研究论文

基于ELM的AIGC用户信任研究

  • 周涛 ,
  • 方晓倩 ,
  • 邓胜利
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  • 1 杭州电子科技大学管理学院 杭州 310018;
    2 杭州电子科技大学中国科教评价研究院 杭州 310018;
    3 武汉大学信息管理学院 武汉 430072
周涛,教授,博士,博士生导师,E-mail:zhoutao@hdu.edu.cn;方晓倩,硕士研究生;邓胜利,教授,博士,博士生导师。

收稿日期: 2024-08-16

  修回日期: 2024-12-12

  网络出版日期: 2025-07-14

基金资助

本文系国家社会科学基金项目“基于隐私悖论的生成式AI用户信息披露形成机理与引导策略研究”(项目编号:24BGL310)研究成果之一。

Research on AIGC User Trust Based on the ELM

  • Zhou Tao ,
  • Fang Xiaoqian ,
  • Deng Shengli
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  • 1 School of Management, Hangzhou Dianzi University, Hangzhou 310018;
    2 Chinese Academy of Science and Education Evaluation, Hangzhou Dianzi University, Hangzhou 310018;
    3 School of Information Management, Wuhan University, Wuhan 430072
Zhou Tao, professor, PhD, doctoral supervisor. E-mail: zhoutao@hdu.edu.cn; Fang Xiaoqian, master candidate; Deng Shengli, professor, PhD, doctoral supervisor.

Received date: 2024-08-16

  Revised date: 2024-12-12

  Online published: 2025-07-14

Supported by

This work is supported by the National Social Science Fund of China project titled “Research on the Formation Mechanism and Guidance Tactics of Generative AI User Information Disclosure Based on the Privacy Paradox” (Grant No. 24BGL310).

摘要

[目的/意义] 研究AIGC用户信任形成机理,从而采取措施建立用户信任,促进其采纳和使用行为。[方法/过程] 基于ELM,考察中心因素和外围因素对AIGC用户信任的影响,采用SEM和fsQCA进行数据分析。[结果/结论] 中心因素(感知准确性、感知个性化、内容可解释性)和外围因素(拟人化、感知偏见、隐私风险)、信任倾向显著影响用户对AIGC的信任。此外,算法素养对信任具有调节作用。因此,AIGC平台需要关注中心因素和外围因素两方面,进而提升用户信任,促进其使用行为。

关键词: AIGC; 信任; ELM

本文引用格式

周涛 , 方晓倩 , 邓胜利 . 基于ELM的AIGC用户信任研究[J]. 图书情报工作, 2025 , 69(14) : 61 -72 . DOI: 10.13266/j.issn.0252-3116.2025.14.006

Abstract

[Purpose/Significance] This research examines AIGC user trust mechanism in order to engender user trust and facilitate user adoption. [Method/Process] Based on ELM, this research identified the effect of both central and peripheral factors on AIGC user trust. SEM and fsQCA were used to analyze the data. [Result/Conclusion] Central factors (perceived accuracy, perceived personalization and content explainability), peripheral factors (anthropomorphism, perceived bias and privacy risk), and trust propensity significantly affect AIGC user trust. In addition, algorithm literacy has a moderation effect on trust. Thus, AIGC platforms should focus on both central and peripheral factors in order to improve user trust and facilitate usage behavior.

Key words: AIGC; trust; ELM

参考文献

[1] 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.
[2] 陈永伟. 超越ChatGPT:生成式AI的机遇、风险与挑战[J]. 山东大学学报(哲学社会科学版), 2023(3): 127-143. (CHEN Y. Beyond ChatGPT: opportunities, risks, and challenges from Generative AI [J]. Journal of Shandong University (philosophy and social sciences), 2023(3): 127-143.)
[3] CHOUDHURY A, SHAMSZARE H. Investigating the impact of user trust on the adoption and use of ChatGPT: survey analysis [J]. Journal of medical internet research, 2023, 25: e47184.
[4] LIU K, TAO D. The roles of trust, personalization, loss of privacy, and anthropomorphism in public acceptance of smart healthcare services [J]. Computers in human behavior, 2022, 127(2): 107026.
[5] JAIN G, PAREEK S, CARLBRING P. Revealing the source: how awareness alters perceptions of AI and human-generated mental health responses [J]. Internet interventions, 2024, 36: 100745.
[6] RAHMAN M S, SABBIR M M, ZHANG J, et al. Examining students’ intention to use ChatGPT: does trust matter? [J]. Australasian journal of educational technology, 2023, 39(6): 51-71.
[7] MAHESHWARI G. Factors influencing students’ intention to adopt and use ChatGPT in higher education: a study in the Vietnamese context [J]. Education and information technologies, 2023: 1-29.
[8] POLYPORTIS A, PAHOS N. Understanding students’ adoption of the ChatGPT chatbot in higher education: the role of anthropomorphism, trust, design novelty and institutional policy [J]. Behaviour & information technology, 2024: 1-22.
[9] DAHRI N A, YAHAYA N, AL-RAHMI W M, et al. Extended TAM based acceptance of AI-powered ChatGPT for supporting metacognitive self-regulated learning in education: a mixed-methods study [J]. Heliyon, 2024, 10(8): e29317.
[10] HYUN BAEK T, KIM M. Is ChatGPT scary good? How user motivations affect creepiness and trust in generative artificial intelligence [J]. Telematics and informatics, 2023, 83: 102030.
[11] KIM J H, KIM J, KIM C, et al. Do you trust ChatGPTs? Effects of the ethical and quality issues of generative AI on travel decisions [J]. Journal of travel & tourism marketing, 2023, 40(9): 779-801.
[12] 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, 2024: 27.
[13] PHAM H C, DUONG C D, NGUYEN G K H. What drives tourists’ continuance intention to use ChatGPT for travel services? A stimulus-organism-response perspective [J]. Journal of retailing and consumer services, 2024, 78: 103758.
[14] 初彦伯, 王萍, 胡姝姝. 社交媒体健康信息用户信任形成机理研究[J]. 现代情报, 2025, 45(1): 97-111. (CHU Y, WANG P, HU S. Research on the mechanism of user trust formation in social media health information [J]. Journal of modern information, 2025, 45(1): 97-111.)
[15] 谭春辉, 李玥澎, 周一夫, 等. 虚拟学术社区用户间人际信任的影响因素[J]. 图书馆论坛, 2022, 42(6): 141-149. (TAN C, LI Y, ZHOU Y et al. Factors influencing interpersonal trust among users in virtual academic communities [J]. Library tribune, 2022, 42(6): 141-149.)
[16] 王晰巍, 罗然, 刘宇桐, 等. 智慧图书馆在线聊天机器人使用行为影响因素及实证研究[J]. 情报学报, 2023, 42(2): 217-230. (WANG X, LUO R, LIU Y et al. Influencing factors and empirical research on the usage behavior of smart library online chatbots [J]. Journal of the China Society for Scientific and Technical Information, 2023, 42(2): 217-230)
[17] 叶凤云, 常琳, 秦琴, 等. 基于虚假信息特征的社交媒体大学生用户感知信任研究[J]. 情报理论与实践, 2024, 47(10): 118-127. (YE F, CHANG L, QIN Q et al. Research of social media college students’ perceived trust on false information features [J]. Information studies: theory & application, 2024, 47(10): 118-127.)
[18] 张嵩, 孙鑫玥, 陈昊. APP隐私侵犯场景下的用户信任修复机制研究——基于公平理论的多重中介模型[J]. 图书情报工作, 2023, 67(13): 36-48. (ZHANG S, SUN X, CHEN H. Research on user trust repair mechanism in APP privacy violation scenarios: a multi-mediator model based on justice theory [J]. Library and information service, 2023, 67(13): 36-48.)
[19] PETTY R E, CACIOPPO J T, The elaboration likelihood model of persuasion in communication and persuasion: central and peripheral routes to attitude change [M]. New York: Springer, 1986.
[20] KITCHEN P, KERR G, E. SCHULTZ D, et al. The elaboration likelihood model: review, critique and research agenda [J]. European journal of marketing, 2014, 48(11/12): 2033-2050.
[21] BHATTACHERJEE A, SANFORD C. Influence processes for information technology acceptance: An elaboration likelihood model [J]. MIS quarterly, 2006, 30(4): 805-825.
[22] SANG W, JI S, PU B, et al. The effect of tourism live streaming information quality and source credibility on users’ continuous viewing intention: an application of elaboration likelihood model [J]. Asia pacific journal of tourism research, 2023, 28(3): 177-190.
[23] SHAO Z, ZHANG L, PAN Z, et al. Uncovering the dual influence processes for click-through intention in the mobile social platform: an elaboration likelihood model perspective [J]. Information & management, 2023, 60(5): 103799.
[24] WANG X, TANG J, LI Q, et al. Will you buy my books? Investigating influential factors for the sales of user-generated e-books on a social commerce platform [J]. Information technology and management, 2023, 11(1): 1-16.
[25] LIU Q, SU F, MU A, et al. Understanding social media information sharing in individuals with depression: insights from the elaboration likelihood model and schema activation theory [J]. Psychology research and behavior management, 2024, 4(2): 1587-1609.
[26] ZAWIAH M, AL-ASHWAL F Y, GHARAIBEH L, et al. ChatGPT and clinical training: perception, concerns, and practice of pharm-D students [J]. Journal of multidisciplinary healthcare, 2023, 16: 4099-4110.
[27] CHOUDHURY A, CHAUDHRY Z. Large language models and user trust: consequence of self-referential learning loop and the deskilling of health care professionals [J]. Journal of medical internet research, 2024, 26: e56764.
[28] KUANG Y R, ZOU M X, NIU H Q, et al. ChatGPT encounters multiple opportunities and challenges in neurosurgery [J]. International journal of surgery, 2023, 109(10): 2886-2891.
[29] MORATH B, CHIRIAC U, JASZKOWSKI E, et al. Performance and risks of ChatGPT used in drug information: an exploratory real-world analysis [J]. European journal of hospital pharmacy, 2024, 10(1): 491-497.
[30] AMARO I, BARRA P, GRECA A D, et al. Believe in artificial intelligence? A user study on the ChatGPT’s fake information impact [J]. IEEE transactions on computational social systems, 2024, 11(4): 5168-5177.
[31] TOSSELL C C, TENHUNDFELD N L, MOMEN A, et al. Student perceptions of ChatGPT use in a college essay assignment: implications for learning, grading, and trust in artificial intelligence [J]. IEEE transactions on learning technologies, 2024, 17: 1069-1081.
[32] ALMAIAH M A, JALIL M A, MAN M. Extending the TAM to examine the effects of quality features on mobile learning acceptance [J]. Journal of computers in education, 2016, 3(4): 453-485.
[33] BALASUBRAMANIAM N, KAUPPINEN M, RANNISTO A, et al. Transparency and explainability of AI systems: From ethical guidelines to requirements [J]. Information and software technology, 2023, 159: 107197.
[34] SHIN D, KOERBER A, LIM J S. Impact of misinformation from generative AI on user information processing: how people understand misinformation from generative AI [J]. New media & society, 2024, 3(1): 1-12.
[35] SHIN D. The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI [J]. International journal of human-computer studies, 2021, 146: 102551.
[36] WANG C, LIU S, YANG H, et al. Ethical considerations of using ChatGPT in health care [J]. Journal of medical internet research, 2023, 25: e48009.
[37] CHAZETTE L, BRUNOTTE W, SPEITH T. Exploring explainability: a definition, a model, and a knowledge catalogue [C]//MOREIRA A. Proceedings of the 29th international requirements engineering conference. Los Alamitos, California: IEEE Press, 2021:197-208.
[38] WANG P, DING H. The rationality of explanation or human capacity? Understanding the impact of explainable artificial intelligence on human-AI trust and decision performance [J]. Information processing & management, 2024, 61(4): 103732.
[39] SIAU K, WANG W. Building trust in artificial intelligence, machine learning, and robotics [J]. Cutter business technology journal, 2018, 31(2): 47-53.
[40] WANG N, PYNADATH D V, HILL S G. Trust calibration within a human-robot team: Comparing automatically generated explanations [C]//BARTNECK C, NAGAI Y. Proceedings of the 11th ACM/IEEE international conference on human-robot interaction. Christchurch, New Zealand: IEEE Press, 2016: 109-116.
[41] BARTNECK C, KULIĆ D, CROFT E, et al. Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots [J]. International journal of social robotics, 2009, 1(2): 71-81.
[42] YE Y, YOU H, DU J. Improved trust in human-robot collaboration with ChatGPT [J]. IEEE access, 2023, 11(1): 55748-55754.
[43] HOU T-Y, TSENG Y-C, YUAN C W. Is this AI sexist? The effects of a biased AI’s anthropomorphic appearance and explainability on users’ bias perceptions and trust [J]. International journal of information management, 2024, 76(6): 102775.
[44] CHENG X, ZHANG X, COHEN J, et al. Human vs. AI: Understanding the impact of anthropomorphism on consumer response to chatbots from the perspective of trust and relationship norms [J]. Information processing & management, 2022, 59(3): 102940.
[45] KLEIN K, MARTINEZ L F. The impact of anthropomorphism on customer satisfaction in chatbot commerce: an experimental study in the food sector [J]. Electronic commerce research, 2023, 23(4): 2789-2825.
[46] OOI K-B, TAN G W-H, AL-EMRAN M, et al. The potential of generative artificial intelligence across disciplines: Perspectives and future directions [J]. Journal of computer information systems, 2025, 65(1): 76-107
[47] HABBAL A, ALI M K, ABUZARAIDA M A. Artificial intelligence trust, risk and security management (AI TRiSM): frameworks, applications, challenges and future research directions [J]. Expert systems with applications, 2024, 240: 122442.
[48] KINNEY M, ANASTASIADOU M, NARANJO-ZOLOTOV M, et al. Expectation management in AI: a framework for understanding stakeholder trust and acceptance of artificial intelligence systems [J]. Heliyon, 2024, 10(7): e28562.
[49] FUREY E, BLUE J. Can I trust her? Intelligent personal assistants and GDPR [C]// CHEN S X. Proceedings of the international symposium on networks, computers and communications. Istanbul, Turkey: IEEE Press, 2019:1-6
[50] ZHOU C, LIU X, YU C, et al. Trust in AI-augmented design: Applying structural equation modeling to AI-augmented design acceptance [J]. Heliyon, 2024, 10(1): e23305.
[51] CHENG Y, JIANG H. How do AI-driven chatbots impact user experience? Examining gratifications, perceived privacy risk, satisfaction, loyalty, and continued use [J]. Journal of broadcasting & electronic media, 2020, 64(4): 592-614.
[52] FAST N J, JAGO A S. Privacy matters… or does it? Algorithms, rationalization, and the erosion of concern for privacy [J]. Current opinion in psychology, 2020, 31(2): 44-48.
[53] LEFKELI D, KARATAŞ M, GÜRHAN-CANLI Z. Sharing information with AI (versus a human) impairs brand trust: the role of audience size inferences and sense of exploitation [J]. International journal of research in marketing, 2024, 41(1): 138-155.
[54] RODRÍGUEZ CARDONA D, JANSSEN A, GUHR N, et al. A matter of trust? Examination of chatbot usage in insurance business [C]//BUI T. Proceedings of the 54th Hawaii international conference on system sciences. Hawaii: AIS Press, 2021:556-565. 55] MAYER R C, DAVIS J H, SCHOORMAN F D. An integrative model of organizational trust [J]. Academy of management review, 1995, 20(3): 709-734.
[56] HALLIKAINEN H, LAUKKANEN T. National culture and consumer trust in e-commerce [J]. International journal of information management, 2018, 38(1): 97-106.
[57] KIM G, SHIN B, LEE H G. Understanding dynamics between initial trust and usage intentions of mobile banking [J]. Information systems journal, 2009, 19(3): 283-311.
[58] DOGRUEL L, MASUR P, JOECKEL S. Development and validation of an algorithm literacy scale for internet users [J]. Communication methods and measures, 2022, 16(2): 115-133.
[59] 袁昀, 袁勤俭. 详尽可能性模型及其在信息系统研究领域的应用与展望[J]. 现代情报, 2023, 43(5): 156-167. (YUAN Y, YUAN Q. Elaboration likelihood model and its application and prospect in the field of information system research [J]. Journal of modern information, 2023, 43(5): 156-167)
[60] KIM H. The indirect effect of source information on psychological reactance against antismoking messages through perceived bias [J]. Health communication, 2017, 32(5): 650-656.
[61] WANG B, RAU P-L P, YUAN T. Measuring user competence in using artificial intelligence: validity and reliability of artificial intelligence literacy scale [J]. Behaviour & information technology, 2023, 42(9): 1324-1337.
[62] LI S, PENG K, ZHU B, et al. Research on users’ privacy-sharing intentions in the health data tracking system providing personalized services and public services [J]. Sustainability, 2023, 15(22): 15709.
[63] PAPPAS I O, WOODSIDE A G. Fuzzy-set qualitative comparative analysis (fsQCA): guidelines for research practice in Information Systems and marketing [J]. International journal of information management, 2021, 58(6): 102310.
[64] MIKALEF P, PATELI A. Information technology-enabled dynamic capabilities and their indirect effect on competitive performance: findings from PLS-SEM and fsQCA [J]. Journal of business research, 2017, 70(1): 1-16.
[65] VIMALKUMAR M, SHARMA S K, SINGH J B, et al. ‘Okay google, what about my privacy?’: user’s privacy perceptions and acceptance of voice based digital assistants [J]. Computers in human behavior, 2021, 120(7): 106763.
[66] CHUNG M. What’s in the black box? How algorithmic knowledge promotes corrective and restrictive actions to counter misinformation in the USA, the UK, South Korea and Mexico [J]. Internet research, 2023, 33(5): 1971-1989.
[67] 戴思源, 李珍珍. 人工智能素养的提升会增加个人的信息隐私忧虑吗?——基于保护意识和技术信任的中介效应研究[J]. 广西师范大学学报(哲学社会科学版), 2023, 59(4): 46-57. (DAI S, LI Z. Will the improvement of artifical intelligence literacy bring about more concerns for personal information privacy? A research on mesomeric effect based on protection consciousness and technology trust [J]. Journal of Guangxi Normal University (philosophy and social sciences edition), 2023, 59(4): 46-57.)
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