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.
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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
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Investigating Trust in Expert System Advice for Business Ethics Audits
Kirchebner, T., Schlögl, S., Bass, E., & Dilger, T. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-81635-3_26
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Klinker, K., Huck-Fries, V., Przybilla, L., Wüller, H., Moker, A., Decker, S., Filar, K., Schmitt, D., Müller, J., Brosi, P., et al. (2021) · Springer eBooks · doi:10.1007/978-3-662-63099-0_4
Larasati, R. A. (2019) · Open Research Online - ORO (The Open University)
THE TREATMENT OF ROTO-LATERAL CURVATURE OF THE SPINE.
McKenzie, B. E. (1912) · Europe PMC (PubMed Central)
Krüeger, F. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108770880.002
Human Resources Information Systems Implementation and Influences in Higher Education
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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 ›
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