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Government adoption of generative artificial intelligence and ambidextrous innovation

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Listed:
  • Zhou, Zhikai
  • Liu, Dewen
  • Chen, Zhongjie
  • Pancho, Martin

Abstract

Every information technological revolution has brought about new possibilities for governmental organizational innovation, and the rapid development of Generative artificial intelligence (Gen-AI) is poised to profoundly impact government governance models and public service supply methods. Understanding the factors influencing government adoption of Gen-AI, and analyzing the impact of such adoption on governmental organizational innovation behavior, have emerged as urgent and cutting-edge topics. Based on the Technology-Organization-Environment (TOE) framework and the ambidextrous organization theory, this study systematically analyzes the three-layered driving factors that influence government organizations' adoption of Gen-AI, and examines the impact of Gen-AI on exploratory and exploitative innovation within government organizations. Furthermore, it delves into the influence mechanisms of technology adoption on different innovation behaviors from the meso-institutional and micro-implementation perspectives. At the theoretical level, this study constructs a conceptual framework for understanding the adoption of Gen-AI technology, extends the application scope of the TOE theory and enhances its explanatory power, while also providing new insights into the complexity of technology-enabled organizational innovation. At the practical level, it offers a more strategic perspective and profound implications for government organizations to maintain innovative vitality and achieve sustainable development amidst the wave of intelligent transformation.

Suggested Citation

  • Zhou, Zhikai & Liu, Dewen & Chen, Zhongjie & Pancho, Martin, 2025. "Government adoption of generative artificial intelligence and ambidextrous innovation," International Review of Economics & Finance, Elsevier, vol. 98(C).
    • Handle: RePEc:eee:reveco:v:98:y:2025:i:c:s1059056025001169
    DOI: 10.1016/j.iref.2025.103953
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    as
    1. Hashimy, Loha & Geetika, Jain & Grifell-Tatje, Emili, 2023. "Determinants of Blockchain Adoption as Decentralized Business Model by Spanish Firms: – An Innovation Theory Perspective," MPRA Paper 119903, University Library of Munich, Germany.
    2. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    3. Klockmann, Victor & von Schenk, Alicia & Villeval, Marie Claire, 2022. "Artificial intelligence, ethics, and intergenerational responsibility," Journal of Economic Behavior & Organization, Elsevier, vol. 203(C), pages 284-317.
    4. Haefner, Naomi & Wincent, Joakim & Parida, Vinit & Gassmann, Oliver, 2021. "Artificial intelligence and innovation management: A review, framework, and research agenda✰," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    5. Haneem, Faizura & Kama, Nazri & Taskin, Nazim & Pauleen, David & Abu Bakar, Nur Azaliah, 2019. "Determinants of master data management adoption by local government organizations: An empirical study," International Journal of Information Management, Elsevier, vol. 45(C), pages 25-43.
    6. Brougham, David & Haar, Jarrod, 2018. "Smart Technology, Artificial Intelligence, Robotics, and Algorithms (STARA): Employees’ perceptions of our future workplace," Journal of Management & Organization, Cambridge University Press, vol. 24(2), pages 239-257, March.
    7. Thrane, Sof & Blaabjerg, Steen & Møller, Rasmus Hannemann, 2010. "Innovative path dependence: Making sense of product and service innovation in path dependent innovation processes," Research Policy, Elsevier, vol. 39(7), pages 932-944, September.
    8. Jing Li & Changhui Zhou & Edward J. Zajac, 2009. "Control, collaboration, and productivity in international joint ventures: theory and evidence," Strategic Management Journal, Wiley Blackwell, vol. 30(8), pages 865-884, August.
    9. Kuziemski, Maciej & Misuraca, Gianluca, 2020. "AI governance in the public sector: Three tales from the frontiers of automated decision-making in democratic settings," Telecommunications Policy, Elsevier, vol. 44(6).
    10. Yogesh K. Dwivedi & Nripendra P. Rana & Anand Jeyaraj & Marc Clement & Michael D. Williams, 2019. "Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model," Information Systems Frontiers, Springer, vol. 21(3), pages 719-734, June.
    11. Mariana Mazzucato, 2016. "From market fixing to market-creating: a new framework for innovation policy," Industry and Innovation, Taylor & Francis Journals, vol. 23(2), pages 140-156, February.
    12. wael AL-khatib, Ayman, 2023. "Drivers of generative artificial intelligence to fostering exploitative and exploratory innovation: A TOE framework," Technology in Society, Elsevier, vol. 75(C).
    13. Ghosh, Arghya & Kato, Takao & Morita, Hodaka, 2017. "Incremental innovation and competitive pressure in the presence of discrete innovation," Journal of Economic Behavior & Organization, Elsevier, vol. 135(C), pages 1-14.
    14. Premkumar, G. & Roberts, Margaret, 1999. "Adoption of new information technologies in rural small businesses," Omega, Elsevier, vol. 27(4), pages 467-484, August.
    15. Ida Merete Enholm & Emmanouil Papagiannidis & Patrick Mikalef & John Krogstie, 2022. "Artificial Intelligence and Business Value: a Literature Review," Information Systems Frontiers, Springer, vol. 24(5), pages 1709-1734, October.
    16. Gary C. Moore & Izak Benbasat, 1991. "Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation," Information Systems Research, INFORMS, vol. 2(3), pages 192-222, September.
    17. Gupta, Manjul & Esmaeilzadeh, Pouyan & Uz, Irem & Tennant, Vanesa M., 2019. "The effects of national cultural values on individuals' intention to participate in peer-to-peer sharing economy," Journal of Business Research, Elsevier, vol. 97(C), pages 20-29.
    18. Jan Jöhnk & Malte Weißert & Katrin Wyrtki, 2021. "Ready or Not, AI Comes— An Interview Study of Organizational AI Readiness Factors," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(1), pages 5-20, February.
    19. Kevin Zheng Zhou & Fang Wu, 2010. "Technological capability, strategic flexibility, and product innovation," Strategic Management Journal, Wiley Blackwell, vol. 31(5), pages 547-561, May.
    20. Mahmoud Moussa & Adela McMurray & Nuttawuth Muenjohn, 2018. "A Conceptual Framework of the Factors Influencing Innovation in Public Sector Organizations," Journal of Developing Areas, Tennessee State University, College of Business, vol. 52(3), pages 231-245, July-Sept.
    21. George A. Boyne, 2002. "Public and Private Management: What’s the Difference?," Journal of Management Studies, Wiley Blackwell, vol. 39(1), pages 97-122, January.
    22. Bernd W. Wirtz & Jan C. Weyerer & Carolin Geyer, 2019. "Artificial Intelligence and the Public Sector—Applications and Challenges," International Journal of Public Administration, Taylor & Francis Journals, vol. 42(7), pages 596-615, May.
    23. Jiang, Zihao & Liu, Zhiying, 2022. "Policies and exploitative and exploratory innovations of the wind power industry in China: The role of technological path dependence," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    24. Md Golam Rabiul Alam & Abdul Kadar Muhammad Masum & Loo-See Beh & Choong Seon Hong, 2016. "Critical Factors Influencing Decision to Adopt Human Resource Information System (HRIS) in Hospitals," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-22, August.
    25. James G. March, 1991. "Exploration and Exploitation in Organizational Learning," Organization Science, INFORMS, vol. 2(1), pages 71-87, February.
    26. James Simmie, 2012. "Path Dependence and New Path Creation in Renewable Energy Technologies," European Planning Studies, Taylor & Francis Journals, vol. 20(5), pages 729-731, May.
    27. Muhammad M. Kamal & Ray Hackney & Kashif Sarwar, 2013. "Investigating Factors Inhibiting e-Government Adoption in Developing Countries: The Context of Pakistan," Journal of Global Information Management (JGIM), IGI Global, vol. 21(4), pages 77-102, October.
    28. Kakatkar, Chinmay & Bilgram, Volker & Füller, Johann, 2020. "Innovation analytics: Leveraging artificial intelligence in the innovation process," Business Horizons, Elsevier, vol. 63(2), pages 171-181.
    29. Danny Miller, 1983. "The Correlates of Entrepreneurship in Three Types of Firms," Management Science, INFORMS, vol. 29(7), pages 770-791, July.
    30. Frey, Carl Benedikt & Osborne, Michael A., 2017. "The future of employment: How susceptible are jobs to computerisation?," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 254-280.
    31. Ross, Stephen A, 1973. "The Economic Theory of Agency: The Principal's Problem," American Economic Review, American Economic Association, vol. 63(2), pages 134-139, May.
    32. Charles Williams & Will Mitchell, 2004. "Focusing Firm Evolution: The Impact of Information Infrastructure on Market Entry by U.S. Telecommunications Companies, 1984--1998," Management Science, INFORMS, vol. 50(11), pages 1561-1575, November.
    33. Hong Chen & Ling Li & Yong Chen, 2021. "Explore success factors that impact artificial intelligence adoption on telecom industry in China," Journal of Management Analytics, Taylor & Francis Journals, vol. 8(1), pages 36-68, January.
    34. Daqing Zheng & Jin Chen & Lihua Huang & Cheng Zhang, 2013. "E-government adoption in public administration organizations: integrating institutional theory perspective and resource-based view," European Journal of Information Systems, Taylor & Francis Journals, vol. 22(2), pages 221-234, March.
    35. Alam, Mohammad Zahedul & Hoque, Md. Rakibul & Hu, Wang & Barua, Zapan, 2020. "Factors influencing the adoption of mHealth services in a developing country: A patient-centric study," International Journal of Information Management, Elsevier, vol. 50(C), pages 128-143.
    36. 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).
    37. Lian, Jiunn-Woei & Yen, David C. & Wang, Yen-Ting, 2014. "An exploratory study to understand the critical factors affecting the decision to adopt cloud computing in Taiwan hospital," International Journal of Information Management, Elsevier, vol. 34(1), pages 28-36.
    38. David J. Teece & Gary Pisano & Amy Shuen, 1997. "Dynamic capabilities and strategic management," Strategic Management Journal, Wiley Blackwell, vol. 18(7), pages 509-533, August.
    39. Amit Kumar Bhardwaj & Arunesh Garg & Yuvraj Gajpal, 2021. "Determinants of Blockchain Technology Adoption in Supply Chains by Small and Medium Enterprises (SMEs) in India," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-14, June.
    40. Pierson, Paul, 2000. "Increasing Returns, Path Dependence, and the Study of Politics," American Political Science Review, Cambridge University Press, vol. 94(2), pages 251-267, June.
    41. Chin-Lung Hsu & Judy Chuan-Chuan Lin, 2016. "Factors affecting the adoption of cloud services in enterprises," Information Systems and e-Business Management, Springer, vol. 14(4), pages 791-822, November.
    42. Junrie B. Matias & Alexander A. Hernandez, 2021. "Cloud Computing Adoption Intention by MSMEs in the Philippines," Global Business Review, International Management Institute, vol. 22(3), pages 612-633, June.
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    JEL classification:

    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • H83 - Public Economics - - Miscellaneous Issues - - - Public Administration
    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management

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