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AI-driven business model innovation: A systematic review and research agenda

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  • Jorzik, Philip
  • Klein, Sascha P.
  • Kanbach, Dominik K.
  • Kraus, Sascha

Abstract

Recent years have seen a surge in research on artificial intelligence (AI)-driven business model innovation (BMI), reflecting its profound impact across industries. However, the field’s current state remains fragmented due to varied conceptual lenses and units of analysis. Existing literature predominantly emphasizes the technological aspects of AI implementation in business models (BMs), treating BMI as a byproduct. Additionally, there is a lack of coherent understanding regarding the scope of BMI propelled by AI. To address these gaps, our study systematically reviews 180 articles, offering two key contributions: (1) a structured analysis of evolving research dimensions in AI-driven BMI, differentiating between static and dynamic views of BMI, and (2) a framework presenting distinct research perspectives on AI-driven BMI, each addressing specific managerial focuses. This synthesis facilitates a comprehensive understanding of the field, enabling the identification of research gaps and proposing future avenues for advancing knowledge on the management of AI-driven BMI.

Suggested Citation

  • Jorzik, Philip & Klein, Sascha P. & Kanbach, Dominik K. & Kraus, Sascha, 2024. "AI-driven business model innovation: A systematic review and research agenda," Journal of Business Research, Elsevier, vol. 182(C).
  • Handle: RePEc:eee:jbrese:v:182:y:2024:i:c:s0148296324002686
    DOI: 10.1016/j.jbusres.2024.114764
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    1. Philipp C. Sauer & Stefan Seuring, 2023. "How to conduct systematic literature reviews in management research: a guide in 6 steps and 14 decisions," Review of Managerial Science, Springer, vol. 17(5), pages 1899-1933, July.
    2. Di Vaio, Assunta & Palladino, Rosa & Hassan, Rohail & Escobar, Octavio, 2020. "Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review," Journal of Business Research, Elsevier, vol. 121(C), pages 283-314.
    3. Mostaghel, Rana & Oghazi, Pejvak & Parida, Vinit & Sohrabpour, Vahid, 2022. "Digitalization driven retail business model innovation: Evaluation of past and avenues for future research trends," Journal of Business Research, Elsevier, vol. 146(C), pages 134-145.
    4. Vangelis Marinakis & Themistoklis Koutsellis & Alexandros Nikas & Haris Doukas, 2021. "AI and Data Democratisation for Intelligent Energy Management," Energies, MDPI, vol. 14(14), pages 1-14, July.
    5. Shrutika Mishra & A. R. Tripathi, 2021. "AI business model: an integrative business approach," Journal of Innovation and Entrepreneurship, Springer, vol. 10(1), pages 1-21, December.
    6. Coskun-Setirek, Abide & Tanrikulu, Zuhal, 2021. "Digital innovations-driven business model regeneration: A process model," Technology in Society, Elsevier, vol. 64(C).
    7. Hongyi Mao & Tao Zhang & Qing Tang, 2021. "Research Framework for Determining How Artificial Intelligence Enables Information Technology Service Management for Business Model Resilience," Sustainability, MDPI, vol. 13(20), pages 1-14, October.
    8. Vlačić, Božidar & Corbo, Leonardo & Costa e Silva, Susana & Dabić, Marina, 2021. "The evolving role of artificial intelligence in marketing: A review and research agenda," Journal of Business Research, Elsevier, vol. 128(C), pages 187-203.
    9. Ma, Yulun & Hu, Yue, 2021. "Business Model Innovation and Experimentation in Transforming Economies: ByteDance and TikTok," Management and Organization Review, Cambridge University Press, vol. 17(2), pages 382-388, May.
    10. Fredström, Ashkan & Wincent, Joakim & Sjödin, David & Oghazi, Pejvak & Parida, Vinit, 2021. "Tracking innovation diffusion: AI analysis of large-scale patent data towards an agenda for further research," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    11. Michael Weber & Moritz Beutter & Jörg Weking & Markus Böhm & Helmut Krcmar, 2022. "AI Startup Business Models," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 64(1), pages 91-109, February.
    12. Shahriar Akter & Katina Michael & Muhammad Rajib Uddin & Grace McCarthy & Mahfuzur Rahman, 2022. "Transforming business using digital innovations: the application of AI, blockchain, cloud and data analytics," Annals of Operations Research, Springer, vol. 308(1), pages 7-39, January.
    13. Burström, Thommie & Parida, Vinit & Lahti, Tom & Wincent, Joakim, 2021. "AI-enabled business-model innovation and transformation in industrial ecosystems: A framework, model and outline for further research," Journal of Business Research, Elsevier, vol. 127(C), pages 85-95.
    14. Martin Eling & Davide Nuessle & Julian Staubli, 2022. "The impact of artificial intelligence along the insurance value chain and on the insurability of risks," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 47(2), pages 205-241, April.
    15. Haftor, Darek M. & Costa Climent, Ricardo & Lundström, Jenny Eriksson, 2021. "How machine learning activates data network effects in business models: Theory advancement through an industrial case of promoting ecological sustainability," Journal of Business Research, Elsevier, vol. 131(C), pages 196-205.
    16. Sjödin, David & Parida, Vinit & Palmié, Maximilian & Wincent, Joakim, 2021. "How AI capabilities enable business model innovation: Scaling AI through co-evolutionary processes and feedback loops," Journal of Business Research, Elsevier, vol. 134(C), pages 574-587.
    17. Bernd W. Wirtz & Wilhelm M. Müller, 2019. "An integrated artificial intelligence framework for public management," Public Management Review, Taylor & Francis Journals, vol. 21(7), pages 1076-1100, July.
    18. Yasheng Chen & Mohammad Islam Biswas & Md. Shamim Talukder, 2022. "The role of artificial intelligence in effective business operations during COVID-19," International Journal of Emerging Markets, Emerald Group Publishing Limited, vol. 18(12), pages 6368-6387, June.
    19. Mariani, Marcello M. & Machado, Isa & Nambisan, Satish, 2023. "Types of innovation and artificial intelligence: A systematic quantitative literature review and research agenda," Journal of Business Research, Elsevier, vol. 155(PB).
    20. Chatterjee, Sheshadri & Chaudhuri, Ranjan & Vrontis, Demetris & Jabeen, Fauzia, 2022. "Digital transformation of organization using AI-CRM: From microfoundational perspective with leadership support," Journal of Business Research, Elsevier, vol. 153(C), pages 46-58.
    21. Jin, Byoungho Ellie & Shin, Daeun Chloe, 2020. "Changing the game to compete: Innovations in the fashion retail industry from the disruptive business model," Business Horizons, Elsevier, vol. 63(3), pages 301-311.
    22. Yun, JinHyo Joseph & Won, DongKyu & Jeong, EuiSeob & Park, KyungBae & Yang, JeongHo & Park, JiYoung, 2016. "The relationship between technology, business model, and market in autonomous car and intelligent robot industries," Technological Forecasting and Social Change, Elsevier, vol. 103(C), pages 142-155.
    23. Battisti, Sandro & Agarwal, Nivedita & Brem, Alexander, 2022. "Creating new tech entrepreneurs with digital platforms: Meta-organizations for shared value in data-driven retail ecosystems," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    24. Francesca Iandolo & Francesca Loia & Irene Fulco & Chiara Nespoli & Francesco Caputo, 2021. "Combining Big Data and Artificial Intelligence for Managing Collective Knowledge in Unpredictable Environment—Insights from the Chinese Case in Facing COVID-19," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 12(4), pages 1982-1996, December.
    25. Sjödin, David & Parida, Vinit & Kohtamäki, Marko, 2023. "Artificial intelligence enabling circular business model innovation in digital servitization: Conceptualizing dynamic capabilities, AI capacities, business models and effects," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    26. Yun, JinHyo Joseph & Won, DongKyu & Park, KyungBae & Jeong, EuiSeob & Zhao, Xiaofei, 2019. "The role of a business model in market growth: The difference between the converted industry and the emerging industry," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 534-562.
    27. Sunil Mithas & Zhi‐Long Chen & Terence J.V. Saldanha & Alysson De Oliveira Silveira, 2022. "How will artificial intelligence and Industry 4.0 emerging technologies transform operations management?," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4475-4487, December.
    28. Füller, Johann & Hutter, Katja & Wahl, Julian & Bilgram, Volker & Tekic, Zeljko, 2022. "How AI revolutionizes innovation management – Perceptions and implementation preferences of AI-based innovators," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    29. Jana Gerlach & Paul Hoppe & Sarah Jagels & Luisa Licker & Michael H. Breitner, 2022. "Decision support for efficient XAI services - A morphological analysis, business model archetypes, and a decision tree," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2139-2158, December.
    30. B. Demil & X. Lecocq, 2010. "Business model evolution : in search of dynamic consistency," Post-Print hal-00572915, HAL.
    31. Matti Mäntymäki & Sami Hyrynsalmi & Antti Koskenvoima, 2020. "How Do Small and Medium-Sized Game Companies Use Analytics? An Attention-Based View of Game Analytics," Information Systems Frontiers, Springer, vol. 22(5), pages 1163-1178, October.
    32. Sascha Kraus & Matthias Breier & Weng Marc Lim & Marina Dabić & Satish Kumar & Dominik Kanbach & Debmalya Mukherjee & Vincenzo Corvello & Juan Piñeiro-Chousa & Eric Liguori & Daniel Palacios-Marqués &, 2022. "Literature reviews as independent studies: guidelines for academic practice," Review of Managerial Science, Springer, vol. 16(8), pages 2577-2595, November.
    33. Subin Liengpunsakul, 2021. "Artificial Intelligence and Sustainable Development in China," Chinese Economy, Taylor & Francis Journals, vol. 54(4), pages 235-248, July.
    34. Diane Coyle & David Nguyen, 2019. "Cloud Computing, Cross-Border Data Flows and New Challenges for Measurement in Economics," National Institute Economic Review, National Institute of Economic and Social Research, vol. 249(1), pages 30-38, August.
    35. Dominik Dellermann & Nikolaus Lipusch & Philipp Ebel & Jan Marco Leimeister, 2019. "Design principles for a hybrid intelligence decision support system for business model validation," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(3), pages 423-441, September.
    36. Josef Åström & Wiebke Reim & Vinit Parida, 2022. "Value creation and value capture for AI business model innovation: a three-phase process framework," Review of Managerial Science, Springer, vol. 16(7), pages 2111-2133, October.
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