图书情报工作

图书情报工作 >

2025 , Vol. 69 >Issue 17: 60 - 71

DOI: https://doi.org/10.13266/j.issn.0252-3116.2025.17.006

研究论文

AI助手用户信任的关键影响因素及其效应的元分析

  • 张露月 ,
  • 张赟 ,
  • 帅青红 ,
  • 邓婉秋
展开
  • 1 西南财经大学管理科学与工程学院 成都 611130;
    2 智能金融教育部工程研究中心 成都 611130
张露月，博士研究生；张赟，副主任，副教授，博士；帅青红，教授，博士；邓婉秋，博士研究生，通信作者，E-mail: WanqiuD@163.com。

收稿日期: 2024-08-22

  修回日期: 2025-02-17

  网络出版日期: 2025-08-20

收起

Key Influencing Factors on User Trust and Its Effects: A Meta-Analysis of AI Assistant

  • Zhang Luyue ,
  • Zhang Yun ,
  • Shuai Qinghong ,
  • Deng Wanqiu
Expand
  • 1 School of Management Science and Engineering, Southwestern University of Finance and Economics, Chengdu 611130;
    2 Engineering Research Center of Intelligent Finance, Ministry of Education, Chengdu 611130
Zhang Luyue, Doctoral Candidate; Zhang Yun, Deputy Director, Associate Professor, PhD; Shuai Qinghong, Professor, PhD; Deng Wanqiu, Doctoral Candidate, Corresponding Author, E-mail: WanqiuD@163.com.

Received date: 2024-08-22

  Revised date: 2025-02-17

  Online published: 2025-08-20

Fold

摘要

[目的/意义] 随着AI技术的高速发展，AI助手在零售、金融、医疗等行业的渗透率持续提升，用户对其使用意愿的影响机制成为学界关注焦点。用户信任作为采纳该产品的重要驱动因素，现有研究对其关键影响因素及效应尚未得到一致结论，急需进行系统的整合评估。[方法/过程] 以国内外已发表的65篇实证研究文献为样本，利用元分析定量分析针对AI助手的用户信任影响因素及其与使用意愿的关系，并检验用户文化背景和产品使用场景对这一关系的调节效应。[结果/结论] 产品拟人化、感知有用性、感知易用性、社会临场感、社会影响、产品性能和感知风险是影响用户信任的7个重要因素，其中除感知风险对于用户信任具有显著的负向影响，其余因素均对用户信任具有显著的正向影响，且用户信任对于使用意愿具有显著的正向影响，但不同研究间存在一定的异质性。通过调节效应分析发现，用户文化背景和AI助手产品使用场景是产生异质性的主要原因。研究结果为未来的深入研究提供了理论依据，并为AI助手产品的交互设计与应用推广提供指导建议。

关键词： AI助手; 用户信任; 影响因素; 元分析

本文引用格式

张露月 , 张赟 , 帅青红 , 邓婉秋 . AI助手用户信任的关键影响因素及其效应的元分析[J]. 图书情报工作, 2025 , 69(17) : 60 -71 . DOI: 10.13266/j.issn.0252-3116.2025.17.006

Abstract

[Purpose/Significance] With the rapid development of artificial intelligence (AI) technology, the penetration rate of AI assistants continues to increase in industries such as retail, finance, and healthcare. The influence mechanism of users on their usage intention has also attracted extensive attention from scholars. User trust is an important driving factor for the product adoption, but the existing research has not yet reached a consensus on the key influencing factors of trust and their effects. So, a systematic review and evaluation of these factors is urgently needed. [Method/Process] The study took 65 empirical research papers published at home and abroad as samples. It quantitatively analyzed the factors influencing user trust in AI assistants and its relationship between trust and use intention using meta-analysis. Then, it tested the moderating effect of user cultural background and product usage scenarios on this relationship. [Result/Conclusion] The results show that product anthropomorphic traits, perceived usefulness, perceived ease of use, social presence, social influence, product performance and perceived risk are seven important factors influencing user trust. Perceived risk has a significantly negative impact on user trust, the other factors have a significantly positive impact on user trust. In addition, user trust has a significantly positive impact on usage intention, but there is some heterogeneity among different studies. Through moderating effect analysis, it finds that user cultural background and AI assistant product use scenarios are the main reasons for heterogeneity. The research provides a theoretical basis for future in-depth studies and offer guiding suggestions for the interactive design and promotion of AI assistant products.

Key words： AI assistant; user trust; influencing factors; meta-analysis

参考文献

[1] RUST R T, HUANG M H. The service revolution and the transformation of marketing science[J]. Marketing science, 2014, 33(2): 206-221.
[2] MAEDCHE A, LEGNER C, BENLIAN A, et al. AI-based digital assistants[J]. Business & information systems engineering, 2019, 61(4): 535-544.
[3] NIRALA K K, SINGH N K, PURANI V S. A survey on providing customer and public administration based services using AI: chatbot[J]. Multimedia tools and applications, 2022, 81(16): 22215-22246.
[4] MCALLISTER D J, LEWICKI R J, CHATURVEDI S. Trust in developing relationships: from theory to measurement.[J]. Academy of management proceedings, 2006, 2006(1): G1-G6.
[5] MARI A, MANDELLI A, ALGESHEIMER R. Empathic voice assistants: enhancing consumer responses in voice commerce[J]. Journal of business research, 2024, 175: 114566.
[6] MUNNUKKA J, TALVITIE-LAMBERG K, MAITY D. Anthropomorphism and social presence in human–virtual service assistant interactions: the role of dialog length and attitudes[J]. Computers in human behavior, 2022, 135: 107343.
[7] 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.
[8] FU J, MOUAKKET S, SUN Y. The role of chatbots’ human-like characteristics in online shopping[J]. Electronic commerce research and applications, 2023, 61: 101304.
[9] 陈倩倩, 陈义涛. 智能语音产品人机信任的实证考察——基于人际吸引的中介作用[J]. 技术经济与管理研究, 2021(8): 31-35. (CHEN Q Q, CHEN Y T. An empirical study on human-robot trust of intelligent voice products：mediating effect of interpersonal attraction[J]. Journal of technical economics & management, 2021(8): 31-35.)
[10] LI J, WU L, QI J, et al. Determinants affecting consumer trust in communication with AI chatbots: the moderating effect of privacy concerns[J]. Journal of organizational and end user computing, 2023, 35(1): 1-24.
[11] 冉龙亚, 陈涛, 孙宁华. 技术特性对政务智能聊天机器人初始信任的影响[J]. 情报杂志, 2022, 41(7): 138-144. (RAN L Y, CHEN T, SUN N H. The influence of technical characteristics on the initial trust of government AI-based chatbots[J]. Journal of intelligence, 2022, 41(7): 138-144.)
[12] OH C S, BAILENSON J N, WELCH G F. A systematic review of social presence: definition, antecedents, and implications[J]. Frontiers in robotics and AI, 2018, 5: 114.
[13] SILVA F A, SHOJAEI A S, BARBOSA B. Chatbot-based services: a study on customers’ reuse intention[J]. Journal of theoretical and applied electronic commerce research, 2023, 18(1): 457-474.
[14] NG Y L. A longitudinal model of continued acceptance of conversational artificial intelligence[J]. Information technology & people, 2024.
[15] UYSAL E, ALAVI S, BEZENÇON V. Trojan horse or useful helper? A relationship perspective on artificial intelligence assistants with humanlike features[J]. Journal of the academy of marketing science, 2022, 50(6): 1153-1175.
[16] KIM J (Sunny), ERDEM M, KIM B. Hi Alexa. Do hotel guests have privacy concerns with you?A cross-cultural study[J]. Journal of hospitality marketing & management, 2024, 33(3): 360-383.
[17] ZHAN X, ABDI N, SEYMOUR W, et al. Healthcare voice AI assistants: factors influencing trust and intention to use[J]. Proceedings of the ACM on human-computer interaction, 2024, 8(CSCW1): 1-37.
[18] SINGH C, DASH M K, SAHU R, et al. Investigating the acceptance intentions of online shopping assistants in E-commerce interactions: mediating role of trust and effects of consumer demographics[J]. Heliyon, 2024, 10(3): e25031.
[19] GLASS G V. Primary, secondary, and meta-analysis of research[J]. Educational researcher, 1976, 5(10): 3-8.
[20] 朱玲玲, 茆意宏. 在线社区用户承诺影响因素研究：一项元分析[J]. 图书情报工作, 2023, 67(3): 106-118.(ZHU L L, MAO Y H. Research on influencing factors of user commitment in online communities: a meta-analysis[J]. Library and information service, 2023, 67(3): 106-118.)
[21] 苗蕊, 吕成戍, 鲁颜. 企业社交媒体使用与员工行为及心理结果间关系的元分析[J]. 南开管理评论, 2024, 27(1): 200-212.(MIAO R, LV C S, LU Y. A meta-analysis of the relationship between enterprise social media usage and employees’ behavioral and psychological outcomes[J]. Nankai business review, 2024, 27(1): 200-212.)
[22] 蒲青云, 黄体杨. 信息素养自我效能感影响因素元分析[J]. 图书情报工作, 2023, 67(17): 77-87.(PU Q Y, HUANG T Y. A meta-analysis of the influencing factors of information literacy self-efficacy[J]. Library and information service, 2023, 67(17): 77-87.)
[23] DAVIS F D. Perceived usefulness, perceived ease of use, and user acceptance of information technology[J]. MIS quarterly, 1989: 319-340.
[24] ZHANG T, TAO D, QU X, et al. The roles of initial trust and perceived risk in public’s acceptance of automated vehicles[J]. Transportation research part C: emerging technologies, 2019, 98: 207-220.
[25] CHOUNG H, DAVID P, ROSS A. Trust in AI and its role in the acceptance of AI technologies[J]. International journal of human-computer interaction, 2023, 39(9): 1727-1739.
[26] LEE K M, JUNG Y, KIM J, et al. Are physically embodied social agents better than disembodied social agents?The effects of physical embodiment, tactile interaction, and people’s loneliness in human–robot interaction[J]. International journal of human-computer studies, 2006, 64(10): 962-973.
[27] VAN DOORN J, MENDE M, NOBLE S M, et al. Domo Arigato Mr. Roboto: emergence of automated social presence in organizational frontlines and customers’ service experiences[J]. Journal of service research, 2017, 20(1): 43-58.
[28] LEE S K, KAVYA P, LASSER S C. Social interactions and relationships with an intelligent virtual agent[J]. International journal of human-computer studies, 2021, 150: 102608.
[29] CHOI S, JANG Y, KIM H. Exploring factors influencing students’ intention to use intelligent personal assistants for learning[J]. Interactive learning environments, 2023: 1-14.
[30] HOMANS G C. Social behavior as exchange[J]. American journal of sociology, 1958, 63(6): 597-606.
[31] WANG W, BENBASAT I. Recommendation agents for electronic commerce: effects of explanation facilities on trusting beliefs[J]. Journal of management information systems, 2007, 23(4): 217-246.
[32] OLDEWEME A, MÄRTINS J, WESTMATTELMANN D, et al. The role of transparency, trust, and social influence on uncertainty reduction in times of pandemics: empirical study on the adoption of COVID-19 tracing apps[J]. Journal of medical internet research, 2021, 23(2): e25893.
[33] MOLM L D, TAKAHASHI N, PETERSON G. Risk and trust in social exchange: an experimental test of a classical proposition[J]. American journal of sociology, 2000, 105(5): 1396-1427.
[34] AJZEN I. The theory of planned behavior[J]. Organizational behavior and human decision processes, 1991, 50(2): 179-211.
[35] AGARIYA A K, SINGH D. What really defines relationship marketing? a review of definitions and general and sector-specific defining constructs[J]. Journal of relationship marketing, 2011, 10(4): 203-237.
[36] WANG S F, CHEN C C. Exploring designer trust in artificial intelligence-generated content: TAM/TPB model study[J]. Applied sciences, 2024, 14(16): 6902.
[37] 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.
[38] 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.
[39] CASSELL J, BICKMORE T. Negotiated collusion: modeling social language and its relationship effects in intelligent agents[J]. User modeling and user-adapted interaction, 2003, 13(1): 89-132.
[40] KIESLER S, POWERS A, FUSSELL S R, et al. Anthropomorphic interactions with a robot and robot-like agent[J]. Social cognition, 2008, 26(2): 169-181.
[41] GHAZALI E, MUTUM D S, LUN N K. Expectations and beyond: the nexus of AI instrumentality and brand credibility in voice assistant retention using extended expectation confirmation model[J]. Journal of consumer behaviour, 2024, 23(2): 655-675.
[42] PITARDI V, MARRIOTT H R. Alexa, she’s not human but… unveiling the drivers of consumers’ trust in voice-based artificial intelligence[J]. Psychology & marketing, 2021, 38(4): 626-642.
[43] SONG S W, SHIN M. Uncanny valley effects on chatbot trust, purchase intention, and adoption intention in the context of e-commerce: the moderating role of avatar familiarity[J]. International journal of human-computer interaction, 2024, 40(2): 441-456.
[44] ZHOU H, LIU J, CUI X. Research on influencing factors of adoption behavior of mobile readers based on meta-analysis[J]. Mathematical problems in engineering, 2021, 2021(1): 5082594.
[45] MOU J, SHIN D H, COHEN J F. Trust and risk in consumer acceptance of e-services[J]. Electronic commerce research, 2017, 17(2): 255-288.
[46] PETERSON R A, BROWN S P. On the use of beta coefficients in meta-analysis.[J]. Journal of applied psychology, 2005, 90(1): 175-181.
[47] DUVAL S, ROTHSTEIN H R, SUTTON A J, et al. Publication bias in meta-analysis: prevention, assessment and adjustments[J]. Hoboken: John Wiley & Sons, 2005: 127-144.
[48] KANG K, ALNEFAIE A. Cultural Transformations of e-commerce consumer behavior and intention toward using artificial intelligence (AI) assistants[M]. the 57th Hawaii international conference on system sciences, 2024.
[49] HOFSTEDE G. The Cultural relativity of organizational practices and theories[J]. Journal of international business studies, 1983, 14(2): 75-89.
[50] BATRA R, AHTOLA O T. Measuring the hedonic and utilitarian sources of consumer attitudes[J]. Marketing letters, 1991, 2(2): 159-170.
[51] DHAR R, WERTENBROCH K. Consumer choice between hedonic and utilitarian goods[J]. Journal of marketing research, 2000, 37(1): 60-71.
[52] LONGONI C, CIAN L. Artificial intelligence in utilitarian vs. hedonic contexts: the “word-of-machine” effect[J]. Journal of marketing, 2022, 86(1): 91-108.
[53] AMEEN N, TARHINI A, REPPEL A, et al. Customer experiences in the age of artificial intelligence[J]. Computers in human behavior, 2021, 114: 106548.
[54] HORTON D, RICHARD WOHL R. Mass communication and para-social interaction: observations on intimacy at a distance[J]. Psychiatry, 1956, 19(3): 215-229.
[55] VENKATESH V, MORRIS M G, DAVIS G B, et al. User acceptance of information technology: toward a unified view[J]. MIS quarterly, 2003: 425-478.
[56] BROADBENT E. Interactions with robots: the truths we reveal about ourselves[J]. Annual review of psychology, 2017, 68(1): 627-652.
[57] BHATTACHERJEE A. Understanding information systems continuance: an expectation-confirmation model[J]. MIS quarterly, 2001: 351-370.
[58] YEN D C, CHEN K, KOH S, et al. An east-west comparison of IS professionals’ performance and knowledge/skills: an empirical study of USA vs Taiwan[J]. Communications of the Association for Information Systems, 2005, 15(1): 162-182.
[59] BASKENTLI S, HADI R, LEE L. How culture shapes consumer responses to anthropomorphic products[J]. International journal of research in marketing, 2023, 40(3): 495-512.
[60] HALL E T. Beyond culture[M]. New York: Anchor Books, 1976.
[61] QIU L, BENBASAT I. Evaluating anthropomorphic product recommendation agents: a social relationship perspective to designing information systems[J]. Journal of management information systems, 2009, 25(4): 145-182.
[62] KLEIN J, MOON Y, PICARD R W. This computer responds to user frustration[C]//CHI’99 extended abstracts on human factors in computing systems. New York, NY: Association for Computing Machinery, 1999: 242-243.
Options
文章导航

/

〈 〉