Science Mapping of Digital Transformation in Business: A Bibliometric Analysis and Research Outlook 3031267648, 9783031267642

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SpringerBriefs in Business Cristina Bota-Avram

Science Mapping of Digital Transformation in Business A Bibliometric Analysis and Research Outlook

SpringerBriefs in Business

SpringerBriefs present concise summaries of cutting-edge research and practical applications across a wide spectrum of fields. Featuring compact volumes of 50 to 125 pages, the series covers a range of content from professional to academic. Typical topics might include: • A timely report of state-of-the art analytical techniques • A bridge between new research results, as published in journal articles, and a contextual literature review • A snapshot of a hot or emerging topic • An in-depth case study or clinical example • A presentation of core concepts that students must understand in order to make independent contributions SpringerBriefs in Business showcase emerging theory, empirical research, and practical application in management, finance, entrepreneurship, marketing, operations research, and related fields, from a global author community. Briefs are characterized by fast, global electronic dissemination, standard publishing contracts, standardized manuscript preparation and formatting guidelines, and expedited production schedules.

Cristina Bota-Avram

Science Mapping of Digital Transformation in Business A Bibliometric Analysis and Research Outlook

Cristina Bota-Avram Accounting and Audit Babes-Bolyai University Cluj Napoca, Romania

ISSN 2191-5490 (electronic) ISSN 2191-5482 SpringerBriefs in Business ISBN 978-3-031-26764-2 ISBN 978-3-031-26765-9 (eBook) https://doi.org/10.1007/978-3-031-26765-9 © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

To my lovely family and accept my apologies for the days, nights, and weekends that I have missed spending with you.

Preface

Digital transformation will affect every company or organization sooner or later, regardless of the industry, country, or type of organization. In recent years, this impact has become disruptive and there is an increasing need for companies to innovate their business models while using various emerging technologies. Digitalization is a complex phenomenon showing an increasing interdisciplinary trend that has aroused an exponential increase in the scientific interest of academics, especially in the last 3 years. The growing interest of researchers in this subject was also generated by the turbulent pandemic context, which we have been through for 3 years. Therefore, research related to digital transformation in business includes results from multiple areas of social sciences. The key goals of this book were twofold. The first goal was to provide a comprehensive overview of the state of digital transformation in business using various bibliometric indicators, such as citation analysis, co-citation analysis, bibliographic coupling, co-occurrence of keywords, burst detection analysis, and timeline analysis, and so the science map of this research field is depicted. The second goal was to provide the intellectual structure of knowledge, to detect the most popular research topics, and to propose a synthesis of the potential directions and opportunities for future research agenda related to this field of research. With the help of advanced bibliometric software, the emerging research trends that have significantly attracted the interest of academics in the research field related to digital transformation in business have been detected. The bibliometric analysis of the current body of knowledge published over the last 20 years reveals the existence of four major thematic clusters: (1) the digital transformation process, (2) digital technologies, (3) the digital economy, and (4) digital disruption. Proceeding to the analysis of the most prestigious articles included in each thematic group, subthemes were produced within their respective topics. Subsequently, the content analysis of these articles helped me suggest research gaps in the existing literature related to digital transformation in business and set several directions for future research in this area.

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Preface

I would like to thank Springer Publishers for the professional realization of this book project and I hope you enjoy reading this book and use my work as a knowledge base for your further research, in this challenging and complex field of research related to digital transformation in business. Cluj-Napoca, Romania

Cristina Bota-Avram

Contents

1

Introduction to the Bibliometric Analysis of Digital Transformation in Business . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2

Bibliometrics Research Methodology . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Data Source and Search Protocol . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Bibliometric Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3

Bibliometrics Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Number of Publications Related to Digital Transformation in Business: Evolution in Time . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Category of Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Number of Citations Related to Digital Transformation in Business: Evolution in Time . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Highly Cited Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4

1 7

. 9 . 9 . 10 . 12

. . 15 . . 15 . . 15 . . 17 . . 17 . . 21

Science Mapping Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Analysis of the Journals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Analysis of the Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Analysis of the Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Analysis of the Keywords . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.1 Co-occurrence of Keywords . . . . . . . . . . . . . . . . . . . . . . . . 4.4.2 Burst Detection Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.3 Timeline View Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

23 23 27 29 33 33 35 36 40

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6

Contents

Content Analysis of Articles Included in the Bibliometric Analysis of Digital Transformation in Business . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Thematic Cluster 1: Digital Transformation Process . . . . . . . . . . . . 5.1.1 Digital Transformation Strategies . . . . . . . . . . . . . . . . . . . . 5.1.2 Phases of Digital Transformation . . . . . . . . . . . . . . . . . . . . 5.1.3 Business Model Innovation in Digital Context . . . . . . . . . . . 5.1.4 Digital Innovation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.5 Digital Transformation in Small and Medium-Sized Enterprises (SMEs) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.6 Impact of Digital Transformation . . . . . . . . . . . . . . . . . . . . 5.1.7 Digital Transformation and Industry 4.0 . . . . . . . . . . . . . . . 5.2 Thematic Cluster 2: Digital Technologies . . . . . . . . . . . . . . . . . . . . 5.2.1 Big Data Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.2 Internet of Things, Blockchain, and Artificial Intelligence . . . 5.2.3 Digital Readiness and Digital Resilience . . . . . . . . . . . . . . . 5.3 Thematic Cluster 3: Digital Economy . . . . . . . . . . . . . . . . . . . . . . . 5.3.1 Smart Circular Economy . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.2 Digital Entrepreneurship . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.3 Digital Sharing Economy . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Thematic Cluster 4: Digital Disruption . . . . . . . . . . . . . . . . . . . . . . 5.4.1 Dynamic Capabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.2 Digital Disruption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.3 Digital Age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

47 49 50 52 52 53 56 57 57 57 58 59 59 60 61 62

Agenda for Future Research and Conclusions . . . . . . . . . . . . . . . . . . . 6.1 Agenda for Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

69 69 69 75

41 41 41 43 44 46

About the Author

Cristina Boța-Avram PhD is an Associate Professor Habilitated, PhD at the Department of Accounting and Audit, Faculty of Economics Sciences and Business Administration, Babeș-Bolyai University, Romania. With over 19 years of experience in academia, she has published as author and co-author of over 70 scientific articles, 4 book chapters, and 3 books. In 2011 she obtained the Award Excellence “Constantin Ionete” of the Financial Audit Magazine under the auspices of the Chamber of Financial Auditors from Romania, for the scientific series of studies entitled “The contribution of the audit function in the vision of corporate governance codes at the European level.” Her research interests include governance, corruption, digitalization, audit, ethical behavior, sustainable business performance, and sustainability reporting assurance. Currently, she is a member of the Body of Expert and Licensed Accountants of Romania since 2010, member of the Chamber of Financial Auditor from Romania since 2021, and member of the Association of Internal Auditors from Romania (Charter of the Institute of Internal Auditors) since 2020.

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Chapter 1

Introduction to the Bibliometric Analysis of Digital Transformation in Business

Digital transformation is a major challenge faced by companies worldwide. Adopting new digitally changed business models is no longer an option; it became a necessity, while the existing technologies, resources, and structures should be re-evaluated to understand what new digital technologies are relevant to the purpose of the business. Thus, the successful implementation of digital transformation across all areas of an organization should be a top management priority, while its corporate business strategies should be perfectly aligned with transformative digital strategies. Therefore, no entity or organization is immune to the effects of digital transformation (Hess et al. 2016; Wang et al. 2020; Chawla and Goyal 2021), especially in the turbulent times we live in today. However, even if this research field is becoming increasingly popular, especially in the last few years, it still lacks a common, widely accepted, and sustainable theoretical foundation (Demlehner and Laumer 2020). In the literature, there are many conceptual approaches in terms of the concepts related to digitalization and digital transformation, while these terms have sometimes been applied differently in various studies (Loebbecke and Picot 2015), and thus far, there is no consensus about the different levels of digitalization (Svadberg et al. 2019). Verhoef et al. (2021) argued that there are three stages of digital transformation: digitization, digitalization, and digital transformation. Although there are various approaches in the literature regarding these terms, a large majority of researchers agree that digitization, digitalization, and digital transformation are different terms, and a more rigorous conceptual foundation needs to be developed. For instance, Ivančić et al. (2019) defined digital transformation as “the continuous process of climbing the scale of digital maturity by employing digital and other technologies along with organizational practices to create a digital culture” (Ivančić et al. 2019, p. 36). Increasing effectiveness in their digital transformation journey will allow companies to provide better value-added services, gain competitive advantages, and become more profitable in the context of increasingly turbulent environments. Therefore, digitalization and digital transformation aroused an exponential increase in academic interest in the last few years, especially if we consider the pandemic context of recent years, which further accentuated the need for © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. Bota-Avram, Science Mapping of Digital Transformation in Business, SpringerBriefs in Business, https://doi.org/10.1007/978-3-031-26765-9_1

1

2

1 Introduction to the Bibliometric Analysis of Digital Transformation. . .

digitalization in various fields of activity. Recently, Verhoef et al. (2021) defined digital transformation as “a change in how a firm employs digital technologies, to develop a new digital business model that helps create and appropriate more value for the firm” (Verhoef et al. 2021, p. 889). Undoubtedly, the complexity of the concept of digital transformation is obvious, and so far, there is no consensus on the general definition of digital transformation. But it is certain that digital transformation becomes an imperative requirement for every business company, from the smallest ones to the largest ones. The study presented in this book is based on the following conceptual approach to digital transformation. Digital transformation supposes the fundamental changes of business strategies in operating and delivering value to its customers through the incorporation of various digital technologies into all areas of a business, while there is an increasing pressure for business leaders to remain competitive, performant, and relevant in the digital age we are going through. Adopting the gap-spotting approach suggested by previous renowned researchers (Alvesson and Sandberg 2011, 2014), next, we will provide the arguments for why this bibliometric analysis is needed. To our knowledge so far, previous research in the field of digital transformation has been enriched with specific literature review studies, while the most relevant are disclosed in Table 1.1. When analyzing the previous literature reviews, one can note that most of these are based on systematic literature review analysis and less on bibliometric analysis. Of course, there were also some bibliometric analyses for various purposes. For instance, Zhu et al. (2021) through a sample of 865 papers selected from the Web of Science database examined the intellectual structure of digital transformation research. Using a sample of 285 articles selected from the Web of Science database, Agostini and Nosella (2021) systematized the previous literature on digital/I4.0 technologies and business models. Sahoo (2022) provided a comprehensive review of the literature of 89 articles on the applicability of big data in manufacturing. The bibliometric analysis proposed in this book is different in several ways. Firstly, I use a combination of two bibliometric software (VosViewer and CiteSpace) to benefit from the advantages of each bibliometric tool and to be able to provide a more comprehensive state-of-the-art of digital transformation in business. Second, the data employed cover a longer period, from 2003 to 2021. Third, I adopted the innovative comparative approach to bibliometrics proposed by Caputo et al. (2021). This approach was advanced by Caputo et al. (2021) to overcome the limitations related to every synthetic indicator and proposed an integration into a comparative analysis of the results of various bibliometric indicators, namely citation analysis, co-citation analysis, bibliographic coupling, and the co-occurrence of keywords. The purpose of the study presented in this book is to provide an informative and comprehensive overview of existing publications through an extensive bibliometric analysis of digital transformation in business research, based on a sample of 896 articles extracted from the Web of Science database for the period 2003–2021, and using two of the most excellent bibliometric software tools, VosViewer and CiteSpace. The bibliometric analysis developed in this study provides relevant answers to the following research questions:

Favoretto, C; Mendes, GHD; Godinho, M; de Oliveira, MG; Ganga, GMD

10.1108/JBIM-10-20200477

10.1108/IJQRM-072021-0247

Rego, BS; Jayantilal, S; Ferreira, JJ; Carayannis, EG Dias, AM; Carvalho, AM; Sampaio, P

10.1007/s13132-02100853-3

Digital Transformation and Strategic Management: a Systematic Review of the Literature Quality 4.0: literature review analysis, definition, and impacts of the digital transformation process on quality Digital transformation of business model in manufacturing companies: challenges and research agenda

Nayernia, H; Bahemia, H; Papagiannidis, S

Zhu, XT; Ge, SL; Wang, NX

10.1016/j. cie.2021.107774

10.1080/ 00207543.2021.2002964

Authors Ardito, L; Cerchione, R; Mazzola, E; Raguseo, E

DOI 10.1108/JKM-04-20210325

A systematic review of the implementation of industry 4.0 from the organizational perspective

Article Industry 4.0 transition: a systematic literature review combining the absorptive capacity theory and the datainformation-knowledge hierarchy Digital transformation: A systematic literature review

Table 1.1 A synthesis of literature review studies in digital transformation

2003– 2019

Systematic literature review

Bibliometric analysis

2015– March 2021

2010– 2020

Systematic literature review through text mining Systematic literature review

Bibliometric analysis

Type of analysis Systematic literature review

2015– 2021

2000– 2020

Period covered 2000– 2021

176 articles (Scopus and Web of Science)

84 articles (Scopus and Web of Science)

45 articles (Web of Science)

97 articles (Scopus)

865 papers (Web of Science)

Database covered 150 papers (Scopus)

(continued)

To identify the challenges of digital transformation in manufacturing companies

To understand how digital transformation has changed business strategies To analyze the state-ofthe-art of Quality 4.0

To examine intellectual structure of the digital transformation research To address the organizational side of implementing I4.0

Purpose To critically review the existing literature on Industry 4.0 from a knowledge management perspective

1 Introduction to the Bibliometric Analysis of Digital Transformation. . . 3

Big data analytics in manufacturing: a bibliometric analysis of research in the field of business management Social Media Adoption, Usage and Impact In Business-To-Business (B2B) Context: A State-OfThe-Art Literature Review The role of digital innovation in knowledge management systems: A systematic literature review

10.1002/bse.2867

Industry 4.0, innovation, and sustainable development: A systematic review and a roadmap to sustainable innovation Artificial intelligence in marketing: A systematic literature review

Sahoo, S

Dwivedi, YK; Ismagilova, E; Rana, NP; Raman, R

Di Vaio, A; Palladino, R; Pezzi, A; Kalisz, DE

10.1007/s10796-02110106-y

10.1016/j. jbusres.2020.09.042

Ghobakhloo, M; Iranmanesh, M; Grybauskas, A; Vilkas, M; Petraite, M Chintalapati, S; Pandey, SK

Authors Agostini, L; Nosella, A

10.1080/ 00207543.2021.1919333

10.1177/ 14707853211018428

DOI 10.1108/BPMJ-03-20210133

Article Industry 4.0 and business models: a bibliometric literature review

Table 1.1 (continued)

Bibliometric analysis

Systematic literature review

2010– 2020

1990– 2020

Bibliometric analysis

Systematic literature review

Content analysis

Type of analysis Bibliometric analysis

1999– 2020

2015– 2020

Until 2020

Period covered 2001– 2020

46 articles (Web of Science)

70 articles (Scopus)

To provide a comprehensive analysis of the use of social media by businessto-business (B2B) companies To understand the role of digital innovation in knowledge management systems

To provide a thorough literature review of the applicability of big data in manufacturing

To explore the use of Artificial intelligence in marketing as an emergent stream of research

57 articles (Scopus, Google Scholar, Sage, Springer, and Emerald) 89 articles (Scopus)

70 articles (Web of Science, Scopus)

Purpose To systematize this body of literature in what regards digital/I4.0 technologies and business models To identify Industry 4.0 functions for sustainable innovation

Database covered 285 articles (Web of Science)

4 1 Introduction to the Bibliometric Analysis of Digital Transformation. . .

Teubner, RA; Stockhinger, J

Hanelt, A; Bohnsack, R; Marz, D; Marante, CA

vom Brocke, J; Schmid, AM; Simons, A; Safrudin, N Ghobakhloo, M

10.1016/j. jsis.2020.101642

10.1111/joms.12639

10.1108/BPMJ-10-20190423

10.1016/j. jclepro.2019.119869

10.1109/ ACCESS.2020.2998754

Industry 4.0, digitization, and opportunities for sustainability

Impact of Big Data and Machine Learning on Digital Transformation in Marketing: A Literature Review

Miklosik, A; Evans, N

Vaska, S; Massaro, M; Bagarotto, EM; Dal Mas, F

10.3389/ fpsyg.2020.539363

The Digital Transformation of Business Model Innovation: A Structured Literature Review Literature review: Understanding information systems strategy in the digital age A Systematic Review of the Literature on Digital Transformation: Insights and Implications for Strategy and Organizational Change IT-enabled organizational transformation: a structured literature review

2014– 2020

2015– 2019

1990– 2017

2000– 2018

2008– 2018

1996– 2020

IBM Sentiment and Context Analysis Systematic literature review

Systematic literature review

Systematic literature review

Systematic literature review

Structured literature review

69 articles (more relevant scientific databases)

72 articles (more relevant scientific databases)

201 articles (more relevant scientific databases)

279 articles (EBSCO)

141 articles (Web of Science)

72 articles (Scopus)

(continued)

To investigate the phenomenon of digital transformation from the perspective of organizational change To determine the state of the art and areas for future research in what regards IT-enabled organizational transformation To model the contextual relationships among the Industry 4.0 sustainability functions To describe the impact of big data and machine learning (ML) on digital transformation of the marketing industry

To understand the impact of digital technologies on business model innovation To understand information systems strategy in the digital age

1 Introduction to the Bibliometric Analysis of Digital Transformation. . . 5

Vial, G

Milian, EZ; Spinola, MD; de Carvalho, MM

Pihir, I; TomicicPupek, K; Furjan, MT

10.1016/j. jsis.2019.01.003

10.1016/j. elerap.2019.100833

10.31341/jios.43.1.3

Understanding digital transformation: A review and a research agenda

Fintechs: A literature review and research agenda

Digital Transformation Playground—Literature Review and Framework of Concepts

Source: author’s synthesis

Authors Fischer, M; Imgrund, F; Janiesch, C; Winkelmann, A

DOI 10.1108/BPMJ-05-20180130

Article Directions for future research on the integration of SOA, BPM, and BRM

Table 1.1 (continued)

Structured literature review

Bibliometric analysis

Bibliometric analysis

1980– 2018

2000– 2019

Type of analysis Structured literature review

2000– 2018

Period covered 2007– 2016

528 articles (Web of Science, Scopus)

179 articles (Web of Science, Scopus)

282 articles (more relevant scientific databases)

Database covered 236 articles (more relevant scientific databases)

Purpose To structure the current state of research on the integration of serviceoriented architectures, business process management, and business rules management To develop a conceptual definition of digital transformation and an inductive framework describing digital transformation To investigate the concept of fintech, to map the literature and point out new routes and opportunities in the field To get some insights about the progressive area of digital transformation

6 1 Introduction to the Bibliometric Analysis of Digital Transformation. . .

References

7

• RQ1: What are the most influential journals, authors, and articles in the research field related to digital transformation in business? • RQ2: What is the intellectual structure of the knowledge related to digital transformation in business? • RQ3: Which are the most popular research topics, key emerging research trends, and guidelines for future research agendas? This research contributes to the research field of digital transformation in business by presenting a quantitative and descriptive overview of all digital transformation research to date by combining multiple bibliometric analysis techniques. As other authors remarked (Bernatović et al. 2021), the major advantages of using bibliometric methods are that they minimize subjectivity and increase the reliability of findings. In summary, the contributions of this study to digital transformation in business research are as follows. First, this study provides a comprehensive assessment of the body of knowledge related to digital transformation in business, while significant insights based on bibliometric techniques are depicted in the most influential journals, authors, and research articles, and the key emerging trends in this research field. Second, the intellectual structure of knowledge related to digital transformation in business is mapped, and the most popular research topics and key directions for future research are identified. Third, as a result of this bibliometric analysis, four major thematic clusters were identified and based on the content analysis of the most prestigious articles included in each thematic group, subthemes were produced within their respective topics. Following this analysis, this book provides a useful summary of future research directions for academics interested in this field.

References Agostini L, Nosella A (2021) Industry 4.0 and business models: a bibliometric literature review. Bus Process Manag J 27(5):1633–1655. https://doi.org/10.1108/BPMJ-03-2021-0133 Alvesson M, Sandberg J (2011) Generating research questions through problematization. Acad Manag Rev 36(2):247–271. https://doi.org/10.5465/amr.2009.0188 Alvesson M, Sandberg J (2014) Habitat and habitus: boxed-in versus box-breaking research. Organ Stud 35(7):967–987. https://doi.org/10.1177/0170840614530916 Bernatović I, Slavec Gomezel A, Černe M (2021) Mapping the knowledge-hiding field and its future prospects: a bibliometric co-citation, co-word, and coupling analysis. Knowl Manag Res Pract:1–16. https://doi.org/10.1080/14778238.2021.1945963 Caputo A, Pizzi S, Pellegrini M, Dabic M (2021) Digitalization and business models: where are we going? A science map of the field. J Bus Res 123:489–501. https://doi.org/10.1016/j.jbusres. 2020.09.053 Chawla RN, Goyal P (2021) Emerging trends in digital transformation: a bibliometric analysis. Benchmarking. https://doi-org.am.e-nformation.ro/10.1108/BIJ-01-2021-0009 Demlehner Q, Laumer S (2020) Why context matters: explaining the digital transformation of the manufacturing industry and the role of the Industry’s characteristics in it. Pacific Asia J Assoc Inf Syst 12(3):57–81. https://doi.org/10.17705/1pais.12303

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Introduction to the Bibliometric Analysis of Digital Transformation. . .

Hess T, Matt C, Benlian A, Wiesbock F (2016) Options for formulating a digital transformation strategy. MIS Q Executive 15(2):123–139 Ivančić L, Bosilj Vukšić V, Spremić M (2019) Mastering the digital transformation process: business practices and lessons learned. Technol Innov Manag Rev 9(2):36–50. https://doi.org/ 10.22215/timreview/1217 Loebbecke C, Picot A (2015) Reflections on societal and business model transformation arising from digitization and big data analytics: a research agenda. J Strateg Inf Syst 24(3):149–157. https://doi.org/10.1016/j.jsis.2015.08.002 Sahoo S (2022) Big data analytics in manufacturing: a bibliometric analysis of research in the field of business management. Int J Prod Res 60(22):6793–6821. https://doi.org/10.1080/00207543. 2021.1919333 Svadberg S, Holand A, Breunig KJ (2019) Beyond the hype: a bibliometric analysis deconstructing research on digitalization. Technol Innov Manag Rev 9(10):38–50. https://doi.org/10.22215/ timreview/1274 Verhoef PC, Broekhuizen T, Bart Y, Bhattacharya A, Qi Dong J, Fabian N, Haenlein M (2021) Digital transformation: a multidisciplinary reflection and research agenda. J Bus Res 122:889– 901. https://doi.org/10.1016/j.jbusres.2019.09.022 Wang H, Feng J, Zhang H, Li X (2020) The effect of digital transformation strategy on performance. Int J Confl Manag 31(3):441–462. https://doi.org/10.1108/IJCMA-09-2019-0166 Zhu XT, Ge SL, Wang NX (2021) Digital transformation: a systematic literature review. Comput Ind Eng 162:Article 107774. https://doi.org/10.1016/j.cie.2021.107774

Chapter 2

Bibliometrics Research Methodology

2.1

Data Source and Search Protocol

A systematic search was conducted on the Web of Science Core Collection (WOS) database in December 2021 to collect literature data. The choice of the Web of Science database is argued by the fact that it yields the highest quality of publications (Caputo et al. 2021; Wang et al. 2021; Raghuram et al. 2019) and it is one of the most reliable sources for indexing highly ranked journals. In addition, as other researchers have remarked (Zhang and Liang 2020), the WOS database provides comprehensive, multidisciplinary citation data and has gradually become one of the main data sources for bibliometric analysis. Figure 2.1 illustrates the research framework used in this bibliometric analysis. The search field used in this analysis was Topic, which contains titles, abstracts, author keywords, and Keywords Plus. The search keywords include “digital* transformation” and “business.” Therefore, the selected keywords must be presented within the titles, abstracts, and keywords of the publications to ensure the comprehensive nature of this search. The selected document types included articles and reviews, the language of publication was English, the time span included all years (1975–2021), and the search time was 20 December 2021. Table 2.1 provides a detailed overview of the search protocols used in the bibliometric analysis. The final sample included 896 documents at the end of the search. The final sample of articles was exported in full records and cited references type in plain text and tab-delimited (Win) file formats as required for the bibliometric software used. Preliminary screening revealed that the first article on digital transformation in business was published in the MIT Sloan Management Review in 2003 (Andal-Ancion et al. 2003), and all collected documents were published from 2003 to 2021. Andal-Ancion et al. (2003) identified 10 specific drivers starting from a sample of 20 large North American and European companies that help transform businesses through the implementation of new information technologies, such as broadband networks, mobile communications, and the Internet.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. Bota-Avram, Science Mapping of Digital Transformation in Business, SpringerBriefs in Business, https://doi.org/10.1007/978-3-031-26765-9_2

9

10

2

DEFINE THE RESEARCH SCOPE Database: WOS database Search protocol: as indicated in Table 2 Time span: 19752021

VOS Viewers; CiteSpace

Bibliometrics Research Methodology

PERFORMANCE ANALYSIS- ACTIVITY INDICATORS OF PUBLICATIONS - Number of publications - Category of publications - Number of citations - Highly cited publications SCIENCE MAPPING ANALYSIS - Citation analysis - Co-citation analysis - Bibliographic coupling -Co-occurence of the keywords - Burst detection analysis - Timeline analysis

DISCUSSIONS -Most popular issues - Agenda for future research - Conclusions and limitations

Fig. 2.1 Research framework used for the bibliometric analysis. Source: author’s own research Table 2.1 Search protocol

Category Search string used in search refine Access Timespan

Document type

Language

Criteria TOPIC: (“digital* transformation”) AND TOPIC: (“business*”) Including both Open Access and others Including only articles from 1975 to 2021. There were already 14 articles published for 2022 which were excluded. The search was limited to documents type as of articles (944) and reviews (57). Other documents such as proceedings paper, book chapter, editorial material, book review, and meeting abstract were excluded. Only articles published in English were selected, the others were excluded.

Final number of remaining articles after manual refine

No. of refined documents 1.729

(14)

(743)

(76) 896

Source: author’s own research

2.2

Bibliometric Methodology

Bibliometric analysis is one of the most recent scientometric disciplines that uses mathematical and statistical methods to analyze and assess scientific publications, allowing researchers to investigate the status and emerging trends in academic literature in a certain research field (Broadus 1987; Khanra et al. 2021). The number

2.2

Bibliometric Methodology

11

of research studies continues to increase in various research fields; therefore, the main advantage of bibliometric research is allowing the summarisation of large amounts of scientific publications to present the state of the intellectual structure and emerging research trends for the research field under enquiry (Donthu et al. 2021). Most recently, bibliometric analysis has increased in popularity in business and management disciplines (Khan et al. 2020; Khan et al. 2021; Wang et al. 2021; Donthu et al. 2021). Compared to other alternative methods for summarizing existing literature in a certain research field (such as systematic literature review or meta-analysis), the major advantage of the bibliometric method is its ability to overcome subjective analysis in literature reviews (Bernatović et al. 2021). Bibliometric research involves a combination of two main procedures: performance analysis and science mapping analysis (Donthu et al. 2021). A performance analysis investigates the activity indicators of publications (Cobo et al. 2011), emphasizing the most significant contributions of research constituents to a given research field. A wide range of techniques can be used, including frequency of publications, frequency of citations, highly cited publications, and publications counted by a unit of analysis (authorship, country, institutions, sources, etc.). Science mapping focuses on the relationship between research constituents, providing a spatial delineation of the ways in which different scientific units of analysis are connected (Caputo et al. 2021; Donthu et al. 2021). Various techniques can be used in science mapping analysis such as citation analysis, co-citation analysis, bibliographic coupling, co-occurrence of keywords, co-authorship analysis, burst detection analysis, and timeline analysis. All of these techniques combined with enrichment techniques, such as network metrics, clustering, and visualization, are beneficial in depicting the intellectual structure of the research field under investigation (Donthu et al. 2021). Citation analysis is a basic technique for science mapping analysis that determines the impact of a publication based on its citation count (Donthu et al. 2021; Khanra et al. 2021). Co-citation analysis is a technique that allows the identification of publications connected when they co-occur in the reference list of another publication. Starting from the assumption that when items are cited together, they are more likely to be thematically similar, co-citation analysis offers a dynamic representation of a topic from the past, considering the period necessary for producing publications and collecting citations (Caputo et al. 2021; Donthu et al. 2021; Wang et al. 2021; Ferreira 2018). Bibliographic coupling analysis investigates the connections between two articles when both articles cite a common third article (Caviggioli and Ughetto 2019), providing a static representation of a topic because the number of cited references in an article does not change over time (Caputo et al. 2021; Ferreira 2018). The co-occurrence of keywords is a form of content analysis provided by authors or journals that measures the frequency with which two keywords occur together among publications, providing meaningful and powerful perspectives in understanding the conceptual structure of a certain research field (Khanra et al. 2021; Wang et al. 2021). In this study, the innovative comparative bibliometric approach proposed by Caputo et al. (2021) was adopted, so the presentation of various bibliometric indicators, such as citation analysis, co-citation

12

2

Bibliometrics Research Methodology

analysis, bibliographic coupling, and co-occurrence of keywords, were presented in a comparative manner. To identify emerging trends and potential challenges in understanding the dynamics of the research field under investigation, burst detection and timeline analyses were also applied in this study. Burst detection analysis emphasizes the most active items, such as subject categories, keywords, or cited references, that registered increasing attention in a certain period of time, and the clusters generated with an increasing citation burst could reflect new trends in research (Li and Xu 2022). Timeline view analysis allows the identification of newly emerging research trends over time so that they can be recognized more easily (Chen et al. 2014; Yin et al. 2020). By applying various clustering algorithms, timeline analysis can reveal the main clusters that can generate potentially transformative changes that contribute to the development of the research field under investigation (Wang et al. 2021). According to the latest practices in recent bibliometric studies published in the field of business and management, two of the most powerful bibliometric software were used: VOSViewer (Van Eck and Waltman 2010) and CiteSpace (Chen 2006; Chen et al. 2014). VosViewer is an excellent bibliometric software for creating knowledge-mapping networks based on various units of analysis. CiteSpace is another popular bibliometric software that allows a better understanding of emerging trends and potential challenges for researchers for a certain research topic (Wang et al. 2021).

References Andal-Ancion A, Cartwright PA, Yip GS (2003) The digital transformation of traditional businesses. MIT Sloan Manag Rev 44(4):34–41 Bernatović I, Slavec Gomezel A, Černe M (2021) Mapping the knowledge-hiding field and its future prospects: a bibliometric co-citation, co-word, and coupling analysis. Knowl Manag Res Pract:1–16. https://doi.org/10.1080/14778238.2021.1945963 Broadus RN (1987) Toward a definition of “bibliometrics”. Scientometrics 12(5–6):373–379. https://doi.org/10.1007/BF02016680 Caputo A, Pizzi S, Pellegrini M, Dabic M (2021) Digitalization and business models: Where are we going? A science map of the field. J Bus Res 123:489–501. https://doi.org/10.1016/j.jbusres. 2020.09.053 Caviggioli F, Ughetto E (2019) A bibliometric analysis of the research dealing with the impact of additive manufacturing on industry, business, and society. Int J Prod Econ 208:254–268. https:// doi.org/10.1016/j.ijpe.2018.11.022 Chen C (2006) CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature. J Am Soc Inf Sci Technol 57(3):359–377. https://doi.org/10.1002/asi.20317 Chen C, Dubin R, Kim MC (2014) Emerging trends and new developments in regenerative medicine: a scientometric update (2000–2014). Expert Opin Biol Ther 14(9):1295–1317. https://doi.org/10.1517/14712598.2014.920813 Cobo MJ, López-Herrera AG, Herrera-Viedma E, Herrera F (2011) An approach for detecting, quantifying, and visualizing the evolution of a research field: a practical application to the fuzzy sets theory field. J. Informetr 5(1):146–166. https://doi.org/10.1016/j.joi.2010.10.002

References

13

Dabic M, Maley J, Dana LP, Novak I, Pellegrini MM, Caputo A (2020) Pathways of SME internationalization: a bibliometric and systematic review. Small Bus Econ 55:705–725. https://doi.org/10.1007/s11187-019-00181-6 Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM (2021) How to conduct a bibliometric analysis: an overview and guidelines. J Bus Res 133:285–296. https://doi.org/10.1016/j.jbusres. 2021.04.070 Ferreira FAF (2018) Mapping the field of arts-based management: bibliographic coupling and co-citation analyses. J Bus Res 85:348–357. https://doi.org/10.1016/j.jbusres.2017.03.026 Khan A, Hassan MK, Paltrinieri A, Dreassi A, Bahoo S (2020) A bibliometric review of takaful literature. Int Rev Econ Finance 69:389–405. https://doi.org/10.1016/j.iref.2020.05.013 Khan IS, Ahmad MO, Majava J (2021) Industry 4.0, and sustainable development: A systematic mapping of the triple bottom line, circular economy, and sustainable business models perspectives. J Clean Prod 297:Article 126655. https://doi.org/10.1016/j.jclepro.2021.126655 Khanra S, Dhir A, Parida V, Kohtamäki M (2021) Servitization research: a review and bibliometric analysis of past achievements and future promises. J Bus Res 131:151–166. https://doi.org/10. 1016/j.jbusres.2021.03.056 Li B, Xu Z (2022) A comprehensive bibliometric analysis of financial innovation. Econ Res-Ekon Istraz 35(1):367–390. https://doi.org/10.1080/1331677X.2021.1893203 Raghuram S, Hill NS, Gibbs JL, Maruping LM (2019) Virtual work: bridging research clusters. Acad Manag Ann 13(1):308–341. https://doi.org/10.5465/annals.2017.0020 Van Eck NJ, Waltman L (2010) Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84(2):523–538. https://doi.org/10.1007/s11192-009-0146-3 Wang X, Xu Z, Qin Y, Skare M (2021) Service networks for sustainable business: a dynamic evolution analysis over half a century. J Bus Res 136:543–557. https://doi.org/10.1016/j. jbusres.2021.07.062 Yin X, Wang H, Wang W, Zhu K (2020) Task recommendation in crowdsourcing systems: bibliometric analysis. Technol Soc 63:Article 101337. https://doi.org/10.1016/j.techsoc.2020. 101337 Zhang K, Liang QM (2020) Recent progress of cooperation on climate mitigation: a bibliometric analysis. J Clean Prod 277:Article 123495. https://doi.org/10.1016/j.jclepro.2020.123495

Chapter 3

Bibliometrics Performance Analysis

3.1

Number of Publications Related to Digital Transformation in Business: Evolution in Time

The dataset consists of 896 documents from 2003 to 2021. Figure 3.1 presents interesting information on the evolution of the number of publications over time. Despite the extended period of publication of these articles (19 years), it may be noted that the increasing attention paid by researchers to this field of research has been particularly emphasized for the period 2018–2021, with an exponential increase in the last 2 years (2020–2021). In fact, from the entire sample of 896 documents, 269 documents (30%) were published in 2020, while almost half of them— 431 documents (48%)—were published only in 2021. In the opinion of the author, an argument for the exponential growth of academic interest in this research field is given by the consequences of the pandemic context that has significantly impacted our lives since 2020. In this vein, Amankwah-Amoah et al. (2021) also provided a pertinent argument, who stated that “COVID-19 is the great accelerator in fasttracking the existing global trend towards embracing modern emerging technologies ushering in transformations in lifestyle, work patterns, and business strategies.” Undoubtedly, this research field will further arouse the interest of researchers in the coming years, in the context of the stringent necessity to integrate digital technologies into all areas of business to provide fundamental changes in the way companies operate and deliver value to customers.

3.2

Category of Publications

For the selected sample of WOS publications, 896 documents on digital transformation in business research refer to 90 categories. The distribution of the top ten categories is shown in Fig. 3.2. It may be noted that the first three largest categories © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. Bota-Avram, Science Mapping of Digital Transformation in Business, SpringerBriefs in Business, https://doi.org/10.1007/978-3-031-26765-9_3

15

16

3

Bibliometrics Performance Analysis

Number of publications related to digital transformation in business 500 450 400 350 300 250 200 150 100 50 0 Number of publications

2003 2006 2010 2011 2012 2015 2016 2017 2018 2019 2020 2021 1 1 1 3 1 3 7 23 54 111 269 431

Fig. 3.1 Number of publications related to digital transformation in business—evolution in time. Source: author’s own research Number of publications in WOS categories 281

Management

233

Business

94

Computer Science Information Systems

81

Environmental Studies

77

Green Sustainable Science Technology Information Science Library Science

77

Environmental Sciences

76 59

Economics

52

Engineering Industrial

31

Operations Research Management Science

0

50

100

150

200

250

300

Fig. 3.2 Top-10 categories of publications related to digitalization transformation in business. Source: author’s own research

are management (281 documents), business (233 documents), and computer science information systems (94 documents) which represent 67.8% of the entire sample. The remaining seven categories were environmental studies (81 documents), green sustainable science technology (77 documents), information science library science (77 documents), environmental sciences (76 documents), economics (59 documents), engineering industry (52 documents), and operations research management science (31 documents). The distribution of publications within the WOS categories highlights that research related to digital transformation in business has been involved in a wide range of areas of business management, computer science, environment, engineering, and economics.

3.4

Highly Cited Publications

3.3

17

Number of Citations Related to Digital Transformation in Business: Evolution in Time

The same increasing trend for the number of publications can also be observed with respect to the number of citations, as shown in Fig. 3.3. An exponential increase in the number of citations may be observed for the period 2019–2021, providing an argument to support the idea of growing interest and influence in this research field for researchers. Thus, a higher number of citations were registered in 2021 (4.970 citations), while the highest number of citations in 2021 was registered for the study conducted by Vial (2019), who proposed a framework for a comprehensive portrait of digital transformation based on a review of 282 publications. Other relevant papers registered a significant number of citations in 2021, such as Matt et al. (2015) (142 citations in 2021), Warner and Wäger (2019) (119 citations in 2021), and Ghobakhloo (2020) (111 citations in 2021).

3.4

Highly Cited Publications

The number of citations received by a publication is a vital indicator of its quality and influence of the publication (Wang et al. 2021). Table 3.1 shows the list of the top 10 highly cited publications related to digital transformation in business within the dataset, ranked according to the number of citations. Table 3.2 provides a list of the top 10 highly cited publications ranked in terms of the average citation per year. According to the data provided in Table 3.1, the most cited publication was the study by Matt et al. (2015), with the highest number of citations (379) and the average number of citations per year 54.14. According to the Sect. 3.3, a significant proportion of the number of citations registered for a publication was registered only in the last year (142 citations in 2021). These studies on digital transformation in Number of citations 6000 4970

5000 4000 3000 2036 2000 1000 0

0

0

1

3

6

6

4

7

7

16

18

27

39

50

95

254

717

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Fig. 3.3 Number of citations for publications related to digital transformation in business – evolution in time. Source: authors’ own research

Zhu, Kevin; Dong, Shutao; Xu, Sean Xin; Kraemer, Kenneth L.

Innovation diffusion in global contexts: determinants of post-adoption digital transformation of European companies Medical Internet of Things and Big Data in Healthcare Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal Open Innovation: Research, Practices, And Policies Servitization and Industry 4.0 convergence in the digital transformation of product firms: A business model innovation perspective Industry 4.0, digitization, and opportunities for sustainability The sharing economy: Your business model's friend or foe? The Role of Dynamic Capabilities in Responding to Digital Disruption: A FactorBased Study of the Newspaper Industry

3

Source: author’s own research

10

9

8

7

Kathan, Wolfgang; Matzler, Kurt; Veider, Viktoria Karimi, Jahangir; Walter, Zhiping

Bogers, Marcel; Chesbrough, Henry; Moedas, Carlos Frank, Alejandro G.; Mendes, Glauco H. S.; Ayala, Nestor F.; Ghezzi, Antonio Ghobakhloo, Morteza

Warner, Karl S. R.; Waeger, Maximilian

Journal of Management Information Systems

California Management Review Technological Forecasting and Social Change Journal of Cleaner Production Business Horizons

Source Title Business & Information Systems Engineering Journal of Strategic Information Systems European Journal of Information Systems Healthcare Informatics Research Long Range Planning

Article

2015

Review

2020

Article

Article

2019

2016

Article

Article

Review

Article

Review

Type Article

2018

2019

2016

2006

2019

Year 2015

137

144

145

161

165

173

257

283

356

No. of citations 379

19.57

24

72.5

53.67

41.25

57.67

42.83

17.69

118.67

Average citations per year 54.14

3

6

5

Dimitrov, Dimiter V.

Vial, Gregory

Understanding digital transformation: A review and a research agenda

2

4

Authors Matt, Christian; Hess, Thomas; Benlian, Alexander

Title Digital Transformation Strategies

Rank 1

Table 3.1 Top-10 Highly cited publications related to digital transformation in business

18 Bibliometrics Performance Analysis

Understanding digital transformation: A review and a research agenda Industry 4.0, digitization, and opportunities for sustainability Impact of COVID-19 pandemic on information management research and practice: Transforming education, work and life

2

Servitization and Industry 4.0 convergence in the digital transformation of product firms: A business model innovation perspective

7

6

Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal Digital Transformation Strategies

5

4

3

Title Digital transformation: A multidisciplinary reflection and research agenda

Rank 1

Frank, Alejandro G.; Mendes, Glauco H. S.; Ayala, Nestor F.; Ghezzi, Antonio

Matt, Christian; Hess, Thomas; Benlian, Alexander

Dwivedi, Yogesh K.; Hughes, D. Laurie; Coombs, Crispin; Constantiou, Ioanna; Duan, Yanqing; Edwards, John S.; Gupta, Babita; Lal, Banita; Misra, Santosh; Prashant, Prakhar; Raman, Ramakrishnan; Rana, Nripendra P.; Sharma, Sujeet K.; Upadhyay, Nitin Warner, Karl S. R.; Waeger, Maximilian

Ghobakhloo, Morteza

Authors Verhoef, Peter C.; Broekhuizen, Thijs; Bart, Yakov; Bhattacharya, Abhi; Dong, John Qi; Fabian, Nicolai; Haenlein, Michael Vial, Gregory

Table 3.2 Top-10 Average cited publications related to digital transformation in business

Business & Information Systems Engineering Technological Forecasting and Social Change

Long Range Planning

Journal of Strategic Information Systems Journal of Cleaner Production International Journal of Information Management

Source Title Journal of Business Research

Article

Article

Article

2015

2019

Article

2020

2019

Review

Review

Type Article

2020

2019

Year 2021

53.67

54.14

57.67

60.5

72.5

118.67

Average citations per year 124

Highly Cited Publications (continued)

161

379

173

121

145

356

No. of citations 124

3.4 19

Title Medical Internet of Things and Big Data in Healthcare Open Innovation: Research, Practices, And Policies Corporate survival in Industry 4.0 era: the enabling role of lean-digitized manufacturing

Source: author’s own research

10

9

Rank 8

Table 3.2 (continued)

Bogers, Marcel; Chesbrough, Henry; Moedas, Carlos Ghobakhloo, Morteza; Fathi, Masood

Authors Dimitrov, Dimiter V.

Source Title Healthcare Informatics Research California Management Review Journal Of Manufacturing Technology Management Article Article

2020

Type Review

2018

Year 2016

38.5

41.25

Average citations per year 42.83

77

165

No. of citations 257

20 3 Bibliometrics Performance Analysis

References

21

business focus mainly on digital transformation strategies and their relationship with other corporate strategies (Matt et al. 2015), building a framework for a better understanding of digital transformation and digital technologies, proposing an examination of the role of dynamic capabilities and accounting for ethical issues (Vial 2019), developing an integrative model to study the determinants of postadoption stages of innovation diffusion, while using enterprise digital transformation as an example of technology-enabled innovations (Zhu et al. 2006), use of digital technologies in medical services (Dimitrov 2016), building dynamic capabilities for digital transformation (Warner and Wäger 2019), discussing the role of servitization and industry 4.0 as two of the most recent trends in transforming manufacturing companies (Frank et al. 2019), the opportunities provided by digital revolution for sustainability of businesses (Ghobakhloo 2020), and the role of dynamic capabilities in responding to digital disruption (Karimi and Walter 2015). When the publications are ranked according to the average citations per year, there are three new documents in Table 3.2 that differ from those in Table 3.1. An interesting phenomenon may be observed in the document with the highest average citations per year (124 citations per year), which was published last year by Verhoef et al. (2021). Drawing on the existing literature, Verhoef et al. (2021) identify three stages of digital transformation: digitization, digitalization, and digital transformation, highlighting growth strategies for digital firms at the same time as the assets and capabilities required to successfully transform digitally.

References Amankwah-Amoah J, Khan Z, Wood G, Knight G (2021) COVID-19 and digitalization: the great acceleration. J Bus Res 136:602–611. https://doi.org/10.1016/j.jbusres.2021.08.011 Dimitrov DV (2016) Medical internet of things and big data in healthcare. Healthc Inform Res 22(3):156–163. https://doi.org/10.4258/hir.2016.22.3.156 Frank AG, Mendes GHS, Ayala NF, Ghezzi A (2019) Servitization and Industry 4.0 convergence in the digital transformation of product firms: a business model innovation perspective. Technol Forecast Soc Change 141:341–351. https://doi.org/10.1016/j.techfore.2019.01.014 Ghobakhloo M (2020) Industry 4.0, digitization, and opportunities for sustainability. J Clean Prod 252:Article 119869. https://doi.org/10.1016/j.jclepro.2019.119869 Karimi J, Walter Z (2015) The role of dynamic capabilities in responding to digital disruption: a factor-based study of the newspaper industry. J Manag Inf Syst 32(1):39–81. https://doi.org/10. 1080/07421222.2015.1029380 Matt C, Hess T, Benlian A (2015) Digital transformation strategies. Bus Inf Syst Eng 57(5): 339–343. https://doi.org/10.1007/s12599-015-0401-5 Verhoef PC, Broekhuizen T, Bart Y, Bhattacharya A, Qi Dong J, Fabian N, Haenlein M (2021) Digital transformation: a multidisciplinary reflection and research agenda. J Bus Res 122:889– 901. https://doi.org/10.1016/j.jbusres.2019.09.022

22

3

Bibliometrics Performance Analysis

Vial G (2019) Understanding digital transformation: a review and a research agenda. J Strateg Inf Syst 28(2):118–144. https://doi.org/10.1016/j.jsis.2019.01.003 Wang X, Xu Z, Qin Y, Skare M (2021) Service networks for sustainable business: a dynamic evolution analysis over half a century. J Bus Res 136:543–557. https://doi.org/10.1016/j. jbusres.2021.07.062 Warner KSR, Wäger M (2019) Building dynamic capabilities for digital transformation: an ongoing process of strategic renewal. Long Range Plan 52(3):326–349. https://doi.org/10.1016/j.lrp. 2018.12.001 Zhu K, Dong S, Xu SX, Kraemer KL (2006) Innovation diffusion in global contexts: determinants of post-adoption digital transformation of European companies. Eur J Inf Syst 15(6):601–616. https://doi.org/10.1057/palgrave.ejis.3000650

Chapter 4

Science Mapping Analysis

4.1

Analysis of the Journals

This study is based on 896 articles on digitalization transformation in businesses, published in 430 journals. Next, through a comparative analysis of various bibliometric indicators, the analysis of journals provides a comprehensive picture of the source titles that have significantly contributed to the development of digitalization transformation in business from the following perspectives: citation analysis, co-citation analysis, and bibliographic coupling. Table 4.1 lists the top 20 journals when analyzed in terms of citations, co-citations, and bibliographic coupling using the bibliometric software VosViewer. When analyzing the dataset consisting of 430 journals and a minimum threshold of five documents per journal, the set obtained contained 36 journals. The highest number of citations was registered for Sustainability (69 documents and 464 citations), the Journal of Business Research (27 documents and 392 citations), the Journal of Strategic Information Systems (7 documents and 381 citations), Business Horizons (16 documents and 336 citations), and The European Journal of Information Systems (5 documents and 329 citations). If the analysis goes deeper and the average number of citations per article published by each journal is analyzed, it may be noted that there are few journals with only a few articles published in this field but with a significant number of citations. Thus, it may be observed that the European Journal of Information Systems with only five documents has an average number of citations per document of 65.8, or the Journal of Strategic Information Systems with only seven documents has an average number of citations per document of 54.42. Regarding co-citation analysis, out of 19,042 cited sources, 172 received more than 40 citations. The articles included in the dataset have mainly cited publications from MIS Quarterly (1204), Journal of Business Research (870), Technological Forecasting and Social Change (767), Strategic Management Journal (687), and Harvard Business Review (681). Referring to the bibliographic coupling analysis of the journals and a minimum threshold of four articles per journal, the set obtained © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. Bota-Avram, Science Mapping of Digital Transformation in Business, SpringerBriefs in Business, https://doi.org/10.1007/978-3-031-26765-9_4

23

329 291 270

5

16

7

European Journal of Information Systems Technological Forecasting and Social Change International Journal of Information Management

California Management Review Industrial Marketing Management

Mis Quarterly Executive

5

8

10

9

7

6

174

240

10

9

253

6

336

16

Business Horizons

381

7

4

3

Source Sustainability Journal of Business Research Journal of Strategic Information Systems

No. citations 464 392

No. articles 69 27

Rank 1 2

Citation analysis

19.33

24.00

42.17

38.57

18.19

65.80

21.00

54.43

Average citations per article 6.72 14.52

10

9

8

7

6

5

4

3

Rank 1 2

MIT Sloan Management Review

Source Mis Quarterly Journal of Business Research Technological Forecasting and Social Change Strategic Management Journal Harvard Business Review Mis Quarterly Executive Industrial Marketing Management Long Range Planning Sustainability

Co-citation analysis

517

522

528

562

628

681

10

9

8

7

6

5

4

3

767

687

Rank 1 2

Information & Management International Journal of Innovation Management International Journal of Innovation and Technology Management

Source Sustainability Journal of Business Research Technological Forecasting and Social Change Journal of Strategic Information Systems Production Planning & Control Business Process Management Journal Electronic Markets

Bibliographic coupling No. Citations 1204 870

Table 4.1 Top 20 Journals – Comparison of citation, co-citation, and bibliographic coupling of journals

1717

2005

2011

2206

2325

2427

2866

3114

Total link strength 9378 6206

24 4 Science Mapping Analysis

52

7

5

6

7

Advanced Engineering Informatics

Journal of Management Studies

Journal of Manufacturing Technology Management Technology Innovation Management Review

Electronic Markets

15

16

17

19

7

80

8

International Journal of Innovation Management

14

18

132

6

IJISPM-International Journal of Information Systems and Project Management

13

51

101

102

107

134

15

Business Process Management Journal

12

143

9

Production Planning & Control

11

7.29

7.43

13.33

20.20

14.57

13.38

22.00

8.93

15.89

19

18

17

16

15

14

13

12

11

International Journal of Information Management Organization Science Academy of Management Review Academy of Management Journal

International Journal of Production Research Journal of Strategic Information Systems International Journal of Production Economics Journal of Cleaner Production Information Systems Research

373

389

396

408

432

19

18

17

16

15

14

13

441

439

12

11

468

473

Business Horizons

European Journal of Information Systems Journal of Business & Industrial Marketing

Pacific Asia Journal of the Association for Information Systems Frontiers in Psychology

Information Systems Frontiers

Industrial Marketing Management

Advanced Engineering Informatics

Journal of Management Studies

(continued)

1222

1264

1360

1558

1573

1613

1617

1645

1707

4.1 Analysis of the Journals 25

Source Information Systems Frontiers

No. articles 6

No. citations 47

Source: author’s own research using VosViewer software

Rank 20

Citation analysis

Table 4.1 (continued) Average citations per article 7.83 Rank 20

Source Journal of Product Innovation Management

Co-citation analysis No. Citations 342 Rank 20

Source Journal of Manufacturing Technology Management

Bibliographic coupling Total link strength 1165

26 4 Science Mapping Analysis

4.2

Analysis of the Authors

27

contains 48 journals out of 430 that meet this requirement. The results indicate that the top five journals in terms of link strength are Sustainability (9738), Journal of Business Research (6206), Technological Forecasting and Social Change (3114), Journal of Strategic Information Systems (2866), and Production, Planning & Control (2427).

4.2

Analysis of the Authors

As shown in Sect. 3.1, this field of research related to digitalization transformation in business is a spectacularly increasing interest in the last few years (2019–2021), and certainly, this trend will continue in the following years. Regarding authorship, the results show that 2570 authors were responsible for the 896 articles included in the sample. The average number of citations per author was 11.12. Table 4.2 lists the top 20 authors when analyzed in terms of citations, co-citations, and bibliographic coupling using the bibliometric software VosViewer. Analyzing the list of the top five positions, it is interesting to note that citation analysis emphasizes the fact that authors with the highest number of citations published a reduced number of articles in the field of digitalization transformation in business. Alexander Benlian (Technische Universität Darmstadt) published two articles with 397 citations, Thomas Hess (University of Munich) published four articles with 391 citations, Christian Matt (Universität Bern) published one article with 377 citations, Gregory Vial (HEC Montreal) published one article with 353 citations, and Shutao Dong (Renmin University of China, Beijing), Kenneth L. Kraemer (Paul Merage School of Business), Sean Xin Xu (Tsinghua University), and Kevin Zhu (University of California) published one article with 282 citations. Regarding results of co-citation analysis in terms of authorship, for the authors cited in the reference list of the publications included in our sample, out of 30,821 cited authors, 230 authors were cited more than 20 times. The data displayed in Table 4.2 revealed that the most cited authors in the references list of the articles included in the data set are David J. Teece (UC Berkeley) – 277 citations, Michael E. Porter (Harvard Business School) – 167 citations, Satish Nambisan (Weatherhead School of Management) – 160 citations, European Commission – 153 citations, and Kathleen Eisenhardt (Stanford University) – 143 citations. Finally, a bibliographic coupling analysis of authorship revealed the authors with the highest link strength. According to Caputo et al. (2021), the highest link strength of bibliographic coupling indicates authors with a higher centrality in the network of citations, while the degree of centrality reveals the number of linkages an author developed in the research network (Donthu et al. 2021). Thus, the authors with the highest link strength in terms of bibliographic coupling are Thomas Hess (University of Munich) – 11,236, Christoph Buck (Universität Bayreuth) – 9330, Michael Rosemann (Queensland University of Technology) – 8813, Stefanie Steinhauser (University of Regensburg) – 8771, and Florian Wiesböck (Institute for Information Systems and New Media) – 8336.

156

2

176 170 170 164 164 164

2 1 1 1 1 1

159 159 159

226

4

1 1 1

282 282 257

1 1 1

Source: author’s own research using VosViewer software

20

17 18 19

11 12 13 14 15 16

10

282 282

No. citations 397 391 377 353

1 1

No. articles 2 4 1 1

89 84 83 79

Osterwalder, A 20

107 107 100 99 98 92

109

116 114 111

143 126

Zott, C Muller, JM Christensen, CM

Hess, T Kane, GC Yin, RK Westerman, G Chesbrough, H Mcafee, A

Bharadwaj, A

Matt, C OECD Hair, JF

Author Teece, Dj Porter, ME Nambisan, S European, Commission Eisenhardt, KM Vial, G

No. citations 277 167 160 153

17 18 19

11 12 13 14 15 16

10

7 8 9

5 6

Rank 1 2 3 4

Co-citation analysis

20

17 18 19

11 12 13 14 15 16

10

7 8 9

5 6

Rank 1 2 3 4

De Massis, Alfredo

Saunila, Minna Ukko, Juhani Kraus, Sascha Baiyere, Abayomi Candelo, Elena Chawla, Raghu Nandan Goyal, Praveen Bresciani, Stefano Frattini, Federico

Rantala, Tero

Klos, Christoph Spieth, Patrick Nasiri, Mina

Wiesboeck, Florian Chierici, Roberto

Author Hess, Thomas Buck, Christoph Rosemann, Michael Steinhauser, Stefanie

Bibliographic coupling

6915

7281 6931 6924

7605 7605 7487 7427 7402 7281

7605

7757 7757 7605

8336 7789

Total link strength 11,236 9330 8813 8771

4

Ayala, Nestor F. Frank, Alejandro G. Mendes, Glauco H. S. Matzler, Kurt

Dong, Shutao Kraemer, Kenneth L. Xu, Sean Xin Zhu, Kevin Dimitrov, Dimiter V. Ghobakhloo, Morteza Ghezzi, Antonio Waeger, Maximilian Warner, Karl S. R. Bogers, Marcel Chesbrough, Henry Moedas, Carlos

5 6

7 8 9

Author Benlian, Alexander Hess, Thomas Matt, Christian Vial, Gregory

Rank 1 2 3 4

Citation analysis

Table 4.2 Top 20 Authors – Comparison of citation, co-citation, and bibliographic coupling of authors

28 Science Mapping Analysis

4.3

4.3

Analysis of the Publications

29

Analysis of the Publications

As mentioned in the previous chapter, the number of publications on digitalization transformation in business (Fig. 3.1) indicates an exponential growth in the period 2019–2021. Table 4.3 lists the top 20 publications when analyzed in terms of citations, co-citations, and bibliographic coupling using the bibliometric software VosViewer. The selected sample included 896 publications, and the average number of citations per publication was 9.34, highlighting increasing academic interest in this field of research. Next, proceeding to the co-citation procedure which allows the analysis of the references cited by the publications included in the dataset, we can observe a comprehensive picture of the main connected references, which constitutes the main theoretical pillars of the research field under investigation (Caputo et al. 2021). When analyzing 896 articles included in the sample, and a minimum threshold of 10 citations of a cited reference was fixed, the set resulted in 308 cited references out of a 44,896 total. Thus, the five most connected references highlighted as the main theoretical pillars of the investigated research field are as follows: • Vial G (2019) Understanding digital transformation: A review and research agenda. Journal of Strategic Information Systems 28(2): 118–144. 10.1016/j. jsis.2019.01.003 – 125 citations. • Matt C, Hess T, Benlian A. (2015) Digital Transformation Strategies. Business & Information Systems Engineering 57(5): 339–343. 10.1007/s12599-015-04015 – 112 citations. • Bharadwaj A, El Sawy OA, Pavlou PA, Venkatraman N (2013) Digital business strategy: toward the next generation of insights. MIS Quarterly 37(2): 471–482. 10.25300/misq/2013/37:2.3 – 101 citations. • Hess T, Matt C, Benlian A, Wiesböck F (2016) Options for Formulating a Digital Transformation Strategy. MIS Quarterly Executive 15(2): 123–139 – 94 citations. • Warner KSR, Wäger M (2019) Building dynamic capabilities for digital transformation: An ongoing strategic renewal process. Long Range Planning 52(3): 326–349. 10.1016/j.lrp.2018.12.001 – 70 citations. To foster an understanding of the theoretical foundations of the 896 articles included in the sample, the network of publications referenced by the 896 documents was analyzed through bibliographic coupling analysis. The largest set of connected articles contained 183 publications (20.42% of the sample). Following the data displayed in Table 4.3, the five studies with the highest indices of bibliographic coupling were as follows: • Chawla RN, Goyal P (2021) Emerging trends in digital transformation: a bibliometric analysis. Benchmarking: An International Journal. 10.1108/BIJ-012021-0009. • Warner KSR, Wäger M (2019) Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long Range Planning 52(3): 326–349. 10.1016/j.lrp.2018.12.001.

6

7

257

170

164

159

Dimitrov DV (2016), 10.4258/ hir.2016.22.3.156

Warner KSR, Wäger M (2019), 10.1016/j.lrp.2018.12.001

Bogers M, Chesbrough H, & Moedas C (2018), 10.1177/ 0008125617745086 Frank AG, Mendes GHS, Ayala NF, & Ghezzi A (2019), 10.1016/j. techfore.2019.01.014

4

5

6

Nambisan S, Lyytinen, K., Majchrzak A, Song M (2017), 10.25300/misq/2017/41:1.03 Berman Saul J(2012), 10.1108/ 10878571211209314

Warner KSR, Wäger M (2019), 10.1016/j.lrp.2018.12.001

Matt C, Hess, T., & Benlian, A (2015), 10.1007/s12599-0150401-5 Bharadwaj A, El Sawy, OA, Pavlou, PA, & Venkatraman, N (2013), 10.25300/misq/ 2013/37:2.3 Hess T, Matt C, Benlian A, Wiesböck F (2016)

Cited Reference Vial G (2019), 10.1016/j. jsis.2019.01.003

6

7

60

5

4

3

2

Rank 1

62

70

94

101

112

No. citations 125

Wessel L, Baiyere A, Ologeanu-Taddei R, Cha J & Blegind Jensen T (2021), 10.17705/1jais.00655 Hanelt A, Bohnsack R, Marz D, & Antunes C (2021), 10.1111/joms.12639 Wiesböck F, Hess T, & Spanjol J (2020), 10.1016/j. im.2020.103389 Buck C., Eder DM, Brügmann J (2021)

Vial G (2019), 10.1016/j. jsis.2019.01.003

Warner KSR, Wäger M (2019), 10.1016/j.lrp.2018.12.001

Articles Chawla RN & Goyal P (2021), 10.1108/BIJ-01-2021-0009

Bibliographic coupling

1452

1459

1469

1469

1757

2008

Total link strength 2137

4

7

5

4

3

282

Zhu K, Dong S, Xu SX, & Kraemer KL (2006), 10.1057/ palgrave.ejis.3000650

3

2

353

2

Articles Matt C, Hess, T., & Benlian, A (2015), 10.1007/s12599-0150401-5 Vial G (2019), 10.1016/j. jsis.2019.01.003

Rank 1

Rank 1

Co-citation analysis

No. citations 377

Citation analysis

Table 4.3 Top 20 most influential articles - Comparison in terms of citations, co-citation, and bibliographic coupling

30 Science Mapping Analysis

14

13

12 12

13

14

121

121

11

130

122

10

137

Karimi J, & Walter Z (2015), 10.1080/ 07421222.2015.1029380 Li L, Su F, Zhang W, & Mao, JY (2018), 10.1111/isj.12153

10

Parviainen P, Tihinen M, Kaariainen J, Teppola S (2017), 10.12821/ ijispm050104 Verhoef PC, Broekhuizen T, Bart Y, Bhattacharya A, Qi Dong J, Fabian N, & Haenlein M (2021), 10.1016/j. jbusres.2019.09.022 Dwivedi YK, Hughes DL, Coombs C, Constantiou I, Duan Y, Edwards JS, . . . Upadhyay N (2020), 10.1016/j. ijinfomgt.2020.102211

9

144

Kathan W, Matzler K, & Veider V (2016), 10.1016/j. bushor.2016.06.006

9

11

8

145

Ghobakhloo M (2020a), 10.1016/j.jclepro.2019.119869

8

Singh AK, Kumar B, Singh G, & Mohan A (2017), 10.1007/ 978-3-319-57699-2_1

13

14

49

12

53

49

11

55

Yoo Y, Henfridsson O, & Lyytinen K (2010), 10.1287/ isre.1100.0322 Teece DJ (2007), 10.1002/ smj.640

Barney J (1991), 10.1177/ 014920639101700108

10

9

58

56

8

60

Eisenhardt KM (1989), 10.2307/258557

Sebastian I, Ross J, Beath C, Mocker M, Moloney K, & Fonstad NO (2017), 10.4324/ 9780429286797-6 Teece DJ, Pisano G, & Shuen A (1998), 10.1002/(sici)10970266(199708)18:73.0.co;2-z

1262 Guo L, Xu L (2021), 10.3390/ su132212844

(continued)

1269

1298

1307

1335

1411

1449

Zhu X., Ge S, Nianxin W (2021), 10.1016/j. cie.2021.107774

Verhoef PC, Broekhuizen T, Bart Y, Bhattacharya A, Qi Dong J, Fabian N, & Haenlein M (2021), 10.1016/j. jbusres.2019.09.022 Buck C, Marques CP, Roseman M (2021), 10.1142/ S0219877021500231 Wu M, Kozanoglu DC, Min C, & Zhang Y (2021), 10.1016/j. aei.2021.101368 Klos C, & Spieth P (2020), 10.1016/j. techfore.2020.120428

Teubner RA, & Stockhinger J (2020), 10.1016/j. jsis.2020.101642

4.3 Analysis of the Publications 31

Schallmo D, Williams CA, & Boardman L (2017), 10.1142/ S136391961740014X Ghobakhloo M, & Fathi M (2020b), 10.1108/JMTM-112018-0417 Ranganathan C, Teo TSH & Dhaliwal J (2011), 10.1016/j. ijinfomgt.2011.02.004

18

Rank 15

16

17

18

19

20

No. citations 118

94

92

91

77

76

Li L, Su F, Zhang W, & Mao JY(2018), 10.1111/isj.12153

Yoo Y, Boland RJ, Lyytinen K, & Majchrzak A (2012), 10.1287/ orsc.1120.0771 Hinings B, Gegenhuber T, & Greenwood R (2018), 10.1016/ j.infoandorg.2018.02.004 Fitzgerald M, Kruschwitz N, Bonnet D, & Welch M (2013)

Teece DJ (2010), 10.1016/j. lrp.2009.07.003

Cited Reference Porter ME & Heppelmann JE (2014)

Co-citation analysis

Source: author’s own research using VosViewer software

20

19

17

16

Articles Trantopoulos K, von Krogh G, Wallin MW, & Woerter M (2017), 10.25300/MISQ/2017/ 41.1.15 Li J, Greenwood D, & Kassem M (2019), 10.1016/j. autcon.2019.02.005 Birkel H, Veile J, Müller J, Hartmann E, & Voigt KI (2019), 10.3390/su11020384

Rank 15

Citation analysis

Table 4.3 (continued)

18

46

42

20

19

17

48

44

16

Rank 15

48

No. citations 48

Soluk J, & Kammerlander N (2021), 10.1080/ 0960085x.2020.1857666 Steinhauser S, Doblinger C, & Hüsig S (2020), 10.1080/ 07421222.2020.1831778

Liu J, Yang W, Liu W (2021), 10.1108/JOCM-02-2020-0043

Articles Culot G, Nassimbeni G, Orzes G, & Sartor M (2020), 10.1016/j. techfore.2020.120092 Van Veldhoven Z, & Vanthienen J (2021), 10.1007/ s12525-021-00464-5 Sousa-Zomer, TT, Neely A, & Martinez V (2020), 10.1108/ ijopm-06-2019-0444

Bibliographic coupling

1169

1174

1206

1218

1248

Total link strength 1262

32 4 Science Mapping Analysis

4.4

Analysis of the Keywords

33

• Vial G (2019) Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems 28(2): 118–144. 10.1016/j. jsis.2019.01.003. • Wessel L, Baiyere A, Ologeanu-Taddei R, Cha J, Blegind Jensen T (2021) Unpacking the Difference Between Digital Transformation and IT-Enabled Organizational Transformation. Journal of the Association for Information Systems 22(1):102–129. 10.17705/1jais.00655. • Hanelt A, Bohnsack R, Marz D, Antunes C (2021) A systematic review of the literature on digital transformation: insights and implications for strategy and organizational change. Journal of Management Studies 58 (5): 1159–1197. 10.1111/joms.12639. It is interesting to note from Table 4.3 that of the top 20 most influential articles in terms of bibliographic coupling, six articles were published in 2021, six articles were published in 2020, and two articles were published in 2019. This result confirms once again the effervescence of this research field, which has registered an exponential growth in recent years, and this trend will certainly continue in the future.

4.4

Analysis of the Keywords

The analysis of keywords allows the assessment of the co-occurrence of keywords and the identification of connections between concepts that co-occur in titles, abstracts, or keywords within the publications included in the dataset (Bernatović et al. 2021). Following the procedure of Wang et al. (2021), keyword analysis was conducted using two powerful bibliometric software programs, VosViewer and CiteSpace, while the interactions between topics related to digital transformation in business were highlighted through co-occurrence analysis, burst detection, and timeline view analysis.

4.4.1

Co-occurrence of Keywords

An effective tool for analyzing the co-occurrence of keywords is VosViewer which allows the visualization of the network diagrams of keywords (Van Eck and Waltman 2010), and the identification of thematic clusters that could represent the theoretical foundational basis for the research field under investigation (Fakhar Namesh et al. 2021). In performing this analysis, the authors’ keywords and Keyword Plus were used. Keyword Plus is an effective tool provided by the Web of Science database, which applies an automatic computer algorithm to provide a list of index terms that appear frequently in the titles of the references of an article. The authors of the publications included in this study’s sample highlighted 2808 keywords (114 keywords co-occurred more than five times), while using index terms

34

4

Science Mapping Analysis

Table 4.4 Top 15 Keywords in terms of co-occurrences Authors’ keywords Rank 1 2 3 4 5 6 7 8 9

Keyword Digital transformation Digitalization Industry 4.0 Big data Artificial intelligence Digitization Innovation Business model Digital economy

10 11 12 13

Covid-19 Sustainability Digital technologies Business model innovation Internet of things Digital innovation

14 15

Keywords Plus Cooccurrences 441 105 54 37 35 35 35 34 31

Rank 1 2 3 4 5 6 7 8 9

28 28 26 24

10 11 12 13

23 22

14 15

Keyword Innovation Management Technology Business Performance Impact Dynamic capabilities Future Informationtechnology Systems Big data Framework Strategy Challenges Digital transformation

Cooccurrences 148 88 82 81 76 73 70 61 61 61 59 59 55 47 47

Source: author’s own research using VosViewer software

generated by Keyword Plus, 1178 keywords (155 keywords co-occurred more than five times) were emphasized. Table 4.4 illustrates the top 15 keywords in terms of co-occurrence using both author keywords and Keyword Plus. To foster a comprehensive understanding of the co-occurrence of keywords, Figs. 4.1 and 4.2 provide a visualization of the co-occurrence network of high-frequency author keywords and Keyword Plus in the literature related to digital transformation in business. Another useful diagram provided by VOSviewer is overlay visualization, where keywords are colored to a score computed based on the average year of occurrence of a keyword, and thus colors vary from blue (the oldest period) to green to yellow (most recent period) (Van Eck and Waltman 2010). Figure 4.3 provides overlay visualization with the authors’ keywords, highlighting the temporal distribution of the keywords in each cluster. Analyzing the overlay diagram, it can be observed that the field of digital transformation has evolved from a previous focus on business model organization, big data, transformation, and the Internet of Things (oldest period) to concepts such as digital strategies, digital entrepreneurship, digitalization, digital economy (recent period), and artificial intelligence, digital technology, and COVID-19 (most recent period).

4.4

Analysis of the Keywords

35

Fig. 4.1 Co-occurrence of author’s keywords network diagram. Source: author’s own research using VosViewer software

4.4.2

Burst Detection Analysis

A very helpful bibliometric software tool for detecting emerging trends for future research to foster an understanding of the body of knowledge related to the research field under enquiry is the CiteSpace tool, created based on a Java application. According to its developers (Chen 2006; Chen et al. 2014), burst detection of subject categories, keywords, and cited references revealed the most active research topics that increased abruptly over time. The burst detection function provided by the CiteSpace bibliometric software is very useful for examining the current and emerging trends in the research under investigation. Table 4.5 illustrates the top ten keywords with strong citation bursts over time, classified according to burst strength. Thus, the results of the burst detection performed using CiteSpace revealed the top ten keywords that increased abruptly over time. This time is indicated by the blue lines, whereas the period interval in which the keyword was found to have a burst is highlighted as a red line segment, showing the beginning and ending years of the burst duration. The relevance of burst

36

4

Science Mapping Analysis

Fig. 4.2 Co-occurrence of Keywords Plus network diagram. Source: author’s own research using VosViewer software

keywords is given by the fact that they represent indicators of frontier topics or emerging trends. According to the data provided in Table 4.5, at the top of the list, the keyword “big data” burst between 2017 and 2019 with a burst strength of 2.24. More recently, the burst detection of keywords highlighted that research topics related to “business model innovation” (2018–2019), “circular economy” (2019–2021), and “IT capability” (2019–2021) have attracted significant interest from researchers in the field of digital transformation in business.

4.4.3

Timeline View Analysis

To clearly identify new developments and emerging trends in the field of research related to digital transformation in business, timeline visualization was generated through the timeline view function provided by CiteSpace. According to its developers (Chen 2006; Chen et al. 2014), timeline visualization allows for the

4.4

Analysis of the Keywords

37

Fig. 4.3 Co-occurrence of author’s keywords overlay diagram. Source: author’s own research using VosViewer software

identification of newly emerging threads of research, allowing them to be recognized more easily. Proceeding to a timeline view analysis, the clusters are arranged on a horizontal timeline, and the direction of the time points to the right. Additionally, the label of each cluster is indicated at the end of the cluster timeline. The generated clusters are numbered in descending order of cluster size, starting from the largest cluster #0, the second largest cluster #1, and so on. The large number of connected curves highlights the symbiotic connection of keywords, indicating the occurrence of emerging research trends in the research field under investigation (Wang et al. 2021). Figure 4.4 presents the eight main clusters with core labels for high-frequency keywords generated after the timeline view analysis. The first and largest cluster (cluster #0) within the selected sample of articles was “digital transformation,” followed by “big data,” “digital economy,” “big data analytics,” “sharing economy,” “digital disruption,” “artificial intelligence,” and “digital age.” As can be seen, most

38

4

Science Mapping Analysis

Table 4.5 Top 10 Keywords with the Strongest Citation Bursts Keywords

Year

Strength

Begin

End

2003 - 2021

Big data

2003

2.24

2017

2019 ■■■■■■■■■■■■■■■■■■■

Circular economy

2003

2.22

2019

2021 ■■■■■■■■■■■■■■■■■■■

Business model innovation

2003

2.15

2018

2019 ■■■■■■■■■■■■■■■■■■■

Internet of service

2003

1.76

2016

2018 ■■■■■■■■■■■■■■■■■■■

Industry 4.0

2003

1.61

2017

2018 ■■■■■■■■■■■■■■■■■■■

IT capability

2003

1.59

2019

2021 ■■■■■■■■■■■■■■■■■■■

Digital platform

2003

1.58

2017

2018 ■■■■■■■■■■■■■■■■■■■

Change management

2003

1.57

2017

2018 ■■■■■■■■■■■■■■■■■■■

Disruptive innovation

2003

1.36

2015

2018 ■■■■■■■■■■■■■■■■■■■

Performance impact

2003

1.36

2006

2011 ■■■■■■■■■■■■■■■■■■■

Source: author’s own research using CiteSpace software

of the clusters are convergent in the last decade of the period analyzed, except for cluster #1 (“big data”) and cluster #7 (“digital age”), whose duration with highfrequency keywords started in 2003. In addition, cluster #2 (“digital economy”) and cluster 3 (“big data analytics”) started their period of high-frequency keywords in 2007. It is interesting to note that all clusters revealed by timeline view analysis are currently active. The most recent cluster is cluster #6 artificial intelligence, which has recently started its duration of high-frequency keywords since 2018. Most likely, this cluster will continue its high-frequency period significantly in the subsequent period. Proceeding to a further analysis of the specific keywords in each cluster, we argue that the research topics in the early stages of this field of research are relatively generalized, such as “internet,” “commerce,” “system,” and “impact,” evolving to “value creation,” “supply chain management,” “information strategy,” “digital transformation,” and “firm performance.” Most recently, keywords have evolved to reflect the specific facets of the digital transformation process in business, such as “business model innovation,” “TOE framework,” “digital entrepreneurship,” “internet of things,” “big data analytics,” “digital platform,” “digital innovation,” “artificial intelligence,” “strategic alignment,” “digital maturity,” “dynamic capabilities,” and “digital government.”

Fig. 4.4 Timeline visualization of high-frequency keywords. Source: author’s own research using CiteSpace software

4.4 Analysis of the Keywords 39

40

4

Science Mapping Analysis

References Bernatović I, Slavec Gomezel A, Černe M (2021) Mapping the knowledge-hiding field and its future prospects: a bibliometric co-citation, co-word, and coupling analysis. Knowl Manag Res Pract:1–16. https://doi.org/10.1080/14778238.2021.1945963 Bharadwaj A, El Sawy OA, Pavlou PA, Venkatraman N (2013) Digital business strategy: toward a next generation of insights. MIS Q 37(2):471–482. https://doi.org/10.25300/MISQ/2013/37:2.3 Caputo A, Pizzi S, Pellegrini M, Dabic M (2021) Digitalization and business models: where are we going? A science map of the field. J Bus Res 123:489–501. https://doi.org/10.1016/j.jbusres. 2020.09.053 Chawla RN, Goyal P (2021) Emerging trends in digital transformation: a bibliometric analysis. Benchmarking. https://doi.org/10.1108/BIJ-01-2021-0009 Chen C (2006) CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature. J Am Soc Inf Sci Technol 57(3):359–377. https://doi.org/10.1002/asi.20317 Chen C, Dubin R, Kim MC (2014) Emerging trends and new developments in regenerative medicine: a scientometric update (2000–2014). Expert Opin Biol Ther 14(9):1295–1317. https://doi.org/10.1517/14712598.2014.920813 Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM (2021) How to conduct a bibliometric analysis: an overview and guidelines. J Bus Res 133:285–296. https://doi.org/10.1016/j.jbusres. 2021.04.070 Fakhar Manesh M, Pellegrini MM, Marzi G, Dabic M (2021) Knowledge management in the fourth industrial revolution: mapping the literature and scoping future avenues. IEEE Trans Eng Manag 68(1):289–300. https://doi.org/10.1109/TEM.2019.2963489 Hanelt A, Bohnsack R, Marz D, Antunes C (2021) A systematic review of the literature on digital transformation: insights and implications for strategy and organizational change. J Manag Stud 58(5):1159–1197. https://doi.org/10.1111/joms.12639 Hess T, Matt C, Benlian A, Wiesbock F (2016) Options for formulating a digital transformation strategy. MIS Q Executive 15(2):123–139 Iwami S, Ojala A, Watanabe C, Neittaanmaki P (2020) A bibliometric approach to finding fields that co-evolved with information technology. Scientometrics 122(1):3–21. https://doi.org/10. 1007/s11192-019-03284-9 Matt C, Hess T, Benlian A (2015) Digital transformation strategies. Bus Inf Syst Eng 57(5): 339–343. https://doi.org/10.1007/s12599-015-0401-5 Van Eck NJ, Waltman L (2010) Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84(2):523–538. https://doi.org/10.1007/s11192-009-0146-3 Vial G (2019) Understanding digital transformation: a review and a research agenda. J Strateg Inf Syst 28(2):118–144. https://doi.org/10.1016/j.jsis.2019.01.003 Wang X, Xu Z, Qin Y, Skare M (2021) Service networks for sustainable business: a dynamic evolution analysis over half a century. J Bus Res 136:543–557. https://doi.org/10.1016/j. jbusres.2021.07.062 Warner KSR, Wäger M (2019) Building dynamic capabilities for digital transformation: an ongoing process of strategic renewal. Long Range Plan 52(3):326–349. https://doi.org/10.1016/j.lrp. 2018.12.001 Wessel L, Baiyere A, Ologeanu-Taddei R, Cha J, Blegind Jensen T (2021) Unpacking the difference between digital transformation and IT-enabled organizational transformation. J Assoc Inf Syst 22(1):102–129. https://doi.org/10.17705/1jais.00655

Chapter 5

Content Analysis of Articles Included in the Bibliometric Analysis of Digital Transformation in Business

5.1 5.1.1

Thematic Cluster 1: Digital Transformation Process Digital Transformation Strategies

The process of exploring new digital technologies and exploiting their benefits supposes complex transformations of key business operations while impacting product and business processes and organizational structures. Such complex transformations should not be initiated without a well-defined digital transformation strategy that should help management integrate the coordination, prioritization, and implementation of digital transformation within a firm (Matt et al. 2015). If previous researchers (Bharadwaj et al. 2013) argued that it is critical to ensure alignment between business strategies and IT strategies through a “digital business strategy,” Matt et al. (2015) argued that the digital transformation strategy is a much more complex concept. Thus, in Matt et al.’s (2015) vision, digital business strategy should be approached as a holistic concept and should include desired future business opportunities and strategies for entities that are partly or fully based on digital technologies. In this vein, Matt et al. (2015) proposed addressing the digital transformation process from two perspectives: (1) operational strategy (products, markets, processes) and (2) functional strategy (finance, human resources, IT, etc.). Furthermore, in establishing a digital transformation framework, four essential dimensions must be considered: the use of technologies, changes in value creation, structural changes, and financial aspects (Matt et al. 2015). Subsequently, this digital transformation framework was enhanced, starting from the successful lessons of three German media companies in approaching the digital transformation process. Therefore, Hess et al. (2016) provided a list of 11 strategic questions and possible answers that managers could use to customize their digital transformation strategy specifically for their industry or business model. Later, other authors (McGrath and McManus 2020) proposed an innovative methodology that companies can use to successfully face digital challenges and © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. Bota-Avram, Science Mapping of Digital Transformation in Business, SpringerBriefs in Business, https://doi.org/10.1007/978-3-031-26765-9_5

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learn their way to a new business model, namely the discovery-driven planning (DDP) strategy. McGrath and McManus (2020) argue that a step-by-step digital transformation process works better for traditional incumbent companies than the all-or-nothing approach, which is specific to a start-up’s pivot. Thus, the proposed DDP strategy includes the following five key steps: 1. Define operating experience. 2. Focus on specific problems: Identify the results and progress metrics. 3. Identify your competition: cast a wide net. 4. Look for platforms: Do not forget implications for the ecosystem. 5. Test Your Assumptions: Failures are also a lesson. In addition, these authors argued that in the case of incumbent firms, a discovery-driven planning strategy in designing digital transformation is much more recommended because by starting small and learning a lot, a company can obtain early supporters and early adopters. As people become more comfortable with digitization projects and more aware of the positive impact of digital technologies on financial performance indicators, extensive organizational digital transformation can be implemented. Other studies further highlight the huge differences that could exist between a well-designed digital transformation strategy and its successful implementation. For instance, Correani et al. (2020) argued that, according to recent estimations, 66–84% of digital transformation projects fail because of various inconsistencies between digital strategy formulation and their implementation. Starting from the analysis of three case studies of firms (ABB, CNH Industrial, and Vodafone), proposed a framework that should help companies implement their digital transformation strategies successfully while innovating their business models. According to this proposed framework (Correani et al. 2020), the first step in the successful implementation of a digital transformation strategy is to clearly define the scope of transformation. Second, companies must ensure that they have proper access to data sources (external and internal), which is an enabler of digital transformation. Digital platforms need to be properly developed because they often collect end-user data, so they should be designed and protected in compliance with the law. Companies should focus on people and employees with specific skills and competencies to fully understand the benefits of new digital technologies. Second, the role of business partners should not be neglected, while establishing business agreements with partners that can provide the organization with new data, capabilities, and knowledge that are imperative to the successful implementation of the digital transformation strategy is quite important. Furthermore, after data are collected, cleaned, and securely stored, specific artificial intelligence (AI) techniques should be implemented to ensure the proper extraction and exploration of data to deliver value to the organizational knowledge base. Companies must then properly define the processes and procedures necessary to support information extraction and knowledge generation processes. These procedures and processes should be agile to support the company in its process of adapting to rapid changes and opportunities provided by digital transformation. Finally, the information and knowledge generated by digital transformation should be used to support newly transformed activities, tasks, and services that generate value for customers (Correani et al. 2020). Starting from an analysis of the digitalization efforts of 26 leading manufacturers, Björkdahl (2020) analyzed the difficulties encountered in their process of

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digitalization, arguing that many companies are not yet perfectly aware of the benefits of digital transformation due to a short-term approach, which focuses on increasing efficiency through digitalization rather than following a growth agenda. Successful business leaders in implementing digital transformation adopt long-term digital transformation, starting from customer needs and generating value for customers. Thus, the implementation of digital transformation will strengthen the core business, gaining significant advantages over its competitors (Björkdahl 2020). Even if there are multiple approaches to designing and implementing a digital transformation strategy, almost all researchers agree that digitalization is a complex and multidimensional process that must be perceived holistically, while its tendency to embrace all the domains of the company is increasingly obvious (Porfirio et al. 2021).

5.1.2

Phases of Digital Transformation

As noted above, the process of digital transformation is complex and involves several phases of digitalization. Verhoef et al. (2021) noted that more research is needed to understand how companies undergo the phases of digital transformation. As with many other researchers, Verhoef et al. (2021) highlighted three stages of digital transformation, digitization, digitalization, and digital transformation, highlighting growth strategies for digital firms at the same time as the assets and capabilities required to successfully transform digitally. The need to distinguish between digitization, digitalization, and digital transformation in terms of scale and scope has also been clearly highlighted by other researchers (Tilson et al. 2010; Hess et al. 2016; Saarikko et al. 2020). Digitization is a component of digitalization, which, in turn, is a component of the digital transformation process (Saarikko et al. 2020). Digitization refers to the conversion of information from analog to digital or the automatization of business processes through information and communication technologies (Hess et al. 2016). Or, in other words, digitization is the technical process of transferring analog signals to digital signals (Tilson et al. 2010). Even if the terms “digitization”and “digitalization” are often used interchangeably, Saarikko et al. (2020) argued that there are significant conceptual differences. Thus, if digitization supposes a system of information and communication technologies in terms of its capabilities, digitalization provides answers regarding the relevance of these technologies to a specific business process or organization. Digital transformation is a complex concept, focusing on the changes those digital technologies can produce in a company’s business model, products, processes, and organizational structure (Hess et al. 2016). Vial (2019) defined digital transformation as a process that “aims to improve an entity by triggering significant changes in its properties through combinations of information, computing, communication, and connectivity technologies” (Vial 2019, p. 118). Certainly, almost all researchers agree that digitization, digitalization, and digital transformation are different terms, and a more rigorous conceptual foundation for these terms needs to be developed. There

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are many conceptual approaches in the literature regarding these concepts, and these terms are applied differently in various studies (Loebbecke and Picot 2015), but so far there is no consensus on the different levels of digitalization (Svadberg et al. 2019). Another research gap identified by Verhoef et al. (2021) refers to the apparent lack of significant empirical research on the impact of different phases of digital transformation on firm performance, assuming that incumbent companies go through stages of digitization, digitalization, and digital performance. Therefore, further studies could be conducted to fully investigate how companies move through various digital transformation phases and how their performance is affected by each phase.

5.1.3

Business Model Innovation in Digital Context

According to some authors, Vaska et al. (2021), the digital transformation of business model innovation is an emerging research topic within this field of research with increasing interest from researchers in recent years. Recent scientific contributions on this topic (Andersen et al. 2022, Buck et al. 2021, Do Vale et al. 2021, Klos et al. 2021, Frank et al. 2019, Latilla et al. 2019, Teece 2018) started to investigate and identify theoretical frameworks that could explain the impact of digital transformation on business model innovation, many of these contributions arguing that a real digital transformation demands holistic transformations of business model. For instance, Klos et al. (2021) applied a case study to 15 incumbents to investigate how these firms have changed their business models, providing a holistic approach to how business model innovation could be impacted during the digital transformation process. Their findings argue for the significance of a preparatory phase in which the strategic course is established, and the effectiveness of the digital transformation process increases when a single person, namely, the chief digital officer, is responsible. Starting from the argument provided by Foss and Saebi (2017), according to three levels of variables that influence the business model innovation process (the macro, company, and micro levels), Do Vale et al. (2021) highlighted that business model innovation in the context of digital transformation is a process in which implementation (top-down) and emergence (bottom-up) are intertwined, while lower-level and middle-level managers can find short-term solutions to better satisfy customers’ needs and improve coordination between organizational processes and various channels. The impact of digital transformation on business model innovation in small and medium-sized enterprises (SMEs) has also attracted the attention of researchers. Andersen et al. (2022) conducted a case study of 18 SMEs, aiming to investigate how their business models were impacted by their digital ventures. Their findings revealed four critical activities related to business model innovation: (1) assessing the external environment to identify new opportunities, (2) communicating a sense

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of urgency, (3) experimenting with new opportunities, and (4) handling decisionmaking with a mixture of intuition and data. An interesting perspective was provided by Frank et al. (2019), who showed the positive impact of two innovation trajectories, servitization and Industry 4.0, on the innovation of business models in their digital ventures. Servitization provides strategic and competitive advantages for companies adopting this form of business model innovation, focusing on the conversion process from product-centric to service-oriented business models. Industry 4.0, a concept that includes various business dimensions such as manufacturing, product development, supply chain, and working processes, is supported by certain emerging technologies such as the Internet of Things, cloud services, big data, and analytics. As a result of their findings, Frank et al. (2019) proposed a conceptual framework in which these two concepts (servitization and Industry 4.0) are connected from the business model innovation point of view, approached from two perspectives: levels of digitization (determined by the usage of digital technologies in the context of Industry 4.0), and servitization types. Another valuable theoretical perspective on the digital transformation of the business model is provided by Latilla et al. (2019), who, through a longitudinal case study of energy utility, identified the mechanisms through the business model had been innovated and the organizational ambidexterity and changes requested by the digital transformation journey. Therefore, in the context of challenging digital transformation, companies must revise their traditional business models to look for valuable solutions that offer customers a new value proposition. An open door to future research agenda is suggested by Latilla et al. (2019), who highlighted the research gap in the effect of the cultural dimension as a critical issue of the organizational change process, especially in the context of the interconnection between organizational changes and business model innovation generated by the process of digital transformation. In a recent study in the form of an interview (Hinterhuber and Nilles 2022), the chief digital information officer (CDIO) of Henkel offered an interesting perspective on digital transformation as the Holy Grail, “a force that is not easy to find, not easy to capture, and that has the potential to dramatically improve the customer experience.” (Hinterhuber and Nilles 2022, p. 261). Furthermore, in the vision of Henkel CDIO to ensure the effectiveness and productivity of digital transformation ventures and digital growth, small agile teams with end-to-end responsibility for project delivery, start-up mentality, and customer obsession are necessary. Finally, as Vaska et al. (2021) also noted, most of these recent contributions argued for the general idea that digital transformation generates the necessity of new conceptualizations for business models, while new ways to create and deliver value are emerging.

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Content Analysis of Articles Included in the Bibliometric Analysis. . .

Digital Innovation

Even if the terms “digital transformation” and “digital innovation” are often used interchangeably, it is important to note that they do not have the same meaning. Even though the importance of digital innovation is widely acknowledged among academics and practitioners, there is still no consensus on its conceptualization. Most research considers that digital transformation is about the ample process of transforming a company and its business model by adopting digital technologies to support its business goals, while digital innovation is the final result of adopting digital technologies, referring to the outcomes and what results from the complex transformation process. Other researchers have approached this concept as a process, focusing on its phases, underlying mechanisms, barriers, and enabling factors (Urbinati et al. 2022). Thus, from the previous literature, various conceptualizations of digital innovation can be observed. For instance, Khin and Ho (2020) define digital innovation as the integration of innovative and emerging digital technologies to support the digitalization of non-tech businesses such as banking, healthcare, retail, and manufacturing. Other researchers (Kohli and Melville 2019, Hensen and Dong 2020, Schneckenberg et al. 2021) have approached digital innovation as the company’s choice to implement digitally improved value-adding activities, transform business models, and enable new product and service offerings to provide new value-added to customer needs. Among its main benefits, digital innovation could provide companies with increased effectiveness of business functions and processes in organizations, could be an enabler of cost savings, and could reveal new ways to create and deliver value to satisfy customer expectations. As with other topics discussed above, digital innovation is also an emerging research area that has aroused the interest of researchers in recent years due to the increasing need for novel digital solutions; this topic will exert an increasing domination in the business world, emerging rapidly, and reaching everyday products through embedded digital technologies (Jahanmir and Cavadas 2018). Some scientific contributions in this field focus on investigating the impact of various factors on digital innovation. Khin and Ho (2020) examined the effect of digital orientation and digital capability on digital innovation through data from a survey of 105 small to medium-sized IT companies in Malaysia, emphasizing that digital orientation and digital capability exert a positive effect on digital innovation. Furthermore, the effects of technological orientation and digital capability on business performance are mediated by digital innovation. Their findings provide valuable arguments that encourage companies to embrace new digital technologies to become leaders in innovation and to increase company performance. Other previous contributions in this field have sought answers and explanations addressing the various concerns that have emerged in the process of digital transformation toward digital innovation. For instance, Svahn et al. (2017), through a longitudinal case study of Volvo Cars, argued that incumbent firms face four competing concerns in their process of embracing digital innovation: capacity (existing resources versus required resources), focus (product versus process), collaboration (internal versus external),

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and governance (control versus flexibility), while these concerns are systemically interconnected. El-Haddadeh (2020) also expressed concerns regarding the potential impact on the working environment when companies use digital innovation as a driver to improve the effectiveness of business performance in organizations, requiring careful consideration of the associated factors that determine the adoption of digital innovation. Some recent preoccupations in this research stream were related to the impact of dynamic capabilities on digital innovation in terms of the creation of new products, processes, or other solutions as a response to customer needs using a wide range of digital technologies (Tortora et al. 2021). Previous researchers have argued that companies with stronger dynamic capabilities are more capable of supporting digital innovation processes (Inigo et al. 2017, Zhou et al. 2019; Warner and Wager 2019, Matarazzo et al. 2021). However, there are some research gaps in terms of how to generate useful dynamic capabilities to successfully manage the digital innovation process. Tortora et al. (2021) attempted to fill this research gap through certain contributions. First, they attempt to identify the main critical dynamic capabilities that refer to the digital transformation of firms that support digital innovation from a management perspective. Second, they were interested in determining how these dynamic capabilities should be managed in the context of digital disruptions. Third, these authors were interested in understanding what kind of critical capabilities can provide the required support to the organization in finding new business opportunities and managing new valuable combinations to improve the competitiveness of companies and their digital innovation processes (Tortora et al. 2021). Finally, it is worth mentioning a recent systematic review of the literature on digital innovation in knowledge management systems realized by Di Vaio et al. (2021), which highlighted that the existing literature on knowledge management systems admitted the impact of digital innovation on business performance, improving the efficiency of organizational processes and structures, while using a combination of human and technological resources provides a significant competitive advantage.

5.1.5

Digital Transformation in Small and Medium-Sized Enterprises (SMEs)

The journey of digital transformation and its impact on small and medium-sized enterprises (SMEs) is another research stream that has gained increasing interest from researchers in the last few years; however, it remains an understudied topic in the existing literature. The way SMEs have driven their digital transformation journey has aroused the interest of researchers due to their limited resources, capabilities, and know-how (Li et al. 2018, Kääriäinen et al. 2020, North et al. 2020). Notable research on this topic was conducted by Li et al. (2018), who investigated how entrepreneurs of SMEs with insufficient capabilities and limited resources managed to implement digital transformation in their entities. Conducting

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qualitative research on digital transformation driven by seven SMEs on the Alibaba digital platform, Li et al. (2018) provide a model that fosters the understanding of both digital entrepreneurship and digital transformation, while also highlighting the benefits of the digital platform service for SMEs. Recently, applying a framework of dynamic capabilities, Cannas (2021) attempted to explain through conceptual and empirical investigation how and to what extent agri-food SMEs face a turbulent external environment during their digital transformation journey, highlighting that digitalization reshapes not only corporate and organizational procedures, structures, and mindsets but also societal ones. In addition, the same author underlined the idea that personal capabilities held by entrepreneurs and managers, such as creativity, empathy, and intuition, may be very helpful in successfully facing disruptive changes triggered by digitalization. Similarly, focusing more on capability development than on processes of digital transformation, North et al. (2020) proposed guidance for SMEs to support their assessment of their digital maturity and their capabilities co-related with each level in digital growth, emphasizing the relationship between dynamic capabilities and digitalization. To test their proposed framework, North et al. (2020) conducted a pilot study among 52 SME managers and owners to assess their digital maturity and understanding of digitalization challenges and opportunities. Their findings revealed a moderate level of digital maturity from the SMEs employed in the study. While these companies have advanced in the implementation of various digital initiatives, they still do not have a digital strategy to guide their digital growth, and their dynamic capabilities are still limited. Similarly, Garzoni et al. (2020) attempted to define a roadmap for the digital transformation of SMEs in a region where their digital maturity profile is still low. For this purpose, they investigated how digital technologies generated transformative changes in the business models of manufacturing SMEs in the Apulia region of southern Italy. Their findings highlight a four-level approach to SMEs’ engagement in the adoption of digital technologies, namely digital awareness, digital enquirement, digital collaboration, and digital transformation. Furthermore, many researchers agree that digital transformation metamorphoses the way SMEs create and capture value (Bharadwaj et al. 2013; Lucas et al. 2013; Chen et al. 2016), and companies must rethink and innovate their business models (Bouwman et al. 2019). One of the relevant studies that analyzed how SMEs can handle the impact of digitalization on the innovation of their business model is Bouwman et al. (2019), who conducted an empirical study on 321 European SMEs, highlighting the positive effect on firm performance due to the allocation of more resources in their business model innovation process in the context of digital transformation. Another positive impact of digital transformation on SMEs was highlighted by Dethine et al. (2020), who argued that digitalization fosters the internationalization of SMEs. In this vein, Dethine et al. (2020), while investigating the impact of digital transformation on SMEs’ internationalization capacity, identified three categories of digital facilitators for companies’ export practices: e-commerce (referring to the organization of the supply chain organization), e-marketing (referring to customer relations and communications), and e-business (referring to the impact on the internal functioning of the company and its processes

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as a whole). Similarly, Herve et al. (2020) emphasize that the more an SME digitalizes its functions, the more entrepreneurial behavior will be stimulated, leading to successful strategic decisions on expanding business in foreign markets.

5.1.6

Impact of Digital Transformation

The impact of digital transformation on company performance and its competitive advantages in various fields of activities is another topic of interest that will increasingly arouse researchers’ interest in the coming period, a subject as interesting as challenging (Leão and Silva 2021). Attempting to foster an understanding of the benefits of digital transformation for organizations, Ivančić et al. (2019) applied a holistic qualitative investigation of digital transformation in three companies and found that to ensure successful digital transformation of the entity, there are some special factors that need to be considered: an organizational structure supporting a digital culture, innovation management, interest in talent development, digital mindset, and digital skills. Another interesting view is provided by D'Ippolito et al. (2019), who showed that the impact of digital transformation on firms’ competitiveness could also be felt by incumbent firms with slower response processes and competencies that can be outperformed by smaller and agile players with a much faster capacity to innovate their business models because of digital transformation. If some authors (Dalenogare et al. 2018) highlighted the positive impact of digital transformation on firm performance, others (Tortorella et al. 2020) went further and examined the mediating role of organizational learning capabilities for 135 firms that have initiated their digital transformation implementation and its impact on operational performance. Therefore, the empirical findings of Tortorella et al. (2020) argue that if companies focus only on applying novel digital technologies without considering the role of organizational learning capabilities, they will not lead to superior performance outputs. In addition, companies must simultaneously consider the development of their sociocultural factors to benefit as much as possible from digital transformation. Similarly, Sousa-Zomer et al. (2020) examined the role of essential capabilities in digital transformation and its impact on the competitive advantage of firms. Thus, they identified three main micro-foundations of dynamic capabilities (digital-savvy skills, digital intensity, and context for action and interaction) that combine to build a digital transforming capability that contributes to a direct and positive impact on firm performance. An interesting perspective on the impact of digital transformation on organizational performance was also provided by Wang et al. (2020), who analyzed whether the digital transformation strategy could positively impact the organizational performance of firms while also considering the moderating role of cognitive conflict between digital transformation strategy and performance. Their findings resulting from the Chinese context revealed that the digital transformation strategy exerts a positive impact on short- and long-term financial performance, while the moderating

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role of cognitive conflict showed that the impact of digital transformation on longterm financial performance is significantly influenced by higher cognitive conflict. To examine the holistic impact of the key antecedents of digital transformation on manufacturing firm performance, Singh et al. (2021) identified the antecedents of digital transformation, namely organizational culture, competitive pressure, cognitive readiness, organizational mindfulness, strategic alignment, and IT readiness. Following their empirical investigation, Singh et al. (2021) found that competitive pressure, organizational mindfulness, IT readiness, and strategic alignment exert a significant impact on digital transformation. In addition, they provided empirical evidence on the mediating role of digital transformation on firm performance, concluding that firm performance can be shaped by antecedents and digital transformation. Recently, a systematic review of the literature on the impact of digital transformation on firm competitive advantages by Leão and Silva (2021) emphasized that most existing studies in the literature argued for positively signed impact directions, which are, on average, beneficial for companies that implement digital transformations. Furthermore, the findings of Leão and Silva (2021) confirm that digital transformation has a broader spectrum of impact scopes and dynamics on the competitiveness of firms, while these impacts vary from minor efficiency improvements through the implementation of value-added products and services to increasing competitiveness in terms of cost reduction. Although most of the impacts were highlighted as positive, Leão and Silva (2021) found that there are also some negative impacts, especially regarding human resources, owing to the increasing stress and tensions triggered by the increased complexity and dynamics of newly digitally transformed daily processes and activities. Finally, it is worth mentioning a recent study by Usai et al. (2021), which dismantles the generally accepted idea that increased use of digital technologies will certainly improve innovation performance of firms, emphasizing one of the main limitations of previous studies given by their “undifferentiated approach toward the vast ocean of digital technologies” (Usai et al. 2021, p. 327). Usai et al. (2021) argued and provided empirical evidence from a large sample of firms operating in the European Union, confirming that the most frequently used digital technologies exert a very low impact on innovation performance, while the most reliable enabler of innovation performance is represented by constant R&D efforts and expenses.

5.1.7

Digital Transformation and Industry 4.0

Industry 4.0, a recent concept that has recently received increasing attention from researchers, refers to the current trend of revolutionizing or digitalizing manufacturing companies through the integration of new technologies, including cloud computing, the Internet of Things (IoT), artificial intelligence, cyber-physical systems, and artificial intelligence in their manufacturing technologies and operations. Most of the time, researchers have used this concept interchangeably with the Fourth

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Industrial Revolution (e.g., Xu et al. 2018; Sony and Naik 2020; Pozzi et al. 2021). However, some researchers (Tirabeni et al. 2019) have argued that these two concepts are not entirely similar. Therefore, Tirabeni et al. (2019) highlighted that the concept of a fourth industrial revolution indicates significant technological developments over the years in various sectors of activity, while the concept of Industry 4.0 is more specific to the manufacturing sector; thus, a significant difference between these two terms refers to the scope. However, by reviewing the literature, we can identify many conceptual approaches to the concept of Industry 4.0. For instance, Frank et al. (2019) defined Industry 4.0 “as a new industrial maturity stage of product firms, based on the connectivity provided by the industrial Internet of things, where the companies’ products and process are interconnected and integrated to achieve higher value for both customers and the companies’ internal processes” (Frank et al. 2019, p. 343). Pozzi et al. (2021) defined Industry 4.0 as a combination of nine technological pillars (Internet of Things (IoT), horizontal and vertical system integration, simulation, autonomous robots, big data and analytics, augmented reality, additive manufacturing, cloud computing, and cybersecurity) implemented to digitally transform traditional manufacturing systems. Dalenogare et al. (2018) argued that the digital transformation of manufacturing firms should be seen as a transition process through companies moving from previous industrial stages to an interconnected smart factory in Industry 4.0, supported by the integration of related technologies for industrial performance. Thus, a smart factory will be equipped with Industry 4.0-related digital technologies that will allow a more effective collection and analysis of data as a basis for better decision-making. According to Pozzi et al. (2021), all of these will offer new strategic opportunities to increase competitiveness through the optimization of cost, quality, service levels, and flexibility, leading to enhanced automation, better integration, and predictive maintenance of manufacturing processes, finally providing a new level of effectiveness and responsiveness to customer needs not previously possible. Undoubtedly, the impact of Industry 4.0 and its challenges and opportunities are expected to be more significant for manufacturing companies (Pozzi et al. 2021), which is why more studies and investigations are needed to identify the critical factors that determine the successful implementation of Industry 4.0 in the manufacturing sector. In this vein, a notable contribution was provided by Pozzi et al. (2021), who analyzed eight case studies of Italian manufacturers after the implementation of Industry 4.0. Thus, a sum of critical success factors for the implementation of Industry 4.0 technologies was identified by Pozzi et al. (2021) such as continuous improvement/lean management, quality and flexibility-based competition, top management leadership, establishment of inter-functional teams, conducting of preparatory activities, project planning, and training activities. In the same vein, another relevant study was conducted by Nimawat and Gidwani (2020), which, by using the analytic hierarchy process and analytic network process techniques, highlighted the priorities of important factors toward the status of Industry 4.0 implementation in Indian manufacturing industries, identifying four criteria and 16 critical factors for the successful implementation of Industry 4.0. Additionally,

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Sony and Naik (2020), based on an in-depth analysis of 84 articles, identified 10 critical success factors necessary for the successful implementation of Industry 4.0. These include (1) alignment of the Industry 4.0 initiatives with organizational strategies; (2) top management support; (3) support for employees in facing new challenges; (4) looking for manufacturing smart products and services; (5) sustained efforts to digitalize the entire supply chain management; (6) digitization of the organization; (7) structural change management due to vertical, horizontal, and end-to-end integration; (8) well-planned and scheduled strategic project management; (9) ensuring cyber security for the management of digital networking of people, products, services, and machines; and (10) the relevance of Industry 4.0 for sustainability in business.

5.2 5.2.1

Thematic Cluster 2: Digital Technologies Big Data Analytics

Big data analytics is a new term that incorporates two concepts: big data and business analytics (Sahoo 2022). Big data are a collection of extremely complicated and large datasets to be handled with traditional data processing technologies, including structured and unstructured data. Therefore, it needs to be computationally analyzed to extract information that can reveal relevant patterns, trends, or associations as significant support for improving business decision-making (Lamba and Singh 2018). Business analytics refers to the extensive use of advanced statistics, quantitative analysis, mathematical explanatory, and predictive models used by management in their analysis of big data as support for making more informed business decisions and actions (Cao et al. 2015). Undoubtedly, this topic is emerging as a hot topic among academics and practitioners and is considered a new enabler of business efficiency and effectiveness due to its high operational and strategic potential (Wamba et al. 2017). In reviewing the literature, it can be noted that many research studies have sought to propose various frameworks for the successful implementation of big data analytics projects. For instance, Dutta and Bose (2015) developed a new holistic framework to conceptualize, plan, and successfully implement big data analytics projects, proposing the validation of this framework through the observation of a case study at a manufacturing Indian company that had implemented the proposed framework. Based on an empirical survey employing feedback from 740 responses collected from UK businesses, Cao et al. (2015) proposed a research model linking business analytics to decision-making effectiveness at the organizational level, through the mediation of a data-driven environment, highlighting positive effects on information processing capabilities, and thus an enhanced capacity of strategic decision-making. Other researchers were interested in highlighting the various effects of the implementation of big data analytics. For instance, Loebbecke and Picot (2015) revealed societal changes and business metamorphoses under the potential effect of

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Thematic Cluster 2: Digital Technologies

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technological advances triggered by digitization and big data analytics. Dubey et al. (2017) empirically investigated the effects of big data analytics on social and environmental performance while highlighting the significant impact of organizational culture on social and environmental sustainability. Wamba et al. (2017) conducted another remarkable study in this direction, which proposed a big data analytics capability model and examined its direct effects on firm performance as well as the mediating role of process-oriented dynamic capabilities. Based on data from 297 Chinese IT managers and business analysts with big data analytics experience, their findings revealed direct and indirect impacts of big data analytics on firm performance; thus, big data analytics can be capitalized as a source of sustainable competitive advantage (Wamba et al. 2017). Similarly, Ciampi et al. (2021) also argued the significance of big data analytics capabilities for business performance in highly dynamic environments, analyzing its impact on business model innovation. Their findings demonstrated that big data analytics capabilities exert both direct and indirect positive effects on business model innovation under the moderating role of entrepreneurial orientation. Caputo et al. (2019) examined the relationships among soft skills, information technologies, big data, and firm performance using structural equation modeling. Their findings revealed the existence of strong relations between some elements of human resources’ personality, such as work motivation, social competencies, and firms’ economic performance, under the moderating role of firms’ investment in big data projects. Barlette and Baillette (2022) argued that in the context of turbulent environments, big data analytics is an enabler of organizational agility, a valuable resource for the identification of opportunities and threats. Finally, in the context of the increasing relevance of big data analytics for achieving business performance, Ram and Zhang (2022) emphasize the need for academics to pay more attention to the adoption of big data analytics among business-to-business (B2B) organizations. In this vein, the authors developed a guideline framework based on a new four-category classification scheme that is useful for managers to understand the needs of B2B organizations to adopt big data analytics, including innovation, operational efficiency, customer satisfaction, and digital transformation.

5.2.2

Internet of Things, Blockchain, and Artificial Intelligence

The Internet of Things (IoT) applications, along with other applications such as big data analytics, artificial intelligence, and cloud computing, have significantly metamorphosed our lives, including our way of doing business, ensuring greater autonomy of various processes, and self-optimization (Qu et al. 2018). There is no unique definition of this term to be widely accepted by academics and researchers, and the literature provides various conceptual approaches to IoT that reflect academic debates on the etymology of this term (Sestino et al. 2020). For example, Gubbi

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et al. (2013) define IoT as “Interconnection of sensing and actuating devices providing the ability to share information across platforms through a unified framework, developing a common operating picture for enabling innovative applications” (Gubbi et al. 2013, p. 1647). The IoT in the vision of Perera et al. (2015) represents a “network of networks where, typically, a massive number of objects/ things/sensors/devices are connected through communications and information infrastructure to provide value-added services” (Perera et al. 2015, p. 585). Recently, Sestino et al. (2020) argued that the new vision of this concept should be approached as a global infrastructure where physical and virtual objects are connected. Within this network infrastructure, things can automatically communicate with other things, providing value-added services for the benefit of mankind. However, regardless of how we define this term, one thing is certain. This is an emerging topic that is still in the early stages of evolution and development. According to Ahmed et al. (2017), IoT technologies are shifting the focus of business processes from physical products to data-based services and are considered one of the main drivers of a company’s digital transformation (Pflaum and Golzer 2018). In addition, Sestino et al. (2020) argued that IoT and digitalization are naturally related because IoT technologies ensure the necessary technological basis for redesigning the production process of products and services, contributing to the delivery of new advanced or efficient products and services, and finally allowing the capture of large amounts of data as a basis for predicting behavior, consumption trends, and choices. Furthermore, Sestino et al. (2020) argued that big data and IoT technologies are two faces of the same coin, and therefore, these two should be integrated into a precise framework to facilitate the digital transformation of businesses. According to Sandner et al. (2020), blockchain technology, along with other applications such as IoT and artificial intelligence, is recognized as a technological innovation with significant potential to overcome and improve traditional business processes while creating new or innovating existing business models. Blockchain technology is a shared and decentralized distributed ledger in which all transactions are recorded in lists and stored in blocks on network nodes. Each node can synchronize data and record transactions issued for every event (Lohmer and Lasch 2020). This distributed ledger is protected by private keys to sign transactions, hash functions to link blocks, and established mechanisms to ensure the irreversibility of the recorded data (Lohmer and Lasch 2020, Wang et al. 2019, Bahga and Madisetti 2016). Owing to its characteristics, blockchain technologies could be a relevant enabler of trust, transparency, security, and privacy in business processes (Sandner et al. 2020), while other researchers (Lohmer and Lasch 2020) highlighted the importance of this technology for the rapid development of the manufacturing industry in the context of digital transformation. As Salah et al. (2019) state, blockchain and artificial intelligence technologies have become two of the most trending and disruptive technologies. Thus, if the blockchain could manage interactions among participants without an intermediary or trusted third party, on the other hand, artificial intelligence allows the improvement of business processes by detecting patterns and optimizing outputs through the

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Thematic Cluster 2: Digital Technologies

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equipping of machines with intelligence and decision-making capabilities similar to humans (Salah et al. 2019). Thus, according to Sjodin et al. (2021), AI radically transforms how companies create, deliver, and capture value. Focusing on the manufacturing sector and providing insights from a case study of six leading manufacturers, Sjodin et al. (2021) identified three sets of critical artificial intelligence capabilities (data pipeline, algorithm development, and artificial intelligence democratization) integrated into a framework. In addition, these authors argued for the necessity of focusing on agile customer co-creation, data-driven delivery operations, and scalable ecosystem integration when firms innovate their business models. In addition, it is worth mentioning that the recent bibliometric framework developed by Wu et al. (2021) attempted to provide a comprehensive definition of digital transformation, including the technological capabilities required by artificial intelligence as one of the emerging technologies used to enable a successful digital transformation journey. Undoubtedly, this topic will attract the interest of researchers and practitioners in the coming years, especially if we consider the increasing number of companies adopting these emerging technologies. Therefore, according to some estimations, the worldwide number of connected devices for IoT technologies is expected to reach 43 billion by 2023 (Sestino et al. 2020), whereas the McKinsey Global Institute estimated the economic impact of IoT technologies to be $11.1 trillion per year by 2025 (Ceipek et al. 2020). Recently, Akter et al. (2022) found that investments in artificial intelligence in organizations across all industries are expected to reach $191 billion by 2025, with a compound annual growth rate of 36.6%, while blockchain technology is increasing at a fast pace, delivering a business value of more than $3 trillion by 2030. Therefore, in the context of the turbulent times we live today and the increasing need for effective digital transformation of businesses in various industries, researchers should expand their studies to identify relevant frameworks to guide companies on their digital transformation journey through the implementation of recent technological innovations, such as IoT technologies, blockchain, and artificial intelligence. For example, Liu et al. (2021) recently proposed a framework for SMEs to follow as a guide in their digital transformation journey with the Internet of things and cloud computing. Akter et al. (2022) investigated digital transformation in business through the lens of emerging technologies such as artificial intelligence, blockchain technology, cloud computing, and data analytics (ABCD technologies). Following a multidisciplinary approach and the dynamic nature of innovation, their findings highlight the potential of these technologies through diverse applications in a variety of vertical sectors. Through a survey of 84 organizations, Dehghani et al. (2022) examined the determinant factors for digital transformations within the food industry by adopting blockchain technology from various perspectives such as organizational policy, adoption strategies, and potential innovations that could positively impact business processes. Bhatti et al. (2021) proposed an investigation of digital business transformation within the telecom sector in China through an analysis of the impact of data quality and technology competence on overall strategic performance under the mediating roles of big data, Internet of Things, and blockchain technologies. Their findings highlighted a significant

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relationship between data quality, technological competence, and strategic performance under the mediating role of big data analytics and the Internet of Things, while blockchain technology was revealed to be an insignificant mediator. Finally, Sandner et al. (2020) remarked that IoT, blockchain, and artificial intelligence are emerging technologies that will drive the next trends in digital transformation, while these technologies will converge and facilitate the development of new autonomous business models, and thus a step further for the digital transformation of industrial organizations.

5.2.3

Digital Readiness and Digital Resilience

Undoubtedly, we are living in a growing digital world and are moving faster to an increasingly dependent digital future. Therefore, companies must be “future ready” when embarking on their digital transformation journey, looking for the best ways to use their digital capabilities to transform a traditional firm into a leader in the digital economy era (Weill and Woerner 2018). In this context, two other concepts will require more and more attention from academics, namely, digital readiness and digital resilience. The significance of these two terms is obvious, especially in the context of the increasing level of digitalization in society; therefore, we need to ensure that the workforce feels comfortable and confident in learning and applying these new digital skills. Attention should be paid to these terms, as emphasized by Verhoef et al. (2021), who stated that researchers should be more focused on digital resilience and therefore investigate “whether incumbent firms are able to compete with (new) digital players and accommodate exogenous shocks from disruptive digital technologies” (Verhoef et al. 2021, p. 896). In addition, the same authors argued for the need to pay more attention to the examination of how companies’ digital readiness may help the transition through the phases of digital transformation and toward the more digitalized workflows enabled by these new emerging technologies. In this vein, it is worth mentioning the empirical work of Pirola et al. (2019), who proposed a comprehensive assessment model for digital readiness levels based on an empirical approach on data from 20 Italian manufacturing SMEs, while highlighting the priorities needed to achieve a successful digital transformation journey. Recently, admitting that there are only a few contributions in the literature regarding the assessment of digital readiness during digital transformation, Lassnig et al. (2022) attempted to fill this gap by providing relevant contributions for a better understanding of digital readiness based on a study employing 409 companies that participated in the digital readiness check (DRC) in the regions of Salzburg (Austria) and Bavaria (Germany) through an online assessment for self-evaluating the digital readiness of companies. Their findings revealed differences between SMEs and large enterprises and provided insight into the categories of strategy, employees, initiation of business transactions, and supply chain aspects.

5.3

Thematic Cluster 3: Digital Economy

5.3 5.3.1

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Thematic Cluster 3: Digital Economy Smart Circular Economy

The concept of circular economy is one that is increasingly trending, especially in the current turbulent times in which it is imperative to achieve the integration of economic activity and environmental performance in a sustainable way to ensure business performance (Murray et al. 2017; Gupta et al. 2021). In this context, various digital technologies such as the Internet of Things, big data analytics, cloud computing, and artificial intelligence are significant enablers for ensuring the transition to a smart circular economy (Kristoffersen et al. 2021). As some authors have noted in recent years, the circular economy and these emerging technologies represent some of the most important paradigms that drove academia, policymaking, and industry in recent years, but even so, there is still much to be done (Rosa et al. 2019; Kristoffersen et al. 2021; Kristoffersen et al. 2020). As Kristoffersen et al. (2021) state, the smart circular economy is an emerging field, and more research is needed to investigate the link between the organizational and digital capabilities of firms and their circular strategies to innovate their business models. Among the relevant pieces of work recently developed in this research direction, it is worth mentioning the study of Kristoffersen et al. (2021), who provided one of the first empirical evidences on the link between firms’ business analytics capabilities and the circular economy, and their effect on firm performance. In this vein, the authors created an instrument to measure the business analytics capability of firms in the circular economy and investigated the relationship between the specific business analytics capability of the circular economy, the implementation of the circular economy, the capacity to orchestrate resources, and the performance of the company. Additionally, they tested the proposed research model using survey data from 125 top-level managers in companies across Europe. Bag et al. (2020) investigated how digital transformation impacted the optimization process and circular economy performance based on survey data from South African manufacturing firms and found a positive effect on the optimization process of business practices, which plays a key role in improving circular economy performance. Finally, Rajput and Singh (2020) proposed a model to optimize the trade-off between energy consumption and machine processing costs to achieve a circular economy and cleaner production in the context of Industry 4.0.

5.3.2

Digital Entrepreneurship

The intersection of emerging technologies and entrepreneurial processes has led to a new emerging concept, namely digital entrepreneurship (Nambisan 2017). The same authors also advanced a new research agenda that required building or explaining existing entrepreneurship theories from the perspective of digital technologies, while

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paying careful attention to the impact of these technologies on shaping entrepreneurial objectives and behaviors (Nambisan 2017). Some progress has been made in this research direction, but there is still much to be done, while the investigation of how entrepreneurs can drive digital transformation remains under-researched (Li et al. 2018; Jafari-Sadeghi et al. 2021). For instance, Jawad et al. (2020), based on empirical data collected from 1445 semi-structured interviews with entrepreneurs from a sample of developing countries, highlight the influence of digital entrepreneurship on the business environment in developing countries. Furthermore, their findings confirmed that administrative efforts to increase digitalization would lead to the revitalization of entrepreneurship in emerging economies, ensuring better premises for sustainable economic growth. Basly and Hammouda (2020) suggested a conceptual model of the adoption of digital entrepreneurship in family firms, describing the main variables that influence the adoption of digital entrepreneurship and then analyzing them under the moderating influence of family involvement in the firm. Gavrila Gavrila and De Lucas Ancillo (2022) discussed the implications and relationships between entrepreneurship, innovation, and digital transformation in the context of the COVID-19 pandemic, which was found to be a strong accelerator of both people’s habits and firms’ digital transformation toward new sustainable business models. Among the relevant works recently developed in this research direction, it is worth mentioning the study by Jafari-Sadeghi et al. (2021) that examined the effects of digital transformation on technology entrepreneurship and technology market expansion. Based on data derived from development indicators and ease of doing business from 28 European countries, these authors advanced a new perspective of digital entrepreneurship from the perspective of digital transformation, structured into three clusters (technology readiness, digital technology exploration, and digital technology exploitation). Their research is relevant because it provides new perspectives on how business models should be metamorphosized in the context of significant challenges triggered by the digital transformation of businesses and societies (Jafari-Sadeghi et al. 2021).

5.3.3

Digital Sharing Economy

The sharing economy in the context of digital transformation is another emerging research topic that remains under-researched and needs further development from both academics and practitioners. The sharing economy is an evolving phenomenon born in the Internet age (Belk 2014) that has attracted increasing interest over the last few years (Acquier et al. 2017; Li et al. 2021). The sharing economy represents the supply of underutilized goods and services in exchange for monetary or non-monetary benefits (Belk 2014; Leung et al. 2019, Nadeem and Al-Imamy 2020; Li et al. 2021). This emerging business model based on collaborative consumption has gained popularity due to the structural changes in people’s consumption behavior and values that shift from owning to sharing (Li et al. 2021). The exponential development of this concept in recent years has been enabled by the

5.4

Thematic Cluster 4: Digital Disruption

59

development of various digital platforms (Nadeem and Al-Imamy 2020; Cheng and Edwards 2019). Various digital platforms, such as websites and digital applications, have the power to connect millions of consumers in a mutual exchange relationship (Nadeem and Al-Imamy 2020). Recently, a relevant study in this research direction by Pouri and Hilty (2021) provided important contributions to the foundations of the theoretical framework and defined the digital sharing economy as the “class of resource allocation systems based on sharing practices which are coordinated by digital online platforms and performed by individuals and possibly (non) commercial organizations with the aim to provide access to material or immaterial resources” (Pouri and Hilty 2021, p. 130). While digitalization through various emerging technologies is a strong enabler for these new forms of sharing goods and services, Pouri and Hilty (2021) provide a new conceptual approach that addresses three fundamental aspects: technical aspects of sharing (shareability of resources), social aspects of sharing (sharing practices including economic transactions), and coordination aspects (organization and scalability of sharing). Undoubtedly, the digital sharing economy provides significant opportunities for individuals and companies to generate extra income, and increasing financial potential is undisputable (Kathan et al. 2016). Thus, we argue that a more in-depth examination of the various factors that could influence the development of the sharing economy in the context of digital transformation is certainly needed.

5.4 5.4.1

Thematic Cluster 4: Digital Disruption Dynamic Capabilities

According to Teece (2007), one of the most cited studies in this field, dynamic capabilities consist of distinct skills, processes, procedures, organizational structures, decision rules, and disciplines that provide the basis for business entities to reconfigure their capacities to ensure long-term business performance. In the vision of the same author, companies with stronger dynamic capabilities are highly entrepreneurial because they not only manage to adapt to business ecosystems, but also succeed through innovation and collaboration in their partnerships with other entities and institutions. The author also argued that dynamic capabilities include three broad clusters: sensing opportunities (and threats), seizing opportunities, and transforming the organization’s business model and a larger resource base. Warner and Wager (2019) highlight the need for incumbent firms to build the strong dynamic capabilities necessary to successfully complete their digital transformation journey and remain relevant in the emerging digital economy. Thus, the same authors proposed a process model comprising nine micro-foundations to emphasize the generic contingency factors that trigger, enable, and hinder the building of dynamic capabilities for digital transformation.

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Furthermore, Sousa-Zomer et al. (2020) argued that incumbent firms face significant challenges in building and maintaining the new digital capabilities needed to successfully face the changes triggered by a dynamic fast-evolving digital environment due to the firms’ tendency to remain more static over time. Sousa-Zomer et al. (2020) continued the idea of Warner and Wager (2019) and proposed a framework that was tested using data from a large sample of US companies. Within this framework, the authors identified three main micro-foundations that, when combined, build a digital transformation capability (digital-savvy skills, digital intensity, and context for action and interaction) while also testing the impact of digital transformation capabilities on the competitive advantage of firms. Khin and Ho (2020), using dynamic capabilities theory, conceptualized digital orientation and digital capabilities as driving factors of digital innovation. Therefore, based on survey data from 105 SMEs in Malaysia, these authors found that digital orientation and capabilities have a positive effect on digital innovation and that digital innovation mediates the effect of technological orientation and capabilities on financial and non-financial performance. Finally, Aghimien et al. (2022) conducted a scientometric review of the literature on dynamic capabilities in the context of digitalization for organizations operating in the architecture, engineering, construction, and operations sectors. The authors argued for the necessity for these organizations to have the capabilities to sense and seize opportunities and threats within the rapidly evolving digital business environment. To this end, these companies should focus on enhancing their dynamic capabilities related to the adoption of digitalization.

5.4.2

Digital Disruption

Digital transformation through digital technologies and their applications has the power to disrupt a wide range of industries, transform the ways of doing business, reshape managerial and organizational structures and mindsets, and innovate existing business models (North et al. 2020; Cannas 2021). Undoubtedly, digital disruption continues to exert a major impact on businesses and on the restructuring of the world’s economy (Wirtz et al. 2022). As some authors have noted, digital technology can be “transformative or disruptive depending on one’s perspective and, more importantly, one’s ability to harness its potential” (Saarikko et al. 2020, p. 3). Christensen (2006) advanced the development of disruptive innovation theory, which has created a significant impact and larger debates within this field of research (Yu and Hang 2010). Later, Karimi and Walter (2015), arguing that disruptive innovation theory provides explanations in the success or failure of companies in responding to disruptive innovation, conducted a study where the role of dynamic capabilities in the performance of response to digital disruption was empirically investigated. Their findings suggest that first-order dynamic capabilities created by changing, expanding, or adapting existing resources, processes, and values of a firm

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Thematic Cluster 4: Digital Disruption

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are positively correlated with building digital platform capabilities and that these capabilities clearly impact response performance to digital disruption. Debates on digital disruption are increasing in academia, and they are approached from various perspectives. For example, Murdoch and Fichter (2017) explored the effects of digital transformation in shifting existing conceptions of technology adoption from the perspective of employees in their workplaces, providing recommendations for organizational leaders to ensure a soft transition from digital to digital. Cozzolino et al. (2018) admitted that there is still a significant research gap regarding the strategies and actions incumbent companies should adopt to adapt their business models after digital disruption. In this vein, the authors analyzed the case study of a major Italian news media publisher reacting to the disruption and identified the drivers and impeding factors of business model adaptation, how incumbent firms could adapt their business strategies to cope with different components of the disruption process, and how a closed business model could be renewed to develop an open, platform-based business model to seize and sense external opportunities and threats, bear lower costs, and avoid the negative effects of disruption. Finally, Al-Edenat (2021) examined the effects of disruptive change, technological process innovation, and Industry 4.0 on digital transformation, trying to identify the capabilities needed to successfully face the challenges resulting from digital disruption. Their findings highlight the direct impact of disruptive change, innovation in technological processes, and digital transformation, while Industry 4.0 moderates the relationship between disruptive change and digital transformation.

5.4.3

Digital Age

The term digital age has emerged in the context of the most recent research in the fields of digitalization and digital transformation, while the approaches of this term vary from different perspectives. For instance, while Ruiz et al. (2018) stated that the digital age offers unprecedented opportunities for firms to access new knowledge for innovation, Troise (2022) examined the main benefits and risks of knowledge visualization in the current digital age, underlining that the main benefits are related to stakeholder engagement, flexibility, knowledge transfer, signaling role, agility, and interactivity, whereas the risks identified refer to complexity, absorptive capacity, divergences, capabilities, and ineffectiveness. Spieth et al. (2021) discussed interrelated technological frames (personal attitude, application value, organizational influence, industrial influence, and supervisor influence) that could impact corporate strategies in coping with the challenges provoked by the digital age. Zhao et al. (2022) discussed the importance of entrepreneurial social networks in the context of the digital age, providing empirical evidence that business development is enabled by online networks. Finally, Menz et al. (2021) introduced this term of the digital age as a successor to the industrial age, discussing the implications of the digital age on corporate strategies, focusing on three directions: corporate

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(competitive) advantage; company scale, scope, boundaries; and internal structure and design.

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Chapter 6

Agenda for Future Research and Conclusions

6.1

Agenda for Future Research

The key goals of the bibliometric study presented in this book are twofold. The first goal was to provide a comprehensive overview of the state of digital transformation in business, while the second goal was to propose a synthesis of potential directions and opportunities for future research agendas related to this field of research. Starting with the content analysis presented in the previous chapter, next, Table 6.1 presents an agenda for future research directions resulting from the content analysis of each identified thematic cluster.

6.2

Conclusions

The purpose of the bibliometric study presented in this book was to assess the published flow of knowledge in the research field related to digital transformation in business and then to generate a summary of future research directions that could provide opportunities and challenges for academics in this field. For this purpose, bibliometric analysis was used to identify the most influential journals, authors, and research articles, and the key emerging trends in this field. Additionally, the intellectual structure of knowledge related to digital transformation in business was evaluated, and the most popular research topics and key directions for future research were derived to ensure that the goals of this study were met. This study contributes to the current body of knowledge by analyzing and assessing the literature on digital transformation in business for almost 20 years. As shown above, this research field has attracted growing interest in the last few years (2019–2021), and this trend will continue in the following years more intensively. From the top 20 most influential articles in terms of bibliographic coupling, more than half of the articles were published in the last 2 years (12 articles published © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. Bota-Avram, Science Mapping of Digital Transformation in Business, SpringerBriefs in Business, https://doi.org/10.1007/978-3-031-26765-9_6

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Agenda for Future Research and Conclusions

Table 6.1 Directions for further research agenda Thematic Cluster Thematic cluster 1: Digital transformation process

Subthemes • Digital transformation strategies

• Phases of digital transformation

• Business model innovation

Directions for future research ✓ More specific guidelines for entities on how to formulate, implement, and evaluate digital transformation strategies across industries and business models, especially in the context of a long-term approach (Matt et al. 2015; Hess et al. 2016; Björkdahl 2020) ✓ Clarification of differences between digital transformation frameworks for B2C companies compared to B2B companies (Matt et al. 2015) ✓ Identification of well-argued answers to some key questions in any digital transformation strategy for various industries and business models: How can data and digital capabilities be used to deliver new value to the customers of the entity? What enablers and capabilities are needed to support the digital transformation process and achieve its successful implementation? (McGrath and McManus 2020; Björkdahl 2020) ✓ Ensuring consistency between digital strategy formulation and its successful implementation (Correani et al. 2020) ✓ More rigorous conceptual foundation of the phases of digital transformation: digitization, digitalization, and digital transformation (Loebbecke and Picot 2015; Svadberg et al. 2019; Vial 2019; Verhoef et al. 2021) ✓ More in-depth investigation on the ways companies move through various digital transformation phases and how their performance is affected by each phase of digital transformation (Verhoef et al. 2021) ✓ More in-depth understanding of the impact of digital transformation on business model innovation, including in the case of small and medium-sized enterprises (SMEs) (Klos et al. 2021; Andersen et al. 2022) ✓ New conceptualization of business models innovation, considering new ways for value creation and capture in the context of digital transformation (Vaska et al. 2021) ✓ More in-depth investigation of the impact of cultural dimension over the interconnections between organizational changes and business model innovation in the context of digital transformation journey (Latilla et al. 2019) (continued)

6.2

Conclusions

71

Table 6.1 (continued) Thematic Cluster

Subthemes • Digital innovation

• Digital transformation in small and medium-sized enterprises (SMEs)

• Impact of digital transformation

Directions for future research ✓ More clarification or even a consensus on the conceptualization of digital innovation: a process or the final outcome of adopting digital technologies (Urbinati et al. 2022) ✓ More empirical investigations of the driven factors related to digital innovation in various industries (Khin and Ho 2020) ✓ Identification of solutions in what regard the main critical dynamic capabilities for supporting digital innovation and their effective management in the context of digital disruption (Tortora et al. 2021) ✓ Debates and more empirical investigations on the role of knowledge management systems and their impact on transforming business models toward sustainability-oriented business models through digital innovation (Di Vaio et al. 2021) ✓ More in-depth understanding through case studies and empirical investigation about how SMEs experiment with their business models innovation in the context of digital transformation journey (Bouwman et al. 2019) ✓ More guidance in supporting SMEs in fostering their digital transformation strategy considering their specificity of limited resources, capabilities, and know-how (Li et al. 2018; Kääriäinen et al. 2020; North et al. 2020; Cannas 2021) ✓ Developing a set of key performance indicators to allow the dynamic measurement and forecasting of the effectiveness of SMEs toward the digital transformation (Garzoni et al. 2020) ✓ More in-depth analysis on the evaluation of the main impacts of digital transformation on firms’ competitive advantage, positive or negative (SousaZomer et al. 2020; Wang et al. 2020; Leão and Silva 2021) ✓ More in-depth examination through case studies and empirical approaches of the holistic impact of key antecedents of digital transformation on firm (continued)

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Table 6.1 (continued) Thematic Cluster

Subthemes

• Digital transformation under Industry 4.0

Thematic cluster 2: Digital technologies

• Big Data Analytics

• Internet of things, blockchain, and artificial intelligence

• Digital readiness

Directions for future research performance in various fields of activities (Singh et al. 2021) ✓ More in-depth analysis (empirical and case studies) on the critical factors determinant of the successful implementation of Industry 4.0 related technologies in manufacturing companies (Calabrese et al. 2022; Nimawat and Gidwani 2020; Sony and Naik 2020; Pozzi et al. 2021) ✓ More research as support in developing an implementation framework, considering critical factors, to ensure the alignment of existing business models with Industry 4.0 initiatives (Sony and Naik 2020; Calabrese et al. 2022) ✓ More in-depth analyses (and their validation through empirical and case studies) in determining the critical success factors that influence the successful implementation of big data analytics projects in various fields of activity (Dutta and Bose 2015; Cao et al. 2015; Lamba and Singh 2018; Sahoo 2022) ✓ More in-depth research on the expected impact of big data analytics on business performance within various industries (Ciampi et al. 2021; Barlette and Baillette 2022; Caputo et al. 2019; Wamba et al. 2017) ✓ More in-depth research to fill the research gaps in the adoption and use of IoT technologies, especially as an enabler of many business processes (Sestino et al. 2020) ✓ More extended studies to identify relevant frameworks to guide companies on their digital transformation journey, through the implementation of recent technological innovations such as IoT technologies, blockchain, and artificial intelligence within diverse contexts, different industries, countries, and organizational types (Liu et al. 2021; Akter et al. 2022; Wu et al. 2021; Bhatti et al. 2021; Sandner et al. 2020) ✓ More in-depth analysis on how digital readiness and digital resilience of companies may facilitate the transition of (continued)

6.2 Conclusions

73

Table 6.1 (continued) Thematic Cluster

Thematic cluster 3: Digital economy

Subthemes

• Smart circular economy

• Digital entrepreneurship

• Digital sharing economy

Directions for future research firms through the phases of digital transformation in various contexts, industries, or organizational types (Verhoef et al. 2021; Lassnig et al. 2022; Pirola et al. 2019) ✓ More in-depth analysis on how digital resilience and digital readiness could be measured through the phases of digital transformation (Verhoef et al. 2021; Lassnig et al. 2022; Pirola et al. 2019) ✓ More in-depth research about the role and relevancy of these emerging technologies, dynamic capabilities of firms, and their strategies for ensuring an effective transition to a smart circular economy (Kristoffersen et al. 2021; Kristoffersen et al. 2020; Bag et al. 2020; Rosa et al. 2019) ✓ More in-depth analysis for developing models or frameworks on how entrepreneurs could drive digital transformation, in various industries, countries, or types of organizations (Jafari-Sadeghi et al. 2021; Basly and Hammouda 2020; Jawad et al. 2020; Li et al. 2018) ✓ More in-depth investigation about the influence of cultural factors across countries on the adoption of digital entrepreneurship (Jafari-Sadeghi et al. 2021) ✓ More in-depth investigation about the determinants of digital entrepreneurship, while considering the interconnections between internal and external factors that could affect the efficiency and effectiveness of digital business transformation (Satalkina and Steiner 2020) ✓ More in-depth investigation for understanding the impact of economic, social, and environmental factors that could influence the ecosystem of sharing economy in the context of digital transformation (Li et al. 2021; Pouri and Hilty 2021; Nadeem and Al-Imamy 2020; Leung et al. 2019) ✓ More research for developing theoretical and practical frameworks that clarify the underlying structure and (continued)

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Table 6.1 (continued) Thematic Cluster

Thematic cluster 4: Digital disruption

Subthemes

• Dynamic capabilities

• Digital disruption

• Digital age

Directions for future research mechanisms of the digital sharing economy (Leung et al. 2019) ✓ More in-depth research about the ways in which companies should build stronger dynamic capabilities and the micro-foundations needed to successfully implement in practice digital transformation in various industries and national cultural contexts (Sousa-Zomer et al. 2020; Warner and Wager 2019; Khin and Ho 2020) ✓ More in-depth research frameworks to provide guidance on how incumbent firms should adapt their business models after digital disruption (Cozzolino et al. 2018; Al-Edenat 2021) ✓ More in-depth theoretical and empirical investigations on how behavior, knowledge, and strategies at corporate level are reshaped in the context of challenges yielded by digital age (Spieth et al. 2021; Menz et al. 2021; Zhao et al. 2022).

Source: author’s own research

in 2020–2021), thus confirming once again the effervescence of this research field. By using various bibliometric indicators, such as citation analysis, co-citation analysis, bibliographic coupling, co-occurrence of keywords, burst detection analysis, and timeline analysis, the science map of this research field is depicted. With the help of advanced bibliometric software, emerging research trends have significantly attracted the interest of academics in the field of digital transformation in business. The bibliometric analysis identified four major thematic clusters: (1) the digital transformation process, (2) digital technologies, (3) the digital economy, and (4) digital disruption. Proceeding to the analysis of the most prestigious articles included in each thematic group, subthemes were produced within their respective topics. Subsequently, the content analysis of these articles helped us suggest research gaps in the existing literature related to digital transformation in business and set directions for researchers to strengthen the understanding of this challenging and complex research field.

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