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Value creation and value capture for AI business model innovation: a three-phase process framework

Author

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  • Josef Åström

    (Luleå University of Technology)

  • Wiebke Reim

    (Luleå University of Technology)

  • Vinit Parida

    (Luleå University of Technology)

Abstract

The rise of AI technologies is generating novel opportunities for companies to create additional value for their customers by applying a proactive approach, managing uncertainty, and thus improving cost efficiency and increasing revenue. However, AI technology capabilities are not enough—companies need to understand how the technology can be commercialized through appropriate AI business model innovation. When emerging technologies are introduced, business-model concepts often need to be significantly altered. This is necessary to fully capitalize on disruptive technologies because it is just as important to innovate the business model as it is to build advanced technology solutions. Therefore, the purpose of this study is to explain how AI providers align value-creation and value-capture dimensions in order to develop commercially viable AI business models. To fulfill our stated purpose, this study has adopted an inductive and exploratory single case-study approach centered on a market-leading provider of AI-related services. The findings are consolidated into a process framework that explicitly illustrates the key activities that companies need to perform concerning value creation and value capture for AI business model innovation and commercialization. The framework explains that AI providers need to follow three phases—namely, identifying prerequisites for AI value creation, matching value capture mechanisms, and developing AI business model offer. We also find that AI providers need to test and develop multiple AI business models and operate them simultaneously to ensure commercial success.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:rvmgts:v:16:y:2022:i:7:d:10.1007_s11846-022-00521-z
    DOI: 10.1007/s11846-022-00521-z
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    References listed on IDEAS

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    1. Teece, David J., 2018. "Profiting from innovation in the digital economy: Enabling technologies, standards, and licensing models in the wireless world," Research Policy, Elsevier, vol. 47(8), pages 1367-1387.
    2. Mirella Muhic & Lars Bengtsson, 2021. "Dynamic capabilities triggered by cloud sourcing: a stage-based model of business model innovation," Review of Managerial Science, Springer, vol. 15(1), pages 33-54, January.
    3. Henry Chesbrough & Richard S. Rosenbloom, 2002. "The role of the business model in capturing value from innovation: evidence from Xerox Corporation's technology spin-off companies," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 11(3), pages 529-555, June.
    4. Erik Brynjolfsson & Daniel Rock & Chad Syverson, 2018. "Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 23-57, National Bureau of Economic Research, Inc.
    5. Ricarda B. Bouncken & Sascha Kraus & Norat Roig-Tierno, 2021. "Knowledge- and innovation-based business models for future growth: digitalized business models and portfolio considerations," Review of Managerial Science, Springer, vol. 15(1), pages 1-14, January.
    6. Ajay Agrawal & Joshua S. Gans & Avi Goldfarb, 2019. "Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction," Journal of Economic Perspectives, American Economic Association, vol. 33(2), pages 31-50, Spring.
    7. Iain M. Cockburn & Rebecca Henderson & Scott Stern, 2018. "The Impact of Artificial Intelligence on Innovation: An Exploratory Analysis," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 115-146, National Bureau of Economic Research, Inc.
    8. Mahka Moeen & Rajshree Agarwal, 2017. "Incubation of an industry: Heterogeneous knowledge bases and modes of value capture," Strategic Management Journal, Wiley Blackwell, vol. 38(3), pages 566-587, March.
    9. Sven M. Laudien & Robin Pesch, 2019. "Understanding the influence of digitalization on service firm business model design: a qualitative-empirical analysis," Review of Managerial Science, Springer, vol. 13(3), pages 575-587, June.
    10. Sascha Kraus & Norat Roig-Tierno & Ricarda B. Bouncken, 2019. "Digital innovation and venturing: an introduction into the digitalization of entrepreneurship," Review of Managerial Science, Springer, vol. 13(3), pages 519-528, June.
    11. Jay Pil Choi & Chaim Fershtman & Neil Gandal, 2010. "Network Security: Vulnerabilities And Disclosure Policy," Journal of Industrial Economics, Wiley Blackwell, vol. 58(4), pages 868-894, December.
    12. Visnjic, Ivanka & Jovanovic, Marin & Neely, Andy & Engwall, Mats, 2017. "What brings the value to outcome-based contract providers? Value drivers in outcome business models," International Journal of Production Economics, Elsevier, vol. 192(C), pages 169-181.
    13. Iain M. Cockburn & Rebecca Henderson & Scott Stern, 2018. "The Impact of Artificial Intelligence on Innovation," NBER Working Papers 24449, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Xiangqian Li & Qiang Qiang & Li Huang & Cunquan Huang, 2022. "How Knowledge Sharing Affects Business Model Innovation: An Empirical Study from the Perspective of Ambidextrous Organizational Learning," Sustainability, MDPI, vol. 14(10), pages 1-17, May.
    2. Bahoo, Salman & Cucculelli, Marco & Qamar, Dawood, 2023. "Artificial intelligence and corporate innovation: A review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    3. Martin R. W. Hiebl & David I. Pielsticker, 2023. "Automation, organizational ambidexterity and the stability of employee relations: new tensions arising between corporate entrepreneurship, innovation management and stakeholder management," The Journal of Technology Transfer, Springer, vol. 48(6), pages 1978-2006, December.
    4. Chamindika Weerakoon & Sarath S. Kodithuwakku, 2023. "Configurations of Business Model Innovation: Unpacking the Practice Adopted by Firms in an Emerging Market Context," Journal of Entrepreneurship and Innovation in Emerging Economies, Entrepreneurship Development Institute of India, vol. 32(1), pages 218-259, March.
    5. Denis E. Matytsin & Valentin A. Dzedik & Galina A. Markeeva & Saglar B. Boldyreva, 2023. "“Smart” outsourcing in support of the humanization of entrepreneurship in the artificial intelligence economy," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-8, December.
    6. Luis J. Callarisa-Fiol & Miguel Ángel Moliner-Tena & Rosa Rodríguez-Artola & Javier Sánchez-García, 2023. "Entrepreneurship innovation using social robots in tourism: a social listening study," Review of Managerial Science, Springer, vol. 17(8), pages 2945-2971, November.
    7. Cristina Sbirneciu & Nicoleta Valentina Florea, 2023. "Evaluating the Impact of Emerging Technologies on the ECB's Mandate: Can the European Central Bank Use Distributed Ledger Technology and Digital Euro to Advance Financial Inclusion in Europe?," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 1059-1070, August.
    8. Yutong Liu & Peiyi Song, 2023. "Digital Transformation and Green Innovation of Energy Enterprises," Sustainability, MDPI, vol. 15(9), pages 1-15, May.
    9. Durst, Susanne & Davila, Andrés & Foli, Samuel & Kraus, Sascha & Cheng, Cheng-Feng, 2023. "Antecedents of technological readiness in times of crises: A comparison between before and during COVID-19," Technology in Society, Elsevier, vol. 72(C).
    10. Miguel-Ángel Galindo-Martín & María-Soledad Castaño-Martínez & María-Teresa Méndez-Picazo, 2023. "Digitalization, entrepreneurship and competitiveness: an analysis from 19 European countries," Review of Managerial Science, Springer, vol. 17(5), pages 1809-1826, July.
    11. Ricarda B. Bouncken & Sascha Kraus & Antonio Lucas Ancillo, 2022. "Management in times of crises: reflections on characteristics, avoiding pitfalls, and pathways out," Review of Managerial Science, Springer, vol. 16(7), pages 2035-2046, October.

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