隨著高齡化、少子化與家庭結構小型化趨勢日益明顯，兼具情感互動與日常陪伴功能之AI陪伴機器人逐漸受到重視，而此類產品之應用不僅限於高齡照護或兒童陪伴，亦可能進一步延伸至一般消費者之日常陪伴、情感互動與娛樂情境。相較於一般功能導向之服務型機器人，AI陪伴機器人不僅需具備一定之技術功能，更須透過外觀形象與互動方式建立使用者之心理連結與信任感。因此，本研究旨在探討AI陪伴機器人之外觀設計與行為風格，如何影響消費者之感知溫暖、感知能力與感知擬人化，並進一步透過信任度與感知價值影響其購買意圖。
本研究採用2×2情境式實驗設計，將外觀設計區分為毛茸可愛感與機械科技感，行為風格區分為友善的與嘲諷的，共設計四種情境問卷。研究對象為具備基本數位產品使用經驗之一般大眾，採便利抽樣方式進行資料蒐集，共回收449份有效問卷。資料分析方面，本研究使用SPSS 29.0與SmartPLS 4.0進行敘述性統計、信效度分析、偏最小平方法結構方程模型分析與多群組分析，以驗證研究假設。
研究結果顯示，外觀設計與行為風格皆會顯著影響消費者對AI陪伴機器人之感知溫暖、感知能力與感知擬人化，其中又以行為風格對感知溫暖之影響最為明顯。感知溫暖、感知能力與感知擬人化皆會正向影響信任度，而信任度進一步正向影響感知價值，感知價值則會正向影響購買意圖。中介效果分析結果亦顯示，外觀設計與行為風格可透過心理感知、信任度與感知價值之連續中介歷程，進一步影響消費者之購買意圖。此外，外觀設計與行為風格之交互作用對感知能力與感知擬人化具有顯著負向影響，但效果量有限，顯示不同設計特徵之搭配未必會產生明顯正向加乘效果，仍須考量整體角色形象與互動一致性。
整體而言，本研究結果指出，消費者對AI陪伴機器人之接受，並非僅取決於外觀吸引力或功能表現，而是取決於外觀設計與行為風格所共同形成之心理知覺，以及後續信任與價值評估。實務上，企業在設計AI陪伴機器人時，除應重視造型與材質等視覺線索外，更應強化互動語氣、情緒支持與回應方式之一致性，以提升消費者信任、感知價值與購買意圖。

This study investigates how the appearance design and behavioral style of AI companion robots influence consumers’ purchase intentions. With increasing demand for emotional interaction and daily companionship, these robots are not limited to elderly care or child companionship but may also serve general consumers in everyday and entertainment contexts.
This study adopts a 2×2 scenario-based experimental design, comparing furry-cute and mechanical-technological appearances with friendly and sarcastic interaction styles. Four scenarios were developed, and 449 valid questionnaires were collected from general consumers with basic digital product experience. Data were analyzed using SPSS 29.0 and SmartPLS 4.0 through descriptive statistics, reliability and validity analysis, PLS-SEM, mediation analysis, and multigroup analysis.
The results show that both appearance design and behavioral style significantly influence perceived warmth, competence, and anthropomorphism, with behavioral style having the strongest effect on perceived warmth. These perceptions positively influence trust, which further enhances perceived value and purchase intention. Mediation results support the sequential pathway from design cues to perceptions, trust, value, and purchase intention. Some interaction effects are significant and negative, but their effect sizes are limited, suggesting cautious interpretation.
Overall, AI companion robot acceptance is influenced not only by visual attractiveness or functions, but also by psychological perceptions, trust, and perceived value. Companies should align appearance, interaction tone, emotional support, and response style to strengthen consumer acceptance.

內政部戶政司. (2025). 民國114年10月戶口統計資料分析. https://www.moi.gov.tw/News_Content.aspx?n=9&s=333130
內政部統計處. (2025). 114年第48週內政統計通報(114年6月底65歲以上長者457萬人，其中一人戶占25.8%). https://www.moi.gov.tw/News_Content.aspx?n=2&sms=9009&s=334815
國家發展委員會. (2024). 中華民國人口推估（2024年至2070年）. https://pop-proj.ndc.gov.tw/News.aspx?n=3&sms=10347
Afroogh, S., Akbari, A., Malone, E., Kargar, M., & Alambeigi, H. (2024). Trust in AI: progress, challenges, and future directions. Humanities and Social Sciences Communications, 11(1), 1568.
Ahmed, E., Buruk, O. O., & Hamari, J. (2024). Human–robot companionship: Current trends and future agenda. International Journal of Social Robotics, 16(8), 1809–1860.
AIST. (2004). Seal-Type Robot "PARO" to Be Marketed with Best Healing Effect in the World. https://www.aist.go.jp/aist_e/list/latest_research/2004/20041208_2/20041208_2.html
Anzabi, N., & Umemuro, H. (2023). Influence of social robots’ benevolence and competence on perceived trust in human-robot interactions. The Japanese Journal of Ergonomics, 59(6), 258–273.
Aquilino, L., Di Dio, C., Manzi, F., Massaro, D., Bisconti, P., & Marchetti, A. (2025). Decoding trust in artificial intelligence: A systematic review of quantitative measures and related variables. Informatics,
Bayor, L., Weinert, C., Maier, C., & Weitzel, T. (2025). Social-oriented communication with AI companions: benefits, costs, and contextual patterns. Business & Information Systems Engineering, 67(5), 637–655.
Belanche, D., Casaló, L. V., Schepers, J., & Flavián, C. (2021). Examining the effects of robots' physical appearance, warmth, and competence in frontline services: The Humanness‐Value‐Loyalty model. Psychology & Marketing, 38(12), 2357–2376.
Blut, M., Chaney, D., Lunardo, R., Mencarelli, R., & Grewal, D. (2024). Customer perceived value: a comprehensive meta-analysis. Journal of service Research, 27(4), 501–524.
Bosch. (2017). Mayfield Robotics Introduces Kuri: A First-of-Its-Kind Home Robot. https://www.bosch-presse.de/pressportal/de/media/dam_images/pi9519/pressrelease_kuri_ces_launch.pdf
Bosch. (2018). Mayfield Robotics. https://www.bosch-business-innovations.com/media/downloads/mayfield_robotics_exit_story_2mb_update.pdf
Cai, N., Gao, S., & Yan, J. (2024). How the communication style of chatbots influences consumers’ satisfaction, trust, and engagement in the context of service failure. Humanities and Social Sciences Communications, 11(1), 1–11.
Cao, N., Isa, N. M., Perumal, S., & Chen, C. (2025). Perceived value, consumer engagement, and purchase intention in virtual influencer marketing: The role of source credibility and generational cohort. Journal of Theoretical and Applied Electronic Commerce Research, 20(2), 150.
Chaturvedi, R., Verma, S., Das, R., & Dwivedi, Y. K. (2023). Social companionship with artificial intelligence: Recent trends and future avenues. Technological Forecasting and Social Change, 193, 122634.
Chauhan, S., Kapoor, S., Nagpal, M., Choudhary, G., & Dutt, V. (2025). Building trust in the age of human-machine interaction: insights, challenges, and future directions. Frontiers in Robotics and AI, 12, 1535082.
Chen, Q. Q., & Park, H. J. (2021). How anthropomorphism affects trust in intelligent personal assistants. Industrial Management & Data Systems, 121(12), 2722–2737.
Cheng, W., & Hwang, J. (2025). Robots' anthropomorphic designs: Psychological mechanism of consumer acceptance in restaurant contexts of service success, failure, and recovery. International Journal of Hospitality Management, 131, 104322.
Chi, O. H., Chi, C. G., Gursoy, D., & Nunkoo, R. (2023). Customers’ acceptance of artificially intelligent service robots: The influence of trust and culture. International Journal of Information Management, 70, 102623.
Chi, R., Zhang, J., & Pan, M. (2024). The effect of anthropomorphic competence-warmth congruence of service robots on recommendation intention. Current Psychology, 43(7), 6570–6583.
Christoforakos, L., Gallucci, A., Surmava-Große, T., Ullrich, D., & Diefenbach, S. (2021). Can robots earn our trust the same way humans do? A systematic exploration of competence, warmth, and anthropomorphism as determinants of trust development in HRI. Frontiers in Robotics and AI, 8, 640444.
Chung, H., Kang, H., & Jun, S. (2023). Verbal anthropomorphism design of social robots: Investigating users’ privacy perception. Computers in Human Behavior, 142, 107640.
Cohen, J. (2013). Statistical power analysis for the behavioral sciences. routledge.
Cucciniello, I., Sangiovanni, S., Maggi, G., & Rossi, S. (2023). Mind perception in HRI: Exploring users’ attribution of mental and emotional states to robots with different behavioural styles. International Journal of Social Robotics, 15(5), 867–877.
Daly, S. J., Wiewiora, A., & Hearn, G. (2025). Shifting attitudes and trust in AI: Influences on organizational AI adoption. Technological Forecasting and Social Change, 215, 124108.
David, D., Hayotte, M., Thérouanne, P., d'Arripe-Longueville, F., & Milhabet, I. (2022). Development and validation of a social robot anthropomorphism scale (SRA) in a french sample. International Journal of Human-Computer Studies, 162, 102802.
Della Corte, V., Sepe, F., Gursoy, D., & Prisco, A. (2023). Role of trust in customer attitude and behaviour formation towards social service robots. International Journal of Hospitality Management, 114, 103587.
Deng, Z., & Yan, J. (2025). The Effect of Perceived Warmth, Competence, and Social Presence of AI-Driven Chabots on Consumers’ Engagement and Satisfaction. SAGE Open, 15(3), 21582440251365438.
Duong, N. T., Lin, H.-H., Wu, T.-L., & Wang, Y.-S. (2025). Understanding consumer trust dynamics and purchase intentions in a multichannel live streaming e-commerce context: A trust transfer perspective. International Journal of Human–Computer Interaction, 41(14), 9123–9136.
Economic, D. o. (2024). World Population Prospects 2024: Summary of Results. Stylus Publishing, LLC.
Elshaer, I. A., Azazz, A. M., Fayyad, S., Aljoghaiman, A., Fathy, E. A., & Fouad, A. M. (2025). From asymmetry to satisfaction: The dynamic role of perceived value and trust to boost customer satisfaction in the tourism industry. Tourism and Hospitality, 6(2), 68.
Fiske, S. T. (2018). Stereotype content: Warmth and competence endure. Current directions in psychological science, 27(2), 67–73.
Ghosh, M. (2024). Meta-analytic review of online purchase intention: conceptualising the study variables. Cogent Business & Management, 11(1), 2296686.
Grand View Research. (2024). Healthcare Companion Robots Market (2025 - 2030). https://www.grandviewresearch.com/industry-analysis/healthcare-companion-robots-market-report
Grassini, S., Thorp, S., Cipriani, E., Mannari, V., & Sevic, A. (2025). Context matters: Analyzing the factors influencing artificial intelligence acceptance across five different applications. Computers in Human Behavior Reports, 100834.
Gul, A., Turner, L., & Fuentes, C. (2025). Conventions and research challenges in considering trust with socially assistive robots for older adults. Frontiers in Robotics and AI, 12, 1631206.
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. Springer.
Halkias, G., & Diamantopoulos, A. (2020). Universal dimensions of individuals' perception: Revisiting the operationalization of warmth and competence with a mixed-method approach. International Journal of Research in Marketing, 37(4), 714–736.
Helal, N. A., Hassan, A., Badr, N. L., & Afify, Y. M. (2024). A contextual-based approach for sarcasm detection. Scientific Reports, 14(1), 15415.
Henschel, A., Laban, G., & Cross, E. S. (2021). What makes a robot social? A review of social robots from science fiction to a home or hospital near you. Current robotics reports, 2(1), 9–19.
Heppner, H., Schiffhauer, B., & Seelmeyer, U. (2024). Conveying chatbot personality through conversational cues in social media messages. Computers in Human Behavior: Artificial Humans, 2(1), 100044.
Hernandez, I., & Chekili, A. (2024). The silicon service spectrum: warmth and competence explain people's preferences for AI assistants. Frontiers in Social Psychology, 2, 1396533.
Hu, Y. (2021). An improvement or a gimmick? The importance of user perceived values, previous experience, and industry context in human–robot service interaction. Journal of Destination Marketing & Management, 21, 100645.
International Federation of Robotics. (2025). World Robotics 2025 report – SERVICE ROBOTS – released by IFR. https://ifr.org/ifr-press-releases/news/service-robots-see-global-growth-boom
Irfan, B., Kuoppamäki, S., & Skantze, G. (2024). Recommendations for designing conversational companion robots with older adults through foundation models. Frontiers in Robotics and AI, 11, 1363713.
Janson, A. (2023). How to leverage anthropomorphism for chatbot service interfaces: The interplay of communication style and personification. Computers in Human Behavior, 149, 107954.
Jibo, I. (2017). Meet Jibo, Your New Best Friend: Introducing The First Social Robot For the Home. https://www.prnewswire.com/news-releases/meet-jibo-your-new-best-friend-introducing-the-first-social-robot-for-the-home-300542856.html
Jung, H. W., Park, J. Y., Holoubek, T., Kim, W. J., & Park, J. (2025). Socially assistive robots in mental healthcare: principles and conceptual framework for user-centered design. International Journal of Social Robotics, 17(11), 2827–2851.
Kulms, P., & Kopp, S. (2018). A social cognition perspective on human–computer trust: the effect of perceived warmth and competence on trust in decision-making with computers. Frontiers in Digital Humanities, 5, 352444.
Li, B., Nan, Y., & Yao, R. (2022). Warmth or competence? The effects of cool and cuteness on the perceived quality of digital products. Asia Pacific Journal of Marketing and Logistics, 34(9), 1880–1904.
Li, L., Li, Y., Song, B., Shi, Z., & Wang, C. (2022). How human-like behavior of service robot affects social distance: A mediation model and cross-cultural comparison. Behavioral Sciences, 12(7), 205.
Li, M., & Suh, A. (2022). Anthropomorphism in AI-enabled technology: A literature review. Electronic markets, 32(4), 2245–2275.
Li, X., Zhou, T., Hu, C., & Liu, H. (2025). The effects of the human-like features of generative AI on usage intention and the moderating role of information overload. Scientific Reports, 15(1), 35077.
Liu, W., Jiang, M., Li, W., & Mou, J. (2024). How does the anthropomorphism of AI chatbots facilitate users' reuse intention in online health consultation services? The moderating role of disease severity. Technological Forecasting and Social Change, 203, 123407.
Liu, X. S., Yi, X. S., & Wan, L. C. (2022). Friendly or competent? The effects of perception of robot appearance and service context on usage intention. Annals of Tourism Research, 92, 103324.
Lumer, E., & Buschmeier, H. (2023). Should robots be polite? Expectations about politeness in human–robot interaction. Frontiers in Robotics and AI, 10, 1242127.
Malhotra, G., & Ramalingam, M. (2025). Perceived anthropomorphism and purchase intention using artificial intelligence technology: examining the moderated effect of trust. Journal of Enterprise Information Management, 38(2), 401–423.
McKee, K. R., Bai, X., & Fiske, S. T. (2023). Humans perceive warmth and competence in artificial intelligence. Iscience, 26(8).
Murtaza, Z., Sharma, I., & Carbonell, P. (2024). Examining chatbot usage intention in a service encounter: Role of task complexity, communication style, and brand personality. Technological Forecasting and Social Change, 209, 123806.
Nazaretsky, T., Mejia-Domenzain, P., Swamy, V., Frej, J., & Käser, T. (2025). The critical role of trust in adopting AI-powered educational technology for learning: An instrument for measuring student perceptions. Computers and Education: Artificial Intelligence, 8, 100368.
Neuenswander, K. L., Dash, A., Koya, P. D., Lin, L., Gillespie, G. S., & Stroessner, S. J. (2025). Measuring fundamental aspects of the social perception of robots: development and validation of a shortened version of the RoSAS (RoSAS-SF). International Journal of Social Robotics, 17(6), 1097–1112.
Noh, H.-H., Rim, H. B., & Lee, B.-K. (2025). Exploring user attitudes and trust toward ChatGPT as a social actor: A UTAUT-based analysis. SAGE Open, 15(2), 21582440251345896.
Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual review of psychology, 63(1), 539–569.
Randall, N., & Šabanović, S. (2025). Do You Want Me?: Exploring Differences in Consumer Home Robot Preferences, Perceptions, and Purchase Intent. ACM Transactions on Human-Robot Interaction, 14(3), 1–39.
Rather, R. A. (2025). Does consumers’ reveal engagement behaviours in artificial intelligence (AI)-based technologies? The dynamics of perceived value and self-congruence. International Journal of Hospitality Management, 126, 103989.
Rosén, J., Lindblom, J., Lamb, M., & Billing, E. (2024). Previous experience matters: an in-person investigation of expectations in human–robot interaction. International Journal of Social Robotics, 16(3), 447–460.
Sairanen, M., Aarikka-Stenroos, L., & Kaipainen, J. (2024). Customer-perceived value in the circular economy: A multidimensional framework. Industrial Marketing Management, 117, 321–343.
Senachai, P., & Julagasigorn, P. (2024). Retail mix instruments influencing customer perceived value and customer engagement: a conceptual framework and research agenda. Humanities and Social Sciences Communications, 11(1), 1–14.
Shi, W., Li, L., Zhang, Z., Li, M., & Li, J. (2025). Research on driving factors of consumer purchase intention of artificial intelligence creative products based on user behavior. Scientific Reports, 15(1), 17400.
Song, Y., & Luximon, Y. (2021). The face of trust: The effect of robot face ratio on consumer preference. Computers in Human Behavior, 116, 106620.
SONY. (1999). Sony Launches Four-Legged Entertainment Robot. https://www.sony.com/en/SonyInfo/News/Press/199905/99-046/
Spicer, D. (2023). How Old Is Your Furby? https://computerhistory.org/blog/how-old-is-your-furby/
Tai, Y.-H., Nwachukwu, P. T. T., LePage, B. A., & Fang, W.-T. (2024). Examining customer intentions to purchase intelligent robotic products and services in Taiwan using the theory of planned behaviour. BMC psychology, 12(1), 351.
Veseli, A., Bajraktari, D., & Bajraktari, A. (2026). AI-Driven Personalization and Traveler Satisfaction: The Role of Trust and Perceived Value, and Technology Readiness. Tourism and Hospitality, 7(4), 100.
Wang, S., Yan, Q., & Wang, L. (2025). Task-oriented vs. social-oriented: chatbot communication styles in electronic commerce service recovery. Electronic Commerce Research, 25(3), 1793–1825.
Wistedt, U., & Safari, A. (2026). Exploring purchase intention in cross-border e-commerce: a relational view. International Marketing Review, 43(7), 50–83.
Xiong, L., Gao, R., & Jeong, A. (2025). Sarc7: Evaluating Sarcasm Detection and Generation with Seven Types and Emotion-Informed Techniques. Proceedings of the 9th Widening NLP Workshop,
Xu, Y. W., Chi, C. G., Gursoy, D., & Cai, R. R. (2025). Rethinking AI anthropomorphism: A holistic conceptualization and scale across AI systems and service contexts. Technology in Society, 103189.
Xue, J., Liu, Y., Ren, Z., & Wu, Y. (2025). Effect of AI empathy perception on employees' prosocial behavior: mediating role of warmth and moderating role of AI anthropomorphism. Frontiers in Psychology, 16, 1706756.
Zhang, W., Slade, E. L., & Pantano, E. (2024). Humanlike service robots: A systematic literature review and research agenda. Psychology & Marketing, 41(12), 3157–3181.