

References Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016). How to improve firm performance using big data analytics capability and business strategy alignment? International Journal of Production Economics, 182, 113–131. Ardito, L., Scuotto, V., Del Giudice, M., & Petruzzelli, A. M. (2018). A bibliometric analysis of research on big data analytics for business and management. Management Decision, 57 (8), 1993–2009. Arnaboldi, M., Azzone, G., & Sidorova, Y. (2017). Governing social media: The emergence of hybridised boundary objects. Accounting, Auditing & Accountability Journal, 30 (4), 821–849. Bedeley, R. T., Ghoshal, T., Iyer, L. S., & Bhadury, J. (2018). Business analytics and organizational value chains: A relational mapping. Journal of Computer Information Systems, 58(2), 151–161. Braganza, A., Brooks, L., Nepelski, D., Ali, M., & Moro, R. (2017). Resource management in big data initiatives: Processes and dynamic capabilities. Journal of Business Research, 70, 328–337. Bresciani, S., Ferraris, A., & Del Giudice, M. (2017). The management of organizational ambidexterity through alliances in a new context of analysis: Internet of Things (IoT) smart city projects. Technological Forecasting and Social Change, 136, 331–338. Caputo, A., Marzi, G., Pellegrini, M. M., & Rialti, R. (2018). Conflict management in family businesses: A bibliometric analysis and systematic literature review. International Journal of Conflict Management, 29 (4), 519–542. Centobelli, P., Cerchione, R., & Esposito, E. (2018). Aligning enterprise knowledge and knowledge management systems to improve efficiency and

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3 From Big Data to Performance: The Importance of Ambidexterity, Agility and BDA Integration in Business Processes—A Theory-Based Framework

Abstract This chapter explores how an ambidextrous organizations’ agility is affected by the BDA capable BPMS, a specific kind of BDA capable IS (Big Data Analytics capable Business Process Management Systems). Specifically, the BDA capable BPMS functionalities and its positive effects on organizational dynamism and reactiveness towards challenges are studied. A theoretical assessment of the possibility of BDA capable BPMS in improving organizational agility, with a specific focus on the ambidextrous organizations, has been explained. A theoretical framework explaining some traits of the phenomenon has also been explained. The results highlight how BDA capable BPMS may increase the agility of an ambidextrous organization and by the implementation of such BPMS, ambidextrous organizations may be able to better achieve market capitalization and operational adjustment agility. Keywords Ambidexterity · BDA · Business Process Management · Management Information Systems · Performance

© The Author(s) 2020 R. Rialti and G. Marzi, Ambidextrous Organizations in the Big Data Era, https://doi.org/10.1007/978-3-030-36584-4_3

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The Integration of BDA in Modern Organizations: Perspectives About Information Systems, Ambidexterity, and Agility

As observed in the previous chapter, the current literature is trying to observe how big data could be turned into value. Some scholars, following a bit too naive approach, pointed out that the more a manager is informed, the better are his decisions. Yet, what matters the most is how the development of BDA could re-shape an organization and make it more profitable. Several scholars did try to explore the connection between BDA, Information Systems, Ambidexterity, and Agility. Therefore, the present chapter summarizes those contributions using as guiding light the theoretical exploration of BDA presented by Rialti, Marzi, Silic, and Ciappei (2018). As stressed through the previous chapter, BDA can change how employees work, how managers decide and act for the success of the organization. BDA capabilities related to people populating the organization necessarily involve a cultural shift. This notwithstanding, what has been observed over the last five years, is that the biggest structural change occurs as a direct consequence of the need to integrate BDA capabilities within existing information-related infrastructures. This phenomenon is related to the fact that BDA capabilities related to infrastructure and systems are frequently one of the primary requirements in the development of organization-wide BDA capabilities. In fact, first of all, the development of the infrastructural component of BDA capability may be outsourced to a specialized provider. Thus, any organization may simply ask a trusted provider to start the implementation even before the full development of all the individual skills. Next, before starting to exploit the potential of big data any organization should at least be endowed by some basic infrastructure to collect and process such datasets. As in any situation requiring the need to develop some information system, one of the key problems may be associated with the loss of flexibility that the organization may suffer because of the change. Indeed, any organization developing a new system may have to face opposition from

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employees, disruption of its internal operation, and all the kinds of technical difficulties. Yet, in the specific case of BDA capable IS, such systems may provide a valid help in the pursuit of organizational ambidexterity provided they are properly implemented. How such a phenomenon occurs, and how such IS may improve ambidexterity by providing the most relevant information about emerging opportunities, will be explored using dynamic capabilities as the main theoretical lens. In particular, this chapter’s focus will be on the impact of BDA capable IS on business processes management. In order to do so, the first section presents a review of literature on the importance of agility and ambidexterity for any organization. The next sections will provide a notion of ambidextrous organization. Finally, it will be assessed, moving from existing research, why BDA capable IS could help an organization become ambidextrous with the improvement in business processes management. Hence, the focus of this section will be on a specific kind of BDA capable IS, namely BDA capable Business Process Management Systems (BPMS).

3.1.1 The Relevance of Agility and Ambidexterity in Modern Organizations Several theories have been proposed to justify the inability of an organization to continually improve current businesses, to embrace and implement innovations, or to adapt according to the changing environment (Aydiner, Tatoglu, Bayraktar, Zaim, & Delen, 2019; Raisch, Birkinshaw, Probst, & Tushman, 2009). Their conclusion is that the improvements or adaptations are not possible as the organizations do not have agility (O’Reilly & Tushman, 2008; Tushman & O’Reilly, 1996). However, organizations can work on improving their agility and performance by using dynamic capabilities approach or cross-functional teams, as proposed by Aime, Humphrey, DeRue, and Paul (2014). The idea of separating organizations, which successfully survived in the competitive environment, into explorative and exploitative units was brought back after seeing the poor status of organizations that attempted to improve their agility (Khan & Wisner, 2019). These types of organizations have been

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recognized as ambidextrous organizations. According to Tushman and O’Reilly (1996), these organizations have certain characteristic features: shared culture and common vision, a decentralized structure, and separate units for exploration and exploitation. Scholars have found how such organizations successfully strive in an environment that is changing constantly (Gibson & Birkinshaw, 2004; He & Wong, 2004). Ambidexterity has been considered to be associated with both, increase in performance; and organization agility (Lee, Sambamurthy, Lim, & Wei, 2015; O’Reilly & Tushman, 2013). Previous literature has emphasized that ambidextrous organizations are more likely to adapt to changing situations as they have a higher level of agility (Del Giudice & Della Peruta, 2016). In addition, scholars in information systems have also discovered that an organization may completely exploit the benefits of ambidexterity if it has adequate IS (Gothelf, 2014). The crucial role of such systems has been highlighted in BPM and, in turn, for improving the level of customers’ satisfaction, allowing quick responses to changes in the market and promoting intra-organizational collaboration, which are all characteristics of ambidextrous organizations’ agility (Gao, 2013; Wamba & Mishra, 2017). In the present digital era, for BPM, conventional information systems may be insufficient for enabling organizations to benefit from the opportunities that rise from the complex digital environment around them (Del Giudice & Straub, 2011). Hence, information systems need BDA capabilities for efficient business process management so that the ever-increasing amount of unstructured data can be explored and exploited for any potential opportunities (Lopez-Nicolas & Soto-Acosta, 2010; Sivarajah, Kamal, Irani, & Weerakkody, 2017). It has been theorized by several scholars that information systems may have an important role in an ambidextrous organization (Roberts & Grover, 2012; Tallon & Pinsonneault, 2011), but most of the literature has not taken into account how big data has revolutionized the information systems’ capabilities and characteristics (Yin & Kainak, 2015). Currently, in process management, processes can be continuously monitored by BDA capable IS and it can provide the right information to the right users involved (Gao, 2013), which may influence ambidextrous organizations’ agility.

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Before understanding the dynamics of this phenomenon, it is necessary to properly consider the notion of the modern ambidextrous organization and its traits.

3.2

The Ambidextrous Organization in Big Data Era

3.2.1 The Ambidextrous Organization and Agility Duncan (1976) was the first to theorize the ambidextrous organization as an organizational competitive response to the shift experienced by organizations in the 1970s to a dynamic competitive environment from a static environment. During the past years, several contributions in the form of empirical and theoretical evidence have enriched the concept of ambidexterity in organizational studies (Gibson & Birkinshaw, 2004; Raisch et al., 2009). Several studies have shown that the survival and performance of an organization are determined by its ability to strive for two different things at the same time. This has been reported in several studies of different things, such as Adler, Goldoftas, and Levine (1999) study of manufacturing efficiency and flexibility, Porter’s (1980) manuscript dealing with differentiation and low-cost strategic positioning, or a study by Bartlett and Ghoshal (1989) on worldwide integration and local responsiveness. As a result, an ambidextrous organization has been identified as an organization that is capable of exploring the environment and exploiting emerging opportunities simultaneously. In other words, if an organization is able to pursue current processes while being able to continuously adapt to the changing competitive environment, such an organization is referred to as an ambidextrous organization (Duncan, 1976; Junni, Sarala, Taras, & Tarba, 2013). Further explorations include the organizations’ ability to search, discover and innovate, take the risk, and to prepare the field for the exploitation task. According to Duncan (1976), exploitation includes the organization’s ability to implementing the explored innovation, producing, optimizing, and executing the tasks (Duncan, 1976).

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The very notion of ambidexterity lies in its theoretical foundations within the realm of dynamic capabilities, with some scholars asserting that the ambidexterity may itself be classified among organizational dynamic capabilities (O’Reilly & Tushman, 2008). Indeed, higher exploration and exploitation capabilities are necessarily related to the organizational capability to amass and deploy existing resources to solve different problems and to react to environmental changes. This perspective has some flaws. Principally, ambidexterity itself is not a capability of an organization that could not invest in something specific to pursue ambidexterity aprioristically. Indeed, ambidexterity can be visualized as the result of the combination of several dynamic capabilities coexisting within the organization. For example, the capability to scan the market could matter. Similarly, an ambidextrous organization may be characterized by greater efficiency and efficacy than a counterpart. Yet, organizational ambidexterity does not derive from capabilities alone. Indeed, organizational traits and elements related to organizational design may play a significant role. In particular, ambidexterity inherently incorporates the concept of agility (Lu & Ramamurthy, 2011). Zain, Rose, Abdullah, and Masrom (2005) identified that an organization targeting for agility has “to be quick in assembling its technology, employees, and management with communication infrastructure in responding to changing customer demands in a market environment of continuous and unanticipated change” (p. 831). Several manuscripts reflect that ambidextrous organizations show agility. In this perspective, Lubatkin, Simsek, Ling, and Veiga (2006) have observed how an ambidextrous organization may be able to react quickly to changes in the market while focusing on maintaining a level of satisfaction amongst existing customers. This reflects the level of how much customers are satisfied with the offerings of the organization (Rialti, Zollo, Pellegrini, & Ciappei, 2017). The organizations’ ability to conveniently adapt to change or the organizational and technical flexibility, has developed as a vital competitive feature of an ambidextrous organization’s agility (O’Reilly & Tushman, 2008, 2013). Nowadays, ambidextrous organizations, characterized by agility, have been conceptualized as organizations roughly based on the widely known agile principles. In recent years, because of the rise of the digital age, a

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new paradigm coming from software development proposes several tools and principles to improve flexibility and performance (Gothelf, 2014). These tools belong to the comprehensive paradigm of Agile and its principles of agility. Briefly, agile approach suggests to (1) build projects around motivated individuals selected on their interests and supported by collaborative environment, (2) respond to change rather than follow it by planning only small stable tasks and accepting changes even later during the project, (3) welcome change in the project, even late if this improves the customer’s competitive advantage, (4) stimulate communication between individuals and teams by regular face-to-face meetings and daily briefings, (5) give continuous attention to product or service technical excellence, (6) embrace customer collaboration in key business processes. Finally, the last two principles provide that, (7) organizations should ensure stakeholder’s corporate responsibility (revise again), and (8) ensure socio-cultural responsibility. Because of the demonstrated efficacy and effectiveness of Agile approach, these principles have been seminally incorporated into organizational studies and several scholars have started to integrate organizational ambidexterity with agile principles in order to provide a holistic approach to organizational flexibility and performance, laying the foundations of the Agile organization approach (Chan, Ngai, & Moon, 2017; Zhang, 2005). However, the literature around Agile organization is still fragmentary and the most relevant contributions are mainly coming from practitioners who show to mangers several ways and benefits coming from the inclusion of Agile principles inside their organizations (Gothelf, 2014). Consequently, in the absence of a theoretical harmonization of Agile organization principles, Table 3.1 gives a summary of the most recent and valuable contributions and their connection with the ambidexterity organizational paradigm. Thus, ambidextrous organizations with characteristic features of agility require dynamic business process management systems so that they can increase dynamism, make process monitoring better and, as a result, increase performance (Kortmann, Gelhard, Zimmermann, & Piller, 2014; Lin, Yang, & Demirkan, 2007). Table 3.1 lists the reports of ambidextrous organizations’ agility and their significance.

Develop customer satisfaction

Rapidly respond to changes in market

Achieve organizational flexibility

Achieve technical flexibility

AP2

AP3

AP4

Assumption

AP1

Principles of agile organization (AP), or characterizing agile organizations An ambidextrous behavior results in better performance in fulfilling costumers’ needs A combination of market exploration and exploitation has balancing interaction effects on ability of an organization to adapt to changes in market An ambidextrous organization is aligned environmentally, efficient and fosters relationship amongst contextual structures and performance of the organization Organization’s response to uncertainty in the environment is affected positively by operational ambidexterity and it promotes manufacturing flexibility and improves the organization’s performance

Evidences of agile organization principles in ambidextrous organizational paradigm

Table 3.1 Agile organization principles and ambidextrous organization paradigm

Kortmann et al. (2014) and Liu, Liao, and Li (2018)

Raisch et al. (2009) and Tai, Wang, and Yeh (2019)

O’Reilly and Tushman (2008), Voss and Voss (2013), and Monferrer Tirado, Moliner Tena, and Estrada Guillén (2019)

Lubatkin et al. (2006) and Peng, Lin, Peng, and Chen (2019)

Pertinent literature

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Assumption

Embrace dynamic BPM

Pursue strategic collaboration with partners that is effective

Ensure stakeholder responsibility

Manage socio-cultural responsibilities

Principles of agile organization (AP), or characterizing agile organizations

AP5

AP6

AP7

AP8

An organization’s process and performance can be positively affected by resource flexibility and coordination flexibility Ambidextrous organizations can make improved strategic collaboration with partners and this way, they can effectively assimilate their knowledge in the shared project For an organization’s sustainable well-being, stakeholders’ commitment can be assured by an ambidextrous approach to the management of stakeholders Ambidextrous organizations have a better balance between sustainability, profitability and employers’ well-being

Evidences of agile organization principles in ambidextrous organizational paradigm

Hahn, Pinkse, Preuss, and Figge (2016) and Fu et al. (2019)

Fu, Chen, Huang, Li, and Köseoglu (2019)

Lin et al. (2007) and Heckmann and Maedche (2018)

Wei, Yi, and Guo (2014), Kortmann et al. (2014), and Heckmann and Maedche (2018)

Pertinent literature

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The eight pieces of evidence summarized in Table 3.1 reflect the same observations made by Lu and Ramamurthy (2011), who focused on organizations that were striving for organizational agility. While pursuing organizational agility, there are two possible outcomes: market capitalizing agility and operational adjustment agility. Market capitalizing agility defines the ability of an organization to react quickly and exploit any changes to address customers’ needs via uninterrupted monitoring and quick improvement of product/service. The latter, operational adjustment agility is the ability of an organization to physically and swiftly cope with changes in the market or demands within its internal business processes. Like previous studies, the eight pieces of evidence in Fig. 3.1 combine to form the two sides of agility in an organization as descriptive characteristics of market capitalizing agility and operational adjustment agility (Dove, 2001; Lu & Ramamurthy, 2011). Hence, by following the agile principles, an organization pursuing ambidexterity may streamline its operations while being capable to better identify opportunities emerging in the market. Obviously, such capabilities require a paradigmatic cultural shift occurring within the organization. In fact, most of the people need to collaborate so that they can make everything work as expected and need to be coordinated to ensure that agility and ambidexterity resist during challenging times. Therefore, with the increase in the environmental dynamism, because of the dual capacity of exploration and exploitation, an ambidextrous

Agile Organization Market Capitalizing Agility

Operational Adjustment Agility

AP1-AP2-AP6-AP7-AP8

AP3-AP4-AP5

Fig. 3.1 Market capitalizing agility and operational adjustment agility and their role in shaping an agile organization (Source Authors’ elaboration)

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organization shows more possibility of gaining benefit from it (Lee et al., 2015). Consequently, two main results of the organizational pursuit of agility—market capitalizing agility and operational adjustment agility— may be considered important so that the organization can take complete benefit of exploring and exploiting opportunities efficiently and simultaneously (Sambamurthy, Bharadwaj, & Grover, 2003). However, several studies have emphasized the key role played by IS in enabling the required workflow of information to completely promote agility in the entire organization. Consequently, it is difficult to identify differences between an ambidextrous organization with a high degree of agility and an ambidextrous organization with an efficient and flexible IS (Morris & McManus, 2002; Tallon & Pinsonneault, 2011). This is especially true in the present digital era which features the growing requirement of expertly collecting and exploiting the ever-increasing quantity of data. The next section explores the role of big data in BPMg and the importance of BDA capable IS (Gao, 2013) for the agility of ambidextrous organizations.

3.2.2 Big Data, Business Process Management Systems and Ambidextrous Organizations’ Agility Management information systems are crucial for any organization. The systems are necessary to collect, organize and analyze data generated from processes and operations. Proper information flows are necessary to make everyone working within the organization aware of what’s going on and accordingly take better decisions. In particular, such information flows are extremely useful to help managers deal with different situations. For organizations pursuing ambidexterity through agility, these systems are even more important than for static organizations. Particularly, they provide the information required to identify opportunities emerging in the market and to inform the right person within the organization. Ambidextrous agile organizations are established on the use of information that ensures teamwork, allows the management for new

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projects, and ensures that everyone is acting correctly. Information and information systems are found to be related to dynamism. In the big data era, the situation is a bit different. As previously explained, big data are datasets that are large, heterogeneous, and unstructured and they cannot be reviewed or analyzed by typical methods and tools (Waller & Fawcett, 2013; Yin & Kaynak, 2015). In this perspective, organizations wishing to obtain agility and ambidexterity should implement BDA capable IS (Hartmann, Zaki, Feldmann, & Neely, 2016). Multiple implementations of BDA capable IS within organizations that were reported to be successful indicate that big data is the next revolutionary aspect in management (McAfee & Brynjolfsson, 2012). Big data analytics tools give managers the ability “to measure and know radically more about their business and to directly translate that knowledge into decision making and performance” (McAfee & Brynjolfsson, 2012, p. 4). Fast and effective decision making by managers may be enabled by BDA systems (Bresciani, Ferraris, & Del Giudice, 2018; Provost & Fawcett, 2013). According to Davenport (2014), with the help of big data, the internal processes can be streamlined by lowering the number of bottlenecks which will improve their efficiency. Hofacker, Malthouse, and Sultan (2016) report that consumer behavior patterns can be identified by the managers on the basis of insights from the customeroriginated big data, and they can then tailor the products and their prices as per the identified preferences of the customer. As the organizations adopt the BDA capable IS, it may lead to organizations gaining better customer-centricity, optimization of operations, better management of risk, improved utilization of workforce, and even new business models (Bughin, Chui, & Manyika, 2010). Scholars and practitioners of information systems have emphasized the importance of integrating BDA capabilities into business process management systems (BPMS) to reveal the potential of big data (Gao, 2013; Wamba & Mishra, 2017). BDA capable of BPMS are a particular kind of BDA capable IS that span the complete organization, are capable of accumulating a large quantity of data from processes that are involved in the business, and can analyze those in real-time and communicate

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results to all the stakeholders (Wamba, Dubey, Gunasekaran, & Akter, 2019). BDA capable BPMS has several features in common with traditional BPM systems that aim for fostering efficiency, innovation, and effectiveness, but it is different in certain fundamental aspects. Implementing BDA in BPMS may give “better real-time situational awareness to process participants and have the ability to tailor their responses appropriately” (Gao, 2013, p. 4). According to many scholars, to leverage big data, BDA capable BPMS should be analytical , automatic , adaptive and agile (Clarke, 2016; Dezi, Santoro, Gabteni, & Pellicelli, 2018; Vera-Baquero, Colomo Palacios, Stantchev, & Molloy, 2015; Vossen, 2013). BDA capable BPMS should also give the system the ability of advanced analysis while data is being automatically processed from many different business processes (Wamba et al., 2019). In addition, this system can develop inter-organizational information exchange if it is applied in two or more organizations that are related (Vera-Baquero et al., 2015) and such BPMS will have the ability to adapt to situations that are continuously changing and complex data that is evolving. Organizational flexibility, agility, and dynamism can be potentially fostered by the BDA capable BPMS. Specifically, such systems may give better customer satisfaction, quick responses to changes in the market and improved collaboration within the organization (Braganza, Brooks, Nepelski, Ali, & Moro, 2017; Gao, 2013). Therefore, the implementation of BPMS is associated with the organizations’ market capitalization agility. It is also possible to observe relationship of BPMS with operational adjustment agility (Lu & Ramamurthy, 2011). Simultaneous analysis of data emerging from multiple internal business processes may be done by BDA capable BPMS. As a result of such analysis, the efficiency of internal business processes can be improved by the managers. Organizations’ exploration and exploitation capabilities may potentially be improved by the BDA capable BPMS (Sivarajah, Kamal, Irani, & Weerakkody, 2017). For ambidextrous organizations, information systems have been extensively studied but the topic of BDA capable BPMS has not been focussed on to such an extent. Because of the inherent features of BDA capable BPMS, these systems may be responsible for increasing the pursuit of

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agility by ambidextrous organizations. Market capitalization and operational agility may also be improved by these systems. The next section is dedicated to explaining the development of a conceptual framework showing the significance of BDA capable BPMS.

3.3

A Conceptual Framework on Big Data, Business Process Management Systems, and Agility

In this section, the significance of BDA capable BPMS for the agility of an ambidextrous organization and the outcomes of implementing these systems are discussed. The propositions underlying the developed model will also be explained, followed by the conceptual framework. As discussed earlier, the competitive landscape has been dramatically changed by big data. The competitive edge for organizations may be gained by adopting information systems that are able to extract meaningful information from datasets that are unstructured. In this sense, most of the literature has stressed how, in order to exploit the potential of big data availability, it is important to develop flexible BDA capable IS. Indeed, BDA capable IS is one of the three constituent BDA capabilities. Among this aforementioned IS, BDA capable BPMS emerges as a primary solution for this (Vossen, 2013) as it is capable of collecting data from different processes in business, analyzing them, and providing the right information to the appropriate users who are associated with business process management (Gao, 2013). Data can be managed by organizations in a better way if BDA capable BPMS is adopted. They may collect data from customers and external stakeholders allowing them to observe existing customers’ activities and interactions on an online platform and, additionally, to gather feedback on products (Hofacker et al., 2016). Customers’ satisfaction can be increased by using BDA capable BPMS as organizations can provide tailored products and services to them. Consequently, these systems play a key role in improving the reactiveness of organizations toward changes in the market (Sivarajah et al., 2017). While considering stakeholder

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management, interactions can be monitored by these systems which can result in process improvement and giving organizations the ability to do real-time data analysis (Hahn et al., 2016). Thus, thanks to BDA capable BPMS, organizations could also ensure stakeholder’s responsibility and develop new CSR practices. On the other hand, BDA capable BPMS can also be used to detect obstruction in internal business processes, like production processes or processes utilized for sharing information, because they have analytical capabilities and by adopting internal business processes management perspective. According to Zain, BDA capable BPMS can improve organizations’ dynamism resulting in improvement in the decision-making process speed and thereby, they could support the pursuit for agility by ambidextrous organizations as it is associated with the concept of quickness in the decision making process and subsequent actions. Therefore, these systems could also give real-time operational adjustment and an improvement in efficiency (Carillo, 2017). Based on these considerations, the following is proposed: P1: BDA capable BPMS could foster ambidextrous organizations’ agility. According to Lu and Ramamurthy (2011), an ambidextrous organizations’ pursuit of agility is associated with and characterized by the attention of an organization toward its customer’s satisfaction level, the ability to react swiftly to any change in the market, and the existence of an effective means of direct collaboration with partners. For ambidextrous organizations targeting to achieve market capitalization agility, it is essential characteristics that they should be capable of identifying and capitalizing on opportunities in emerging markets (Gandomi & Haider, 2015). Similarly, an ambidextrous organization with a high degree of agility may possibly attain operational adjustment agility. Organizations may be able to identify internal processes’ inefficiencies and adjusting the quantity of production based on evolving needs if they are dedicated to embracing dynamic BPM and to achieving technical and organizational flexibility (Wei et al., 2014). According to Lu and Ramamurthy (2011), there is a potential for a higher level of production efficiency in such organizations. There are two possible results of the pursuit of agility by

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an ambidextrous organization: market capitalizing agility and operational adjustment agility (Gothelf, 2014; Sivarajah et al., 2017). The possibility of recognizing opportunities in the market and acquiring the benefits are the result of the agility of ambidextrous organization connected with its exploration and exploitation capabilities (Junni et al., 2013). Therefore, the next propositions are: P2: Ambidextrous organizations pursuing agility may achieve market capitalizing agility. P3: Ambidextrous organizations pursuing agility may achieve operational adjustment agility. For any ambidextrous organization pursuing organizational agility, an important role as facilitator may be played by information systems such as BDA capable BPMS (Morris & McManus, 2002; Tallon & Pinsonneault, 2011). These systems give organizations the ability to collecting and analyzing information from both sources, external and internal, simultaneously (Nazir & Pinsonneault, 2012; Warren, Moffitt, & Byrnes, 2015). Subsequently, BDA capable BPMS are essential for improving the speed of the process of decision-making and reaping the benefits that arise from the pursuit of agility (Davenport, 2014). BDA capable BPMS’ analytic capabilities allow them to detect market opportunities or production process inefficiencies and to adapt themselves to increasing environmental complexities ensuring their functionality over time (Gao, 2013; Wamba & Mishra, 2017). Automation of these systems enables the information workflow needed by ambidextrous organizations (O’Reilly & Tushman, 2013). Therefore, market capitalization agility and operational adjustment agility may be achieved by an ambidextrous organization characterized by a high level of agility (Lu & Ramamurthy, 2011). BDA capable BPMS may allow an ambidextrous organization to better identify opportunities in the market, exploit them and adjust internal processes (Overby, Bharadwaj, & Sambamurthy, 2006); facilitating parallel functions of exploration and exploitation (Gao, 2013; Wamba & Mishra, 2017). In the present environment where the data analytics capabilities are essential, this statement is particularly true (Chen et al., 2014). Therefore, the following is proposed:

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P4: BDA capable BPMS may act as a facilitator for ambidextrous organizations characterized by a high degree of agility to achieve market capitalizing and operational adjustment agility. Additionally, dynamic business process management and adoptive innovative information systems are more likely to be embraced by an organization that has achieved operational adjustment agility (Janssen, van der Voort, & Wahyudi, 2017). It is hypothesized that ambidextrous organizations characterized by a high level of agility may be more prone to adopting innovative business process management systems or to improve existing ones (Kohlborn, Mueller, Poeppelbuss, & Roeglinger, 2014). Therefore, the proposition is: P5: Organizations that have achieved operational adjustment agility are more disposed to innovate current process management systems or to adopt more innovative ones. In the end, we assumed that market capitalizing agility and operational adjustment agility could influence overall organization performances. Indeed, an organization capable to detect opportunities arising in the environment while re-adjusting its internal productive capacity could have better performances than its competitors. Such performances may also be more sustainable over time. Therefore, the proposition is: P6: Market capitalizing agility and operational adjustment agility could influence performances. These 6 propositions have been used to develop the following conceptual framework (see Fig. 3.2). In addition to this, in the following framework we have also shown all the three capabilities together (and the position of BDA capable BPMS among these), their impact on organizations’ characteristics, and the potential final linkage between market capitalizing agility and operational adjustment agility and improved organizational performances.

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BDA Managerial Capabilities BDA Personell Capabilities

BDA Capabilities

P5

Evidences of Ambidextrous Organizations’ Agility

BDA Infrastructure Flexibility Which is related to BDA capable IS like

Organizational Outcomes AP1 - Develop customers' satisfaction AP2- Rapidly respond to market changes AP6- Effective strategic collaborations with partners AP7- Ensure Stakeholders Responsibility AP8- CSR Practices

BDA Capable BPMS That could deal with BP data and communicate information to the right actor Analytical - Automatic Adaptive - Agile

P1

E3- Achieve org. flexibility E4- Achieve tech. flexibility E5- Embrace dynamic BPM

Outcomes of Ambidextrous Organizations’ Agility

P2

P3

Market Capitalizing Agility

Operational Adjustment Agility P4

P6

Improved Organizational Performances

Fig. 3.2 The importance of BDA capable BPMS for ambidextrous organizations’ agility

3.4

Insights Emerging from the Developed Framework

3.4.1 Theoretical Implications for Researchers During the development of the framework, several preliminary insights were obtained on the significance of implementing BDA capable BPMS in an ambidextrous organization to improve its agility. It has emerged that systems that are able to collect unstructured data and analyzing them in real-time, may give ambidextrous organizations looking to increase

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their dynamism certain competitive advantage (Vossen, 2013). BDA capable BPMS may offer the informative potential of big data (Gao, 2013) and managers may receive real-time information regarding any changes happening within the organizations and at the levels of customers and partners (Wamba & Mishra, 2017). The continuous flow of information enables managers and individuals involved in process management to act swiftly and implement strategies in response to environmental changes, for the organization’s benefit (Provost & Fawcett, 2013). Because of their inherent characteristics, BDA capable BPMS may increase the flexibility of an ambidextrous organization. Since BDA capable BPMS can adapt automatically to different situations, they may prove to be the ultimate savior for the organization’s survival in the current competitive environment. The flow of information from such systems allows managers to know their business and competitors, better than before (Wamba et al., 2019). Such systems may also allow managers to know their customers well, better collaboration amongst partners, and embrace an internal and external BPM that is constant (Janssen et al., 2017). Such BPMS may increase the agility of ambidextrous organizations and, as a result, promote the achievement of market capitalizing agility and operational agility (Nazir & Pinsonneault, 2012). An organization capable of achieving market capitalizing agility and operational agility may identify market opportunities. Based on these opportunities, the organization may adjust its internal production in response to the demand. BDA capable BPMS may, thus, increase capabilities for exploration and exploitation of an ambidextrous organization (Wamba & Mishra, 2017). Two streams of literature exploring (i) the importance of BPMS (Wamba & Mishra, 2017) and (ii) the characteristics and potential of organizational ambidexterity (Lee, Kim, & Joshi, 2017) contribute to these findings. The main contribution to the first literature stream is about better identification of the potential and the characteristics of BDA capable BPMS, which has been quite neglected in the literature about BPM with few exceptions (Kohlborn et al., 2014; Mishra, Luo, Jiang, Papadopoulos, & Dubey, 2017; Motamarri, Akter, & Yanamandram, 2017). These manuscripts supplemented the literature on the ambidextrous organization by giving some understandings on the significance

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of BDA capable BPMS in increasing dynamism and achieving market capitalizing and operational adjustment agility (Vossen, 2013). Exploration and exploitation capabilities of ambidextrous organizations may be increased by such BPM systems (Gao, 2013).

3.4.2 Preliminary Suggestions for Managers During the implementation of such BPMS some challenges may emerge, although BDA in general and capable BPMS in particular have the potential of increasing ambidextrous organization agility. Most of these criticalities are immediately observable if we think about the characteristics of BDA capable BPMS. Hence, it is first essential to understand the technical challenges related to BDA capable BPMS for appreciating the significance of coupling agility of ambidextrous organizations with BDA capable BPMS. It is critical to completely understand the origins of big data and the role played by information systems in organizations. As compared to small data sets, big data is different in terms of its analytics architecture, procedures, tools, underlying data processes, and methods (for big data, data lake is used rather than data warehouse) (LaValle, Lesser, Shockley, Hopkins, & Kruschwitz, 2011). For example, instead of relational data model, schema-based data retrieval, static data, and monolithic systems with vertical scaling that are generally used with small data, big data uses NoSQL (Not Only SQL) data model, schemaless data retrieval, real-time data, and data system architecture that is horizontally scaled (Vossen, 2013). Because of all these differences, there is increased complexity and it is a substantially challenging task to manage and process big data (IBM, 2012). While adopting BDA capable BPMS for pursuing agility, the most common mistake made by organizations is that they believe BDA as an architectural design where the database is considered as a static repository and it has fixed schemas and it does not have any dynamic dimension (Yin & Kaynak, 2015). The first step is to understand the organization of big data so that the best architectural approach can be identified. Usually, an engine (e.g., Hadoop, Apache Spark, etc.) for processing big data is implemented initially. It has the

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required physical infrastructure, which comprises of complex configurations of servers, large storage, and other components needed to run and process the big data. With growing data, more resources (e.g., hardware) will be required, which means that the organization must go for the iterative and time-consuming processes of buying more resources. In order to avoid all this accumulation of resources, a trend has been witnessed where organizations are turning to the use of cloud through cloud providers (e.g., Amazon AWS). According to Lu and Ramamurthy (2011), the cloud provides an agile infrastructure where based on the demand, resources are pulled in or out. This is a favorable approach from the infrastructure standpoint when organizations consider pursuing agility. In order to deliver on several premises, the agility of an ambidextrous organization must be integrated with big data successfully. There is an ongoing discussion on this relationship between agility and architectural design (Chen, Kazman, & Haziyev, 2016). As explained above, the BDA infrastructure building processes characterize an “up-front” approach in which everything needs to be arranged before the analysis of data. This is a sort of contradiction with the connotation of agility itself (Bellomo, Gorton, & Kazman, 2015). The alignment between the two worlds is shown in studies by Gao (2013) and Chen et al. (2016). The iterative nature of the big data is well supported by agility. For implementing and following this iterative nature of the big data, one potential way is to develop systems that are easy to iterate, modify, adapt, or replace. This is in line with the ability of the most BDA capable BPMS to adapt. As organizations wish to combine agility with big data, a cultural shift is important. They need to look beyond their current thoughts and go for exploration and change beyond the present state of mind. This will improve speed and reduce costs in the organization as the organization will achieve flexibility related to agility. We can refer to the aforementioned as “big data culture” where the employees’ mindset needs to be adapted to the new approach and the entire organization has to work on it (Lu & Ramamurthy, 2011; Wamba & Mishra, 2017). For example, if a bank wants to launch new services and products and remain competitive, a new model of working by implementation and launch of digital initiatives needs to be adopted before

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their competition adopts them (Waller & Fawcett, 2013). If a new technology infrastructure implementation has to be speedier than the implementation by traditional means, then it must be implemented with a data lake that would contain data that is structured and unstructured, e.g., Twitter data (Caputo, Marzi, & Pellegrini, 2016). To create systems that would respond to particular requests by users and their needs, the alignment between the IT department and the business team is very crucial (Gao, 2013; Wamba & Mishra, 2017). Most importantly, the entire process must be sufficiently iterative so that there is no loss of time between different trials and implementations. Despite such problems, an ambidextrous organization would be able to implement systems, which will provide managers with a real-time glimpse of customer’s data (internal or external). This will bring the well-defined competitive benefit to an agile organization as it will give them the ability to get real-time information about any service, product, or procedure that the organization wants to improve or implement. This falls in line with the above-hypothesized consequences of market capitalizing agility and operational adjustment agility (Wamba & Mishra, 2017). Hence, a BDA capable BPMS that is designed, projected and implemented properly, may result in an increase in the agility of an ambidextrous organization and allows achieving market capitalizing agility and operational adjustment agility. This is achievable because of the dynamism and the ability to extricate information from such systems (Chen et al., 2014). The partnership between BDA capable BPMS and ambidextrous organization will remain effective till the organizations are capable of doing a cultural shift and following the right implementation stages that will utilize dynamic capabilities of big data (Erevelles, Fukawa, & Swayne, 2016).

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Going a Bit Further, the Need for Empirical Evidence

This section identifies the main characteristics of BDA capable BPMS and the effects of these on ambidextrous organization agility. It also discusses the function of such systems in increasing dynamism and reactiveness in response to changes in the environment of the organization and, as a result, organizational agility (Zain et al., 2005). The implementation of such BPM systems shows that the principal result is the achievement of market capitalization and operational adjustment agility (Lu & Ramamurthy, 2011). Within an ambidextrous organization, increased exploration and exploitation capabilities are associated with the implementation of BDA capable BPMS. Particularly, the attention is drawn on the significance of integrating BDA with BPM by providing some theoretical evidence. The findings are, however, limited in several aspects. The principal limitation is the fact that the theoretical framework is based on the findings from present studies. In order to simplify the results, it is essential to inspect the phenomenon by observing what is occurring in the real world. In fact, on the one hand, it is required to focus on the implementation of BDA capable IS as a whole ensemble of tools (and thus not only BDA capable BPMS). On the other hand, it is necessary to quantify the relationship between BDA capabilities, ambidexterity, and organizational dynamism. It will also be imperative to observe the effect of all these elements on organizational performances. This is done in the next chapter.

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4 Agility Through BDA and Ambidexterity: Some Empirical Evidence from Managers’ Experiences

Abstract This chapter describes the micro-linkages that exist between Big Data Analytics (BDA) capabilities and an organization’s pursuit of strategic agility. One of the factors that influence this relationship is organizational ambidexterity. In order to assess this, dynamic capabilities are considered as the main theoretical framework. Multiple regressions are the main methodological approach used for this. The developed structural model is used to test on 250 survey responses collected from managers of large European organizations. The results show that the capabilities of BDA are an important precursor of an organization’s strategic agility. In this sense, managers, who want to exploit the potential of big data completely, must invest in the improvement of procedures of knowledge management across their organizations. Keywords Big data · BDA capabilities · Knowledge management · Ambidexterity · Strategic flexibility

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The Need for Empirical Evidence

As stressed in the previous chapters, big data are an extremely large amount of unstructured data that is accessible everywhere in real-time and cannot be analyzed by using conventional methods because of its intricacies. In addition to this, using evidence acquired from big data, managers can make better decisions (Ferraris, Mazzoleni, Devalle, & Couturier, 2018). The existence of big data is related to superior organizational performance in terms of ambidexterity, flexibility, and agility (Rialti, Marzi, Silic, & Ciappei, 2018). The current chapter presents a research setting and framework inspired by ongoing studies by the authors of the present book (Rialti, Marzi, Caputo, & Mayah, 2019). What is clear at this point in the book is that Big Data Analytics (BDA) is required so that big data can be analyzed. According to a report by Wamba et al. (2017), organizations are tremendously impacted by BDA and through BDA, data use, processes, possible bottlenecks in workflow, and performance of employees can be monitored by the managers (Akter, Fosso Wamba, & Dewan, 2017). Comprehensibly, powerful computational techniques are required so that trends and patterns can be established from the large socio-economic datasets (Labrinidis & Jagadish, 2012). From the BDA, data comparison can result in the extraction of new insights that are different compared to what can be obtained from static data sources like surveys, official statistics, and archival data (George, Haas, & Pentland, 2014). While many researchers have observed the importance of BDA capabilities, but for the use of big data, an empirical exploration of the phenomenon is missing. This can be attempted through the opinions of managers dealing with big data. In particular, empirical evidence is needed to better explore the linkages existing between BDA, ambidexterity, and agility. While it has been noticed that BDA systems and overall capabilities may help managers in obtaining the information, they require to manage complex environments, the link between BDA and agility needs to be properly explored.

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In order to do so, multiple regressions are used to analyze whether BDA influences the explorative and exploitative capabilities of an organization, which are elements of ambidexterity. It is also required to observe how an increase in explorative and exploitative capabilities may influence agility and performance. The objective of this final chapter is to specify a validation to the hypothesized framework. Finally, guidelines about the definitions of the notion of ambidextrous and agile organizations in the big data era will be provided.

4.2

BDA Capabilities, Ambidexterity, Agility, and Organizational Performance

Modern businesses need BDA to manage big data and exploit the opportunities emerging from the market. BDA has accordingly been seen to have a remarkable effect on organizations (Wamba et al., 2017). Data’s dimensions necessitate the usage of new computational methods to detect trends and establish patterns within these extremely large socioeconomic datasets (Labrinidis & Jagadish, 2012). Thanks to the use of BDA, new insights can be obtained from these datasets. This is particularly true when it is compared to official statistics, surveys, and archival data sources, which tend to remain static fundamentally (George et al., 2014). Hence, BDA could be extremely useful also in the case it is necessary to compare dynamic information with the static one. In detail, it may be possible to integrate static information with dynamic insights, thus creating an auto-poietic circle of information generation. BDA also assists organizations (depending on the type of information at their disposal) to navigate, plan, compete with, and adapt to the continually changing business environment. As previously assessed, in order to fully obtain benefits from the informative power of big data, an organization should necessarily cultivate a series of extreme capabilities to master the insights that are given by big data (Rialti et al., 2018). Accordingly, it is important to ensure the development of integrated BDA managerial capabilities, BDA personnel

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capabilities, and BDA infrastructure flexibility (including BDA capable IS). While information about these three capabilities was provided also in the preceding chapters, it is time to explore them in detail in order to support the following analyses. 1. BDA Managerial Capabilities: These capabilities are related to managerial skills to deal with big data. Thus, they relate to previous managerial expertise concerning these datasets. In detail, a manager could be considered skilled in big data if (or when) he/she is knowledgeable about the main elements composing a big data infrastructure/architecture. As an example, a manager is skilled enough in big data if he/she is holding a certain degree of knowledge in: a. Big data characteristics (or the 7Vs); b. Sources of big data (i.e. social media, websites, embedded sensors in machines, mobile technologies, deep web, dark web…); c. Internet of Things (IoT; or the systems required to connect to the internet machinery usually involved in the production process or business processes); d. Data ingestion tools, or tools that allow an organization to collect data and to transfer them to a storage platform (i.e. Apache NiFi, Kafka, Gobblin, Flume…); e. Data storage platforms (like data lakes or cloud systems); f. Visualization layers (i.e. IBM Watson, Tableau, QlikView, PowerBI…); g. Analytics engines (i.e. Statistical analytics open-source software, text analytics tools, search engine…); h. Cybersecurity systems and procedures. Such capabilities are vital in making the choice and execution of the correct BDA infrastructure. Indeed, only a knowledge of the basics of big data science may allow a manager to select the right solution for a business. Next, they are necessary in the exact moment managers are harvesting information from the datasets. Individual capabilities from managers could in fact help them in understanding which data are more

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important than others, thus allowing them to take business decisions according to the new insights. Thus, the decision-maker must hold certain skills in order to determine a technical solution and understand the information extracted from the dataset. 2. BDA Personnel Capabilities: These capabilities are of the lower level; yet, they are indispensable within an organization. Employees with the necessary skills can identify the right data, analyze this data, and maintain the integrity of the company’s infrastructure (Wamba et al., 2017). The importance of these capabilities is intrinsically related to the fact that employees are the ones dealing both with data analytics procedures and systems management. On the one hand, indeed, once the data have been collected someone should deal with them and their analysis. Hereby, the degree of competence of data analysts, data scientists, data architects could represent a noteworthy competitive advantage. On the other hand, they should also be capable to detect any potential problem affecting the infrastructure. Thus, it is important to consider that the functioning of the systems is related to individuals’ capabilities to properly handle their complexity. 3. BDA Infrastructural capabilities/ Flexibility: This group of capabilities includes all the structural elements allowing the identification of sources of data, the collection of data, their storage, and the subsequent analysis. Therefore, they consist of all the information systems able to collect, cleanse, store, process, and analyze rigid or non-self-adaptable big data to different kinds of data, which would guarantee the flow of data in any state (Wamba et al., 2017). Indeed, it was stressed how organizations have formerly writhed to decipher large datasets by means of traditional data-mining techniques (Labrinidis & Jagadish, 2012). The effective use of the right BDA infrastructural tools (or architectures) is now essential in enabling organizations to distribute data formats in a well-timed way. Data complexity indeed requires custombuilt solutions frequently based on cloud computing and data lake for storage, cloud computing for processing, and open source software

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(i.e. Apache, SparQL) and coding language (i.e. Python, R) for the analysis. Yet, these infrastructures should not represent a burden on the organization implementing them. Here the importance of flexibility emerges. BDA infrastructure, whilst simultaneously allowing management to decodify and transform data in order to make decisions that improve organizational performance, should also be capable of transferring the correct information to the right person within the organization (Rialti et al., 2018; Vera-Baquero, Colomo-Palacios, & Molloy, 2013). In addition, in respect of flexibility, such systems should be scalable. BDA capable IS should then be capable to be modified to the changing needs of an organization when data changes in size and format. Finally, such systems should be reliable and capable to ensure operational continuity even amidst complexity. In this sense, these two latter characteristics have been related by research and anecdotal evidence to the fact that they are built on cloud-based platforms. In the case such systems are effectively built to be flexible, it has been observed how they could dramatically improve supply-chain management and marketing (Erevelles, Fukawa, & Swayne, 2016). While several types of research have explored the importance of BDA capabilities, previous studies have not focused on the empirical investigation of the effect of BDA capabilities on ambidexterity (perceived as exploitative innovation and explorative innovation), agility and performance. Hereby, thanks to the test of a conceptual model, the next paragraphs will contain an investigation of the phenomenon. Following the standard structure of this typology of researches, the next section will contain the hypotheses upon which the model is built.

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Validating the Theory and the Main Hypothesis by Empirical Investigation

4.3.1 Organizational Ambidexterity and BDA Capabilities As already observed, it is almost essential for organizations to separately establish their explorative and exploitative units in order to survive and compete successfully in a constantly changing and challenging environment. Such organizations fulfill the criteria of “ambidextrous organization” (O’Reilly & Tushman, 2013). Ambidextrous organizations efficiently explore their business environment and get a competitive edge over their competitors by exploiting potential opportunities. The exploration capability of an organization is measured in terms of its willingness to investigate, take calculated risks, recognize the environment, and take innovative measures to reach the level of exploitation. Exploitation also depends on an organization’s adaptability according to the environment, produce, improve and accomplish the tasks. It will be pertinent to observe that an ambidextrous organization should quickly respond to changes in their business environment while ensuring that their existing consumers remain adequately satisfied (Lubatkin, Simsek, Ling, & Veiga, 2006). Thus ambidextrous organizations have to be very agile in order to not only earn valuable profits from new opportunities but also ensure that they constantly monitor and value consumer’s opinions and intelligently respond to requests. Information plays the most vital role for an organization to achieve ambidexterity, and when right information is transferred to the right person in an organization, this information plus knowledge of consumer’s perceptions about the strategies, provide the required platform to transform the organization into an agile and ambidextrous organization (Wamba et al., 2017). The importance of Management Information Systems for ambidextrous organizations has been identified and explained in a large number of research papers (Scuotto, Del Giudice, Bresciani, & Meissner, 2017). Identifying the right opportunities to be exploited is possible only by useful information gained through BPMS and utilized by BDA capable

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managers and employees. Successful and expanding business organizations need to have information about their environment, market trends, competitors’ strategies, emerging and future changes occurring within the organization’s industry. Information is the prerequisite by which necessary knowledge to successfully run a business organization is gained. This knowledge is organized and established a set of justified beliefs to enhance organization’s performance by acting effectively and efficiently (Ferraris et al., 2018), and are the corner stones for successfully detecting new opportunities (O’Connor & Kelly, 2017). Similarly, this knowledge derived from useful information helps in decision making to exploit the new emerged opportunities. Agile organizations have set up knowledge management mechanisms and procedures along with management information systems in order to ensure that information is distributed to every relevant employee of the organization through proper knowledge channels. Efficient and effective management of knowledge includes technological dimensions such as knowledge discovery, collaboration, business intelligence, distributed learning, knowledge mapping, knowledge application, opportunity generation, and knowledge security (Al Ahbabi, Singh, Balasubramanian, & Gaur, 2019). Knowledge managed in this way serves as the linkage between BDA and ambidexterity. Currently, more attention is being paid to the interdependency between Knowledge Management and BDA (Obitade, 2019). BDA and KM complement each other by combining and utilizing the knowledge of business intelligence and human knowledge. Ambidextrous organizations share this combined knowledge and information with relevant stakeholders (Xu et al., 2016). Organizations then direct this time-specific knowledge, process customer’s information, and competitors’ information to get useful insight for exploring and exploiting the emerged opportunities. BDA has the capability to fetch digital and customized knowledge by using various tools such as search engines, web analytics, pay-perclick management, search analytics, and customer analytics (Xu & Duan, 2019). The same is true about gaining customer’s preferences knowledge. BDA is capable to provide new insight about customer’s preferences and opinions by generating new and accurate knowledge. It is easy to infer

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that BDA can guide the organizations to ambidexterity and ambidexterity, in turn, can improve an organization’s knowledge management capabilities. Hence BDA, while using a dynamic capability perspective, serves as a prerequisite of organizational ambidexterity. An organization equipped with improved access to information and properly trained staff has a competitive advantage to exploit the opportunities. BDA and Big Data culture encourages collaboration and distributed learning among employees of the organization. This will topple the communicational, operational, and geographical gaps in organizations, which have previously been their characteristics (Bedford, Bisbe, & Sweeney, 2019). Consequently, BDA has become an ideal tool for organizations seeking to make use of big data in order to better manage their resources to exploit opportunities already present in the market. Therefore, the main objective of any organization implementing BDA and big data culture is to improve organization’s capabilities by gaining new knowledge in order to secure a competitive advantage in the market. BDA enables organizations’ managers to generate, access, categorize, and interpret useful information and knowledge (Chen, Chiang, & Storey, 2012), leading to improved organizational management. In a nutshell, BDA capabilities have proved to be extremely helpful for organizations seeking to achieve ambidexterity. It means BDA capabilities are directly linked with the organization’s dynamic capabilities which are the soul of ambidexterity (Wamba et al., 2017). Thus, we hypothesize: H1: Organization ambidexterity is directly linked with BDA capabilities.

4.3.2 The Relationship Between Organizational Performances, Agility and, Ambidexterity It is a generally established fact among most of the organizational literature that ambidextrous organizations have a competitive edge over others in the market and can easily survive and adapt the changes in their business environment (Raisch & Birkinshaw, 2008). It is also considered true

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that an organization in order to be ahead of its competitors needs to be agile. Highly agile organizations are capable of managing their resources in a changing environment in order to remain successful. This emphasizes better management of resources and reconfiguration to survive and gain a competitive advantage in a constantly evolving market (Eisenhardt & Martin, 2000). Such an organization is always in a better position to identify new resources or potentially flawed technologies and techniques present in the market. An agile organization because of its experience in deploying the latest technologies and resources can quickly adapt to the changing environment and gains the upper hand in exploiting the new opportunities. As mentioned earlier the characteristic of being agile depends also on innate factors such as inclinations of organization’s managers to educate the employees, to facilitate them in improving their problem-solving skills, responding efficiently to different and challenging situations. Apart from these factors managers’ experience in managing their resources also plays a key role. Hence, agility is also directly linked with accumulated technological capabilities (Tuan, 2016). The information derived from this experience in resource management is part of the process of the explorative innovation. However, the process of exploitation derives its strength mainly from improved explorative and resource deployment capabilities for rapidly and efficiently responding to evolving market challenges (Lee, Sambamurthy, Lim, & Wei, 2015). Researches indicate that strategic agility is very helpful in identifying important changes in the market. This earlier identification of changes allows organizations to strategize their resource deployment leading to ambidexterity (Wei, Yi, & Guo, 2014). Ambidexterity becomes even more important because of its positive impact on the organization’s performance variables and the organization’s adaptability in a turbulent environment (O’Reilly & Tushman, 2013). So an ambidextrous organization can efficiently adapt and respond to market changes. Thus, we hypothesize: H2: Organizational ambidexterity derived from BDA could influence organizational agility.

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This notwithstanding, ambidexterity and agility could have an effect on modern organization performances (Dezi, Santoro, Gabteni, & Pellicelli, 2018). Indeed, such organization has the systems and capabilities (the BDA capabilities) to extract information from digital environments and transmitting them to the right actor while simultaneously being capable to re-deploy the existing resources. This could evidently represent a competitive advantage for a business, which will be, in turn, capable to achieve better performance (Mardi, Arief, Furinto, & Kumaradjaja, 2018). Hereby, we hypothesize: H3: As a consequence of the development of BDA capabilities modern ambidextrous organizations could achieve better performances.

4.3.3 Proposed Model Moving from the previous four hypotheses, we have developed a model (see Fig. 4.1). As it is possible to observe from the following figure, the focus of the empirical investigation relates to the exploration of the relationship between BDA capabilities and performances trough ambidexterity and agility. Thus, we aimed to demonstrate how the achievement of agility and ambidexterity derived from BDA capabilities (including information systems) could improve performance. H1 BDA Capabilities 1- Managerial Capabilities 2- Personnel Capabilities 3- Infrastructural Flexibility

Fig. 4.1 Research model

H2 Organizational Ambidexterity 1- Enhanced Exploration 2-Enhanced Exploitation

H3

Organizational Agility

Improved Performances

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Methods

Multiple regressions were the statistical tool selected as the main methodology of analysis. Indeed, such a methodology has been historically considered quite valuable in the case a researcher wishes to empirically explore the relationship between several variables organized as shown in the previous model. In order to collect the data necessary for the analysis, first of all, the authors collected responses from several Italian managers dealing with big data and BDA. The very first phase of the analysis then deals with the identification of the right subjects for this analysis while developing the survey. Then, we ran a confirmatory factor analysis to develop variables from existing items. Finally, we tested the relationships existing within the variables.

4.4.1 Sampling To empirically exploring the phenomenon shown above, a survey was conducted on selected managers working in Italian organizations. Because of the nature of the research, the authors selected managers only from big organizations as these are the only organizations which can invest to develop required BDA capabilities and have the first-hand experience of utilizing big data (De Mauro, Greco, Grimaldi, & Ritala, 2018). Some of the screening criteria of the survey were experienced in handling big data and in BDA, respondents’ designation in the organization, and industry. The authors were particularly trying to get information about the manager’s experiences relevant to big data from the manufacturing industry. This approach was helpful in getting relevant data in a relatively short period of time. The authors also hired a research company for collecting the data. Additionally, the use of a market research company has been recommended by existing research in business and management (Sarmento & Simões, 2019). Regarding the context of our research, we selected Italy

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for several different motivations. On the one hand, it is the home country of both the authors; therefore, it was deemed easier to contact managers and to obtain responses from them. On the other hand, Italy is right now one of the few countries where businesses are investing the greatest amount of resources to analyze big data. Indeed, the government is providing relevant incentives to technology-intensive businesses (repetition, already used in the previous two sentences). Moreover, Telecommunication infrastructure in Italy allows for fast and effective data generation and transfer, thus proving more opportunities for companies wishing to exploit them. At the end of July 2019, 250 completed surveys out of 555 were returned. A response rate of 45.10% was achieved, in line with previous research targeting managers. In order to get additional information about the respondents, we considered 6 control variables (gender, age, education, experience, industry and business turnaround). Such 6 control variables were used as they were the most diffused in several kinds of research on related topics (i.e. Wamba et al., 2017). As far as the structure of the sample is concerned, it is the following: a. Gender: 120 respondents were men (48%) and 129 were women (52%). b. Age: most of the respondents aged between 30 and 39 (100 of them, the 40%), the rest instead were mostly between 40 and 49 (55) and older than 50. Interestingly, 75 of the respondents were less than 25. By triangulating the data, it emerged how these were mostly junior managers or entrepreneurs. c. Instruction: most of the responders hold a bachelor’s degree (117 out of 250, 46.8%). 75 out of 250 stopped their instruction period at high school. Only 62 respondents hold a master’s degree (44 of them) or a Ph.D. (18 of them). In this perspective, while it is true that big data management requires education, it was observed (again by triangulating the data) that most of them compensated for the lack of a master degree with experience within businesses. d. Experience: Half of the respondents (125, 50% of them) hold more than 10 years of experience in management information systems. 122

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Table 4.1 Sample characteristics Control variable Industry Adult/college education Broadcasting Computer/electronic Electricity/oil and gas Finance/insurance Hotel and tourism Information/data processing/communication Manufacturing Marketing Retail and wholesale Scientific/technical services Pharmacy/healthcare in general Telecommunication Other Organization’s turnaround 50–99 million (e) 100 million–1 billion (e) More than 1 billion

Frequency 2 1 28 8 20 6 27 16 12 31 43 29 14 22 192 43 15

of them reported between 1 and 10 years of experience. 3 of the showed less than one year of experience in the industry. Collecting information about the experience has been fundamental. In fact, the experience is fundamental to develop problem-solving skills which may represent a relevant advantage in respect to dealing with new problems like the one emerging while developing BDA solutions. In the end, we also investigated from which industry most of the respondents were coming from. Information about this data is provided in Table 4.1.

4.4.2 Measures The survey that was administered on behalf of the authors consisted of 73 items, 6 of which were the aforementioned control variables. Respondents rated every single item using a 7 points-Likert scale: 1 was labeled as “strongly disagree” and 7 was labeled as “fully agree”.

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The 49-items scale developed by Wamba et al. (2017) was used to measure Organizational BDA capabilities. Such a scale is organized as follows. 11 items concern BDA Infrastructure Flexibility; as an example, we asked managers to rate whether “Software applications can be easily used across multiple analytics platforms”. 21 items measure BDA management capabilities, the exemplary item includes “I constantly monitor the performance of the analytics function”. Finally, 17 items were used to assess BDA personnel capabilities. An explicative question may, in this case, be “Our analytics personnel are very capable in decision support systems (e.g., expert systems, artificial intelligence, data warehousing, mining, marts, etc.)”. Then, the authors measured ambidexterity through the 8-items scale created by Jansen, Tempelaar, Van den Bosch, and Volberda (2009). Such a scale explores ambidexterity through the analysis of a business explorative and exploitative innovation capacity. The original developer suggested that the item should be treated as an aggregated construct, and this method was followed. An example of the items contained in this scale may be represented by the following one “We frequently utilize new opportunities in new markets”. Organization agility, finally, was assessed using the 6-item scale by Cegarra-Navarro, Soto-Acosta, and Wensley (2016). Among the items, an explicatory one may be “We continuously search for forms to reinvent or redesign our organization”. Finally, the performance was measured using the scale from Gibson and Birkinshaw (2004). The scale consists of 3 items, among these ones, a relevant one may be represented by “The organization it’s achieving its full potential”.

4.4.3 Statistical Analysis and Hypotheses Testing The authors used SPSS v.25 and its plug-ins as the main methodological instrument to run the statistical analysis of the variables and to tests the hypotheses. First, we conducted an analysis of the individual items building our constructs and then ran a confirmatory analysis. Ambidexterity and BDA capabilities are first or second-order constructs. We used AMOS v. 25 to conduct CFA as recommended by (Arbuckle, 2013). The authors also

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evaluated composite reliability (CR) for each latent variable in order to evaluate the internal consistency of various indicators. All the CR values exceeded 0.6 as required (Bagozzi & Yi, 1989). Moreover, all the Cronbach’s α for each individual variable, including the ones present in the first and second-order constructs were evaluated. Since all the variables fulfilled the acceptable requirement (α > 0.70), the authors moved to the next step of the analysis. Moving from these preliminary findings, the regression analysis was performed. Regression analysis was performed as it is a simple, while powerful, procedure to observe how two or more variables of interest related each other. In particular, regression analysis is particularly useful when a researcher wishes to explore the relationship between a dependent variable (which is the variable that we are wishing to predict—in our case represented by performance) and independent variables (which are the ones hypothesized to have an impact on the dependent one—in our case ambidexterity, agility and performance). Accordingly, we proceeded with the analysis itself. To what concern the first variable a relationship exists between BDA capabilities and Ambidexterity (R 2 = 0.565; β = 0.752). The first hypothesis is then confirmed and BDA capabilities influence ambidexterity as was hypothesized. Next, it emerged how ambidexterity influences agility (R 2 = 0.691; β = 0.831). Thus, the second hypothesis is also confirmed and ambidexterity deriving from BDA capabilities could influence agility. Finally, it was observed how agility influence organizational performances (R 2 = 0.358; β = 0.599). Thus, also hypotheses three were also supported.

4.5

Discussion and Managerial Implications

According to the study of BDA, it has been found that BDA can play a very important part in enhancing the capabilities and productivity of big organizations. Similarly, ambidexterity is also equally important in such organizations for the ability to handle the problems of the current

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era as well as having the capability of adapting to whatever will happen in the future. The process is carried out by employing useful information garnered through the internally available big data datasets. Big data datasets contain vast amounts of complex and intricate information about business and many different natured costumers. BDA capabilities are critical in organizing scattered data into useful information in the forms of different trends, predictions, forecasts, etc. (Erevelles et al., 2016). The BDA capabilities allow organizations to have a better understanding of businesses, competitors, and customers (Chen et al., 2012; Xu et al., 2016). Consequently, the ambidexterity is the utmost benefit that the businesses can reap from the big data as it allows them to adapt to the fast approaching and distant changes in trends in industry as found in the study (Caputo, Marzi, & Pellegrini, 2016; Rialti et al., 2018; Trequattrini, Shams, Lardo, & Lombardi, 2016). Finding the empirical relationship between BDA capabilities and strategic agility is the research purpose of this study hence contributing toward the current work on this topic. This discussion helps to understand the issues in a much innovative manner because all the previous studies on this subject have either focused only on explaining BDA theoretically (Rialti et al., 2018) or have directed all the resources in exploring different tenets of agility like business agility, team agility etc. (Dubey, Gunasekaran, & Childe, 2018). In this research on agility, the study develops on the relationship between ambidexterity and different types of agility. Then it moves on to examine the importance of the BDA capabilities and reviews how it extracts the right information from big data and then disperses it across the organization. This ability to extract the data and raw knowledge from big data, processing it to make the information proper and relevant and then disseminating it to the concerned managerial and executive quarters is one of the most critical capabilities of BDA. Managers are also expected to pay very keen attention to the process of flowing-in of data and then to be able to develop such structural arrangements that may enhance the BDA capabilities of the organization. This will diminish the hitches during the process of practically employing BDA capabilities (Gupta & George, 2016). Therefore, BDA capabilities are the key to increasing production and efficient performance because

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they enhance the ambidextrous practices of an organization. Due to this reason, managers should employ both BDA capabilities and ambidexterity practices which ultimately yield every kind of agility. In short, it can be maintained that this sheds light on the fact that the agility; particularly strategic agility, can be achieved by employing BDA capabilities at every managerial level in an organization. Therefore, for this very reason, expert and smart managers make sure that their organization invests in these capabilities and that every employee of the organization has access to the appropriate information that comes out of this system. For example, managers might develop a data lake where information about every matter of the organization is available. The employees may approach this data lake and the partners of the organization and use it for the benefit of the organization. Through this process of disseminating the knowledge throughout the organization and even outside it, the organization makes the work of its employees easier by making relevant information available to them, which allows them to use innovative ways to complete their tasks (Gupta & Giri, 2018). Therefore, by only investing in BDA capabilities, organizations can reap both benefits of ambidexterity as well as agility. In summary, the best managerial practices emerging from the current study can be summarized as follows: a. Organization should invest in BDA capabilities to reap the benefits deriving from the digital era. Indeed, information matters for organizations wishing to overcome competitors as information are the building blocks of organizational capacity to understand consumers’ needs and identify the right strategic reactions. Yet, infrastructure is not sufficient as it was observable. Thus, while information system matters, they should be supported by managerial capabilities and personnel capabilities. It is not sufficient in fact to select the right tools, it is necessary to have people capable to handle them and to extract the most relevant information from these tools. The propensity to trust data and not only intuition is also fundamental for any manager wishing to strive in the big data era.

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The first implication is then related to the need for developing a big data mindset. Hence, first of all, we suggest managers invest in education concerning data analytics and data infrastructure. A suggestion may be attending higher education programs about big data, which are more and more diffused. In respect of employees, instead, the first action is surely investing in people already with a certain degree in BDA. Yet, as hinted in the previous pages, the whole culture needs to be reshaped. Personnel should, in fact, be made aware that by new BDA capable IS they could improve the ways they work and benefit from the reduction in their workloads. b. Organizations (and managers) should not be afraid of investing in BDA architectures. Indeed, one of the main concerns for most of the managers while talking about information systems is related to implementation times and to technical challenges that may temporarily reduce organizational capacity to react to change. In this sense, through this text, we observed how BDA infrastructures, due to their characteristics, could enhance organizational capacity to react. This phenomenon relates to the fact that thanks to artificial intelligence, cloud computing and cloud storage such infrastructure may be almost completely intangible (thus, there may not be the need for internal lakes or servers) and could decide autonomously which computational technique is the best to solve the problem. Obviously, also time and implementation problems matter. Fortunately, big data consultants are becoming increasingly available and may help the organization in successfully selecting the right solution. c. BDA may increase performance. In the current era, what may change the economic success of a business is information. Information drives organization decisions and may increase the performances of a business. This is achieved by businesses following industry 4.0 paradigms (i.e. machines endowed with sensors and actuators connected to the internet) that could constantly monitor internal processes. Thus, information from the outside, together with information from inside, could allow a business to be innovative and adjust capacity at the same time. The third suggestion suggests businesses invest in BDA and also in systems integration in order to be capable to connect information from markets with information about supply chain and production.

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BDA capabilities have then emerged as the right choice for any business wishing to succeed in the digital era. Yet, they may come at a relevant cost if not properly managed. This notwithstanding, in the case the organization is ready to accept and properly implement them, benefits may exceed the burdens.

4.6

Conclusions, Limitations, and Suggestions for Future Research

This study has examined the characteristics of BDA capabilities in enhancing an organization’s agility and augmenting its ambidexterity. Through BDA capabilities, an organization can easily find, store, and make crucial information accessible throughout its organization while simultaneously enhancing the management capabilities for fostering the market agility making the exploration and exploitation of details as accurate as possible. This information may include identifying the right customers’ preferences and the prevailing trends in the market (Lu & Ramamurthy, 2011). The interdependence and interaction of these two factors not only make organizations ambidextrous but also allows them to showcase strategic agility resulting in a better market position (O’Reilly & Tushman, 2013). This study showcases its findings on the significance of possessing critical and effective information systems that play a noteworthy role in the success of the organization. These information systems comprise of strategic agility and BDA capabilities. A limitation of this approach is that in the sampling process only BDA and agility management capabilities are considered. The whole focus was put on these two factors to find if they have an impact on ambidexterity and agility. Further, only organizations of one nation have been considered, as a sample. Due to this reason, there is a need to conduct the research about organizations in other countries as well and to examine whether the results are similar or not and the framework can be applied to all small and medium enterprises (SMEs).

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5 Conclusion

Abstract Thanks to the results of the empirical analysis, in conjunction with the exploration of existing theory, it was possible to observe the importance of each individual Big Data Analytics (BDA) capability to achieve ambidexterity (i.e. exploration and exploitation capacities), organizational agility, and better performances. Such insights, whether contextualized in the current digital environment (with business evolving toward Industry 4.0), allowed the authors to develop a framework for modern ambidextrous and agile organizations to follow. This section, then, contains some concluding remarks about the project. Keywords Big data · Ambidextrous organizations · Agile organization · Digital technologies · Performances In the present book, we tried to provide a managerial perspective on how big data can be helpful in improving the organization’s strategy, operational activity, and performance. As we show in Chapter 1, big data are defined as a managerial revolution. They differ from the traditional large dataset due to the 7Vs characteristics that make them a valuable success factor for several modern companies. © The Author(s) 2020 R. Rialti and G. Marzi, Ambidextrous Organizations in the Big Data Era, https://doi.org/10.1007/978-3-030-36584-4_5

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From the analysis of the literature, it has been observed that the studies on big data are clustered around five main areas of interest. The first is related to supply chain management and how big data can influence the performance of the entire supply chain. Next, we saw how big data could impact the production process and how they can be a source of valuable information to optimize the production and the manufacturing process. In the third cluster, we observed that big data are able to provide a huge amount of information for the organization’s strategic decision-making process. In the fourth cluster, we learned how it is possible to exploit the accumulated knowledge with the help of big data. Finally, in the fifth cluster, we explored the performance outcomes of big data showing that they are able to influence directly or indirectly the performance of the organization. Next, we explored the role of big data in being able to improve the performances and the outcomes of the Business Process Management System. In doing so, we took in consideration the main agile organization principles emerging from the literature and we applied them to the big data (Singh & Del Giudice, 2019). It has emerged that when an organization has an Information System that encompasses the characteristics of big data, it produces a series of positive cascading effects on the whole organization (Dubey, Gunasekaran, Childe, Blome, & Papadopoulos, 2019). In detail, when an organization effectively implements an efficient and effective big data Information System, it is able to increase the customers’ satisfaction, better respond to the market changes, increase the effectiveness of the collaborations, and be more flexible at all organizational levels. These positive characteristics ultimately result in a more flexible operation and market response. Finally, in Chapter 4, we stressed the role of Big Data Analytics (BDA). In fact, during the whole book, we gently explored the various facets of big data. However, the readers should have perceived the increasing importance of the BDA in extracting information from big data datasets. Chapter 4, therefore, focuses on the role of Analytics in extracting information from huge sets of data, namely big data. Through empirical research, we have demonstrated what managerial practices are required to activate and foster the positive performance of BDA in relation to the agility of the organization.

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In exploring these three macro-areas, an attempt has been made to give the reader a comprehensive overview of what big data means, in particular why big data and BDA matter so much for managers (Popoviˇc, Hackney, Tassabehji, & Castelli, 2018). Along with the definition of this concept, our aim was to provide a series of best practices and managerial insights stemming from the practices and validated by a rigorous scientific method. We hope that the readers found this reading interesting and applicable to their everyday business practices (Dubey, Gunasekaran, Childe, Roubaud et al., 2019). In conclusion to the present work, we would like to be thankful to the colleagues that peer-reviewed this book and to all the practitioners who gave us precious insights to better explore the topic of big data and BDA.

References Dubey, R., Gunasekaran, A., Childe, S. J., Blome, C., & Papadopoulos, T. (2019). Big data and predictive analytics and manufacturing performance: Integrating institutional theory, resource-based view and big data culture. British Journal of Management, 30 (2), 341–361. Dubey, R., Gunasekaran, A., Childe, S. J., Roubaud, D., Wamba, S. F., Giannakis, M., & Foropon, C. (2019). Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain. International Journal of Production Economics, 210, 120–136. Popoviˇc, A., Hackney, R., Tassabehji, R., & Castelli, M. (2018). The impact of big data analytics on firms’ high value business performance. Information Systems Frontiers, 20 (2), 209–222. Singh, S. K., & Del Giudice, M. (2019). Big data analytics, dynamic capabilities and firm performance. Management Decision, 57 (8), 1729–1733.

Index

A

B

Acquisition 10 Adaptive 51 Agile 51 Agile organization 45, 46, 49, 60, 71, 76, 78, 94 Agility 3, 6, 7, 13, 14, 25, 40–42, 44, 45, 48–61, 70, 71, 74, 78, 79, 83–86, 88, 94 Ambidexterity 3, 6, 11, 16, 17, 21, 33, 40–46, 48, 49, 57, 61, 70, 71, 74–79, 83–86, 88 Ambidextrous organizations 42, 44, 45, 47, 49, 51, 53–58, 75–77 Analytical 51, 53 Automatic 51

BDA capabilities 3, 15, 29, 40, 42, 50, 52, 61, 70, 74, 77, 79, 80, 83–86, 88 Bibliometric method 16 Big Data 1–4, 6–18, 20, 22, 23, 28–30, 32, 40, 50–52, 58, 59, 70, 77, 80, 81, 85, 87, 93–95 Big Data Analytics (BDA) 3, 6, 7, 10–18, 20–23, 25, 26, 28–32, 40–42, 50–55, 57–61, 70–77, 80, 84, 94, 95 Big Data Era 13, 43, 50, 71, 86 Business Process Management Systems (BPMS) 10, 41, 42, 45, 50–61, 75, 94

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 R. Rialti and G. Marzi, Ambidextrous Organizations in the Big Data Era, https://doi.org/10.1007/978-3-030-36584-4

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Index

C

Cleansing 10

58, 61, 72, 73, 75, 76, 79, 81, 86–88, 94 Integration 10, 29, 43, 87 Interpretation 10

D

Decision making 6, 10, 29, 50, 53, 76 Digital technologies 93 Digital transformation 1 Dynamic capabilities 3, 7, 12–14, 16–18, 20, 22, 23, 27, 28, 32, 41, 44, 60, 77 Dynamic environment 43 Dynamism 6, 7, 14, 25, 45, 48, 50, 51, 53, 57, 58, 60, 61

K

Knowledge management 31, 76, 77

M

Management revolution 8 Mindsets 10 Modeling 10 Multiple regressions 3, 71, 80

E

P

Exploitation 11, 14, 30, 42–44, 46, 48, 51, 54, 57, 58, 61, 75, 78, 88 Exploration 3, 11, 16, 19, 20, 25, 40, 42–44, 46, 48, 51, 54, 57–59, 61, 70, 75, 79, 88

Performance 3, 6, 11, 14, 15, 17, 18, 22, 23, 26, 31, 32, 41, 43, 45, 50, 55, 61, 70, 71, 74, 76, 78, 79, 83–85, 87, 93, 94

S

Strategic flexibility 70 F

Flexibility 6, 7, 12–15, 25, 40, 43–46, 51, 53, 57, 59, 70, 72–74, 83

I

Industry 4.0 28, 87 Information System (IS) 2, 7, 10–16, 28–32, 40–42, 49–52, 54, 55,

V

Value 8, 9, 15, 30, 40 Variability 8, 9 Variety 8 Velocity 8 Veracity 8, 9 Visualization 8, 9, 19 Volume 8