Impact of Recommendation Systems on AI-enabled Customer Experience Mediator Role of Perceived Usefulness and Perceived Trust
Main Article Content
Keywords
Artificial Intelligence, Recommendation Systems, AI-enabled Customer Experience, Perceived Usefulness, Controllability
Abstract
Artificial intelligence (AI) is revolutionising the way customers interact with brands. AI-based customer experiences lack empirical research. This study aims to analyse how and to what extent integrating AI through recommendation systems in online purchases can lead to better AI-based customer experiences. We propose a theoretical model based on the theory of trust and commitment and the technology acceptance model. We conducted an online survey with customers who have experience with AI-powered online recommendation ads. We analysed 220 responses using PLS-SEM. The results of this research show that perceived trust in recommendation systems and customisation of recommendation systems have a positive impact on AI-based customer experience. Additionally, this study indicates that customisation of recommendation systems and controllability of recommendation systems have a positive impact on perceived usefulness and also have a positive impact on perceived trust. By examining the concept of controllability in AI-driven recommendation systems, an underexplored factor in the literature, this research offers a new perspective on AI-based customer experience. Finally, this research highlights the mediating role of perceived trust in the relationship between controllability and AI-based customer experience, and in the relationship between customisation and AI-based customer experience.
References
Abdullah, F., Ward, R., & Ahmed, E. (2016). Investigating the influence of the most commonly used external variables of TAM on students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios. Computers in Human Behavior, 63, 75–90. https://doi.org/10.1016/j.chb.2016.05.014
Acharya, N., Sassenberg, A. M., & Soar, J. (2022). Consumers’ behavioural intentions to reuse recommender systems: Assessing the effects of trust propensity, trusting beliefs and perceived usefulness. Journal of Theoretical and Applied Electronic Commerce Research, 18(1), 55–78. https://doi.org/10.3390/jtaer18010004
Adawiyah, S. R., Purwandari, B., Eitiveni, I., & Purwaningsih, E. H. (2024). The influence of AI and AR technology in personalised recommendations on customer usage intention: a case study of cosmetic products on shopee. Applied Sciences, 14(13), 5786 https://doi.org/10.3390/app14135786
Akram, S., & Kortam, W. (2020). The impact of customer experience in online brand communities on customer engagement and purchase intentions among Arab internet users: theoretical analysis, conceptual framework and research agenda. Business and Management Studies, 6(3), 26–54. https://doi.org/10.11114/bms.v6i3.5021
Alsharari, N. M., Al-Shboul, M., & Alteneiji, S. (2020). Implementation of cloud ERP in the SME: evidence from UAE. Journal of Small Business and Enterprise Development, 27(2), 299–327. https://doi.org/10.1108/JSBED-01-2019-0007
Amazon (2023). Annual Report. https://www.annualreports.com/HostedData/AnnualReports/PDF/NASDAQ_AMZN_2023.pdf
Ameen, N., Tarhini, A., Reppel, A., & Anand, A. (2021). Customer experiences in the age of artificial intelligence. Computers in Human Behavior, 114, 106548. https://doi.org/10.1016/j.chb.2020.106548
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
Bach, T. A., Khan, A., Hallock, H., Beltrão, G., & Sousa, S. (2024). A systematic literature review of user trust in AI-enabled systems: An HCI perspective. International Journal of Human–Computer Interaction, 40(5), 1251–1266. https://doi.org/10.1080/10447318.2022.2138826
Bakalov, F., Meurs, M. J., König-Ries, B., Sateli, B., Witte, R., Butler, G., & Tsang, A. (2013, March). An Approach to Controlling User Models and Personalization Effects in Recommender Systems. In Proceedings of the 2013 International Conference on Intelligent User Interfaces (pp. 49–56). https://doi.org/10.1145/2449396.2449405
Bawack, R. E., Wamba, S. F., & Carillo, K. D. A. (2021). Exploring the role of personality, trust, and privacy in customer experience performance during voice shopping: Evidence from SEM and fuzzy set qualitative comparative analysis. International Journal of Information Management, 58, 102309. https://doi.org/10.1016/j.ijinfomgt.2021.102309
Bhuiyan, M. S. (2024). The role of AI-enhanced personalization in customer experiences. Journal of Computer Science and Technology Studies, 6(1), 162–169. https://doi.org/10.32996/jcsts.2024.6.1.17
Bitkina, O. V., Jeong, H., Lee, B. C., Park, J., Park, J., & Kim, H. K. (2020). Perceived trust in artificial intelligence technologies: A preliminary study. Human Factors and Ergonomics in Manufacturing & Service Industries, 30(4), 282–290. https://doi.org/10.1002/hfm.20839
Bleier, A., Harmeling, C. M., & Palmatier, R. W. (2019). Creating effective online customer experiences. Journal of marketing, 83(2), 98–119. https://doi.org/10.1177/0022242918809930
Bostandjiev, S., O’Donovan, J., & Höllerer, T. (2012, September). TasteWeights: A visual Interactive Hybrid Recommender System. In Proceedings of the Sixth ACM Conference on Recommender Systems (pp. 35–42). https://doi.org/10.1145/2365952.2365964
Bourke, S., McCarthy, K., & Smyth, B. (2011, August). Using Social Ties in Group Recommendation. In AICS 2011: Proceedings of the 22nd Irish Conference on Artificial Intelligence and Cognitive Science, 31 August–2 September 2011: University of Ulster-Magee. Intelligent Systems Research Centre. https://hdl.handle.net/10197/3448
Brill, T. M., Munoz, L., & Miller, R. J. (2019). Siri, Alexa, and other digital assistants: a study of customer satisfaction with artificial intelligence applications. Journal of Marketing Management, 35(15–16), 1401–1436. https://doi.org/10.1080/0267257X.2019.1687571
Brown, S. C., & Greene, J. A. (2006). The wisdom development scale: Translating the conceptual to the concrete. Journal of College Student Development, 47(1), 1–19. https://doi.org/10.1353/csd.2006.0002
Cai, R., Cain, L. N., & Jeon, H. (2022). Customers’ perceptions of hotel AI-enabled voice assistants: does brand matter? International Journal of Contemporary Hospitality Management, 34(8), 2807–2831. https://doi.org/10.1108/IJCHM-10-2021-1313
Champiri, Z. D., Mujtaba, G., Salim, S. S., & Chong, C. Y. (2019, January). User Experience and Recommender Systems. In 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) (pp. 1–5). IEEE. https://doi.org/10.1109/ICOMET.2019.8673410
Chang, W., & Park, J. (2024). A comparative study on the effect of ChatGPT recommendation and AI recommender systems on the formation of a consideration set. Journal of Retailing and Consumer Services, 78, 103743. https://doi.org/10.1016/j.jretconser.2024.103743
Chiou, J. S., & Droge, C. (2006). Service quality, trust, specific asset investment, and expertise: Direct and indirect effects in a satisfaction-loyalty framework. Journal of the Academy of Marketing Science, 34(4), 613–627. https://doi.org/10.1177/0092070306286934
Chong, L., Zhang, G., Goucher-Lambert, K., Kotovsky, K., & Cagan, J. (2022). Human confidence in artificial intelligence and in themselves: The evolution and impact of confidence on adoption of AI advice. Computers in Human Behavior, 127, 107018. https://doi.org/10.1016/j.chb.2021.107018
Choung, H., David, P., & Ross, A. (2023). Trust in AI and its role in the acceptance of AI technologies. International Journal of Human–Computer Interaction, 39(9), 1727-1739. https://doi.org/10.1080/10447318.2022.2050543
Chua, A. Y., Pal, A., & Banerjee, S. (2023). AI-enabled investment advice: Will users buy it? Computers in Human Behavior, 138, 107481. https://doi.org/10.1016/j.chb.2022.107481
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates, Publishers. https://www.scirp.org/reference/ReferencesPapers?ReferenceID=2041144
Corritore, C. L., Marble, R. P., Wiedenbeck, S., Kracher, B., & Chandran, A. (2005). Measuring online trust of websites: Credibility, perceived ease of use, and risk. https://www.researchgate.net/publication/220890100_Measuring_Online_Trust_of_Websites_Credibility_Perceived_Ease_of_Use_and_Risk
Daqar, M. A. A., & Smoudy, A. K. (2019). The role of artificial intelligence on enhancing customer experience. International Review of Management and Marketing, 9(4), 22. https://doi.org/10.32479/irmm.8166
Davis, F. D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly, 13(3). https://doi.org/10.2307/249008
Davis, F. D. (1989). Technology acceptance model: TAM. Al-Suqri, MN, Al-Aufi, AS: Information Seeking Behavior and Technology Adoption, 205(219), 5. https://doi.org/10.4018/978-1-4666-8156-9.ch013
Dayal, U., Hsu, M., & Ladin, R. (2001, September). Business Process Coordination: State of the Art, Trends, and Open Issues. In the International Conference on Very Large Data Bases, 1, 3–13. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=9cf8b207ae28f55de602452148f21fd412300737
Deldjoo, Y., Jannach, D., Bellogin, A., Difonzo, A., & Zanzonelli, D. (2024). Fairness in recommender systems: research landscape and future directions. User Modeling and User-Adapted Interaction, 34(1), 59–108. https://doi.org/10.48550/arXiv.2205.11127
Deng, Z., Hong, Z., Ren, C., Zhang, W., & Xiang, F. (2018). What predicts patients’ adoption intention toward mHealth services in China: Empirical study. JMIR mHealth and uHealth, 6(8), e9316. https://doi.org/10.2196/mhealth.9316
El Gharbaoui, O., El Boukhari, H., & Salmi, A. (2024). The transformative power of recommender systems in enhancing citizens’ satisfaction: Evidence from the Moroccan public sector. Innovative Marketing, 20(3), 224–36. https://doi.org/10.21511/im.20(3).2024.18
Ewerhard, A. C., Sisovsky, K., & Johansson, U. (2019). Consumer decision-making of slow moving consumer goods in the age of multi-channels. The International Review of Retail, Distribution and Consumer Research, 29(1), 1–22. https://doi.org/10.1080/09593969.2018.1537191
Ferrario, A., Loi, M., & Viganò, E. (2020). In AI we trust incrementally: A multi-layer model of trust to analyze human-artificial intelligence interactions. Philosophy & Technology, 33(3), 523–539. https://doi.org/10.1007/s13347-019-00378-3
Foroudi, P., Gupta, S., Sivarajah, U., & Broderick, A. (2018). Investigating the effects of smart technology on customer dynamics and customer experience. Computers in Human Behavior, 80, 271–282. https://doi.org/10.1016/j.chb.2017.11.014
Forrester Consulting. (2015). Digital Transformation in the Age of the Customer. https://www.the-digital-insurer.com/wp-content/uploads/2016/02/646-Accenture-Digital-Transformation-In-The-Age-Of-The-Customer
Forrester Consulting. (2023). Forrester’s US 2023 Customer Experience Index: Brands’ CX Quality Falls For A Second Consecutive Year. Forrester Consulting website. https://www.forrester.com/press-newsroom/forrester-us-2023-customer-experience-index/
Frimousse, S., et Peretti, J. M. (2019). ‘Expérience collaborateur’ et ‘Expérience client’: comment l’entreprise peut-elle utiliser l’Intelligence Artificielle pour progresser? Question (s) de management, 1, 135–156. https://doi.org/10.3917/qdm.191.0135
Frow, P., & Payne, A. (2007). Towards the ‘perfect’ customer experience. Journal of Brand Management, 15(2), 89–101. https://doi.org/10.1057/palgrave.bm.2550120
Gao, Y., & Liu, H. (2023). Artificial intelligence-enabled personalization in interactive marketing: a customer journey perspective. Journal of Research in Interactive Marketing, 17(5), 663–680. https://doi.org/10.1108/JRIM-01-2022-0023
Ge, Y., Liu, S., Fu, Z., Tan, J., Li, Z., Xu, S., & Zhang, Y. (2024). A survey on trustworthy recommender systems. ACM Transactions on Recommender Systems, 3(2), 1–68. https://doi.org/10.1145/3652891
Gefen, D. (2003). TAM or just plain habit: A look at experienced online shoppers. Journal of Organizational and End User Computing, 15(3), 1–13. https://doi.org/10.4018/joeuc.2003070101
Gera, R., & Kumar, A. (2023). Artificial Intelligence In Consumer Behaviour: A Systematic Literature Review of Empirical Research Papers Published In Marketing Journals (2000-2021). Academy of Marketing Studies Journal, 27(S1). https://doi.org/10.1108/GKMC-04-2023-0130
Gharahighehi, A., Pliakos, K., & Vens, C. (2021). Recommender systems in the real estate market – a survey. Applied Sciences, 11(16), 7502. https://doi.org/10.3390/app11167502
Gogua, M. M., & Smirnova, M. M. (2020). Revisiting personalization through customer experience journey. Vestnik of Saint Petersburg University. Management 19 (4): 430–460. http://www.vestnikmanagement.spbu.ru/archive/pdf/858.pdf
Grewal, D., Hulland, J., Kopalle, P. K., & Karahanna, E. (2020). The future of technology and marketing: A multidisciplinary perspective. Journal of the Academy of Marketing Science, 48, 1–8. https://doi.org/10.1007/s11747-019-00711-4
Gursoy, D., Chi, O. H., Lu, L., & Nunkoo, R. (2019). Consumers acceptance of artificially intelligent (AI) devices used in service delivery. International Journal of Information Management, 49, 157–169. https://doi.org/10.1016/j.ijinfomgt.2019.03.008
Hair Jr, J. F., Babin, B. J., & Krey, N. (2017). Covariance-based structural equation modeling in the Journal of Advertising: Review and recommendations. Journal of Advertising, 46(1), 163–177. https://doi.org/10.1080/00913367.2017.1281777
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. https://doi.org/10.1007/978-3-030-80519-7
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2010). Multivariate data analysis (7. Baskı). Pearson. Hallahan, TA, Faff, RW, McKenzie, MD (2004). An Empirical Investigation of Personal Financial Risk Tolerance. Financial Services Review-Greenwıch, 13(1), 57-78. https://www.scirp.org/reference/referencespapers?referenceid=2989262
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40, 414–433. https://doi.org/10.1007/s11747-011-0261-6
Harman, J. L., O’Donovan, J., Abdelzaher, T., & Gonzalez, C. (2014, October). Dynamics of Human Trust in Recommender Systems. In Proceedings of the 8th ACM Conference on Recommender Systems (pp. 305–308). https://doi.org/10.1145/2645710.2645761
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, 115–135. https://doi.org/10.1007/s11747-014-0403-8
Hoyer, W. D., Kroschke, M., Schmitt, B., Kraume, K., & Shankar, V. (2020). Transforming the customer experience through new technologies. Journal of Interactive Marketing, 51(1), 57–71. https://doi.org/10.1016/j.intmar.2020.04.001
Iacoviello, A & Downie, A. (2024). 7 Customer Experience Trends in 2024. IBM. https://www.ibm.com/blog/customer-experience-trends/
Ittelson, W. H., Proshansky, H. M., Rivlin, L. G., & Winkel, G. H. (1974). An Introduction to Environmental Psychology. Holt, Rinehart & Winston. https://www.scirp.org/reference/referencespapers?referenceid=1628241
Jannach, D. (2010). Recommender Systems: An Introduction. Cambridge University Press. https://doi.org/10.1017/CBO9780511763113
Jiang, Y., Shang, J., & Liu, Y. (2010). Maximizing customer satisfaction through an online recommendation system: A novel associative classification model. Decision Support Systems, 48(3), 470–479. https://doi.org/10.1016/j.dss.2009.06.006
Jin, Y., Chen, L., Cai, W., & Pu, P. (2021, November). Key Qualities of Conversational Recommender Systems: From Users’ Perspective. In Proceedings of the 9th International Conference on Human-Agent Interaction (pp. 93–102). https://doi.org/10.1145/3472307.3484164
Kaduškeviit Vaida, Mackevi?i?t? Božena (2022), When Users Meet AI: Customer Acceptance of Recommendation Systems in Online Shopping. Proceedings of the European Marketing Academy, 50th, (111795). https://proceedings.emac-online.org/pdfs/R2022-111795.pdf
Kailani, C., & Ciobotar, N. (2015). Experiential Marketing: An efficient Tool to Leverage Marketing Communication Impact on Consumer Behaviour. In International Conference on Marketing and Business Development, (Vol. 1, No. 1, pp. 281–287). Bucharest University of Economic Studies Publishing House. https://ideas.repec.org/a/aes/icmbdj/v1y2015i1p281-287.html
Kanungo, R. P., Liu, R., & Gupta, S. (2024). Cognitive analytics enabled responsible artificial intelligence for business model innovation: A multilayer perceptron neural networks estimation. Journal of Business Research, 182, 114788. https://doi.org/10.1016/j.jbusres.2024.114788
Kim, G. S., Park, S. B., & Oh, J. (2008). An examination of factors influencing consumer adoption of short message service (SMS). Psychology & Marketing, 25(8), 769–786. https://doi.org/10.1002/mar.20238
Kim, H. G., Kim, G. Y., & Kim, J. Y. (2019). Music recommendation system using human activity recognition from accelerometer data. IEEE Transactions on Consumer Electronics, 65(3), 349–358. https://doi.org/10.1109/TCE.2019.2924177
KITRUM. (2023). Recommendation Systems in E-commerce: How It Works? https://kitrum.com/blog/recommendation-systems-in-e-commerce-how-it-works/?utm_source=chatgpt.com
Knijnenburg, B. P., Willemsen, M. C., Gantner, Z., Soncu, H., & Newell, C. (2012). Explaining the user experience of recommender systems. User Modeling and User-adapted Interaction, 22, 441–504. https://doi.org/10.1007/s11257-011-9118-4
Komiak S.X., Benbasat I. (2006) The effects of personalization and familiarity on trust and adoption of recommendation agents. MIS Quarterly, 30(4), 941–960. https://doi.org/10.2307/25148760
Konstan, J. A., & Riedl, J. (2012). Recommender systems: from algorithms to user experience. User Modeling and User-adapted Interaction, 22, 101–123. https://doi.org/10.1007/s11257-011-9112-x
Kowatsch, T., & Maass, W. (2010). In-store consumer behavior: How mobile recommendation agents influence usage intentions, product purchases, and store preferences. Computers in Human Behavior, 26(4), 697–704. https://doi.org/10.1016/j.chb.2010.01.006
Kumar, N., & Benbasat, I. (2006). Research note: the influence of recommendations and consumer reviews on evaluations of websites. Information Systems Research, 17(4), 425–439. https://doi.org/10.1287/isre.1060.0107
Lambillotte, L., Magrofuoco, N., Poncin, I., & Vanderdonckt, J. (2022). Enhancing playful customer experience with personalization. Journal of Retailing and Consumer Services, 68, 103017. https://doi.org/10.1016/j.jretconser.2022.103017
Lee, G.-G. and Lin, H.-F. (2005) Customer Perceptions of E-Service Quality in Online Shopping. International Journal of Retail & Distribution Management, 33, 161-176. https://doi.org/10.1108/09590550510581485
Lee, Y., & Kozar, K. A. (2012). Understanding of website usability: Specifying and measuring constructs and their relationships. Decision Support Systems, 52(2), 450–463. https://doi.org/10.1016/j.dss.2011.10.004
Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 69–96. https://doi.org/10.1509/jm.15.0420
Li, Q., Choi, I., & Kim, J. (2020). Evaluation of recommendation systems for sustainable E-commerce: accuracy, diversity and customer satisfaction. Preprints. https://doi.org/10.20944/preprints202001.0015.v1
Li, S. S., & Karahanna, E. (2015). Online recommendation systems in a B2C E-commerce context: a review and future directions. Journal of the Association for Information Systems, 16(2), 2. https://doi.org/10.17705/1jais.00389
Liang, T. P., Chen, H. Y., Turban, E. (2012). Effect of Personalization on the Perceived Usefulness of Online Customer Services: A Dual-core Theory. In Proceedings of the 11th International Conference on Electronic Commerce (pp. 279–288). https://doi.org/10.1145/1593254.1593296
Ling, K. C., Chai, L. T., & Piew, T. H. (2010). The effects of shopping orientations, online trust and prior online purchase experience toward customers’ online purchase intention. International Business Research, 3(3), 63. https://doi.org/10.5539/ibr.v3n3p63
Liu, H., Shao, M., Liu, X., & Zhao, L. (2021). Exploring the influential factors on readers’ continuance intentions of E-Book APPs: personalization, usefulness, playfulness, and satisfaction. Frontiers in Psychology, 12, 640110. https://doi.org/10.3389/fpsyg.2021.640110
Liu, W., & Wang, Y. (2024). Evaluating trust in recommender systems: A user study on the impacts of explanations, agency attribution, and product types. International Journal of Human–Computer Interaction, 1–13. https://doi.org/10.1080/10447318.2024.2313921
Londa, D. R., Worang, F. G., & Arie, F. V. (2022). The influence of perceived credibility, perceived ease of use, and perceived usefulness toward customer satisfaction in using BSGtouch. Jurnal EMBA: Jurnal Riset Ekonomi, Manajemen, Bisnis dan Akuntansi, 10(4), 934–945. https://ejournal.unsrat.ac.id/index.php/emba/article/view/43902
Lutz, K., Williams, J. R., & Purakal, J. D. (2023). Assessment of emergency department health care providers’ readiness for managing intimate partner violence and correlation with perceived cultural competence. Journal of emergency nursing, 49(5), 724-732. https://doi.org/10.1016/j.jen.2023.04.009
Lv, L., Chen, S., Liu, G. G., & Benckendorff, P. (2024). Enhancing customers’ life satisfaction through AI-powered personalized luxury recommendations in luxury tourism marketing. International Journal of Hospitality Management, 123, 103914. https://doi.org/10.1016/j.ijhm.2024.103914
Madhuri, A., Shireesha, M., Reddy, S. M., & Kumar, B. R. (2024). Exploring the Role of Personalization in E-commerce: Impacts on Consumer Trust and Purchase Intentions. European Economic Letters. https://www.eelet.org.uk/index.php/journal/article/view/2385/2142
Martin, J., Mortimer, G., & Andrews, L. (2015). Re-examining online customer experience to include purchase frequency and perceived risk. Journal of Retailing and Consumer Services, 25, 81–95. https://doi.org/10.1016/j.jretconser.2015.03.008
Meyer, C., & Schwager, A. (2007). Understanding customer experience. Harvard Business Review, 85(2), 116. https://www.academia.edu/8781181/Understanding_Customer_Experience
Mican, D., Sitar-Tăut, D. A., & Moisescu, O. I. (2020). Perceived usefulness: A silver bullet to assure user data availability for online recommendation systems. Decision Support Systems, 139, 113420. https://doi.org/10.1016/j.dss.2020.113420
Mocean, L., & Pop, C. M. (2012). Marketing recommender systems: a new approach in the digital economy. Informatica Economica, 16(4), 142. https://www.researchgate.net/publication/281082534_Marketing_Recommender_Systems_A_New_Approach_in_Digital_Economy
Morgan, R. M., & Hunt, S. D. (1994). The commitment-trust theory of relationship marketing. Journal of Marketing, 58(3), 20–38. https://doi.org/10.2307/1252308
Moriuchi, E. (2019). Okay, Google!: An empirical study on voice assistants on consumer engagement and loyalty. Psychology & Marketing, 36(5), 489–501. https://doi.org/10.1002/mar.21192
Nilashi, M., Jannach, D., bin Ibrahim, O., Esfahani, M. D., & Ahmadi, H. (2016). Recommendation quality, transparency, and website quality for trust-building in recommendation agents. Electronic Commerce Research and Applications, 19, 70–84. https://doi.org/10.1016/j.elerap.2016.09.003
Niska, M. (2020). Engaging Customers with Recommendations – A Study about How Customers Can Be Engaged Using a Recommender System. Dissertation. Linköping University. https://liu.diva-portal.org/smash/get/diva2:1460556/FULLTEXT01.pdf
Nunes, P. F., & Kambil, A. (2001). Personalization? No thanks. Harvard Business Review, 79(4), 32–33. https://hbr.org/2001/04/personalization-no-thanks
Pappas, I. O., Kourouthanassis, P. E., Giannakos, M. N., & Lekakos, G. (2017). The interplay of online shopping motivations and experiential factors on personalized e-commerce: A complexity theory approach. Telematics and Informatics, 34(5), 730–742. https://doi.org/10.1016/j.tele.2016.08.021
Park, D. H., Kim, H. K., Choi, I. Y., & Kim, J. K. (2012). A literature review and classification of recommender systems research. Expert Systems with Applications, 39(11), 10059–10072. https://doi.org/10.1016/j.eswa.2012.02.038
Peng, M., Xu, Z. & Huang, H. (2021). How does information overload affect consumers’ online decision process? An event-related potential study. Frontiers in Neuroscience, 15, 695852. https://doi.org/10.3389/fnins.2021.695852
Ping, Y., Li, Y., & Zhu, J. (2024). Beyond accuracy measures: the effect of diversity, novelty and serendipity in recommender systems on user engagement. Electronic Commerce Research, 1-28.https://link.springer.com/article/10.1007/s10660-024-09813-w
Pu, P., Chen, L., & Hu, R. (2012). Evaluating recommender systems from the user’s perspective: survey of the state of the art. User Modeling and User-Adapted Interaction, 22, 317–355. https://doi.org/10.1007/s11257-011-9115-7
Purwanto P., Kuswandi K., Fatmah F. (2020). Interactive Applications with Artificial Intelligence: The Role of Trust among Digital Assistant Users. Foresight and STI Governance, 14(2), 64–75. https://cyberleninka.ru/article/n/interactive-applications-with-artificial-intelligence-the-role-of-trust-among-digital-assistant-users/viewer
Rafiq, F., Dogra, N., Adil, M., & Wu, J. Z. (2022). Examining consumer’s intention to adopt AI-chatbots in tourism using partial least squares structural equation modeling method. Mathematics, 10(13), 2190. https://doi.org/10.3390/math10132190
Rai, A. (2020). Explainable AI: From black box to glass box. Journal of the Academy of Marketing Science, 48, 137–141. https://doi.org/10.1007/s11747-019-00710-5
Rane, N., Paramesha, M., Choudhary, S., & Rane, J. (2024). Artificial intelligence in sales and marketing: Enhancing customer satisfaction, experience and loyalty. Experience and Loyalty (17 May 2024). https://doi.org/10.2139/ssrn.4831903
Resnick, P., & Varian, H. R. (1997). Recommender systems. Communications of the ACM, 40(3), 56-58. https://dl.acm.org/doi/10.1145/245108.245121
Rodríguez, M., Paredes, F., & Yi, G. (2016). Towards future customer experience: trends and innovation in retail. Форсайт, 10(3), (eng), 18–28. https://doi.org/10.17323/1995-459X.2016.3.18.28
Ruan, Y., & Mezei, J. (2022). When do AI chatbots lead to higher customer satisfaction than human frontline employees in online shopping assistance? Considering product attribute type. Journal of Retailing and Consumer Services, 68, 103059. https://doi.org/10.1016/j.jretconser.2022.103059
Sboui, M., Baati, O., & Sfar, N. (2024). Influencing factors and consequences of chatbot initial trust in AI telecommunication services: A study on Generation Z. The TQM Journal. https://doi.org/10.1108/TQM-02-2024-0085
Schnall, R., Higgins, T., Brown, W., Carballo-Dieguez, A., & Bakken, S. (2015). Trust, perceived risk, perceived ease of use and perceived usefulness as factors related to mHealth technology use. In MEDINFO 2015: eHealth-enabled Health, (pp. 467–471). IOS Press. https://pubmed.ncbi.nlm.nih.gov/26262094/
Schrotenboer, D. W. (2019). The Impact of Artificial Intelligence along the Customer Journey: A Systematic Literature Review. University of Tewnte. https://purl.utwente.nl/essays/78520
Sciascio, C. D., Brusilovsky, P., Trattner, C., & Veas, E. (2019). A roadmap to user-controllable social exploratory search. ACM Transactions on Interactive Intelligent Systems, 10(1), 1–38. https://doi.org/10.1145/3241382
Searby, S. (2003). personalization – an overview of its use and potential. BT Technology Journal, 21(1), 13–19. https://doi.org/10.1023/A:1022439824138
Shah, T. R., Kautish, P., & Mehmood, K. (2023). Influence of robot service quality on customers’ acceptance in restaurants. Asia Pacific Journal of Marketing and Logistics, 35(12), 3117–3137. https://doi.org/10.1108/APJML-09-2022-0780
Shen, A. (2014). Recommendations as personalized marketing: insights from customer experiences. Journal of Services Marketing, 28(5), 414–427. https://doi.org/10.1108/JSM-04-2013-0083/10.1108/JSM-04-2013-0083
Shen, A., & Ball, A. D. (2009). Customer perceptions and sustained usage of recommender systems. International Journal of Electronic Business, 7(6), 642–663. https://doi.org/10.1504/IJEB.2009.029056
Sheng, X., & Zolfagharian, M. (2014). Consumer participation in online product recommendation services: augmenting the technology acceptance model. Journal of Services Marketing, 28(6), 460–470. https://doi.org/10.1108/JSM-04-2013-0098
Sheridan, J. (2017). An Exploration into the Potential Impact in Adopting a personalization Strategy and How It Could Affect the Customer’s Experience on a Company’s Own Website (doctoral dissertation, Dublin, National College of Ireland). https://norma.ncirl.ie/2919/1/johnsheridan.pdf
Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human-Computer Studies, 146, 102551. https://doi.org/10.1016/j.ijhcs.2020.102551
Sicular, S. & Vashisth, S. (2020). Hype Cycle for Artificial Intelligence, 2020 Gartner. https://www.gartner.com/en/documents/3988006
Sieger, L. N., Hermann, J., Schomäcker, A., Heindorf, S., Meske, C., Hey, C. C., & Doğangün, A. (2022, December). User Involvement in Training Smart Home Agents: Increasing Perceived Control and Understanding. In Proceedings of the 10th International Conference on Human-Agent Interaction (pp. 76–85). https://doi.org/10.1145/3527188.3561914
Silva-Aravena, F., Morales, J., Sáez, P., Jorquera, J., & Cornide-Reyes, H. (2024). Use of Artificial Intelligence as a Mechanism to Evaluate Customer Experience. Literature Review. In International Conference on Human-Computer Interaction (pp. 338–354). Springer, Cham. https://doi.org/10.1007/978-3-031-61305-0_24
Smits, A., Detweiler, C., Bartels, E., Pott, K., van der Stappen, E., van Rossen, S., & van Turhout, K. (2023, August). Assessing the Utility of an Interaction Qualities Framework in Systematizing the Evaluation of User Control. In IFIP Conference on Human-Computer Interaction (pp. 185–195). Cham: Springer Nature Switzerland. https://www.hu.nl/onderzoek/publicaties/assessing-the-utility-of-an-interaction-qualities-framework-in-systematizing-the-evaluation-of
Suhir, M., & Imam Suyadi, R. (2014). Pengaruh persepsi risiko, kemudahan dan manfaat terhadap keputusan pembelian secara online. Jurnal Administrasi Bisnis, 8(1). https://www.neliti.com/publications/79853/pengaruh-persepsi-risiko-kemudahan-dan-manfaat-terhadap-keputusan-pembelian-seca
Suresh, A., & Rani, N. J. (2020). Consumer perception towards artificial intelligence in E-commerce with reference to Chennai city, India. Journal of Information Technology and Economic Development, 11(1), 1–14. https://rochester.userservices.exlibrisgroup.com/permalink/01ROCH_INST/ts7lro/cdi_proquest_journals_2412995876
Syaharani, D. P., & Yasa, N. K. (2022). The role of trust as mediation between perceived usefulness and perceived ease of use on repurchase intention. European Journal of Development Studies, 2(3), 36–40. https://doi.org/10.24018/ejdevelop.2022.2.3.91
Tang, J. H., Chen, M. C., Yang, C. Y., Chung, T. Y., & Lee, Y. A. (2016). Personality traits, interpersonal relationships, online social support, and Facebook addiction. Telematics and Informatics, 33(1), 102–108. https://doi.org/10.1016/j.tele.2015.06.003
Teng, Z., Wu, A., & Song, Z. (2024, June). Research on the influence of the artificial intelligence chatbots’ service quality on the consumers’ purchase intention. In International Conference on Human-Computer Interaction (pp. 119–130). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-76806-4_9
Tintarev, N., & Masthoff, J. (2007, April). A survey of explanations in recommender systems. In 2007 IEEE 23rd international conference on data engineering workshop (pp. 801-810). IEEE. https://ieeexplore.ieee.org/abstract/document/4401070
Trawnik, A., Al-Masaeed, S., Alsoud, M., & Alkufahy, A. (2022). Understanding artificial intelligence experience: A customer perspective. International Journal of Data and Network Science, 6(4), 1471–1484. https://doi.org/10.5267/j.ijdns.2022.5.004
Tsai, C. H., & Brusilovsky, P. (2021). The effects of controllability and explainability in a social recommender system. User Modeling and User-Adapted Interaction, 31, 591–627. https://doi.org/10.1007/s11257-020-09281-5
Tulcanaza-Prieto, A. B., Cortez-Ordoñez, A., & Lee, C. W. (2023). Influence of customer perception factors on AI-enabled customer experience in the Ecuadorian banking environment. Sustainability, 15(16), 12441. https://doi.org/10.20944/preprints202306.1938.v1
Urbach, N., & Ahlemann, F. (2010). Structural equation modeling in information systems research using partial least squares. Journal of Information Technology Theory and Application, 11(2), 5–40. https://aisel.aisnet.org/jitta/vol11/iss2/2
Vashishth, T. K., Sharma, K. K., Kumar, B., Chaudhary, S., & Panwar, R. (2025). Enhancing customer experience through AI-enabled content personalization in e-commerce marketing. Advances in Digital Marketing in the Era of Artificial Intelligence, 7–32. https://doi.org/10.1201/9781003450443-2
Verhoef, P. C. (2020). Customer experience creation in today’s digital world. In The Routledge companion to strategic marketing (pp. 107-122). Routledge https://pure.rug.nl/ws/portalfiles/portal/790710188/Customer_experience_creation_in_today_s_digital_world.pdf
Verhoef, P. C., Lemon, K. N., Parasuraman, A., Roggeveen, A., Tsiros, M., & Schlesinger, L. A. (2009). Customer experience creation: Determinants, dynamics and management strategies. Journal of Retailing, 85(1), 31–41. https://doi.org/10.1016/j.jretai.2008.11.001
Walls, A. R., Okumus, F., Wang, Y. R., & Kwun, D. J. W. (2011). An epistemological view of consumer experiences. International Journal of Hospitality Management, 30(1), 10–21. https://doi.org/10.1016/j.ijhm.2010.03.008
Wang, W., Guan, J., Guo, Z., Chan, J. H., & Qi, X. (2021). Customer experience and brand loyalty in the full-service hotel sector: the role of brand affect. International Journal of Contemporary Hospitality Management, 33(5), 1620-1645. https://doi.org/10.1108/IJCHM-10-2020-1177
Wedel, M., Rust, R. T., & Chung, T. S. (2009, October). Up close And Personalized: A Marketing View of Recommendation Systems. In Proceedings of the Third ACM Conference on Recommender Systems (pp. 3–4). https://doi.org/10.1145/1639714.1639716
Widiar, G., Yuniarinto, A., & Yulianti, I. (2023). Perceived ease of use’s effects on behavioral intention mediated by perceived usefulness and trust. Interdisciplinary Social Studies, 2(4), 1829–1844. https://doi.org/10.55324/iss.v2i4.397
Xiao, B., & Benbasat, I. (2007). E-commerce product recommendation agents: Use, characteristics, and impact. MIS Quarterly, 137–209. https://doi.org/10.2307/25148784
Yassin, F. M., Ouarda, W., & Alimi, A. M. (2022). Fuzzy ontology as a basis for recommendation systems for traveler’s preference. Multimedia Tools and Applications, 81(5), 6599–6631. https://doi.org/10.1007/s11042-021-11780-5
Yi, S., & Liu, X. (2020). Machine learning based customer sentiment analysis for recommending shoppers, shops based on customers’ reviews. Complex & Intelligent Systems, 6(3), 621–634. https://doi.org/10.1007/s40747-020-00155-2
Yoon, N., & Lee, H. K. (2021). AI recommendation service acceptance: Assessing the effects of perceived empathy and need for cognition. Journal of Theoretical and Applied Electronic Commerce Research, 16(5), 1912-1928. https://www.mdpi.com/0718-1876/16/5/107
Zaid, S., & Patwayati, P. (2021). Impact of customer experience and customer engagement on satisfaction and loyalty: A case study in Indonesia. The Journal of Asian Finance, Economics and Business, 8(4), 983–992. https://doi.org/10.13106/JAFEB.2021.VOL8.NO4.0983
Zamri, N., & Idris, I. (2013, June). The effects of attitude, social influences and perceived behavioral control on intention to purchase online shopping apparels in Malaysia: Case study on Zalora. In the 3rd International Conference on Management, Penang. https://doi.org/10.4236/jssm.2015.83032
Zanker, M., Rook, L., & Jannach, D. (2019). Measuring the impact of online personalization: Past, present and future. International Journal of Human-Computer Studies, 131, 160–168. https://doi.org/10.1016/j.ijhcs.2019.06.006
Zarrad, A. (2024). Artificial Intelligence in Tunisian International Hotel Industry. (Doctoral dissertation.) https://hdl.handle.net/10198/30160
Zhang, J., Li, Q., & Luximon, Y. (2021). Building Trust in Mobile Medical Consultations: The Roles of Privacy Concerns, Personality Traits, and Social Cues. In Proceedings of the 23rd HCI International Conference, Washington, DC, USA, 24–29 July 2021, (pp. 293–30). https://doi.org/10.1007/978-3-030-78108-8_22
Zhang, K. Z., & Benyoucef, M. (2016). Consumer behavior in social commerce: A literature review. Decision Support Systems, 86, 95–108. https://doi.org/10.1016/j.dss.2016.04.001
Zhang, X., Guo, X., Lai, K. H., Guo, F., & Li, C. (2014). Understanding gender differences in m-health adoption: a modified theory of reasoned action model. Telemedicine and e-Health, 20(1), 39–46. https://doi.org/10.1089/tmj.2013.0092
Acharya, N., Sassenberg, A. M., & Soar, J. (2022). Consumers’ behavioural intentions to reuse recommender systems: Assessing the effects of trust propensity, trusting beliefs and perceived usefulness. Journal of Theoretical and Applied Electronic Commerce Research, 18(1), 55–78. https://doi.org/10.3390/jtaer18010004
Adawiyah, S. R., Purwandari, B., Eitiveni, I., & Purwaningsih, E. H. (2024). The influence of AI and AR technology in personalised recommendations on customer usage intention: a case study of cosmetic products on shopee. Applied Sciences, 14(13), 5786 https://doi.org/10.3390/app14135786
Akram, S., & Kortam, W. (2020). The impact of customer experience in online brand communities on customer engagement and purchase intentions among Arab internet users: theoretical analysis, conceptual framework and research agenda. Business and Management Studies, 6(3), 26–54. https://doi.org/10.11114/bms.v6i3.5021
Alsharari, N. M., Al-Shboul, M., & Alteneiji, S. (2020). Implementation of cloud ERP in the SME: evidence from UAE. Journal of Small Business and Enterprise Development, 27(2), 299–327. https://doi.org/10.1108/JSBED-01-2019-0007
Amazon (2023). Annual Report. https://www.annualreports.com/HostedData/AnnualReports/PDF/NASDAQ_AMZN_2023.pdf
Ameen, N., Tarhini, A., Reppel, A., & Anand, A. (2021). Customer experiences in the age of artificial intelligence. Computers in Human Behavior, 114, 106548. https://doi.org/10.1016/j.chb.2020.106548
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
Bach, T. A., Khan, A., Hallock, H., Beltrão, G., & Sousa, S. (2024). A systematic literature review of user trust in AI-enabled systems: An HCI perspective. International Journal of Human–Computer Interaction, 40(5), 1251–1266. https://doi.org/10.1080/10447318.2022.2138826
Bakalov, F., Meurs, M. J., König-Ries, B., Sateli, B., Witte, R., Butler, G., & Tsang, A. (2013, March). An Approach to Controlling User Models and Personalization Effects in Recommender Systems. In Proceedings of the 2013 International Conference on Intelligent User Interfaces (pp. 49–56). https://doi.org/10.1145/2449396.2449405
Bawack, R. E., Wamba, S. F., & Carillo, K. D. A. (2021). Exploring the role of personality, trust, and privacy in customer experience performance during voice shopping: Evidence from SEM and fuzzy set qualitative comparative analysis. International Journal of Information Management, 58, 102309. https://doi.org/10.1016/j.ijinfomgt.2021.102309
Bhuiyan, M. S. (2024). The role of AI-enhanced personalization in customer experiences. Journal of Computer Science and Technology Studies, 6(1), 162–169. https://doi.org/10.32996/jcsts.2024.6.1.17
Bitkina, O. V., Jeong, H., Lee, B. C., Park, J., Park, J., & Kim, H. K. (2020). Perceived trust in artificial intelligence technologies: A preliminary study. Human Factors and Ergonomics in Manufacturing & Service Industries, 30(4), 282–290. https://doi.org/10.1002/hfm.20839
Bleier, A., Harmeling, C. M., & Palmatier, R. W. (2019). Creating effective online customer experiences. Journal of marketing, 83(2), 98–119. https://doi.org/10.1177/0022242918809930
Bostandjiev, S., O’Donovan, J., & Höllerer, T. (2012, September). TasteWeights: A visual Interactive Hybrid Recommender System. In Proceedings of the Sixth ACM Conference on Recommender Systems (pp. 35–42). https://doi.org/10.1145/2365952.2365964
Bourke, S., McCarthy, K., & Smyth, B. (2011, August). Using Social Ties in Group Recommendation. In AICS 2011: Proceedings of the 22nd Irish Conference on Artificial Intelligence and Cognitive Science, 31 August–2 September 2011: University of Ulster-Magee. Intelligent Systems Research Centre. https://hdl.handle.net/10197/3448
Brill, T. M., Munoz, L., & Miller, R. J. (2019). Siri, Alexa, and other digital assistants: a study of customer satisfaction with artificial intelligence applications. Journal of Marketing Management, 35(15–16), 1401–1436. https://doi.org/10.1080/0267257X.2019.1687571
Brown, S. C., & Greene, J. A. (2006). The wisdom development scale: Translating the conceptual to the concrete. Journal of College Student Development, 47(1), 1–19. https://doi.org/10.1353/csd.2006.0002
Cai, R., Cain, L. N., & Jeon, H. (2022). Customers’ perceptions of hotel AI-enabled voice assistants: does brand matter? International Journal of Contemporary Hospitality Management, 34(8), 2807–2831. https://doi.org/10.1108/IJCHM-10-2021-1313
Champiri, Z. D., Mujtaba, G., Salim, S. S., & Chong, C. Y. (2019, January). User Experience and Recommender Systems. In 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) (pp. 1–5). IEEE. https://doi.org/10.1109/ICOMET.2019.8673410
Chang, W., & Park, J. (2024). A comparative study on the effect of ChatGPT recommendation and AI recommender systems on the formation of a consideration set. Journal of Retailing and Consumer Services, 78, 103743. https://doi.org/10.1016/j.jretconser.2024.103743
Chiou, J. S., & Droge, C. (2006). Service quality, trust, specific asset investment, and expertise: Direct and indirect effects in a satisfaction-loyalty framework. Journal of the Academy of Marketing Science, 34(4), 613–627. https://doi.org/10.1177/0092070306286934
Chong, L., Zhang, G., Goucher-Lambert, K., Kotovsky, K., & Cagan, J. (2022). Human confidence in artificial intelligence and in themselves: The evolution and impact of confidence on adoption of AI advice. Computers in Human Behavior, 127, 107018. https://doi.org/10.1016/j.chb.2021.107018
Choung, H., David, P., & Ross, A. (2023). Trust in AI and its role in the acceptance of AI technologies. International Journal of Human–Computer Interaction, 39(9), 1727-1739. https://doi.org/10.1080/10447318.2022.2050543
Chua, A. Y., Pal, A., & Banerjee, S. (2023). AI-enabled investment advice: Will users buy it? Computers in Human Behavior, 138, 107481. https://doi.org/10.1016/j.chb.2022.107481
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates, Publishers. https://www.scirp.org/reference/ReferencesPapers?ReferenceID=2041144
Corritore, C. L., Marble, R. P., Wiedenbeck, S., Kracher, B., & Chandran, A. (2005). Measuring online trust of websites: Credibility, perceived ease of use, and risk. https://www.researchgate.net/publication/220890100_Measuring_Online_Trust_of_Websites_Credibility_Perceived_Ease_of_Use_and_Risk
Daqar, M. A. A., & Smoudy, A. K. (2019). The role of artificial intelligence on enhancing customer experience. International Review of Management and Marketing, 9(4), 22. https://doi.org/10.32479/irmm.8166
Davis, F. D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly, 13(3). https://doi.org/10.2307/249008
Davis, F. D. (1989). Technology acceptance model: TAM. Al-Suqri, MN, Al-Aufi, AS: Information Seeking Behavior and Technology Adoption, 205(219), 5. https://doi.org/10.4018/978-1-4666-8156-9.ch013
Dayal, U., Hsu, M., & Ladin, R. (2001, September). Business Process Coordination: State of the Art, Trends, and Open Issues. In the International Conference on Very Large Data Bases, 1, 3–13. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=9cf8b207ae28f55de602452148f21fd412300737
Deldjoo, Y., Jannach, D., Bellogin, A., Difonzo, A., & Zanzonelli, D. (2024). Fairness in recommender systems: research landscape and future directions. User Modeling and User-Adapted Interaction, 34(1), 59–108. https://doi.org/10.48550/arXiv.2205.11127
Deng, Z., Hong, Z., Ren, C., Zhang, W., & Xiang, F. (2018). What predicts patients’ adoption intention toward mHealth services in China: Empirical study. JMIR mHealth and uHealth, 6(8), e9316. https://doi.org/10.2196/mhealth.9316
El Gharbaoui, O., El Boukhari, H., & Salmi, A. (2024). The transformative power of recommender systems in enhancing citizens’ satisfaction: Evidence from the Moroccan public sector. Innovative Marketing, 20(3), 224–36. https://doi.org/10.21511/im.20(3).2024.18
Ewerhard, A. C., Sisovsky, K., & Johansson, U. (2019). Consumer decision-making of slow moving consumer goods in the age of multi-channels. The International Review of Retail, Distribution and Consumer Research, 29(1), 1–22. https://doi.org/10.1080/09593969.2018.1537191
Ferrario, A., Loi, M., & Viganò, E. (2020). In AI we trust incrementally: A multi-layer model of trust to analyze human-artificial intelligence interactions. Philosophy & Technology, 33(3), 523–539. https://doi.org/10.1007/s13347-019-00378-3
Foroudi, P., Gupta, S., Sivarajah, U., & Broderick, A. (2018). Investigating the effects of smart technology on customer dynamics and customer experience. Computers in Human Behavior, 80, 271–282. https://doi.org/10.1016/j.chb.2017.11.014
Forrester Consulting. (2015). Digital Transformation in the Age of the Customer. https://www.the-digital-insurer.com/wp-content/uploads/2016/02/646-Accenture-Digital-Transformation-In-The-Age-Of-The-Customer
Forrester Consulting. (2023). Forrester’s US 2023 Customer Experience Index: Brands’ CX Quality Falls For A Second Consecutive Year. Forrester Consulting website. https://www.forrester.com/press-newsroom/forrester-us-2023-customer-experience-index/
Frimousse, S., et Peretti, J. M. (2019). ‘Expérience collaborateur’ et ‘Expérience client’: comment l’entreprise peut-elle utiliser l’Intelligence Artificielle pour progresser? Question (s) de management, 1, 135–156. https://doi.org/10.3917/qdm.191.0135
Frow, P., & Payne, A. (2007). Towards the ‘perfect’ customer experience. Journal of Brand Management, 15(2), 89–101. https://doi.org/10.1057/palgrave.bm.2550120
Gao, Y., & Liu, H. (2023). Artificial intelligence-enabled personalization in interactive marketing: a customer journey perspective. Journal of Research in Interactive Marketing, 17(5), 663–680. https://doi.org/10.1108/JRIM-01-2022-0023
Ge, Y., Liu, S., Fu, Z., Tan, J., Li, Z., Xu, S., & Zhang, Y. (2024). A survey on trustworthy recommender systems. ACM Transactions on Recommender Systems, 3(2), 1–68. https://doi.org/10.1145/3652891
Gefen, D. (2003). TAM or just plain habit: A look at experienced online shoppers. Journal of Organizational and End User Computing, 15(3), 1–13. https://doi.org/10.4018/joeuc.2003070101
Gera, R., & Kumar, A. (2023). Artificial Intelligence In Consumer Behaviour: A Systematic Literature Review of Empirical Research Papers Published In Marketing Journals (2000-2021). Academy of Marketing Studies Journal, 27(S1). https://doi.org/10.1108/GKMC-04-2023-0130
Gharahighehi, A., Pliakos, K., & Vens, C. (2021). Recommender systems in the real estate market – a survey. Applied Sciences, 11(16), 7502. https://doi.org/10.3390/app11167502
Gogua, M. M., & Smirnova, M. M. (2020). Revisiting personalization through customer experience journey. Vestnik of Saint Petersburg University. Management 19 (4): 430–460. http://www.vestnikmanagement.spbu.ru/archive/pdf/858.pdf
Grewal, D., Hulland, J., Kopalle, P. K., & Karahanna, E. (2020). The future of technology and marketing: A multidisciplinary perspective. Journal of the Academy of Marketing Science, 48, 1–8. https://doi.org/10.1007/s11747-019-00711-4
Gursoy, D., Chi, O. H., Lu, L., & Nunkoo, R. (2019). Consumers acceptance of artificially intelligent (AI) devices used in service delivery. International Journal of Information Management, 49, 157–169. https://doi.org/10.1016/j.ijinfomgt.2019.03.008
Hair Jr, J. F., Babin, B. J., & Krey, N. (2017). Covariance-based structural equation modeling in the Journal of Advertising: Review and recommendations. Journal of Advertising, 46(1), 163–177. https://doi.org/10.1080/00913367.2017.1281777
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. https://doi.org/10.1007/978-3-030-80519-7
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2010). Multivariate data analysis (7. Baskı). Pearson. Hallahan, TA, Faff, RW, McKenzie, MD (2004). An Empirical Investigation of Personal Financial Risk Tolerance. Financial Services Review-Greenwıch, 13(1), 57-78. https://www.scirp.org/reference/referencespapers?referenceid=2989262
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40, 414–433. https://doi.org/10.1007/s11747-011-0261-6
Harman, J. L., O’Donovan, J., Abdelzaher, T., & Gonzalez, C. (2014, October). Dynamics of Human Trust in Recommender Systems. In Proceedings of the 8th ACM Conference on Recommender Systems (pp. 305–308). https://doi.org/10.1145/2645710.2645761
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, 115–135. https://doi.org/10.1007/s11747-014-0403-8
Hoyer, W. D., Kroschke, M., Schmitt, B., Kraume, K., & Shankar, V. (2020). Transforming the customer experience through new technologies. Journal of Interactive Marketing, 51(1), 57–71. https://doi.org/10.1016/j.intmar.2020.04.001
Iacoviello, A & Downie, A. (2024). 7 Customer Experience Trends in 2024. IBM. https://www.ibm.com/blog/customer-experience-trends/
Ittelson, W. H., Proshansky, H. M., Rivlin, L. G., & Winkel, G. H. (1974). An Introduction to Environmental Psychology. Holt, Rinehart & Winston. https://www.scirp.org/reference/referencespapers?referenceid=1628241
Jannach, D. (2010). Recommender Systems: An Introduction. Cambridge University Press. https://doi.org/10.1017/CBO9780511763113
Jiang, Y., Shang, J., & Liu, Y. (2010). Maximizing customer satisfaction through an online recommendation system: A novel associative classification model. Decision Support Systems, 48(3), 470–479. https://doi.org/10.1016/j.dss.2009.06.006
Jin, Y., Chen, L., Cai, W., & Pu, P. (2021, November). Key Qualities of Conversational Recommender Systems: From Users’ Perspective. In Proceedings of the 9th International Conference on Human-Agent Interaction (pp. 93–102). https://doi.org/10.1145/3472307.3484164
Kaduškeviit Vaida, Mackevi?i?t? Božena (2022), When Users Meet AI: Customer Acceptance of Recommendation Systems in Online Shopping. Proceedings of the European Marketing Academy, 50th, (111795). https://proceedings.emac-online.org/pdfs/R2022-111795.pdf
Kailani, C., & Ciobotar, N. (2015). Experiential Marketing: An efficient Tool to Leverage Marketing Communication Impact on Consumer Behaviour. In International Conference on Marketing and Business Development, (Vol. 1, No. 1, pp. 281–287). Bucharest University of Economic Studies Publishing House. https://ideas.repec.org/a/aes/icmbdj/v1y2015i1p281-287.html
Kanungo, R. P., Liu, R., & Gupta, S. (2024). Cognitive analytics enabled responsible artificial intelligence for business model innovation: A multilayer perceptron neural networks estimation. Journal of Business Research, 182, 114788. https://doi.org/10.1016/j.jbusres.2024.114788
Kim, G. S., Park, S. B., & Oh, J. (2008). An examination of factors influencing consumer adoption of short message service (SMS). Psychology & Marketing, 25(8), 769–786. https://doi.org/10.1002/mar.20238
Kim, H. G., Kim, G. Y., & Kim, J. Y. (2019). Music recommendation system using human activity recognition from accelerometer data. IEEE Transactions on Consumer Electronics, 65(3), 349–358. https://doi.org/10.1109/TCE.2019.2924177
KITRUM. (2023). Recommendation Systems in E-commerce: How It Works? https://kitrum.com/blog/recommendation-systems-in-e-commerce-how-it-works/?utm_source=chatgpt.com
Knijnenburg, B. P., Willemsen, M. C., Gantner, Z., Soncu, H., & Newell, C. (2012). Explaining the user experience of recommender systems. User Modeling and User-adapted Interaction, 22, 441–504. https://doi.org/10.1007/s11257-011-9118-4
Komiak S.X., Benbasat I. (2006) The effects of personalization and familiarity on trust and adoption of recommendation agents. MIS Quarterly, 30(4), 941–960. https://doi.org/10.2307/25148760
Konstan, J. A., & Riedl, J. (2012). Recommender systems: from algorithms to user experience. User Modeling and User-adapted Interaction, 22, 101–123. https://doi.org/10.1007/s11257-011-9112-x
Kowatsch, T., & Maass, W. (2010). In-store consumer behavior: How mobile recommendation agents influence usage intentions, product purchases, and store preferences. Computers in Human Behavior, 26(4), 697–704. https://doi.org/10.1016/j.chb.2010.01.006
Kumar, N., & Benbasat, I. (2006). Research note: the influence of recommendations and consumer reviews on evaluations of websites. Information Systems Research, 17(4), 425–439. https://doi.org/10.1287/isre.1060.0107
Lambillotte, L., Magrofuoco, N., Poncin, I., & Vanderdonckt, J. (2022). Enhancing playful customer experience with personalization. Journal of Retailing and Consumer Services, 68, 103017. https://doi.org/10.1016/j.jretconser.2022.103017
Lee, G.-G. and Lin, H.-F. (2005) Customer Perceptions of E-Service Quality in Online Shopping. International Journal of Retail & Distribution Management, 33, 161-176. https://doi.org/10.1108/09590550510581485
Lee, Y., & Kozar, K. A. (2012). Understanding of website usability: Specifying and measuring constructs and their relationships. Decision Support Systems, 52(2), 450–463. https://doi.org/10.1016/j.dss.2011.10.004
Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 69–96. https://doi.org/10.1509/jm.15.0420
Li, Q., Choi, I., & Kim, J. (2020). Evaluation of recommendation systems for sustainable E-commerce: accuracy, diversity and customer satisfaction. Preprints. https://doi.org/10.20944/preprints202001.0015.v1
Li, S. S., & Karahanna, E. (2015). Online recommendation systems in a B2C E-commerce context: a review and future directions. Journal of the Association for Information Systems, 16(2), 2. https://doi.org/10.17705/1jais.00389
Liang, T. P., Chen, H. Y., Turban, E. (2012). Effect of Personalization on the Perceived Usefulness of Online Customer Services: A Dual-core Theory. In Proceedings of the 11th International Conference on Electronic Commerce (pp. 279–288). https://doi.org/10.1145/1593254.1593296
Ling, K. C., Chai, L. T., & Piew, T. H. (2010). The effects of shopping orientations, online trust and prior online purchase experience toward customers’ online purchase intention. International Business Research, 3(3), 63. https://doi.org/10.5539/ibr.v3n3p63
Liu, H., Shao, M., Liu, X., & Zhao, L. (2021). Exploring the influential factors on readers’ continuance intentions of E-Book APPs: personalization, usefulness, playfulness, and satisfaction. Frontiers in Psychology, 12, 640110. https://doi.org/10.3389/fpsyg.2021.640110
Liu, W., & Wang, Y. (2024). Evaluating trust in recommender systems: A user study on the impacts of explanations, agency attribution, and product types. International Journal of Human–Computer Interaction, 1–13. https://doi.org/10.1080/10447318.2024.2313921
Londa, D. R., Worang, F. G., & Arie, F. V. (2022). The influence of perceived credibility, perceived ease of use, and perceived usefulness toward customer satisfaction in using BSGtouch. Jurnal EMBA: Jurnal Riset Ekonomi, Manajemen, Bisnis dan Akuntansi, 10(4), 934–945. https://ejournal.unsrat.ac.id/index.php/emba/article/view/43902
Lutz, K., Williams, J. R., & Purakal, J. D. (2023). Assessment of emergency department health care providers’ readiness for managing intimate partner violence and correlation with perceived cultural competence. Journal of emergency nursing, 49(5), 724-732. https://doi.org/10.1016/j.jen.2023.04.009
Lv, L., Chen, S., Liu, G. G., & Benckendorff, P. (2024). Enhancing customers’ life satisfaction through AI-powered personalized luxury recommendations in luxury tourism marketing. International Journal of Hospitality Management, 123, 103914. https://doi.org/10.1016/j.ijhm.2024.103914
Madhuri, A., Shireesha, M., Reddy, S. M., & Kumar, B. R. (2024). Exploring the Role of Personalization in E-commerce: Impacts on Consumer Trust and Purchase Intentions. European Economic Letters. https://www.eelet.org.uk/index.php/journal/article/view/2385/2142
Martin, J., Mortimer, G., & Andrews, L. (2015). Re-examining online customer experience to include purchase frequency and perceived risk. Journal of Retailing and Consumer Services, 25, 81–95. https://doi.org/10.1016/j.jretconser.2015.03.008
Meyer, C., & Schwager, A. (2007). Understanding customer experience. Harvard Business Review, 85(2), 116. https://www.academia.edu/8781181/Understanding_Customer_Experience
Mican, D., Sitar-Tăut, D. A., & Moisescu, O. I. (2020). Perceived usefulness: A silver bullet to assure user data availability for online recommendation systems. Decision Support Systems, 139, 113420. https://doi.org/10.1016/j.dss.2020.113420
Mocean, L., & Pop, C. M. (2012). Marketing recommender systems: a new approach in the digital economy. Informatica Economica, 16(4), 142. https://www.researchgate.net/publication/281082534_Marketing_Recommender_Systems_A_New_Approach_in_Digital_Economy
Morgan, R. M., & Hunt, S. D. (1994). The commitment-trust theory of relationship marketing. Journal of Marketing, 58(3), 20–38. https://doi.org/10.2307/1252308
Moriuchi, E. (2019). Okay, Google!: An empirical study on voice assistants on consumer engagement and loyalty. Psychology & Marketing, 36(5), 489–501. https://doi.org/10.1002/mar.21192
Nilashi, M., Jannach, D., bin Ibrahim, O., Esfahani, M. D., & Ahmadi, H. (2016). Recommendation quality, transparency, and website quality for trust-building in recommendation agents. Electronic Commerce Research and Applications, 19, 70–84. https://doi.org/10.1016/j.elerap.2016.09.003
Niska, M. (2020). Engaging Customers with Recommendations – A Study about How Customers Can Be Engaged Using a Recommender System. Dissertation. Linköping University. https://liu.diva-portal.org/smash/get/diva2:1460556/FULLTEXT01.pdf
Nunes, P. F., & Kambil, A. (2001). Personalization? No thanks. Harvard Business Review, 79(4), 32–33. https://hbr.org/2001/04/personalization-no-thanks
Pappas, I. O., Kourouthanassis, P. E., Giannakos, M. N., & Lekakos, G. (2017). The interplay of online shopping motivations and experiential factors on personalized e-commerce: A complexity theory approach. Telematics and Informatics, 34(5), 730–742. https://doi.org/10.1016/j.tele.2016.08.021
Park, D. H., Kim, H. K., Choi, I. Y., & Kim, J. K. (2012). A literature review and classification of recommender systems research. Expert Systems with Applications, 39(11), 10059–10072. https://doi.org/10.1016/j.eswa.2012.02.038
Peng, M., Xu, Z. & Huang, H. (2021). How does information overload affect consumers’ online decision process? An event-related potential study. Frontiers in Neuroscience, 15, 695852. https://doi.org/10.3389/fnins.2021.695852
Ping, Y., Li, Y., & Zhu, J. (2024). Beyond accuracy measures: the effect of diversity, novelty and serendipity in recommender systems on user engagement. Electronic Commerce Research, 1-28.https://link.springer.com/article/10.1007/s10660-024-09813-w
Pu, P., Chen, L., & Hu, R. (2012). Evaluating recommender systems from the user’s perspective: survey of the state of the art. User Modeling and User-Adapted Interaction, 22, 317–355. https://doi.org/10.1007/s11257-011-9115-7
Purwanto P., Kuswandi K., Fatmah F. (2020). Interactive Applications with Artificial Intelligence: The Role of Trust among Digital Assistant Users. Foresight and STI Governance, 14(2), 64–75. https://cyberleninka.ru/article/n/interactive-applications-with-artificial-intelligence-the-role-of-trust-among-digital-assistant-users/viewer
Rafiq, F., Dogra, N., Adil, M., & Wu, J. Z. (2022). Examining consumer’s intention to adopt AI-chatbots in tourism using partial least squares structural equation modeling method. Mathematics, 10(13), 2190. https://doi.org/10.3390/math10132190
Rai, A. (2020). Explainable AI: From black box to glass box. Journal of the Academy of Marketing Science, 48, 137–141. https://doi.org/10.1007/s11747-019-00710-5
Rane, N., Paramesha, M., Choudhary, S., & Rane, J. (2024). Artificial intelligence in sales and marketing: Enhancing customer satisfaction, experience and loyalty. Experience and Loyalty (17 May 2024). https://doi.org/10.2139/ssrn.4831903
Resnick, P., & Varian, H. R. (1997). Recommender systems. Communications of the ACM, 40(3), 56-58. https://dl.acm.org/doi/10.1145/245108.245121
Rodríguez, M., Paredes, F., & Yi, G. (2016). Towards future customer experience: trends and innovation in retail. Форсайт, 10(3), (eng), 18–28. https://doi.org/10.17323/1995-459X.2016.3.18.28
Ruan, Y., & Mezei, J. (2022). When do AI chatbots lead to higher customer satisfaction than human frontline employees in online shopping assistance? Considering product attribute type. Journal of Retailing and Consumer Services, 68, 103059. https://doi.org/10.1016/j.jretconser.2022.103059
Sboui, M., Baati, O., & Sfar, N. (2024). Influencing factors and consequences of chatbot initial trust in AI telecommunication services: A study on Generation Z. The TQM Journal. https://doi.org/10.1108/TQM-02-2024-0085
Schnall, R., Higgins, T., Brown, W., Carballo-Dieguez, A., & Bakken, S. (2015). Trust, perceived risk, perceived ease of use and perceived usefulness as factors related to mHealth technology use. In MEDINFO 2015: eHealth-enabled Health, (pp. 467–471). IOS Press. https://pubmed.ncbi.nlm.nih.gov/26262094/
Schrotenboer, D. W. (2019). The Impact of Artificial Intelligence along the Customer Journey: A Systematic Literature Review. University of Tewnte. https://purl.utwente.nl/essays/78520
Sciascio, C. D., Brusilovsky, P., Trattner, C., & Veas, E. (2019). A roadmap to user-controllable social exploratory search. ACM Transactions on Interactive Intelligent Systems, 10(1), 1–38. https://doi.org/10.1145/3241382
Searby, S. (2003). personalization – an overview of its use and potential. BT Technology Journal, 21(1), 13–19. https://doi.org/10.1023/A:1022439824138
Shah, T. R., Kautish, P., & Mehmood, K. (2023). Influence of robot service quality on customers’ acceptance in restaurants. Asia Pacific Journal of Marketing and Logistics, 35(12), 3117–3137. https://doi.org/10.1108/APJML-09-2022-0780
Shen, A. (2014). Recommendations as personalized marketing: insights from customer experiences. Journal of Services Marketing, 28(5), 414–427. https://doi.org/10.1108/JSM-04-2013-0083/10.1108/JSM-04-2013-0083
Shen, A., & Ball, A. D. (2009). Customer perceptions and sustained usage of recommender systems. International Journal of Electronic Business, 7(6), 642–663. https://doi.org/10.1504/IJEB.2009.029056
Sheng, X., & Zolfagharian, M. (2014). Consumer participation in online product recommendation services: augmenting the technology acceptance model. Journal of Services Marketing, 28(6), 460–470. https://doi.org/10.1108/JSM-04-2013-0098
Sheridan, J. (2017). An Exploration into the Potential Impact in Adopting a personalization Strategy and How It Could Affect the Customer’s Experience on a Company’s Own Website (doctoral dissertation, Dublin, National College of Ireland). https://norma.ncirl.ie/2919/1/johnsheridan.pdf
Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human-Computer Studies, 146, 102551. https://doi.org/10.1016/j.ijhcs.2020.102551
Sicular, S. & Vashisth, S. (2020). Hype Cycle for Artificial Intelligence, 2020 Gartner. https://www.gartner.com/en/documents/3988006
Sieger, L. N., Hermann, J., Schomäcker, A., Heindorf, S., Meske, C., Hey, C. C., & Doğangün, A. (2022, December). User Involvement in Training Smart Home Agents: Increasing Perceived Control and Understanding. In Proceedings of the 10th International Conference on Human-Agent Interaction (pp. 76–85). https://doi.org/10.1145/3527188.3561914
Silva-Aravena, F., Morales, J., Sáez, P., Jorquera, J., & Cornide-Reyes, H. (2024). Use of Artificial Intelligence as a Mechanism to Evaluate Customer Experience. Literature Review. In International Conference on Human-Computer Interaction (pp. 338–354). Springer, Cham. https://doi.org/10.1007/978-3-031-61305-0_24
Smits, A., Detweiler, C., Bartels, E., Pott, K., van der Stappen, E., van Rossen, S., & van Turhout, K. (2023, August). Assessing the Utility of an Interaction Qualities Framework in Systematizing the Evaluation of User Control. In IFIP Conference on Human-Computer Interaction (pp. 185–195). Cham: Springer Nature Switzerland. https://www.hu.nl/onderzoek/publicaties/assessing-the-utility-of-an-interaction-qualities-framework-in-systematizing-the-evaluation-of
Suhir, M., & Imam Suyadi, R. (2014). Pengaruh persepsi risiko, kemudahan dan manfaat terhadap keputusan pembelian secara online. Jurnal Administrasi Bisnis, 8(1). https://www.neliti.com/publications/79853/pengaruh-persepsi-risiko-kemudahan-dan-manfaat-terhadap-keputusan-pembelian-seca
Suresh, A., & Rani, N. J. (2020). Consumer perception towards artificial intelligence in E-commerce with reference to Chennai city, India. Journal of Information Technology and Economic Development, 11(1), 1–14. https://rochester.userservices.exlibrisgroup.com/permalink/01ROCH_INST/ts7lro/cdi_proquest_journals_2412995876
Syaharani, D. P., & Yasa, N. K. (2022). The role of trust as mediation between perceived usefulness and perceived ease of use on repurchase intention. European Journal of Development Studies, 2(3), 36–40. https://doi.org/10.24018/ejdevelop.2022.2.3.91
Tang, J. H., Chen, M. C., Yang, C. Y., Chung, T. Y., & Lee, Y. A. (2016). Personality traits, interpersonal relationships, online social support, and Facebook addiction. Telematics and Informatics, 33(1), 102–108. https://doi.org/10.1016/j.tele.2015.06.003
Teng, Z., Wu, A., & Song, Z. (2024, June). Research on the influence of the artificial intelligence chatbots’ service quality on the consumers’ purchase intention. In International Conference on Human-Computer Interaction (pp. 119–130). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-76806-4_9
Tintarev, N., & Masthoff, J. (2007, April). A survey of explanations in recommender systems. In 2007 IEEE 23rd international conference on data engineering workshop (pp. 801-810). IEEE. https://ieeexplore.ieee.org/abstract/document/4401070
Trawnik, A., Al-Masaeed, S., Alsoud, M., & Alkufahy, A. (2022). Understanding artificial intelligence experience: A customer perspective. International Journal of Data and Network Science, 6(4), 1471–1484. https://doi.org/10.5267/j.ijdns.2022.5.004
Tsai, C. H., & Brusilovsky, P. (2021). The effects of controllability and explainability in a social recommender system. User Modeling and User-Adapted Interaction, 31, 591–627. https://doi.org/10.1007/s11257-020-09281-5
Tulcanaza-Prieto, A. B., Cortez-Ordoñez, A., & Lee, C. W. (2023). Influence of customer perception factors on AI-enabled customer experience in the Ecuadorian banking environment. Sustainability, 15(16), 12441. https://doi.org/10.20944/preprints202306.1938.v1
Urbach, N., & Ahlemann, F. (2010). Structural equation modeling in information systems research using partial least squares. Journal of Information Technology Theory and Application, 11(2), 5–40. https://aisel.aisnet.org/jitta/vol11/iss2/2
Vashishth, T. K., Sharma, K. K., Kumar, B., Chaudhary, S., & Panwar, R. (2025). Enhancing customer experience through AI-enabled content personalization in e-commerce marketing. Advances in Digital Marketing in the Era of Artificial Intelligence, 7–32. https://doi.org/10.1201/9781003450443-2
Verhoef, P. C. (2020). Customer experience creation in today’s digital world. In The Routledge companion to strategic marketing (pp. 107-122). Routledge https://pure.rug.nl/ws/portalfiles/portal/790710188/Customer_experience_creation_in_today_s_digital_world.pdf
Verhoef, P. C., Lemon, K. N., Parasuraman, A., Roggeveen, A., Tsiros, M., & Schlesinger, L. A. (2009). Customer experience creation: Determinants, dynamics and management strategies. Journal of Retailing, 85(1), 31–41. https://doi.org/10.1016/j.jretai.2008.11.001
Walls, A. R., Okumus, F., Wang, Y. R., & Kwun, D. J. W. (2011). An epistemological view of consumer experiences. International Journal of Hospitality Management, 30(1), 10–21. https://doi.org/10.1016/j.ijhm.2010.03.008
Wang, W., Guan, J., Guo, Z., Chan, J. H., & Qi, X. (2021). Customer experience and brand loyalty in the full-service hotel sector: the role of brand affect. International Journal of Contemporary Hospitality Management, 33(5), 1620-1645. https://doi.org/10.1108/IJCHM-10-2020-1177
Wedel, M., Rust, R. T., & Chung, T. S. (2009, October). Up close And Personalized: A Marketing View of Recommendation Systems. In Proceedings of the Third ACM Conference on Recommender Systems (pp. 3–4). https://doi.org/10.1145/1639714.1639716
Widiar, G., Yuniarinto, A., & Yulianti, I. (2023). Perceived ease of use’s effects on behavioral intention mediated by perceived usefulness and trust. Interdisciplinary Social Studies, 2(4), 1829–1844. https://doi.org/10.55324/iss.v2i4.397
Xiao, B., & Benbasat, I. (2007). E-commerce product recommendation agents: Use, characteristics, and impact. MIS Quarterly, 137–209. https://doi.org/10.2307/25148784
Yassin, F. M., Ouarda, W., & Alimi, A. M. (2022). Fuzzy ontology as a basis for recommendation systems for traveler’s preference. Multimedia Tools and Applications, 81(5), 6599–6631. https://doi.org/10.1007/s11042-021-11780-5
Yi, S., & Liu, X. (2020). Machine learning based customer sentiment analysis for recommending shoppers, shops based on customers’ reviews. Complex & Intelligent Systems, 6(3), 621–634. https://doi.org/10.1007/s40747-020-00155-2
Yoon, N., & Lee, H. K. (2021). AI recommendation service acceptance: Assessing the effects of perceived empathy and need for cognition. Journal of Theoretical and Applied Electronic Commerce Research, 16(5), 1912-1928. https://www.mdpi.com/0718-1876/16/5/107
Zaid, S., & Patwayati, P. (2021). Impact of customer experience and customer engagement on satisfaction and loyalty: A case study in Indonesia. The Journal of Asian Finance, Economics and Business, 8(4), 983–992. https://doi.org/10.13106/JAFEB.2021.VOL8.NO4.0983
Zamri, N., & Idris, I. (2013, June). The effects of attitude, social influences and perceived behavioral control on intention to purchase online shopping apparels in Malaysia: Case study on Zalora. In the 3rd International Conference on Management, Penang. https://doi.org/10.4236/jssm.2015.83032
Zanker, M., Rook, L., & Jannach, D. (2019). Measuring the impact of online personalization: Past, present and future. International Journal of Human-Computer Studies, 131, 160–168. https://doi.org/10.1016/j.ijhcs.2019.06.006
Zarrad, A. (2024). Artificial Intelligence in Tunisian International Hotel Industry. (Doctoral dissertation.) https://hdl.handle.net/10198/30160
Zhang, J., Li, Q., & Luximon, Y. (2021). Building Trust in Mobile Medical Consultations: The Roles of Privacy Concerns, Personality Traits, and Social Cues. In Proceedings of the 23rd HCI International Conference, Washington, DC, USA, 24–29 July 2021, (pp. 293–30). https://doi.org/10.1007/978-3-030-78108-8_22
Zhang, K. Z., & Benyoucef, M. (2016). Consumer behavior in social commerce: A literature review. Decision Support Systems, 86, 95–108. https://doi.org/10.1016/j.dss.2016.04.001
Zhang, X., Guo, X., Lai, K. H., Guo, F., & Li, C. (2014). Understanding gender differences in m-health adoption: a modified theory of reasoned action model. Telemedicine and e-Health, 20(1), 39–46. https://doi.org/10.1089/tmj.2013.0092
Article Sidebar
Published
2025-04-14
Article Details
How to Cite
Jlassi, E., Chaabouni, A., & Triki, M. (2025). Impact of Recommendation Systems on AI-enabled Customer Experience: Mediator Role of Perceived Usefulness and Perceived Trust. Journal of Telecommunications and the Digital Economy, 13(1), 160-191. https://doi.org/10.18080/jtde.v13n1.1137
Issue
Section
Special Issue: Driving the Digital Economy
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright Telecommunications Association Inc.
How to Cite
Jlassi, E., Chaabouni, A., & Triki, M. (2025). Impact of Recommendation Systems on AI-enabled Customer Experience: Mediator Role of Perceived Usefulness and Perceived Trust. Journal of Telecommunications and the Digital Economy, 13(1), 160-191. https://doi.org/10.18080/jtde.v13n1.1137
