Abstract
Artificial intelligence (AI) voice assistants possess significant market potential and offer diverse services through voice interaction. However, the influence of anthropomorphic features on consumers’ mind perception and continued use intention, particularly across various age groups, remains underexplored. To address this research gap, we employ mind perception theory, the stimulus–organism-response model, and cognitive load theory to conduct a research model. Using a sample of 303 survey responses, we evaluate the research model and hypotheses through partial least squares analysis. Findings reveal that these features positively affect alleviating loneliness and enhancing perceived usefulness. Additionally, the alleviation of loneliness and perceived usefulness contribute to consumers’ continued use intention and mediate the relationship between anthropomorphic features and continued use intention. Furthermore, the effect of anthropomorphic features on mind perception varies across age groups. This research enhances understanding of the influence of anthropomorphic features on consumers’ mind perception and continued use intention of AI voice assistants, providing valuable insights for product developers and marketers to enhance the consumer experience.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Data Availability
Data is available upon reasonable request.
References
Aaker, J., Vohs, K. D., & Mogilner, C. (2010). Nonprofits are seen as warm and for-profits as competent: Firm stereotypes matter. Journal of Consumer Research, 37(2), 224–237. https://doi.org/10.1086/651566
Aldossari, M. Q., & Sidorova, A. (2020). Consumer acceptance of Internet of Things (IoT): Smart home context. Journal of Computer Information Systems, 60(6), 507–517. https://doi.org/10.1080/08874417.2018.1543000
Ashfaq, M., Yun, J., Yu, S., & Loureiro, S. M. C. (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
Benitez, J., Henseler, J., Castillo, A., & Schuberth, F. (2020). How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research. Information & Management, 57(2), 103168. https://doi.org/10.1016/j.im.2019.05.003
Benlian, A., Klumpe, J., & Hinz, O. (2020). Mitigating the intrusive effects of smart home assistants by using anthropomorphic design features: A multimethod investigation. Information Systems Journal, 30(6), 1010–1042. https://doi.org/10.1111/isj.12243
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921
Cao, X., Gong, M., Yu, L., & Dai, B. (2020). Exploring the mechanism of social media addiction: An empirical study from WeChat users. Internet Research, 30(4), 1305–1328. https://doi.org/10.1108/INTR-08-2019-0347
Charness, N., & Boot, W. R. (2009). Aging and information technology use: Potential and barriers. Current Directions in Psychological Science, 18(5), 253–258. https://doi.org/10.1111/j.1467-8721.2009.01647.x
Cheng, Y., & Jiang, H. (2020). 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. https://doi.org/10.1080/08838151.2020.1834296
Cheng, X., Zhang, X., Cohen, J., & Mou, J. (2022). Human vs AI: Understanding the impact of anthropomorphism on consumer response to chatbots from the perspective of trust and relationship norms. Information Processing & Management, 59(3), 102940. https://doi.org/10.1016/j.ipm.2022.102940
Cho, W.-C., Lee, K. Y., & Yang, S.-B. (2018). What makes you feel attached to smartwatches? The stimulus–organism–response (S–O–R) perspectives. Information Technology & People, 32(2), 319–343. https://doi.org/10.1108/ITP-05-2017-0152
CNNIC. (2022). The 49th statistical report on China’s internet development. Retrieved from http://www.cnnic.net.cn/hlwfzyj/hlwxzbg/ Accessed 15 May 2022
Cuddy, A. J., Fiske, S. T., & Glick, P. (2008). Warmth and competence as universal dimensions of social perception: The stereotype content model and the BIAS map. Advances in Experimental Social Psychology, 40, 61–149. https://doi.org/10.1016/S0065-2601(07)00002-0
de Kervenoael, R., Hasan, R., Schwob, A., & Goh, E. (2020). Leveraging human-robot interaction in hospitality services: Incorporating the role of perceived value, empathy, and information sharing into visitors’ intentions to use social robots. Tourism Management, 78, 104042. https://doi.org/10.1016/j.tourman.2019.104042
Delgosha, M. S., & Hajiheydari, N. (2021). How human users engage with consumer robots? A dual model of psychological ownership and trust to explain post-adoption behaviours. Computers in Human Behavior, 117, 106660. https://doi.org/10.1016/j.chb.2020.106660
Ding, Y. (2019). Looking forward: The role of hope in information system continuance. Computers in Human Behavior, 91, 127–137. https://doi.org/10.1016/j.chb.2018.09.002
Epley, N., Waytz, A., & Cacioppo, J. T. (2007). On seeing human: A three-factor theory of anthropomorphism. Psychological Review, 114(4), 864. https://doi.org/10.1037/0033-295X.114.4.864
Fan, A., Wu, L. L., & Mattila, A. S. (2016). Does anthropomorphism influence customers’ switching intentions in the self-service technology failure context? Journal of Services Marketing, 30(7), 713–723. https://doi.org/10.1108/JSM-07-2015-0225
Ghasemaghaei, M., Hassanein, K., & Benbasat, I. (2019). Assessing the design choices for online recommendation agents for older adults: Older does not always mean simpler information technology. MIS Quarterly, 43(1), 329–346. https://doi.org/10.25300/MISQ/2019/13947
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://doi.org/10.1016/j.chb.2019.01.020
Gray, K., & Wegner, D. M. (2010). Blaming God for our pain: Human suffering and the divine mind. Personality and Social Psychology Review, 14(1), 7–16. https://doi.org/10.1177/1088868309350299
Gray, H. M., Gray, K., & Wegner, D. M. (2007). Dimensions of mind perception. Science, 315(5812), 619–619. https://doi.org/10.1126/science.1134475
Guo, X., Zhang, X., & Sun, Y. (2016). The privacy–personalization paradox in mHealth services acceptance of different age groups. Electronic Commerce Research and Applications, 16, 55–65. https://doi.org/10.1016/j.elerap.2015.11.001
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414–433. https://doi.org/10.1007/s11747-011-0261-6
Hair, J. F., Jr., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
Han, S., & Yang, H. (2018). Understanding adoption of intelligent personal assistants: A parasocial relationship perspective. Industrial Management & Data Systems, 118(3), 618–636. https://doi.org/10.1108/IMDS-05-2017-0214
Hollender, N., Hofmann, C., Deneke, M., & Schmitz, B. (2010). Integrating cognitive load theory and concepts of human–computer interaction. Computers in Human Behavior, 26(6), 1278–1288. https://doi.org/10.1016/j.chb.2010.05.031
Hu, X., Huang, Q., Zhong, X., Davison, R. M., & Zhao, D. (2016). The influence of peer characteristics and technical features of a social shopping website on a consumer’s purchase intention. International Journal of Information Management, 36(6), 1218–1230. https://doi.org/10.1016/j.ijinfomgt.2016.08.005
Hu, Q., Lu, Y., Pan, Z., Gong, Y., & Yang, Z. (2021). Can AI artifacts influence human cognition? The effects of artificial autonomy in intelligent personal assistants. International Journal of Information Management, 56, 102250. https://doi.org/10.1016/j.ijinfomgt.2020.102250
Huang, Y., Gursoy, D., Zhang, M., Nunkoo, R., & Shi, S. (2021). Interactivity in online chat: Conversational cues and visual cues in the service recovery process. International Journal of Information Management, 60, 102360. https://doi.org/10.1016/j.ijinfomgt.2021.102360
Kim, S., & Choudhury, A. (2021). Exploring older adults’ perception and use of smart speaker-based voice assistants: A longitudinal study. Computers in Human Behavior, 124, 106914. https://doi.org/10.1016/j.chb.2021.106914
Kim, A., Cho, M., Ahn, J., & Sung, Y. (2019). Effects of gender and relationship type on the response to artificial intelligence. Cyberpsychology, Behavior, and Social Networking, 22(4), 249–253. https://doi.org/10.1089/cyber.2018.0581
Kim, B., de Visser, E., & Phillips, E. (2022). Two uncanny valleys: Re-evaluating the uncanny valley across the full spectrum of real-world human-like robots. Computers in Human Behavior, 135, 107340. https://doi.org/10.1016/j.chb.2022.107340
Kock, N., & Lynn, G. (2012). Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. Journal of the Association for Information Systems, 13(7). https://doi.org/10.17705/1jais.00302
Lee, S., Lee, N., & Sah, Y. J. (2020). Perceiving a mind in a chatbot: Effect of mind perception and social cues on co-presence, closeness, and intention to use. International Journal of Human-Computer Interaction, 36(10), 930–940. https://doi.org/10.1080/10447318.2019.1699748
Lee, K. Y., Sheehan, L., Lee, K., & Chang, Y. (2021). The continuation and recommendation intention of artificial intelligence-based voice assistant systems (AIVAS): The influence of personal traits. Internet Research, 31(5), 1899–1939. https://doi.org/10.1108/INTR-06-2020-0327
Li, X., & Sung, Y. (2021). Anthropomorphism brings us closer: The mediating role of psychological distance in user–AI assistant interactions. Computers in Human Behavior, 118, 106680. https://doi.org/10.1016/j.chb.2021.106680
Li, Q., Guo, X., Bai, X., & Xu, W. (2018). Investigating microblogging addiction tendency through the lens of uses and gratifications theory. Internet Research, 28(5), 1228–1252. https://doi.org/10.1108/IntR-03-2017-0092
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(3), 575–599. https://doi.org/10.1007/s12525-020-00454-z
Li, X., Zhu, X., Lu, Y., Shi, D., & Deng, W. (2023). Understanding the continuous usage of mobile payment integrated into social media platform: The case of WeChat pay. Electronic Commerce Research and Applications, 60, 101275. https://doi.org/10.1016/j.elerap.2023.101275
Liang, H., Saraf, N., Hu, Q., & Xue, Y. (2007). Assimilation of enterprise systems: The effect of institutional pressures and the mediating role of top management. MIS Quarterly, 31(1), 59–87. https://doi.org/10.2307/25148781
Liew, T. W., Tan, S.-M., Tan, T. M., & Kew, S. N. (2020). Does speaker’s voice enthusiasm affect social cue, cognitive load and transfer in multimedia learning? Information and Learning Sciences, 121(3/4), 117–135. https://doi.org/10.1108/ILS-11-2019-0124
Liu, K., & Tao, D. (2022). The roles of trust, personalization, loss of privacy, and anthropomorphism in public acceptance of smart healthcare services. Computers in Human Behavior, 127, 107026. https://doi.org/10.1016/j.chb.2021.107026
Lu, L., Cai, R., & Gursoy, D. (2019). Developing and validating a service robot integration willingness scale. International Journal of Hospitality Management, 80, 36–51. https://doi.org/10.1016/j.ijhm.2019.01.005
Lv, X., Yang, Y., Qin, D., Cao, X., & Xu, H. (2022). Artificial intelligence service recovery: The role of empathic response in hospitality customers’ continuous usage intention. Computers in Human Behavior, 126, 106993. https://doi.org/10.1016/j.chb.2021.106993
Ma, X., Zhang, X., Guo, X., Lai, K.-H., & Vogel, D. (2021). Examining the role of ICT usage in loneliness perception and mental health of the elderly in China. Technology in Society, 67, 101718. https://doi.org/10.1016/j.techsoc.2021.101718
Mahapatra, S. (2019). Smartphone addiction and associated consequences: Role of loneliness and self-regulation. Behaviour & Information Technology, 38(8), 833–844. https://doi.org/10.1080/0144929X.2018.1560499
Malhotra, G., & Ramalingam, M. (2023). Perceived anthropomorphism and purchase intention using artificial intelligence technology: Examining the moderated effect of trust. Journal of Enterprise Information Management. https://doi.org/10.1108/JEIM-09-2022-0316
McLean, G., & Osei-Frimpong, K. (2019). Hey Alexa… examine the variables influencing the use of artificial intelligent in-home voice assistants. Computers in Human Behavior, 99, 28–37. https://doi.org/10.1016/j.chb.2019.05.009
Mehrabian, A., & Russell, J. (1974). An approach to environmental psychology. The MIT Press.
Mishra, A., Shukla, A., & Sharma, S. K. (2022). Psychological determinants of users’ adoption and word-of-mouth recommendations of smart voice assistants. International Journal of Information Management, 67, 102413. https://doi.org/10.1016/j.ijinfomgt.2021.102413
Moussawi, S., & Benbunan-Fich, R. (2021). The effect of voice and humour on users’ perceptions of personal intelligent agents. Behaviour & Information Technology, 40(15), 1603–1626. https://doi.org/10.1080/0144929X.2020.1772368
Mulcahy, R., Letheren, K., McAndrew, R., Glavas, C., & Russell-Bennett, R. (2019). Are households ready to engage with smart home technology? Journal of Marketing Management, 35(15–16), 1370–1400. https://doi.org/10.1080/0267257X.2019.1680568
Mulcahy, R., Letheren, K., McAndrew, R., Glavas, C., & Russell-Bennett, R. (2022). Are households ready to engage with smart home technology? The Role of Smart Technologies in Decision Making (pp. 4–33). Routledge.
Munnukka, J., Talvitie-Lamberg, K., & Maity, D. (2022). Anthropomorphism and social presence in human–virtual service assistant interactions: The role of dialog length and attitudes. Computers in Human Behavior, 135, 107343. https://doi.org/10.1016/j.chb.2022.107343
Nikou, S. (2019). Factors driving the adoption of smart home technology: An empirical assessment. Telematics and Informatics, 45, 101283. https://doi.org/10.1016/j.tele.2019.101283
Odekerken-Schröder, G., Mele, C., Russo-Spena, T., Mahr, D., & Ruggiero, A. (2020). Mitigating loneliness with companion robots in the COVID-19 pandemic and beyond: An integrative framework and research agenda. Journal of Service Management, 31(6), 1149–1162. https://doi.org/10.1108/JOSM-05-2020-0148
Pal, D., Babakerkhell, M. D., & Zhang, X. (2021). Exploring the determinants of users’ continuance usage intention of smart voice assistants. IEEE Access, 9, 162259–162275. https://doi.org/10.1109/ACCESS.2021.3132399
Park, E. (2020). User acceptance of smart wearable devices: An expectation-confirmation model approach. Telematics and Informatics, 47, 101318. https://doi.org/10.1016/j.tele.2019.101318
Park, K., Kwak, C., Lee, J., & Ahn, J.-H. (2018). The effect of platform characteristics on the adoption of smart speakers: Empirical evidence in South Korea. Telematics and Informatics, 35(8), 2118–2132. https://doi.org/10.1016/j.tele.2018.07.013
Pelau, C., Dabija, D.-C., & Ene, I. (2021). What makes an AI device human-like? The role of interaction quality, empathy and perceived psychological anthropomorphic characteristics in the acceptance of artificial intelligence in the service industry. Computers in Human Behavior, 122, 106855. https://doi.org/10.1016/j.chb.2021.106855
Pentina, I., Hancock, T., & Xie, T. (2023). Exploring relationship development with social chatbots: A mixed-method study of replika. Computers in Human Behavior, 140, 107600. https://doi.org/10.1016/j.chb.2022.107600
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
Qiu, L., & Benbasat, I. (2009). Evaluating anthropomorphic product recommendation agents: A social relationship perspective to designing information systems. Journal of Management Information Systems, 25(4), 145–182. https://doi.org/10.2753/MIS0742-1222250405
Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). Editor’s comments: A critical look at the use of PLS-SEM in" MIS Quarterly". MIS Quarterly, 36(1), iii–xiv. https://doi.org/10.2307/41410402
Saunders, C., Wiener, M., Klett, S., & Sprenger, S. (2017). The impact of mental representations on ICT-related overload in the use of mobile phones. Journal of Management Information Systems, 34(3), 803–825. https://doi.org/10.1080/07421222.2017.1373010
Schanke, S., Burtch, G., & Ray, G. (2021). Estimating the impact of “humanizing” customer service chatbots. Information Systems Research, 32(3), 736–751. https://doi.org/10.1287/isre.2021.1015
Shao, Z., & Chen, K. (2021). Understanding individuals’ engagement and continuance intention of MOOCs: The effect of interactivity and the role of gender. Internet Research, 31(4), 1262–1289. https://doi.org/10.1108/INTR-10-2019-0416
Sharma, M., Joshi, S., Luthra, S., & Kumar, A. (2022). Impact of digital assistant attributes on millennials’ purchasing intentions: A multi-group analysis using PLS-SEM, artificial neural network and fsQCA. Information Systems Frontiers, 1–24. https://doi.org/10.1007/s10796-022-10339-5
Shin, J., Park, Y., & Lee, D. (2018). Who will be smart home users? An analysis of adoption and diffusion of smart homes. Technological Forecasting and Social Change, 134, 246–253. https://doi.org/10.1016/j.techfore.2018.06.029
Sundar, S. S., Go, E., Kim, H.-S., & Zhang, B. (2015). Communicating art, virtually! Psychological effects of technological affordances in a virtual museum. International Journal of Human-Computer Interaction, 31(6), 385–401. https://doi.org/10.1080/10447318.2015.1033912
Tang, J., & Zhang, P. (2020). The impact of atmospheric cues on consumers’ approach and avoidance behavioral intentions in social commerce websites. Computers in Human Behavior, 108, 105729. https://doi.org/10.1016/j.chb.2018.09.038
Tenenhaus, M., Vinzi, V. E., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159–205. https://doi.org/10.1016/j.csda.2004.03.005
Troshani, I., Rao Hill, S., Sherman, C., & Arthur, D. (2021). Do we trust in AI? Role of anthropomorphism and intelligence. Journal of Computer Information Systems, 61(5), 481–491. https://doi.org/10.1080/08874417.2020.1788473
Tu, Q., Wang, K., & Shu, Q. (2005). Computer-related technostress in China. Communications of the ACM, 48(4), 77–81. https://doi.org/10.1145/1053291.1053323
Van Pinxteren, M. M., Wetzels, R. W., Rüger, J., Pluymaekers, M., & Wetzels, M. (2019). Trust in humanoid robots: Implications for services marketing. Journal of Services Marketing, 33(4), 507–518. https://doi.org/10.1108/JSM-01-2018-0045
Wang, W. (2017). Smartphones as social actors? Social dispositional factors in assessing anthropomorphism. Computers in Human Behavior, 68, 334–344. https://doi.org/10.1016/j.chb.2016.11.022
Wang, W.-T., Ou, W.-M., & Chen, W.-Y. (2019). The impact of inertia and user satisfaction on the continuance intentions to use mobile communication applications: A mobile service quality perspective. International Journal of Information Management, 44, 178–193. https://doi.org/10.1016/j.ijinfomgt.2018.10.011
Wang, C., Teo, T. S. H., & Janssen, M. (2021). Public and private value creation using artificial intelligence: An empirical study of AI voice robot users in Chinese public sector. International Journal of Information Management, 61, 102401. https://doi.org/10.1016/j.ijinfomgt.2021.102401
Xiang, Y., & Chae, S. W. (2022). Influence of perceived interactivity on continuous use intentions on the Danmaku video sharing platform: Belongingness perspective. International Journal of Human-Computer Interaction, 38(6), 573–593. https://doi.org/10.1080/10447318.2021.1952803
Xiang, G., Chen, Q., & Li, Q. (2022). How attachment affects users’ continued use intention of tourism mobile platform: A user experience perspective. Frontiers in Psychology, 13, 995384. https://doi.org/10.3389/fpsyg.2022.995384
Xiao, X., Sarker, S., Wright, R. T., Sarker, S., & Mariadoss, B. J. (2020). Commitment and replacement of existing SaaS-delivered applications: A mixed-methods investigation. MIS Quarterly, 44(4), 1811–1857. https://doi.org/10.25300/MISQ/2020/13216
Xie, Y., Zhao, S., Zhou, P., & Liang, C. (2023). Understanding continued use intention of AI assistants. Journal of Computer Information Systems, 1–14. https://doi.org/10.1080/08874417.2023.2167134
Xu, X., Yao, Z., & Teo, T. S. (2020). Moral obligation in online social interaction: Clicking the “like” button. Information & Management, 57(7), 103249. https://doi.org/10.1016/j.im.2019.103249
Yam, K. C., Bigman, Y., & Gray, K. (2021). Reducing the uncanny valley by dehumanizing humanoid robots. Computers in Human Behavior, 125, 106945. https://doi.org/10.1016/j.chb.2021.106945
Yang, H., Lee, H., & Zo, H. (2017). User acceptance of smart home services: An extension of the theory of planned behavior. Industrial Management & Data Systems, 117(1), 68–89. https://doi.org/10.1108/IMDS-01-2016-0017
Yang, S., Huang, L., Zhang, Y., Zhang, P., & Zhao, Y. C. (2021). Unraveling the links between active and passive social media usage and seniors’ loneliness: A field study in aging care communities. Internet Research, 31(6), 2167–2189. https://doi.org/10.1108/INTR-08-2020-0435
Yu, P., Li, H., & Gagnon, M.-P. (2009). Health IT acceptance factors in long-term care facilities: A cross-sectional survey. International Journal of Medical Informatics, 78(4), 219–229. https://doi.org/10.1016/j.ijmedinf.2008.07.006
Zhou, X., Kim, S., & Wang, L. (2019). Money helps when money feels: Money anthropomorphism increases charitable giving. Journal of Consumer Research, 45(5), 953–972. https://doi.org/10.1093/jcr/ucy012
Zhou, S., Li, T., Yang, S., & Chen, Y. (2022). What drives consumers’ purchase intention of online paid knowledge? A stimulus-organism-response perspective. Electronic Commerce Research and Applications, 52, 101126. https://doi.org/10.1016/j.chb.2023.107708
Zhou, P., Zhao, S., Ma, Y., Liang, C., & Zhu, J. (2023). What influences user participation in an online health community? The stimulus-organism-response model perspective. Aslib Journal of Information Management, 75(2), 364–389. https://doi.org/10.1108/AJIM-12-2021-0383
Zhu, L., Li, H., Wang, F.-K., He, W., & Tian, Z. (2020). How online reviews affect purchase intention: A new model based on the stimulus-organism-response (SOR) framework. Aslib Journal of Information Management, 72(4), 463–488. https://doi.org/10.1108/AJIM-11-2019-0308
Funding
This study was supported by the National Social Science Fund of China with a grant number of 21AZD116.
Author information
Authors and Affiliations
Corresponding author
Additional information
Responsible Editor: Babak Abedin
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Zhou, P., Xie, Y. & Liang, C. How to increase consumers’ continued use intention of artificial intelligence voice assistants? The role of anthropomorphic features. Electron Markets 33, 60 (2023). https://doi.org/10.1007/s12525-023-00681-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12525-023-00681-0
Keywords
- Artificial intelligence
- Voice assistant
- Anthropomorphism
- Mind perception theory
- Loneliness
- Age difference