从心智知觉到信任：生成式AI可供性对游客信息搜索意愿的影响研究

doi:10.12054/lydk.bisu.335

旅游导刊 ›› 2026, Vol. 10 ›› Issue (4): 90-114.DOI: 10.12054/lydk.bisu.335

• 研究论文 • 上一篇

从心智知觉到信任：生成式AI可供性对游客信息搜索意愿的影响研究

宋潇潇¹, 谷慧敏¹^,², 凌小蝶³, 梅冬¹^,⁴()

¹ 北京第二外国语学院旅游科学学院北京 100024
² 新疆大学旅游学院新疆乌鲁木齐 830046
³ 对外经济贸易大学国际经济贸易学院北京 100029
⁴ 社会科学文献出版社博士后科研工作站北京 100029

收稿日期:2026-03-28 修回日期:2026-05-13 出版日期:2026-08-31 发布日期:2026-09-10
通讯作者: 梅冬（1993— ），男，山东济宁人，社会科学文献出版社博士后科研工作站、北京第二外国语学院在站博士后，研究方向为农文旅融合、旅游消费行为，E-mail：meidong@bisu.edu.cn。
作者简介:宋潇潇（1995— ），女，山东威海人，北京第二外国语学院旅游科学学院助理教授，研究方向为旅游与酒店业数字营销、服务AI；
谷慧敏（1964— ），女，湖北钟祥人，北京第二外国语学院旅游科学学院教授，研究方向为旅游和酒店管理、共享经济、企业社会责任；
凌小蝶（1997— ），女，广西玉林人，对外经济贸易大学国际经济贸易学院博士后，研究方向为游客体验、旅游与酒店智能技术。
基金资助:
国家自然科学基金青年科学基金项目“生成式人工智能驱动的个性化推荐对旅游信息搜索行为及决策的影响研究”(72502012);国家自然科学基金重点项目“文旅深度融合的游客体验管理研究”(72332010);2025年度北京第二外国语学院科研项目“生成式AI驱动的旅游服务创新：场景应用、影响机制与数智治理研究”(KYZX25A012)

From Mind Perception to Trust: The Impact of Generative AI Affordances on Tourists’ Information Search Intention

SONG Xiaoxiao¹, GU Huimin¹^,², LING Xiaodie³, MEI Dong¹^,⁴()

¹ School of Tourism Sciences, Beijing International Studies University, Beijing 100024, China
² College of Tourism, Xinjiang University, Urumqi 830046, China
³ School of International Trade and Economics, University of International Business and Economics, Beijing 100029, China
⁴ Postdoctoral Research Workstation of Social Sciences Academic Press, Beijing 100029, China

Received:2026-03-28 Revised:2026-05-13 Online:2026-08-31 Published:2026-09-10

摘要/Abstract

摘要：

生成式AI在旅游信息服务领域的应用日益广泛，并显著改变了游客的信息获取与决策方式。本研究基于技术可供性理论与心智知觉理论，构建并检验了生成式AI可供性属性对游客信息搜索意愿影响的三阶段理论模型，对696份问卷数据进行了结构方程模型分析。研究结果表明，生成式AI的功能可供性（智能性、透明度）可以显著提升游客的感知能动性，而交互可供性（信息可达性、个性化）可以显著提升游客的情感体验；感知能动性与情感体验能够进一步增强游客信任，并提升游客使用生成式AI进行旅游信息搜索的意愿。本研究将技术可供性理论引入旅游信息搜索情境，不仅扩展了该理论的应用边界，也为生成式AI在旅游信息服务领域的推广与优化提供了理论依据和实践参考。

关键词: 生成式人工智能; 技术可供性; 感知能动性; 信任; 旅游信息搜索

Abstract:

Generative artificial intelligence (gen AI) is increasingly applied in the field of tourism information services, significantly transforming how tourists acquire information and make decisions. Based on technology affordance and mind perception theories, this study constructs and tests a three-stage theoretical model examining the influence of gen AI on tourists’ intention to search for travel information. Structural equation modeling was conducted using data from 696 questionnaires. The results indicate that functional affordances of gen AI (intelligence and transparency) significantly enhance tourists’ perceived agency, while interactive affordances (information accessibility and personalization) significantly improve tourists’ emotional experience. Perceived agency and emotional experience further strengthen tourists’ trust, thereby increasing their intention to use gen AI for information search. By introducing technology affordance theory into the context of tourism information search, this study not only extends the theoretical boundary of technology affordance theory but also provides theoretical guidance and practical implications for the promotion and optimization of gen AI in tourism information services.

Keywords: generative AI; technology affordances; perceived agency; trust; travel information search

中图分类号:

宋潇潇, 谷慧敏, 凌小蝶, 梅冬. 从心智知觉到信任：生成式AI可供性对游客信息搜索意愿的影响研究[J]. 旅游导刊, 2026, 10(4): 90-114.

SONG Xiaoxiao, GU Huimin, LING Xiaodie, MEI Dong. From Mind Perception to Trust: The Impact of Generative AI Affordances on Tourists’ Information Search Intention[J]. Tourism and Hospitality Prospects, 2026, 10(4): 90-114.

图/表 6

参考文献 64

[1]	Ali F, Yasar B, Ali L, et al. Antecedents and consequences of travelers’ trust towards personalized travel recommendations offered by ChatGPT[J]. International Journal of Hospitality Management, 2023, 114: 103588.
[2]	Christensen J, Hansen J M, Wilson P. Understanding the role and impact of generative artificial intelligence (AI) hallucination within consumers’ tourism decision-making processes[J]. Current Issues in Tourism, 2025, 28(4): 545-560.
[3]	Clegg M, Hofstetter R, De Bellis E, et al. Unveiling the mind of the machine[J]. Journal of Consumer Research, 2024, 51(2): 342-361.
[4]	Dwivedi Y K, Kshetri N, Hughes L, et al. “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy[J]. International Journal of Information Management, 2023, 71: 102642.
[5]	Glikson E, Woolley A W. Human trust in artificial intelligence: review of empirical research[J]. Academy of Management Annals, 2020, 14(2): 627-660.
[6]	Gray H M, Gray K, Wegner D M. Dimensions of mind perception[J]. Science, 2007, 315(5812): 619.
[7]	Gray K, Wegner D M. Feeling robots and human zombies: mind perception and the uncanny valley[J]. Cognition, 2012, 125(1): 125-130.
[8]	Gray K, Young L, Waytz A. Mind perception is the essence of morality[J]. Psychological Inquiry, 2012, 23(2): 101-124.
[9]	Gursoy D. A critical review of determinants of information search behavior and utilization of online reviews in decision making process (invited paper for “luminaries” special issue of International Journal of Hospitality Management)[J]. International Journal of Hospitality Management, 2019, 76: 53-60.
[10]	Gursoy D, Cai R Y. Artificial intelligence: an overview of research trends and future directions[J]. International Journal of Contemporary Hospitality Management, 2025, 37(1): 1-17.
[11]	Hsu C L, Liao Y C. Exploring the linkages between perceived information accessibility and microblog stickiness: the moderating role of a sense of community[J]. Information & Management, 2014, 51(7): 833-844.
[12]	Jaiswal R, Gupta S, Chen P J. GenAI personalization: antecedents, outcomes, mediators, and moderators[J]. International Journal of Contemporary Hospitality Management, 2026, 38(13): 134-158.
[13]	Kim J H, Kim J, Baek T H, et al. ChatGPT personalized and humorous recommendations[J]. Annals of Tourism Research, 2025, 110: 103857.
[14]	Kim J H, Kim J, Kim S, et al. Effects of AI ChatGPT on travelers’ travel decision-making[J]. Tourism Review, 2024, 79(5): 1038-1057.
[15]	Kim J H, Kim J, Park J, et al. When ChatGPT gives incorrect answers: the impact of inaccurate information by generative AI on tourism decision-making[J]. Journal of Travel Research, 2025, 64(1): 51-73.
[16]	Kim T, Kim M J, Promsivapallop P. Investigating the influence of generative AI’s credibility and utility on travel consumer behaviour and recommendations through the lens of personal innovativeness[J]. Current Issues in Tourism, 2025, 28(14): 2220-2224.
[17]	Komiak S Y X, Benbasat I. The effects of personalization and familiarity on trust and adoption of recommendation agents[J]. MIS Quarterly, 2006, 30(4): 941-960.
[18]	Lee J C, Cao H Z. System transparency, artificial intelligence (AI) self-efficacy, job satisfaction and resistance in AI-enabled accounting systems[J/OL]. Industrial Management & Data Systems, 2026. https://doi.org/10.1108/IMDS-01-2025-0075.
[19]	Leonardi P M. When flexible routines meet flexible technologies: affordance, constraint, and the imbrication of human and material agencies[J]. MIS quarterly, 2011, 35(1): 147-167.
[20]	Leung X Y, Zhang H Y, Lyu J Y, et al. Why do hotel frontline employees use service robots in the workplace? A technology affordance theory perspective[J]. International Journal of Hospitality Management, 2023, 108: 103380.
[21]	Li C X, Lin Y X, Chen R Q, et al. How do users adopt AI-generated content (AIGC)? An exploration of content cues and interactive cues[J]. Technology in Society, 2025, 81: 102830.
[22]	Li H Y, Xi J B, Hsu C H C, et al. Generative artificial intelligence in tourism management: an integrative review and roadmap for future research[J]. Tourism Management, 2025, 110: 105179.
[23]	Li Q W, Sun Y Q, Yang B. Affordances of, attachment to and satisfaction with generative artificial intelligence: an affordance actualization perspective[J]. Information Technology & People, 2025, 39(2): 1064-1090.
[24]	Li Y, Lee S O. Navigating the generative AI travel landscape: the influence of ChatGPT on the evolution from new users to loyal adopters[J]. International Journal of Contemporary Hospitality Management, 2025, 37(4): 1421-1447.
[25]	Li Y N, Zhao L, Cao C C, et al. The double-edged sword effect of algorithmic transparency: an empirical study of gig workers’ work disengagement under algorithmic management[J]. Information & Management, 2025, 62(2): 104100.
[26]	Li Y, Zhou X Y, Jiang X, et al. How service robots’ human-like appearance impacts consumer trust: a study across diverse cultures and service settings[J]. International Journal of Contemporary Hospitality Management, 2024, 36(9): 3151-3167.
[27]	Lu L, Cai R Y, Gursoy D. Developing and validating a service robot integration willingness scale[J]. International Journal of Hospitality Management, 2019, 80: 36-51.
[28]	Mayer R C, Davis J H, Schoorman F D. An integrative model of organizational trust[J]. The Academy of Management Review, 1995, 20(3): 709-734.
[29]	McAllister D J. Affect- and cognition-based trust as foundations for interpersonal cooperation in organizations[J]. The Academy of Management Journal, 1995, 38(1): 24-59.
[30]	Mich L, Garigliano R. ChatGPT for e-Tourism: a technological perspective[J]. Information Technology & Tourism, 2023, 25(1): 1-12.
[31]	Moussawi S, Koufaris M, Benbunan-Fich R. How perceptions of intelligence and anthropomorphism affect adoption of personal intelligent agents[J]. Electronic Markets, 2021, 31(2): 343-364.
[32]	Niu B, Mvondo G F N. I am ChatGPT, the ultimate AI Chatbot! Investigating the determinants of users’ loyalty and ethical usage concerns of ChatGPT[J]. Journal of Retailing and Consumer Services, 2024, 76: 103562.
[33]	Park H E. The double-edged sword of generative artificial intelligence in digitalization: an affordances and constraints perspective[J]. Psychology & Marketing, 2024, 41(11): 2924-2941.
[34]	Park K, Yoon H Y. AI algorithm transparency, pipelines for trust not prisms: mitigating general negative attitudes and enhancing trust toward AI[J]. Humanities and Social Sciences Communications, 2025, 12(1): 1-13.
[35]	Pillai R, Sivathanu B. Adoption of AI-based chatbots for hospitality and tourism[J]. International Journal of Contemporary Hospitality Management, 2020, 32(10): 3199-3226.
[36]	Pitardi V, Wirtz J, Paluch S, et al. Service robots, agency and embarrassing service encounters[J]. Journal of Service Management, 2022, 33(2): 389-414.
[37]	Rai A. Explainable AI: from black box to glass box[J]. Journal of the Academy of Marketing Science, 2020, 48(1): 137-141.
[38]	Saleh M I. Generative artificial intelligence in hospitality and tourism: future capabilities, AI prompts and real-world applications[J]. Journal of Hospitality Marketing & Management, 2025, 34(4): 467-498.
[39]	Schulz-Schaeffer I. Why generative AI is different from designed technology regarding task-relatedness, user interaction, and agency[J]. Big Data & Society, 2025, 12(3): 20539517251367452.
[40]	Seyfi S, Kim M J, Nazifi A, et al. Understanding tourist barriers and personality influences in embracing generative AI for travel planning and decision-making[J]. International Journal of Hospitality Management, 2025, 126: 104105.
[41]	Shi J, Lee M, Girish V G, et al. Embracing the ChatGPT revolution: unlocking new horizons for tourism[J]. Journal of Hospitality and Tourism Technology, 2024, 15(3): 433-448.
[42]	Shi S, Gong Y H, Gursoy D. Antecedents of trust and adoption intention toward artificially intelligent recommendation systems in travel planning: a heuristic-systematic model[J]. Journal of Travel Research, 2021, 60(8): 1714-1734.
[43]	Shin S, Kim J, Lee E, et al. ChatGPT for trip planning: the effect of narrowing down options[J]. Journal of Travel Research, 2025, 64(2): 247-266.
[44]	Teo H H, Chan H C, Wei K K, et al. Evaluating information accessibility and community adaptivity features for sustaining virtual learning communities[J]. International Journal of Human-Computer Studies, 2003, 59(5): 671-697.
[45]	Vanneste B S, Puranam P. Artificial intelligence, trust, and perceptions of agency[J]. Academy of Management Review, 2025, 50(4): 726-744.
[46]	Wanner J, Herm L V, Heinrich K, et al. The effect of transparency and trust on intelligent system acceptance: evidence from a user-based study[J]. Electronic markets, 2022, 32(4): 2079-2102.
[47]	Waytz A, Gray K, Epley N, et al. Causes and consequences of mind perception[J]. Trends in Cognitive Sciences, 2010, 14(8): 383-388.
[48]	Weith H, Matt C. Information provision measures for voice agent product recommendations: the effect of process explanations and process visualizations on fairness perceptions[J]. Electronic Markets, 2023, 33(1): 57.
[49]	Wong A, Wong J. Service robot acceptance in museums: an empirical study using the service robot acceptance model (sRAM)[J]. Journal of Services Marketing, 2024, 38(5): 619-635.
[50]	Xie L S, Liu C M, Li D M. Proactivity or passivity? An investigation of the effect of service robots’ proactive behaviour on customer co-creation intention[J]. International Journal of Hospitality Management, 2022, 106: 103271.
[51]	Yang H J, Song H Q, Xia L T, et al. Understanding the consequences of robotic interaction quality and outcome quality: a three-phased affordance theory-based approach[J]. Journal of Hospitality Marketing & Management, 2024, 33(6): 763-783.
[52]	Zhang M Y, Yi F R, Gursoy D. The effects of generative artificial intelligence on consumers in hospitality and tourism: a systematic review and future research directions[J]. International Journal of Hospitality Management, 2026, 133: 104452.
[53]	崔淼, 钱金芳, 王之澜. 持续用户生成内容的实现机制: 可供性-实现视角下Bilibili案例研究[J]. 南开管理评论, 2024, 27(1): 40-52.
	[CUI Miao, QIAN Jinfang, WANG Zhilan. Actualization mechanism of sustainable user generated content: a case study of Bilibili based on affordance actualization theory[J]. Nankai Business Review, 2024, 27(1): 40-52.]
[54]	邓克涛, 张贵红. 算法透明度: 从理论到实践的探索与反思[J]. 科学学研究, 2024, 42(7): 1354-1360.
	[DENG Ketao, ZHANG Guihong. Algorithmic transparency: exploration and reflection from theory to practice[J]. Studies in Science of Science, 2024, 42(7):1354-1360.]
[55]	邓士昌, 许祺, 张晶晶, 等. 基于心灵知觉理论的AI服务用户接受机制及使用促进策略[J]. 心理科学进展, 2022, 30(4): 723-737.
	[DENG Shichang, XU Qi, ZHANG Jingjing, et al. User acceptance mechanism and usage promotion strategy of AI services based on mind perception theory[J]. Advances in Psychological Science, 2022, 30(4): 723-737.]
[56]	李军, 宋晨鹏, 叶浩彬. 智能服务情境下消费者对服务型机器人使用意愿研究[J]. 旅游学刊, 2023, 38(6): 136-150.
	[LI Jun, SONG Chenpeng, YE Haobin. Consumers’ intention to use service robots in intelligent service environment[J]. Tourism Tribune, 2023, 38(6): 136-150.]
[57]	林美珍, 杨兴鑫, 王彦文, 等. 酒店服务机器人拟人化对顾客服务失败容忍度的影响: 任务类型的调节作用[J]. 旅游导刊, 2024, 8(6): 28-55.
	[LIN Meizhen, YANG Xingxin, WANG Yanwen, et al. The influence of hotel service robot anthropomorphism on customer tolerance for service failures: the moderating role of task type[J]. Tourism and Hospitality Prospects, 2024, 8(6): 28-55.]
[58]	陆金凤, 朱国玮, 罗映宇, 等. 从决策式到生成式: 人工智能管理研究的理论框架与未来议题[J]. 管理世界, 2026, 42(2): 208-241.
	[LU Jinfeng, ZHU Guowei, LUO Yingyu, et al. From discriminative to generative AI: theoretical framework and future agenda in artificial intelligence management[J]. Journal Management World, 2026, 42(2): 208-241.]
[59]	吕巍, 杨颖, 张雁冰. AI个性化推荐下消费者感知个性化对其点击意愿的影响[J]. 管理科学, 2020, 33(5): 44-57.
	[LYU Wei, YANG Ying, ZHANG Yanbing. Effects of consumers’ perceived personalization on their click-through intention under AI personalized recommendations[J]. Journal of Management Science, 2020, 33(5): 44-57.]
[60]	吕兴洋, 杨玉帆, 许双玉, 等. 以情补智: 人工智能共情回复的补救效果研究[J]. 旅游学刊, 2021, 36(8): 86-100.
	[LYU Xingyang, YANG Yufan, XU Shuangyu, et al. Emotional intelligence: the effect of AI empathic response on customers’ continuance intention of AI service[J]. Tourism Tribune, 2021, 36(8): 86-100.]
[61]	乔向杰, 赵子惠, 刘丁菀. 生成式AI行程规划持续使用意愿的双阶段适配机制[J]. 旅游学刊, 2026, 41(2): 32-47.
	[QIAO Xiangjie, ZHAO Zihui, LIU Dingwan. A dual-stage adaptation mechanism of continuous usage intention for generative AI in itinerary planning[J]. Tourism Tribune, 2026, 41(2): 32-47.]
[62]	舒伯阳, 邱海莲, 李明龙. 社会化视角下接待业服务机器人对顾客体验的影响研究[J]. 旅游导刊, 2020, 4(2): 9-25.
	[SHU Boyang, QIU Hailian, LI Minglong. The influence of service robots on customer experience in the hospitality industry: a socialization perspective[J]. Tourism and Hospitality Prospects, 2020, 4(2): 9-25.]
[63]	苏郁锋, 周翔. “直播电商”情境下数字机会共创机制研究: 基于数字可供性视角的质性研究[J]. 南开管理评论, 2023, 26(1): 106-119.
	[SU Yufeng, ZHOU Xiang. Digital opportunity co-creation mechanism within live broadcast e-commerce context: a qualitative study based on the digital affordance theory[J]. Nankai Business Review, 2023, 26(1): 106-119.]
[64]	谢卫红, 曾思敏, 彭铁鹏, 等. 技术可供性: 概念内涵、理论框架及展望[J]. 科技管理研究, 2022, 42(5): 210-218.
	[XIE Weihong, ZENG Simin, PENG Tiepeng, et al. Technology affordance: conceptual connotation, theoretical framework and prospect[J]. Science and Technology Management Research, 2022, 42(5): 210-218.]

变量 Variable	题项 Item	标准化因子载荷 Standardized factor loading	克隆巴赫系数 Cronbach’s α	rho_A	平均提取方差值 AVE
智能性IT	IT1 我认为生成式AI在搜索旅游信息时是负责任的	0.848	0.708	0.735	0.772
智能性IT	IT2 我认为生成式AI在搜索旅游信息时是智能的	0.908	0.708	0.735	0.772
透明度TP	TP1 生成式AI会解释其为何提供这些旅游建议与任务提示	0.849	0.804	0.817	0.718
	TP2 生成式AI能够帮助我理解其提供旅游建议与任务提示的理由	0.881
	TP3 生成式AI会定期反馈我在搜索旅游信息过程中的表现	0.811
信息可达性 IA	IA1 生成式AI使我能够轻松获取多样的旅游信息	0.795	0.707	0.707	0.630
	IA2 通过生成式AI，我可以获得全面的旅游信息	0.792
	IA3 生成式AI提供的旅游信息能够及时更新并反映实时变化	0.795
个性化 PER	PER1生成式AI会根据我的个人需求调整旅游信息搜索结果	0.839	0.704	0.707	0.629
	PER2生成式AI能够为我提供定制化的旅游信息搜索结果	0.785
	PER3 生成式AI会根据我的输入提供个性化的旅游信息搜索结果	0.754
感知能动性 PA	PA1 在旅游信息搜索中，生成式AI能够自主制定旅行规划	0.848	0.881	0.890	0.738
	PA2在旅游信息搜索中，生成式AI能够进行自我控制	0.811
	PA3在旅游信息搜索中，生成式AI能够形成自己的判断	0.882
	PA4在旅游信息搜索中，生成式AI具备思考能力	0.893
情感体验 EE	EE1 如果我使用生成式AI进行旅游信息搜索，我是否会感到愉快	0.757	0.704	0.708	0.628
	EE2 如果我使用生成式AI进行旅游信息搜索，我是否会感到满意	0.782
	EE3 如果我使用生成式AI进行旅游信息搜索，我是否会感到放松	0.836
信任 TRU	TRU1 在搜索旅游相关信息时，我信任生成式AI	0.829	0.708	0.716	0.632
	TRU2 我认为生成式AI能够给出可靠且诚实的旅游建议	0.734
	TRU3 我认为生成式AI提供的旅游信息是真实可信的	0.819
搜索意愿 SI	SI1 我未来会使用生成式AI搜索旅游信息	0.876	0.705	0.705	0.772
搜索意愿 SI	SI2 下次旅游时，我计划使用生成式AI搜索旅游信息	0.881	0.705	0.705	0.772

变量 Variable	题项 Item	标准化因子载荷 Standardized factor loading	克隆巴赫系数 Cronbach’s α	rho_A	平均提取方差值 AVE
智能性IT	IT1 我认为生成式AI在搜索旅游信息时是负责任的	0.848	0.708	0.735	0.772
智能性IT	IT2 我认为生成式AI在搜索旅游信息时是智能的	0.908	0.708	0.735	0.772
透明度TP	TP1 生成式AI会解释其为何提供这些旅游建议与任务提示	0.849	0.804	0.817	0.718
	TP2 生成式AI能够帮助我理解其提供旅游建议与任务提示的理由	0.881
	TP3 生成式AI会定期反馈我在搜索旅游信息过程中的表现	0.811
信息可达性 IA	IA1 生成式AI使我能够轻松获取多样的旅游信息	0.795	0.707	0.707	0.630
	IA2 通过生成式AI，我可以获得全面的旅游信息	0.792
	IA3 生成式AI提供的旅游信息能够及时更新并反映实时变化	0.795
个性化 PER	PER1生成式AI会根据我的个人需求调整旅游信息搜索结果	0.839	0.704	0.707	0.629
	PER2生成式AI能够为我提供定制化的旅游信息搜索结果	0.785
	PER3 生成式AI会根据我的输入提供个性化的旅游信息搜索结果	0.754
感知能动性 PA	PA1 在旅游信息搜索中，生成式AI能够自主制定旅行规划	0.848	0.881	0.890	0.738
	PA2在旅游信息搜索中，生成式AI能够进行自我控制	0.811
	PA3在旅游信息搜索中，生成式AI能够形成自己的判断	0.882
	PA4在旅游信息搜索中，生成式AI具备思考能力	0.893
情感体验 EE	EE1 如果我使用生成式AI进行旅游信息搜索，我是否会感到愉快	0.757	0.704	0.708	0.628
	EE2 如果我使用生成式AI进行旅游信息搜索，我是否会感到满意	0.782
	EE3 如果我使用生成式AI进行旅游信息搜索，我是否会感到放松	0.836
信任 TRU	TRU1 在搜索旅游相关信息时，我信任生成式AI	0.829	0.708	0.716	0.632
	TRU2 我认为生成式AI能够给出可靠且诚实的旅游建议	0.734
	TRU3 我认为生成式AI提供的旅游信息是真实可信的	0.819
搜索意愿 SI	SI1 我未来会使用生成式AI搜索旅游信息	0.876	0.705	0.705	0.772
搜索意愿 SI	SI2 下次旅游时，我计划使用生成式AI搜索旅游信息	0.881	0.705	0.705	0.772

变量 Variable	智能性 IT	透明度 TP	信息可达性 IA	个性化 PER	感知能动性 PA	情感体验 EE	信任 TRU	搜索意愿 SI
智能性 IT	0.879
透明度 TP	0.381	0.847
信息可达性 IA	0.430	0.403	0.794
个性化 PER	0.315	0.333	0.361	0.793
感知能动性 PA	0.299	0.373	0.272	0.217	0.859
情感体验 EE	0.383	0.389	0.433	0.435	0.217	0.792
信任 TRU	0.417	0.400	0.541	0.423	0.299	0.479	0.795
搜索意愿 SI	0.265	0.174	0.261	0.298	0.132	0.254	0.446	0.879

变量 Variable	智能性 IT	透明度 TP	信息可达性 IA	个性化 PER	感知能动性 PA	情感体验 EE	信任 TRU	搜索意愿 SI
智能性 IT	0.879
透明度 TP	0.381	0.847
信息可达性 IA	0.430	0.403	0.794
个性化 PER	0.315	0.333	0.361	0.793
感知能动性 PA	0.299	0.373	0.272	0.217	0.859
情感体验 EE	0.383	0.389	0.433	0.435	0.217	0.792
信任 TRU	0.417	0.400	0.541	0.423	0.299	0.479	0.795
搜索意愿 SI	0.265	0.174	0.261	0.298	0.132	0.254	0.446	0.879

变量 Variable	智能性IT	透明度 TP	信息可达性IA	个性化 PER	感知能动性PA	情感体验EE	信任 TRU	搜索意愿SI
智能性 IT	—
透明度 TP	0.497	—
信息可达性 IA	0.609	0.531	—
个性化 PER	0.440	0.444	0.513	—
感知能动性 PA	0.369	0.438	0.340	0.272	—
情感体验 EE	0.537	0.514	0.610	0.615	0.273	—
信任 TRU	0.594	0.529	0.775	0.595	0.376	0.674	—
搜索意愿 SI	0.374	0.229	0.369	0.423	0.171	0.357	0.626	—

从心智知觉到信任：生成式AI可供性对游客信息搜索意愿的影响研究

From Mind Perception to Trust: The Impact of Generative AI Affordances on Tourists’ Information Search Intention

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Metrics

路径 Path	路径系数 Path coefficient	t值 t value
智能性→感知能动性 IT→PA	0.184***	5.252
透明度→感知能动性 TP→PA	0.303***	7.108
信息可达性→情感体验 IA→EE	0.318***	9.281
个性化→情感体验 PER→EE	0.320***	9.003
感知能动性→信任 PA→TRU	0.205***	6.184
情感体验→信任 EE→TRU	0.434***	13.229
信任→搜索意愿 TRU→SI	0.446***	14.291

[1]	张凌云. 中国式现代化背景下旅游基础理论创新与创建[J]. 旅游导刊, 2024, 8(4): 1-8.
[2]	王璐, 郑向敏, 李勇泉. 网络点评对民宿顾客住宿意愿的影响——信任的中介作用探讨[J]. 旅游导刊, 2020, 4(1): 79-94.