DETERMINANTS OF GENERATIVE ARTIFICIAL INTELLIGENCE ADOPTION IN INSURANCE COMPANIES
DOI:
https://doi.org/10.33736/ijbs.9562.2025Keywords:
Generative artificial intelligence, Insurance company, TOE frameworkAbstract
This study aims to investigate the factors that influence the adoption of Generative artificial intelligence (GEN-AI) in insurance companies by utilizing the technology-organization-environment (TOE) framework.
This study employs the TOE framework to examine the factors that impact the GEN-AI adoption at organizational level and was conducted on 307 insurance company managers and analysed using partial least squares (PLS). The research offers insurance companies and policy maker insights and recommendations for GEN-AI adoption. The empirical results reveal that relative advantage (RA), perceived compatibility (PC) and top management support (TMS) significantly influence GEN-AI adoption, CP, OR have positive effect on TMS, but perceived compatibility (PC) cannot significantly influence GEN-AI adoption. TMS mediates between competitive pressure(CP) and GEN-AI in insurance firms, and it also mediates between organizational readiness (OR) and GEN-AI. This study provides countermeasure advice to AI technology developers, insurance company manager, and practitioners.
References
Aattouri I., Mouncif H., & Rida M. (2023). Modeling of an artificial intelligence based enterprise callbot with natural language processing and machine learning algorithms. International Journal of Artificial Intelligence, 12(2), 943. doi:10.11591/ijai.v12.i2.pp943-955
https://doi.org/10.11591/ijai.v12.i2.pp943-955
Agrawal K. (2024). Towards Adoption of Generative AI in Organizational Settings. Journal of Computer Information Systems, 64(5), 636-651. doi:10.1080/08874417.2023.2240744
https://doi.org/10.1080/08874417.2023.2240744
Al-khatib A. W. (2023). Drivers of generative artificial intelligence to fostering exploitative and exploratory innovation: A TOE framework. Technology in Society, 75(2), 12. doi:10.1016/j.techsoc.2023.102403
https://doi.org/10.1016/j.techsoc.2023.102403
Al Halbusi H., Klobas J. E., & Ramayah T. (2023). Green core competence and firm performance in a post-conflict country, Iraq. Business Strategy and the Environment, 32(6), 2702-2714. doi:10.1002/bse.3265
https://doi.org/10.1002/bse.3265
Ali S. S., Kaur R., Gupta H., Ahmad Z., & Elnaggar G. (2024). Determinants of an Organization's Readiness for Drone Technologies Adoption. Ieee Transactions on Engineering Management, 71, 43-57. doi:10.1109/tem.2021.3083138 Amin A., Bhuiyan M. R. I., Hossain R., Molla C., Poli T. A., & Milon M. N. U. (2024). The adoption of Industry 4.0 technologies by using the technology organizational environment framework: The mediating role to manufacturing performance in a developing country. Business Strategy and Development, 7(2), 363. doi:10.1002/bsd2.363
https://doi.org/10.1002/bsd2.363
Atta A. A. B. (2024). Adoption of fintech products through environmental regulations in Jordanian commercial banks. Journal of Financial Reporting and Accounting, 13(2), 14. doi:10.1108/jfra-09-2023-0507
https://doi.org/10.1108/JFRA-09-2023-0507
Badghish S., & Soomro Y. A. (2024). Artificial Intelligence Adoption by SMEs to Achieve Sustainable Business Performance: Application of Technology-Organization-Environment Framework. Sustainability, 16(5), 24. doi:10.3390/su16051864
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. doi:10.1016/j.techfore.2022.122264
https://doi.org/10.1016/j.techfore.2022.122264
Baig M. I., Yadegaridehkordi E., & Nizam Bin Md Nasir M. H. (2023). Influence of big data adoption on sustainable marketing and operation of SMEs: a hybrid approach of SEM-ANN. Management Decision, 61(7), 2231-2253. doi:10.1108/Md-06-2022-0778
https://doi.org/10.1108/MD-06-2022-0778
Baiod W., & Hussain M. M. (2024). The impact and adoption of emerging technologies on accounting: perceptions of Canadian companies. International Journal of Accounting and Information Management, 32(4), 557-592. doi:10.1108/ijaim-05-2023-0123
https://doi.org/10.1108/IJAIM-05-2023-0123
Bin-Nashwan S. A., Li J. Z., Jiang H. C., Bajary A. R., & Ma'aji M. M. (2025). Does AI adoption redefine financial reporting accuracy, auditing efficiency, and information asymmetry? An integrated model of TOE-TAM-RDT and big data governance. Computers in Human Behavior Reports, 17(3), 14. doi:10.1016/j.chbr.2024.100572
https://doi.org/10.1016/j.chbr.2024.100572
Brislin R. W. (1980). Cross-cultural research methods: Strategies, problems, applications. In Environment and culture (pp. 47-82): Springer.
https://doi.org/10.1007/978-1-4899-0451-5_3
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. doi:10.1108/EJIM-02-2023-0156
https://doi.org/10.1108/EJIM-02-2023-0156
Cain M. K., Zhang Z., & Yuan K.-H. (2017). Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation. Behavior research methods, 49, 1716-1735. doi:10.3758/s13428-016-0814-1
https://doi.org/10.3758/s13428-016-0814-1
Chatterjee S., Rana N. P., Dwivedi Y. K., & Baabdullah A. M. (2021). Understanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model. Technological Forecasting and Social Change, 170(12), 14. doi:10.1016/j.techfore.2021.120880
https://doi.org/10.1016/j.techfore.2021.120880
Chen D. Q., Preston D. S., & Swink M. (2015). How the Use of Big Data Analytics Affects Value Creation in Supply Chain Management. Journal of Management Information Systems, 32(4), 4-39. doi:10.1080/07421222.2015.1138364
https://doi.org/10.1080/07421222.2015.1138364
Chen H., Li L., & Chen Y. (2020). Explore success factors that impact artificial intelligence adoption on telecom industry in China. Journal of Management Analytics, 8(1), 36-68. doi:10.1080/23270012.2020.1852895
https://doi.org/10.1080/23270012.2020.1852895
Chen Y., Hu Y., Zhou S., & Yang S. (2022). Investigating the determinants of performance of artificial intelligence adoption in hospitality industry during COVID-19. International Journal of Contemporary Hospitality Management, 35(8), 2868-2889. doi:10.1108/ijchm-04-2022-0433Chin W. W., Marcolin B. L., & Newsted P. R. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research, 14(2), 189-217. doi:10.1287/isre.14.2.189.16018
https://doi.org/10.1287/isre.14.2.189.16018
Dai J., Montabon F. L., & Cantor D. E. (2014). Linking rival and stakeholder pressure to green supply management: Mediating role of top management support. Transportation Research Part E-Logistics and Transportation Review, 71, 173-187. doi:10.1016/j.tre.2014.09.002
https://doi.org/10.1016/j.tre.2014.09.002
Das S. D., & Bala P. K. (2023). What drives MLOps adoption? An analysis using the TOE framework. Journal of Decision Systems, 33(3), 376-412. doi:10.1080/12460125.2023.2214306
https://doi.org/10.1080/12460125.2023.2214306
Deng S. C., Zhang J. J., Lin Z. N., & Li X. Q. (2024). Service staff makes me nervous: Exploring the impact of insecure attachment on AI service preference. Technological Forecasting and Social Change, 198(13), 89-93. doi:10.1016/j.techfore.2023.122946
https://doi.org/10.1016/j.techfore.2023.122946
Hair J. F., Hult G. T. M., Ringle C. M., Sarstedt M., Danks N. P., & Ray S. (2022). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook: Springer Nature.
https://doi.org/10.1007/978-3-030-80519-7
Harman H. H. (1976). Modern factor analysis: University of Chicago press.
Hashimy L., Jain G., & Grifell-Tatjé E. (2023). Determinants of blockchain adoption as decentralized business model by Spanish firms-an innovation theory perspective. Industrial Management Data Systems, 123(1), 204-228. doi:10.1108/IMDS-01-2022-0030
https://doi.org/10.1108/IMDS-01-2022-0030
Henseler J., Ringle C. M., & Sarstedt M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the academy of marketing science, 43(1), 115-135. doi:10.1007/s11747-014-0403-8
https://doi.org/10.1007/s11747-014-0403-8
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, 12(6), 25. doi:10.1177/02666669231166889
https://doi.org/10.1177/02666669231166889
Iranmanesh., Lim K. H., Foroughi B., Hong M. C., & Ghobakhloo M. (2023). Determinants of intention to adopt big data and outsourcing among SMEs: organisational and technological factors as moderators. Management Decision, 61(1), 201-222. doi:10.1108/md-08-2021-1059
https://doi.org/10.1108/MD-08-2021-1059
Isiaku L., & Adalier A. (2024). Determinants of business intelligence systems adoption in Nigerian banks: The role of perceived usefulness and ease of use. Information Development, 21. doi:10.1177/02666669241307024
https://doi.org/10.1177/02666669241307024
Javaid., Haleem A., Singh R. P., & Suman R. (2022). Artificial Intelligence Applications for Industry 4.0: A Literature-Based Study. Journal of Industrial Integration and Management-Innovation and Entrepreneurship, 7(1), 83-111. doi:10.1142/s2424862221300040
https://doi.org/10.1142/S2424862221300040
Kapadiya K., Patel U., Gupta R., Alshehri M. D., Tanwar S., Sharma G., & Bokoro P. N. (2022). Blockchain and AI-Empowered Healthcare Insurance Fraud Detection: an Analysis, Architecture, and Future Prospects. Ieee Access, 10, 79606-79627. doi:10.1109/access.2022.3194569
https://doi.org/10.1109/ACCESS.2022.3194569
Kock N., & Lynn G. S. (2012). Lateral Collinearity and Misleading Results in Variance-Based SEM: An Illustration and Recommendations. Journal of the Association for Information Systems, 13(7), 546-580. doi:10.17705/1jais.00302 Le L. T. N., Jeenanunta C., Ueki Y., Intalar N., & Komolavanij S. (2025). The Role of Managerial Competencies in Driving Industry 4.0 Adoption: A Comparative Study of Thailand and Vietnam's Manufacturing Sectors. Sustainability, 17(1), 28. doi:10.3390/su17010077
https://doi.org/10.3390/su17010077
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. doi:10.1016/j.technovation.2022.102623
https://doi.org/10.1016/j.technovation.2022.102623
Maroufkhani P., Iranmanesh M., & Ghobakhloo M. (2023). Determinants of big data analytics adoption in small and medium-sized enterprises (SMEs). Industrial Management & Data Systems, 123(1), 278-301. doi:10.1108/imds-11-2021-0695
https://doi.org/10.1108/IMDS-11-2021-0695
Mezghani K., Alsadi A. K., & Alaskar T. H. (2022). Study of the environmental factors' effects on big data analytics adoption in supply chain management. International Journal of E-Business Research, 18(1), 1-20. doi:10.4018/IJEBR.309395
https://doi.org/10.4018/IJEBR.309395
Min S., & Kim B. (2024). Adopting Artificial Intelligence Technology for Network Operations in Digital Transformation. Administrative Sciences, 14(4), 70. doi:10.3390/admsci14040070
https://doi.org/10.3390/admsci14040070
Mondal S., Das S., & Vrana V. G. (2023). How to bell the cat? A theoretical review of generative artificial intelligence towards digital disruption in all walks of life. Technologies, 11(2), 44.
https://doi.org/10.3390/technologies11020044
Na S., Heo S., Han S., Shin Y., & Roh Y. (2022). Acceptance Model of Artificial Intelligence (AI)-Based Technologies in Construction Firms: Applying the Technology Acceptance Model (TAM) in Combination with the Technology-Organisation-Environment (TOE) Framework. Buildings, 12(2), 17. doi:10.3390/buildings12020090
https://doi.org/10.3390/buildings12020090
Naeem S., Azam M., Boulos M. N. K., & Bhatti R. (2024). Leveraging the TOE Framework: Examining the Potential of Mobile Health (mHealth) to Mitigate Health Inequalities. Information, 15(4), 176. doi:10.3390/info15040176
https://doi.org/10.3390/info15040176
Ngah A. H., Ramayah T., Ali M. H., & Khan M. I. (2020). Halal transportation adoption among pharmaceuticals and comestics manufacturers. Journal of Islamic Marketing, 11(6), 1619-1639. doi:10.1108/jima-10-2018-0193
https://doi.org/10.1108/JIMA-10-2018-0193
Nordin S. M., Zolkepli I. A., Rizal A. R. A., Tariq R., Mannan S., & Ramayah T. (2022). Paving the way to paddy food security: A multigroup analysis of agricultural education on Circular Economy Adoption. Journal of Cleaner Production, 375, 9. doi:10.1016/j.jclepro.2022.134089
https://doi.org/10.1016/j.jclepro.2022.134089
Owens E., Sheehan B., Mullins M., Cunneen M., Ressel J., & Castignani G. (2022). Explainable Artificial Intelligence (XAI) in Insurance. Risks, 10(12), 230. doi:10.3390/risks10120230
https://doi.org/10.3390/risks10120230
Pan Y., Froese F., Liu N., Hu Y. Y., & Ye M. L. (2022). The adoption of artificial intelligence in employee recruitment: The influence of contextual factors. International Journal of Human Resource Management, 33(6), 1125-1147. doi:10.1080/09585192.2021.1879206
https://doi.org/10.1080/09585192.2021.1879206
Pathak A., & Bansal V. (2025). Technology or Organization: What is More Important for Artificial Intelligence Adoption? Tehnicki Glasnik-Technical Journal, 19(1), 136-141. doi:10.31803/tg-20240512171214
https://doi.org/10.31803/tg-20240512171214
Phuoc N. V. (2022). The critical factors impacting artificial intelligence applications adoption in Vietnam: a structural equation modeling analysis. Economies, 10(6), 129. doi:10.3390/economies10060129
https://doi.org/10.3390/economies10060129
Pillai R., Sivathanu B., Mariani M., Rana N. P., Yang B., & Dwivedi Y. K. (2021). Adoption of AI-empowered industrial robots in auto component manufacturing companies. Production Planning & Control, 33(16), 1517-1533. doi:10.1080/09537287.2021.1882689
https://doi.org/10.1080/09537287.2021.1882689
Pizam A., Ozturk A. B., Balderas-Cejudo A., Buhalis D., Fuchs G., Hara T., . . . Chaulagain S. (2022). Factors affecting hotel managers' intentions to adopt robotic technologies: A global study. International Journal of Hospitality Management, 102(5), 15. doi:10.1016/j.ijhm.2022.103139
https://doi.org/10.1016/j.ijhm.2022.103139
Podsakoff P. M., Podsakoff N. P., Williams L. J., Huang C., & Yang J. (2024). Common method bias: It's bad, it's complex, it's widespread, and it's not easy to fix. Annual Review of Organizational Psychology Organizational Behavior, 11(1), 17-61. doi:10.1146/annurev-orgpsych-110721-040030
https://doi.org/10.1146/annurev-orgpsych-110721-040030
Preacher K. J., & Hayes A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior research methods, 40(3), 879-891. doi:10.3758/BRM.40.3.879
https://doi.org/10.3758/BRM.40.3.879
Ramayah T., Cheah J., Chuah F., Ting H., & Memon M. A. (2018). Partial least squares structural equation modeling (PLS-SEM) using smartPLS 3.0. An updated guide practical guide to statistical analysis, 1(1), 1-72.
Rodrigues A. R. D., Ferreira F. A., Teixeira F. J., & Zopounidis C. (2022). Artificial intelligence, digital transformation and cybersecurity in the banking sector: A multi-stakeholder cognition-driven framework. Research in International Business Finance Research Letters, 60(8), 30-41. doi:10.1016/j.ribaf.2022.101616
https://doi.org/10.1016/j.ribaf.2022.101616
Seethamraju R., & Hecimovic A. (2023). Adoption of artificial intelligence in auditing: An exploratory study. Australian Journal of Management, 48(4), 780-800. doi:10.1177/03128962221108440
https://doi.org/10.1177/03128962221108440
Sharma C., Bharadwaj S. S., Gupta N., & Jain H. (2023). Robotic process automation adoption: contextual factors from service sectors in an emerging economy. Journal of Enterprise Information Management, 36(1), 252-274. doi:10.1108/jeim-06-2021-0276
https://doi.org/10.1108/JEIM-06-2021-0276
Shavneet S., & Gurmeet S. (2024). Why Do SMEs Adopt Artificial Intelligence-Based Chatbots? Ieee Transactions on Engineering Management, 71, 1773-1786. doi:10.1109/tem.2022.3203469
https://doi.org/10.1109/TEM.2022.3203469
Shmueli, Sarstedt M., Hair J. F., Cheah J. H., Ting H., Vaithilingam S., & Ringle C. M. (2019). Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322-2347. doi:10.1108/ejm-02-2019-0189
https://doi.org/10.1108/EJM-02-2019-0189
Smit D., Eybers S., & Smith J. (2021, Dec 06-10). A Data Analytics Organisation's Perspective on Trust and AI Adoption. Paper presented at the 2nd Southern African Conference forArtificial Intelligence Research (SACAIR), Ctr AI Res, ELECTR NETWORK.
Somya G., Wafa G., Dharen K. P., & Sah G. P. (2022). Artificial intelligence adoption in the insurance industry: Evidence using the technology-organization-environment framework. Research in International Business and Finance, 63(8), 15. doi:10.1016/j.ribaf.2022.101757
https://doi.org/10.1016/j.ribaf.2022.101757
Sun S., Hall D. J., & Cegielski C. G. (2020). Organizational intention to adopt big data in the B2B context: An integrated view. Industrial Marketing Management, 86, 109-121. doi:10.1016/j.indmarman.2019.09.003
https://doi.org/10.1016/j.indmarman.2019.09.003
Teo H. H., Wei K. K., & Benbasat I. (2003). Predicting intention to adopt interorganizational linkages: An institutional perspective. Mis Quarterly, 27(1), 19-49. doi:10.2307/30036518 Thanabalan P., Vafaei-Zadeh A., Hanifah H., & Ramayah T. (2024). Big Data Analytics Adoption in Manufacturing Companies: The Contingent Role of Data-Driven Culture. Information Systems Frontiers, 27. doi:10.1007/s10796-024-10491-0
https://doi.org/10.1007/s10796-024-10491-0
Thong J. Y. (1999). An integrated model of information systems adoption in small businesses. Journal of Management Information Systems, 15(4), 187-214.
https://doi.org/10.1080/07421222.1999.11518227
Van L., Truong H. T. H., Vo-Thanh T., Nguyen H. T. T., Dang-Van T., & Nguyen N. (2024). Determinants of blockchain technology adoption in small and medium hospitality and tourism enterprises. Journal of Hospitality Marketing & Management, 33(7), 867-897. doi:10.1080/19368623.2024.2335931
https://doi.org/10.1080/19368623.2024.2335931
Wiredu J., Yang Q., Sampene A. K., Gyamfi B. A., & Asongu S. A. (2024). The effect of green supply chain management practices on corporate environmental performance: Does supply chain competitive advantage matter? Business Strategy and the Environment, 33(3), 2578-2599. doi:10.1002/bse.3606
https://doi.org/10.1002/bse.3606
Wook J. J. (2020). Case Studies for Insurance Service Marketing Using Artificial Intelligence(AI) in the InsurTech Industry. [인슈어테크(InsurTech)산업에서의 인공지능(AI)을 활용한보험서비스 마케팅사례 연구]. Journal of Digital Convergence, 18(10), 175-180. doi:10.14400/jdc.2020.18.10.175
Yuangao C., Hu Y., Zhou S., & Yang S. (2023). Investigating the determinants of performance of artificial intelligence adoption in hospitality industry during COVID-19. International Journal of Contemporary Hospitality Management, 35(8), 2868-2889. doi:10.1108/IJCHM-04-2022-0433
https://doi.org/10.1108/IJCHM-04-2022-0433
Yubo S., Ramayah T., & Hongmei L. (2022). Research on the Impact of Intelligent Manufacturing Technology on Business Model Based on CNKI's Evidence. Global Business & Management Research, 14, 266-283.
Yubo S., Ramayah T., Hongmei L., Yifan Z., & Wenhui W. (2023). Analysing the current status, hotspots, and future trends of technology management: Using the WoS and scopus database. Heliyon, 9(9), 7-29. doi:10.1016/j.heliyon.2023.e19922
https://doi.org/10.1016/j.heliyon.2023.e19922
Zhou B. R., & Zheng L. (2023). Technology-pushed, market-pulled, or government-driven? The adoption of industry 4.0 technologies in a developing economy. Journal of Manufacturing Technology Management, 34(9), 115-138. doi:10.1108/jmtm-09-2022-0313

Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 UNIMAS Publisher

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright Transfer Statement for Journal
1) In signing this statement, the author(s) grant UNIMAS Publisher an exclusive license to publish their original research papers. The author(s) also grant UNIMAS Publisher permission to reproduce, recreate, translate, extract or summarize, and to distribute and display in any forms, formats, and media. The author(s) can reuse their papers in their future printed work without first requiring permission from UNIMAS Publisher, provided that the author(s) acknowledge and reference publication in the Journal.
2) For open access articles, the author(s) agree that their articles published under UNIMAS Publisher are distributed under the terms of the CC-BY-NC-SA (Creative Commons Attribution-Non Commercial-Share Alike 4.0 International License) which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original work of the author(s) is properly cited.
3) For subscription articles, the author(s) agree that UNIMAS Publisher holds copyright, or an exclusive license to publish. Readers or users may view, download, print, and copy the content, for academic purposes, subject to the following conditions of use: (a) any reuse of materials is subject to permission from UNIMAS Publisher; (b) archived materials may only be used for academic research; (c) archived materials may not be used for commercial purposes, which include but not limited to monetary compensation by means of sale, resale, license, transfer of copyright, loan, etc.; and (d) archived materials may not be re-published in any part, either in print or online.
4) The author(s) is/are responsible to ensure his or her or their submitted work is original and does not infringe any existing copyright, trademark, patent, statutory right, or propriety right of others. Corresponding author(s) has (have) obtained permission from all co-authors prior to submission to the journal. Upon submission of the manuscript, the author(s) agree that no similar work has been or will be submitted or published elsewhere in any language. If submitted manuscript includes materials from others, the authors have obtained the permission from the copyright owners.
5) In signing this statement, the author(s) declare(s) that the researches in which they have conducted are in compliance with the current laws of the respective country and UNIMAS Journal Publication Ethics Policy. Any experimentation or research involving human or the use of animal samples must obtain approval from Human or Animal Ethics Committee in their respective institutions. The author(s) agree and understand that UNIMAS Publisher is not responsible for any compensational claims or failure caused by the author(s) in fulfilling the above-mentioned requirements. The author(s) must accept the responsibility for releasing their materials upon request by Chief Editor or UNIMAS Publisher.
6) The author(s) should have participated sufficiently in the work and ensured the appropriateness of the content of the article. The author(s) should also agree that he or she has no commercial attachments (e.g. patent or license arrangement, equity interest, consultancies, etc.) that might pose any conflict of interest with the submitted manuscript. The author(s) also agree to make any relevant materials and data available upon request by the editor or UNIMAS Publisher.