Design, development and evaluation of a human-computer trust scale
Abstract
New technologies, data, and algorithms impact nearly every aspect of daily life. Unfortunately, many of these algorithms operate like black boxes and cannot explain their results even to their programmers, let alone to end-users. As more and more tasks get delegated to such intelligent systems and the nature of user interactions with them becomes increasingly complex, it is important to understand the amount of trust that a user is willing to place on such systems. However, attempts at quantifying trust have either been limited in their scope or not empirically thorough. To address this, we build on prior work which empirically modelled trust in user-technology interactions and describe the development and evolution of a human computer trust scale. We present results of two studies (N=118 & N=183) which were undertaken to assess the reliability and validity of the proposed scale. Our study contributes to the literature by (a) developing a multi-dimensional scale to assess user trust in HCI and (b) being the first study to use the concept of design fiction and future scenarios to study trust.
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The Human-Computer Trust Scale (Gulati et al., 2019), which evaluates the degree of trust users place in automation, was adapted in this study to assess trust in Generative AI for real-world problems and complex tasks, and has been validated with strong reliability (Cronbach's alpha = 0.84).
“The Human- Computer Trust Scale (Gulati et al., 2019) evaluates the degree of trust users place in automation, adapted in this study to assess trust in Generative AI for real- world problems and complex tasks.”
From The role of critical thinking on undergraduates' reliance behaviours on generative <scp>AI</scp> in problem‐solving (2025) · cites this paper as Gulati et al., 2019
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Student Interaction with ChatGPT Can Promote Complex Critical Thinking Skills
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Beyond the “Wow” Factor: Using Generative AI for Increasing Generative Sense-Making
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- Authors
- Gulati, S., Sousa, S., & Lamas, D.
- Affiliations
- Tallinn University (EE)
- School of Digital Technologies, Tallinn University, Tallinn, Estonia
- Corresponding author
- Gulati, Siddharth
- Venue
- Behaviour & Information Technology
- Publisher
- Informa UK Limited
- Published
- 2019-08-31
- Volume
- 38
- Issue
- 10
- Pages
- 1004-1015
- ISSN
- 0144-929X (ISSN-L)
- 1362-3001
- OpenAlex
- W2970250414
- Cited by
- 229
- References
- 4
- Citations by year
- 2026: 63 · 2025: 65 · 2024: 38 · 2023: 23 · 2022: 15 · 2021: 17 · 2020: 7 · 2019: 1
- FWCI
- 12.57
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Behaviour and Information Technology (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Keywords
- black-box algorithms, scale reliability and validity, design fiction, future scenarios, user-technology interaction
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