IDEAS home Printed from https://ideas.repec.org/a/spr/infotm/v26y2025i3d10.1007_s10799-024-00422-5.html
   My bibliography  Save this article

A moderated model of artificial intelligence adoption in firms and its effects on their performance

Author

Listed:
  • Jing Chen

    (Wagner College)

  • Saeed Tajdini

    (Indiana University Southeast)

Abstract

Leveraging two prominent theories of technology adoption in firms, this study examines the organizational determinants of the adoption intensity of artificial intelligence (AI) and its effects on firms’ performance, under the moderating effects of technological turbulence. To conduct this study, a unique dataset was compiled via a survey of US-based managers involved with technology and AI adoption in high-tech goods and services, leading to 226 usable responses. Structural Equation Modeling was then applied to test the proposed model. The findings uncover the influence of technological, organizational, and environmental factors on the firms’ AI adoption intensity. Additionally, a positive correlation is observed between AI adoption intensity and firms' performance. Lastly, technological turbulence emerges as a crucial environmental factor moderating the effects of antecedents on AI. Given the feeble adoption of AI in firms despite its documented role in firms’ success, the current study can offer a road map to successfully implementing AI in firms and, thus, improving their performance.

Suggested Citation

  • Jing Chen & Saeed Tajdini, 2025. "A moderated model of artificial intelligence adoption in firms and its effects on their performance," Information Technology and Management, Springer, vol. 26(3), pages 407-419, September.
    • Handle: RePEc:spr:infotm:v:26:y:2025:i:3:d:10.1007_s10799-024-00422-5
    DOI: 10.1007/s10799-024-00422-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10799-024-00422-5
    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/s10799-024-00422-5?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Lucia-Palacios, Laura & Bordonaba-Juste, Victoria & Polo-Redondo, Yolanda & Grünhagen, Marko, 2014. "Technological opportunism effects on IT adoption, intra-firm diffusion and performance: Evidence from the U.S. and Spain," Journal of Business Research, Elsevier, vol. 67(6), pages 1178-1188.
    2. Weijters, Bert & Cabooter, Elke & Schillewaert, Niels, 2010. "The effect of rating scale format on response styles: The number of response categories and response category labels," International Journal of Research in Marketing, Elsevier, vol. 27(3), pages 236-247.
    3. Mohamad Reza Sanaei & Farzad Movahedi Sobhani, 2018. "Information technology and e-business marketing strategy," Information Technology and Management, Springer, vol. 19(3), pages 185-196, September.
    4. Mustak, Mekhail & Salminen, Joni & Plé, Loïc & Wirtz, Jochen, 2021. "Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda," Journal of Business Research, Elsevier, vol. 124(C), pages 389-404.
    5. Louis Raymond & Sylvestre Uwizeyemungu & Bruno Fabi & Josée St-Pierre, 2018. "IT capabilities for product innovation in SMEs: a configurational approach," Information Technology and Management, Springer, vol. 19(1), pages 75-87, March.
    6. Chen, Chien-Wei & Lien, Nai-Hwa, 2013. "Technological opportunism and firm performance: Moderating contexts," Journal of Business Research, Elsevier, vol. 66(11), pages 2218-2225.
    7. Sunčica Rogić & Ljiljana Kašćelan & Vladimir Kašćelan & Vladimir Đurišić, 2022. "Automatic customer targeting: a data mining solution to the problem of asymmetric profitability distribution," Information Technology and Management, Springer, vol. 23(4), pages 315-333, December.
    8. Rampersad, Giselle, 2020. "Robot will take your job: Innovation for an era of artificial intelligence," Journal of Business Research, Elsevier, vol. 116(C), pages 68-74.
    9. Volkmar, Gioia & Fischer, Peter M. & Reinecke, Sven, 2022. "Artificial Intelligence and Machine Learning: Exploring drivers, barriers, and future developments in marketing management," Journal of Business Research, Elsevier, vol. 149(C), pages 599-614.
    10. Gerald Zaltman, 2016. "Marketing’s forthcoming Age of imagination," AMS Review, Springer;Academy of Marketing Science, vol. 6(3), pages 99-115, December.
    11. Zhen Shao & Yuqiang Feng & Qing Hu, 2016. "Effectiveness of top management support in enterprise systems success: a contingency perspective of fit between leadership style and system life-cycle," European Journal of Information Systems, Taylor & Francis Journals, vol. 25(2), pages 131-153, March.
    12. Michael Song & Cornelia Droge & Sangphet Hanvanich & Roger Calantone, 2005. "Marketing and technology resource complementarity: an analysis of their interaction effect in two environmental contexts," Strategic Management Journal, Wiley Blackwell, vol. 26(3), pages 259-276, March.
    13. Jane E. Dutton & Robert B. Duncan, 1987. "The influence of the strategic planning process on strategic change," Strategic Management Journal, Wiley Blackwell, vol. 8(2), pages 103-116, March.
    14. Aboelmaged, Mohamed Gamal, 2014. "Predicting e-readiness at firm-level: An analysis of technological, organizational and environmental (TOE) effects on e-maintenance readiness in manufacturing firms," International Journal of Information Management, Elsevier, vol. 34(5), pages 639-651.
    15. Mekhail Mustak & Joni Salminen & Loïc Plé & Jochen Wirtz, 2021. "Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda," Post-Print hal-03269994, HAL.
    16. Toorajipour, Reza & Sohrabpour, Vahid & Nazarpour, Ali & Oghazi, Pejvak & Fischl, Maria, 2021. "Artificial intelligence in supply chain management: A systematic literature review," Journal of Business Research, Elsevier, vol. 122(C), pages 502-517.
    17. Papadas, Karolos-Konstantinos & Avlonitis, George J. & Carrigan, Marylyn, 2017. "Green marketing orientation: Conceptualization, scale development and validation," Journal of Business Research, Elsevier, vol. 80(C), pages 236-246.
    18. Jens Butschan & Sven Heidenreich & Benjamin Weber & Tobias Kraemer, 2019. "TACKLING HURDLES TO DIGITAL TRANSFORMATION — THE ROLE OF COMPETENCIES FOR SUCCESSFUL INDUSTRIAL INTERNET OF THINGS (IIoT) IMPLEMENTATION," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 23(04), pages 1-34, May.
    19. Voola, Ranjit & Casimir, Gian & Carlson, Jamie & Anushree Agnihotri, M., 2012. "The effects of market orientation, technological opportunism, and e-business adoption on performance: A moderated mediation analysis," Australasian marketing journal, Elsevier, vol. 20(2), pages 136-146.
    20. Richard P. Bagozzi, 1983. "A Holistic Methodology for Modeling Consumer Response to Innovation," Operations Research, INFORMS, vol. 31(1), pages 128-176, February.
    21. 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.
    22. Thomas Davenport & Abhijit Guha & Dhruv Grewal & Timna Bressgott, 2020. "How artificial intelligence will change the future of marketing," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 24-42, January.
    23. Abhijit Guha & Dhruv Grewal, 2022. "How robots will affect the future of retailing," AMS Review, Springer;Academy of Marketing Science, vol. 12(3), pages 245-252, December.
    24. Stewart, David W., 2009. "Marketing accountability: Linking marketing actions to financial results," Journal of Business Research, Elsevier, vol. 62(6), pages 636-643, June.
    25. Olan, Femi & Ogiemwonyi Arakpogun, Emmanuel & Suklan, Jana & Nakpodia, Franklin & Damij, Nadja & Jayawickrama, Uchitha, 2022. "Artificial intelligence and knowledge sharing: Contributing factors to organizational performance," Journal of Business Research, Elsevier, vol. 145(C), pages 605-615.
    26. Jens Butschan & Sven Heidenreich & Benjamin Weber & Tobias Kraemer, 2019. "Tackling Hurdles to Digital Transformation — The Role of Competencies for Successful Industrial Internet of Things (IIoT) Implementation," World Scientific Book Chapters, in: Joe Tidd (ed.), Digital Disruptive Innovation, chapter 7, pages 169-208, World Scientific Publishing Co. Pte. Ltd..
    27. Kinkel, Steffen & Baumgartner, Marco & Cherubini, Enrica, 2022. "Prerequisites for the adoption of AI technologies in manufacturing – Evidence from a worldwide sample of manufacturing companies," Technovation, Elsevier, vol. 110(C).
    28. 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.
    29. Ming-Hui Huang & Roland T. Rust, 2021. "A strategic framework for artificial intelligence in marketing," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 30-50, January.
    30. Cook, William A. & Talluri, Vijay S., 2004. "How the Pursuit of ROMI Is Changing Marketing Management," Journal of Advertising Research, Cambridge University Press, vol. 44(3), pages 244-254, September.
    Full references (including those not matched with items on IDEAS)

    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. Broekhuizen, Thijs & Dekker, Henri & de Faria, Pedro & Firk, Sebastian & Nguyen, Dinh Khoi & Sofka, Wolfgang, 2023. "AI for managing open innovation: Opportunities, challenges, and a research agenda," Journal of Business Research, Elsevier, vol. 167(C).
    2. Hermann, Erik & Puntoni, Stefano, 2024. "Artificial intelligence and consumer behavior: From predictive to generative AI," Journal of Business Research, Elsevier, vol. 180(C).
    3. Mariani, Marcello M. & Machado, Isa & Magrelli, Vittoria & Dwivedi, Yogesh K., 2023. "Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions," Technovation, Elsevier, vol. 122(C).
    4. Giacomo Zatini, 2025. "Conditions of use and impacts of artificial intelligence in marketing practices: a mixed-method literature review," Italian Journal of Marketing, Springer, vol. 2025(3), pages 293-329, September.
    5. Wenkai Zhou & Chi Zhang & Linwan Wu & Meghana Shashidhar, 2023. "ChatGPT and marketing: Analyzing public discourse in early Twitter posts," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 693-706, December.
    6. Nur Areena Aqilah Mohd Sapri & Asma Nadia Zanol Rashid & Wan Ainol Mursyida Ahmad Tarmizi, 2024. "Leveraging AI for Effective Content Marketing in Libraries: Maximizing User Engagement," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(9), pages 3510-3521, September.
    7. Erik Hermann, 2022. "Leveraging Artificial Intelligence in Marketing for Social Good—An Ethical Perspective," Journal of Business Ethics, Springer, vol. 179(1), pages 43-61, August.
    8. 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).
    9. Jeon, Yongwoog Andrew, 2022. "Let me transfer you to our AI-based manager: Impact of manager-level job titles assigned to AI-based agents on marketing outcomes," Journal of Business Research, Elsevier, vol. 145(C), pages 892-904.
    10. Manis, K.T. & Madhavaram, Sreedhar, 2023. "AI-Enabled marketing capabilities and the hierarchy of capabilities: Conceptualization, proposition development, and research avenues," Journal of Business Research, Elsevier, vol. 157(C).
    11. Roy, Sanjit K. & Tehrani, Ali N. & Pandit, Ameet & Apostolidis, Chrysostomos & Ray, Subhasis, 2025. "AI-capable relationship marketing: Shaping the future of customer relationships," Journal of Business Research, Elsevier, vol. 192(C).
    12. Truong Thi Hue & Ta Huy Hung, 2025. "Impact of artificial intelligence on branding: a bibliometric review and future research directions," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-11, December.
    13. Wondwesen Tafesse & Bronwyn Wood, 2024. "Hey ChatGPT: an examination of ChatGPT prompts in marketing," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(4), pages 790-805, December.
    14. Rituparna Basu & Md. Nayeem Aktar & Satish Kumar, 2025. "The interplay of artificial intelligence, machine learning, and data analytics in digital marketing and promotions: a review and research agenda," Journal of Marketing Analytics, Palgrave Macmillan, vol. 13(2), pages 267-287, June.
    15. Blasco-Arcas, Lorena & Lee, Hsin-Hsuan Meg & Kastanakis, Minas N. & Alcañiz, Mariano & Reyes-Menendez, Ana, 2022. "The role of consumer data in marketing: A research agenda," Journal of Business Research, Elsevier, vol. 146(C), pages 436-452.
    16. Sagarika Mishra & Michael T. Ewing & Holly B. Cooper, 2022. "Artificial intelligence focus and firm performance," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1176-1197, November.
    17. O. C. Ferrell & Dana E. Harrison & Linda K. Ferrell & Haya Ajjan & Bryan W. Hochstein, 2024. "A theoretical framework to guide AI ethical decision making," AMS Review, Springer;Academy of Marketing Science, vol. 14(1), pages 53-67, June.
    18. V. G. P. Lakshika & B. T. K. Chathuranga & P. G. S. A. Jayarathne, 2025. "The evolving role of AI and ML in digital promotion: a systematic review and research agenda," Journal of Marketing Analytics, Palgrave Macmillan, vol. 13(2), pages 288-307, June.
    19. Hoffmann, Stefan & Lasarov, Wassili & Dwivedi, Yogesh K., 2024. "AI-empowered scale development: Testing the potential of ChatGPT," Technological Forecasting and Social Change, Elsevier, vol. 205(C).
    20. Goodell, John W. & Kumar, Satish & Lim, Weng Marc & Pattnaik, Debidutta, 2021. "Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:infotm:v:26:y:2025:i:3:d:10.1007_s10799-024-00422-5. 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.