Emotional banking with a digital avatar: A PLS-SEM based study based on para-social relationship theory
Vol 8, Issue 6, 2024
VIEWS - 229 (Abstract) 149 (PDF)
Abstract
Purpose: The major objective of this study is to measure the impact of various attributes, such as social attraction, physical attraction, and task attraction on para-social relationships. The study also seeks to measure how the para-social relationship mediates the association between the three attributes (above-mentioned) on perceived credibility and informational influence, and consumers’ intention to purchase banking products. Study design/methodology: PLS-SEM has been used as it is believed to be most suited for the study due to the multivariate non-normality in the data, and the small sample size. Data has been collected using the 5-point Likert scale from approximately 151 respondents, who were selected using the non-random sampling method based on purposive sampling coupled with convenience-based sampling. The data was collected from January 2023 to August 2023. Findings: Largely, the findings reveal that both social and physical attractions do have a positive impact on the para-social relationship, further leading to perceived credibility and informational influence. Notably, this perceived credibility and informational influence lead to consumers’ intentions to purchase banking products, albeit with the use of artificial intelligence-based chatbots and digital assistants. Originality: This is possibly among the first-ever studies extending the para-social theory for purchasing banking products and services using artificial intelligence-based chatbots and virtual assistants.
Keywords
Full Text:
PDFReferences
Aamodt, A., & Nygård, M. (1995). Different roles and mutual dependencies of data, information, and knowledge—An AI perspective on their integration. Data & Knowledge Engineering, 16, 191-222.
Adam, A., & Sizemore, B. (2013). Parasocial Romance: A Social Exchange Perspective. Interpersona: An International Journal on Personal Relationships, 7(1), 12–25. https://doi.org/10.5964/ijpr.v7i1.106
Aggarwal, P., & McGill, A. L. (2012). When Brands Seem Human, Do Humans Act Like Brands? Automatic Behavioral Priming Effects of Brand Anthropomorphism. Journal of Consumer Research, 39(2), 307–323. https://doi.org/10.1086/662614
Alan M. R., Mary M. S. (2000). Impact of motivation, attraction and parasocial interaction on talk radio listening. Journal of broadcasting and electronic media, 44(4), 635-652. https://doi.org/10.1207/s15506878jobem4404_7
Andrew R. C. (2023). AI-Human Romances Are Flourishing—And This Is Just the Beginning. TIME. Available online: https://time.com/6257790/ai-chatbots-love/ (accessed on 23 February 2023).
Antheunis, M. L., Valkenburg, P. M., & Peter, J. (2012). The quality of online, offline, and mixed mode relationships among the users of social networking site. Cyberpsychology: Journal of Psychological Research on Cyberspace, 6(3). https://doi.org/10.1016/j.chb.2021.106919
Ashfaq, M. (2020). I, Chatbot: Modeling the determinants of users’ satisfaction and continuance intention of AI-powered service agents. Telematics and Informatics, 54, 101473. https://doi.org/10.1016/j.tele.2020.101473
Athapaththu, J.C., & Kulathunga, D. (2018). Factors Affecting Online Purchase Intention: Effects of Technology and Social Commerce. International Business Research., https://doi.org/10.5539/IBR.V11N10P111
Auter, P. J. (1992). Psychometric: TV that talks back: An experimental validation of a parasocial interaction scale. Journal of Broadcasting & Electronic Media, 36(2), 173–181. https://doi.org/10.1080/08838159209364165
Avolio, B. J., Yammarino, F. J., & Bass, B. M. (1991). Identifying Common Methods Variance With Data Collected From A Single Source: An Unresolved Sticky Issue. Journal of Management, 17(3), 571–587. https://doi.org/10.1177/014920639101700303
Aw, E.C.X.; Labrecque, L.I. (2020). Celebrity endorsement in social media contexts: Understanding the role of parasocial interactions and the need to belong. Journal of Consumer Marketing, 37, 895-908. https://doi.org/10.47191/jefms/v6-i10-14
Bartneck, C., Kulić, D., Croft, E., et al. (2008). Measurement Instruments for the Anthropomorphism, Animacy, Likeability, Perceived Intelligence, and Perceived Safety of Robots. International Journal of Social Robotics, 1(1), 71–81. https://doi.org/10.1007/s12369-008-0001-3
Bearden, W. O., Netemeyer, R. G., & Teel, J. E. (1989). Measurement of Consumer Susceptibility to Interpersonal Influence. Journal of Consumer Research, 15(4), 473. https://doi.org/10.1086/209186
Beattie, A., Edwards, A. P., & Edwards, C. (2020). A Bot and a Smile: Interpersonal Impressions of Chatbots and Humans Using Emoji in Computer-mediated Communication. Communication Studies, 71(3), 409–427. https://doi.org/10.1080/10510974.2020.1725082
Becker, J., Ringle, C.M., & Sarstedt, M. (2018). Estimating moderating effects in pls-sem and plsc-sem: interaction term generation*data treatment. Journal of applied structural equation modeling; https://doi.org/10.47263/jasem.2(2)01
Becker, J.-M., Klein, K., & Wetzels, M. (2012). Hierarchical Latent Variable Models in PLS-SEM: Guidelines for Using Reflective-Formative Type Models. Long Range Planning, 45(5–6), 359–394. https://doi.org/10.1016/j.lrp.2012.10.001
Bhattacharjee A and Sanford C. (2006). Influence processes for information technology acceptance: an elaboration likelihood model. MIS Quarterly, 30(4), 805-825. https://doi.org/10.2307/25148755
Blut, M., Wang, C., Wünderlich, N.V. et al. (2021). Understanding anthropomorphism in service provision: a meta-analysis of physical robots, chatbots, and other AI. Journal of academic marketing sciences, 49(4), 632-658. https://doi.org/10.1007/s11747-020-00762-y
Bond, B. J. (2021). he development and influence of parasocial relationships with television characters: A longitudinal experimental test of prejudice reduction through parasocial contact. Communication Research, 48(4), 573-593. https://doi.org/10.1177/0093650219900632
Bonus, J. A., Matthews, N. L., & Wulf, T. (2021). The impact of moral expectancy violations on audiences’ parasocial relationships with movie heroes and villains. Communication Research, 48(4), 550-572. https://doi.org/10.1177/0093650219886516
Burleigh, T. J., Schoenherr, J. R., and Lacroix, G. L. (2013). Does the uncanny valley exist? An empirical test of the relationship between eeriness and the human likeness of digitally created faces. Comput. Hum. Behav., 29, 759–771. https:// 10.1016/j.chb.2012.11.021
Burnkrant, R. E., Ain C. (1975). Informational and Normative Social Influence in Buyer Behavior. Journal of Consumer Research, 2(3), 206-215.
Cheah, J. H., Roldán, J. L., Ciavolino, E., et al. (2021). Sampling weight adjustments in partial least squares structural equation modeling: guidelines and illustrations. Total Quality Management & Business Excellence, 32(13-14), 1594-1613. https://doi.org/10.1002/mar.21640
Ciechanowski, L., Przegalinska, A., Magnuski, M., & Gloor, P. (2019). In the shades of the uncanny valley: An experimental study of human-chatbot interaction. Future Generation of Computer Systems, 92, 539-548. https://doi.org/10.1016/j.future.2018.01.055
Cohen, J. (2013). Statistical Power Analysis for the Behavioral Sciences. Academic Press.
Conway, J. C., & Rubin, A. M. (1991). Psychological predictors of television viewing motivation. Communication Research, 18(4), 443–463. https://doi.org/10.1177/009365091018004001
Croes, E. A., & Antheunis, M. L. (1972). Can we be friends with Mitsuku? A longitudinal study on the process of relationship formation between humans and a social chatbot. Journal of Social and Personal Relationships. 38(1), 279. https://doi.org/10.1177%2F0265407520959463
Crolic, C., Thomaz, F., Hadi, R., & Stephen, A. T. (2022). Blame the bot: Anthropomorphism and anger in customer–chatbot interactions. Journal of marketing, 86(1), 132-148; https://doi.org/10.1177/00222429211045687
Dai, Y., & Walther, J. B. (2018). Vicariously Experiencing Parasocial Intimacy with Public Figures Through Observations of Interactions on Social Media. Human Communication Research, 44(3), 322–342. https://doi.org/10.1093/hcr/hqy003
Darics, E. (2017). E-Leadership or “How to Be Boss in Instant Messaging?” The Role of Nonverbal Communication. International Journal of Business Communication, 57(1), 3–29. https://doi.org/10.1177/2329488416685068
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. https://doi.org/10.1108/ijrdm-12-2019-0406
Deutsch, M., & Gerard, H. B. (1955). A study of normative and informational social influences upon individual judgment. The Journal of Abnormal and Social Psychology, 51(3), 629–636. https://doi.org/10.1037/h0046408
Diaz, M., Saez-Pons, J., Heerink, M., et al. (2013). Emotional factors in robot-based assistive services for elderly at home. 2013 IEEE RO-MAN. https://doi.org/10.1109/roman.2013.6628396
Dixson, M. D., Greenwell, M. R., Rogers-Stacy, C., et al. (2016). Nonverbal immediacy behaviors and online student engagement: bringing past instructional research into the present virtual classroom. Communication Education, 66(1), 37–53. https://doi.org/10.1080/03634523.2016.1209222
Donghee, S. (2021). The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human Computer Studies, 146. https://doi.org/10.1016/j.ijhcs.2020.102551
Edwards, C., Edwards, A., Stoll, B., et al. (2019). Evaluations of an artificial intelligence instructor’s voice: Social Identity Theory in human-robot interactions. Computers in Human Behavior, 90, 357–362. https://doi.org/10.1016/j.chb.2018.08.027
Epley, N., Waytz, A., & Cacioppo, J. T. (2007). On seeing human: A three-factor theory of anthropomorphism. Psychological Review, 114(4), 864–886. https://doi.org/10.1037/0033-295x.114.4.864
Erebak, S., & Turgut, T. (2018). Caregivers’ attitudes toward potential robot coworkers in elder care. Cognition, Technology & Work, 21(2), 327–336. https://doi.org/10.1007/s10111-018-0512-0
Esposito, A., Amorese, T., Cuciniello, M., et al. (2019). Elder user’s attitude toward assistive virtual agents: the role of voice and gender. Journal of Ambient Intelligence and Humanized Computing, 12(4), 4429–4436. https://doi.org/10.1007/s12652-019-01423-x
Faul, F., Erdfelder, E., Buchner, A., et al. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160. https://doi.org/10.3758/brm.41.4.1149
Ferrario, A., Loi, M., & Viganò, E. (2019). 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
Fetscherin, M. (2014). What type of relationship do we have with loved brands? Journal of Consumer Marketing, 31(6/7), 430–440. https://doi.org/10.1108/jcm-05-2014-0969
Field, A. (2009). Discovering statistics using SPSS. Sage publications Ltd.
Foster, G. (2005). Making friends: A nonexperimental analysis of social pair formation. Human relations, 58(11), 1443-1465. https://doi.org/10.1177/0018726705061313
Fournier, S. (1998). Consumers and Their Brands: Developing Relationship Theory in Consumer Research. Journal of Consumer Research, 24(4), 343–353. https://doi.org/10.1086/209515
Geisser, S. (1974). A predictive approach to the random effect model. Biometrika, 61(1), 101-107. https://doi.org/10.2307/2334290
George, D., & Mallery, M. (2010). SPSS for Window Step by Step: A Simple Guide and Referenc17.0 update, 10 ed. Pearson.
Giles, D. C. (2002). Parasocial interaction: A review of the literature and a model for future research.Media Psychology, 4(3), 279-305. https://doi.org/10.1207/S1532785XMEP0403_04
Go, E., & Sundar, S. S. (2019). Humanizing chatbots: The effects of visual, identity and conversational cues on humanness perceptions. Computers in human behavior., 97, 304-316; https:// 10.1016/JCHB.2019.01.020
Gobron, S., Ahn, J., Thalmann, D., et al. (2013). Impact study of nonverbal facial cues on spontaneous chatting with virtual humans. J. Virtual Reality Broadcast, 19, 1-17.
Gong, L., Nass, C. (2007). When a talking-face computer agent is half-human and half-humanoid: Human identity and consistency preference. Human research communication, 93, 163-193. https://doi.org/10.1111/j.1468-2958.2007.00295.x
Gorry, G. A., Westbrook, R. A. (2011). Once more, with feeling: Empathy and technology in customer care. Business Horizons, 125-134. Business Horizons, 54(2), 125-134.
Grant, A. E., Guthrie, K. K., & Ball-Rokeach, S. J. (1991). Television Shopping: A Media System Dependency Perspective. Communication Research, 18, 773-798. https://doi.org/10.1177/009365091018006004
Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). California, Sage Publications.
Hair, J.F., Risher, J.J., Sarstedt, M. and 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
Han, S., & Yang, H. (2018). Understanding adoption of intelligent personal assistants. Industrial Management & Data Systems, 118(3), 618–636. https://doi.org/10.1108/imds-05-2017-0214
Heider, F. (1958). The psychology of interpersonal relations. Wiley.
Hellweg, S. A., Andersen, P. A. (1989). An analysis of source valence instrumentation in the organizational communication literature. Management Communication Quartely, 3, 132-159. https://doi.org/10.1177/0893318989003001009
Henkel, A. P., Čaić, M., Blaurock, M., Okan, M. (2020). Robotic transformative service research: deploying social robots for consumer well-being during Covid-19 and beyond. J Serv Management, 31(6), 1131-1148. https:// 10.1108/JOSM-05-2020-0145
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the academy of marketing science, 43(1), 115-135. https://doi.org/10.1007/s11747-014-0403-8
Hertzog, M. (2008). Considerations in Determining Sample Size for Pilot Studies. Research in Nursing & Health, 31, 180-191. http://doi.org/10.1002/nur.20247
Horton, D., & Richard Wohl, R. (1956). Mass Communication and Para-Social Interaction. Psychiatry, 19(3), 215–229. https://doi.org/10.1080/00332747.1956.11023049
Huang, D.-H., & Chueh, H.-E. (2021). Chatbot usage intention analysis: Veterinary consultation. Journal of Innovation & Knowledge, 6(3), 135–144. https://doi.org/10.1016/j.jik.2020.09.002
Huang, M.-H., & Rust, R. T. (2020). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50. https://doi.org/10.1007/s11747-020-00749-9
Hulland, J., Baumgartner, H., & Smith, K. M. (2017). Marketing survey research best practices: evidence and recommendations from a review of JAMS articles. Journal of the Academy of Marketing Science, 46(1), 92–108. https://doi.org/10.1007/s11747-017-0532-y
Indriasari, E., Gaol, F. L., & Matsuo, T. (2019). Digital Banking Transformation: Application of Artificial Intelligence and Big Data Analytics for Leveraging Customer Experience in the Indonesia Banking Sector. 2019 8th International Congress on Advanced Applied Informatics (IIAI-AAI). https://doi.org/10.1109/iiai-aai.2019.00175
Janarthanam, S. (2017). ands-on chatbots and conversational UI development: build chatbots and voice user interfaces with Chatfuel, Dialogflow, Microsoft Bot Framework, Twilio, and Alexa Skills. Packt Publishing Ltd.
Joseph, W.B. (1982). The credibility of physically attractive communicators: A review. Journal of advertising, 11(3), 15–24. https://doi.org/10.1080/00913367.1982.10672807
Keller, E., Berry, J. (2003). The Influentials: One American in Ten Tells the Other NINE how to Vote, Where to Eat, and What to Buy. Simon and Schuster.
Kepuska, V., & Bohouta, G. (2018). Next-generation of virtual personal assistants (Microsoft Cortana, Apple Siri, Amazon Alexa and Google Home). 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC). https://doi.org/10.1109/ccwc.2018.8301638
Kim, H. C., & Kramer, T. (2015). Do Materialists Prefer the “Brand-as-Servant”? The Interactive Effect of Anthropomorphized Brand Roles and Materialism on Consumer Responses. Journal of Consumer Research, 42(2), 284–299. https://doi.org/10.1093/jcr/ucv015
Kim, J., Rubin, AM. (1997). The variable influence of audience activity on media effects. Communication Research, 24(2), 107-135. https://doi.org/10.1177/009365097024002001
Kim, J., Song, H. (2016). Celebrity’s self-disclosure on Twitter and parasocial relationships: A mediating role of social presence. Comput. Hum. Behav., 62, 570-577. https://doi.org/10.1016/j.chb.2016.03.083
Kim, K. S. (2018). The effects of interpersonal attraction on service justice. Journal of service marketing, 32, 728-738. https://doi.org/10.1108/JSM-06-2017-0200
Kompatsiari, K., Ciardo, F., Tikhanoff, V., et al. (2019). It’s in the eyes: the engaging role of eye contact in HRI. International Journal of Social Robotics, 1-11. https://doi.org/10.1007/s12369-019-00565-4
Kong, H. (2013). Face interface will empower employee. IJACT, 5, 193-199.
Labrecque, L. I. (2014). Fostering Consumer–Brand Relationships in Social Media Environments: The Role of Parasocial Interaction. Journal of Interactive Marketing, 28(2), 134–148. https://doi.org/10.1016/j.intmar.2013.12.003
Lee, M.-S., & Park, J. (2017). Television Shopping at Home to Alleviate Loneliness Among Older Consumers. ASIA MARKETING JOURNAL, 18(4), 139. https://doi.org/10.15830/amj.2017.18.4.139
Lee, M.-S., & Park, J. (2017). Television Shopping at Home to Alleviate Loneliness Among Older Consumers. Asia Marketing Journal, 18(4). https://doi.org/10.53728/2765-6500.1439
Levinger, G. (1980). Toward the analysis of close relationships. Journal of Experimental Social Psychology, 6, 510-544. https://doi.org/10.1016/0022-1031(80)90056-6
Lim, C. M., & Kim, Y.-K. (2011). Older consumers’ TV home shopping: Loneliness, parasocial interaction, and perceived convenience. Psychology & Marketing, 28(8), 763-780. https://doi.org/10.3390/su14159476
Lo, S. K. (2008). The nonverbal communication functions of emoticons in computer-mediated communication. Cyberpsychology and behaviour, 11, 595-597. http://doi.org/10.1089/cpb.2007.0132
Lunardo, R. (2016). The interacting effect of virtual agents’ gender and dressing style on attractiveness and subsequent consumer online behavior. Journal of retail consumer services, 30, 59-66. https://doi.org/10.1016/j.jretconser.2016.01.006
MacKenzie, S. B., & Podsakoff, P. M. (2012). Common method bias in marketing: Causes, mechanisms, and procedural remedies. Journal of retailing, 88(4), 542-555. https:// doi.org/10.1016/j.jretai.2012.08.0
McCarthy, J., & Hayes, P. J. (1981). Some Philosophical Problems from the Standpoint of Artificial Intelligence. Readings in Artificial Intelligence, 431–450. https://doi.org/10.1016/b978-0-934613-03-3.50033-7
McCroskey, J. C., & McCain, T. A. (1974). The measurement of interpersonal attraction. Speech Monographs, 41(3), 261–266. https://doi.org/10.1080/03637757409375845
McCroskey, J. C., & Young, T. J. (1981). Ethos and credibility: The construct and its measurement after three decades. Communication studies, 24-34. https://doi.org/10.1177/0893318989003001009
McCroskey, J. C., Hamilton, P. R., & Weiner, A. N. (1974). Human Communication Research, 42-52. https://doi.org/10.1111/j.1468-2958.1974.tb00252.x
McCroskey, J.C., Larson, C.E., Knapp M.L. (1981). An introduction to Interpersonal Communication. Prentice Hall.
McCroskey, L. L., McCroskey, J. C., & Richmond, V. P. (2006). Analysis and improvement of the mesaurement of interpersonal attraction and homophily. Communication quarterly, 54(1), 1-31. https://doi.org/10.1080/01463370500270322
McCroskey, L., McCroskey, J., & Richmond, V. (2006). Analysis and Improvement of the Measurement of Interpersonal Attraction and Homophily. Communication Quarterly, 54(1), 1–31. https://doi.org/10.1080/01463370500270322
McCruskey, J.C., McCain, T.A. (1974). The measurement of interpersonal attraction. Speech monograph, 41, 261-266. https://doi.org/10.1080/03637757409375845
McKinsey & Company. (2021). Building the AI Bank of the future. McKinsey.
Miller, D. T., Downs, J. S., & Prentice, D. A. (1998). Minimal conditions for the creation of a unit relationship: The social bond between birthmates. European Journal of Social Psychology, 28, 475-481.
Mittal, B. (1999). The Advertising of Services: Meeting the Challenge of Intangibility. Journal of Service Research, 2(1), 98-116. https://doi.org/10.1177/109467059921008
Montano, D.E., Kasprzyk, D. (2015) Theory of Reasoned Action, Theory of Planned Behavior, and the Integrated Behavioral Model. In: Karen, G., Barbara, R. and Viswanath, K. (editors). Health Behavior: Theory, Research and Practice book, 5th ed. Jossey-Bass, San Francisco, pp. 95-124.
Mori, M. (1970). The uncanny valley. Energy, 7, 33-35.
Mou, Y., & Xu, K. (2017). The media inequality: Comparing the initial human-human and human-AI social interactions. Computers in Human Behavior, 72, 432–440. https://doi.org/10.1016/j.chb.2017.02.067
Nass, C., & Moon, Y. (2000). Machines and Mindlessness: Social Responses to Computers. Journal of Social Issues, 56(1), 81–103. Portico. https://doi.org/10.1111/0022-4537.00153
Ned Kock. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e Collaboration, 11(4), 1-10; https://doi.org/10.4018/ijec.2015100101
Newberry, C.R.; Klemz, B.R.; Boshoff, C. (2003). Managerial implications of predicting purchase behavior from purchase intentions: A retail patronage case study. J. Serv. Mark, 17, 609-620. https://doi.org/10.1108/08876040310495636
Ng, M., Coopamootoo, K.P., Toreini, E., et al. (2020). Simulating the Effects of Social Presence on Trust, Privacy Concerns & Usage Intentions in Automated Bots for Finance. 2020 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW), 190-199.
Niculescu, A., Hofs, D., van Dijk, B., and Nijholt, A. (2010). How the agent’s gender influences users’ evaluation of a QA system. Proceedings of the International Conference on User Science and Engineering (i-USEr 2010). Shah Alam, Selangor, Malaysia: IEEE. https://doi.org/10.3389/fcomp.2023.1138501
Nitzl, C., Roldan, J. L., & Cepeda, G. (2016). Mediation analysis in partial least squares path modeling. Industrial Management & Data Systems, 116(9), 1849–1864. https://doi.org/10.1108/imds-07-2015-0302
Noor, N., Rao Hill, S., & Troshani, I. (2021). Artificial Intelligence Service Agents: Role of Parasocial Relationship. Journal of Computer Information Systems, 62(5), 1009–1023. https://doi.org/10.1080/08874417.2021.1962213
Novak, T. P., & Hoffman, D. L. (2018). Relationship journeys in the internet of things: a new framework for understanding interactions between consumers and smart objects. Journal of the Academy of Marketing Science, 47(2), 216–237. https://doi.org/10.1007/s11747-018-0608-3
Novikova, J. (2016). Designing emotionally expressive behaviour: Intelligibility and predictability in human-robot interaction. University of bath.
Pelachaud, C. (2009). Modelling multimodal expression of emotion in a virtual agent. Philosophical Transactions of the Royal Society B: Biological Sciences, 364, 3539-3548. https:// doi.org/10.1098/rstb.2009.0186
Pornpitakpan, C. (2004). The effect of celebrity endorsers’ perceived credibility on product purchase intention: The case of Singaporeans. Journal of international consumer marketing, 16(2), 55-74. https://doi.org/10.1300/J046v16n02_04
Putri, A. (1998). What sample size is "enough" in internet survey research? Interpersonal Computing and Technology: An Electronic Journal for the 21st Century AECT. 6. AECT.
Rawlins, W. K. (1992). Friendship matters: communication, dialectics, and the life course. (1992). Choice Reviews Online, 30(01), 30-0615-30–0615. https://doi.org/10.5860/choice.30-0615
rice, L.L., Feick, L.F. and Higie, R.H. (1987). Preference heterogeneity and co-orientation as determinants of referent influence in the choice of service providers. University of Pittsburgh.
Rihl, A., & Wegener, C. (2017). YouTube celebrities and parasocial interaction: Using feedback channels in mediatized relationships. Convergence: The International Journal of Research into New Media Technologies, 25(3), 554–566. https://doi.org/10.1177/1354856517736976
Ring, L., Utami, D., and Bickmore, T. (2014). The right agent for the job? The effects of agent visual appearance on task domain. Proceedings of International Conference on Intelligent Virtual Agents (IVA 2014). Springer International Publishing. pp. 374-384.
Ringle, C. M., Wende, S., Becker, J.-M. (2015). SmartPLS 3. Boenningstedt: SmartPLS GmbH. Germany.
Rubin, A. M., & Rubin, R. B. (1985). Interface of personal and mediated communication: A research agenda. Critical Studies in Mass Communication, 2(1), 36–53. https://doi.org/10.1080/15295038509360060
Rubin, R. B., & McHugh, M. P. (1987). Development of parasocial interaction relationships. Journal of Broadcasting & Electronic Media, 31(3), 279–292. https://doi.org/10.1080/08838158709386664
Rungtusanatham, M., Miller, J. W., & Boyer, K. K. (2014). Theorizing, testing, and concluding for mediation in SCM research: tutorial and procedural recommendations. Journal of Operations Management, 32(3), 99-113. https://doi.org/10.1016/j.jom.2014.01.002
Rust, R. T. (2019). The future of marketing. Int J Res Mark., 7(1), 15-26. https://doi.org/10.1016/j.ijresmar.2019.08.002
Rzepka, C., Berger, B., & Hess, T. (2022). Voice assistant vs. Chatbot–examining the fit between conversational agents’ interaction modalities and information search tasks. Information Systems Frontiers, 24(3), 839-856. https://doi.org/10.1007/s10796-021-10226-5
Salem, M., Eyssel, F., Rohlfing, K., et al. (2013). To Err is Human(-like): Effects of Robot Gesture on Perceived Anthropomorphism and Likability. International Journal of Social Robotics, 5(3), 313–323. https://doi.org/10.1007/s12369-013-0196-9
Sarstedt, M., Ringle, C. M., & Hair, J.F. (2017). Treating Unobserved Heterogeneity in PLS-SEM: A Multi-method Approach. in Noonan R and Latan H (editors). Partial Least Squares Structural Equation Modelling: Basic Concepts, Methodological Issues and Applications. pp. 199-217.
Sarstedt, M., Bengart, P., Shaltoni, A. M., et al. (2017). The use of sampling methods in advertising research: a gap between theory and practice. International Journal of Advertising, 37(4), 650–663. https://doi.org/10.1080/02650487.2017.1348329
Saxena, C., & Kaur, V. (2018). Identification of limiting factors of customer complaint redressal system of banks: A study of banks of Punjab from bankers’ perspective. International Journal of Creative Research Thoughts, 6(2), 583-589.
Sharma, A., Dwivedi, Y. K., Arya, V., et al. (2021). Does SMS advertising still have relevance to increase consumer purchase intention? A hybrid PLS-SEM-neural network modelling approach. Computers in Human Behavior, 124, 106919. https://doi.org/10.1016/j.chb.2021.106919
Sheehan, B., Jin, H. S., & Gottlieb, U. (2020). Customer service chatbots: Anthropomorphism and adoption. Journal of business research, 115, 14-24. https:// 10.1016/j.jbusres.2020.04.030
Shmueli, G., Sarstedt, M., Hair, J.F., et al. (2019). Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322-2347. https://doi.org/10.1108/EJM-02-2019-0189
Sienkiewicz, A. (2021). Chatbot Statistics and Trends You Need to Know in 2021. Available online: https://www.tidio.com/blog/chatbot-statistics/ (accessed on 16 January 2021).
Simons, H. W., Berkowitz, N. N., & Moyer, R. J. (1970). Similarity, credibility, and attitude change: A review and a theory. Psychological Bulletin, 73(1), 1–16. https://doi.org/10.1037/h0028429
Sokolova, K., & Kefi, H. (2020). Instagram and YouTube bloggers promote it, why should I buy? How credibility and parasocial interaction influence purchase intentions. Journal of Retailing and Consumer Services, 53, 101742. https://doi.org/10.1016/j.jretconser.2019.01.011
Sparrow, R. (2020). Do Robots Have Race?: Race, Social Construction, and HRI. IEEE Robotics & Automation Magazine, 27(3), 144–150. https://doi.org/10.1109/mra.2019.2927372
Stone, M. (1974). Cross‐Validatory Choice and Assessment of Statistical Predictions. Journal of the Royal Statistical Society: Series B (Methodological), 36(2), 111–133. Portico. https://doi.org/10.1111/j.2517-6161.1974.tb00994.x
Straßmann, C., and Krämer, N. C. (2017). A categorization of virtual agent appearances and a qualitative study on age-related user preferences. Proceedings of International Conference on Intelligent Virtual Agents (IVA 2017). Springer International Publishing. pp. 413-422.
Stroessner, S. J., & Benitez, J. (2019). The social perception of humanoid and non-humanoid robots. International Journal of Social Robotics, 11(2), 305-315. https://doi.org/10.1007/s12369-018-0502-7
Su, B.-C.; Wu, L.-W.; Chang, Y.-Y.-C.; Hong, R.-H. (2021). Influencers on Social Media as References: Understanding the Importance of Parasocial Relationships. Sustainability, 13, 10919. https://doi.org/10.3390/su131910919
Tesser, A. (1988). Toward a self-evaluation maintenance model of social behavior. Advances in Experimental Social Psychology, 21, 181-227. https://doi.org/10.1016/S0065-2601(08)60227-0
Thomas MJ, Wirtz B.W. and Weyerer J.C. (2019). Determinants of online review credibility and its impact on consumers purchase intention. Journal of Electronic Commerce Research, 20(1), 1-20.
Tillmann-Healy, L. M. (2003). Friendship as Method. Qualitative Inquiry, 9(5), 729–749. https://doi.org/10.1177/1077800403254894
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/10.3390/su151612441
Winterich, K. P., & Nenkov, G. Y. (2015). Save Like the Joneses. Journal of Service Research, 18(3), 384–404. https://doi.org/10.1177/1094670515570268
Wünderlich, N.V., and Paluch, S. (2017). A nice and friendly chat with a bot. 38th International conference on information systems, Association for Information Systems. pp. 1-11.
Xiang, L., Zheng, X., Lee, M. K. O., et al. (2016). Exploring consumers’ impulse buying behavior on social commerce platform: The role of parasocial interaction. International Journal of Information Management, 36(3), 333–347. https://doi.org/10.1016/j.ijinfomgt.2015.11.002
Xie T, Yang X, Rose D. (2023). Converse Task-Oriented Dialogue System Simplifies Chatbot Building, Handles Complex Tasks. Salesforce AI Research. Available online: https://blog.salesforceairesearch.com/converse-task-oriented-dialogue-system/ (accessed on 1 November 2023).
Xu, K, & Lombard, M. (2016). Media are social actors: Expanding the CASA paradigm in the 21st Century . Presented at the Annual Conference of the International Communication Association, Fukuoka, Japan, (pp. Presented at the Annual Conference of the International Communication Association, Fukuoka, Japan); https://doi.org/10.1177/1461444819851479
Xuan, C. L. (2003). Inducing AI powered chatbots use for customer purchase: the role of information value and innovative technology. Journal of systems and information technology; https://doi.org/10.47672/ejt.1561
Yao, W., Baumann, C., Tan, L.P. (2015). Wine Brand Category Choice and Confucianism: A Purchase Motivation Comparison of Caucasian, Chinese and Korean Consumers. In: Martínez-López, F., Gázquez-Abad, J., Sethuraman, R. (editors). Advances in National Brand and Private Label Marketing. Proceedings in Business and Economics. Springer.
Yuan, C. L., Kim, J., & Kim, S. J. (2016). Parasocial relationship effects on customer equity in the social media context. Journal of business research, 69(9), 3795-3803. https://doi.org/10.1016/j.jbusres.2015.12.071
Zhang, T., Kaber, DB., Zhu, B., et al. (2010). Service robot feature design effects on user perceptions and emotional responses. Intelligent service robotics, 3(2), 73-88. https://doi.org/10.1177/1541931213571285
Zheng, X., Men, J., Xiang, L., Yang, F. (2020). Role of technology attraction and parasocial interaction in social shopping websites. International Journal of Information Management, 51, 102-104. https://doi.org/10.1016/j.ijinfomgt.2019.102043
Zhou, T. (2021). Understanding online health community users information adoption intention: an elaboration likelihood model perspective. Online Information Review, 46(1), 134-146; https://doi.org/10.1108/oir-09-2020-0412
DOI: https://doi.org/10.24294/jipd.v8i6.4086
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Nishi Malhotra, P. Saravanan, Pankaj Shah
License URL: https://creativecommons.org/licenses/by/4.0/
This site is licensed under a Creative Commons Attribution 4.0 International License.