AI聊天機器人已經普及並融入我們的日常生活，市場規模不斷擴大，也引來許多競爭對手。使用者由於可以選擇不同平台上的AI聊天機器人，往往同時使用多個平台，或者從舊平台轉移到新平台。因此，如何維持使用者的持續使用與訂閱意圖成為重要的議題。而擬人化是一種常見的市場行銷方式，但是仍未有人討論擬人化AI聊天機器人是否可以讓使用者持續使用與訂閱。
因此，本研究旨在研究可愛化或表情化的AI聊天機器人對使用者的感受影響，並且分析這些感受對AI聊天機器人持續使用和訂閱意圖的影響。同時，本研究利用延伸型整合科技接受模型(UTAUT2)作為衡量感受之基礎架構。本研究使用問卷調查蒐集資料，並利用SPSS Statistic 29.0和SmartPLS4進行MANOVA和結構方程模型分析方法，以檢驗可愛化或表情化是否最終會影響持續使用意圖和持續訂閱意圖。
本研究MANOVA結果證實，可愛化或表情化的AI聊天機器人可以提升使用者的績效預期與享樂動機，而本研究進行結構方程模型分析，結果顯示績效預期與享樂動機會正向影響進使用者的持續使用與訂閱意圖。因此，服務提供商亦可透過可愛化或表情化AI聊天機器人以提高持續使用與訂閱的行為意圖。

AI chatbots have become popular and integrated into our daily lives, with the market continually expanding and attracting numerous competitors. Users often engage with multiple platforms simultaneously or migrate from old platforms to new ones, given the variety of AI chatbots available. Therefore, maintaining users' intention to continue using and subscribing has become a crucial issue.
Anthropomorphism is a common marketing approach, but its impact on user continuance and subscription intention to anthropomorphized AI chatbots remains underexplored. Therefore, this study aims to investigate how cuteness or facial expression on AI chatbots influence user perceptions and analyze the effects of these perceptions on user continuance and subscription intention. The study utilizes the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) as the foundational framework for measuring perceptions. Data is collected through a questionnaire survey, and MANOVA and Structural Equation Modeling (SEM) using SPSS Statistics 29.0 and SmartPLS4 are employed to examine whether cuteness or expressiveness ultimately affects continuance and subscription intention.
The MANOVA results confirm that cuteness or facial expression on AI chatbots enhances users' performance expectancy and hedonic motivation. The SEM analysis in this study indicates that performance expectancy and hedonic motivation positively influence users’ continuance and subscription intention. Therefore, service providers can enhance behavioral intentions for continuance and subscription with cuteness or facial expression on AI chatbots.

Abushanab, E., & Pearson, J. M. (2007). Internet banking in Jordan: the unified theory of acceptance and use of technology (UTAUT) perspective. Journal of Systems and Information Technology, 9(1), 78–97. https://doi.org/10.1108/13287260710817700
Adapa, A., Nah, F. F.-H., Hall, R. H., Siau, K., & Smith, S. N. (2018). Factors influencing the adoption of smart wearable devices. International Journal of Human–Computer Interaction, 34(5), 399–409. https://doi.org/10.1080/10447318.2017.1357902
Aggarwal, McGill, A. L. (2007). Is That Car Smiling at Me? Schema Congruity as a Basis for Evaluating Anthropomorphized Products. Journal of Consumer Research, 34(4), 468–479. https://doi.org/10.1086/518544
Aggarwal, P., & McGill, A. (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
Ahmed, M. H., Bogale, A. D., Tilahun, B., Kalayou, M. H., Klein, J., Mengiste, S. A., & Endehabtu, B. F. (2020). Intention to use electronic medical record and its predictors among health care providers at referral hospitals, north-West Ethiopia, 2019: Using unified theory of acceptance and use technology 2 (UTAUT2) model. BMC Medical Informatics and Decision Making, 20(1), 1–11. https://doi.org/10.1186/s12911-020-01222-x
Alalwan, A. A., Dwivedi, Y. K., & Rana, N. P. (2017). Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust. International Journal of Information Management, 37(3), 99–110. https://doi.org/10.1016/j.ijinfomgt.2017.01.002
Al-Azawei, A., & Alowayr, A. (2020). Predicting the intention to use and hedonic motivation for mobile learning: A comparative study in two Middle Eastern countries. Technology in Society, 62, 101325. https://doi.org/10.1016/j.techsoc.2020.101325
Al-Emran, M., Al-Qudah, A. A., Abbasi, G. A., Al-Sharafi, M. A., & Iranmanesh, M. (2023). Determinants of Using AI-Based Chatbots for Knowledge Sharing: Evidence From PLS-SEM and Fuzzy Sets (fsQCA). IEEE Transactions on Engineering Management, 1–15. https://doi.org/10.1109/TEM.2023.3237789
Anthony, B., Kamaludin, A., & Romli, A. (2021). Predicting academic staffs behaviour intention and actual use of blended learning in higher education: Model development and validation. Technology, Knowledge and Learning, 1–47. https://doi.org/10.1007/s10758-021-09579-2
Beverland M B, Farrelly F J. (2010). The quest for authenticity in consumption: Consumers’ purposive choice of authentic cues to shape experienced outcomes. Journal of Consumer Research, 36(5), 838–856. https://doi.org/10.1086/615047
Bettman, Luce, M. F., and Payne, J. W. (1998). Constructive Consumer Choice Processes, Journal of consumer research, 25(3), 187–217. https://doi.org/10.1086/209535
Brown, S. A., & Venkatesh, V. (2005). Model of Adoption of Technology in the Household: A Baseline Model Test and Extension Incorporating Household Life Cycle. MIS Quarterly, 29 (4), 399–426. https://doi.org/10.2307/25148690
Buck, R.W.; Savin, V.J.; Miller, R.E.; Caul, W.F. (1972) Communication of affect through facial expressions in humans. J. Pers. Soc. Psychol, 23(3), 362– 371. https://doi.org/10.1037/h0033171
Caudwell, C., & Lacey, C. (2020). What do home robots want? The ambivalent power of cuteness in robotic relationships. Convergence, 26(4), 956–968. https://doi.org/10.1177/1354856519837792
C. Bartneck, T. Suzuki, T. Kanda, and T. Nomura (2006). The Influence of People’s Culture and Prior Experiences with Aibo on Their Attitude towards Robots. AI & Society (21), 217–230. https://doi.org/10.1007/s00146-006-0052-7
Chakava, M. H., Mberia, H. K., & Gatero, G. (2018). Relationship between performance expectancy and use of new media in scholarly communication by academic staff in public universities in Kenya. IOSR Journal of Humanities and Social Science, 23(6), 49–59. https://doi.org/10.9790/0837-2306044959
Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling. Education and Information Technologies, 25(5), 3443–3463. https://doi.org/10.1007/s10639-020-10159-7
Childers, T. L., Carr, C. L., Peck, J., and Carson, S. (2001). Hedonic and Utilitarian Motivations for Online Retail Shopping Behavior. Journal of Retailing, 77 (4), 511– 535. https://doi.org/10.1016/S0022-4359(01)00056-2
Christou, P., Simillidou, A., & Stylianou, M. C. (2020). Tourists’ perceptions regarding the use of anthropomorphic robots in tourism and hospitality. International Journal of Contemporary Hospitality Management, 32(11), 3665–3683. https://doi.org/10.1108/IJCHM-05-2020-0423
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
de Graaf M, Allouch S, Van Dijk JA (2016). Long-term acceptance of social robots in domestic environments: insights from a user’s perspective. In: AAAI Spring Symposia.
Dietvorst BJ, Simmons JP, Massey C (2018). Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them. Management Science. 64(3), 1155–1170. https://doi.org/10.1287/mnsc.2016.2643
Eckhardt, A., Laumer, S., & Weitzel, T. (2009). Who influences whom? Analyzing workplace referents’ social influence on its adoption and non-adoption. Journal of Information Technology, 24(1), 11–24. https://doi.org/10.1057/jit.2008.31
Edlund, J., Beskow, J., (2007). Pushy versus meek – using avatars to influence turn-taking behaviour. In: INTERSPEECH-2007 (pp.682–685). https://doi.org/10.21437/Interspeech.2007-289
Ekman, P. (1972). Universal and cultural differences in facial expression of emotion. In J. K. Cole (Ed.), Nebraska Symposium on Motivation (pp. 207-286). Lincoln, NE: University of Nebraska Press.
Elfenbein, H. A., & Ambady, N. (2002a). On the universality and cultural specificity of emotion recognition: A meta-analysis. Psychological Bulletin, 128, 203–235. https://doi.org/10.1037/0033-2909.128.2.203
Epley, Waytz, A., and 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
Fox, J., Ahn, S. J., Janssen, J. H., Yeykelis, L., Segovia, K. Y., & Bailenson, J. N. (2015). Avatars versus agents: A meta-analysis quantifying the effect of agency on social influence. Human-Computer Interaction, 30(5), 401–432. https://doi.org/10.1080/07370024.2014.921494
Froehlich A (2018) Pros and cons of chatbots in the IT helpdesk. Informationweek.com. Accessed October 18, 2018, https://www.informationweek.com/strategic-cio/it-strategy/pros-and-cons-of-chatbots-in-the-it-helpdesk/a/d-id/1332942.
Glocker, M. L., Langleben, D. D., Ruparel, K., Loughead, J. W., Valdez, J. N., Griffin, M. D., et al. (2009). Baby schema modulates the brain reward system in nulliparous women. Proceedings of the National Academy of Sciences of the United States of America, 106(22), 9115–9119. https://doi.org/10.1073/pnas.0811620106
Golle, J., Lisibach, S., Mast, F. W., & Lobmaier, J. S. (2013). Sweet puppies and cute babies: Perceptual adaptation to babyfacedness transfers across species. PloS one, 8(3), e58248. https://doi.org/10.1371/journal.pone.0058248
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2010). SEM: an introduction. Multivariate Data Analysis: A Global Perspective (7th ed., pp. 629-686). Upper Saddle River, NJ: Pearson Education.
Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (3rd ed.). Sage.
Hellén, K., & Sääksjärvi, M. (2013). Development of a scale measuring childlike anthropomorphism in products. Journal of Marketing Management, 29(1-2), 141– 157. https://doi.org/10.1080/0267257X.2012.759989
Holbrook M B, Woodside A G. (2008). Animal companions, consumption experiences, and the marketing of pets: Transcending boundaries in the animal human distinction. Journal of Business Research, 61(5), 377–381. https://doi.org/10.1016/j.jbusres.2007.06.024
Huang, D.-H., & Chueh, H.-E. (2022). Behavioral intention to continuously use learning apps: A comparative study from Taiwan universities. Technological Forecasting and Social Change, 177, 121531. https://doi.org/10.1016/j.techfore.2022.121531
Izard, C. E. (1971). The Face of Emotion. New York: Appleton-Century-Crofts.
Izard, C. E. (1977). Human Emotions. New York: Plenum Press.
Izard, C. E. (2007). Basic emotions, natural kinds, emotional schemas, and a new paradigm. Perspectives on Psychological Science, 2, 260-280. https://doi.org/10.1111/j.1745-6916.2007.00044.x
Keating, C. F., Randall, D. W., Kendrick, T., & Gutshall, K. A. (2003). Do babyfaced adults receive more help? The (cross-cultural) case of the lost resume. Journal of Nonverbal Behavior, 27(2), 89–109. https://doi.org/10.1023/A:1023962425692
Kirmani, A. R. (2022). Artificial intelligence-enabled science poetry. ACS Energy Letters, 8, 574–576. https://doi.org/10.1021/acsenergylett.2c02758
Klecková J. (2009) Important nonverbal attributes for spontaneous speech recognition. In: ICONS ’09: proceedings of the 2009 fourth international conference on systems. IEEE Computer Society, Washington, DC, USA, 13–16. https://doi.org/10.1109/ICONS.2009.41
Lee, H. C., Chang, C. T., Chen, Y. H., & Huang, Y. S. (2018). The spell of cuteness in food consumption? It depends on food type and consumption motivation. Food Quality and Preference, 65, 110–117. https://doi.org/10.1016/j.foodqual.2017.11.002
Lefkoff-Hagius, Mason, C. H. (1993). Characteristic, Beneficial, and Image Attributes in Consumer Judgments of Similarity and Preference. Journal of Consumer Research, 20(1), 100-110. https://doi.org/10.1086/209336
Lin, Y. T., Doong, H. S., & Eisingerich, A. B. (2021). Avatar design of virtual salespeople: Mitigation of recommendation conflicts. Journal of Service Research, 24(1), 141–159. https://doi.org/10.1177/1094670520964872
Liu, X., Zheng, Y., Du, Z., Ding, M., Qian, Y., Yang, Z., & Tang, J. (2023). GPT understands, too. arXiv. https://doi.org/10.48550/arXiv.2103.10385
Lorenz, K. (1943). Die angeborenen formen möglicher erfahrung. Zeitschrift für Tierpsychologie, 5(2), 235–409. https://doi.org/10.1111/j.1439-0310.1943.tb00655.x
Lucas, G. M., Gratch, J., King, A., & Morency, L. P. (2014). It’s only a computer: Virtual humans increase willingness to disclose. Computers in Human Behavior, 37, 94–100. https://doi.org/10.1016/j.chb.2014.04.043
Lu, J., Yao, J. E., & Yu, C.-S. (2005). Personal innovativeness, social influences and adoption of wireless internet services via mobile technology. Journal of Strategic Information Systems, 14, 245–268. https://doi.org/10.1016/j.jsis.2005.07.003
Lv, X. Y., Liu, Y., Luo, J. J., Liu, Y. Q., & Li, C. X. (2021). Does a cute artificial intelligence assistant soften the blow? The impact of cuteness on customer tolerance of assistant service failure. Annals of Tourism Research, 87. https://doi.org/10.1016/j.annals.2020.103114
Madhavan, P., Wiegmann, D. A., & Lacson, F. C. (2006). Automation failures on tasks easily performed by operators undermine trust in automated aids. Human Factors, 48(2), 241–256. https://doi.org/10.1518/001872006777724408
Mandler, G. (1984). Mind and body: Psychology of Emotion and Stress. New York: Norton.
Mehrabian A. (1968) Communication without words. Psychol Today 2 (4), 53–56. https://doi.org/10.4324/9781315080918-15
Morreall J. (1991) Cuteness. British Journal of Aesthetics, 31(1), 39–47. https://doi.org/10.1093/bjaesthetics/32.2.162
Nenkov, G. Y., & Scott, M. L. (2014). So cute I could eat it up: Priming effects of cute products on indulgent consumption. Journal of Consumer Research, 41(2), 326–341. https://doi.org/10.1086/676581
Nikolopoulou, K., Gialamas, V., & Lavidas, K. (2021). Habit, hedonic motivation, performance expectancy and technological pedagogical knowledge affect teachers’ intention to use mobile internet. Computers and Education Open, 2, 100041. https://doi.org/10.1016/j.caeo.2021.100041
Nittono, H., Fukushima, M., Yano, A., & Moriya, H. (2012). The power of kawaii: Viewing cute images promotes a careful behavior and narrows attentional focus. PLoS One, 7(9), Article e46362. https://doi.org/10.1371/journal.pone.0046362
Rafaeli, S., & Noy, A. (2005). Social presence: Influence on bidders in internet auctions. Electronic Markets, 15(2), 158–175. https://doi.org/10.1080/10196780500083886
Sanders J. T. (1992) On ‘‘cuteness’’. British Journal of Aesthetics, 32(2), 162–165. https://doi.org/10.1093/bjaesthetics/32.2.162
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2022). Partial least squares structural equation modeling. In Handbook of market research, 587–632. https://doi.org/10.1007/978-3-319-05542-8_15-2
Sherman, G. D., Haidt, J., & Coan, J. A. (2009). Viewing cute images increases behavioral carefulness. Emotion, 9(2), 282–286. https://doi.org/10.1037/a0014904
Sing, C. C., Teo, T., Huang, F., Chiu, T. K. F., & Xing Wei, W. (2022). Secondary school students’ intentions to learn AI: Testing moderation effects of readiness, social good and optimism. Educational Technology Research and Development, 70(4). https://doi.org/765–782. 10.1007/s11423-022-10111-1
Sprengelmeyer, R., Perrett, D. I., Fagan, E. C., Cornwell, R. E., Lobmaier, J. S., Sprengelmeyer, A., et al. (2009). The cutest little baby face: A hormonal link to sensitivity to cuteness in infant faces. Psychological Science, 20(2), 149–154. https://doi.org/10.1111/j.1467-9280.2009.02272.x
Sun, J., Nazlan, N. H., Leung, X. Y., & Bai, B. (2020). A cute surprise: Examining the influence of meeting giveaways on word-of-mouth intention. Journal of Hospitality and Tourism Management, 45, 456–463. https://doi.org/10.1016/j.jhtm.2020.10.003
Terblanche, N., & Kidd, M. (2022). Adoption factors and moderating effects of age and gender that influence the intention to use a nondirective reflective coaching chatbot. SAGE Open, 12(2), 215824402210961. https://doi.org/10.1177/21582440221096136
T. Nomura, T. Kanda, T. Suzuki, & K. Kato (2008). Prediction of Human Behavior in Human-Robot Interaction Using Psychological Scales for Anxiety and Negative Attitudes Toward Robots. IEEE Transactions on Robotics, (24), 442– 451. https://doi.org/10.1109/TRO.2007.914004
Van Raaij, E. M., & Schepers, J. J. L. (2008). The acceptance and use of a virtual learning environment in China. Computers & Education, 50(3), 838–852. https://doi.org/10.1016/j.compedu.2006.09.001
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance information technology: toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
Wang, Y.-S., Wu, M.-C., & Wang, H.-Y. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British Journal of Educational Technology, 40(1), 92–118. https://doi.org/10.1111/j.1467-8535.2007.00809.x
Wen Wan, Peng Chen, R., and Jin, L. (2017). Judging a Book by Its Cover? The Effect
of Anthropomorphism on Product Attribute Processing and Consumer Preference.
Journal Of Consumer Research, 43(6), 1008-1030. https://doi.org/10.1093/jcr/ucw074
Wilson HJ, Daugherty PR, Morini-Bianzino N (2017) The jobs that artificial intelligence will create. MIT Sloan Management Rev. 58(4). https://doi.org/10.7551/mitpress/11645.003.0020
Zhang, H., Lu, Y., Shi, X., Tang, Z., & Zhao, Z. (2012). Mood and social presence on consumer purchase behaviour in C2C E-commerce in Chinese culture. Electronic Markets, 22(3), 143–154. https://doi.org/10.1007/s12525-012-0097-z
Zhou, L. L., Owusu-Marfo, J., Asante Antwi, H., Antwi, M. O., Kachie, A. D. T., & Ampon-Wireko, S. (2019). Assessment of the social influence and facilitating conditions that support nurses’ adoption of hospital electronic information management systems (HEIMS) in Ghana using the unified theory of acceptance and use of technology (UTAUT) model. BMC Medical Informatics and Decision Making, 19(1), 1–9. https://doi.org/10.1186/s12911-019-0956-z