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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

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References

  1. 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. DOI: https://doi.org/10.1007/s12525-020-00414-7
    View in Google Scholar
  2. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. DOI: https://doi.org/10.1016/0749-5978(91)90020-T
    View in Google Scholar
  3. Alagarsamy, S., & Mehrolia, S. (2023). Exploring chatbot trust: Antecedents and behavioural outcomes. Heliyon, 9(5), e16074. DOI: https://doi.org/10.1016/j.heliyon.2023.e16074
    View in Google Scholar
  4. Alsharhan, A., Al-Emran, M., & Shaalan, K. (2023). Chatbot adoption: A multiperspective systematic review and future research agenda. IEEE Transactions on Engineering Management. DOI: https://doi.org/10.1109/TEM.2023.3298360
    View in Google Scholar
  5. 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. DOI: https://doi.org/10.18535/ijsrm/v7i10.em02
    View in Google Scholar
  6. 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. DOI: https://doi.org/10.1016/j.jretconser.2021.102798
    View in Google Scholar
  7. 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. DOI: https://doi.org/10.1016/j.chb.2018.03.051
    View in Google Scholar
  8. Armstrong, J. S., & Overton, T. S. (1977). Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14(3), 396–402. DOI: https://doi.org/10.1177/002224377701400320
    View in Google Scholar
  9. 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. DOI: https://doi.org/10.1002/cb.1590
    View in Google Scholar
  10. Bagozzi, R. P., & Yi, Y. (1989). On the use of structural equation models in experimental designs. Journal of Marketing Research, 26(3), 271–284. DOI: https://doi.org/10.1177/002224378902600302
    View in Google Scholar
  11. Bagozzi, R. P., Yi, Y., & Phillips, L. W. (1991). Assessing construct validity in organizational research. Administrative Science Quarterly, 36(6), 421–458. DOI: https://doi.org/10.2307/2393203
    View in Google Scholar
  12. 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. DOI: https://doi.org/10.1016/j.jretconser.2015.05.008
    View in Google Scholar
  13. Bethlehem, J. (2010). Selection bias in web surveys. International Statistical Review, 78(2), 161–188. DOI: https://doi.org/10.1111/j.1751-5823.2010.00112.x
    View in Google Scholar
  14. 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. DOI: https://doi.org/10.1016/j.jretconser.2023.103674
    View in Google Scholar
  15. 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. DOI: https://doi.org/10.1108/IJQSS-07-2015-0054
    View in Google Scholar
  16. 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. DOI: https://doi.org/10.1177/1094670516662352
    View in Google Scholar
  17. 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. DOI: https://doi.org/10.1007/978-3-319-70284-1_30
    View in Google Scholar
  18. Brosseau-Liard, P. E., & Savalei, V. (2014). Adjusting incremental fit indices for nonnormality. Multivariate Behavioral Research, 49(5), 460–470. DOI: https://doi.org/10.1080/00273171.2014.933697
    View in Google Scholar
  19. 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. DOI: https://doi.org/10.1080/00273171.2012.715252
    View in Google Scholar
  20. 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. DOI: https://doi.org/10.1002/9781118540190.wbeic102
    View in Google Scholar
  21. 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. DOI: https://doi.org/10.1108/IJRDM-08-2020-0312
    View in Google Scholar
  22. 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. DOI: https://doi.org/10.1016/j.jbusres.2022.02.088
    View in Google Scholar
  23. 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. DOI: https://doi.org/10.1002/mar.21862
    View in Google Scholar
  24. 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. DOI: https://doi.org/10.1080/08838151.2020.1834296
    View in Google Scholar
  25. 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. DOI: https://doi.org/10.1111/1468-5973.12319
    View in Google Scholar
  26. 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. DOI: https://doi.org/10.1108/JPBM-05-2020-2907
    View in Google Scholar
  27. Cheung, C., Chiu, P.-Y., & Lee. M. (2011). Online social networks: Why do students use Facebook? Computers in Human Behavior, 27, 1337–1343. DOI: https://doi.org/10.1016/j.chb.2010.07.028
    View in Google Scholar
  28. 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
  29. Chung, K., & Park, R.C. (2019). Chatbot-based healthcare service with a knowledge base for cloud computing. Cluster Computing, 22(S1), 1925–1937. DOI: https://doi.org/10.1007/s10586-018-2334-5
    View in Google Scholar
  30. 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. DOI: https://doi.org/10.1016/j.jbusres.2018.10.004
    View in Google Scholar
  31. 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. DOI: https://doi.org/10.1002/mar.21135
    View in Google Scholar
  32. 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
  33. Dale, R. (2016). The return of the chatbots. Natural Language Engineering, 22(5), 811–817. DOI: https://doi.org/10.1017/S1351324916000243
    View in Google Scholar
  34. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Information Systems Quarterly, 13(3), 319–340. DOI: https://doi.org/10.2307/249008
    View in Google Scholar
  35. 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. DOI: https://doi.org/10.1108/IJRDM-12-2019-0406
    View in Google Scholar
  36. 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. DOI: https://doi.org/10.2139/ssrn.4035662
    View in Google Scholar
  37. 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
  38. 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. DOI: https://doi.org/10.1287/mnsc.2016.2643
    View in Google Scholar
  39. 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. DOI: https://doi.org/10.1080/13527266.2022.2032798
    View in Google Scholar
  40. 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
  41. 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
  42. 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. DOI: https://doi.org/10.1016/j.jbusres.2020.08.058
    View in Google Scholar
  43. 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
  44. 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
  45. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. DOI: https://doi.org/10.1177/002224378101800104
    View in Google Scholar
  46. 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. DOI: https://doi.org/10.1016/j.chb.2017.10.003
    View in Google Scholar
  47. 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. DOI: https://doi.org/10.1108/JSIT-06-2015-0052
    View in Google Scholar
  48. 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. DOI: https://doi.org/10.1057/s41599-023-02239-z
    View in Google Scholar
  49. 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. DOI: https://doi.org/10.1109/TEM.2003.817277
    View in Google Scholar
  50. 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. DOI: https://doi.org/10.1016/j.emj.2007.08.005
    View in Google Scholar
  51. 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
  52. 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. DOI: https://doi.org/10.1007/s12599-022-00755-x
    View in Google Scholar
  53. 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. DOI: https://doi.org/10.1037/0003-066X.59.2.93
    View in Google Scholar
  54. Grimes, G. M., Schuetzler, R. M., & Giboney, J. S. (2021). Mental models and expectation violations in conversational AI interactions. Decision Support System, 144, 113515. DOI: https://doi.org/10.1016/j.dss.2021.113515
    View in Google Scholar
  55. 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
  56. 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. DOI: https://doi.org/10.7906/indecs.19.3.6
    View in Google Scholar
  57. 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. DOI: https://doi.org/10.35940/ijitee.J9366.0981119
    View in Google Scholar
  58. 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. DOI: https://doi.org/10.1108/IJRDM-09-2015-0140
    View in Google Scholar
  59. 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
  60. 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. DOI: https://doi.org/10.6007/IJARBSS/v11-i2/9191
    View in Google Scholar
  61. 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. DOI: https://doi.org/10.1016/j.jbusres.2009.01.019
    View in Google Scholar
  62. 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. DOI: https://doi.org/10.1509/jmkg.70.3.21
    View in Google Scholar
  63. 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. DOI: https://doi.org/10.1007/s12525-023-00629-4
    View in Google Scholar
  64. 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. DOI: https://doi.org/10.1080/10705519909540118
    View in Google Scholar
  65. Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155–172. DOI: https://doi.org/10.1177/1094670517752459
    View in Google Scholar
  66. 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. DOI: https://doi.org/10.3390/joitmc7020125
    View in Google Scholar
  67. 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. DOI: https://doi.org/10.1080/08838151.2020.1834297
    View in Google Scholar
  68. Jain, R., Aagja, J., & Bagdare, S. (2017). Customer experience – A review and research agenda. Journal of Service Theory and Practice, 27(3), 642–662. DOI: https://doi.org/10.1108/JSTP-03-2015-0064
    View in Google Scholar
  69. 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. DOI: https://doi.org/10.21511/im.18(3).2022.02
    View in Google Scholar
  70. 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. DOI: https://doi.org/10.3390/jtaer17010011
    View in Google Scholar
  71. 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. DOI: https://doi.org/10.1002/cb.2034
    View in Google Scholar
  72. Jo, H. (2022). Antecedents of continuance intention of social networking services (SNS): Utilitarian, hedonic, and social contexts. Mobile Information Systems, 2022, 7904124. DOI: https://doi.org/10.1155/2022/7904124
    View in Google Scholar
  73. 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
  74. Kasilingam, D. L. (2020). Understanding the attitude and intention to use smartphone chatbots for shopping. Technology in Society, 62, 101280. DOI: https://doi.org/10.1016/j.techsoc.2020.101280
    View in Google Scholar
  75. 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. DOI: https://doi.org/10.1016/j.knosys.2006.11.014
    View in Google Scholar
  76. 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. DOI: https://doi.org/10.1108/BFJ-07-2022-0564
    View in Google Scholar
  77. 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. DOI: https://doi.org/10.1016/j.tourman.2022.104537
    View in Google Scholar
  78. Kim, M. S. (2018). Factors influencing willingness to provide personal information for personalized recommendations. Computers in Human Behavior, 88, 143–152. DOI: https://doi.org/10.1016/j.chb.2018.06.031
    View in Google Scholar
  79. 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
  80. 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. DOI: https://doi.org/10.3758/s13428-018-1151-3
    View in Google Scholar
  81. 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. DOI: https://doi.org/10.1016/j.chb.2022.107513
    View in Google Scholar
  82. 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. DOI: https://doi.org/10.1080/10919392.2023.2215666
    View in Google Scholar
  83. 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. DOI: https://doi.org/10.1016/j.chb.2012.08.009
    View in Google Scholar
  84. 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. DOI: https://doi.org/10.1016/j.indmarman.2021.08.011
    View in Google Scholar
  85. 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. DOI: https://doi.org/10.1016/j.ijhcs.2017.02.005
    View in Google Scholar
  86. Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69–96. DOI: https://doi.org/10.1509/jm.15.0420
    View in Google Scholar
  87. 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. DOI: https://doi.org/10.1177/0022243718818423
    View in Google Scholar
  88. 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. DOI: https://doi.org/10.1007/s12525-020-00454-z
    View in Google Scholar
  89. 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. DOI: https://doi.org/10.1016/j.ijinfomgt.2014.12.004
    View in Google Scholar
  90. 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. DOI: https://doi.org/10.1016/j.sbspro.2011.09.082
    View in Google Scholar
  91. 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. DOI: https://doi.org/10.1007/s10257-015-0277-4
    View in Google Scholar
  92. 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. DOI: https://doi.org/10.1080/10705511.2010.488999
    View in Google Scholar
  93. 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. DOI: https://doi.org/10.1002/mar.21491
    View in Google Scholar
  94. 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. DOI: https://doi.org/10.4102/sajim.v23i1.1299
    View in Google Scholar
  95. 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. DOI: https://doi.org/10.1016/j.chb.2014.05.042
    View in Google Scholar
  96. 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. DOI: https://doi.org/10.1016/j.dss.2010.11.015
    View in Google Scholar
  97. MacKenzie, S. B., & Podsakoff, P. M. (2012). Common method bias in marketing: Causes, mechanisms, and procedural remedies. Journal of Retailing, 88(4), 542–555. DOI: https://doi.org/10.1016/j.jretai.2012.08.001
    View in Google Scholar
  98. 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. DOI: https://doi.org/10.1016/j.jbusres.2023.113838
    View in Google Scholar
  99. 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. DOI: https://doi.org/10.1108/IJCHM-07-2019-0605
    View in Google Scholar
  100. 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. DOI: https://doi.org/10.1016/j.chb.2019.05.009
    View in Google Scholar
  101. 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. DOI: https://doi.org/10.1177/0022243718822827
    View in Google Scholar
  102. 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. DOI: https://doi.org/10.15358/2511-8676-2020-1-35
    View in Google Scholar
  103. Meyer, C., & Schwager, A. (2007). Understanding customer experience. Harvard Business Review, 85(2), 116–126.
    View in Google Scholar
  104. 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
  105. 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. DOI: https://doi.org/10.1016/j.procs.2022.03.055
    View in Google Scholar
  106. 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. DOI: https://doi.org/10.1016/j.jretconser.2020.102404
    View in Google Scholar
  107. Morgan, R. M., & Hunt, S. D. (1994). The commitment-trust theory of relationship marketing. Journal of Marketing, 58(3), 20–38. DOI: https://doi.org/10.1177/002224299405800302
    View in Google Scholar
  108. 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. DOI: https://doi.org/10.1080/0965254X.2020.1740766
    View in Google Scholar
  109. 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. DOI: https://doi.org/10.1007/s12525-020-00411-w
    View in Google Scholar
  110. 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. DOI: https://doi.org/10.5539/ijbm.v8n6p128
    View in Google Scholar
  111. Nunnally, J. C. (1978). Psychometric theory. New York: McGraw Hill.
    View in Google Scholar
  112. 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
  113. 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. DOI: https://doi.org/10.2753/JEC1086-4415180400
    View in Google Scholar
  114. Pullman, M. E., & Gross, M. A. (2004). Ability of experience design elements to elicit emotions and loyalty behaviors. Decision Sciences, 35(3), 551–578. DOI: https://doi.org/10.1111/j.0011-7315.2004.02611.x
    View in Google Scholar
  115. 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. DOI: https://doi.org/10.1002/mar.21548
    View in Google Scholar
  116. 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. DOI: https://doi.org/10.1108/IJOEM-08-2021-1214
    View in Google Scholar
  117. 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. DOI: https://doi.org/10.1002/mar.21106
    View in Google Scholar
  118. 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. DOI: https://doi.org/10.1016/j.jretconser.2020.102176
    View in Google Scholar
  119. 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. DOI: https://doi.org/10.1016/j.chb.2021.107034
    View in Google Scholar
  120. Rogers, E. M. (1983). Diffusion of innovations. New York: Free Press.
    View in Google Scholar
  121. 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. DOI: https://doi.org/10.1111/j.1468-2370.2010.00280.x
    View in Google Scholar
  122. 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. DOI: https://doi.org/10.3390/jtaer18040086
    View in Google Scholar
  123. 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
  124. 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. DOI: https://doi.org/10.1089/cpb.2004.7.384
    View in Google Scholar
  125. 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. DOI: https://doi.org/10.24136/oc.2022.006
    View in Google Scholar
  126. 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
  127. 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
  128. Tabachnick, B. G., Fidell, L. S., & Ullman, J. B. (2013). Using multivariate statistics. Vol. 6. Boston, MA: Pearson.
    View in Google Scholar
  129. Talwar, S., Dhir, A., Kaur, P., & Mäntymäki, M. (2020). Barriers toward purchasing from online travel agencies. International Journal of Hospitality Management, 89, 102593. DOI: https://doi.org/10.1016/j.ijhm.2020.102593
    View in Google Scholar
  130. 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. DOI: https://doi.org/10.1016/j.jdmm.2015.08.002
    View in Google Scholar
  131. Trevinal, A. M., & Stenger, T. (2014). Toward a conceptualization of the online shopping experience. Journal of Retailing and Consumer Services, 21(3), 314–326. DOI: https://doi.org/10.1016/j.jretconser.2014.02.009
    View in Google Scholar
  132. 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. DOI: https://doi.org/10.1080/15332861.2019.1567188
    View in Google Scholar
  133. 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. DOI: https://doi.org/10.1108/JRIM-12-2019-0200
    View in Google Scholar
  134. 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. DOI: https://doi.org/10.1177/1094670516679272
    View in Google Scholar
  135. Viglia, G., & Dolnicar, S. (2020). A review of experiments in tourism and hospitality. Annals of Tourism Research, 80, 102858. DOI: https://doi.org/10.1016/j.annals.2020.102858
    View in Google Scholar
  136. Walczuch, R., & Lundgren, H. (2004). Psychological antecedents of institution-based consumer trust in e-retailing. Information & Management, 42(1), 159–177. DOI: https://doi.org/10.1016/j.im.2003.12.009
    View in Google Scholar
  137. 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. DOI: https://doi.org/10.3390/su151310028
    View in Google Scholar
  138. 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. DOI: https://doi.org/10.1177/2515245920918253
    View in Google Scholar
  139. 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. DOI: https://doi.org/10.2991/icst-18.2018.97
    View in Google Scholar
  140. 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. DOI: https://doi.org/10.1016/S0149-2063_03_00084-9
    View in Google Scholar
  141. 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. DOI: https://doi.org/10.1080/13645579.2021.1961187
    View in Google Scholar
  142. 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. DOI: https://doi.org/10.1177/1094670512448413
    View in Google Scholar
  143. 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
  144. 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. DOI: https://doi.org/10.1080/0144929X.2020.1743362
    View in Google Scholar
  145. 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. DOI: https://doi.org/10.1108/BFJ-09-2022-0818
    View in Google Scholar
  146. 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
  147. 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. DOI: https://doi.org/10.1016/j.jbusres.2022.06.044
    View in Google Scholar
  148. 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. DOI: https://doi.org/10.1108/JHTT-02-2022-0041
    View in Google Scholar

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