The DOI matches its Crossref record; 79 of the 81 references it cites were found in a registry or an index.
Trust in a specific technology: An investigation of its components and measures
Abstract
Trust plays an important role in many Information Systems (IS)-enabled situations. Most IS research employs trust as a measure of interpersonal or person-to-firm relations, such as trust in a Web vendor or a virtual team member. Although trust in other people is important, this article suggests that trust in the Information Technology (IT) itself also plays a role in shaping IT-related beliefs and behavior. To advance trust and technology research, this article presents a set of trust in technology construct definitions and measures. We also empirically examine these construct measures using tests of convergent, discriminant, and nomological validity. This study contributes to the literature by providing: (a) a framework that differentiates trust in technology from trust in people, (b) a theory-based set of definitions necessary for investigating different kinds of trust in technology, and (c) validated trust in technology measures useful to research and practice.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Trustworthiness judgments in humans or technology have different characteristics, yet they both are premised on the same underlying contextual conditions: risk, uncertainty and lack of control (McKnight et al. 2011).”
From Explaining Why the Computer Says No: Algorithmic Transparency Affects the Perceived Trustworthiness of Automated Decision‐Making (2023) · cites this paper as McKnight et al. 2011
In the citing paper
“Trust in technology has previously been seen in the light of the technology's functionality, ability to provide help and operational reliability and the technology provider's integrity, benevolence and competence-including privacy aspects (McKnight et al., 2011;Prakash and Das, 2020;Ta et al., 2020).”
From My Chatbot Companion - a Study of Human-Chatbot Relationships (2021) · cites this paper as McKnight et al., 2011
In the citing paper
“In the canonical models of trust formation, such (Giddens, 1990; Mayer et al., 1995; McKnight et al., 2011; Misztal, 1996) trust is described to have a number of different components. First, trust depends on the personal characteristics of the trustor, such as their propensity to trust, something which I do not discuss in this article. Second, trust is a factor of the perceived or actual char”
From Mediated trust: A theoretical framework to address the trustworthiness of technological trust mediators (2021) · cites this paper as McKnight et al., 2011
In the citing paper
“Nevertheless, trust is a complex concept and is built upon human users’ interaction and consequent assessment of the system from cognitive, emotional, and social dimensions [91-93].”
From Generative AI in medical practice: in-depth exploration of privacy and security challenges (2024) · cites this paper as [93]
In the citing paper
“Individual differences of users (e.g., gender, culture, age, and personality) are closely related to the propensity for or tendency toward technology users may manifest or develop (Gefen et al., 2008; McKnight et al. 2011).”
From Why do users trust algorithms? A review and conceptualization of initial trust and trust over time (2022) · cites this paper as McKnight et al. 2011
In the citing paper
“Proposition 6b. The greater the reliability an algorithm demonstrates, the higher the probability of building trust in AI algorithms over time.”
From Why do users trust algorithms? A review and conceptualization of initial trust and trust over time (2022) · cites this paper as McKnight et al., 2011
In the citing paper
“One important factor for initial trust is the user's propensity to trust, which, being tacit, bestows a positive impact on this starting phase and tends to remain stable over time (McKnight et al. 2011).”
From Why do users trust algorithms? A review and conceptualization of initial trust and trust over time (2022) · cites this paper as McKnight et al. 2011
In the citing paper
“Specific trust in people and trust in technology not only differ in terms of the nature of the object of dependence but also on important trusting beliefs.”
From Trustworthy artificial intelligence (2021) · cites this paper as McKnight et al. 2011
In the citing paper
“Interpersonal trusting beliefs reflect judgments that the other party has suitable attributes and motives for performing as expected in a risky situation (Mayer et al. 1995), whereas technology-related trust necessarily reflects beliefs about a technology's characteristics rather than its motives (McKnight et al. 2011).”
From Trustworthy artificial intelligence (2021) · cites this paper as McKnight et al. 2011
In the citing paper
“Most IS research nowadays employs a dualistic perspective on trust (see Table 1).First, trust in a specific person or organization (a moral and volitional agent) (Lankton et al. 2015;McKnight et al. 2011), such as trust in an e-vendor (Gefen et al. 2003) or virtual team members (Robert et al. 2009).”
From Trustworthy artificial intelligence (2021) · cites this paper as McKnight et al. 2011
In the citing paper
“In contrast, individuals' trust in technology commonly concerns the technology's functionality (i.e., providing features needed to complete a task), its helpfulness (i.e., help functions will provide necessary advice), and its reliability (i.e., technology will consistently operate properly) (McKnight et al. 2011;Thatcher et al. 2010).”
From Trustworthy artificial intelligence (2021) · cites this paper as McKnight et al. 2011
In the citing paper
“Trust plays a particularly important role in almost any ISenabled situation in which either uncertainty prevails or undesirable outcomes are possible (McKnight et al. 2011).”
From Trustworthy artificial intelligence (2021) · cites this paper as McKnight et al. 2011
In the citing paper
“Specific trust in persons and technology can be further positioned in a nomological net of trust, comprising general trusting beliefs and institution-based trust as antecedents and trusting intentions as consequence of specific trust in persons or technology (McKnight et al. 2011;McKnight et al. 2002; see Fig. 1).”
From Trustworthy artificial intelligence (2021) · cites this paper as McKnight et al. 2011
In the citing paper
“The trust literature suggests a causal ordering among trust constructs, such that an individual's general trusting beliefs in people or technology directly influences institutionbased trust and indirectly shapes trust in a specific person or technology (McKnight et al. 2011;McKnight and Chervany 2001).”
From Trustworthy artificial intelligence (2021) · cites this paper as McKnight et al. 2011
In the citing paper
“In line with the view of institutions-based trust in technology being an antecedent to trust in a specific technology (McKnight et al. 2011), we argue, that the limited availability of large, high-quality data in certain areas, which constraints our ability to promote human well-being through AI in those areas, could lead society to perceive the entire class of AIbased systems as not beneficent (”
From Trustworthy artificial intelligence (2021) · cites this paper as McKnight et al. 2011
Citations
Cites
Evaluating Structural Equation Models with Unobservable Variables and Measurement Error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.2307/3151312 · Cited by 109,314
Evaluating structural equation models with unobservable variables and measurement error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800104 · Cited by 109,190
Hu, L., & Bentler, P. M. (1999) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705519909540118 · Cited by 108,788
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003) · Journal of Applied Psychology · doi:10.1037/0021-9010.88.5.879 · Cited by 82,194
Tabachnick, B. G., & Fidell, L. S. (1983) · Cited by 79,882
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483
Structural equation modeling in practice: A review and recommended two-step approach
Anderson, J. C., & Gerbing, D. W. (1988) · Psychological Bulletin · doi:10.1037/0033-2909.103.3.411 · Cited by 41,941
Significance tests and goodness of fit in the analysis of covariance structures.
Bentler, P. M., & Bonett, D. G. (1980) · Psychological Bulletin · doi:10.1037/0033-2909.88.3.588 · Cited by 19,933
An integrative model of organizational trust
Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995) · The Academy of Management Review · doi:10.2307/258792 · Cited by 10,736
Trust: The Social Virtues and the Creation of Prosperity
Schneider, P. A., & Fukuyama, F. (1996) · Journal of Marketing · doi:10.2307/1251846 · Cited by 10,493
Flagged first, then by citations
2 of the 81 references couldn’t be matched to a known paper.
Research works printed but not linked: 8
- Berscheid E., Close Relationships
- 10.5465/amr.1998.926629 · DOI · Paper page
- Golembiewski R. T. and Mcconkie M. 1975. The centrality of interpersonal trust in group processes. In Theories of Group Processes G. L. Cooper Ed. John Wiley & Sons London 131--185. Golembiewski R. T. and Mcconkie M. 1975. The centrality of interpersonal trust in group processes. In Theories of Group Processes G. L. Cooper Ed. John Wiley & Sons London 131--185.
- 10.5555/2017317.2017325 · DOI
- Lewicki R. J., 1996, Organizations: Frontiers of Theory and Research
- 10.5465/amr.1995.9508080335 · DOI · Paper page
- Mcknight D. H., 2005, Trust in information technology. In The Blackwell Encyclopedia of Management, Management Information Systems, G. B. Davis, Ed., Blackwell, Malden, MA, 7, 329
- 10.5465/amr.1998.926622 · DOI · Paper page
Cited by
Sorted by citations
Items 201–300 of 999
Gbongli, K., Xu, Y., Amedjonekou, K. M., & Kovács, L. (2020) · Sustainability · doi:10.3390/su12041288 · Cited by 52
Using Social and Behavioral Science to Support COVID-19 Pandemic Response
Cucchiarini, V., Lunn, P., Robertson, D. A., & Viale, R. (2023) · Springer eBooks · doi:10.1007/978-3-319-57365-6_319-1 · Cited by 47
Sullivan, Y. W., de Bourmont, M., & Dunaway, M. (2020) · Annals of Operations Research · doi:10.1007/s10479-020-03702-9 · Cited by 44
The Unexplored Link between Communication and Trust in Aviation Maintenance Practice
Chatzi, A. V., Martin, W., Bates, P. R., & Murray, P. S. (2019) · Aerospace · doi:10.3390/aerospace6060066 · Cited by 44
“Risky transitions?” Risk perceptions, public concerns, and energy infrastructure in Germany
Linzenich, A., Zaunbrecher, B. S., & Ziefle, M. (2020) · Energy Research & Social Science · doi:10.1016/j.erss.2020.101554 · Cited by 44
Łapińska, J., Escher, I., Górka, J., Sudolska, A., & Brzustewicz, P. (2021) · Energies · doi:10.3390/en14071942 · Cited by 55
Trust in service robot: the role of appearance anthropomorphism
Qin, M., Li, S., Zhu, W., & Qiu, S. (2023) · Current Issues in Tourism · doi:10.1080/13683500.2023.2295923 · Cited by 43
Cha, M., & Lee, H.-J. (2021) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2021.102515 · Cited by 43
Healthcare Voice AI Assistants: Factors Influencing Trust and Intention to Use
Zhan, X., Abdi, N., Seymour, W., & Such, J. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3637339 · Cited by 46
Perceptions and drivers of the metaverse adoption: A mixed‐methods study
Adhini, N. V., & Prasad, C. V. V. S. N. V. (2024) · International Journal of Consumer Studies · doi:10.1111/ijcs.13069 · Cited by 44
Living with Autonomy: Public Perceptions of an AI-Mediated Future
Kaßens-Noor, E., Wilson, M. I., Kotval-K, Z., Cai, M., & Decaminada, T. (2021) · Journal of Planning Education and Research · doi:10.1177/0739456x20984529 · Cited by 45
Self-concept related to information and communication technology: Scale development and validation
Schauffel, N., Schmidt, I., Peiffer, H., & Ellwart, T. (2021) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2021.100149 · Cited by 49
Understanding Security in Smart City Domains From the ANT-Centric Perspective
Fan, J., Yang, W., Liu, Z., Kang, J., Niyato, D. T., Lam, K., & Du, H. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3252040 · Cited by 47
Rupp, M. A., Michaelis, J. R., McConnell, D. S., & Smither, J. A.-A. (2016) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1541931213601329 · Cited by 45
A qualitative systematic review of trust in technology
Schuetz, S. W., Kuai, L., Lacity, M. C., & Steelman, Z. (2024) · Journal of Information Technology · doi:10.1177/02683962241254392 · Cited by 47
Khoso, A. K., Honggang, W., & Darazi, M. A. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-06362-x · Cited by 51
Scholz, D. D., Kraus, J., & Miller, L. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2307691 · Cited by 47
Hou, T.-Y., Tseng, Y.-C., & Yuan, C. W. (2024) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2024.102775 · Cited by 43
TOE drivers for cloud transformation: direct or trust-mediated?
Li, M., Zhao, D., & Yu, Y. (2015) · Asia Pacific Journal of Marketing and Logistics · doi:10.1108/apjml-03-2014-0040 · Cited by 41
Towards the Development of an Inter-cultural Scale to Measure Trust in Automation
Chien, S.-Y. J., Semnani‐Azad, Z., Lewis, M., & Sycara, K. P. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-07308-8_4 · Cited by 40
Ackerhans, S., Wehkamp, K., Petzina, R., Dumitrescu, D., & Schultz, C. (2025) · JMIR Formative Research · doi:10.2196/64266 · Cited by 38
Trust in Fintech: Risk, Governance, and Continuance Intention
Xia, H., Lu, D., Lin, B., Nord, J. H., & Zhang, Z. (2022) · Journal of Computer Information Systems · doi:10.1080/08874417.2022.2093295 · Cited by 47
Human trust in otherware – a systematic literature review bringing all antecedents together
Saßmannshausen, T. M., Burggräf, P., Hassenzahl, M., & Wagner, J. (2022) · Ergonomics · doi:10.1080/00140139.2022.2120634 · Cited by 42
Who is responsible? US Public perceptions of AI governance through the lenses of trust and ethics
David, P., Choung, H., & Seberger, J. S. (2024) · Public Understanding of Science · doi:10.1177/09636625231224592 · Cited by 33
Understanding human-AI trust in education
Pitts, G., & Motamedi, S. (2025) · Telematics and Informatics Reports · doi:10.1016/j.teler.2025.100270 · Cited by 58
Faqih, K. M. S., & Jaradat, M.-I. R. M. (2015) · International Business Research · doi:10.5539/ibr.v8n9p142 · Cited by 44
Modelling Trust: An Empirical Assessment
Gulati, S., Sousa, S., & Lamas, D. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-68059-0_3 · Cited by 42
Building Consumer Trust to Improve Internet of Things (IoT) Technology Adoption
Alhogail, A., & Alshahrani, M. (2018) · Advances in Neuroergonomics and Cognitive Engineering · doi:10.1007/978-3-319-94866-9_33 · Cited by 30
Understanding trust on social networking sites among tertiary students: An empirical study in Ghana
Koranteng, F. N., Wiafe, I., Katsriku, F. A., & Apau, R. (2019) · Applied Computing and Informatics · doi:10.1016/j.aci.2019.07.003 · Cited by 38
Hassan, M. U., Iqbal, M. S., & Habibah, U. (2020) · SAGE Open · doi:10.1177/2158244020924412 · Cited by 42
Explainable Deep Learning for False Information Identification: An Argumentation Theory Approach
Lee, K., & Ram, S. (2023) · Information Systems Research · doi:10.1287/isre.2020.0097 · Cited by 37
Vongvit, R., Maeng, K., & Lee, S. C. (2024) · Travel Behaviour and Society · doi:10.1016/j.tbs.2024.100788 · Cited by 44
Trust in context: The impact of regulation on blockchain and DeFi
Bodó, B., & Filippi, P. D. (2024) · Regulation & Governance · doi:10.1111/rego.12637 · Cited by 48
A Longitudinal Simulator Study to Explore Drivers' Behaviour in Level 3 Automated Vehicles
Large, D. R., Burnett, G., Salanitri, D., Lawson, A. R., & Box, E. (2019) · International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI) · doi:10.1145/3342197.3344519 · Cited by 37
Consumers' willingness to share digital footprints on social media: the role of affective trust
Muhammad, S. S., Dey, B. L., Alwi, S. F. S., Kamal, M. M., & Asaad, Y. (2022) · Information Technology and People · doi:10.1108/itp-10-2020-0694 · Cited by 46
Public Acceptance towards Emerging Autonomous Vehicle Technology: A Bibliometric Research
Ho, J. S., Tan, B. C., Lau, T. C., & Khan, N. (2023) · Sustainability · doi:10.3390/su15021566 · Cited by 36
Trust and AI weight: human-AI collaboration in organizational management decision-making
Wen, Y., Wang, J., & Chen, X. (2025) · Frontiers in Organizational Psychology · doi:10.3389/forgp.2025.1419403 · Cited by 57
Trust in Management Information Systems (MIS)
Meeßen, S. M., Thielsch, M. T., & Hertel, G. (2019) · Zeitschrift für Arbeits- und Organisationspsychologie A&O · doi:10.1026/0932-4089/a000306 · Cited by 44
Trust in blockchain-based systems
Becker, M. Y., & Bodó, B. (2021) · Internet Policy Review · doi:10.14763/2021.2.1555 · Cited by 43
‘Are You OK?’ Students’ Trust in a Chatbot Providing Support Opportunities
Pesonen, J. A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77943-6_13 · Cited by 45
Effects of Humanlikeness and Conversational Breakdown on Trust in Chatbots for Customer Service
Law, E. L., Følstad, A., & van As, N. (2022) · Nordic Conference on Human-Computer Interaction · doi:10.1145/3546155.3546665 · Cited by 37
Wald, R., Piotrowski, J. T., Araujo, T., & van Oosten, J. M. F. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107526 · Cited by 36
Duenser, A., & Douglas, D. (2023) · IEEE Intelligent Systems · doi:10.1109/mis.2023.3322586 · Cited by 35
Reliable Visual Question Answering: Abstain Rather Than Answer Incorrectly
Whitehead, S., Petryk, S., Shakib, V., Gonzalez, J. E., Darrell, T., Rohrbach, A., & Rohrbach, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20059-5_9 · Cited by 47
von Kalckreuth, N., & Feufel, M. A. (2023) · JMIR Human Factors · doi:10.2196/45503 · Cited by 33
Exploring the key factors influencing the actual usage of digital tax platforms
Abu-Silake, S. A., Alshurafat, H., Alaqrabawi, M., & Shehadeh, M. (2024) · Discover Sustainability · doi:10.1007/s43621-024-00241-2 · Cited by 57
What drives individuals’ trusting intention in digital platforms? An exploratory meta-analysis
Oesterreich, T. D., Anton, E., Hettler, F. M., & Teuteberg, F. (2024) · Management Review Quarterly · doi:10.1007/s11301-024-00477-2 · Cited by 59
Mentalizing Without a Mind: Psychotherapeutic Potential of Generative AI
Yirmiya, K., & Fonagy, P. (2025) · Journal of Medical Internet Research · doi:10.2196/79156 · Cited by 46
Examining the infusion of mobile technology by healthcare practitioners in a hospital setting
O’Connor, Y., & O’Reilly, P. (2016) · Information Systems Frontiers · doi:10.1007/s10796-016-9728-9 · Cited by 35
Is data-driven decision-making driven only by data? When cognition meets data
Zaitsava, M., Marku, E., & Guardo, M. C. D. (2022) · European Management Journal · doi:10.1016/j.emj.2022.01.003 · Cited by 43
Croes, E. A. J., Antheunis, M. L., Goudbeek, M. B., & Wildman, N. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2075574 · Cited by 33
Intelligent autonomous agents and trust in virtual reality
Sun, N., & Botev, J. (2021) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2021.100146 · Cited by 36
Yu, T., Tian, Y., Chen, Y., Huang, Y., Pan, Y., & Jang, W. (2025) · Systems · doi:10.3390/systems13060461 · Cited by 47
Söllner, M., & Leimeister, J. M. (2013) · Alexandria (UniSG) (University of St.Gallen) · Cited by 33
Sharing gaze rays for visual target identification tasks in collaborative augmented reality
Erickson, A., Norouzi, N., Kim, K., Schubert, R., Jules, J., LaViola, J. J., Bruder, G., & Welch, G. (2020) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-020-00330-2 · Cited by 33
Adoption of Mobile Government Cloud from the Perspective of Public Sector
Liang, Y., Wang, W., Dong, K., Zhang, G., & Qi, G. (2021) · Mobile Information Systems · doi:10.1155/2021/8884594 · Cited by 35
Safety Technology Adoption: Predicting Intention to Use Car Dashcams in an Emerging Country
Zadeh, A. V., Ng, S.-X., Hanifah, H. M., Teoh, A. P., & Nawaser, K. (2021) · International Journal of Innovation and Technology Management · doi:10.1142/s021987702150022x · Cited by 33
Trust transfer effects and associated risks in telemedicine adoption
Kuen, L., Schürmann, F., Westmattelmann, D., Hartwig, S., Tzafrir, S. S., & Schewe, G. (2023) · Electronic Markets · doi:10.1007/s12525-023-00657-0 · Cited by 39
You, S., & Robert, L. (2024) · MIS Quarterly · doi:10.25300/misq/2023/17403 · Cited by 36
Trkman, M., Popovič, A., & Trkman, P. (2022) · Government Information Quarterly · doi:10.1016/j.giq.2022.101787 · Cited by 47
When the machine learns from users, is it helping or snooping?
Lee, S., Moon, W.-K., Lee, J.-G., & Sundar, S. S. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107427 · Cited by 33
Security Risks and User Perception towards Adopting Wearable Internet of Medical Things
Thapa, S., Bello, A., Maurushat, A., & Farid, F. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20085519 · Cited by 31
A model validation of robo-advisers for stock investment
Shiva, A., Kushwaha, B. P., & Rishi, B. (2023) · Borsa Istanbul Review · doi:10.1016/j.bir.2023.09.005 · Cited by 41
Social touch to build trust: A systematic review of technology-mediated and unmediated interactions
Valori, I., Jung, M. M., & Fairhurst, M. T. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.108121 · Cited by 31
Cui, Y., & Hai, Z. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13448-1 · Cited by 41
Lelescu, A., Sava, S., Grosseck, G., & Maliţa, L. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-06253-1 · Cited by 43
An investigation of technology trust and habit in IT use continuance: a study of a social network
Ambalov, I. A. (2021) · Journal of Systems and Information Technology · doi:10.1108/jsit-05-2019-0096 · Cited by 38
Koh, L. Y., Wu, M., Wang, X., & Yuen, K. F. (2023) · Journal of Environmental Management · doi:10.1016/j.jenvman.2023.117480 · Cited by 31
When Algorithms Err : Differential Impact of Early vs. Late Errors on Users’ Reliance on Algorithms
Kim, A., Yang, M., & Zhang, J. (2020) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3557889 · Cited by 42
Consumers acceptance of service robots in hotels: A meta-analytic review
Begum, N., Faisal, M. N., Sobh, R., Nunkoo, R., & Rana, N. P. (2024) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2024.104052 · Cited by 38
Development of a Cloud Trust Label: A Delphi Approach
Lynn, T., van der Werff, L., Hunt, G., & Healy, P. D. (2016) · Journal of Computer Information Systems · doi:10.1080/08874417.2016.1153887 · Cited by 30
Protecting a whale in a sea of phish
Pienta, D., Thatcher, J. B., & Johnston, A. C. (2020) · Journal of Information Technology · doi:10.1177/0268396220918594 · Cited by 28
Self-citationWamba, S. F., de Queiroz, M. M., & Hamzi, L. (2023) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.122912 · Cited by 31
Tripp, J. F., McKnight, D. H., & Lankton, N. K. (2022) · European Journal of Information Systems · doi:10.1080/0960085x.2022.2062469 · Cited by 33
Self-citationJang, C. (2023) · Behaviour and Information Technology · doi:10.1080/0144929x.2023.2246590 · Cited by 36
Virtual Teams: A Smart Literature Review of Four Decades of Research
Kimura, T. (2024) · Human Behavior and Emerging Technologies · doi:10.1155/2024/8373370 · Cited by 32
In blockchain we trust? Demystifying the “trust” mechanism in blockchain ecosystems
Toufaily, É., & Zalan, T. (2024) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2024.123574 · Cited by 37
Lu, C.-C. A., Yeh, C.-C. R., & Lai, C.-C. (2024) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2024.104032 · Cited by 38
Trust and acceptance of AI caregiving robots: The role of ethics and self-efficacy
Lin, C. S., Kuo, Y.-F., & Wang, T.-Y. (2024) · Comput. Hum. Behav. Artif. Humans · doi:10.1016/j.chbah.2024.100115 · Cited by 37
Wang, X., Lin, X., & Shao, B. (2024) · Journal of Business Ethics · doi:10.1007/s10551-024-05894-7 · Cited by 33
Joshi, H. (2025) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2025.100332 · Cited by 41
AI in education: The mediating role of perceived trust in adoption decisions of school leaders
Abdelazim, A., Breiki, M. A., & Khlaif, Z. N. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13596-4 · Cited by 38
Pal, D., Funilkul, S., & Papasratorn, B. (2019) · IEEE Access · doi:10.1109/access.2019.2960467 · Cited by 35
Meng, F., Guo, X., Zhang, X., Peng, Z. C., & Lai, K. (2020) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2020.403 · Cited by 30
Trust in open data applications through transparency
Wiencierz, C., & Lünich, M. (2020) · New Media & Society · doi:10.1177/1461444820979708 · Cited by 33
Müller, L. S., Reiners, S., Becker, J., & Hertel, G. (2023) · Journal of Business Research · doi:10.1016/j.jbusres.2023.113927 · Cited by 46
Müller, L. S., Nohe, C., Reiners, S., Becker, J., & Hertel, G. (2023) · Behaviour and Information Technology · doi:10.1080/0144929x.2023.2196598 · Cited by 39
Building consumer trust in the cloud: an experimental analysis of the cloud trust label approach
van der Werff, L., Fox, G., Masevic, I., Emeakaroha, V. C., Morrison, J. P., & Lynn, T. (2019) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-019-0129-8 · Cited by 29
The effects of religiosity and gender on attitudes and trust toward autonomous vehicles
Modliński, A., Gwiaździński, E., & Karpińska-Krakowiak, M. (2022) · The Journal of High Technology Management Research · doi:10.1016/j.hitech.2022.100426 · Cited by 32
A nascent design theory for explainable intelligent systems
Herm, L.-V., Steinbach, T., Wanner, J., & Janiesch, C. (2022) · Electronic Markets · doi:10.1007/s12525-022-00606-3 · Cited by 35
The Relationship Between Presence and Trust in Virtual Reality
Salanitri, D., Lawson, G., & Waterfield, B. C. (2016) · European Conference on Cognitive Ergonomics · doi:10.1145/2970930.2970947 · Cited by 28
Rowe, F., Medina, M. J., Journé, B., Coëtard, E., & Myers, M. D. (2023) · Journal of Information Technology · doi:10.1177/02683962231207108 · Cited by 40
A Trust Label System for Communicating Trust in Cloud Services
Emeakaroha, V. C., Fatema, K., van der Werff, L., Healy, P. D., Lynn, T., & Morrison, J. P. (2016) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2016.2553036 · Cited by 27
The NFT Purchasing Process and the Challenges to Trust at Each Stage
Zarifis, A., & Castro, L. A. (2022) · Sustainability · doi:10.3390/su142416482 · Cited by 27
Relationship Between Trust and Usability in Virtual Environments: An Ongoing Study
Salanitri, D., Hare, C., Borsci, S., Lawson, G., Sharples, S., & Waterfield, B. C. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-20901-2_5 · Cited by 27
Liu, W., & Wang, Y. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2313921 · Cited by 27
The Role of Explainability in Collaborative Human-AI Disinformation Detection
Schmitt, V., Villa-Arenas, L. F., Feldhus, N., Meyer, J., Spang, R. P., & Möller, S. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3659031 · Cited by 35
Does Technology Trust Substitute Interpersonal Trust?
Li, X., Rong, G., & Thatcher, J. B. (2012) · Journal of Organizational and End User Computing · doi:10.4018/joeuc.2012040102 · Cited by 32
Self-citationEvidence for Acceptance of Ride-Hailing Services in Iran
Akbari, M., Seyyedamiri, N., Zúñiga, M. Á., Padash, H., & Shakiba, H. (2020) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/0361198120942224 · Cited by 28
Metadata
- Authors
- Mcknight, D. H., Carter, M., Thatcher, J. B., & Clay, P. F.
- Affiliations
- Michigan State University (US)
- Clemson University (US)
- Washington State University (US)
- Venue
- ACM Transactions on Management Information Systems
- Published
- 2011-06
- Volume
- 2
- Issue
- 2
- Pages
- 1-25
- ISSN
- 2158-656X (ISSN-L)
- 2158-6578
- OpenAlex
- W2037881738
- Semantic Scholar
- 1581959e828d2701b72702264bca37ca7f408124
- Cited by
- 1,185
- References
- 81
- Citations by year
- 2026: 273 · 2025: 182 · 2024: 147 · 2023: 131 · 2022: 110 · 2021: 95 · 2020: 74 · 2019: 38 · 2018: 36 · 2017: 15 · 2016: 19 · 2015: 14 · 2014: 23 · 2013: 22 · 2012: 4
- FWCI
- 37.97
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Transactions on Management Information Systems (publishedVersion)
- Research Explorer (The University of Manchester) (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Keywords
- trust in technology, interpersonal trust, trust measurement, construct validity, convergent validity, discriminant validity, nomological network, technology adoption, information systems
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
