The Role of AI Chatbots on E-Commerce Platforms: Understanding its Influence on Customer Trust and Dependability
Abstract
This study investigates the role of AI chatbots in enhancing customer trust and dependability on e-commerce platforms. With the changing landscape of business due to advancements AI chatbots have become crucial tools for customer engagement and simplifying transactions. The study explores factors that affect customer trust, such as chatbot knowledge, humanlike qualities, responsiveness and brand reputation. By collecting data from 209 e-commerce users in Indonesia through a survey using quantitative methods the study discovered that expertise and responsiveness play a significant role in enhancing trust in AI chatbots leading to increased reliance by customers on these systems. Surprisingly, brand reputation alone did not have a direct effect on chatbot trust. The research emphasizes the need to optimize chatbot features to build user trust and suggests that while humanlike characteristics can improve interactions the main focus should be on efficiency. These findings provide insights for businesses looking to use AI chatbots effectively to enhance customer satisfaction and loyalty in today's competitive e-commerce environment.
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Trust directly forecasts reliance in simplified models.
“trust directly forecasts reliance in simplified models [53]”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [53]
Cites
Predictive model assessment in PLS-SEM: guidelines for using PLSpredict
Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019) · European Journal of Marketing · doi:10.1108/ejm-02-2019-0189 · Cited by 4,618 · see citing works
Human trust in artificial intelligence: Review of empirical research
Glikson, E., & Woolley, A. W. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0057 · Cited by 2,921 · see citing works
Descriptive statistics and normality tests for statistical data
Gupta, A., Mishra, P., Pandey, C., Singh, U., Sahu, C., & Keshri, A. (2019) · Annals of Cardiac Anaesthesia · doi:10.4103/aca.aca_157_18 · Cited by 2,628 · see citing works
AI-based chatbots in customer service and their effects on user compliance
Adam, M., Wessel, M., & Benlian, A. (2021) · Electronic Markets · doi:10.1007/s12525-020-00414-7 · Cited by 1,195 · see citing works
Blut, M., Wang, C., Wünderlich, N. V., & Brock, C. (2021) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-020-00762-y · Cited by 1,194 · see citing works
My Chatbot Companion - a Study of Human-Chatbot Relationships
Skjuve, M., Følstad, A., Fostervold, K. I., & Brandtzaeg, P. B. (2021) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2021.102601 · Cited by 797 · see citing works
Iglesias, O., Marković, S., Bagherzadeh, M., & Singh, J. J. (2018) · Journal of Business Ethics · doi:10.1007/s10551-018-4015-y · Cited by 574 · see citing works
Chen, J., Le, T.-T.-Y., & Florence, D. (2021) · International Journal of Retail & Distribution Management · doi:10.1108/ijrdm-08-2020-0312 · Cited by 486 · see citing works
Roy, R., & Naidoo, V. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2020.12.051 · Cited by 404 · see citing works
Gao, J., Siddik, A. B., Abbas, S. K., Hamayun, M., Masukujjaman, M., & Alam, S. S. (2023) · Sustainability · doi:10.3390/su15021594 · Cited by 332 · see citing works
Cheng, X., Bao, Y., Zarifis, A., Gong, W., & Mou, J. (2022) · Internet Research · doi:10.1108/intr-08-2020-0460 · Cited by 300 · see citing works
Consumer Marketing Strategy and E-Commerce in the Last Decade: A Literature Review
Rosário, A. T., & Raimundo, R. J. G. (2021) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer16070164 · Cited by 289 · see citing works
Role of cognitive absorption in building user trust and experience
Balakrishnan, J., & Dwivedi, Y. K. (2021) · Psychology and Marketing · doi:10.1002/mar.21462 · Cited by 268 · see citing works
The impact of chatbots on customer loyalty: a systematic literature review
Jenneboer, L., Herrando, C., & Constantinides, E. (2022) · Journal of Theoretical and Applied Electronic Commerce Research · doi:10.3390/jtaer17010011 · Cited by 249 · see citing works
An Initial Model of Trust in Chatbots for Customer Service—Findings from a Questionnaire Study
Nordheim, C. B., Følstad, A., & Bjørkli, C. A. (2019) · Interacting with Computers · doi:10.1093/iwc/iwz022 · Cited by 233 · see citing works
Decoding Gen Z: AI's influence on brand trust and purchasing behavior
Guerra-Tamez, C. R., Flores, K. K., Serna-Mendiburu, G. M., Robles, D. C., & Cortés, J. I. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1323512 · Cited by 141 · see citing works
Nadarzynski, T., Knights, N., Husbands, D., Graham, C. A., Llewellyn, C. D., Buchanan, T., Montgomery, I., & Ridge, D. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000492 · Cited by 106 · see citing works
Consumer Trust: Meta-Analysis of 50 Years of Empirical Research
Khamitov, M., Rajavi, K., Huang, D.-W., & Hong, Y. (2023) · Journal of Consumer Research · doi:10.1093/jcr/ucad065 · Cited by 99 · see citing works
Gender Differences in Perception of Artificial Intelligence-Based Tools
Ofosu‐Ampong, K. (2023) · Journal of Digital Art & Humanities · doi:10.33847/2712-8149.4.2_6 · Cited by 97 · see citing works
Xu, Y., Zhang, J., & Deng, G. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.902782 · Cited by 92 · see citing works
Sustainable customer relationship management
Ferrer-Estévez, M., & Chalmeta, R. (2022) · Marketing Intelligence & Planning · doi:10.1108/mip-06-2022-0266 · Cited by 80 · see citing works
Fakfare, P. (2021) · International Journal of Gastronomy and Food Science · doi:10.1016/j.ijgfs.2021.100392 · Cited by 78 · see citing works
Determinants Affecting Consumer Trust in Communication With AI Chatbots
Li, J., Wu, L., Qi, J., Zhang, Y., Wu, Z., & Hu, S. (2023) · Journal of Organizational and End User Computing · doi:10.4018/joeuc.328089 · Cited by 73 · see citing works
Text-Based Chatbot in Financial Sector: A Systematic Literature Review
Wube, H. D., Esubalew, S. Z., Weldesellasie, F. F., & Debelee, T. G. (2022) · Data Science in Finance and Economics · doi:10.3934/dsfe.2022011 · Cited by 55 · see citing works
E-commerce adoption in ASEAN: who and where?
Ayob, A. H. (2021) · Future Business Journal · doi:10.1186/s43093-020-00051-8 · Cited by 43 · see citing works
An empirical analysis of the information security culture key factors framework
Tolah, A., Furnell, S., & Papadaki, M. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102354 · Cited by 41 · see citing works
Towards Human-Centered Design of AI Service Chatbots: Defining the Building Blocks
Hartikainen, M., & Väänänen, K. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35894-4_5 · Cited by 5 · see citing works
Assistive Chatbots for healthcare: a succinct reviewPreprint
Bhattacharya, B. S. (2023) · doi:10.36227/techrxiv.23741469 · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Le, Designing a concept of chatbot mobile application to en-hance travel experiences using user-centred approach
- Hair, A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)
Cited by
Sorted by citations
Balaskas, S. (2026) · Information · doi:10.3390/info17030222 · Cited by 8 · see citing works
Lead Sphere: An AI-Driven CRM for Intelligent Lead Management and Automated Project Execution
Tirpude, S., Rathi, P., Rodge, P., & Thakare, L. P. (2025) · International Conference for Advancement in Technology (ICONAT) · doi:10.1109/iconat66879.2025.11362369
DoraReviewer: Vietnamese Virtual Reviewer with GraphRAG and LLM
Trinh, T. T. N., Ngo, A. H. L., Nguyen, T. H. T., & Ly, K. D. D. (2025) · RIVF International Conference on Computing and Communication Technologies (RIVF) · doi:10.1109/rivf68649.2025.11365128
Competitiveness and Productivity in SMEs
Rismawati, R., Djusmin, V. B., & Bongso, G. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-2817-1.ch007
Paramaiah, C., Nalluri, V., Chang, J., Dalavai, S. B., Veetil, A. K. V., & Kumar, S. (2026) · Asian Journal of Interdisciplinary Research · doi:10.54392/ajir26112
AI Chatbots and Prospect Guidance in E-Commerce
TAHIRI, A., benyahya, Z., & Sabiri, B. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-0512-5.ch009
- Authors
- Christopher, Y., Sundjaja, A. M., & Mulvono
- Affiliations
- Binus University (ID)
- ORCID
- Sundjaja, Arta Moro
- Venue
- 2024 9th International Conference on Information Technology and Digital Applications (ICITDA)
- Publisher
- IEEE
- Published
- 2024-11-07
- Pages
- 1-8
- OpenAlex
- W4405845659
- Semantic Scholar
- 942136baef57e5cf10e6864c6d5bda794deb9f6a
- Cited by
- 5
- References
- 27
- Citations by year
- 2026: 3 · 2025: 2
- FWCI
- 1.44
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Information Technology and Digital Applications (ICITDA) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Customer Service Quality and Loyalty
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Keywords
- AI chatbots, e-commerce platforms, consumer trust, dependability, chatbot responsiveness, brand reputation, customer satisfaction, customer loyalty
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