Artificial Intelligence Service Agents: Role of Parasocial Relationship

References

• Noor N, Rao Hill S, Troshani I. Recasting service quality for AI-based service. Australas Marketing J. 2021:183933492110050. doi:10.1177/183933492110050.
   Google Scholar
• Brill TM, Munoz L, Miller RJ. Siri, Alexa, and other digital assistants: a study of customer satisfaction with artificial intelligence applications. J. Mark. Manag 2019;35(15–16):1401–36. doi:10.1080/0267257X.2019.1687571.
   Web of Science ®Google Scholar
• Lee KM, Park N, Song H. Can a robot be perceived as a developing creature? Effects of a robot’s long-term cognitive developments on its social presence and people’s social responses toward it. Hum Commun Res. 2005;31(4):538–63. doi:10.1111/j.1468-2958.2005.tb00882.x. Cited in: Scopus
   Web of Science ®Google Scholar
• De Keyser A, Köcher S, Alkire L, Verbeeck C, Kandampully J. Frontline service technology infusion: conceptual archetypes and future research directions. J Serv Manage. 2019;30(1):156–83. doi:10.1108/JOSM-03-2018-0082.
   Web of Science ®Google Scholar
• Huang M-H, Rust RT. Engaged to a robot? The role of AI in service. J Serv Res. 2020;24(1):30–41. doi:10.1177/1094670520902266.
   Web of Science ®Google Scholar
• Wirtz J, Patterson PG, Kunz WH, Gruber T, Lu VN, Paluch S, Martins A. Brave new world: service robots in the frontline. J Serv Manage. 2018;29(5):907–31. doi:10.1108/JOSM-04-2018-0119
   Google Scholar
• Davenport T, Guha A, Grewal D, Bressgott T. How artificial intelligence will change the future of marketing. J Acad Marketing Sci. 2020;48(1):24–42. doi:10.1007/s11747-019-00696-0.
   Web of Science ®Google Scholar
• Rust RT. The future of marketing. Int J Res Mark. 2019;37(1):15–26. doi:10.1016/j.ijresmar.2019.08.002.
   Web of Science ®Google Scholar
• Huang M-H, Rust RT. Artificial intelligence in service. J Serv Res. 2018;21(2):155–72. doi:10.1177/1094670517752459.
   Web of Science ®Google Scholar
• Huang M-H, Rust RT. A strategic framework for artificial intelligence in marketing. J Acad Marketing Sci. 2021;49(1):30–50. doi:10.1007/s11747-020-00749-9.
   Web of Science ®Google Scholar
• Shankar V. How artificial intelligence (AI) is reshaping retailing. J Retail. 2018;94(4):vi–xi. doi:10.1016/S0022-4359(18)30076-9.
   Web of Science ®Google Scholar
• Mordor Intelligence M. Social robot market - Growth, trends and forecasts (2020-2025). Mordor Intelligence; 2020 [cited 2020 22 October]. Available from: https://www.mordorintelligence.com/industry-reports/social-robots-market
   Google Scholar
• Adroit Market Research A. Intelligent Virtual Assistant (IVA) Market to grow at 33% CAGR during forecast period (2020-2025) - insights on growth drivers, size and share analysis, key trends, leading players, and business opportunities: adroit market research. GlobeNewswire; 2020 [cited 2020 26 February]. Available from: https://www.globenewswire.com/news-release/2020/02/24/1988963/0/en/Intelligent-Virtual-Assistant-IVA-Market-to-grow-at-33-CAGR-during-forecast-period-2020-2025-Insights-on-Growth-Drivers-Size-and-Share-Analysis-Key-Trends-Leading-Players-and-Busin.html
   Google Scholar
• Bock DE, Wolter JS, Ferrell O. Artificial intelligence: disrupting what we know about services. J Serv Marketing. 2020;34(3):317–34. doi:10.1108/JSM-01-2019-0047.
   Web of Science ®Google Scholar
• Pantano E, Pizzi G. Forecasting artificial intelligence on online customer assistance: evidence from chatbot patents analysis. J. Retail. Consum. Serv 2020;55(102096):1–9. doi:10.1016/j.jretconser.2020.102096.
   Google Scholar
• Meyer P, Jonas JM, Roth A. Frontline employees’ acceptance of and resistance to service robots in stationary retail - an exploratory interview study. SMR - J Serv Manage Res. 2020;4(1):21–34. doi:10.15358/2511-8676-2020-1-21.
   Google Scholar
• Meyer-Waarden L, Pavone G, Poocharoentou T, Prayatsup P, Ratinaud M, Tison A, Torné S. How service quality influences customer acceptance and usage of chatbots?SMR - J Serv Manage Res. 2020;4(1):35–51. doi:10.15358/2511-8676-2020-1-35
   Google Scholar
• Cane S, McCarthy R. Analyzing the factors that affect information systems use: a task-technology fit meta-analysis. J Comput Inf Syst. 2009;50(1):108–23. doi:10.1080/08874417.2009.11645368.
   Web of Science ®Google Scholar
• Konok V, Korcsok B, Miklósi Á, Gácsi M. Should we love robots?–The most liked qualities of companion dogs and how they can be implemented in social robots. Comput Human Behav. 2018;80:132–42. doi:10.1016/j.chb.2017.11.002.
   Web of Science ®Google Scholar
• Pesce NL. A quarter of us want to have sex with Alexa. MarketWatch; 2017 [cited 2020 28 June]. Available from: https://www.marketwatch.com/story/a-quarter-of-us-want-to-have-sex-with-alexa-2017-04-06-148821
   Google Scholar
• Peeters A, Haselager P. Designing virtuous sex robots. Int J Soc Robot. 2021;13(1):55–66. doi:10.1007/s12369-019-00592-1.
   Web of Science ®Google Scholar
• Van Doorn J, Mende M, Noble SM, Hulland J, Ostrom AL, Grewal D, Petersen JA. Domo arigato Mr. Roboto: emergence of automated social presence in organizational frontlines and customers’ service experiences. J Serv Res. 2017;20(1):43–58. doi:10.1177/1094670516679272
   Google Scholar
• Kiron D, Unruh G. Even if AI can cure loneliness - should it? MIT Sloan Manage Rev. 2019;60(2):1–4. Cited in: eLibrary; ProQuest One Academic
   Web of Science ®Google Scholar
• Lu V, Wirtz J, Kunz W, Paluch S, Gruber T, Martins A, Patterson PG. Service robots, customers and service employees: what can we learn from the academic literature and where are the gaps? J Serv Theor Pract. 2020;30(3):361–91. doi:10.1108/JSTP-04-2019-0088.
   Web of Science ®Google Scholar
• Horton D, Wohl RR. Mass communication and para-social interaction: observations on intimacy at a distance. Psychiatry. 1956;19(3):215–29. doi:10.1080/00332747.1956.11023049.
   PubMed Web of Science ®Google Scholar
• Horton D, Strauss A. Interaction in audience-participation shows. Am J Sociol. 1957;62(6):579–87. doi:10.1086/222106.
   Web of Science ®Google Scholar
• Han S, Yang H. Understanding adoption of intelligent personal assistants: a parasocial relationship perspective. Indus Manage Data Syst. 2018;118(3):618–36. doi:10.1108/IMDS-05-2017-0214.
   Web of Science ®Google Scholar
• Cronin JJJ, Brady MK, Hult GTM. Assessing the effects of quality, value, and customer satisfaction on consumer behavioral intentions in service environments. J Retail. 2000;76(2):193–218. doi:10.1016/S0022-4359(00)00028-2.
   Web of Science ®Google Scholar
• Rubin RB, McHugh MP. Development of parasocial interaction relationships. J Broadcast Electron Media. 1987;31(3):279–92. [Conference Paper]. Cited in: Scopus. doi:10.1080/08838158709386664.
   Web of Science ®Google Scholar
• Henkel AP, Čaić M, Blaurock M, Okan M. Robotic transformative service research: deploying social robots for consumer well-being during Covid-19 and beyond. J Serv Manage. 2020;31(6):1131–48. doi:10.1108/JOSM-05-2020-0145.
   Web of Science ®Google Scholar
• Zeithaml VA, Berry LL, Parasuraman A. The behavioral consequences of service quality. J Mark. 1996;60(2):31–46. doi:10.1177/002224299606000203.
   Web of Science ®Google Scholar
• Lim JS, Choe M-J, Zhang J, Noh G-Y. The role of wishful identification, emotional engagement, and parasocial relationships in repeated viewing of live-streaming games: a social cognitive theory perspective. Comput Human Behav. 2020;108(106327):1–10. doi:10.1016/j.chb.2020.106327.
   Google Scholar
• Troebs -C-C, Wagner T, Heidemann F. Transformative retail services: elevating loyalty through customer well-being. J. Retail. Consum. Serv 2018;45:198–206. doi:10.1016/j.jretconser.2018.09.009.
   Web of Science ®Google Scholar
• Su L, Huang S, Chen X. Effects of service fairness and service quality on tourists’ behavioral intentions and subjective well-being. J Travel Tour Mark. 2015;32(3):290–307. doi:10.1080/10548408.2014.896766.
   Web of Science ®Google Scholar
• Su L, Swanson SR, Chen X. The effects of perceived service quality on repurchase intentions and subjective well-being of Chinese tourists: the mediating role of relationship quality. Tourism Manage. 2016;52:82–95. doi:10.1016/j.tourman.2015.06.012.
   Web of Science ®Google Scholar
• Xu Y, Shieh C-H, van Esch P, Ling I-L. AI customer service: task complexity, problem-solving ability, and usage intention. Australas. Mark. J 2020;28(4):189–99. doi:10.1016/j.ausmj.2020.03.005.
   Web of Science ®Google Scholar
• Van Der Heijden H. User acceptance of hedonic information systems. MIS Quarterly. 2004;28(4):695–704. doi:10.2307/25148660.
   Web of Science ®Google Scholar
• Sillice MA, Morokoff PJ, Ferszt G, Bickmore T, Bock BC, Lantini R, Velicer WF. Using relational agents to promote exercise and sun protection: assessment of participants’ experiences with two interventions. J Med Internet Res. 2018;20(2):e48. doi:10.2196/jmir.7640
   Google Scholar
• Wolters MK, Kelly F, Kilgour J. Designing a spoken dialogue interface to an intelligent cognitive assistant for people with dementia. Health Informatics J. 2016;22(4):854–66. doi:10.1177/1460458215593329.
   PubMed Web of Science ®Google Scholar
• Pandey AK, Gelin R. A mass-produced sociable humanoid robot: pepper: the first machine of its kind. IEEE Robot Autom Mag. 2018;25(3):40–48. doi:10.1109/MRA.2018.2833157.
   Web of Science ®Google Scholar
• Coghlan S, Vetere F, Waycott J, Neves BB. Could social robots make us kinder or crueller to humans and animals? Int J Soc Robot. 2019;11(5):741–51. doi:10.1007/s12369-019-00583-2.
   Web of Science ®Google Scholar
• Ta V, Griffith C, Boatfield C, Wang X, Civitello M, Bader H, DeCero E, Loggarakis A. User experiences of social support from companion chatbots in everyday contexts: thematic analysis. J Med Internet Res. 2020;22(3):e16235. doi:10.2196/16235
   Google Scholar
• Ewers K, Baier D, Höhn N. Siri, do I like you? Digital voice assistants and their acceptance by consumers. SMR - J Serv Manage Res. 2020;4(1):52–66. doi:10.15358/2511-8676-2020-1-52.
   Google Scholar
• Hoy MB, Alexa S. Cortana, and more: an introduction to voice assistants. Med Ref Serv Q. 2018;37(1):81–88. doi:10.1080/02763869.2018.1404391.
   PubMedGoogle Scholar
• Rubin AM, Perse EM, Powell RA. Loneliness, parasocial interaction, and local television news viewing. Hum Commun Res. 1985;12(2):155–80. doi:10.1111/j.1468-2958.1985.tb00071.x.
   Web of Science ®Google Scholar
• Dibble JL, Hartmann T, Rosaen SF. Parasocial interaction and parasocial relationship: conceptual clarification and a critical assessment of measures. Hum Commun Res. 2016;42(1):21–44. doi:10.1111/hcre.12063.
   Web of Science ®Google Scholar
• Rosaen SF, Dibble JL. Clarifying the role of attachment and social compensation on parasocial relationships with television characters. Commun Stud. 2016;67(2):147–62. doi:10.1080/10510974.2015.1121898.
   Web of Science ®Google Scholar
• Hartmann T. Parasocial interactions and paracommunication with new media characters. New York: Routledge; 2008.
   Google Scholar
• Hartmann T, Stuke D, Daschmann G. Positive parasocial relationships with drivers Affect suspense in racing sport spectators. J Media Psychol Theor Meth Appl. 2008;20(1):24–34. doi:10.1027/1864-1105.20.1.24.
   Google Scholar
• Tsiotsou RH. The role of social and parasocial relationships on social networking sites loyalty. Comput Human Behav. 2015;48:401–14. doi:10.1016/j.chb.2015.01.064.
   Web of Science ®Google Scholar
• Zheng X, Men J, Xiang L, Yang F. Role of technology attraction and parasocial interaction in social shopping websites. Int J Inf Manage. 2020;51(102043):1–13. doi:10.1016/j.ijinfomgt.2019.102043. Cited in: Scopus
   Google Scholar
• Lu L, Cai R, Gursoy D. Developing and validating a service robot integration willingness scale. Int. J. Hosp. Manag 2019;80:36–51. doi:10.1016/j.ijhm.2019.01.005.
   Web of Science ®Google Scholar
• Gursoy D, Chi OH, Lu L, Nunkoo R. Consumers acceptance of artificially intelligent (AI) device use in service delivery. Int J Inf Manage. 2019;49:157–69. doi:10.1016/j.ijinfomgt.2019.03.008.
   Web of Science ®Google Scholar
• Song SY Modeling the consumer acceptance of retail service robots; 2017.
   Google Scholar
• Niemelä M, Arvola A, Aaltonen I Monitoring the acceptance of a social service robot in a shopping mall: first results. In: Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-robot Interaction; 2017. p. 225–26. Vienna Austria.
   Google Scholar
• Troshani I, Rao Hill S, Sherman C, Arthur D. Do we trust in AI? Role of anthropomorphism and intelligence. J Comput Inf Syst. 2020:1–11. doi:10.1080/08874417.2020.1788473.
   Google Scholar
• Kepuska V, Bohouta G Next-generation of virtual personal assistants (microsoft cortana, apple siri, amazon alexa and google home). In: 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC). Las Vegas, NV, USA: IEEE; 2018. p. 99–103.
   Google Scholar
• Qiu L, Benbasat I. Evaluating anthropomorphic product recommendation agents: a social relationship perspective to designing information systems. Manag. Inf. Syst 2009;25(4):145–81. doi:10.2753/MIS0742-1222250405.
   Web of Science ®Google Scholar
• Fetscherin M. What type of relationship do we have with loved brands? J Consum Marketing. 2014;31(6/7):430–40. doi:10.1108/JCM-05-2014-0969.
   Google Scholar
• Venkatesh V, Thong JYL, Xu X. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly. 2012;36(1):157–78. doi:10.2307/41410412.
   Web of Science ®Google Scholar
• Allam H, Bliemel M, Spiteri L, Blustein J, Ali-Hassan H. Applying a multi-dimensional hedonic concept of intrinsic motivation on social tagging tools: a theoretical model and empirical validation. Int J Inf Manage. 2019;45:211–22. doi:10.1016/j.ijinfomgt.2018.11.005.
   Web of Science ®Google Scholar
• Aldossari MQ, Sidorova A. Consumer acceptance of Internet of Things (IoT): smart home context. J Comput Inf Syst. 2020;60(6):507–17. doi:10.1080/08874417.2018.1543000.
   Web of Science ®Google Scholar
• Suki NM, Suki NM. Exploring the relationship between perceived usefulness, perceived ease of use, perceived enjoyment, attitude and subscribers’ intention towards using 3G mobile services. J Inf Technol Manage. 2011;22:1–7.
   Google Scholar
• Teo T, Noyes J. An assessment of the influence of perceived enjoyment and attitude on the intention to use technology among pre-service teachers: a structural equation modeling approach. Comput Educ. 2011;57(2):1645–53. doi:10.1016/j.compedu.2011.03.002.
   Web of Science ®Google Scholar
• Hamari J. Why do people buy virtual goods? Attitude toward virtual good purchases versus game enjoyment. Int J Inf Manage. 2015;35(3):299–308. doi:10.1016/j.ijinfomgt.2015.01.007.
   Web of Science ®Google Scholar
• Ramayah T, Ignatius J. Impact of perceived usefulness, perceived ease of use and perceived enjoyment on intention to shop online. ICFAI J Syst Manage (IJSM). 2005;3:36–51.
   Google Scholar
• Fryer LK, Ainley M, Thompson A, Gibson A, Sherlock Z. Stimulating and sustaining interest in a language course: an experimental comparison of Chatbot and Human task partners. Comput Human Behav. 2017;75:461–68. doi:10.1016/j.chb.2017.05.045.
   Web of Science ®Google Scholar
• Giles DC. Parasocial interaction: a review of the literature and a model for future research. Media Psychol. 2002;4(3):279–305. doi:10.1207/S1532785XMEP0403_04. Cited in: Scopus
   Web of Science ®Google Scholar
• Diener E. Subjective well-being. Psychol Bull. 1984;95(3):542–75. doi:10.1037/0033-2909.95.3.542.
   PubMed Web of Science ®Google Scholar
• Anderson L, Ostrom AL, Corus C, Fisk RP, Gallan AS, Giraldo M, Mende M, Mulder M, Rayburn SW, Rosenbaum MS, et al. Transformative service research: an agenda for the future. J Bus Res. 2013;66(8):1203–10. doi:10.1016/j.jbusres.2012.08.013.
   Web of Science ®Google Scholar
• Diener E, Emmons RA, Larsen RJ, Griffin S. The satisfaction with life scale. J Pers Assess. 1985;49(1):71–75. doi:10.1207/s15327752jpa4901_13.
   PubMed Web of Science ®Google Scholar
• Diener E, Suh EM, Lucas RE, Smith HL. Subjective well-being: three decades of progress. Psychol Bull. 1999;125(2):276–302. doi:10.1037/0033-2909.125.2.276.
   Web of Science ®Google Scholar
• Larsen RJ, Diener E, Emmons RA. An evaluation of subjective well-being measures. Soc Indic Res. 1985;17(1):1–17. doi:10.1007/BF00354108.
   Web of Science ®Google Scholar
• Pavot W, Diener E. The affective and cognitive context of self-reported measures of subjective well-being. Soc Indic Res. 1993;28(1):1–20. doi:10.1007/BF01086714.
   Web of Science ®Google Scholar
• Lyubomirsky S, Lepper HS. A measure of subjective happiness: preliminary reliability and construct validation. Soc Indic Res. 1999;46(2):137–55. doi:10.1023/A:1006824100041.
   Web of Science ®Google Scholar
• Anderson L, Ostrom AL. Transformative service research: advancing our knowledge about service and well-being. J Serv Res. 2015;18(3):243–49. doi:10.1177/1094670515591316.
   Web of Science ®Google Scholar
• Ostrom AL, Parasuraman A, Bowen DE, Patricio L, Voss CA. Service research priorities in a rapidly changing context. J Serv Res. 2015;18(2):127–59. doi:10.1177/1094670515576315.
   Web of Science ®Google Scholar
• Rafaeli A, Altman D, Gremler DD, Huang M-H, Grewal D, Iyer B, Parasuraman A, De Ruyter K. The future of frontline research: invited commentaries. J Serv Res. 2017;20(1):91–99. doi:10.1177/1094670516679275.
   Web of Science ®Google Scholar
• Rosenbaum MS. Transformative service research: focus on well-being. The Serv Indus J. 2015;35(7–8):363–67. doi:10.1080/02642069.2015.1025061.
   Web of Science ®Google Scholar
• Parasuraman A. Technology Readiness Index (TRI) a multiple-item scale to measure readiness to embrace new technologies. J Serv Res. 2000;2(4):307–20. doi:10.1177/109467050024001.
   Google Scholar
• Venkatesh V, Bala H. Technology acceptance model 3 and a research agenda on interventions. Decis Sci. 2008;39(2):273–315. doi:10.1111/j.1540-5915.2008.00192.x.
   Web of Science ®Google Scholar
• Chiu C-M, Cheng H-L, Huang H-Y, Chen C-F. Exploring individuals’ subjective well-being and loyalty towards social network sites from the perspective of network externalities: the Facebook case. Int J Inf Manage. 2013;33(3):539–52. doi:10.1016/j.ijinfomgt.2013.01.007.
   Web of Science ®Google Scholar
• Cohen S. Social relationships and health. Am Psychologist. 2004;59(8):676–84. doi:10.1037/0003-066X.59.8.676.
   PubMed Web of Science ®Google Scholar
• Gefen D, Straub DW. Gender differences in the perception and use of e-mail: an extension to the technology acceptance model. MIS Quarterly. 1997;21(4):389–400. doi:10.2307/249720.
   Web of Science ®Google Scholar
• Ramkissoon H, Nunkoo R. More than just biological sex differences: examining the structural relationship between gender identity and information search behavior. J. Hosp. Tour. Res 2012;36(2):191–215. doi:10.1177/1096348010388662.
   Web of Science ®Google Scholar
• Kamide H, Kawabe K, Shigemi S, Arai T. Development of a psychological scale for general impressions of humanoid. Adv Robot. 2013;27(1):3–17. doi:10.1080/01691864.2013.751159.
   Web of Science ®Google Scholar
• Schiappa E, Allen M, Gregg PB. Parasocial relationships and television: a meta-analysis of the effects. New York: Routledge; 2007.
   Google Scholar
• Hu M. The influence of a scandal on parasocial relationship, parasocial interaction, and parasocial breakup. Psychol. Pop. Media Cult 2016;5(3):217–31. doi:10.1037/ppm0000068.
   Web of Science ®Google Scholar
• Lin J-SC, Hsieh P-L. Assessing the self-service technology encounters: development and validation of SSTQUAL scale. J. Retail 2011;87(2):194–206. doi:10.1016/j.jretai.2011.02.006.
   Web of Science ®Google Scholar
• Orel FD, Kara A. Supermarket self-checkout service quality, customer satisfaction, and loyalty: empirical evidence from an emerging market. J. Retail. Consum. Serv 2014;21(2):118–29. doi:10.1016/j.jretconser.2013.07.002.
   Google Scholar
• Considine E, Cormican K. Self-service technology adoption: an analysis of customer to technology interactions. Procedia Comput Sci. 2016;100:103–09. doi:10.1016/j.procs.2016.09.129.
   Google Scholar
• Moussawi S, Koufaris M Perceived intelligence and perceived anthropomorphism of personal intelligent agents: scale development and validation. In: Proceedings of the 52nd Hawaii international conference on system sciences; 2019. p. 115–24. Hawaii, USA. Available from: https://hdl.handle.net/10125/59452
   Google Scholar
• Bartneck C, Kulić D, Croft E, Zoghbi S. Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots. Int J Soc Robot. 2009;1(1):71–81. doi:10.1007/s12369-008-0001-3.
   Web of Science ®Google Scholar
• Weiss A, Bartneck C Meta analysis of the usage of the godspeed questionnaire series. In: 2015 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). Kobe, Japan: IEEE; 2015. p. 381–88.
   Google Scholar
• Whang C, Im H. I Like Your Suggestion!” the role of humanlikeness and parasocial relationship on the website versus voice shopper’s perception of recommendations. Psychol Marketing. 2020;38(4):581–595.
   Web of Science ®Google Scholar
• Netemeyer RG, Bearden WO, Sharma S. Scaling procedures: issues and applications. London, United Kingdom: Sage Publications; 2003.
   Google Scholar
• Dabholkar PA. Consumer evaluations of new technology-based self-service options: an investigation of alternative models of service quality. Int J Res Mark. 1996;13(1):29–51. doi:10.1016/0167-8116(95)00027-5.
   Google Scholar
• Liu F, Xiao B, Lim ET, Tan C-W. The art of appeal in electronic commerce: understanding the impact of product and website quality on online purchases. Internet Res. 2017;27(4):752–71. doi:10.1108/IntR-09-2016-0280.
   Web of Science ®Google Scholar
• Basak E, Calisir F. An empirical study on factors affecting continuance intention of using Facebook. Comput Human Behav. 2015;48:181–89. doi:10.1016/j.chb.2015.01.055.
   Web of Science ®Google Scholar
• Vimalkumar M, Sharma SK, Singh JB, Dwivedi YK. ‘Okay google, what about my privacy?’: user’s privacy perceptions and acceptance of voice based digital assistants. Comput Human Behav. 2021;120:106763. doi:10.1016/j.chb.2021.106763.
   Web of Science ®Google Scholar
• PwC. Artificial intelligence: touchpoints with consumers; 2018. Available from: https://www.pwc.at/de/publikationen/branchen-und-wirtschaftsstudien/ai_intelligence_2018.pdf
   Google Scholar
• Oppenheimer DM, Meyvis T, Davidenko N. Instructional manipulation checks: detecting satisficing to increase statistical power. J Exp Soc Psychol. 2009;45(4):867–72. doi:10.1016/j.jesp.2009.03.009.
   Web of Science ®Google Scholar
• MacKenzie SB, Podsakoff PM. Common method bias in marketing: causes, mechanisms, and procedural remedies. J Retail. 2012;88(4):542–55. doi:10.1016/j.jretai.2012.08.001.
   Web of Science ®Google Scholar
• Herzog AR, Bachman JG. Effects of questionnaire length on response quality. Public Opin Q. 1981;45(4):549–59. doi:10.1086/268687.
   Web of Science ®Google Scholar
• Olson C, Kemery K. Voice report; 2019. Available from: https://advertiseonbing-blob.azureedge.net/blob/bingads/media/insight/whitepapers/2019/04%20apr/voice-report/bingads_2019_voicereport.pdf
   Google Scholar
• Hair JF, Risher JJ, Sarstedt M, Ringle CM. When to use and how to report the results of PLS-SEM. Eur Bus Rev. 2019;31(1):2–24. doi:10.1108/EBR-11-2018-0203.
   Web of Science ®Google Scholar
• Sarstedt M, Hair JF Jr, Nitzl C, Ringle CM, Howard MC. Beyond a tandem analysis of SEM and PROCESS: use of PLS-SEM for mediation analyses. Int J Market Res. 2020;62(3):288–99. doi:10.1177/1470785320915686.
   Web of Science ®Google Scholar
• Hair JF, Black WC, Babin BJ, Anderson RE. Multivariate data analysis, Eighth. London, United Kingdom: Cengage Learning EMEA; 2019.
   Google Scholar
• Benitez J, Henseler J, Castillo A, Schuberth F. How to perform and report an impactful analysis using partial least squares: guidelines for confirmatory and explanatory IS research. J Inf Manage. 2020;57(2):103168. doi:10.1016/j.im.2019.05.003.
   Google Scholar
• Hair JF, Sarstedt M, Ringle CM. Rethinking some of the rethinking of partial least squares. Eur J Mark. 2019;53(4):566–84. doi:10.1108/EJM-10-2018-0665.
   Web of Science ®Google Scholar
• Fernandes T, Oliveira E. Understanding consumers’ acceptance of automated technologies in service encounters: drivers of digital voice assistants adoption. J Bus Res. 2021;122:180–91. doi:10.1016/j.jbusres.2020.08.058.
   Web of Science ®Google Scholar
• Henseler J, Hubona G, Ray PA. Using PLS path modeling in new technology research: updated guidelines. Ind. Manag. Data Syst 2016;116(1):2–20. doi:10.1108/IMDS-09-2015-0382.
   Web of Science ®Google Scholar
• Bagozzi RP, Yi Y. Specification, evaluation, and interpretation of structural equation models. J Acad Marketing Sci. 2012;40(1):8–34. doi:10.1007/s11747-011-0278-x.
   Web of Science ®Google Scholar
• Bagozzi RP, Yi Y. On the evaluation of structural equation models. J Acad Marketing Sci. 1988;16(1):74–94. doi:10.1007/BF02723327.
   Google Scholar
• Henseler J, Ringle CM, Sarstedt M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J Acad Marketing Sci. 2015;43(1):115–35. doi:10.1007/s11747-014-0403-8.
   Web of Science ®Google Scholar
• Fuller CM, Simmering MJ, Atinc G, Atinc Y, Babin BJ. Common methods variance detection in business research. J Bus Res. 2016;69(8):3192–98. doi:10.1016/j.jbusres.2015.12.008.
   Web of Science ®Google Scholar
• Kock N. Common method bias in PLS-SEM: a full collinearity assessment approach. Int. J. e-Collab 2015;11:1–10.
   Google Scholar
• De Graaf MM, Allouch SB. Exploring influencing variables for the acceptance of social robots. Rob Auton Syst. 2013;61(12):1476–86. doi:10.1016/j.robot.2013.07.007.
   Web of Science ®Google Scholar
• Sundar SS, Jung EH, Waddell TF, Kim KJ. Cheery companions or serious assistants? Role and demeanor congruity as predictors of robot attraction and use intentions among senior citizens. Int J Hum Comput Stud. 2017;97:88–97. doi:10.1016/j.ijhcs.2016.08.006.
   Web of Science ®Google Scholar
• Robinson H, MacDonald B, Broadbent E. The role of healthcare robots for older people at home: a review. Int J Soc Robot. 2014;6(4):575–91. doi:10.1007/s12369-014-0242-2.
   Web of Science ®Google Scholar
• Hartmann T. Parasocial interaction, parasocial relationships, and well-being. In: The Routledge handbook of media use and well-being: international perspectives on theory and research on positive media effects;  New York, USA: Routledge; 2016. p. 131–44.
   Google Scholar
• Venkatesh V, Thong JYL, Xu X. Unified theory of acceptance and use of technology: a synthesis and the road ahead. JAIS. 2016;17(5):328–76. doi:10.17705/1jais.00428.
   Google Scholar
• Shi S, Chen Y, Chow WS. Key values driving continued interaction on brand pages in social media: an examination across genders. Comput Human Behav. 2016;62:578–89. doi:10.1016/j.chb.2016.04.017.
   Web of Science ®Google Scholar
• Fung P This is why AI has a gender problem. World Economic Forum; 2019 [cited 2020 27 October]. Available from: https://www.weforum.org/agenda/2019/06/this-is-why-ai-has-a-gender-problem
   Google Scholar
• Rojas-Méndez JI, Parasuraman A, Papadopoulos N. Demographics, attitudes, and technology readiness: a cross-cultural analysis and model validation. Marketing Intell Plann. 2017;35(1):18–39. doi:10.1108/MIP-08-2015-0163.
   Web of Science ®Google Scholar
• Roberts J, Yaya L, Manolis C. The invisible addiction: cell-phone activities and addiction among male and female college students. J Behav Addict. 2014;3(4):254–65. doi:10.1556/jba.3.2014.015.
   PubMed Web of Science ®Google Scholar
• Junco R, Merson D, Salter DW. The effect of gender, ethnicity, and income on college students’ use of communication technologies. Cyberpsychol Behav Soc Netw. 2010;13(6):619–27. doi:10.1089/cyber.2009.0357.
   PubMed Web of Science ®Google Scholar
• Junco R, Cole‐Avent GA. An introduction to technologies commonly used by college students. New Dir Stud Serv. 2008;2008(124):3–17. doi:10.1002/ss.292.
   Google Scholar
• Geser H Are girls (even) more addicted? Some gender patterns of cell phone usage. Sociology in Switzerland: Sociology of the Mobile Phone; 2006 [cited 2021 24 May]. Available from: https://socio.ch/mobile/t_geser3.pdf
   Google Scholar
• MacDorman KF, Vasudevan SK, Ho -C-C. Does Japan really have robot mania? Comparing attitudes by implicit and explicit measures. AI Soc. 2009;23(4):485–510. doi:10.1007/s00146-008-0181-2.
   Google Scholar
• Funk C, Tyson A, Kennedy B, Johnson C 4. Publics express a mix of views on AI, childhood vaccines, food and space issues. Pew Research Center; 2020 [cited 2021 13 January]. Available from: https://www.pewresearch.org/science/2020/09/29/publics-express-a-mix-of-views-on-ai-childhood-vaccines-food-and-space-issues/
   Google Scholar
• Greenwood DN, Long CR. Attachment, belongingness needs, and relationship status predict imagined intimacy with media figures. Communic Res. 2011;38(2):278–97. doi:10.1177/0093650210362687.
   Web of Science ®Google Scholar