Assessing the Adoption Intention of Computational Intelligence Technologies in the E-Commerce Industry

Authors

Figo Fernando

Management Department, Universitas Internasional Batam, Batam (Indonesia)

Renny Christiarini

Management Department, Universitas Internasional Batam, Batam (Indonesia)

Article Information

DOI: 10.47772/IJRISS.2025.930000081

Subject Category: Marketing

Volume/Issue: 9/30 | Page No: 634-645

Publication Timeline

Submitted: 2025-12-10

Accepted: 2025-12-16

Published: 2026-04-21

Abstract

Rapid advances in information technology have brought significant changes to the role of e-commerce, as well as changing the way electronic customers engage with interactive marketing, in connection with the presence of computational intelligence. This study aims to analyze the influence of CI service quality, CI information quality, social influence on e-customer perceived value, intention to adopt CI, and use behavior. The research approach used is quantitative, with SEM-PLS data processing using SMARTPLS software. The results of the study revealed that CI Service quality and CI information quality have a significant influence on E-Customer Perceived Value. Social influence and E-customer perceived value have a significant influence on intention to adopt AI, intention to adopt AI has a significant influence on use behavior and is able to mediate the relationship between social influence and use behavior. However, E-Customer Perceived Value is not able to mediate the relationship between CI Service quality and CI information quality on Use behavior.

Keywords

CI service quality, CI information quality, social influence

Downloads

References

1. Ayaz, A., & Yanartaş, M. (2020). An analysis on the unified theory of acceptance and use of technology theory (UTAUT): Acceptance of electronic document management system (EDMS). Computers in Human Behavior Reports, 2, 100032. [Google Scholar] [Crossref]

2. Aytekin, A., Özköse, H., & Ayaz, A. (2022). Unified theory of acceptance and use of technology (UTAUT) in mobile learning adoption: Systematic literature review and bibliometric analysis. COLLNET Journal of Scientometrics and Information Management, 16(1), 75-116. [Google Scholar] [Crossref]

3. Bag, S., Srivastava, G., Bashir, M. M. A., Kumari, S., Giannakis, M., & Chowdhury, A. H. (2022). Journey of customers in this digital era: Understanding the role of artificial intelligence technologies in user engagement and conversion. Benchmarking: An International Journal, 29(7), 2074-2098. [Google Scholar] [Crossref]

4. Behera, R. K., Bala, P. K., & Rana, N. P. (2024). Assessing the intention to adopt computational intelligence in interactive marketing. Journal of Retailing and Consumer Services, 78, 103765. [Google Scholar] [Crossref]

5. Bajunaied, K., Hussin, N., & Kamarudin, S. (2023). Behavioral intention to adopt FinTech services: An extension of unified theory of acceptance and use of technology. Journal of Open Innovation: Technology, Market, and Complexity, 9(1), 100010. [Google Scholar] [Crossref]

6. Blut, M., Chong, A. Y. L., Tsiga, Z., & Venkatesh, V. (2022, January). Meta-analysis of the unified theory of acceptance and use of technology (UTAUT): challenging its validity and charting a research agenda in the red ocean. Association for Information Systems. [Google Scholar] [Crossref]

7. Chopdar, P. K. (2022). Adoption of Covid-19 contact tracing app by extending UTAUT theory: Perceived disease threat as moderator. Health Policy and Technology, 11(3), 100651. [Google Scholar] [Crossref]

8. Faqih, K. M. (2020). The influence OF perceived usefulness, social influence, internet self-efficacy and compatibility ON USERS’INTENTIONS to adopt e-learning: investigating the moderating effects OF culture. IJAEDU-International E-Journal of Advances in Education, 5(15), 300-320. [Google Scholar] [Crossref]

9. Gao, Y., & Liu, H. (2022). Artificial intelligence-enabled personalization in interactive marketing: a customer journey perspective. Journal of Research in Interactive Marketing. [Google Scholar] [Crossref]

10. GC, S. B., Bhandari, P., Gurung, S. K., Srivastava, E., Ojha, D., & Dhungana, B. R. (2024). Examining the role of social influence, learning value and habit on students’ intention to use ChatGPT: the moderating effect of information accuracy in the UTAUT2 model. Cogent Education, 11(1), 2403287. [Google Scholar] [Crossref]

11. Gligor, D., & Bozkurt, S. (2021). The role of perceived social media agility in customer engagement. Journal of Research in Interactive Marketing, 15(1), 125-146. [Google Scholar] [Crossref]

12. Guerra-Montenegro, J., Sanchez-Medina, J., Lana, I., Sanchez-Rodriguez, D., Alonso-Gonzalez, I., & Del Ser, J. (2021). Computational Intelligence in the hospitality industry: A systematic literature review and a prospect of challenges. Applied Soft Computing, 102, 107082 [Google Scholar] [Crossref]

13. Guetz, B., & Bidmon, S. (2022). The impact of social influence on the intention to use physician rating websites: moderated mediation analysis using a mixed methods approach. Journal of Medical Internet Research, 24(11), e37505. [Google Scholar] [Crossref]

14. Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook (p. 197). Springer Nature. [Google Scholar] [Crossref]

15. Jiang, L., Zhou, W., Ren, Z., & Yang, Z. (2022). Make the apps stand out: discoverability and perceived value are vital for adoption. Journal of Research in Interactive Marketing, 16(4), 494-513 [Google Scholar] [Crossref]

16. Jiang, Y., Wang, X., & Yuen, K. F. (2021). Augmented reality shopping application usage: The influence of attitude, value, and characteristics of innovation. Journal of Retailing and Consumer Services, 63, 102720. [Google Scholar] [Crossref]

17. Li, Y., & Shang, H. (2020). Service quality, perceived value, and citizens’ continuous-use intention regarding e-government: Empirical evidence from China. Information & Management, 57(3), 103197. [Google Scholar] [Crossref]

18. Li, Z., Du, N., Wang, B., & Oteng-Darko, C. (2022). Impact of social influence on users' continuance intention toward sports and fitness applications. Frontiers in Public Health, 10, 1031520. [Google Scholar] [Crossref]

19. Lin, W. R., Lin, C. Y., & Ding, Y. H. (2020). Factors affecting the behavioral intention to adopt mobile payment: An empirical study in Taiwan. Mathematics, 8(10), 1851. [Google Scholar] [Crossref]

20. Maria, I., Wijaya, V., & Keni, K. (2021). Pengaruh information quality dan service quality terhadap perceived value dan konsekuensinya terhadap customer engagement behavior intention (Studi pada social commerce Instagram). Jurnal Muara Ilmu Ekonomi dan Bisnis, 5(2), 321-334. [Google Scholar] [Crossref]

21. Moorthy, K., Chun T'ing, L., Ming, K. S., Ping, C. C., Ping, L. Y., Joe, L. Q., & Jie, W. Y. (2019). Behavioral intention to adopt digital library by the undergraduates. International Information & Library Review, 51(2), 128-144. [Google Scholar] [Crossref]

22. Naqvi, M.H.A., Jiang, Y., & Naqvi, M. (2021). Generating customer engagement in electronic- brand communities: a stimulus–organism–response perspective. Asia Pacific Journal of Marketing and Logistics. 33(7), 1535-1555. [Google Scholar] [Crossref]

23. Nguyen, V. T. (2021). Determinants of intention to use google lens. Int. J. Inf. Sci. Technol, 5(2), 4-11. [Google Scholar] [Crossref]

24. Patma, T. S., Fienaningsih, N., Rahayu, K. S., & Artatanaya, I. G. L. S. (2021). Impact of Information Quality on Customer Perceived Value, Experience Quality, and Customer Satisfaction from Using GoFood Aplication. Journal of Indonesian Economy and Business: JIEB., 36(1), 51-61. [Google Scholar] [Crossref]

25. Purwanto, E., & Loisa, J. (2020). The intention and use behaviour of the mobile banking system in Indonesia: UTAUT Model. Technology Reports of Kansai University, 62(06), 2757-2767. [Google Scholar] [Crossref]

26. Putri, W. K., & Pujani, V. (2019). The influence of system quality, information quality, e-service quality and perceived value on Shopee consumer loyalty in Padang City. The International Technology Management Review, 8(1), 10-15. [Google Scholar] [Crossref]

27. Radhamani, R., Kumar, D., Nizar, N., Achuthan, K., Nair, B., & Diwakar, S. (2021). What virtual laboratory usage tells us about laboratory skill education pre-and post-COVID-19: Focus on usage, behavior, intention and adoption. Education and information technologies, 26(6), 7477-7495. [Google Scholar] [Crossref]

28. Rahardja, U., Hongsuchon, T., Hariguna, T., & Ruangkanjanases, A. (2021). Understanding impact sustainable intention of s-commerce activities: The role of customer experiences, perceived value, and mediation of relationship quality. Sustainability, 13(20), 11492. [Google Scholar] [Crossref]

29. Rahi, S., Ghani, M. A., & Ngah, A. H. (2020). Factors propelling the adoption of internet banking: the role of e-customer service, website design, brand image and customer satisfaction. International Journal of Business Information Systems, 33(4), 549-569. [Google Scholar] [Crossref]

30. Rahi, S., & Ishaq, M. (2020). Factors Influencing online Shopping behavior With Mediating Role of Customer Perceived Value. Economic and Social Development: Book of Proceedings, 128-141. [Google Scholar] [Crossref]

31. Rana, S., Tandon, U., & Kumar, H. (2023). Understanding medical service quality, system quality and information quality of Tele-Health for sustainable development in the Indian context. Kybernetes. [Google Scholar] [Crossref]

32. Samudro, A., Sumarwan, U., Simanjuntak, M., & Yusuf, E. (2020). Assessing the effects of perceived quality and perceived value on customer satisfaction. Management Science Letters, 10(5), 1077-1084. [Google Scholar] [Crossref]

33. Sartono, Y., Astuti, E. S., Wilopo, W., & Noerman, T. (2024). Sustainable Digital Transformation: Its Impact on Perceived Value and Adoption Intention of Industry 4.0 in Moderating Effects of Uncertainty Avoidance. F1000Research, 13. [Google Scholar] [Crossref]

34. Senali, M.G., Cripps, H., Meek, S., & Ryan, M.M. (2022). A comparison of Australians, Chinese and Sri Lankans' payment preference at point-of-sale, Marketing Intelligence & Planning, 40(1), 18-32. [Google Scholar] [Crossref]

35. Sharma, N., & Fatima, J. (2024). Influence of perceived value on omnichannel usage: Mediating and moderating roles of the omnichannel shopping habit. Journal of Retailing and Consumer Services, 77, 103627. [Google Scholar] [Crossref]

36. Shiau, W. L., & Huang, L. C. (2023). Scale development for analyzing the fit of real and virtual world integration: an example of Pokémon Go. Information Technology & People, 36(2), 500-531. [Google Scholar] [Crossref]

37. Song, M., Xing, X., Duan, Y., Cohen, J., & Mou, J. (2022). Will artificial intelligence replace human customer service? The impact of communication quality and privacy risks on adoption intention. Journal of Retailing and Consumer Services, 66, 102900. [Google Scholar] [Crossref]

38. Sreelakshmi, C. C., & Prathap, S. K. (2023). Effect of COVID-19 health threat on consumer’s perceived value towards mobile payments in India: a means-end model. Journal of Financial Services Marketing, 1-25. [Google Scholar] [Crossref]

39. Stahl, B. C. (2021). Artificial intelligence for a better future: an ecosystem perspective on the ethics of AI and emerging digital technologies (p. 124). Springer Nature. [Google Scholar] [Crossref]

40. Statista.com (2024). E-Commerce in Indonesia. Diakses dari: https://www-statista-com.translate.goog/topics/5742/e-commerce-in-indonesia/?_x_tr_sl=en&_x_tr_tl=id&_x_tr_hl=id&_x_tr_pto=tc [Google Scholar] [Crossref]

41. Tsai, P. H., & Tang, J. W. (2023). Consumers' switching intention towards E-commerce platforms’ store-to-store pickup services: The application of the extended PPM model. Journal of Retailing and Consumer Services, 75, 103535. [Google Scholar] [Crossref]

42. Tseng, T.H., Lee, C.T., Huang, H.-T., & Yang, W.H. (2022). Success factors driving consumer reuse intention of mobile shopping application channel. International Journal of Retail & Distribution Management, 50(1), 76-99. [Google Scholar] [Crossref]

43. Tumewah, E., & Kurniawan, Y. (2020). The effect of m-banking service quality and customer perceived value to satisfaction and loyalty of bank XYZ customers. International Journal of Management and Humanities, 4(6), 132-138. [Google Scholar] [Crossref]

44. Uzir, M. U. H., Al Halbusi, H., Thurasamy, R., Hock, R. L. T., Aljaberi, M. A., Hasan, N., & Hamid, M. (2021). The effects of service quality, perceived value and trust in home delivery service personnel on customer satisfaction: Evidence from a developing country. Journal of Retailing and Consumer Services, 63, 102721. [Google Scholar] [Crossref]

45. Xin, B., Hao, Y., & Xie, L. (2023). Strategic product showcasing mode of E-commerce live streaming. Journal of Retailing and Consumer Services, 73, 103360. [Google Scholar] [Crossref]

46. Xue, L., Rashid, A. M., & Ouyang, S. (2024). The Unified Theory of Acceptance and Use of Technology (UTAUT) in Higher Education: A Systematic Review. SAGE Open, 14(1), 21582440241229570. [Google Scholar] [Crossref]

Metrics

Views & Downloads

Similar Articles