Issue

Vol 40 No 3 (2025): September

Date Log

Submitted
Nov 30, 2023
Accepted
Dec 3, 2024
Published
Sep 29, 2025
Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Copyright

Upon acceptance of an article, authors transfer copyright to the JIEB as part of a journal publishing agreement, but authors still have the right to share their article for personal use, internal institutional use, and for any use permitted under the CC BY-SA license

Open Access

Articles are freely available to the public without any subscription with permitted reuse. For open access articles, permitted third party (re)use is defined by the following Creative Commons user licenses: Creative Commons Attribution (CC BY-SA).

Factors Affecting Customer Trust in Chatbot Usage: Evidence from Indonesia

https://doi.org/10.22146/jieb.v40i3.11047
Amelia Amelia
amelia@unmuha.ac.id
Department of Management, Faculty of Economics, Universitas Muhammadiyah Aceh, Aceh, 23245, Indonesia
Fani Sartika
Department of Management, Faculty of Economics, Universitas Muhammadiyah Aceh, Aceh, 23245, Indonesia

Corresponding Author(s) : Amelia Amelia

amelia@unmuha.ac.id

Journal of Indonesian Economy and Business, Vol 40 No 3 (2025): September

Abstract

Introduction/Main Objectives: Customer trust is critical in ensuring the successful implementation of chatbots. Building trust is essential to ensure that users feel confident in using chatbot across various contexts, including customer service. Background Problems: Despite its importance, there is limited understanding of how specific chatbot features influence customer trust, especially within the Indonesian context. Novelty: Drawing principally on the Technology Acceptance Model (TAM), this empirical study develops and tests a model that incorporates anthropomorphism, the attribution of human-like qualities, to provide a more comprehensive explanation of customer trust.  Research Methods: This study utilizes quantitative analysis of data gathered from 368 customers to examine the relationships between perceived usefulness, ease of use, anthropomorphism, and trust. A structured survey was administered, and statistical techniques were employed to validate the proposed model and determine the significance of each factor. Finding/Results: The analysis reveals that perceived usefulness, ease of use, and anthropomorphism are all significant predictors of trust in chatbots. Among these, ease of use emerges as the most influential factor, emphasizing its pivotal role in fostering trust. Conclusion: This study provides practical guidance for managers and developers aiming to design trust-enhancing chatbots. Key strategies include integrating human-like features, focusing on usability, and highlighting the practical benefits offered by chatbots. These approaches can improve customer engagement, enhance interaction quality, and support the succesful implementation of chatbot technologies in Indonesia.

Keywords

Chatbot perceived usefulness perceived ease of use anthropomoprhism customer trust
Amelia, A., & Sartika, F. (2025). Factors Affecting Customer Trust in Chatbot Usage: Evidence from Indonesia. Journal of Indonesian Economy and Business, 40(3), 327 -. https://doi.org/10.22146/jieb.v40i3.11047
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. REFERENCES
  2. Al-Adwan, A. S., Kokash, H., Adwan, A. A., Alhorani, A., & Yaseen, H. (2020). Building customer loyalty in online shopping: The role of online trust, online satisfaction and electronic word of mouth. International Journal of Electronic Marketing and Retailing, 11(3), 278-306.
  3. Alboqami, H. (2023). Factors Affecting Consumers Adoption of AI-Based Chatbots: The Role of Anthropomorphism. American Journal of Industrial and Business Management, 13(4), 195-214.
  4. Amalia, A., & Suprayogi, M. S. (2019). Social Media Chatbots for Collaborated Engagement Marketing. CHANNEL: Jurnal Komunikasi, 7(1), 33-42.
  5. Amelia, A., Mathies, C., & Patterson, P. G. (2022). Customer acceptance of frontline service robots in retail banking: A qualitative approach. Journal of Service Management, 33(2), 321-341. doi:10.1108/JOSM-10-2020-0374
  6. Amelia, A., Sartika, F., Murad, S., & Ufra, N. (2024). Determinants of Customer Satisfaction In Chatbot Use. Almana: Jurnal Manajemen dan Bisnis, 8(1), 46-55.
  7. Ashfaq, M., Yun, J., Yu, S., & Loureiro, S. M. C. (2020). I, Chatbot: Modeling the determinants of users’ satisfaction and continuance intention of AI-powered service agents. Telematics and Informatics, 54, 101473.
  8. Ba, S., & Pavlou, P. A. (2002). Evidence of the effect of trust building technology in electronic markets: Price premiums and buyer behavior. MIS Quarterly, 26(3), 243-268.
  9. Blut, M., Wang, C., Wünderlich, N. V., & Brock, C. (2021). Understanding anthro-pomorphism in service provision: a meta-analysis of physical robots, chatbots, and other AI. Journal of the Academy of Marketing Science, 49, 632-658. doi:10.1007/s11747-020-00762-y
  10. Chen, J.-S., Le, T.-T.-Y., & Florence, D. (2021). Usability and responsiveness of artificial intelligence chatbot on online customer experience in e-retailing. International Journal of Retail & Distribution Management, 49(11), 1512-1531.
  11. Cheng, X., Zhang, X., Cohen, J., & Mou, J. (2022). Human vs. AI: Understanding the impact of anthropomorphism on consumer response to chatbots from the perspective of trust and relationship norms. Information Processing Management, 59(3), 102940.
  12. Chung, M., Ko, E., Joung, H., & Kim, S. J. (2020). Chatbot e-service and customer satisfaction regarding luxury brands. Journal of Business Research, 117, 587-595. doi:10.1016/j.jbusres.2018.10.004
  13. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319-340.
  14. De Visser, E. J., Monfort, S. S., McKendrick, R., Smith, M. A., McKnight, P. E., Krueger, F., & Parasuraman, R. (2016). Almost human: Anthropomorphism increases trust resilience in cognitive agents. Journal of Experimental Psychology: Applied, 22(3), 331.
  15. Douglas, S. P., & Craig, C. S. (2007). Collaborative and iterative translation: An alternative approach to back translation. Journal of International Marketing, 15(1), 30-43.
  16. Epley, N., Waytz, A., Akalis, S., & Cacioppo, J. T. (2008). When we need a human: Motivational determinants of anthropomor¬phism. Social Cognition, 26(2), 143-155.
  17. Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: an integrated model. MIS Quarterly, 27(1), 51-90.
  18. George, A., & Kumar, G. G. (2013). Antecedents of customer satisfaction in internet banking: Technology acceptance model (TAM) redefined. Global Business Review, 14(4), 627-638.
  19. Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2014). Multivariate data analysis (7th edition ed.). Essex: Pearson Education Ltd.
  20. Han, J.-H., Jae, S.-H., Kim, B.-H., & Park, J.-S. (2015). Effects of consumer trust and perceived usefulness on mobile payments and online shopping website loyalty. Journal of Digital Convergence, 13(12), 75-87.
  21. Han, M. C. (2021). The impact of anthropomorphism on consumers’ purchase decision in chatbot commerce. Journal of Internet Commerce, 20(1), 46-65. doi:10.1080/15332861.2020.1863022
  22. Hancock, P. A., Billings, D. R., Schaefer, K. E., Chen, J. Y., De Visser, E. J., & Parasuraman, R. (2011). A meta-analysis of factors affecting trust in human-robot interaction. Human Factors, 53(5), 517-527. doi:10.1177/0018720811417254
  23. Harman, H. H. (1976). Modern factor analysis: University of Chicago press.
  24. Heerink, M., Kröse, B., Evers, V., & Wielinga, B. (2010). Assessing acceptance of assistive social agent technology by older adults: The almere model. International Journal of Social Robotics, 2(4), 361-375.
  25. katadata.co.id. (2022). Teknologi Chatbot Digandrungi, Pengguna Startup Kata.ai Tumbuh 170%. Retrieved from https://katadata.co.id/yuliawati/digital/6295e9c6832d5/teknologi-chatbot-digandrungi-pengguna-startup-kataai-tumbuh-170
  26. Korzynski, P., Mazurek, G., Altmann, A., Ejdys, J., Kazlauskaite, R., Paliszkiewicz, J., . . . Ziemba, E. (2023). Generative artificial intelligence as a new context for management theories: analysis of ChatGPT. Central European Management Journal, 31(1), 3-13.
  27. Lappeman, J., Marlie, S., Johnson, T., & Poggenpoel, S. (2023). Trust and digital privacy: willingness to disclose personal information to banking chatbot services. Journal of Financial Services Marketing, 28(2), 337-357.
  28. Larasetiati, M., & Ali, H. (2019). Model of consumer trust: analysis of perceived usefulness and security toward repurchase intention in online travel agent. Saudi Journal of Economics and Finance, 3(8), 350-357.
  29. Lei, S. I., Shen, H., & Ye, S. (2021). A comparison between chatbot and human service: customer perception and reuse intention. International Journal of Contemporary Hospitality Management, 33(11), 3977-3995.
  30. Li, M.-W., Teng, H.-Y., & Chen, C.-Y. (2020). Unlocking the customer engagement-brand loyalty relationship in tourism social media: The roles of brand attachment and customer trust. Journal of Hospitality and Tourism Management, 44, 184-192.
  31. Lim, Y. J., Osman, A. B., & Halim, M. S. B. A. (2014). Perceived usefulness and trust towards consumer behaviors: a perspective of consumer online shopping. Journal of Asian Scientific Research, 4(10), 541.
  32. MihirContractor. (2021). Chatbot Market and Current Important Statistics. Retrieved from https://www.datasciencecentral.com/chatbot-market-and-current-important-statistics/
  33. Mostafa, R. B., & Kasamani, T. (2022). Antecedents and consequences of chatbot initial trust. European Journal of Marketing, 56(6), 1748-1771.
  34. Mou, J., & Cohen, J. F. (2014). A longitudinal Study of Trust and Perceived Usefulness in Consumer Acceptance of an eService: the Case of Online Health Services. Paper presented at the PACIS.
  35. Mozafari, N., Weiger, W. H., & Hammerschmidt, M. (2021). Trust me, I'm a bot–repercussions of chatbot disclosure in different service frontline settings. Journal of Service Management, 33(2), 221-245.
  36. Nass, C., & Moon, Y. (2000). Machines and mindlessness: Social responses to computers. Journal of Social Issues, 56(1), 81-103.
  37. Nunkoo, R., & Ramkissoon, H. (2012). Structural equation modelling and regres¬-sion analysis in tourism research. Current Issues in Tourism, 15(8), 777-802.
  38. Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903. doi:10.1037/0021-9010.88.5.879
  39. Podsakoff, P. M., & Organ, D. W. (1986). Self-reports in organizational research: Problems and prospects. Journal of Management, 12(4), 531-544.
  40. Rese, A., Ganster, L., & Baier, D. (2020). Chatbots in retailers’ customer commu-nication: How to measure their acceptance? Journal of Retailing and Consumer Services, 56, 102176.
  41. Sarkar, S., Chauhan, S., & Khare, A. (2020). A meta-analysis of antecedents and consequences of trust in mobile commerce. International Journal of Information Management, 50, 286-301 . doi:10.1016/j.ijinfomgt.2019.08.008
  42. Schmidt, P., Biessmann, F., & Teubner, T. (2020). Transparency and trust in artificial intelligence systems. Journal of Decision Systems, 29(4), 260-278.
  43. Selamat, M. A., & Windasari, N. A. (2021). Chatbot for SMEs: Integrating customer and business owner perspectives. Technology in Society, 66, 101685.
  44. Soares, A. M., Camacho, C., & Elmashhara, M. G. (2022). Understanding the impact of chatbots on purchase intention. Paper presented at the World Conference on Information Systems and Technologies.
  45. Sugiyono. (2022). Metode Penelitian Kuantitatif, Kualitatif, dan R&D (2 Cetakan 4 ed.). Bandung: Alfabeta.
  46. van Pinxteren, M. M., Wetzels, R. W., Rüger, J., Pluymaekers, M., & Wetzels, M. (2019). Trust in humanoid robots: Implications for services marketing. Journal of Services Marketing, 33(4), 507-518. doi:10.1108/JSM-01-2018-0045
  47. Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178.
  48. Wilson, N. (2019). The impact of perceived usefulness and perceived ease-of-use toward repurchase intention in the Indonesian e-commerce industry. Jurnal Manajemen Indonesia, 19(3), 241-249.
  49. Wilson, N., Keni, K., & Tan, P. H. P. (2021). The role of perceived usefulness and perceived ease-of-use toward satisfaction and trust which influence computer consumers' loyalty in China. Gadjah Mada International Journal of Business, 23(3), 262-294.
  50. Wirtz, J., Patterson, P. G., Kunz, W. H., Gruber, T., Lu, V. N., Paluch, S., & Martins, A. (2018). Brave new world: Service robots in the frontline. Journal of Service Management, 29(5), 907-931. doi:10.1108/JOSM-04-2018-0119
  51. Ye, Y., You, H., & Du, J. (2023). Improved trust in human-robot collaboration with ChatGPT. IEEE Access.
  52. Zhang, B., Zhu, Y., Deng, J., Zheng, W., Liu, Y., Wang, C., & Zeng, R. (2023). “I am here to assist your tourism”: predicting continuance intention to use ai-based chatbots for tourism. does gender really matter? International Journal of Human–Computer Interaction, 39(9), 1887-1903.
Read More

Author Biographies

Amelia Amelia, Department of Management, Faculty of Economics, Universitas Muhammadiyah Aceh, Aceh, 23245, Indonesia

Amelia is a lecturer and researcher at the Magister Management Program, Postgraduate Program, Universitas Muhammadiyah Aceh, Indonesia where she has been a faculty member since 2015. She earned her Bachelor’s degree in Management at the Faculty of Economics, Universitas Indonesia, Indonesia. She completed her Master’s degree in Institute of International Management Business Administration (IIMBA) at the National Cheng Kung University, Taiwan. She also earned her Ph.D. in Marketing from the UNSW Business School, UNSW Sydney, Australia. Her research interests lie in the areas of marketing and consumer behavior, with recent works exploring topics such as service robots, chatbots and SME competitiveness. She has collaborated actively with scholars from diverse fields and has succesfully published articles in several top-tier international journals, presented her research at leading academic conferences, and contributed to scholarly books.

Fani Sartika, Department of Management, Faculty of Economics, Universitas Muhammadiyah Aceh, Aceh, 23245, Indonesia

Fani is a lecturer and researcher at the Department of Management, Universitas Muhammadiyah Aceh (UNMUHA), Indonesia, where she has been a faculty member since 2016. She earned her Bachelor’s degree in Management at the Faculty of Economics, Syiah Kuala University, Indonesia. She did her Master’s degree in Marketing Management, also at Syiah Kuala University. Her research interests lie in the areas of marketing strategy, digital marketing and e-commerce, marketing communications, and consumer behaviors. She has collaborated actively with other researchers from various fields in many research projects. She successfully published articles in different reputable journals.

Download this PDF file
PDF
Statistic
Read Counter : 174 Download : 128