Home⇛JPAIR Multidisciplinary Research Journal⇛vol. 63 no. 1 (2026)

The Lived Experiences Of Bpo Executives’ Adoption Of Artificial Intelligence (Ai): An Utaut-Based Framework

Allan E. Cruz

Discipline: management studies

 

Abstract:

The rapid advancements in the use of Artificial Intelligence (AI) in the BPO industry pose opportunities and challenges to BPO executives. This study explored the lived experiences of BPO executives’ adoption of AI, using the Unified Theory of Acceptance and Use of Technology (UTAUT) as the guiding framework. The goal of the study is to gain insight into the experiences of BPO executives in their AI adoption journey in contact center operations. Phenomenological inquiry, purposive and snowball sampling techniques, and semi-structured interviews were used to gather data. Using Interpretative Phenomenological Analysis (IPA), findings revealed four (4) superordinate themes: 1) Key Challenges in AI Adoption; 2) Strategies for AI Adoption; 3) The Impact and Benefits of AI; and 4) The Evolving Role of People. Key challenges in AI adoption can be addressed using sound strategies. AI can drive performance and is generally easy to use, while social influence and facilitating conditions enable effective and continued use of AI. While AI offers numerous benefits, it also poses a threat to employees. AI can create new jobs, complement some jobs, but can also lead to job insecurity. There is a need for employees to be equipped with the skills and domain expertise necessary to adapt to the changing nature of contact center operations.



References:

  1. Abosamaha, A. J., Ahmad, W., & Herzallah, F. (2025). The status of E-Municipality adoption in Palestine: A Dual-Factor Perspective by Integrating SQB and UTAUT. In Studies in systems, decision and control (pp. 491–506). https://doi.org/10.1007/978-3-031-87550-2_31
  2. Alalwan, A., Dwivedi, Y., & Rana, N. (2017). Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust. International Journal                        of            Information            Management,            37(3),            99–110. https://doi.org/10.1016/j.ijinfomgt.2017.01.002
  3. Al-Saedi, K., Al-Emran, M., Ramayah, T., & Abusham, E. (2020). Developing a general extended UTAUT model for M-payment adoption. Technology in Society, 62, 101293. https://doi.org/10.1016/j.techsoc.2020.10129
  4. Ali, R. (2025, March 5). What is operational efficiency? A definition and guide. Oracle NetSuite.                       https://www.netsuite.com/portal/resource/articles/financial-management/operational-efficiency.shtml
  5. Ali, T., Hussain, I., & Anwer, S. (2024). Examine How the Rise of AI and Automation Affects Job Security, Stress Levels, and Mental Health in the Workplace. Bulletin of Business and Economics. https://doi.org/10.61506/01.00506
  6. Arora, M., & Mittal, A. (2024). Employees’ change in perception when artificial intelligence integrates with human resource management: a mediating role of AI-tech trust. Benchmarking an International Journal. https://doi.org/10.1108/bij-11-2023-0795
  7. Bai, X., & Yang, L. (2025). Exploring the determinants of AIGC usage intention based on the extended AIDUA model: a multi-group structural equation modeling analysis. Frontiers in psychology, 16, 1589318. https://doi.org/10.3389/fpsyg.2025.1589318
  8. Bayaga, A., & Du Plessis, A. (2023). Ramifications of the Unified Theory of Acceptance and Use of Technology (UTAUT) among developing countries’ higher education staffs. Education and Information Technologies, 29(8), 9689–9714. https://doi.org/10.1007/s10639-023-12194-6
  9. Belghiti, A. E., Sbai, H., & Asri, H. (2025). AI Systems Quality: A Data-Centric Perspective. In Lecture notes in networks and systems (pp. 73–83). https://doi.org/10.1007/978-3-031-95326-2_8  
  10. Camilleri, M. A. (2024). Factors affecting performance expectancy and intentions to use ChatGPT: Using SmartPLS to advance an information technology acceptance framework. Technological Forecasting and Social Change, 201, 123247. https://doi.org/10.1016/j.techfore.2024.123247
  11. Chand, S., & Kumar, B. (2024). Applying the UTAUT model to understand m-payment adoption: A case study of the western part of Fiji. Journal of the Knowledge Economy. Advance online publication. https://doi.org/10.1007/s13132-023-01722-x
  12. Chatterjee, S., Rana, N. P., Khorana, S., Mikalef, P., & Sharma, A. (2021). Assessing Organizational Users’ Intentions and Behavior to AI Integrated CRM Systems: a Meta-UTAUT Approach. Information Systems Frontiers, 25(4), 1299–1313. https://doi.org/10.1007/s10796-021-10181-1
  13. Cheng, B., Lin, H., & Kong, Y. (2023). Challenge or hindrance? How and when organizational artificial intelligence adoption influences employee job crafting. Journal         of    Business                          Research,  164,                      113987 https://doi.org/10.1016/j.jbusres.2023.113987
  14. Chugh, S. (2025). Maximizing ROI with ServiceNow AI Capabilities and Avoiding Value Traps. In: ServiceNow’s Intelligent IT Service Management. Apress, Berkeley, CA. https://doi.org/10.1007/979-8-8688-1706-9_6
  15. Cooper, R. (2024). Why AI projects fail: Lessons from new product development. IEEEEngineering                 Management                 Review,                 52(1),                 1–8. https://doi.org/10.1109/EMR.2024.3419268
  16. Cucio, M., & Hennig, T. (2025). Artificial intelligence and the Philippine labor market (IMF Working Paper No. 2025/043). International Monetary Fund. https://doi.org/10.5089/9798229001977.001
  17. Cuthbertson, L. M. (2019). The journey to radiographer advanced practice: a methodological reflection on the use of interpretative phenomenological analysis to explore perceptions and experiences. Journal of Radiotherapy in Practice, 19(2), 116–121. https://doi.org/10.1017/s1460396919000621
  18. Cuthbertson, L., Robb, Y., & Blair, S. (2019). Theory and application of research principles and philosophical underpinning for a study utilising interpretative phenomenological                                          analysis.            Radiography,            26(2),            e94–e102. https://doi.org/10.1016/j.radi.2019.11.092
  19. Daruhadi, G. (2024). Phenomenological method as a theoretical basis of qualitative methods. International Journal of Social Health, 3(9), 599–613. https://doi.org/10.58860/ijsh.v3i9.238
  20. Delve, Ho, L., & Limpaecher, A. (2023c, June 08). What is Interpretive Phenomenological Analysis (IPA)? https://delvetool.com/blog/interpretive-phenomenological-analysis
  21. Desiderio, L. (2024, October 3). AI slowly taking over IT-BPM sector in Philippines. PhilStar. https://www.philstar.com/business/2024/10/03/2389665/ai-slowly-taking-over-it-bpm-sector-philippines 
  22. Desiderio, L. (2025, January 16). IT-BPM revenues hit $38 billion in 2024. PhilStar. https://www.philstar.com/business/2025/01/16/2414464/it-bpm-revenues-hit-38-billion-2024
  23. Du, L., & Lv, B. (2024). Factors influencing students’ acceptance and use generative artificial intelligence in elementary education: an expansion of the UTAUT model. Education and Information Technologies. https://doi.org/10.1007/s10639-024-12835-4
  24. Fauziawati, D. (2021). The effect of job insecurity on innovative work behavior through organizational commitment in UFO Elektronika employees. Journal of Business     and                          Management                              Review,            2(6),            401–416. https://doi.org/10.47153/jbmr26.1702021
  25. Fu, Q., Nicholson, G. L., & Easton, J. M. (2024). Understanding data quality in a data-driven industry context: Insights from the fundamentals. Journal of Industrial Information Integration, 42, 100729. https://doi.org/10.1016/j.jii.2024.100729
  26. Gartner. (2023, November 9). Invest Implications: Forecast Analysis: Artificial Intelligence Software, 2023-2027, Worldwide. Gartner Research. https://www.gartner.com/en/documents/4925331
  27. Georgieva, K. (2024, January 14). AI will transform the global economy. Let’s make sure   it                        benefits      humanity.              IMF                           Blog. https://www.imf.org/en/Blogs/Articles/2024/01/14/ai-will-transform-the-global-economy-lets-make-sure-it-benefits-humanity
  28. Goldburgh, M., LaChance, M., Komissarchik, J., & others. (2025). Industry perceptions survey on AI adoption and return on investment. Journal of Digital Imaging, 38(3), 663–670. https://doi.org/10.1007/s10278-024-01147-1
  29. Gong, Y., Liu, G., Xue, Y., Li, R., & Meng, L. (2023). A survey on dataset quality in machine learning. Information and Software Technology, 162, 107268. https://doi.org/10.1016/j.infsof.2023.107268
  30. Grassini, S., Aasen, M. L., & Møgelvang, A. (2024). Understanding University Students’ Acceptance of ChatGPT: Insights from the UTAUT2 Model. Applied Artificial Intelligence, 38(1). https://doi.org/10.1080/08839514.2024.2371168
  31. Guo, K., Zhan, C., & Li, X. (2025). Factors influencing Chinese college students’ intention to use AIGC: a study based on the UTAUT model. International Journal of Systems Assurance Engineering and Management. https://doi.org/10.1007/s13198-025-02772-x
  32. Haefner, N., Wincent, J., Parida, V., & Gassmann, O. (2020). Artificial intelligence and innovation management: A review, framework, and research agenda✰. Technological Forecasting              and              Social              Change,              162,              120392.https://doi.org/10.1016/j.techfore.2020.120392
  33. Hailu, D. T., Melaku, M. S., Abebe, S. A., Walle, A. D., Tilahun, K. N., & Gashu, K. D. (2025). A modified UTAUT model for acceptance to use telemedicine services and its predictors among healthcare professionals at public hospitals in North Shewa Zone ofOromia Regional State, Ethiopia. Frontiers in Digital Health, 7, 1469365. https://doi.org/10.3389/fdgth.2025.1469365 
  34. He, X. (2023). Research on the relationship between perceived AI substitution crisis and employees’ negative work behavior: From the perspective of job insecurity. In Proceedings of the 2023 3rd International Conference on Public Management and Intelligent Society (PMIS 2023) (pp. 211–216). Atlantis Press. https://doi.org/10.2991/978-94-6463-200-2_40
  35. Hermita, N., Wijaya, T. T., Yusron, E., Abidin, Y., Alim, J. A., & Putra, Z. H. (2023). Extending unified theory of acceptance and use of technology to understand the acceptance of digital textbook for elementary School in Indonesia. Frontiers in Education, 8. https://doi.org/10.3389/feduc.2023.958800
  36. Hooda, A., Gupta, P., Jeyaraj, A., Giannakis, M., & Dwivedi, Y. K. (2022). The effects of trust on behavioral intention and use behavior within e-government contexts. International Journal of Information Management, 67, 102553. https://doi.org/10.1016/j.ijinfomgt.2022.102553
  37. Inkpen, K., Chappidi, S., Mallari, K., Nushi, B., Ramesh, D., Michelucci, P., Mandava, V., Vepřek, L., & Quinn, G. (2023). Advancing human–AI complementarity: The impact of user expertise and algorithmic tuning on joint decision making. ACM Transactions on Computer-Human Interaction, 30(5), Article 54. https://doi.org/10.1145/3534561
  38. Jain, N. K., Bhaskar, K., & Jain, S. (2021). What drives adoption intention of electric vehicles in India? An integrated UTAUT model with environmental concerns, perceived risk and government support. Research in Transportation Business & Management, 42, 100730. https://doi.org/10.1016/j.rtbm.2021.100730
  39. Jain, R., Garg, N., & Khera, S. N. (2022). Adoption of AI-enabled tools in social development organizations in India: An extension of the UTAUT model. Frontiers in Psychology, 13, 893691. https://doi.org/10.3389/fpsyg.2022.893691
  40. Jocson, L. (2025, February 24). Filipino BPO workers at risk of being displaced by AI— report. BusinessWorld Online. https://www.bworldonline.com/top-stories/2025/02/25/655366/filipino-bpo-workers-at-risk-of-being-displaced-by-ai-report/
  41. Kelly, S., Kaye, S., & Oviedo-Trespalacios, O. (2022). What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics and Informatics, 77, 101925. https://doi.org/10.1016/j.tele.2022.101925
  42. Khan, A. (2024). Simulating Intelligence. In: Artificial Intelligence: A Guide for Everyone. Springer, Cham. https://doi.org/10.1007/978-3-031-56713-1_10
  43. Khan, B., Fatima, H., Qureshi, A., Kumar, S., Hanan, A., Hussain, J., & Abdullah, S. (2023). Drawbacks of artificial intelligence and their potential solutions in the healthcare sector. Deleted Journal, 1(2), 731–738. https://doi.org/10.1007/s44174-023-00063-2 
  44. Kim, B., & Kim, M. (2024). How artificial intelligence-induced job insecurity shapes knowledge dynamics: the mitigating role of artificial intelligence self-efficacy. Journal of Innovation & Knowledge, 9(4), 100590. https://doi.org/10.1016/j.jik.2024.100590
  45. Kim, Y., Blazquez, V., & Oh, T. (2024). Determinants of generative AI system adoption and usage behavior in Korean companies: Applying the UTAUT model. Behavioral Sciences, 14(11), 1035. https://doi.org/10.3390/bs14111035
  46. Koeszegi, S.T. (2024). AI @ Work: Human Empowerment or Disempowerment?. In: Werthner, H., et al. Introduction to Digital Humanism. Springer, Cham. https://doi.org/10.1007/978-3-031-45304-5_12
  47. Lee, K. F. (2025). Artificial intelligence and labour markets in Southeast Asia: An empirical examination. Asian Economics Letters, 6, 132415. https://doi.org/10.46557/001c.132415
  48. Li, N., Zhou, H., Deng, W., Liu, J., Liu, F., & Mikel-Hong, K. (2024). When advanced AI isn’t enough: human factors as drivers of success in generative AI-Human collaborations. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4738829
  49. Maldonado-Canca, L., Cabrera-Sánchez, J., Casado-Molina, A., & Bermúdez-González, G. (2025). AI in Companies’ Production Processes. Journal of Global Information Management, 32(1), 1–29. https://doi.org/10.4018/jgim.366653
  50. Marikyan, D.& Papagiannidis, S. (2025) Unified Theory of Acceptance and Use of Technology: A review. In S. Papagiannidis (Ed), TheoryHub Book. Available at https://open.ncl.ac.uk / ISBN: 9781739604400
  51. Mayer, H., Yee, L., Chui, M., & Roberts, R. (2025, January 28). Superagency in the workplace: Empowering people to unlock AI’s full potential. McKinsey & Company. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work#/
  52. Medaglia, R., Gil-Garcia, J. R., & Pardo, T. A. (2021). Artificial intelligence in government: Taking stock and moving forward. Social Science Computer Review, 41(1), 123–140. https://doi.org/10.1177/08944393211034087
  53. McCombes, S. (2023, June 22). Sampling Methods | Types, Techniques & Examples. Scribbr.                           Retrieved               October               19,               2024,               from https://www.scribbr.com/methodology/sampling-methods/
  54. Mishra, S., Ewing, M. T., & Cooper, H. B. (2022). Artificial intelligence focus and firm performance. Journal of the Academy of Marketing Science, 50(6), 1176–1197. https://doi.org/10.1007/s11747-022-00876-5
  55. Mouka, M. (2025, January 16). The ROI puzzle of AI investments in 2025. The CFO. https://the-cfo.io/2025/01/17/the-roi-puzzle-of-ai-investments-in-2025/
  56. Müller, W. (2025). Determinants of smart contract adoption in supply chains: a UTAUT-based PLS-SEM analysis. Operations Management Research. https://doi.org/10.1007/s12063-025-00560-1
  57. Nguyen, H.-H., & Nguyen, V. A. (2024). An application of model unified theory of acceptance and use of technology (UTAUT): A use case for a system of personalized learning based on learning styles. International Journal of Information and Education Technology, 14(11), 1574–1582. https://doi.org/10.18178/ijiet.2024.14.11.2188 
  58. Oludapo, S., Carroll, N., & Helfert, M. (2024). Why do so many digital transformations fail? A bibliometric analysis and future research agenda. Journal of Business Research, 174, 114528. https://doi.org/10.1016/j.jbusres.2024.114528
  59. Pan, X. (2020). Technology acceptance, technological self-efficacy, and attitude toward technology-based self-directed learning: Learning motivation as a mediator. Frontiers in Psychology, 11, 564294. https://doi.org/10.3389/fpsyg.2020.564294
  60. Piccoli, B., Reisel, W. D., & De Witte, H. (2019). Understanding the relationship between job insecurity and performance: hindrance or challenge Effect? Journal of Career Development, 48(2), 150–165. https://doi.org/10.1177/0894845319833189
  61. Pillai, M. C. (2024). The Evolution of Customer Service: Identifying the impact of artificial intelligence on employment and management in call centres. Journal of Business Management and Information Systems, 11, 52–55. https://doi.org/10.48001/jbmis.2024.si1010
  62. Przegalinska, A., Triantoro, T., Kovbasiuk, A., Ciechanowski, L., Freeman, R. B., & Sowa, K. (2025). Collaborative AI in the workplace: Enhancing organizational performance through resource-based and task-technology fit perspectives. International Journal of                      Information                   Management,                   81,                   102853. https://doi.org/10.1016/j.ijinfomgt.2024.102853
  63. Restuputri, D. P., Masudin, I., Andini, A. P., Handayani, D. I., & Setiawan, M. (2023). Usability Evaluation of Artificial Intelligence for Image Recognition Features in Online Shopping Applications Using the UTAUT Method. In P. Ordóñez de Pablos, M. Almunawar, & M. Anshari (Eds.), Perspectives on the Transition Toward Green and Climate Neutral Economies in Asia (pp. 159-181). IGI Global Scientific Publishing. https://doi.org/10.4018/978-1-6684-8613-9.ch010
  64. Sabbagh, M. A., & Elgeddawy, M. (2025). Change management and AI adoption for enhancing employee experience in higher education institutions, Oman. In Studies in systems, decision and control (pp. 307–315). https://doi.org/10.1007/978-3-031-92240-4_28
  65. Saiju, N., Tamang, N., Tamang, P., Bastola, P., Bhattarai, P., & Neupane, D. (2025). A comparative study of e-books and printed books on academic performance: Perception from the university students. International Journal of Humanities, Education,        and                                Social           Sciences,         3,                          295–311. https://doi.org/10.58578/IJHESS.v3i1.4953
  66. Sharma, R. (2024). AI change Management. In Apress eBooks (pp. 135–150). https://doi.org/10.1007/979-8-8688-0796-1_12
  67. Sharma, S., & Singh, G. (2024). Adoption of artificial intelligence in higher education: an empirical study of the UTAUT model in Indian universities. International Journal of Systems Assurance Engineering and Management. https://doi.org/10.1007/s13198-024-02558-7
  68. Soliman, M. M., Ahmed, E., Darwish, A., & Hassanien, A. E. (2024). Artificial intelligence powered Metaverse: analysis, challenges and future perspectives. Artificial Intelligence Review, 57(2). https://doi.org/10.1007/s10462-023-10641-x
  69. Spreitzenbarth, J. M., Bode, C., & Stuckenschmidt, H. (2024). Artificial intelligence and machine learning in purchasing and supply management: A mixed-methods review of the state-of-the-art in literature and practice. Journal of Purchasing and Supply Management, 30(1), 100896. https://doi.org/10.1016/j.pursup.2024.100896  
  70. Sreeram, S. (2019). Artificial intelligence and jobs of the future: Adaptability is key for human evolution. AI Matters, 4(1), 22–28. https://doi.org/10.1145/3299758.3300060
  71. Szczepański, M. (2019, July). Economic impacts of artificial intelligence (AI). European Parliament. https://www.europarl.europa.eu/RegData/etudes/BRIE/2019/637967/EPRS_BRI%28 2019%29637967_EN.pdf
  72. Takahashi, M., & Katagiri, Y. (2025). Promoting Digital Therapeutics in Japan: Understanding user acceptance through the UTAUT model. In Lecture notes in computer science (pp. 365–379). https://doi.org/10.1007/978-3-031-93227-4_25
  73. Talukder, M. S., Shen, L., Talukder, M. F. H., & Bao, Y. (2018). Determinants of user acceptance and use of open government data (OGD): An empirical investigation in Bangladesh.             Technology             in             Society,             56,             147–156. https://doi.org/10.1016/j.techsoc.2018.09.013
  74. Tomić, N., Kalinić, Z., & Todorović, V. (2023). Using the UTAUT model to analyze user intention to accept electronic payment systems in Serbia. Portuguese Economic Journal, 22(2), 251–270. https://doi.org/10.1007/s10258-022-00210-5
  75. Vijh, G., Sharma, R., & Agrawal, S. (2023). Blockchain-Enabled intelligent Solution using structured equation modelling based on the UTAUT framework. SN Computer Science, 4(6). https://doi.org/10.1007/s42979-023-02150-z
  76. Wamba-Taguimdje, S.-L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E. (2020). Influence of artificial intelligence (AI) on firm performance: The business value of AI-based transformation sprojects. Business Process Management Journal, 26(7), 1893–1924. https://doi.org/10.1108/BPMJ-10-2019-0411
  77. Wang, C., Ahmad, S. F., Ayassrah, A. Y. B. A., Awwad, E. M., Irshad, M., Ali, Y. A., Al-Razgan, M., Khan, Y., & Han, H. (2023). An empirical evaluation of technology acceptance model for Artificial Intelligence in E-commerce. Heliyon, 9(8), e18349. https://doi.org/10.1016/j.heliyon.2023.e18349
  78. Wang, H., Tao, D., Yu, N., & Qu, X. (2020). Understanding consumer acceptance of healthcare wearable devices: An integrated model of UTAUT and TTF. International journal                          of              medical              informatics,              139,              104156.https://doi.org/10.1016/j.ijmedinf.2020.104156
  79. Witkowski, K., Okhai, R., & Neely, S. R. (2024). Public perceptions of artificial intelligence in healthcare: ethical concerns and opportunities for patient-centered care. BMC medical ethics, 25(1), 74. https://doi.org/10.1186/s12910-024-01066-4 
  80. Yin, Z., Kong, H., Baruch, Y., Decosta, P. L., & Yuan, Y. (2024). Interactive effects of AI awareness and change-oriented leadership on employee-AI collaboration: The role of approach and avoidance motivation. Tourism Management, 105, 104966. https://doi.org/10.1016/j.tourman.2024.104966
  81. Yu, S., & Chen, T. (2024). Understanding older adults’ acceptance of chatbots in healthcare delivery: An extended UTAUT model. Frontiers in Public Health, 12, 1435329. https://doi.org/10.3389/fpubh.2024.1435329

Tools


Copyright © 2026  KITE Digital Educational Solutions |  Exclusively distributed by CE-Logic