Construct · 4 names · 4 citations

Where does trust in technology collide?

Different names. Different fields. One construct. One map.

● jangle · one construct, many names Nearest construct by meaningtrustcompare the meanings
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Every construct on the map is compared with every other by the meaning of its definitions, and the closest one is surfaced here. The nearest construct to trust in technology is trust.

Hold this as a prompt, not a verdict. Two constructs sitting close in meaning is a reason to read them side by side and decide for yourself whether they are one idea under two names. It is not proof that they are. Compare the meanings in the Collider.

trust intechnology4 NAMESSpecific technologyMCKNIGHT · SPECIFIC ITTrust in automationLEE & SEE · AUTOMATIONHuman-robot trustHANCOCK · HUMAN-ROBOTOnline trustGEFEN · ONLINE VENDOR

Spokes are the names and the models that coined them; dashed arcs are borrowings. Tap a field for its definition, unit, and measures.

2003Online trust2004Trust in automation2011Specific technology2011Human-robot trust
2026

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Information systems

Trusting beliefs about a specific IT artifact

Trust in technology treated as a set of beliefs a user holds about one particular information technology: that it has the capability to do the job (functionality), that it will offer adequate help when needed (helpfulness), and that it will operate consistently and predictably (reliability). McKnight, Carter, Thatcher, and Clay's 2011 Trust in a specific technology argued that a tool has no free will or moral standing, so the person-like beliefs used for trusting a human should be replaced by these system-like beliefs, and built and validated a scale for them. This treatment ties the words to properties of an artifact rather than to a machine's fitness for a task under risk, to a robot as a social partner, or to a vendor reached through a website.[1]

Unit
A user's standing on three trusting beliefs about one IT artifact
Recovery means
Not applicable
Measured by
Rated on validated scales for functionality, helpfulness, and reliability of a named technology

Human factors engineering

The attitude behind reliance on an automated aid

Trust in technology treated as the attitude that an automated agent will help a person reach a goal in a situation marked by uncertainty and vulnerability, the attitude that guides whether the person relies on the automation or takes over manually. Lee and See's 2004 Trust in automation: Designing for appropriate reliance framed it as a dynamic attitude that should be calibrated to how the automation actually performs, so that reliance is neither too high, which is overtrust, nor too low, which is disuse. This treatment centres on the decision to rely under risk and on the match between trust and true capability, which differs from a fixed profile of beliefs about an artifact, from trust in a robot as a partner, or from trust in an online seller.[2]

Unit
A person's attitude toward relying on an automated aid, calibrated to its performance
Recovery means
Trust that has fallen after an automation failure can rebuild as reliable performance resumes
Measured by
Assessed by self-report trust scales alongside behavioural reliance measures such as how often the operator accepts or overrides the automation

Robotics and human-robot interaction

Willingness to depend on a robot partner

Trust in technology treated as a person's willingness to depend on a robot in a shared task, shaped by what the robot is (its performance and its attributes), by the human, and by the environment they work in together. Hancock, Billings, Schaefer, Chen, de Visser, and Parasuraman's 2011 A meta-analysis of factors affecting trust in human-robot interaction pooled experiments and found that the robot's own performance was the strongest driver of trust, more than the human's traits or the setting. This treatment applies trust to an embodied machine acting as a teammate, which sets it apart from beliefs about a passive IT tool, from calibrated reliance on abstract automation, and from confidence in a commercial website.[3]

Unit
A person's willingness to depend on a specific robot in a joint task
Recovery means
Trust damaged by a robot's error can recover as its later performance proves dependable
Measured by
Aggregated across studies by effect sizes linking trust to robot performance, human factors, and environmental factors

E-commerce and online systems

Person-like trust extended to an online system

Trust in technology treated as the interpersonal trust a shopper places in the party reached through a website, the belief that the online vendor is able, honest, and caring, carried over from face-to-face trust and combined with judgments of how easy and useful the site is to use. Gefen, Karahanna, and Straub's 2003 Trust and TAM in online shopping: An integrated model built trust into the technology acceptance model and showed that trust and perceived usefulness both drive the intention to buy online. This treatment keeps the person-like trusting beliefs about honesty and goodwill and points them at the system and the seller behind it, which is exactly the framing that the specific-technology account set out to replace for tools that have no will of their own.[4]

Unit
A shopper's trusting beliefs about the vendor behind a website
Recovery means
Not applicable
Measured by
Rated on interpersonal trusting-belief scales for ability, integrity, and benevolence, modelled together with perceived ease of use and usefulness

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Nearby collisions

trust6 fields · 11 citationsjingle · one name, five constructsconfidence4 fields · 5 citationsjingle · one word, four constructsacceptance6 fields · 14 citationsjingle · one name, six constructs

References

  1. McKnight, D. H., Carter, M., Thatcher, J. B., & Clay, P. F. (2011). Trust in a specific technology: An investigation of its components and measures. ACM Transactions on Management Information Systems, 2(2), 1–25. https://doi.org/10.1145/1985347.1985353
  2. Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human Factors, 46(1), 50–80. https://doi.org/10.1518/hfes.46.1.50_30392
  3. Hancock, P. A., Billings, D. R., Schaefer, K. E., Chen, J. Y. C., de Visser, E. J., & Parasuraman, R. (2011). A meta-analysis of factors affecting trust in human-robot interaction. Human Factors, 53(5), 517–527. https://doi.org/10.1177/0018720811417254
  4. Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/30036519

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