本研究探討了影響接受 AI 服務系統的因素，並著重在感知信任、互惠利益、社會影響力和感知風險的作用。透過結合中介變數分析和調節變數分析模型，闡明這些變量之間的複雜互動關係。主效應模型強調了感知信任在推動技術接受中的關鍵作用，而中介分析揭示了感知信任在互惠利益和社會影響力對技術接受的影響中起到中介作用。調節效應分析探討了任務複雜性和任務重要性對感知風險與信任關係的影響，結果顯示任務複雜性具有顯著的調節作用，但任務重要性並不顯著。研究結果強調了管理者在設計提升技術採用的策略時，應優先考慮建立信任措施，並關注與任務相關的因素。研究的局限性包括樣本規模、樣本多樣性以及資料的橫斷面特徵，建議未來研究應採用更大、更具多樣性的樣本和縱向設計。此外，當前研究依賴電子郵件廣告作為處理媒介，未來研究應探索替代的溝通渠道以驗證研究結果。這些見解為理解影響技術接受的動態提供了全面的認識，並為在各種情境中提高技術採用提供了實際意義上的建議。

關鍵字: AI 服務系統、感知信任、互惠利益、社會影響力

This study investigates the factors influencing technology acceptance, focusing on the roles of perceived trust, mutual benefits, social influence, and perceived risk. Employing a combination of main effect, mediation, and moderation models, the research aims to elucidate the complex interactions between these variables. The main effect model highlights the critical importance of perceived trust in driving technology acceptance, while the mediation analysis reveals that perceived trust mediates the effects of mutual benefits and social influence on technology acceptance. The moderation analysis examines the influence of task complexity and task importance on the relationship between perceived risk and trust, finding significant moderation by task complexity but not by task importance. The study's findings underscore the need for managers to prioritize trust-building measures and consider task-related factors when designing strategies to enhance technology adoption. Limitations include the sample size, diversity, and the cross-sectional nature of the data, suggesting the need for future research with larger, more diverse samples and longitudinal designs. Additionally, the current study's reliance on email advertisements as the treatment medium suggests that future research should explore alternative communication channels to validate the findings. These insights provide a comprehensive understanding of the dynamics affecting technology acceptance and offer practical implications for enhancing adoption in various contexts.

Adams, J. S. (1965). Inequity in social exchange. In Advances in experimental social psychology (Vol. 2, pp. 267–299). Elsevier.

Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50 (2), 179–211.

Blau, P. (2017). Exchange and power in social life. Routledge.

Campbell, D. J. (1988). Task complexity: A review and analysis. Academy of management review, 13 (1), 40–52.

Castelfranchi, C., & Falcone, R. (2010). Trust theory: A socio-cognitive and computational model. John Wiley & Sons.

Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 1165–1188.

Cialdini, R. B., & Goldstein, N. J. (2004). Social influence: Compliance and conformity. Annu. Rev. Psychol., 55 , 591–621.

Cook, K. S., Cheshire, C., Rice, E. R., & Nakagawa, S. (2013). Social exchange theory. Handbook of social psychology, 61–88.

Cook, K. S., & Emerson, R. M. (1987). Social exchange theory. Newbury Park.

Cropanzano, R., & Mitchell, M. S. (2005). Social exchange theory: An interdisciplinary review. Journal of management, 31 (6), 874–900.

Das, T. K., & Teng, B.-S. (2001). Trust, control, and risk in strategic alliances: An integrated framework. Organization studies, 22 (2), 251–283.

Davenport, T. H., Ronanki, R., et al. (2018). Artificial intelligence for the real world. Harvard business review, 96 (1), 108–116.

Davis, F. D., Bagozzi, R., & Warshaw, P. (1989a). Technology acceptance model. J Manag Sci, 35 (8), 982–1003.

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989b). User acceptance of computer technology: A comparison of two theoretical models. Management science, 35 (8), 982–1003.

Deloitte. (2019). Ai and risk management: Innovating with confidence. Retrieved from https://www2.deloitte.com/tw/tc.html

Eisenhardt, K. M. (1989). Agency theory: An assessment and review. Academy of management review, 14 (1), 57–74.

Fama, E. F., & Jensen, M. C. (2004). Separation of ownership and control. Theories of Corporate Governance. New York, Rutledge.

Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: a perceived risk facets perspective. International journal of human-computer studies, 59 (4), 451–474.

Gefen, D. (2000). E-commerce: the role of familiarity and trust. Omega, 28 (6), 725–737.

Gefen, D., & Straub, D. W. (2004). Consumer trust in b2c e-commerce and
the importance of social presence: experiments in e-products and e-services. Omega, 32 (6), 407–424.

Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14 (2), 627–660.

Hinkin, T. R., & Schriesheim, C. A. (1989). Development and application of new scales to measure the french and raven (1959) bases of social power. Journal of applied psychology, 74 (4), 561.

Hoff, K. A., & Bashir, M. (2015). Trust in automation: Integrating empirical evidence on factors that influence trust. Human factors, 57 (3), 407–434.

Homans, G. C. (1958). Social behavior as exchange. American journal of sociology, 63 (6), 597–606.

Hsu, C.-L., & Lin, J. C.-C. (2008). Acceptance of blog usage: The roles of technology acceptance, social influence and knowledge sharing motivation. Information & management, 45 (1), 65–74.

Hurley, M., & Adebayo, J. (2016). Credit scoring in the era of big data. Yale JL & Tech., 18 , 148.

Jensen, M. C., & Meckling, W. H. (2019). Theory of the firm: Managerial behavior, agency costs and ownership structure. In Corporate governance (pp. 77–132). Gower.

Jiang, Z., & Liang, J. (2017). Cryptocurrency portfolio management with deep reinforcement learning. In 2017 intelligent systems conference (intellisys) (pp. 905–913).

Kelembagaan, D., & Eisenhardt, K. M. (1989). Agency theory: an assessment and review. Academy of management review, 14 (1), 57–74.

Kelley, H. H., et al. (1959). The social psychology of groups. Transaction Publishers.

Liao, Y., Vitak, J., Kumar, P., Zimmer, M., & Kritikos, K. (2019). Understanding the role of privacy and trust in intelligent personal assistant adoption. In Information in contemporary society: 14th international conference, iconference 2019, washington, dc, usa, march 31–april 3, 2019, proceedings 14 (pp. 102–113).

Locke, E. A., & Latham, G. P. (2002). Building a practically useful theory of goal setting and task motivation: A 35-year odyssey. American psychologist, 57 (9), 705.

Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of management review, 20 (3), 709–734.

McKinsey. (2020). The state of ai in 2020. Retrieved from https://
www.mckinsey.com/capabilities/quantumblack/our-insights/
global-survey-the-state-of-ai-in-2020

McKnight, D. H., Choudhury, V., & Kacmar, C. (2002a). Developing and validating trust measures for e-commerce: An integrative typology. Information systems research, 13 (3), 334–359.

McKnight, D. H., Choudhury, V., & Kacmar, C. (2002b). The impact of initial consumer trust on intentions to transact with a web site: a trust building model. The journal of strategic information systems, 11 (3-4), 297–323.

MODEL, A. I. (2003). Trust and tam in online shopping: An integrated model1. MIS Quarterly, 27 (1), 51–90.

Molm, L. D. (1997). Risk and power use: Constraints on the use of coercion in exchange. American Sociological Review, 113–133.

Nguyen, T., et al. (2019). Potential effects of chatbot technology on customer support: A case study (Unpublished master’s thesis).

Palan, S., & Schitter, C. (2018). Prolific. ac—a subject pool for online experiments. Journal of Behavioral and Experimental Finance, 17 , 22–27.

Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International journal of electronic commerce, 7 (3), 101–134.

Pavlou, P. A., & Fygenson, M. (2006). Understanding and predicting electronic commerce adoption: An extension of the theory of planned behavior. MIS quarterly, 115–143.

Petty, R. E., Cacioppo, J. T., Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. Springer.

Qualtrics. (2021). Qualtrics research core: The ultimate guide to survey software. Retrieved from https://www.qualtrics.com/

Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive science, 12 (2), 257–285.

Thomas, P. (2006). Information systems success and technology acceptance within government organization. Information Systems Research.

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46 (2), 186–204.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425 478.

Wood, R., Bandura, A., & Bailey, T. (1990). Mechanisms governing organizational performance in complex decision-making environments. Organizational behavior and human decision processes, 46 (2), 181–201.

Zhang, B., & Dafoe, A. (2019). Artificial intelligence: American attitudes and trends. Available at SSRN 3312874 .