Throughout the decades, numerous theories and models have been developed to explore the factors influencing technology adoption. Among these, two essential theories stand out: the Diffusion of Innovations Theory (DOI) and the Technology, Organization, and Environment Framework (TOE). This article reviews existing research on the application of both the DOI theory and TOE framework to Artificial Intelligence (AI) adoption, exploring how these frameworks elucidate the complexities of integrating AI technology in diverse contexts. The review underscores how each theory addresses different aspects of AI adoption and proposes methods for their integration, aiming to provide a deeper understanding of the factors that promote and impede AI implementation across various organizational contexts.
Abaddi, S. (2024). Factors and moderators influencing artificial intelligence adoption by Jordanian MSMEs. https://doi.org/10.1108/MSAR-10-2023-0049
Abdul Hameed, M., & Counsell, S. (2012). Assessing the influence of environmental and CEO characteristics for adoption of information technology in organizations. Journal of Technology Management & Innovation, 7(1), 64–84.
Agarwal, V., Mathiyazhagan, K., Malhotra, S., & Saikouk, T. (2021). Analysis of challenges in sustainable human resource management due to disruptions by Industry 4.0: an emerging economy perspective. International Journal of Manpower, 43(2), 513–541.
Al-khatib, A. (2023). Technology in Society Drivers of generative artificial intelligence to fostering exploitative and exploratory innovation?: A TOE framework. Technology in Society, 75(October), 102403. https://doi.org/10.1016/j.techsoc.2023.102403
Al-Qaysi, N., Grani?, A., Al-Emran, M., Ramayah, T., Garces, E., & Daim, T. U. (2023). Social media adoption in education: A systematic review of disciplines, applications, and influential factors. Technology in Society, 102249.
Al Wael, H., Abdallah, W., Ghura, H., & Buallay, A. (2024). Factors influencing artificial intelligence adoption in the accounting profession: the case of public sector in Kuwait. Competitiveness Review, 34(1), 3–27. https://doi.org/10.1108/CR-09-2022-0137
Aloini, D., Latronico, L., & Pellegrini, L. (2022). The impact of digital technologies on business models. Insights from the space industry. Measuring Business Excellence, 26(1), 64–80.
Baabdullah, A. M., Alalwan, A. A., Slade, E. L., Raman, R., & Khatatneh, K. F. (2021). SMEs and artificial intelligence (AI): Antecedents and consequences of AI-based B2B practices. Industrial Marketing Management, 98, 255–270.
Badghish, S., & Soomro, Y. A. (2024). Artificial Intelligence Adoption by SMEs to Achieve Sustainable Business Performance: Application of Technology–Organization–Environment Framework. Sustainability (Switzerland) , 16(5). https://doi.org/10.3390/su16051864
Bahoo, S., Cucculelli, M., & Qamar, D. (2023). Artificial intelligence and corporate innovation: A review and research agenda. Technological Forecasting and Social Change, 188, 122264.
Burger, B., Kanbach, D. K., Kraus, S., Breier, M., & Corvello, V. (2023). On the use of AI-based tools like ChatGPT to support management research. European Journal of Innovation Management, 26(7), 233–241.
Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International Journal of Information Management, 48, 63–71.
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., & Eirug, A. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994.
Ghobakhloo, M., & Iranmanesh, M. (2021). Digital transformation success under Industry 4.0: A strategic guideline for manufacturing SMEs. Journal of Manufacturing Technology Management, 32(8), 1533–1556.
Hmoud, H., Al-Adwan, A. S., Horani, O., Yaseen, H., & Zoubi, J. Z. Al. (2023). Factors influencing business intelligence adoption by higher education institutions. Journal of Open Innovation: Technology, Market, and Complexity, 9(3), 100111. https://doi.org/10.1016/j.joitmc.2023.100111
Horani, O. M., Al-adwan, A. S., & Alkhalifah, A. (2023). The critical determinants impacting arti fi cial intelligence adoption at the organizational level. https://doi.org/10.1177/02666669231166889
Horani, O. M., Al-Adwan, A. S., Yaseen, H., Hmoud, H., Al-Rahmi, W. M., & Alkhalifah, A. (2023). The critical determinants impacting artificial intelligence adoption at the organizational level. Information Development. https://doi.org/10.1177/02666669231166889
Hussein, L. A., Baharudin, A. S., Jayaraman, K., & Kiumarsi, S. (2019). B2B e-commerce technology factors with mediating effect perceived usefulness in Jordanian manufacturing SMES. Journal of Engineering Science and Technology, 14(1), 411–429.
Ilin, V., Iveti?, J., & Simi?, D. (2017). Understanding the determinants of e-business adoption in ERP-enabled firms and non-ERP-enabled firms: A case study of the Western Balkan Peninsula. Technological Forecasting and Social Change, 125, 206–223.
Kaur, M., A. G., R., & Vikas, S. (2024). Adoption of Artificial Intelligence in Human Resource Management: A Conceptual Model. Indian Journal of Industrial Relations, 57(2), 331–342. https://search.ebscohost.com/login.aspx?direct=true&AuthType=ip,sso&db=bth&AN=153280404&scope=site&custid=s8448101
Khalid, N. (2020). Artificial intelligence learning and entrepreneurial performance among university students: evidence from malaysian higher educational institutions. Journal of Intelligent & Fuzzy Systems, 39(4), 5417–5435.
Kinkel, S., Baumgartner, M., & Cherubini, E. (2022). Prerequisites for the adoption of AI technologies in manufacturing – Evidence from a worldwide sample of manufacturing companies. Technovation, 110(July), 102375. https://doi.org/10.1016/j.technovation.2021.102375
Kumar, M., Raut, R. D., Mangla, S. K., Ferraris, A., & Choubey, V. K. (2022). The adoption of artificial intelligence powered workforce management for effective revenue growth of micro, small, and medium scale enterprises (MSMEs). Production Planning & Control, 1–17.
Lada, S., Chekima, B., Karim, M. R. A., Fabeil, N. F., Ayub, M. S., Amirul, S. M., Ansar, R., Bouteraa, M., Fook, L. M., & Zaki, H. O. (2023). Determining factors related to artificial intelligence (AI) adoption among Malaysia’s small and medium-sized businesses. Journal of Open Innovation: Technology, Market, and Complexity, 9(4), 100144. https://doi.org/10.1016/j.joitmc.2023.100144
Lai, P. C. (2017). The literature review of technology adoption models and theories for the novelty technology. JISTEM-Journal of Information Systems and Technology Management, 14(1), 21–38.
Management, I., Maroufkhani, P., Iranmanesh, M., & Ghobakhloo, M. (2022). Determinants of big data analytics adoption in small and medium- sized enterprises ( SMEs ). February. https://doi.org/10.1108/IMDS-11-2021-0695
Mariani, M. M., Machado, I., Magrelli, V., & Dwivedi, Y. K. (2023). Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions. Technovation, 122, 102623.
Mikalef, P., & Gupta, M. (2021). Information & Management Artificial intelligence capability?: Conceptualization , measurement calibration , and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3), 103434. https://doi.org/10.1016/j.im.2021.103434
Mikalef, P., Lemmer, K., Schaefer, C., Ylinen, M., Fjørtoft, S. O., Torvatn, H. Y., Gupta, M., & Niehaves, B. (2022). Enabling AI capabilities in government agencies: A study of determinants for European municipalities. Government Information Quarterly, 39(4). https://doi.org/10.1016/j.giq.2021.101596
Nayal, K., Raut, R., Priyadarshinee, P., Narkhede, B. E., Kazancoglu, Y., & Narwane, V. (2022). Exploring the role of artificial intelligence in managing agricultural supply chain risk to counter the impacts of the COVID-19 pandemic. International Journal of Logistics Management, 33(3), 744–772. https://doi.org/10.1108/IJLM-12-2020-0493
Pai, V., & Chandra, S. (2022). Exploring Factors Influencing Organizational Adoption of Artificial Intelligence (AI) in Corporate Social Responsibility (CSR) Initiatives. Pacific Asia Journal of the Association for Information Systems, 14(5), 82–115. https://doi.org/10.17705/1pais.14504
Paiola, M., Schiavone, F., Grandinetti, R., & Chen, J. (2021). Digital servitization and sustainability through networking: Some evidences from IoT-based business models. Journal of Business Research, 132, 507–516.
Pan, Y., Froese, F., Liu, N., Hu, Y., & Ye, M. (2022). The adoption of artificial intelligence in employee recruitment: The influence of contextual factors. The International Journal of Human Resource Management, 33(6), 1125–1147.
Pateli, A., Mylonas, N., & Spyrou, A. (2020). Organizational adoption of social media in the hospitality industry: An integrated approach based on DIT and TOE frameworks. Sustainability, 12(17), 7132.
Pillai, R., & Sivathanu, B. (2020). Adoption of artificial intelligence (AI) for talent acquisition in IT/ITeS organizations. Benchmarking: An International Journal, 27(9), 2599–2629.
Popkova, E. G., De Bernardi, P., Tyurina, Y. G., & Sergi, B. S. (2022). A theory of digital technology advancement to address the grand challenges of sustainable development. Technology in Society, 68, 101831.
Salah, O. H., Yusof, Z. M., & Mohamed, H. (2021). The determinant factors for the adoption of CRM in the Palestinian SMEs: The moderating effect of firm size. PloS One, 16(3), e0243355.
Sharma, S., Singh, G., Islam, N., & Dhir, A. (2022). Why do SMEs adopt artificial intelligence-based chatbots? IEEE Transactions on Engineering Management.
Straub, D., Keil, M., & Brenner, W. (1997). Testing the technology acceptance model across cultures: A three country study. Information & Management, 33(1), 1–11.
Tornatzky, L., & Fleischer, M. (1990). The process of technology innovation, Lexington, MA. Lexington books.
Wang, M. (2022). Drivers of Artificial Intelligence and Their Effects on Supply Chain Resilience and Performance?: An Empirical Analysis on an Emerging Market.
Alka’awneh, S. M. N., Abdul-Halim, H., & Saad, N. H. M. (2025). A Review of Diffusion of Innovations Theory (DOI) and Technology, Organization, and Environment Framework (TOE) in the Adoption of Artificial Intelligence. International Journal of Academic Research in Business and Social Sciences, 15(3), 2118–2129.
Copyright: © 2025 The Author(s)
Published by Knowledge Words Publications (www.kwpublications.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode
