Empirical AI Transformation Research: A Systematic Mapping Study and Future Agenda

1. J. Holmstrom, “From ai to digital transformation: The ai readiness framework,” Business Horizons , 2021.

2. U. Lichtenthaler, “Beyond artificial intelligence: Why companies need to go the extra step,” Journal of Business Strategy , 2018.

3. E. Brynjolfsson, The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies . New York City, New York, USA: Norton & Company, 2014.

4. J. Maclure and S. Russell, AI for Humanity: The Global Challenges . Springer International Publishing, 2021, pp. 116–126.

5. R.C. Schank, “Where’s the ai?” AI Magazine , Vol. 12, No. 4, 1991.

6. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature , Vol. 521, No. 7553, 2015, pp. 436–444.

7. E. Ntoutsi, “Bias in data-driven artificial intelligence systems – an introductory survey,” WIREs Data Mining and Knowledge Discovery , 2020.

8. C. Anderson, Creating a data-driven organization: Practical advice from the trenches . O’Reilly Media, Inc., 2015.

9. K. McElheran, N. Goldschlag, Z. Kroff, D. Beede, L. Foster et al., “Advanced technologies adoption and use by us firms: Evidence from the annual business survey,” in Conference Paper , 2020.

10. M. Cubric, “Drivers, barriers and social considerations for ai adoption in business and management: A tertiary study,” Technology in Society , Vol. 62, 2020, pp. 101 – 257.

11. S. Akter, K. Michael, M. Uddin, G. McCarthy, and M. Rahman, “Transforming business using digital innovations: The application of AI, blockchain, cloud, and data analytics,” 2020.

12. F. Khanboubi and A. Boulmakoul, “Digital Transformation in the Banking Sector: Surveys Exploration and Analytics,” International Journal of Information Systems and Change Management , Vol. 11, No. 2, 2019, pp. 93–127.

13. L. Achtenhagen, L. Melin, and L. Naldi, “Dynamics of business models – Strategizing, critical capabilities and activities for sustained value creation,” Long Range Planning , Vol. 46, No. 6, 2013, pp. 427–442.

14. S. Makridakis, “The Forthcoming Artificial Intelligence (AI) Revolution: Its Impact on Society and Firms,” Futures , Vol. 90, 2017, pp. 46–60.

15. A. Schumacher, S. Erol, and W. Sihn, “A Maturity Model for Assessing Industry 4.0 Readiness and Maturity of Manufacturing Enterprises,” Procedia Cirp , Vol. 52, 2016, pp. 161–166.

16. H.A. Simon, Administrative Behavior: A Study of Decision-making Processes in Administrative Organization , 3rd ed. New York City, New York, USA: Free Press, 1976.

17. A. Ebbage, “Banking on Artificial Intelligence,” Engineering & Technology , Vol. 13, No. 10, 2018, pp. 66–69.

18. H. David, “Why are There Still so Many Jobs? The History and Future of Workplace Automation,” Journal of Economic Perspectives , Vol. 29, No. 3, 2015, pp. 3–30.

19. M. Polanyi, The Tacit Dimension . Chicago, Illinois, USA: University of Chicago Press, 2009.

20. E. Sadler-Smith and E. Shefy, “The Intuitive Executive: Understanding and Applying ‘gut feel’in Decision-making,” Academy of Management Perspectives , Vol. 18, No. 4, 2004, pp. 76–91.

21. M. Tegmark, Life 3.0: Being Human in the Age of Artificial Intelligence , 1st ed. New York City, New York, USA: Alfred A. Knopf, 2017.

22. P. Maroufkhani, W.K.W. Ismail, and M. Ghobakhloo, “Big data analytics adoption model for small and medium enterprises,” Journal of Science and Technology Policy Management , Vol. 11, No. 4, 2020, pp. 483–513.

23. S.L. Wamba-Taguimdje, S.F. Wamba, J.R.K. Kamdjoug, and C.T. Wanko, “Influence of Artificial Intelligence (AI) on Firm Performance: The Business Value of AI-based Transformation Projects,” Business Process Management Journal , Vol. 26, No. 7, 2020.

24. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach . Essex, England: Pearson Education, 2016.

25. L. Deng, “Artificial intelligence in the rising wave of deep learning: The historical path and future outlook [perspectives],” IEEE Signal Processing Magazine , Vol. 35, No. 1, 2018, pp. 180–177.

26. J. Lee, H. Davari, J. Singh, and V. Pandhare, “Industrial Artificial Intelligence for Industry 4.0-based Manufacturing Systems,” Manufacturing Letters , Vol. 18, 2018, pp. 20–23.

27. E.P.R. Service, “The Ethics of Artificial Intelligence: Issues and Initiatives,” European Parliament, Tech. Rep., 2020.

28. E. Peretz-Andersson, “AI Transformation: A Systematic Literature Review (Linked Data Sheet),” https://osf.io/3afw6/?view_only=fd36e2c55f044f1abe55e6e9d1d0f852 , 2021, [Online; accessed 2021-05-21].

29. F. Li, “The digital transformation of business models in the creative industries: A holistic framework and emerging trends,” Technovation , Vol. 92, 2020, p. 102012.

30. L. Heilig, E. Lalla-Ruiz, and V. Stefan, “Digital transformation in maritime ports: analysis and a game theoretic framework,” Netnomics: Economic research and electronic networking , Vol. 18, No. 2, 2017, pp. 227–254.

31. A. Nadeem, B. Abedin, N. Cerpa, and E. Chew, “Digital transformation & digital business strategy in electronic commerce-the role of organizational capabilities,” 2018, pp. 1–8.

32. D. Anderson and L.A. Anderson, Beyond Change Management: Advanced Strategies for Today’s Transformational Leaders . John Wiley and Sons, 2002.

33. H. Tsoukas and R. Chia, “On Organizational Becoming: Rethinking Organizational Change,” Organization Science , Vol. 13, No. 5, 2002, pp. 567–582.

34. H. Mintzberg and F. Westley, “Cycles of Organizational Change,” Strategic Management Journal , Vol. 13, No. S2, 1992, pp. 39–59.

35. H. Arazmjoo and H. Rahmanseresht, “A multi-dimensional meta-heuristic model for managing organizational change,” Management Decision , Vol. 58, No. 3, 2019, pp. 526–543.

36. M.L. Tushman and C.O. III, “Ambidextrous Organizations: Managing Evolutionary and Revolutionary Change,” California Management Review , Vol. 38, No. 4, 1996, pp. 8–29.

37. P. Weill and S.L. Woerner, “Is Your Company Ready for a Digital Future?” MIT Sloan Management Review , Vol. 59, No. 2, 2018, pp. 21–25.

38. R. Ramilo and M.R.B. Embi, “Critical Analysis of Key Determinants and Barriers to Digital Innovation Adoption among Architectural Organizations,” Frontiers of Architectural Research , Vol. 3, No. 4, 2014, pp. 431–451.

39. B. Kitchenham, “Procedures for Performing Systematic Reviews,” Keele University, UK, Tech. Rep., 2004.

40. R. Mallett, J. Hagen-Zanker, R. Slater, and M. Duvendack, “The Benefits and Challenges of Using Systematic Reviews in International Development Research,” Journal of Development Effectiveness , Vol. 4, No. 3, 2012, pp. 445–455.

41. K. Petersen, S. Vakkalanka, and L. Kuzniarz, “Guidelines for conducting systematic mapping studies in software engineering: An update,” Information and Software Technology , Vol. 64, 2015, pp. 1–18.

42. A. Bajaj and O.P. Sangwan, “A Systematic Literature Review of Test Case Prioritization Using Genetic Algorithms,” IEEE Access , Vol. 7, No. 126355–126375, 2019.

43. I.M. Côté, P.S. Curtis, H.R. Rothstein, and G.B. Stewart, “Gathering data: searching literature and selection criteria,” Handbook of meta-analysis in ecology and evolution , 2013, pp. 37–51.

44. A.F. Hayes and K. Krippendorff, “Answering the Call for a Standard Reliability Measure for Coding Data,” Communication Methods and Measures , Vol. 1, No. 1, 2007, pp. 77–89.

45. Q.N. Hong, P. Pluye, S. Fàbregues, G. Bartlett, F. Boardman et al., “Mixed Methods Appraisal Tool (MMAT),” McGill University, Tech. Rep., 2018.

46. C. Wohlin, “Guidelines for Snowballing in Systematic Literature Studies and a Replication in Software Engineering,” in 18th International Conference on Evaluation and Assessment in Software Engineering . New York City, New York, USA: ACM Press, 2014, pp. 1–10.

47. D. Moher, A. Liberati, J. Tetzlaff, D.G. Altman, and P. Group, “Preferred Reporting Items for Systematic Reviews and Meta-analyses: The PRISMA Statement,” PLoS Medicine , Vol. 6, No. 7, 2009.

48. T.D.Cook, D. Campbell, and W.R.Shadish, Experimental and quasi-experimental designs for generalized causal inference . Houghton Mifflin Boston, MA, 2002.

49. S. Elo, M. Kääriäinen, O. Kanste, T. Pölkki, K. Utriainen et al., “Qualitative Content Analysis: A Focus on Trustworthiness,” SAGE Open , 2014, pp. 1–10.

50. U. Flick, The SAGE Handbook of Qualitative Data Analysis . Sage, 2013.

51. K.A. Neuendorf, The Content Analysis Guidebook , 2nd ed. Los Angeles, California, USA: SAGE, 2017.

52. S. Elo and H. Kyngäs, “The Qualitative Content Analysis Process,” Journal of Advanced Nursing , Vol. 62, No. 1, 2008, pp. 107–115.

53. M. Bengtsson, “How to Plan and Perform a Qualitative Study using Content Analysis,” NursingPlus Open , Vol. 2, 2016, pp. 8–14.

54. J. vom Brocke, A. Simons, B. Niehaves, B. Niehaves, K. Riemer et al., “Reconstructing the Giant: On the Importance of Rigour in Documenting the Literature Search Process,” in 17th European Conference on Information Systems , 2009, pp. 2206–2217.

55. A.C. Serban and M.D. Lytras, “Artificial Intelligence for Smart Renewable Energy Sector in Europe—smart Energy Infrastructures for Next Generation Smart Cities,” IEEE Access , Vol. 8, 2020, pp. 77364–77377.

56. M. Allen, The SAGE encyclopedia of Communication Research Methods . Sage Publications, 2017.

57. M.M. Bonanomi, D.M. Hall, S. Staub-French, A. Tucker, and C.M.L. Talamo, “The Impact of Digital Transformation on Formal and Informal Organizational Structures of Large Architecture and Engineering Firms,” Engineering, Construction, and Architectural Management , Vol. 27, No. 4, 2019, pp. 872–892.

58. C.S. Collins and C.M. Stockton, “The Central Role of Theory in Qualitative Research,” International Journal of Qualitative Methods , Vol. 17, 2018, pp. 1–10.

59. J.W. Creswell and J.D. Creswell, Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . Los Angeles, California, USA: Sage, 2018.

60. M. Jocevski, N. Arvidsson, G. Miragliotta, A. Ghezzi, and R. Mangiaracina, “Transitions Towards Omni-channel Retailing Strategies: A Business Model Perspective,” International Journal of Retail & Distribution Management , Vol. 47, No. 2, 2019, pp. 78–93.

61. X. Fan, N. Ning, and N. Deng, “The Impact of the Quality of Intelligent Experience on Smart Retail Engagement,” Marketing Intelligence & Planning , Vol. 38, No. 7, 2020, pp. 877–891.

62. S.F. Wamba and S. Akter, “Understanding Supply Chain Analytics Capabilities and Agility for Data-rich Environments,” International Journal of Operations & Production Management , Vol. 39, No. 6–8, 2019, pp. 887–912.

63. G. Elia, G. Polimeno, G. Solazzo, and G. Passiante, “A Multi-dimension Framework for Value Creation through Big Data,” Industrial Marketing Management , Vol. 90, 2020, pp. 508–522.

64. K. Tiwari and M.S. Khan, “Sustainability Accounting and Reporting in the Industry 4.0,” Journal of Cleaner Production , Vol. 258, 2020.

65. Y. Gong and M. Janssen, “Roles and Capabilities of Enterprise Architecture in Big Data Analytics Technology Adoption and Implementation,” Journal of Theoretical and Applied Electronic Commerce Research , Vol. 16, No. 1, 2021, pp. 37–51.

66. T. Nam, “Technology Usage, Expected Job Sustainability, and Perceived Job Insecurity,” Technological Forecasting and Social Change , Vol. 138, 2019, pp. 155–165.

67. P. Dahlbom, N. Siikanen, P. Sajasalo, and M. Jarvenpää, “Big Data and HR Analytics in the Digital Era,” Baltic Journal of Management , Vol. 15, No. 1, 2020.

68. M. Gotthardt, D. Koivulaakso, O. Paksoy, C. Saramo, M. Martikainen et al., “Current State and Challenges in the Implementation of Smart Robotic Process Automation in Accounting and Auditing,” ACRN Journal of Finance and Risk Perspectives , Vol. 9, 2020, pp. 90–102.

69. M. Barrett, E. Oborn, W.J. Orlikowski, and J. Yates, “Reconfiguring Boundary Relations: Robotic Innovations in Pharmacy Work,” Organization Science , Vol. 23, No. 5, 2011, pp. 1448–1466.

70. W. Leontief, The Long-Term Impact of Technology on Employment and Unemployment . Washington D. C., USA: The National Academies Press, 1983.

71. W. Feng, R. Tu, and Z. Zhou, “Understanding Forced Adoption of Self-service Technology: The Impacts of Users’ Psychological Reactance,” Behaviour & Information Technology , Vol. 38, No. 8, 2019, pp. 820–832.

72. D.J. Bowersox, D.J. Closs, and R. Drayer, “The Digital Transformation: Technology and Beyond,” Supply Chain Management Review , Vol. 9, No. 1, 2005, pp. 22–29.

73. F. Caputo, V. Cillo, E. Candelo, and Y. Liu, “Innovating through Digital Revolution: The Role of Soft Skills and Big Data in Increasing Firm Performance,” Management Decision , Vol. 57, No. 8, 2019, pp. 2032–2051.

74. K. Conboy, P. Mikalef, D. Dennehy, and J. Krogstie, “Using Business Analytics to Enhance Dynamic Capabilities in Operations Research: A Case Analysis and Research Agenda,” European Journal of Operational Research , Vol. 281, No. 3, 2020, pp. 656–672.

75. S. Schneider and M. Leyer, “Me or Information Technology? Adoption of Artificial Intelligence in the Delegation of Personal Strategic Decisions,” Managerial and Decision Economics , Vol. 40, No. 3, 2019, pp. 223–231.

76. W.E. Hilali, A.E. Manouar, and M.A. Idrissi, “Reaching Sustainability During a Digital Transformation: A PLS Approach,” International Journal of Innovation Science , Vol. 12, No. 1, 2020, pp. 52–79.

77. C.D. Francescomarino and F.M. Maggi, “Preface to the Special Issue on Business Process Innovations with Artificial Intelligence,” Journal on Data Semantics , Vol. 8, 2019, pp. 77–77.

78. A. Regnault, T.Willgoss, and S. Barbic, “Towards the Use of Mixed Methods Inquiry as Best Practice in Health Outcomes Research,” Journal of Patient-Reported Outcomes , Vol. 2, No. 19, 2018.

79. A. Farhoomand and R. Wigand, “Editorial: Special Section on Managing e-Business Transformation,” European Journal of Information Systems , Vol. 12, 2003, pp. 249–250.

80. A.D. Chandler, Strategy and Structure: Chapters in the History of the Industrial Enterprise , 3rd ed. MIT press, 2013.

81. B.T. Pentland, C.S. Osborn, G. Wyner, and F. Luconi, Useful Descriptions of Organizational Processes: Collecting Data for the Process Handbook . Center for Coordination Science, Massachusetts Institute of Technology, USA, 1999.

82. R. Bain, “Technology and State Government,” American Sociological Review , Vol. 2, No. 6, 1937, pp. 860–874.

83. E.Romanelli and M. Tushman, “Organizational transformation as punctuated equilibrium: An empirical test,” Academy of Management journal , Vol. 37, No. 5, 1994, pp. 1141–1166.

84. Y. Chen and Z. Lin, “Business intelligence capabilities and firm performance: A study in china,” International Journal of Information Management , Vol. 57, 2021, p. 102232.

85. M. Aboelmaged and S. Mouakket, “Influencing models and determinants in big data analytics research: A bibliometric analysis,” Information Processing & Management , Vol. 57, No. 4, 2020, p. 102234.

86. S.D. R. Balakrishnan, “How do firms reorganize to implement digital transformation?” Strategic Change , Vol. 29, No. 5, 2020, pp. 531–541.

87. F. Brunetti, D.T. Matt, A. Bonfanti, A.D. Longhi, G. Pedrini et al., “Digital Transformation Challenges: Strategies Emerging from a Multi-stakeholder Approach,” The TQM Journal , Vol. 32, No. 4, 2020, pp. 697–724.

88. C. Dremel, M.M. Herterich, J. Wulf, and J. vom Brocke, “Actualizing big data analytics affordances: A revelatory case study,” Information & Management , Vol. 57, No. 1, 2020, p. 103121.

89. J. Lee and D. Kim, “Development of innovative business of telecommunication operator: Case of kt-meg,” International Journal of Asian Business and Information Management (IJABIM) , Vol. 11, No. 3, 2020, pp. 1–14.

90. P. Mikalef, J. Krogstie, I. Pappas, and P. Pavlou, “Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities,” Information & Management , Vol. 57, No. 2, 2020, p. 103169.

91. K. Moore, “Smart connected sensors, cyber-physical networks, and big data analytics systems in internet of things-based real-time production logistics,” Economics, Management, and Financial Markets , Vol. 15, No. 2, 2020, pp. 16–22.

92. N. Nguyen, R. Gosine, and P. Warrian, “A systematic review of big data analytics for oil and gas industry 4.0,” IEEE access , Vol. 8, 2020, pp. 61183–61201.

93. P. Osterrieder, L. Budde, and T. Friedli, “The smart factory as a key construct of industry 4.0: A systematic literature review,” International Journal of Production Economics , Vol. 221, 2020, p. 107476.

94. R. Silva, C. Bernardo, C. Watanabe, R. Silva, and J. Neto, “Contributions of the internet of things in education as support tool in the educational management decision-making process,” International Journal of Innovation and Learning , Vol. 27, No. 2, 2020, pp. 175–196.

95. M. Sott, L. Furstenau, L. Kipper, F. Giraldo, J. LÓPEZ-ROBLES et al., “Precision techniques and agriculture 4.0 technologies to promote sustainability in the coffee sector: state of the art, challenges and future trends,” IEEE Access , Vol. 8, 2020, pp. 149854–149867.

96. A. Tuomi, I. Tussyadiah, E. Ling, G. Miller, and L. Geunhee, “x=(tourism_work) y=(sdg8) while y= true: automate (x),” Annals of Tourism Research , Vol. 84, 2020, p. 102978.

97. Z. Zhang and T. Luo, “Knowledge structure, network structure, exploitative and exploratory innovations,” Technology Analysis & Strategic Management , Vol. 32, No. 6, 2020, pp. 666–682.

98. J. Brock and F.V. Wangenheim, “Demystifying ai: What digital transformation leaders can teach you about realistic artificial intelligence,” California Management Review , Vol. 61, No. 4, 2019, pp. 110–134.

99. D. Kalaivani and P. Sumathi, “Factor based prediction model for customer behavior analysis,” International Journal of System Assurance Engineering and Management , Vol. 10, No. 4, 2019, pp. 519–524.

100. R. Leung, “Smart hospitality: Taiwan hotel stakeholder perspectives,” Tourism Review , 2019.

101. S. Magistretti, C. Dell’Era, and A. Messeni Petruzzelli, “How intelligent is watson? enabling digital transformation through artificial intelligence,” Business Horizons , Vol. 62, No. 6, 2019, pp. 819–829.

102. A. Mitra, S. Gaur, and E. Giacosa, “Combining organizational change management and organizational ambidexterity using data transformation,” Management decision , 2019.

103. L. Pee, S. Pan, and L. Cui, “Artificial intelligence in healthcare robots: A social informatics study of knowledge embodiment,” Journal of the Association for Information Science and Technology , Vol. 70, No. 4, 2019, pp. 351–369.

104. A. Thomas, “Convergence and digital fusion lead to competitive differentiation,” Business Process Management Journal , 2019.

105. K. Warner and M. Wäger, “Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal,” Long range planning , Vol. 52, No. 3, 2019, pp. 326–349.

106. C. Lehrer, A. Wieneke, J.V. Brocke, R. Jung, and S. Seidel, “How big data analytics enables service innovation,” Journal of Strategic Information Systems , Vol. 35, No. 2, 2018.

107. R. Torres, A. Sidorova, and M. Jones, “Enabling firm performance through business intelligence and analytics: A dynamic capabilities perspective,” Information & Management , Vol. 55, No. 7, 2018, pp. 822–839.

108. H. Chen, R. Schütz, R. Kazman, and F. Matthes, “How lufthansa capitalized on big data for business model renovation.” MIS Quarterly Executive , Vol. 16, No. 1, 2017.

109. A. Gunasekaran, T. Papadopoulos, R. Dubey, S. Wamba, S. Childe et al., “Big data and predictive analytics for supply chain and organizational performance,” Journal of Business Research , Vol. 70, 2017, pp. 308–317.

110. R. Basole, “Accelerating digital transformation: Visual insights from the api ecosystem,” IT Professional , Vol. 18, No. 6, 2016, pp. 20–25.

111. M. Hengstler, E. Enkel, and S. Duelli, “Applied artificial intelligence and trust—the case of autonomous vehicles and medical assistance devices,” Technological Forecasting and Social Change , Vol. 105, 2016, pp. 105–120.

112. M. Chalal, X. Boucher, and G. Marquès, “Decision support system for servitization of industrial smes: a modelling and simulation approach,” Journal of Decision Systems , Vol. 24, No. 4, 2015, pp. 355–382.

113. P. O’Donovan, K. Leahy, K. Bruton, and D. O’Sullivan, “An industrial big data pipeline for data-driven analytics maintenance applications in large-scale smart manufacturing facilities,” Journal of Big Data , Vol. 2, No. 1, 2015, pp. 1–26.

114. P. O’donovan, K. Leahy, K. K. Bruton, and D. O’Sullivan, “Big data in manufacturing: a systematic mapping study,” Journal of Big Data , Vol. 2, No. 1, 2015, pp. 1–22.

115. S. LaValle, E. Lesser, R. Shockley, M. Hopkins, and N. Kruschwitz, “Big data, analytics and the path from insights to value,” MIT sloan management review , Vol. 52, No. 2, 2011, pp. 21–32.