本研究以臺灣路口多功能科技執法為背景，探討人工智慧信任、警察執法公平性與罰單經驗對民眾政策支持的影響，並檢視此影響在不同政策情境下的差異。研究採次級資料分析，使用2025年全國性電話調查資料，透過成對樣本t檢定與階層迴歸分析，針對一般政策支持、特定情境支持（繁忙路口）與個人相關支持（常走路段）三種層次進行假設檢驗。結果顯示，三個核心自變項在控制人口統計特徵後均對政策支持具有顯著預測力，其中警察執法公平性為最強預測因子，在抽象情境中明顯優於人工智慧信任，但在個人相關支持情境下兩者差距收窄至近乎相等，顯示技術信任的重要性隨政策個人相關性提升而動態增加；罰單經驗在三種情境中均呈穩定負向影響，支持程序正義觀點而非威懾理論的預期；成對樣本t檢定亦顯示三種情境支持度呈非線性差異，特定情境支持最高，個人相關支持最低。本研究延伸信任理論與程序正義理論在科技執法情境下的適用性，並指出政府推動科技執法時，應優先提升執法公平性知覺，依情境調整技術信任的溝通重點，方能有效回應民眾在不同層次上的顧慮。

This study investigates the effects of AI trust, perceived police fairness, and traffic citation experience on public support for Taiwan's multifunctional tech-assisted traffic enforcement (intersection-based), and examines how these effects vary across different policy contexts. Using secondary data from a 2025 national telephone survey, the study employs paired-sample t-tests and hierarchical regression analysis to examine three levels of policy support: general policy support, context-specific support (busy intersections), and personally relevant support (frequently traveled routes). Results show that all three core predictors significantly predict policy support after controlling for demographic characteristics. Perceived police fairness emerged as the strongest predictor, with greater explanatory power than AI trust in abstract contexts; however, in the personally relevant support context, the gap between the two narrowed to near parity, suggesting that the importance of technological trust increases dynamically as the personal relevance of the policy rises. Citation experience demonstrated a consistently negative effect across all three contexts, supporting procedural justice theory over deterrence theory. Paired-sample t-tests further revealed a nonlinear pattern of contextual variation, with context-specific support being the highest and personally relevant support the lowest. Theoretically, this study extends trust theory and procedural justice theory to technology-based enforcement contexts. Practically, the findings suggest that governments should prioritize improving perceptions of enforcement fairness and tailor communication about technological trust to specific policy contexts, in order to more effectively address public concerns at different levels of policy evaluation.

張淑娟、劉建邦、王志綱、黃祺芳、張智華、黃銘崇（2024）。以AI技術實現適應性號誌控制。運輸學刊，36（3）。https://doi.org/10.6383/JCIT.202409_36(3).0003
黃東益（2025）。公共管理者對人工智慧（AI）的科技框架與公共價值創造——台灣六個直轄市的研究（編號：NSTC112-2410-H-004-083-MY2）。國家科學及技術委員會。
黃耀緯（2024）。國內外交通科技執法作法初探。交通部運輸研究所。https://www.iot.gov.tw/xhr/universal_table/download?file=67867cda085777175b13b5c3
楊漢鵬（2019）。運用科技設備於交通執法之研究。交通學報，19（1），1–12。
廖興中（2024）。人工智慧在公共行政的機會與挑戰。文官制度，16（2），1–19。
賴擁連（2016）。警民接觸經驗與其對警察態度之影響性研究。中國行政評論，22（4）。https://doi.org/10.6635/cpar.2016.22(4).01
Adadi, A., & Berrada, M. (2018). Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). IEEE Access, 6, 52138–52160. https://doi.org/10.1109/ACCESS.2018.2870052
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. https://doi.org/10.1057/s41599-024-04044-8
Bates, L., Allen, S., & Watson, B. (2016). The influence of the elements of procedural justice and speed camera enforcement on young novice driver self-reported speeding. Accident Analysis & Prevention, 92, 34–42. https://doi.org/10.1016/j.aap.2016.03.023
Bradford, B. (2014). Policing and social identity: Procedural justice, inclusion and cooperation between police and public. Policing and Society, 24(1), 22–43. https://doi.org/10.1080/10439463.2012.724068
Brehm, S. S., & Brehm, J. W. (1981). Psychological reactance: A theory of freedom and control. Academic press.
Cheng, Z. (Aaron), Dong, Z., & Pang, M.-S. (2025). Automated Enforcement and Traffic Safety. Management Science, mnsc.2023.00575. https://doi.org/10.1287/mnsc.2023.00575
Cheranchery, M. F., & Greeshma, G. S. (2025). Can AI-powered traffic enforcement system augment road safety by influencing driver behavior and perception? Case Studies on Transport Policy, 21, 101566. https://doi.org/10.1016/j.cstp.2025.101566
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed). L. Erlbaum Associates.
Converse, P. E. (2006). The nature of belief systems in mass publics (1964). Critical Review, 18(1–3), 1–74. https://doi.org/10.1080/08913810608443650
Dahlin, E. (2025). Trust in AI. AI & SOCIETY, 40(8), 6089–6095. https://doi.org/10.1007/s00146-025-02429-0
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008
Dear, M. (1992). Understanding and Overcoming the NIMBY Syndrome. Journal of the American Planning Association, 58(3), 288–300. https://doi.org/10.1080/01944369208975808
Deutsch, M. (1958). Trust and suspicion. Journal of Conflict Resolution, 2(4), 265–279. https://doi.org/10.1177/002200275800200401
Engstrom, D. F., & Haim, A. (2023). Regulating Government AI and the Challenge of Sociotechnical Design. Annual Review of Law and Social Science, 19(1), 277–298. https://doi.org/10.1146/annurev-lawsocsci-120522-091626
Glikson, E., & Woolley, A. W. (2020). Human Trust in Artificial Intelligence: Review of Empirical Research. Academy of Management Annals, 14(2), 627–660. https://doi.org/10.5465/annals.2018.0057
Grimmelikhuijsen, S., & Meijer, A. (2022). Legitimacy of Algorithmic Decision-Making: Six Threats and the Need for a Calibrated Institutional Response. Perspectives on Public Management and Governance, 5(3), 232–242. https://doi.org/10.1093/ppmgov/gvac008
Grosz, B. J., Altman, R., Horvitz, E., Mackworth, A., Mitchell, T., Mulligan, D., & Shoham, Y. (2016). Artificial intelligence and life in 2030: One hundred years study on artificial intelligence. Stanford University.
Guler, A., Kula, S., & Boke, K. (2025). Examining public support for AI in policing: The role of perceived procedural justice. Police Practice and Research, 26(6), 673–695. https://doi.org/10.1080/15614263.2025.2516535
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (Eighth edition). Cengage.
Hartley, J., Ongaro, E., Quick, K., & Schröter, E. (2023). Public management and policing: A dialectical inquiry. Public Management Review, 25(9), 1711–1729. https://doi.org/10.1080/14719037.2023.2224809
Hetherington, M. J. (1998). The Political Relevance of Political Trust. American Political Science Review, 92(4), 791–808. https://doi.org/10.2307/2586304
Hunter, S., & Leyden, K. M. (1995). Beyond NIMBY: Explaining Opposition to Hazardous Waste Facilities. Policy Studies Journal, 23(4), 601–619. https://doi.org/10.1111/j.1541-0072.1995.tb00537.x
Kankanhalli, A., Charalabidis, Y., & Mellouli, S. (2019). IoT and AI for Smart Government: A Research Agenda. Government Information Quarterly, 36(2), 304–309. https://doi.org/10.1016/j.giq.2019.02.003
Kelly, S., Kaye, S.-A., & Oviedo-Trespalacios, O. (2023). What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics and Informatics, 77, 101925. https://doi.org/10.1016/j.tele.2022.101925
Lee, J. D., & See, K. A. (2004). Trust in Automation: Designing for Appropriate Reliance. Human Factors: The Journal of the Human Factors and Ergonomics Society, 46(1), 50–80. https://doi.org/10.1518/hfes.46.1.50_30392
Lee, J., & Moray, N. (1992). Trust, control strategies and allocation of function in human-machine systems. Ergonomics, 35(10), 1243–1270. https://doi.org/10.1080/00140139208967392
Lerner, J. S., Li, Y., Valdesolo, P., & Kassam, K. S. (2015). Emotion and Decision Making. Annual Review of Psychology, 66(1), 799–823. https://doi.org/10.1146/annurev-psych-010213-115043
Lewicki, R., & Bunker, B. (1996). Developing and Maintaining Trust in Working Relations. In Trust in organizations: Frontiers of theory and research (頁 114–139). https://doi.org/10.4135/9781452243610.n7
Lipsky, M. (1980). Street Level Bureaucracy. Russell Sage Foundation; JSTOR.
Maslej, N., Fattorini, L., Perrault, R., Gil, Y., Parli, V., Kariuki, N., Capstick, E., Reuel, A., Brynjolfsson, E., Etchemendy, J., Ligett, K., Lyons, T., Manyika, J., Niebles, J. C., Shoham, Y., Wald, R., Walsh, T., Hamrah, A., Santarlasci, L., & Oak, S. (2025). Artificial Intelligence Index Report 2025. https://doi.org/10.48550/arXiv.2504.07139
Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An Integrative Model of Organizational Trust. The Academy of Management Review, 20(3), 709. https://doi.org/10.2307/258792
Mazerolle, L., Bennett, S., Davis, J., Sargeant, E., & Manning, M. (2013). Legitimacy in Policing: A Systematic Review. Campbell Systematic Reviews, 9(1). https://doi.org/10.4073/csr.2013.1
McCarthy, J. (2007). WHAT IS ARTIFICIAL INTELLIGENCE?
Murphy, K., Mazerolle, L., & Bennett, S. (2014). Promoting trust in police: Findings from a randomised experimental field trial of procedural justice policing. Policing and Society, 24(4), 405–424. https://doi.org/10.1080/10439463.2013.862246
Nagin, D. S. (2013). Deterrence in the Twenty-First Century. Crime and Justice, 42(1), 199–263. https://doi.org/10.1086/670398
O’Hare, M. (1977). Not on my block you don’t—Facilities siting and the strategic importance of compensation (DOE/EV/70012-T3, 5221104; 頁 DOE/EV/70012-T3, 5221104). https://doi.org/10.2172/5221104
Parasuraman, R., & Riley, V. (1997). Humans and Automation: Use, Misuse, Disuse, Abuse. Human Factors, 39(2), 230–253. https://doi.org/10.1518/001872097778543886
Pierce, J. L., Kostova, T., & Dirks, K. T. (2001). Toward a Theory of Psychological Ownership in Organizations. The Academy of Management Review, 26(2), 298. https://doi.org/10.2307/259124
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
Retting, R. A., Farmer, C. M., & McCartt, A. T. (2008). Evaluation of Automated Speed Enforcement in Montgomery County, Maryland. Traffic Injury Prevention, 9(5), 440–445. https://doi.org/10.1080/15389580802221333
Russell, S. J., & Norvig, P. (2021). Artificial intelligence: A modern approach (M. Chang, J. Devlin, A. Dragan, D. Forsyth, I. Goodfellow, J. Malik, V. Mansinghka, J. Pearl, M. J. Wooldridge; Fourth Edition). Pearson.
Schiff, K. J., Schiff, D. S., Adams, I. T., McCrain, J., & Mourtgos, S. M. (2025). Institutional factors driving citizen perceptions of AI in government: Evidence from a survey experiment on policing. Public Administration Review, 85(2), 451–467. https://doi.org/10.1111/puar.13754
Schuman, H., Steeh, C., & Bobo, L. (1997). Racial attitudes in America: Trends and interpretations (Rev. ed). Harvard Univ. Press.
Siau, K., & Wang, W. (2018). Building Trust in Artificial Intelligence, Machine Learning, and Robotics. Cutter Business Technology Journal, 31, 47–53.
Skogan, W. G. (2006). Asymmetry in the Impact of Encounters with Police. Policing and Society, 16(2), 99–126. https://doi.org/10.1080/10439460600662098
Slovic, P. (1987). Perception of Risk. Science, 236(4799), 280–285. https://doi.org/10.1126/science.3563507
Soss, J. (1999). Lessons of Welfare: Policy Design, Political Learning, and Political Action. American Political Science Review, 93(2), 363–380. https://doi.org/10.2307/2585401
Starke, C., Baleis, J., Keller, B., & Marcinkowski, F. (2022). Fairness perceptions of algorithmic decision-making: A systematic review of the empirical literature. Big Data & Society, 9(2), 20539517221115189. https://doi.org/10.1177/20539517221115189
Sunshine, J., & Tyler, T. R. (2003). The Role of Procedural Justice and Legitimacy in Shaping Public Support for Policing. Law & Society Review, 37(3), 513–547. https://doi.org/10.1111/1540-5893.3703002
Thiebes, S., Lins, S., & Sunyaev, A. (2021). Trustworthy artificial intelligence. Electronic Markets, 31(2), 447–464. https://doi.org/10.1007/s12525-020-00441-4
Trope, Y., & Liberman, N. (2010). Construal-level theory of psychological distance. Psychological Review, 117(2), 440–463. https://doi.org/10.1037/a0018963
Turing, A. M. (1950). Computing Machinery and Intelligence. Mind, 59(236), 433–460. https://doi.org/10.1093/mind/LIX.236.433
Tyler, T. R. (1990). Why people obey the law. Yale University Press.
Tyler, T. R. (2006). Psychological Perspectives on Legitimacy and Legitimation. Annual Review of Psychology, 57(1), 375–400. https://doi.org/10.1146/annurev.psych.57.102904.190038
Tyler, T. R., & Huo, Y. J. (2002). Trust in the law: Encouraging public cooperation with the police and courts. Russell Sage Foundation.
Van Ryzin, G. G. (2004). Expectations, performance, and citizen satisfaction with urban services. Journal of Policy Analysis and Management, 23(3), 433–448. https://doi.org/10.1002/pam.20020
Wang, Y.-F., Chen, Y.-C., Chien, S.-Y., & Wang, P.-J. (2024). Citizens’ trust in AI-enabled government systems. Information Polity, 29(3), 293–312. https://doi.org/10.3233/ip-230065
Wirtz, B. W., Weyerer, J. C., & Geyer, C. (2019). Artificial Intelligence and the Public Sector—Applications and Challenges. International Journal of Public Administration, 42(7), 596–615. https://doi.org/10.1080/01900692.2018.1498103
Wolsink, M. (2006). Invalid theory impedes our understanding: A critique on the persistence of the language of NIMBY. Transactions of the Institute of British Geographers, 31(1), 85–91. https://doi.org/10.1111/j.1475-5661.2006.00191.x
Zaller, J. R. (1992). The Nature and Origins of Mass Opinion. Cambridge University Press. https://doi.org/10.1017/CBO9780511818691
Zhou, L., Dai, D., Ren, J., Chen, X., & Chen, S. (2022). What is policy content and how is the public’s policy support? A policy cognition study based on natural language processing and social psychology. Frontiers in Psychology, 13, 941762. https://doi.org/10.3389/fpsyg.2022.941762