76
Views
0
CrossRef citations to date
0
Altmetric
Articles

Impact of artificial intelligence (AI) chatbot characteristics on customer experience and customer satisfaction

ORCID Icon, ORCID Icon, &
Pages 439-457 | Received 08 Mar 2024, Accepted 29 May 2024, Published online: 14 Jun 2024

References

  • Abd-Alrazaq, A. A., Alajlani, M., Alalwan, A. A., Bewick, B. M., Gardner, P., & Househ, M. (2019). An overview of the features of chatbots in mental health: A scoping review. International Journal of Medical Informatics, 132, 103978. https://doi.org/10.1016/j.ijmedinf.2019.103978
  • Aggarwal, P., & McGill, A. L. (2012). When brands seem human, do humans act like brands? Automatic behavioral priming effects of brand anthropomorphism. Journal of Consumer Research, 39(2), 307–323. https://doi.org/10.1086/662614
  • Alaaeldin, R., Asfoura, E., Kassem, G., & Abdel-Haq, M. S. (2021). Developing chatbot system to support decision making based on big data analytics. Journal of Management Information & Decision Sciences, 24(2), 1–15.
  • Alt, M. A., Vizeli, I., & SăplăSăplăCan, Z. (2021). Banking with a chatbot–A study on technology acceptance. Studia Universitatis Babes-Bolyai Oeconomica, 66(1), 13–35. https://doi.org/10.2478/subboec-2021-0002
  • Amin, M., Rezaei, S., & Abolghasemi, M. (2014). User satisfaction with mobile websites: The impact of perceived usefulness (PU), perceived ease of use (PEOU) and trust. Nankai Business Review International, 5(3), 258–274. https://doi.org/10.1108/NBRI-01-2014-0005
  • Ashfaq, M., Yun, J., Yu, S., & Loureiro, S. M. C. (2020). I, chatbot: Modelling the determinants of users’ satisfaction and continuance intention of ai-powered service agents. Telematics and Informatics, 54, Article 101473. https://doi.org/10.1016/j.tele.2020.101473
  • Bai, C., Quayson, M., & Sarkis, J. (2021). COVID-19 pandemic digitization lessons for sustainable development of micro-and small- enterprises. Sustainable Production and Consumption, 27, 1989–2001. https://doi.org/10.1016/j.spc.2021.04.035
  • Byun, S. H., & Cho, C. H. (2020). The effect of the anthropomorphism level and personalization level on AI financial chatbot recommendation messages on customer response. Korean Journal of Advertising and Public Relations, 22(2), 466–502. https://doi.org/10.16914/kjapr.2020.22.2.466
  • Capian. (2023). Schneiderman’s eight golden rules of interface design. https://capian.co/shneiderman-eight-golden-rules-interface-design
  • Chaves, A. P., & Gerosa, M. A. (2021). How should my chatbot interact? A survey on social characteristics in human–chatbot interaction design. International Journal of Human–Computer Interaction, 37(8), 729–758. https://doi.org/10.1080/10447318.2020.1841438
  • 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. https://doi.org/10.1108/IJRDM-08-2020-0312
  • Chin, W. W., & Gopal, A. (1995). Adoption intention in GSS: Relative importance of beliefs. ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 26(2–3), 42–64. https://doi.org/10.1145/217278.217285
  • Choi, S. M., & Choi, D. Y. (2022). The effect of the experience clue of chatbot service in e-commerce on customer experience and trust. The Journal of Information Systems, 31(4), 123–143.
  • Choi, K., Wang, Y., & Sparks, B. (2019). Travel app users’ continued use intentions: It’s a matter of value and trust. Journal of Travel & Tourism Marketing, 36(1), 131–143. https://doi.org/10.1080/10548408.2018.1505580
  • Crolic, C., Thomaz, F., Hadi, R., & Stephen, A. T. (2022). Blame the bot: Anthropomorphism and anger in customer–chatbot interactions. Journal of Marketing, 86(1), 132–148. https://doi.org/10.1177/00222429211045687
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
  • Doll, W. J., & Torkzadeh, G. (1988). The measurement of end-user computing satisfaction. MIS Quarterly, 12(2), 259. https://doi.org/10.2307/248851
  • Epley, N., Waytz, A., & Cacioppo, J. T. (2007). On seeing human: A three-factor theory of anthropomorphism. Psychological Review, 114(4), 864. https://doi.org/10.1037/0033-295X.114.4.864
  • Fernandes, T., & Oliveira, E. (2021). Understanding consumers’ acceptance of automated technologies in service encounters: Drivers of digital voice assistants adoption. Journal of Business Research, 122, 180–191. https://doi.org/10.1016/j.jbusres.2020.08.058
  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
  • Hair, J. F., Jr., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: Updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107–123. https://doi.org/10.1504/IJMDA.2017.087624
  • 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. https://doi.org/10.14400/JDC.2015.13.12.75
  • Hasal, M., Nowaková, J., Ahmed Saghair, K., Abdulla, H., Snášel, V., & Ogiela, L. (2021). Chatbots: Security, privacy, data protection, and social aspects. Concurrency and Computation: Practice and Experience, 33(19), e6426. https://doi.org/10.1002/cpe.6426
  • 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. https://doi.org/10.1007/s11747-014-0403-8
  • Huh, J., Whang, C., & Kim, H. (2023). Building trust with voice assistants for apparel shopping: The effects of social role and user autonomy. Journal of Global Fashion Marketing, 14(1), 5–19. https://doi.org/10.1080/20932685.2022.2085603
  • Iovine, A., Narducci, F., Musto, C., de Gemmis, M., & Semeraro, G. (2023). Virtual customer assistants in finance: From state of the art and practices to design guidelines. Computer Science Review, 47, Article 100534. 100534. https://doi.org/10.1016/j.cosrev.2023.100534
  • Iriani, S. S., & Andjarwati, A. L. (2020). Analysis of perceived usefulness, perceived ease of use, and perceived risk toward online shopping in the era of COVID-19 pandemic. Systematic Reviews in Pharmacy, 11(12), 313–320.
  • Islam, J. U., & Rahman Z. (2016). Examining the effects of brand love and brand image on customer engagement: An empirical study of fashion apparel brands. Journal of Global Fashion Marketing, 7(1), 45–59. https://doi.org/10.1080/20932685.2015.1110041
  • Jaiwant, S. V. (2022). Artificial intelligence and personalized banking. In V. Garg & R. Goel (Eds.), Handbook of research on innovative management using AI in industry 5.0 (pp. 74–87). IGI Global.
  • Jokinen, J. P. (2015). Emotional user experience: Traits, events, and states☆. International Journal of Human-Computer Studies, 76, 67–77. https://doi.org/10.1016/j.ijhcs.2014.12.006
  • Jun, M., & Palacios, S. (2016). Examining the key dimensions of mobile banking service quality: An exploratory study. International Journal of Bank Marketing, 34(3), 307–326. https://doi.org/10.1108/IJBM-01-2015-0015
  • Kassim, N., & Asiah Abdullah, N. (2010). The effect of perceived service quality dimensions on customer satisfaction, trust, and loyalty in e‐commerce settings: A cross cultural analysis. Asia Pacific Journal of Marketing & Logistics, 22(3), 351–371. https://doi.org/10.1108/13555851011062269
  • Kaya, O., Schildbach, J., Ag, D. B., & Schneider, S. (2019). Artificial intelligence in banking. EU Monitor. www.dbresearch.com.
  • Kim, C., Hwang, J. S., & Cho, J. (2015). Relationships among mobile fashion shopping characteristics, perceived usefulness, perceived enjoyment, and purchase intention: Mediating effect of ease of use. Journal of the Korean Society of Clothing and Textiles, 39(2), 161–174. https://doi.org/10.5850/JKSCT.2015.39.2.161
  • Kim, E. Y., & Yang, K. (2018). Self-service technologies (SSTs) streamlining consumer experience in the fashion retail stores: The role of perceived interactivity. Journal of Global Fashion Marketing, 9(4), 287–304. https://doi.org/10.1080/20932685.2018.1503558
  • Koetz, C. (2019). Managing the customer experience: A beauty retailer deploys all tactics. Journal of Business Strategy, 40(1), 10–17. https://doi.org/10.1108/JBS-09-2017-0139
  • Laranjo, L., Dunn, A. G., Tong, H. L., Kocaballi, A. B., Chen, J., Bashir, R., Surian, D., Gallego, B., Magrabi, F., Lau, A. Y., & Coiera, E. (2018). Conversational agents in healthcare: A systematic review. Journal of the American Medical Informatics Association, 25(9), 1248–1258. https://doi.org/10.1093/jamia/ocy072
  • Lee, S., Kim, H. Y., & Song, J. H. (2011). The effect of personalized email messages on consumers’ perceived interactivity, purchase intention and loyalty. The Academy of Customer Satisfaction Management, 13(3), 85–100.
  • Lee, S. J., Lee, J. H., & Chung, D. H. (2021). A study on the factors affecting the acceptance intention of chatbot service in the financial industry. Journal of Korea Technology Innovation Society, 24(5), 845–869. https://doi.org/10.35978/jktis.2021.10.24.5.845
  • Lee, K. M., & Nass, C. (2003). Designing social presence of social actors in human computer interaction. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 289–296). https://doi.org/10.1145/642611.642662
  • Lee, M. K., & Park, H. (2019). Exploring factors influencing usage intention of chatbot – chatbot in financial service. Journal of Korean Society for Quality Management, 47(4), 755–765.
  • Lim, H. A., Im, H., & Lee, G. (2022). The strengths of fashion film series: The effects on character empathy and brand anthropomorphism. Journal of Global Fashion Marketing, 13(4), 289–303. https://doi.org/10.1080/20932685.2022.2097939
  • Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019). Frontiers: Machines vs. humans: The impact of artificial intelligence chatbot disclosure on customer purchases. Marketing Science, 38(6), 937–947. https://doi.org/10.1287/mksc.2019.1192
  • Meng, J., & Dai, Y. (2021). Emotional support from AI chatbots: Should a supportive partner self-disclose or not? Journal of Computer-Mediated Communication, 26(4), 207–222. https://doi.org/10.1093/jcmc/zmab005
  • Mori, M., MacDorman, K. F., & Kageki, N. (2012). The uncanny valley [from the field]. IEEE Robotics & Automation Magazine, 19(2), 98–100. https://doi.org/10.1109/MRA.2012.2192811
  • Ngai, E. W., Lee, M. C., Luo, M., Chan, P. S., & Liang, T. (2021). An intelligent knowledge-based chatbot for customer service. Electronic Commerce Research and Applications, 50, 101098. https://doi.org/10.1016/j.elerap.2021.101098
  • Ng, P. Y., & Sia, J. K.-M. (2023). Managers’ perspectives on restaurant food waste separation intention: The roles of institutional pressures and internal forces. International Journal of Hospitality Management, 108, Article 103362. https://doi.org/10.1016/j.ijhm.2022.103362
  • Nielsen, J. (n.d.). Ten Usability Heuristics. https://pdfs.semanticscholar.org/5f03/b251093aee730ab9772db2e1a8a7eb8522cb.pdf
  • Noh, M.-J., & Jang, H.-Y. (2011). An effect of the quality of the mobile banking and perceived trust on the reuse intention: Focusing on the moderating effects of gender. Journal of Industrial Economics & Business, 24(2), 927–952.
  • Pan, S. L., & Nishant, R. (2023). Artificial intelligence for digital sustainability: An insight into domain-specific research and future directions. International Journal of Information Management, 72, 102668. https://doi.org/10.1016/j.ijinfomgt.2023.102668
  • Park, J., & Joo, J. (2018). A behavioral economic approach to increase users’ intention to continue to use the voice recognition speakers: Anthropomorphism. Design Convergence Tudy, 17(3), 41–53. https://doi.org/10.31678/SDC.70.3
  • Perea y Monsuwé, T., Dellaert, B. G. C., & de Ruyter, K. (2004). What drives consumers to shop online? A literature review. International Journal of Service Industry Management, 15(1), 102–121. https://doi.org/10.1108/09564230410523358
  • Pizzi, G., Vannucci, V., Mazzoli, V., & Donvito, R. (2023). I, chatbot! the impact of anthropomorphism and gaze direction on willingness to disclose personal information and behavioural intentions. Psychology & Marketing, 40(7), 1372–1387. https://doi.org/10.1002/mar.21813
  • Ramjattan, R., Hosein, P., & Henry, N. (2021, December). Using chatbot technologies to help individuals make sound personalized financial decisions. 2021 IEEE International Humanitarian Technology Conference (IHTC) (pp. 1–4). IEEE.
  • Ryu, J., & Yu, J. (2013). The impact of gesture and facial expression on learning comprehension and persona effect of pedagogical agent. Science of Emotion and Sensibility, 16(3), 281–292.
  • Sanjeed, V. K., Kim, M. G., & Wang, C. Y. (2020). Examining the effect of chatbot gender and gender congruence between a chatbot and a customer in a banking context. Journal of Korea Service Management Society, 21(5), 46–73. https://doi.org/10.15706/jksms.2020.21.5.003
  • 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. https://doi.org/10.1016/j.ijinfomgt.2019.08.008
  • Sheehan, B. T. (2018). Customer service chatbots: Anthropomorphism, adoption and word of mouth [ Doctoral dissertation]. Queensland University of Technology. QUT ePrints. https://eprints.qut.edu.au/121188/1/Benjamin_Sheehan_Thesis.pdf
  • Sheng, H., Nah, F. F. H., & Siau, K. (2008). An experimental study on ubiquitous commerce adoption: Impact of personalization and privacy concerns. Journal of the Association for Information Systems, 9(6), 1. https://doi.org/10.17705/1jais.00161
  • Shumanov, M., & Johnson, L. (2021). Making conversations with chatbots more personalized. Computers in Human Behavior, 117, 106627. https://doi.org/10.1016/j.chb.2020.106627
  • Son, S., Bae, J., & Kim, K. H. (2021). The effect of perceived agility on intention to reuse Omni-channel: Focused on mediating effect of integration quality of Omni-channel. Journal of Global Fashion Marketing, 12(4), 375–389. https://doi.org/10.1080/20932685.2021.1947151
  • Srinivasan, S. S., Anderson, R., & Ponnavolu, K. (2002). Customer loyalty in e-commerce: An exploration of its antecedents and consequences. Journal of Retailing, 78(1), 41–50. https://doi.org/10.1016/S0022-4359(01)00065-3
  • Sung, H. (2013). A study on the determinants of attitude toward and intention to use mobile shopping through fashion apps: Comparisons of gender and age group differences. Journal of the Korean Society of Clothing and Textiles, 37(7), 1000–1014. https://doi.org/10.5850/JKSCT.2013.37.7.1000
  • Thong, J. Y. L., Hong, S.-J., & Tam, K. Y. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of Human-Computer Studies, 64(9), 799–810. https://doi.org/10.1016/j.ijhcs.2006.05.001
  • Vasile, F., Smirnova, E., & Conneau, A. (2016). Meta-Prod2Vec: Product embeddings using side-information for recommendation. Proceedings of the 10th ACM Conference on Recommender Systems (RecSys ’16) (pp. 225–232). Association for Computing Machinery.
  • Veeramootoo, N., Nunkoo, R., & Dwivedi, Y. K. (2018). What determines success of an e-government service? Validation of an integrative model of e-filing continuance usage. Government Information Quarterly, 35(2), 161–174. https://doi.org/10.1016/j.giq.2018.03.004
  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
  • Wang, S., Beatty, S. E., & Foxx, W. (2004). Signaling the trustworthiness of small online retailers. Journal of Interactive Marketing, 18(1), 53–69. https://doi.org/10.1002/dir.10071
  • Wang, H., Wang, N., & Yeung, D.-Y. (2015). Collaborative deep learning for recommender systems. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’15) (pp. 1235–1244). Association for Computing Machinery.
  • Weiss, C. (2022). Fashion retailing in the metaverse. Fashion, Style and Popular Culture, 9(4), 523–538. https://doi.org/10.1386/fspc_00159_1
  • 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. https://doi.org/10.1108/JOSM-04-2018-0119
  • Zhang, X., Agarwal, S., Choy, R., Wong, K. J., Lim, L., Lee, Y. Y., & Lu, J. J. (2020, July). Personalized digital customer services for consumer banking call centre using neural networks. 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1–7). IEEE.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.