IDEAS home Printed from https://ideas.repec.org/a/spr/rvmgts/v16y2022i7d10.1007_s11846-022-00521-z.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11846-022-00521-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11846-022-00521-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Madanaguli, Arun & Sjödin, David & Parida, Vinit & Mikalef, Patrick, 2024. "Artificial intelligence capabilities for circular business models: Research synthesis and future agenda," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    3. 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.
    4. Satish Kumar & Weng Marc Lim & Riya Sureka & Charbel Jose Chiappetta Jabbour & Umesh Bamel, 2024. "Balanced scorecard: trends, developments, and future directions," Review of Managerial Science, Springer, vol. 18(8), pages 2397-2439, August.
    5. 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.
    6. 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.
    7. Yutong Liu & Peiyi Song, 2023. "Digital Transformation and Green Innovation of Energy Enterprises," Sustainability, MDPI, vol. 15(9), pages 1-15, May.
    8. 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).
    9. Xiang, Guopeng & Peng, Mixiang & Tang, Fei & Liu, Yuan, 2024. "Unpacking the impact of entrepreneurial learning on business model innovation in internet startups: Mediating roles of digital capabilities," Technology in Society, Elsevier, vol. 77(C).
    10. 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).
    11. Gillner, Sandra, 2024. "We're implementing AI now, so why not ask us what to do? – How AI providers perceive and navigate the spread of diagnostic AI in complex healthcare systems," Social Science & Medicine, Elsevier, vol. 340(C).
    12. Bryan N. Zambrano Manzur & Fabián A. Espinoza Bazán & Pavel Novoa-Hernández & Carlos Cruz Corona, 2024. "In what ways do AI techniques propel decision-making amidst volatility? Annotated bibliography perspectives," Journal of Innovation and Entrepreneurship, Springer, vol. 13(1), pages 1-24, December.
    13. 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.
    14. 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.
    15. Yugang He, 2024. "Artificial intelligence and religious freedom: divergent paths converging on economic expansion," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.
    16. Tachia Chin & Muhammad Waleed Ayub Ghouri & Jiyang Jin & Muhammet Deveci, 2024. "AI technologies affording the orchestration of ecosystem-based business models: the moderating role of AI knowledge spillover," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
    17. Moqaddamerad, Sara & Ali, Murad, 2024. "Strategic foresight and business model innovation: The sequential mediating role of sensemaking and learning," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    18. 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.
    19. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bernardo S Buarque & Ronald B Davies & Ryan M Hynes & Dieter F Kogler, 2020. "OK Computer: the creation and integration of AI in Europe," Cambridge Journal of Regions, Economy and Society, Cambridge Political Economy Society, vol. 13(1), pages 175-192.
    2. Venturini, Francesco, 2022. "Intelligent technologies and productivity spillovers: Evidence from the Fourth Industrial Revolution," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 220-243.
    3. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "Economic Policy for Artificial Intelligence," Innovation Policy and the Economy, University of Chicago Press, vol. 19(1), pages 139-159.
    4. Johnson, Prince Chacko & Laurell, Christofer & Ots, Mart & Sandström, Christian, 2022. "Digital innovation and the effects of artificial intelligence on firms’ research and development – Automation or augmentation, exploration or exploitation?," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    5. Agrawal, Ajay & Gans, Joshua S. & Goldfarb, Avi, 2019. "Exploring the impact of artificial Intelligence: Prediction versus judgment," Information Economics and Policy, Elsevier, vol. 47(C), pages 1-6.
    6. Lundvall, Bengt-Åke & Rikap, Cecilia, 2022. "China's catching-up in artificial intelligence seen as a co-evolution of corporate and national innovation systems," Research Policy, Elsevier, vol. 51(1).
    7. Liu, Liang & Yang, Kun & Fujii, Hidemichi & Liu, Jun, 2021. "Artificial intelligence and energy intensity in China’s industrial sector: Effect and transmission channel," Economic Analysis and Policy, Elsevier, vol. 70(C), pages 276-293.
    8. Hu, Gang-Gao, 2021. "Is knowledge spillover from human capital investment a catalyst for technological innovation? The curious case of fourth industrial revolution in BRICS economies," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    9. Lyu, Wenjing & Liu, Jin, 2021. "Artificial Intelligence and emerging digital technologies in the energy sector," Applied Energy, Elsevier, vol. 303(C).
    10. Yuliya Snihur & Christoph Zott & Raphael (Raffi) Amit, 2021. "Managing the Value Appropriation Dilemma in Business Model Innovation," Strategy Science, INFORMS, vol. 6(1), pages 22-38, March.
    11. Babina, Tania & Fedyk, Anastassia & He, Alex & Hodson, James, 2024. "Artificial intelligence, firm growth, and product innovation," Journal of Financial Economics, Elsevier, vol. 151(C).
    12. Yang, Haochang & Li, Lianshui & Liu, Yaobin, 2022. "The effect of manufacturing intelligence on green innovation performance in China," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    13. Moshe A. Barach & Aseem Kaul & Ming D. Leung & Sibo Lu, 2019. "Strategic Redundancy in the Use of Big Data: Evidence from a Two-Sided Labor Market," Strategy Science, INFORMS, vol. 4(4), pages 298-322, December.
    14. Tania Babina & Alex X. He & Anastassia Fedyk & James Hodson, 2022. "Artificial Intelligence, Firm Growth, and Product Innovation," NBER Chapters, in: Economics of Artificial Intelligence, National Bureau of Economic Research, Inc.
    15. Matheus Eduardo Leusin, 2022. "The Development of Al in Multinational Enterprises - Effects upon Technological Trajectories and Innovation Performance," Bremen Papers on Economics & Innovation 2201, University of Bremen, Faculty of Business Studies and Economics.
    16. Kopka, Alexander & Grashof, Nils, 2022. "Artificial intelligence: Catalyst or barrier on the path to sustainability?," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    17. Christian Rammer & Gastón P Fernández & Dirk Czarnitzki, 2021. "Artificial Intelligence and Industrial Innovation: Evidence from Firm-Level Data," Working Papers of Department of Economics, Leuven 674605, KU Leuven, Faculty of Economics and Business (FEB), Department of Economics, Leuven.
    18. Zhou, Yixiao & Tyers, Rod, 2019. "Automation and inequality in China," China Economic Review, Elsevier, vol. 58(C).
    19. Ekaterina Prytkova, 2021. "ICT's Wide Web: a System-Level Analysis of ICT's Industrial Diffusion with Algorithmic Links," Jena Economics Research Papers 2021-005, Friedrich-Schiller-University Jena.
    20. Charles Ayoubi & Boris Thurm, 2023. "Knowledge diffusion and morality: Why do we freely share valuable information with Strangers?," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 32(1), pages 75-99, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:rvmgts:v:16:y:2022:i:7:d:10.1007_s11846-022-00521-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.