Purchase intentions in a chatbot environment: An examination of the effects of customer experience
Abstract
Research background: Chatbots represent valuable technological tools that allow companies to improve customer experiences, meet their expectations in real time, and provide them with personalized assistance. They have contributed to the transformation of conventional customer service models into online solutions, offering accessibility and efficiency through their integration across various digital platforms. Nevertheless, the existing literature is limited in terms of exploring the potential of chatbots in business communication and studying their impact on the customer's response.
Purpose of the article: The main objective of this study is to examine how consumers perceive chatbots as customer service devices. In particular, the paper aims to analyze the influence of the dimensions of “Information”, “Entertainment”, “Media Appeal”, “Social Presence” and “Risk for Privacy” on the “Customer Experience” and the latter on the “Purchase Intention”, under the consideration of the Uses and Gratifications Theory. Moderations due to Chatbot Usage Frequency for some of the relationships proposed are also analyzed.
Methods: An empirical study was performed through a questionnaire to Spanish consumers. The statistical data analysis was conducted with R software through the lavaan package. To test the hypotheses from the conceptual model a structural equation modelling approach was adopted.
Findings & value added: The results obtained identify the main characteristics of chatbots that can support brands to effectively develop their virtual assistants in order to manage their relational communication strategies and enhance their value proposal through the online customer journey. Findings demonstrate the contribution that chatbot dimensions make to the online consumer experience and its impact on the purchase intention, with the consideration of the moderating effect exercised by the user's level of experience (novice vs. experienced) with the use of chatbots. Regarding managerial implications, this research offers recommendations for e-commerce professionals to manage chatbots more effectively. The “Entertainment” and “Social Presence” dimensions can be operationalized at a visual (e.g., appearance of the avatar and text box, use of designs aligned with the website) and textual level (e.g., style and tone of voice, use of expressions typical of the target audience) to generate a feeling of proximity with the chatbot and facilitate its adoption. “Media Appeal” requires that the chatbot be easy to use, effective, and accessible, to facilitate its usability. Finally, mitigation of “Privacy Risk” concerns should be achieved by presenting an appropriate privacy policy and requesting permission for the use of customers’ private information.
Keywords
artificial intelligence tools, chatbot, customer experience, purchase intention, uses and gratifications theory, usage frequency
References
- Adam, M., Wessel, M., & Benlian, A. (2020). AI-based chatbots in customer service and their effects on user compliance. Electronic Markets, 31(2), 1–20.
View in Google Scholar DOI: https://doi.org/10.1007/s12525-020-00414-7 - Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.
View in Google Scholar DOI: https://doi.org/10.1016/0749-5978(91)90020-T - Alagarsamy, S., & Mehrolia, S. (2023). Exploring chatbot trust: Antecedents and behavioural outcomes. Heliyon, 9(5), e16074.
View in Google Scholar DOI: https://doi.org/10.1016/j.heliyon.2023.e16074 - Alsharhan, A., Al-Emran, M., & Shaalan, K. (2023). Chatbot adoption: A multiperspective systematic review and future research agenda. IEEE Transactions on Engineering Management.
View in Google Scholar DOI: https://doi.org/10.1109/TEM.2023.3298360 - Amenuvor, F., Owusu-Antwi, K., & Basilisco, R. (2019). Customer experience and behavioral intentions: The mediation role of customer perceived value. International Journal of Scientific Research and Management, 7(10), 1359–1374.
View in Google Scholar DOI: https://doi.org/10.18535/ijsrm/v7i10.em02 - Anshu, K., Gaur, L., & Singh, G. (2022). Impact of customer experience on attitude and repurchase intention in online grocery retailing: A moderation mechanism of value co-creation. Journal of Retailing and Consumer Services, 64, 102798.
View in Google Scholar DOI: https://doi.org/10.1016/j.jretconser.2021.102798 - Araujo, T. (2018). Living up to the chatbot hype: The influence of anthropomorphic design cues and communicative agency framing on conversational agent and company perceptions. Computers in Human Behavior, 85, 183–189.
View in Google Scholar DOI: https://doi.org/10.1016/j.chb.2018.03.051 - Armstrong, J. S., & Overton, T. S. (1977). Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14(3), 396–402.
View in Google Scholar DOI: https://doi.org/10.1177/002224377701400320 - Ashraf, R., & Merunka, D. (2017). The use and misuse of student samples: An empirical investigation of European marketing research. Journal of Consumer Behaviour, 16(4), 295–308.
View in Google Scholar DOI: https://doi.org/10.1002/cb.1590 - Bagozzi, R. P., & Yi, Y. (1989). On the use of structural equation models in experimental designs. Journal of Marketing Research, 26(3), 271–284.
View in Google Scholar DOI: https://doi.org/10.1177/002224378902600302 - Bagozzi, R. P., Yi, Y., & Phillips, L. W. (1991). Assessing construct validity in organizational research. Administrative Science Quarterly, 36(6), 421–458.
View in Google Scholar DOI: https://doi.org/10.2307/2393203 - Ben Mimoun, M. S., & Poncin. I. (2015). A valued agent: How ECAs affect website customers’ satisfaction and behaviors. Journal of Retailing and Consumer Services, 26, 70–82.
View in Google Scholar DOI: https://doi.org/10.1016/j.jretconser.2015.05.008 - Bethlehem, J. (2010). Selection bias in web surveys. International Statistical Review, 78(2), 161–188.
View in Google Scholar DOI: https://doi.org/10.1111/j.1751-5823.2010.00112.x - Bilal, M. Zhang, Y., Cai, S., Akram, U., & Halibas, A. (2024). Artificial intelligence is the magic wand making customer-centric a reality! An investigation into the relationship between consumer purchase intention and consumer engagement through affective attachment. Journal of Retailing and Consumer Services, 77, 103674.
View in Google Scholar DOI: https://doi.org/10.1016/j.jretconser.2023.103674 - Bilgihan, A., Kandampully, J., & Zhang, T. C. (2016). Towards a unified customer experience in online shopping environments: Antecedents and outcomes. International Journal of Quality and Service Sciences, 8(1), 102–119.
View in Google Scholar DOI: https://doi.org/10.1108/IJQSS-07-2015-0054 - Blut, M., Wang, C., & Schoefer, K. (2016). Factors influencing the acceptance of self-service technologies: A meta-analysis. Journal of Service Research, 19(4), 396–416.
View in Google Scholar DOI: https://doi.org/10.1177/1094670516662352 - Brandtzaeg, P., & Følstad, A. (2017). Why people use chatbots. In Proceedings of the 4th international conference on internet science, Thessaloniki, Greece, 22-24 November 2017, (pp. 377–392) Springer.
View in Google Scholar DOI: https://doi.org/10.1007/978-3-319-70284-1_30 - Brosseau-Liard, P. E., & Savalei, V. (2014). Adjusting incremental fit indices for nonnormality. Multivariate Behavioral Research, 49(5), 460–470.
View in Google Scholar DOI: https://doi.org/10.1080/00273171.2014.933697 - Brosseau-Liard, P. E., Savalei, V., & Li, L. (2012). An investigation of the sample performance of two nonnormality corrections for RMSEA. Multivariate Behavioral Research, 47(6), 904–930.
View in Google Scholar DOI: https://doi.org/10.1080/00273171.2012.715252 - Burgoon, J. K. (2015). Expectancy violations theory. In C. R. Berger & M. E. Roloff (Eds). The international encyclopedia of interpersonal communication (pp 1–9). New York: Wiley Blackwell.
View in Google Scholar DOI: https://doi.org/10.1002/9781118540190.wbeic102 - Chen, J. S., Tran-Thien-Y, L., & 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.
View in Google Scholar DOI: https://doi.org/10.1108/IJRDM-08-2020-0312 - Chen, Q., Gong, Y., Lu, Y., & Tang, J. (2022). Classifying and measuring the service quality of AI chatbot in frontline service. Journal of Business Research, 145, 552–568.
View in Google Scholar DOI: https://doi.org/10.1016/j.jbusres.2022.02.088 - Chen, S., Li, X., Liu, K., & Wang, X. (2023). Chatbot or human? The impact of online customer service on consumers' purchase intentions. Psychology & Marketing, 40, 2186–2200.
View in Google Scholar DOI: https://doi.org/10.1002/mar.21862 - Cheng, Y., & Jiang, H. (2020a). How do AI-driven chatbots impact user experience? Examining gratifications, perceived privacy risk, satisfaction, loyalty, and continued use. Journal of Broadcasting & Electronic Media, 64(4), 592–614.
View in Google Scholar DOI: https://doi.org/10.1080/08838151.2020.1834296 - Cheng, Y., & Jiang, H. (2020b). AI-Powered mental health chatbots: Examining users’ motivations, active communicative action and engagement after mass-shooting disasters. Journal of Contingencies and Crisis Management, 28, 339–354.
View in Google Scholar DOI: https://doi.org/10.1111/1468-5973.12319 - Cheng, Y., & Jiang, H. (2022). Customer–brand relationship in the era of artificial intelligence: Understanding the role of chatbot marketing efforts. Journal of Product & Brand Management, 31(2), 252–264.
View in Google Scholar DOI: https://doi.org/10.1108/JPBM-05-2020-2907 - Cheung, C., Chiu, P.-Y., & Lee. M. (2011). Online social networks: Why do students use Facebook? Computers in Human Behavior, 27, 1337–1343.
View in Google Scholar DOI: https://doi.org/10.1016/j.chb.2010.07.028 - Chou, E-Y., & Hsu, W-C. (2021). Conversational service experiences in chatbots: A perspective on cognitive load. Management Review, 40, 111–130.
View in Google Scholar - Chung, K., & Park, R.C. (2019). Chatbot-based healthcare service with a knowledge base for cloud computing. Cluster Computing, 22(S1), 1925–1937.
View in Google Scholar DOI: https://doi.org/10.1007/s10586-018-2334-5 - Chung, M. J., Ko, E. J., Joung, H. R., & Kim, S. J. (2020). Chatbot e-service and customer satisfaction regarding luxury brands. Journal of Business Research, 117, 587–595.
View in Google Scholar DOI: https://doi.org/10.1016/j.jbusres.2018.10.004 - Chung, S., Kramer, T., & Wong, E. M. (2018). Do touch interface users feel more engaged? The impact of input device type on online shoppers' engagement, affect, and purchase decisions. Psychology & Marketing, 35(11), 795–806.
View in Google Scholar DOI: https://doi.org/10.1002/mar.21135 - Collier, J. E., & Bienstock, C. C. (2006). How do customers judge quality in an e-tailer? MIT Sloan Management Review, 48(1), 35–40.
View in Google Scholar - Dale, R. (2016). The return of the chatbots. Natural Language Engineering, 22(5), 811–817.
View in Google Scholar DOI: https://doi.org/10.1017/S1351324916000243 - Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Information Systems Quarterly, 13(3), 319–340.
View in Google Scholar DOI: https://doi.org/10.2307/249008 - De Cicco, R., Silva, S. C., & Alparone, F. R. (2020). Millennials' attitude toward chatbots: an experimental study in a social relationship perspective. International Journal of Retail & Distribution Management, 48(11), 1213–1233.
View in Google Scholar DOI: https://doi.org/10.1108/IJRDM-12-2019-0406 - De Keyser, A., & Kunz, W. H. (2022). Living and working with service robots: A TCCM analysis and considerations for future research. Journal of Service Management.
View in Google Scholar DOI: https://doi.org/10.2139/ssrn.4035662 - Delloitte (2023). Unlock the full potential of your e-commerce transformation. Retrieved from https://www2.deloitte.com/content/dam/Deloitte/de/ Documents/customer-marketing/Deloitte_Unlock-eCommerce-Transformation-2023.pdf
View in Google Scholar - Dietvorst, B. J., Simmons, J. P., & Massey, C. (2018). Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them. Management Science, 64(3), 1155–1170.
View in Google Scholar DOI: https://doi.org/10.1287/mnsc.2016.2643 - Dogra, P., & Kaushal, A. (2023). The impact of digital marketing and promotional strategies on attitude and purchase intention towards financial products and service: A Case of emerging economy. Journal of Marketing Communications, 29(4), 403–430.
View in Google Scholar DOI: https://doi.org/10.1080/13527266.2022.2032798 - Elhadidi, A. (2018). Beyond access to social media: A comparison of gratifications, interactivity, and content usage among Egyptian adults. Global Media Journal, 16(30), 1–13.
View in Google Scholar - Emplifi (2022). Top 35+ customer experience statistics to know in 2022. Emplifi Retrieved from https://emplifi.io/resources/blog/customer-experience-statistics (5.08.2022).
View in Google Scholar - 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.
View in Google Scholar DOI: https://doi.org/10.1016/j.jbusres.2020.08.058 - Fitria, T., Simbolon, N., & Afdaleni (2023). Chatbots as online chat conversation in the education sector. International Journal of Computer and Information System, 4(3), 93–104.
View in Google Scholar - Forbes (2017). How chatbots improve customer experience in every industry: An infograph. Forbes Inc. Retrieved from https://www.forbes.com/sites/blakemorgan/2017/06/08/how-chatbots-improve-customer-experience-in-every-industry-a n-infograph/?sh=377115d067df (5.08.2022).
View in Google Scholar - Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
View in Google Scholar DOI: https://doi.org/10.1177/002224378101800104 - Gan, C., & Li, H. (2018). Understanding the effects of gratifications on the continuance intention to use WeChat in China: A perspective on uses and gratifications. Computers in Human Behavior, 78, 306–315.
View in Google Scholar DOI: https://doi.org/10.1016/j.chb.2017.10.003 - Gan, C., & Wang, W. (2015). Uses and gratifications of social media: A comparison of microblog and WeChat. Journal of Systems and Information Technology, 17(4), 1–12.
View in Google Scholar DOI: https://doi.org/10.1108/JSIT-06-2015-0052 - Gao, B. (2023). A uses and gratifications approach to examining users’ continuance intention towards smart mobile learning. Humanities & Social Sciences Communications, 10(726), 1–13.
View in Google Scholar DOI: https://doi.org/10.1057/s41599-023-02239-z - Gefen, D., Karahanna, E., & Straub, D. W. (2003). Inexperience and experience with online stores: The importance of TAM and trust. IEEE Transactions on Engineering Management, 50(3), 307–321.
View in Google Scholar DOI: https://doi.org/10.1109/TEM.2003.817277 - Gentile, C., Spiller, N., & Noci, G. (2007). How to sustain the customer experience: An overview of experience components that co-create value with the customer. European Management Journal, 25(5), 395–410.
View in Google Scholar DOI: https://doi.org/10.1016/j.emj.2007.08.005 - Gnewuch, U., Morana, S., Adam, M. T., & Maedche, A. (2018). “The chatbot is typing...” – the role of typing indicators in human-chatbot interaction. In SIGHCI 2018 proceedings. AIS e-Library. Retrieved from https://aisel.aisnet.org/sighci20 18/14.
View in Google Scholar - Gnewuch, U., Morana, S., Adam, M. T., & Maedche, A. (2022). Opposing effects of response time in human–chatbot interaction: The moderating role of prior experience. Business & Information Systems Engineering, 64(6), 773–791.
View in Google Scholar DOI: https://doi.org/10.1007/s12599-022-00755-x - Gosling, S. D., Vazire, S., Srivastava, S., & John, O. P. (2004). Should we trust web-based studies? A comparative analysis of six preconceptions about internet questionnaires. American Psychologist, 59(2), 93.
View in Google Scholar DOI: https://doi.org/10.1037/0003-066X.59.2.93 - Grimes, G. M., Schuetzler, R. M., & Giboney, J. S. (2021). Mental models and expectation violations in conversational AI interactions. Decision Support System, 144, 113515.
View in Google Scholar DOI: https://doi.org/10.1016/j.dss.2021.113515 - GS1 (2023). Trend research 2023-2024: Innovation in a world of continuous disruption. Retrieved from https://www.gs1.org/docs/innovation/gs1-trend-research-3rd-edition-090823.pdf
View in Google Scholar - Gümüş, N., & Çark, Ö. (2021). The effect of customers’ attitudes towards chatbots on their experience and behavioural intention in Turkey. Interdisciplinary Description of Complex Systems: INDECS, 19(3), 420–436.
View in Google Scholar DOI: https://doi.org/10.7906/indecs.19.3.6 - Gupta, A., & Sharma, D. (2019). Customers’ attitude towards chatbots in banking industry of India. International Journal of Innovative Technology and Exploring Engineering, 8(11), 1222–1225.
View in Google Scholar DOI: https://doi.org/10.35940/ijitee.J9366.0981119 - Hagberg, J., Sundstrom, M., & Egels-Zandén, N. (2016). The digitalization of retailing: An exploratory framework. International Journal of Retail & Distribution Management, 44(7), 694–712.
View in Google Scholar DOI: https://doi.org/10.1108/IJRDM-09-2015-0140 - Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis. Vol. 6. Upper Saddle River, NJ: Prentice Hall.
View in Google Scholar - Haris, J., Rahim, S. A., Haris, M., & Zahari, M. S. (2021). Using the theory of planned behaviour to predict purchase intention towards using Taobao. International Journal of Academic Research in Business and Social Sciences, 11(2), 952–959.
View in Google Scholar DOI: https://doi.org/10.6007/IJARBSS/v11-i2/9191 - Hernández, B., Jiménez, J., & Martín, M.J. (2010). Customer behavior in electronic commerce: The moderating effect of e-purchasing experience. Journal of Business Research, 63(9–10), 964–971.
View in Google Scholar DOI: https://doi.org/10.1016/j.jbusres.2009.01.019 - Homburg, C., Koschate, N., & Hoyer, W. D. (2006). The role of cognition and affect in the formation of customer satisfaction: A dynamic perspective. Journal of Marketing, 70(3), 21–31.
View in Google Scholar DOI: https://doi.org/10.1509/jmkg.70.3.21 - Hsu, P. F., Nguyen, T., Wang, C. Y., & Huang, P. J. (2023). Chatbot commerce—How contextual factors affect Chatbot effectiveness. Electronic Markets, 33(14), 1–22.
View in Google Scholar DOI: https://doi.org/10.1007/s12525-023-00629-4 - Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.
View in Google Scholar DOI: https://doi.org/10.1080/10705519909540118 - Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155–172.
View in Google Scholar DOI: https://doi.org/10.1177/1094670517752459 - Illescas-Manzano, M. D., Vicente-López, N., Afonso-González, N., & Cristofol-Rodríguez, C. (2021). Implementation of chatbot in online commerce, and open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 7(2), 1–20.
View in Google Scholar DOI: https://doi.org/10.3390/joitmc7020125 - Ischen, C., Araujo, T., Noort, G. van, Voorveld, H., & Smit, E. (2020). I am here to assist you today: The role of entity, interactivity and experiential perceptions in chatbot persuasion. Journal of Broadcasting & Electronic Media, 64(4), 615–639.
View in Google Scholar DOI: https://doi.org/10.1080/08838151.2020.1834297 - Jain, R., Aagja, J., & Bagdare, S. (2017). Customer experience – A review and research agenda. Journal of Service Theory and Practice, 27(3), 642–662.
View in Google Scholar DOI: https://doi.org/10.1108/JSTP-03-2015-0064 - Jansom, A., Srisangkhajorn, T., & Limarunothai, W. (2022). How chatbot e-services motivate communication credibility and lead to customer satisfaction: The perspective of Thai consumers in the apparel retailing context. Innovative Marketing, 18(3), 13–25.
View in Google Scholar DOI: https://doi.org/10.21511/im.18(3).2022.02 - Jenneboer, L., Herrando, C., & Constantinides, E. (2022). The impact of chatbots on customer loyalty: A systematic literature review. Journal of Theoretical and Applied Electronic Commerce Research, 17(1), 212–229.
View in Google Scholar DOI: https://doi.org/10.3390/jtaer17010011 - Jiang, K., Qin, M., & Li, S. (2022). Chatbots in retail: How do they affect the continued use and purchase intentions of Chinese consumers? Journal of Consumer Behavior, 21, 756–772.
View in Google Scholar DOI: https://doi.org/10.1002/cb.2034 - Jo, H. (2022). Antecedents of continuance intention of social networking services (SNS): Utilitarian, hedonic, and social contexts. Mobile Information Systems, 2022, 7904124.
View in Google Scholar DOI: https://doi.org/10.1155/2022/7904124 - Juniper Research (2022). Chatbot messaging app accesses to reach 9.5 billion globally by 2026: Driven by online retail growth. Juniper Research. Retrieved from https://www.juniperresearch.com/press/chatbot-messaging-app-accesses-reach-9-bn?ch=chatbot (5.08.2022).
View in Google Scholar - Kasilingam, D. L. (2020). Understanding the attitude and intention to use smartphone chatbots for shopping. Technology in Society, 62, 101280.
View in Google Scholar DOI: https://doi.org/10.1016/j.techsoc.2020.101280 - Kerly, A., Hall, P., & Bull, S. (2007). Bringing chatbots into education: Towards natural language negotiation of open learner models. Knowledge-based Systems, 20(2), 177–185.
View in Google Scholar DOI: https://doi.org/10.1016/j.knosys.2006.11.014 - Khan, Y., Hameed, I., & Akram, U. (2023). What drives attitude, purchase intention and consumer buying behavior toward organic food? A self-determination theory and theory of planned behavior perspective. British Food Journal, 125(7), 2572–2587.
View in Google Scholar DOI: https://doi.org/10.1108/BFJ-07-2022-0564 - Kim, H., So, K. K. F., & Wirtz, J. (2022). Service robots: Applying social exchange theory to better understand human–robot interactions. Tourism Management, 92, 104537.
View in Google Scholar DOI: https://doi.org/10.1016/j.tourman.2022.104537 - Kim, M. S. (2018). Factors influencing willingness to provide personal information for personalized recommendations. Computers in Human Behavior, 88, 143–152.
View in Google Scholar DOI: https://doi.org/10.1016/j.chb.2018.06.031 - Kim, M., & Chang, B. (2020). The effect of service quality on the reuse intention of a chatbot: Focusing on user satisfaction, reliability, and immersion. International Journal of Contents, 16(4), 1–15.
View in Google Scholar - Kolbe, L., & Jorgensen, T. D. (2019). Using restricted factor analysis to select anchor items and detect differential item functioning. Behavior Research Methods, 51, 138–151.
View in Google Scholar DOI: https://doi.org/10.3758/s13428-018-1151-3 - Konya-Baumbach, E., Biller, M., & von Janda, S. (2023). Someone out there? A study on the social presence of anthropomorphized chatbots. Computers in Human Behavior, 139, 107513.
View in Google Scholar DOI: https://doi.org/10.1016/j.chb.2022.107513 - Kopplin, C. (2023). Chatbots in the workplace: A technology acceptance study applying uses and gratifications in coworking spaces. Journal of Organizational Computing and Electronic Commerce, 32(3-4), 232–257.
View in Google Scholar DOI: https://doi.org/10.1080/10919392.2023.2215666 - Ku, Y., Chu, T., & Tseng, C. (2013). Gratifications for using CMC technologies: A comparison among SNS, IM, and e-mail. Computers in Human Behavior, 29(1), 226–234.
View in Google Scholar DOI: https://doi.org/10.1016/j.chb.2012.08.009 - Kushwaha, A. K., Kumar, P., & Kar, A. K. (2021). What impacts customer experience for B2B enterprises on using AI-enabled chatbots? Insights from big data analytics. Industrial Marketing Management, 98, 207–221.
View in Google Scholar DOI: https://doi.org/10.1016/j.indmarman.2021.08.011 - Lee, S., & Choi, J. (2017). Enhancing user experience with conversational agent for movie recommendation: Effects of self-disclosure and reciprocity. International Journal of Human-Computer Studies, 103, 95–105.
View in Google Scholar DOI: https://doi.org/10.1016/j.ijhcs.2017.02.005 - Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69–96.
View in Google Scholar DOI: https://doi.org/10.1509/jm.15.0420 - Leung, E., Paolacci, G., & Puntoni, S. (2018). Man versus machine: Resisting automation in identity-based consumer behavior. Journal of Marketing Research, 55(6), 818–831.
View in Google Scholar DOI: https://doi.org/10.1177/0022243718818423 - Li, L., Lee, K. Y., Emokpae, E., & Yang, S. B. (2021). What makes you continuously use chatbot services? Evidence from Chinese online travel agencies. Electronic Markets, 31, 575–599.
View in Google Scholar DOI: https://doi.org/10.1007/s12525-020-00454-z - Li, M., & Mao, J. (2015). Hedonic or utilitarian? Exploring the impact of communication style alignment on user’s perception of virtual health advisory services. International Journal of Information Management, 35(2), 229–243.
View in Google Scholar DOI: https://doi.org/10.1016/j.ijinfomgt.2014.12.004 - Liang, R. D., & Zhang, J. S. (2011). The effect of service interaction orientation on customer satisfaction and behavioral intention: The moderating effect of dining frequency. Procedia-Social and Behavioral Sciences, 24, 1026–1035.
View in Google Scholar DOI: https://doi.org/10.1016/j.sbspro.2011.09.082 - Liébana-Cabanillas, F., Muñoz-Leiva, F., Sánchez-Fernández, J., & Viedma-del Jesús, M.I. (2016). The moderating effect of user experience on satisfaction with electronic banking: Empirical evidence from the Spanish case. Information Systems and E-Business Management, 14(1), 141–165.
View in Google Scholar DOI: https://doi.org/10.1007/s10257-015-0277-4 - Lin, G. C., Wen, Z., Marsh, H. W., & Lin, H. S. (2010). Structural equation models of latent interactions: Clarification of orthogonalizing and double-mean-centering strategies. Structural Equation Modeling, 17(3), 374–391.
View in Google Scholar DOI: https://doi.org/10.1080/10705511.2010.488999 - Ling, E. C., Tussyadiah, I., Tuomi, A., Stienmetz, J., & Ioannou, A. (2021). Factors influencing users' adoption and use of conversational agents: A systematic review. Psychology & Marketing, 38(7), 1031–1051.
View in Google Scholar DOI: https://doi.org/10.1002/mar.21491 - Lubbe, I., & Ngoma, N. (2021). Useful chatbot experience provides technological satisfaction: An emerging market perspective. South African Journal of Information Management, 23(1), a1299, 1–8.
View in Google Scholar DOI: https://doi.org/10.4102/sajim.v23i1.1299 - Luo, M., & Remus, W. (2014). Uses and gratifications and acceptance of Web-based information services: An integrated model. Computers in Human Behavior, 38, 281–295.
View in Google Scholar DOI: https://doi.org/10.1016/j.chb.2014.05.042 - Luo, M., Chea, S., & Chen, J. (2011). Web-based information service adoption: A comparison of the motivational model and the uses and gratifications theory. Decision Support Systems, 51(1), 21–30.
View in Google Scholar DOI: https://doi.org/10.1016/j.dss.2010.11.015 - MacKenzie, S. B., & Podsakoff, P. M. (2012). Common method bias in marketing: Causes, mechanisms, and procedural remedies. Journal of Retailing, 88(4), 542–555.
View in Google Scholar DOI: https://doi.org/10.1016/j.jretai.2012.08.001 - Mariani, M. M., Hashemi, N., & Wirtz, J. (2023). Artificial intelligence empowered conversational agents: A systematic literature review and research agenda. Journal of Business Research, 161, 113838.
View in Google Scholar DOI: https://doi.org/10.1016/j.jbusres.2023.113838 - McLean, G., Osei-Frimpong, K., Wilson, A., & Pitardi, V. (2020). How live chat assistants drive travel consumers’ attitudes, trust and purchase intentions: The role of human touch. International Journal of Contemporary Hospitality Management, 32(5), 1795–1812.
View in Google Scholar DOI: https://doi.org/10.1108/IJCHM-07-2019-0605 - McLean, G., & Osei-Frimpong, K. (2019). Hey Alexa… examine the variables influencing the use of artificial intelligence in-home voice assistants. Computers in Human Behavior, 99, 28–37.
View in Google Scholar DOI: https://doi.org/10.1016/j.chb.2019.05.009 - Mende, M., Scott, M. L., van Doorn, J., Grewal, D., & Shanks, I. (2019). Service robots rising: How humanoid robots influence service experiences and elicit compensatory consumer responses. Journal of Marketing Research, 56(4), 535–556.
View in Google Scholar DOI: https://doi.org/10.1177/0022243718822827 - Meyer-Waarden, L., Pavone, G., Poocharoentou, T., Prayatsup, P., Ratinaud, M., Tison, A., & Tomé, S. (2020). How service quality influences customer acceptance and usage of chatbots? Journal of Service Management Research, 4(1), 35–51.
View in Google Scholar DOI: https://doi.org/10.15358/2511-8676-2020-1-35 - Meyer, C., & Schwager, A. (2007). Understanding customer experience. Harvard Business Review, 85(2), 116–126.
View in Google Scholar - Ministry of Industry and Tourism (2024). PYME 2024 figures. Ministry of Industry and Tourism. Retrieved from https://industria.gob.es/es-es/estadisticas/paginas /estadisticas-y-publicaciones-sobre-pyme.aspx (18.02.2024).
View in Google Scholar - Misischia, C. V., Poecze, F., & Strauss, C. (2022). Chatbots in customer service: Their relevance and impact on service quality. Procedia Computer Science, 201, 421–428.
View in Google Scholar DOI: https://doi.org/10.1016/j.procs.2022.03.055 - Molinillo, S., Aguilar-Illescas, R., Anaya-Sánchez, R., & Liébana-Cabanillas, F. (2021). Social commerce website design, perceived value and loyalty behavior intentions: The moderating roles of gender, age and frequency of use. Journal of Retailing and Consumer Services, 63, 102404.
View in Google Scholar DOI: https://doi.org/10.1016/j.jretconser.2020.102404 - Morgan, R. M., & Hunt, S. D. (1994). The commitment-trust theory of relationship marketing. Journal of Marketing, 58(3), 20–38.
View in Google Scholar DOI: https://doi.org/10.1177/002224299405800302 - Moriuchi, E., Landers, V. M., Colton, D., & Hair, N. (2021). Engagement with chatbots versus augmented reality interactive technology in e-commerce. Journal of Strategic Marketing, 29(5), 375–389.
View in Google Scholar DOI: https://doi.org/10.1080/0965254X.2020.1740766 - Moussawi, S., Koufaris, M., Benbunan-Fich, R. (2020). How perceptions of intelligence and anthropomorphism affect adoption of personal intelligent agents. Electronic Markets, 31, 343–364.
View in Google Scholar DOI: https://doi.org/10.1007/s12525-020-00411-w - Nasermoadeli, A., Ling, K., & Maghnati, F. (2013). Evaluating the impacts of customer experience on purchase intention. International Journal of Business and Management, 8(6), 128–138.
View in Google Scholar DOI: https://doi.org/10.5539/ijbm.v8n6p128 - Nunnally, J. C. (1978). Psychometric theory. New York: McGraw Hill.
View in Google Scholar - Ontsi (2023). Use of artificial intelligence and big data in Spanish companies. National Observatory of Technology and Society. Retrieved from https://www.ontsi.es/sites/ontsi/files/2023-02/Br%C3%BAjula_IA_Big_data_202 3.pdf (27.09.2023).
View in Google Scholar - Piotrowicz, W., & Cuthbertson, R. (2014). Introduction to the special issue information technology in retail: Toward omnichannel retailing. International Journal of Electronic Commerce, 18(4), 5–16.
View in Google Scholar DOI: https://doi.org/10.2753/JEC1086-4415180400 - Pullman, M. E., & Gross, M. A. (2004). Ability of experience design elements to elicit emotions and loyalty behaviors. Decision Sciences, 35(3), 551–578.
View in Google Scholar DOI: https://doi.org/10.1111/j.0011-7315.2004.02611.x - Rajaobelina, L., Prom, S., Arcand, M., & Ricard, L. (2021). Creepiness: Its antecedents and impact on loyalty when interacting with a chatbot. Psychology & Marketing, 38, 2339–2356.
View in Google Scholar DOI: https://doi.org/10.1002/mar.21548 - Rana, J., Gaur, L., Singh, G., Awan, U., & Rasheed, M. I. (2021). Reinforcing customer journey through artificial intelligence: A review and research agenda. International Journal of Emerging Markets, 17(7), 1738–1758.
View in Google Scholar DOI: https://doi.org/10.1108/IJOEM-08-2021-1214 - Rauschnabel, P. A. (2018). Virtually enhancing the real world with holograms: An exploration of expected gratifications of using augmented reality smart glasses. Psychology and Marketing, 35, 557–572.
View in Google Scholar DOI: https://doi.org/10.1002/mar.21106 - Rese, A., Ganster, L., & Baier, D. (2020). Chatbots in retailers’ customer communication: How to measure their acceptance? Journal of Retailing and Consumer Services, 56, 1–14.
View in Google Scholar DOI: https://doi.org/10.1016/j.jretconser.2020.102176 - Rhim, J., Kwak, M., Gong, Y., & Gweon, G. (2022). Application of humanization to survey chatbots: Change in chatbot perception, interaction experience, and survey data quality. Computers in Human Behavior, 126, 107034.
View in Google Scholar DOI: https://doi.org/10.1016/j.chb.2021.107034 - Rogers, E. M. (1983). Diffusion of innovations. New York: Free Press.
View in Google Scholar - Rose, S., Hair, N., & Clark, M. (2011). Online customer experience: A review of the Business‐to‐Consumer online purchase context. International Journal of Management Reviews, 13(1), 24–39.
View in Google Scholar DOI: https://doi.org/10.1111/j.1468-2370.2010.00280.x - Santos-Jaén, J. M., Gimeno-Arias, F., León-Gómez, A., & Palacios-Manzano, M. (2023). The Business digitalization process in SMEs from the implementation of e-commerce: An empirical analysis. Journal of Theorical and Applied Electronic Commerce Research, 18, 1700–1720.
View in Google Scholar DOI: https://doi.org/10.3390/jtaer18040086 - Satorra, A., & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. von Eye & C. C. Clogg (Eds.). Latent variable analysis: Applications to developmental research (pp. 399–419). Thousand Oaks, CA: Sage.
View in Google Scholar - Song, I., Larose, R., Eastin, M., & Lin, C. (2004). Internet gratifications and Internet addiction: On the uses and abuses of new media. Cyberpsychology & Behavior, 7(4), 384–394.
View in Google Scholar DOI: https://doi.org/10.1089/cpb.2004.7.384 - Stefko, R., Bacik, R., Fedorko, R., & Olearova, M. (2022). Gender-generation characteristic in relation to the customer behavior and purchasing process in terms of mobile marketing. Oeconomia Copernicana, 13(1), 181–223.
View in Google Scholar DOI: https://doi.org/10.24136/oc.2022.006 - Sfenrianto, S., & Vivensius, G. (2020). Analysis on factors influencing customer experience of e-commerce users in Indonesia through the application of chatbot technology. Journal of Theoretical and Applied Information Technology, 98(7), 953–962.
View in Google Scholar - Statista (2022). Number of e-commerce users in Europe from 2017 to 2025. Statista. Retrieved from https://www.statista.com/forecasts/715683/e-commerce-users-in-europe#statisticContainer (5.08.2022).
View in Google Scholar - Tabachnick, B. G., Fidell, L. S., & Ullman, J. B. (2013). Using multivariate statistics. Vol. 6. Boston, MA: Pearson.
View in Google Scholar - Talwar, S., Dhir, A., Kaur, P., & Mäntymäki, M. (2020). Barriers toward purchasing from online travel agencies. International Journal of Hospitality Management, 89, 102593.
View in Google Scholar DOI: https://doi.org/10.1016/j.ijhm.2020.102593 - Tosun, C., Dedeoglu, B. B., & Fyall, A. (2015). Destination service quality, affective image and revisit intention: The moderating role of past experience. Journal of Destination Marketing & Management, 4(4), 222–234.
View in Google Scholar DOI: https://doi.org/10.1016/j.jdmm.2015.08.002 - Trevinal, A. M., & Stenger, T. (2014). Toward a conceptualization of the online shopping experience. Journal of Retailing and Consumer Services, 21(3), 314–326.
View in Google Scholar DOI: https://doi.org/10.1016/j.jretconser.2014.02.009 - Trivedi, J. (2019). Examining the customer experience of using banking chatbots and its impact on brand love: The moderating role of perceived risk. Journal of Internet Commerce, 18(1), 1–21.
View in Google Scholar DOI: https://doi.org/10.1080/15332861.2019.1567188 - Tsai, W. H. S., Liu, Y., & Chuan, C. H. (2021). How chatbots' social presence communication enhances consumer engagement: The mediating role of parasocial interaction and dialogue. Journal of Research in Interactive Marketing, 15(3), 460–482.
View in Google Scholar DOI: https://doi.org/10.1108/JRIM-12-2019-0200 - van Doorn, J., Mende, M., Noble, S. M., Hulland, J., Ostrom, A. L., Grewal, D., & Petersen, J. A. (2017). Domo arigato Mr. Roboto: Emergence of automated social presence in organizational frontlines and customers’ service experiences. Journal of Service Research, 20(1), 43–58.
View in Google Scholar DOI: https://doi.org/10.1177/1094670516679272 - Viglia, G., & Dolnicar, S. (2020). A review of experiments in tourism and hospitality. Annals of Tourism Research, 80, 102858.
View in Google Scholar DOI: https://doi.org/10.1016/j.annals.2020.102858 - Walczuch, R., & Lundgren, H. (2004). Psychological antecedents of institution-based consumer trust in e-retailing. Information & Management, 42(1), 159–177.
View in Google Scholar DOI: https://doi.org/10.1016/j.im.2003.12.009 - Wang, J., & Oh, J. I. (2023). Factors influencing consumers’ continuous purchase intentions on TikTok: An examination from the uses and gratifications (U&G) theory perspective. Sustainability, 15, 1–19.
View in Google Scholar DOI: https://doi.org/10.3390/su151310028 - Wang, Y. A., & Rhemtulla, M. (2021). Power analysis for parameter estimation in structural equation modeling: A discussion and tutorial. Advances in Methods and Practices in Psychological Science, 4(1), 1–17.
View in Google Scholar DOI: https://doi.org/10.1177/2515245920918253 - Wibowo, N., Suryanto, T., Faroqi, A., & Hadiwiyanti, R. (2018). Understanding the dominant factors towards the intention to use Youtube continuously in Indonesia. Atlantis Highlights in Engineering, 1, 465–470.
View in Google Scholar DOI: https://doi.org/10.2991/icst-18.2018.97 - Williams, L. J., Edwards, J. R., & Vandenberg, R. J. (2003). Recent advances in causal modeling methods for organizational and management research. Journal of Management, 29(6), 903–936.
View in Google Scholar DOI: https://doi.org/10.1016/S0149-2063_03_00084-9 - Winton, B. G., & Sabol, M. A. (2022). A multi-group analysis of convenience samples: Free, cheap, friendly, and fancy sources. International Journal of Social Research Methodology, 25(6), 861–876.
View in Google Scholar DOI: https://doi.org/10.1080/13645579.2021.1961187 - Wünderlich, N. V., Wangenheim, F. V., & Bitner, M. J. (2013). High tech and high touch: A framework for understanding user attitudes and behaviors related to smart interactive services. Journal of Service Research, 16(1), 3–20.
View in Google Scholar DOI: https://doi.org/10.1177/1094670512448413 - Yang, Z., & He, L. (2011). Goal, customer experience and purchase intention in a retail context in China: An empirical study. African Journal of Business Management, 5(16), 6738–6746.
View in Google Scholar - Yen, C., & Chiang, M. (2021). Trust me, if you can: A study on the factors that influence consumers’ purchase intention triggered by chatbots based on brain image evidence and self-reported assessments. Behaviour & Information Technology, 40(11), 1177–1194.
View in Google Scholar DOI: https://doi.org/10.1080/0144929X.2020.1743362 - Yeo, S. F., Tan, C. L., Leong, I. Y. C., Palmucci, D. N., & Then, Y. J. (2023). Supplements purchase intention: Young consumer's perspective. British Food Journal, 125(7), 2610–2627.
View in Google Scholar DOI: https://doi.org/10.1108/BFJ-09-2022-0818 - Yuen, M. (2022). Chatbot market in 2022: Stats, trends, and companies in the growing AI chatbot industry. Insider intelligence. Retrieved from https://www.insid erintelligence.com/insights/chatbot-market-stats-trends/ (15.04.2022).
View in Google Scholar - Zhu, Y., Zhang, J., Wu, J., & Liu, Y. (2022). AI is better when I'm sure: The influence of certainty of needs on consumers' acceptance of AI chatbots. Journal of Business Research, 150, 642–652.
View in Google Scholar DOI: https://doi.org/10.1016/j.jbusres.2022.06.044 - Zhu, Y., Zhang, R. R., Zou, Y., & Jin, D. (2023). Investigating customers’ responses to artificial intelligence chatbots in online travel agencies: The moderating role of product familiarity. Journal of Hospitality and Tourism Technology, 14(2), 208–224.
View in Google Scholar DOI: https://doi.org/10.1108/JHTT-02-2022-0041