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Trust in AI and Its Role in the Acceptance of AI Technologies
Open accessAbstract
As AI-enhanced technologies become common in a variety of domains, there is an increasing need to define and examine the trust that users have in such technologies. Given the progress in the development of AI, a correspondingly sophisticated understanding of trust in the technology is required. This paper addresses this need by explaining the role of trust in the intention to use AI technologies. Study 1 examined the role of trust in the use of AI voice assistants based on survey responses from college students. A path analysis confirmed that trust had a significant effect the on intention to use AI, which operated through perceived usefulness and participants’ attitude toward voice assistants. In Study 2, using data from a representative sample of the U.S. population, different dimensions of trust were examined using exploratory factor analysis, which yielded two dimensions: human-like trust and functionality trust. The results of the path analyses from Study 1 were replicated in Study 2, confirming the indirect effect of trust and the effects of perceived usefulness, ease of use, and attitude on intention to use. Further, both dimensions of trust shared a similar pattern of effects within the model, with functionality-related trust exhibiting a greater total impact on usage intention than human-like trust. Overall, the role of trust in the acceptance of AI technologies was significant across both studies. This research contributes to the advancement and application of the TAM in AI-related applications and offers a multidimensional measure of trust that can be utilized in the future study of trustworthy AI.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
Two studies extend the technology acceptance model (TAM) by adding trust in AI, finding that trust indirectly affects intention to use AI through perceived usefulness and attitude. Study 2 identifies two trust dimensions—human-like trust and functionality trust—with functionality trust showing a greater total impact on usage intention than human-like trust.
Key findings
- In Study 1 (N = 312 college students), the path model explained 52% of variance in usage intention (R² = .52); attitude was the strongest direct predictor of behavioral intention (β = .62, p < .001), and trust had a significant indirect effect through perceived usefulness and attitude. Total effects on usage intention were perceived ease of use β = .63, perceived usefulness β = .46, and trust β = .32 (all p < .001).
- In Study 2 (N = 640 representative U.S. adults), exploratory factor analysis yielded two trust dimensions: human-like trust in AI (six items) and functionality trust in AI (five items). The TAM results replicated: attitude predicted intention (β = .65, p < .001), perceived ease of use had total effects of β = .71 in both models, and perceived usefulness had total effects of β = .57 and β = .56 (all p < .001).
- Both trust dimensions had significant indirect effects on intention to use smart technologies, but functionality-related trust had a greater total impact on usage intention (β = .42, p < .001) than human-like trust (β = .36, p < .001).
Methods
- Study 1: online survey conducted October–December 2020 with 312 undergraduate students from a large Midwest university (56% female; ages 18–29, median 21; 96% had used at least one AI voice assistant). Path analysis was conducted in R using lavaan with maximum likelihood estimation and bootstrapped 95% confidence intervals (k = 1,000).
- Study 2: online survey conducted April 2021 using a national Qualtrics panel with quota sampling for gender, age, and race based on U.S. Census data; 640 respondents participated (50.1% female, mean age = 46.43, SD = 17.83; 65.9% White, 12% Black or African American, 12% Hispanic, 5.9% Asian, 4.4% other). Trust items were factor analyzed, yielding two dimensions, and two separate path models tested human-like trust and functionality trust using the same analytic approach as Study 1.
- Measures: perceived ease of use, perceived usefulness, attitude, and behavioral intention were adapted from TAM scales; Study 1 trust in AI voice assistants used four created items; Study 2 trust items were constructed around benevolence/helpfulness, integrity/reliability, and competence/functionality.
Limitations
- Stated in the paper: the studies focused on consumer AI products (voice assistants and smart technologies) associated with lower risk, so findings may not generalize to high-stakes AI such as self-driving cars or healthcare AI; trust is dynamic and may differ across stages such as initial versus ongoing trust; the path models are correlational and cannot guarantee causal effects; and online panel respondents may not be truly representative because they have computer and internet access, skewing toward higher technology acceptance.
- Evident from the text: Study 1 used a convenience sample of college students, limiting generalizability; both studies relied on cross-sectional self-report survey data, so causal direction among trust, perceived usefulness, attitude, and intention cannot be established from these data alone.
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94 Own result · 27 Not checked yet · 18 Own interpretation · 15 Own method · 7 No basis given
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In the paper
“The perceived usefulness of an AI voice assistant also positively influenced users' attitude toward it ( 𝛽 = .42, p < .001)”
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“The validated model presents a high explanatory power, with 52% of variance explained ( R 2 = .52).”
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“the path model demonstrated a good fit with the data: 𝜒 2 (1) = .32, p = .58; RMSEA = .00 [.00, .12]; SRMR = .01; CFI = 1.00; TLI = 1.01.”
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In the paper
“trust perception ( 𝛽 = .39, p < .001)”
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“Moreover, trust positively predicted perceived usefulness.”
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In the paper
“In Study 2, using data from a representative sample of the U.S. population, different dimensions of trust were examined using exploratory factor analysis, which yielded two dimensions: human-like trust and functionality trust.”
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In the paper
“Although trust does not directly affect usage intention, the construct appears to be pivotal in improving user perception of the utility of voice assistants and in building positive attitudes toward them.”
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In the paper
“willingness to use it ( 𝛽 = .20, p < .01)”
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In the paper
“A path analysis confirmed that trust had a significant effect on the intention to use AI, which operated through perceived usefulness and participants' attitude toward voice assistants.”
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“attitude ( 𝛽 = .26, p < .001)”
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In the paper
“All other measures of fit (CFI above .95, SRMR below .05, and RMSEA below .08) showed that the models were adequate in reproducing covariances among the variables.”
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In the paper
“The models fit the data well: human trust in AI, 𝜒 2 (1) = 4.92, p = .03; RMSEA = .08 [.02, .15]; SRMR = .01; CFI = 1.00; TLI = .98; technology-related trust in AI, 𝜒 2 (1) = 4.85, p = .03 RMSEA = .08 [.02, .15]; SRMR = .01; CFI = 1.00; TLI = .98.”
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In the paper
“The results of the path analyses from Study 1 were replicated in Study 2, confirming the indirect effect of trust and the effects of perceived usefulness, ease of use, and attitude on intention to use.”
plaindirect- Asserted: passes — The authors put it forward as true.
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“The values in Table 2 indicate that the constructs are moderately correlated.”
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In the paper
“Trust also predicted positive attitudes, which in turn influenced usage intention (H4b).”
plaindirect- Asserted: passes — The authors put it forward as true.
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“perceived ease of use had the largest total effect on usage intention ( 𝛽 = .63; p < .001), followed by perceived usefulness ( 𝛽 = .46; p < .001) and trust perception ( 𝛽 = .32; p < .001).”
plaindirect- Asserted: passes — The authors put it forward as true.
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“In addition, we found that trust can influence and is influenced by the factors included in the TAM.”
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“Trust was associated with increased perceived usefulness, which in turn increased usage intention (H4a).”
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In the paper
“Items from the three constructs were factor analyzed, which yielded a two-factor structure, based on a principal components exploratory factor analysis (EFA) with an Oblimin rotation. The items measuring benevolence and integrity were grouped together, and competence was treated as another dimension.”
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Perceived ease of use, perceived usefulness, and attitude were positive predictors of usage intention.”
plaindirect- Asserted: passes — The authors put it forward as true.
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“The results of indirect effects revealed that all mediated paths depicted were statistically significant, including the hypothesized (H5) paths (see Table 3).”
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In the paper
“Further, both dimensions of trust shared a similar pattern of effects within the model, with functionality-related trust exhibiting a greater total impact on usage intention than human-like trust.”
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Moreover, trust predicted perceived usefulness, which in turn increased positive attitudes and usage intention (H4c).”
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“In both path models, participants' intention to use a smart technology was predicated on its perceived ease of use (total effect 𝛽 = .71; p < .001; 𝛽 = .71; p < .001) and perceived usefulness (total effect 𝛽 = .57; p < .001; 𝛽 = .56; p < .001).”
plaindirect- Asserted: passes — The authors put it forward as true.
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“both trust dimensions ( 𝛽 = .56, p < .001; 𝛽 = .51, p < .001)”
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In the paper
“Additionally, the results indicate that the two dimensions of trust in AI-the human-like trust dimension and the functionality dimension -significantly predict perceived usefulness and positive attitude toward smart technology, which in turn, predict greater usage intention.”
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In the paper
“The two types of trust were associated with perceived usefulness, which predicted greater usage intention (H4a).”
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In the paper
“Finally, trust dimensions predicted perceived usefulness, which in turn increased positive attitudes and usage intention (H4c).”
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In the paper
“Trust also predicted positive attitudes, which in turn affected usage intention (H4b).”
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“In Study 1 and Study 2, we found that perceived ease of use and perceived usefulness were essential determinants for the use of AI technologies, although perceived ease of use had a consistently greater impact on the acceptance of these technologies.”
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“This dimension was a significant predictor of the acceptance of technology, as was the functionality dimension.”
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“both the human-like and the functionality dimensions of trust had statistically significant indirect effects on the intention to use AI smart technologies, thus supporting H5.”
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In the paper
“Factor 1: Human-like trust in AI”
- Also stated in: § Tables and Figures · plain
“Factor 2: Functionality trust in AI”
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- Also stated in: § Tables and Figures · plain
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This finding suggests that trust in technology is a dynamic concept that is still evolving and variable based on the context and the characteristics of the trusted agent.”
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“Also, current voice assistants are designed to be more human-like than previous attempts, and many individuals are communicating with voice assistants as part of their everyday life in the same way they interact with other humans (Sundar et al., 2017), making trust more relevant.”
What the sources say
- Not checked yetcites Sundar et al., 2017
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“Unlike traditional technologies that rely on user input and the execution of rules programmed by a human, AI implies certain levels of autonomy.”
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“Trust is vital to understanding interpersonal interaction, as it contributes to perceived reliability and integrity.”
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In the paper
“In the current study, building on previous work regarding trust, we focus on the two main dimensions of trust in AI: 1) human-like trust in AI, and 2) functionality trust in AI.”
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In the paper
“trust has been examined as a key factor in the acceptance of emerging technologies (K. Wu et al., 2011).”
What the sources say
- Not checked yetcites K. Wu et al., 2011
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In the paper
“The finding is in line with studies that have tested the impact of these constructs on the adoption of various forms of technology (e.g., Beldad & Hegner, 2018; Choi & Ji, 2015).”
What the sources say
- Not checked yetcites Beldad & Hegner, 2018
- Not checked yetcites Choi & Ji, 2015
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In the paper
“The latter dimension pertains to the competency and expertise of the technological features.”
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In the paper
“The constructs 'perceived ease of use' and 'perceived usefulness' were measured with five items, each adapted from the original TAM scale (Davis, 1989).”
What the sources say
- Not checked yetcites Davis, 1989
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In the paper
“The anthropomorphic nature of voice assistants could make trust more critical.”
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“To ensure the representativeness of participants, we adopted a quota sampling method that set quotas for gender, age, and race based on U.S. Census data.”
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In the paper
“This finding suggests that to motivate individuals to adopt or continue to use an AI voice assistant, they should find the technology easy to use and useful.”
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- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Past studies have applied interpersonal trust to human technology relationships (Calhoun et al., 2019), especially when the technology has human-like characteristics (Gillath et al., 2021).”
What the sources say
- Not checked yetcites Calhoun et al., 2019
- Not checked yetcites Gillath et al., 2021
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“For human-like trust in AI, trust is in the agent, not in the specific actions and operations of the agent (J. D. Lee & See, 2004).”
What the sources say
- Not checked yetcites J. D. Lee & See, 2004
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Ninety-six percent of participants reported that they had used at least one type of AI voice assistant.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Similar to trust in people and trust in technology, trust in automation is summarized in three categories: 1) performance, 2) process, and 3) purpose.”
What the sources say
- Not checked yetcites Lee and See (2004)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Voice assistants are designed to be more human-like (McLean & Osei-Frimpong, 2019), with their voice feature encouraging users to adopt the same social responses that they habitually use in their interpersonal relationships (Reeves & Nass, 1996).”
What the sources say
- Not checked yetcites McLean & Osei-Frimpong, 2019
- Not checked yetcites Reeves & Nass, 1996
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although trust does not directly affect usage intention, the construct appears to be pivotal in improving user perception of the utility of voice assistants and in building positive attitudes toward them.”
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Behavioral intention to use or continue to use an AI voice assistant was measured with three items from the TAM 2 scale (Venkatesh & Davis, 2000).”
What the sources say
- Not checked yetcites Venkatesh & Davis, 2000
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The former dimension pertains to the social and cultural values of the algorithms and the values and ethics that undergird the design of the AI technology.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“We consider that in the context of AI, both trust in people and trust in technology are relevant, since AI technologies are often depicted as being capable of human qualities, including reasoning and motivations, which can induce high expectations and initial trust (Glikson & Woolley, 2020).”
What the sources say
- Not checked yetcites Glikson & Woolley, 2020
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“McKnight et al. (2011) explained that trust in technology is qualitatively different from trust in people.”
What the sources say
- Not checked yetcites McKnight et al. (2011)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The TAM is a theoretical framework for explaining the technology usage behavior that has been validated in different technologies across various populations”
What the sources say
- Not checked yetcites Venkatesh & Davis
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“A total of 640 respondents (50.1% female, age M = 46.43, SD = 17.83, 65.9% White, 12% Black or African American, 12% Hispanic, 5.9% Asian, 4.4% other) participated in the study.”
plainn = 640- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“For instance, Glikson and Woolley (2020) organized the literature on trust in AI based on the factors that shaped users' cognitive and emotional trust.”
What the sources say
- Not checked yetcites Glikson & Woolley, 2020
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“| Basis of trust in people Mayer et al., (1995) | Basis of trust in technology Lankton et al. (2015) | Basis of trust in automation Lee & Moray (1992) |”
What the sources say
- Not checked yetcites Lee & Moray (1992)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Further, human-like trust may explain emotional trust and emotional attachment to technology.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our findings provide further support for the utility of the TAM in explaining the acceptance of AI technologies and the added contribution of trust within the model.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The distinctive perceptions of human-like trust and functionality trust align with studies that propose multiple dimensions and factors of trust in AI and automation (Glikson & Woolley, 2020; Hoff & Bashir, 2015; J. D. Lee & See, 2004).”
What the sources say
- Not checked yetcites Glikson & Woolley, 2020
- Not checked yetcites Hoff & Bashir, 2015
- Not checked yetcites J. D. Lee & See, 2004
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“As past research has already confirmed, trust in AI can be built through embedding specific principles in the design and governance of AI, as suggested in recent publications using the fairness, accountability, transparency, explainability framework (Shin, 2020b; Shin & Park, 2019).”
What the sources say
- Not checked yetcites Shin, 2020b
- Not checked yetcites Shin & Park, 2019
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Still, an opposite direction of the relationship was also found to be valid in another study (Shin, 2020a).”
What the sources say
- Not checked yetcites Shin, 2020a
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“We proposed trust as an antecedent of perceived usefulness based on the findings from the past TAM literature (K. Wu et al., 2011).”
What the sources say
- Not checked yetcites K. Wu et al., 2011
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“First, we focused on AI technology applications in the form of consumer products (i.e., voice assistants and smart technologies), which tend be associated with lower risks.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Hoff and Bashir (2015) suggested three layers of trust in automation (dispositional trust, situational trust, and learned trust) based on their review of trust in AI.”
What the sources say
- Not checked yetcites Hoff & Bashir, 2015
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It appears the duality of AI as both a functional and social technology must be considered when designing trustworthy AI.”
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“The cognitive construct involves the rational evaluation of AI while emotional trust is typically influenced by irrational factors.”
What the sources say
- Not checked yetcites Glikson & Woolley, 2020
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This impact may be because consumers tend to believe that AI technologies are complicated and difficult to use.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“| Basis of trust in people Mayer et al., (1995) | Basis of trust in technology Lankton et al. (2015) | Basis of trust in automation Lee & Moray (1992) |”
What the sources say
- Not checked yetcites Mayer et al., (1995)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The model explains that when people consider an AI technology to be easy to use, they are more likely to trust the technology and consider it useful.”
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“These findings are consistent with Study 1 and prior literature (Gefen et al., 2003; I.-L. Wu & Chen, 2005).”
What the sources say
- Not checked yetcites Gefen et al., 2003
- Not checked yetcites I.-L. Wu & Chen, 2005
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“| Basis of trust in people Mayer et al., (1995) | Basis of trust in technology Lankton et al. (2015) | Basis of trust in automation Lee & Moray (1992) |”
What the sources say
- Not checked yetcites Lankton et al. (2015)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“N/A | 𝛼 = .92 |”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The quote only partly states this claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“𝛼 = .89 | 𝛼 = .90”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“𝛼 = .92 | 𝛼 = .90”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“𝛼 = .98 | 𝛼 = .91”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“𝛼 = .81 | N/A”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“𝛼 = .90 | 𝛼 = .91”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“N/A | 𝛼 = .91 |”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
The paper relies on this without support.
Perception of fairness and justice is associated with human trust in AI.
“Moreover, the perception of fairness and justice will be associated with human trust in AI.”
The paper relies on this without support.
Trust is relevant throughout the lifecycle of a technology.
“Indeed, trust is relevant throughout the lifecycle of a technology.”
The paper relies on this without support.
Users who participate in an experience with technology build ideas about whether it is useful and easy to use based on their experience of use.
“Users who participate in an experience with technology build ideas about whether it is useful and easy to use based on their experience of use.”
The paper relies on this without support.
Trust is a dynamic concept that varies depending on the stage of technology.
“Second, trust is a dynamic concept that varies depending on the stage of technology.”
The paper relies on this without support.
Participants in an online survey are likely to have greater access to and experience with various forms of information technology.
“Participants in an online survey are likely to have greater access to and experience with various forms of information technology.”
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Regarding reliance, Choung et al. (2023) demonstrated that trust significantly influences reliance intentions and Kahr et al. (2024) found that trust and reliance on AI advice were significantly higher when the AI model was highly accurate and that trust increased over repeated interactions.”
From Investigating appropriate reliance on AI-Based decision support systems: the role of expertise, trust, and self-confidence (2025) · cites this paper as Choung et al., 2023
Experimental — accuracy not yet measuredIn the citing paper
“These findings underscore that trust has a significant impact on perceived ease of use, perceived usefulness, and attitudes toward GenAI.”
From Artificial intelligence and communication technologies in academia: faculty perceptions and the adoption of generative AI (2025) · cites this paper as Choung et al., 2022
Statistics recomputed
Every reported test result, recomputed from its test statistic and degrees of freedom. A result shows as not adding up only once an independent check has confirmed it.
- Not confirmedχ²(1) = 0.32reported p = .58 · recomputed p = .572
“χ 2 (1) = .32, p = .58”
- Consistentχ²(1) = 4.92reported p = .03 · recomputed p = .027
“χ 2 (1) = 4.92, p = .03”
- Consistentχ²(1) = 4.85reported p = .03 · recomputed p = .028
“χ 2 (1) = 4.85, p = .03”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
§ Measures
“based on a principal components exploratory factor analysis (EFA) with an Oblimin rotation.”
§ Tables and Figures
“The extraction method was principal axis factoring with an oblique (promax with Kaiser normalization) rotation.”
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
“| TRU → PU→ ATT → BI | .03 | .06 | .16 | H4c |”
Reported 0.03 · recomputed 0.101556
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Tables and Figures
“| Smart technologies have the features necessary to complete key tasks. (Competence) | .08 | .76 | .67 |”
§ Tables and Figures
“| Smart technologies have the features necessary to complete key tasks. (Competence) | -.03 | .87 | .72 |”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ [Figure 2 About Here]
“Regression estimates indicated that all four proposed predictors of AI voice assistant usage intention have statistically significant direct and indirect effects on the dependent variables.”
§ Discussion
“Although trust does not directly affect usage intention, the construct appears to be pivotal in improving user perception of the utility of voice assistants and in building positive attitudes toward them.”
Citations
Cites
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 · cited 5×
“The original TAM proposed perceived usefulness and perceived ease of use to be the two determinants of future usage intention (Davis, 1989).”
BackgroundMethodInfluentiallavaan : An R Package for Structural Equation Modeling
Rosseel, Y. (2012) · Journal of Statistical Software · doi:10.18637/jss.v048.i02 · Cited by 27,955 · cited 1×
“The analysis was conducted in R using the lavaan package (Rosseel, 2012).”
MethodA Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies
Venkatesh, V., & Davis, F. D. (2000) · Management Science · doi:10.1287/mnsc.46.2.186.11926 · Cited by 23,066 · cited 3×
“Later, more elaborate versions of the TAM (i.e., TAM 2, TAM 3) were developed, incorporating additional external factors, such as social norms and perceived enjoyment (Venkatesh & Bala, 2008; Venkatesh & Davis, 2000), and treating attitude as a predictor of behavior.”
BackgroundMethodAn 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 · cited 7×
“In interpersonal relationships, this corresponds to the consistency of behaviors associated with adherence to a set of norms (Mayer et al., 1995).”
BackgroundMethodInfluentialArtificial intelligence: A modern approach
Nilsson, N. J. (1996) · Artificial Intelligence · doi:10.1016/0004-3702(96)00007-0 · Cited by 10,730
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423 · cited 1×
“…growing fear that our reliance on AI comes at the cost of human agency, and that fear is further stoked by the black-box nature of machine learning algorithms, which generate a response that mimics human response without adequate explanation of the underlying process (Barredo Arrieta et al., 2020).”
BackgroundI.—COMPUTING MACHINERY AND INTELLIGENCE
TURING, A. M. (1950) · Mind · doi:10.1093/mind/lix.236.433 · Cited by 10,290
Not So Different After All: A Cross-Discipline View Of Trust
Rousseau, D. M., Sitkin, S. B., Burt, R. S., & Camerer, C. (1998) · Academy of Management Review · doi:10.5465/amr.1998.926617 · Cited by 10,274 · cited 1×
“Trust is defined as “a psychological state comprising the intention to accept vulnerability based upon positive expectations of the intentions or behavior of another” (Rousseau et al., 1998, p. 395).”
BackgroundTrust and TAM in Online Shopping: An Integrated Model1
Gefen, D., Karahanna, E., & Straub, D. W. (2003) · MIS Quarterly · doi:10.2307/30036519 · Cited by 8,635 · cited 6×
“This study follows the hypotheses tested by other TAMrelated studies that have proposed trust as an antecedent of perceived usefulness and subsequent to perceived ease of use while having a direct effect on attitude (Gefen et al., 2003; Kim, 2012; Wu et al., 2011).”
MethodResultInfluentialTechnology Acceptance Model 3 and a Research Agenda on Interventions
Venkatesh, V., & Bala, H. (2008) · Decision Sciences · doi:10.1111/j.1540-5915.2008.00192.x · Cited by 8,159 · cited 1×
“Later, more elaborate versions of the TAM (i.e., TAM 2, TAM 3) were developed, incorporating additional external factors, such as social norms and perceived enjoyment (Venkatesh & Bala, 2008; Venkatesh & Davis, 2000), and treating attitude as a predictor of behavior.”
Background
Research works printed but not linked: 3
- AI HLEG (2019). Ethics guidelines for trustworthy AI. European Commission. https://data.europa.eu/doi/10.2759/346720 · DOI
- Shin D, 2021, New Media & Society, 1
- Wu J., 2007, Journal of Electronic Commerce Research, 8, 128
Cited by
Sorted by citations
What factors contribute to the acceptance of artificial intelligence? A systematic review
Kelly, S., Kaye, S.-A., & Oviedo-Trespalacios, Ó. (2022) · Telematics and Informatics · doi:10.1016/j.tele.2022.101925 · Cited by 1,340
Schiavo, G., Businaro, S., & Zancanaro, M. (2024) · Technology in Society · doi:10.1016/j.techsoc.2024.102537 · Cited by 284
Hyun Baek, T., & Kim, M. (2023) · Telematics and Informatics · doi:10.1016/j.tele.2023.102030 · Cited by 319
Bergdahl, J., Latikka, R., Celuch, M., Savolainen, I., Soares Mantere, E., Savela, N., & Oksanen, A. (2023) · Telematics and Informatics · doi:10.1016/j.tele.2023.102013 · Cited by 204
“Previous studies have also stated that people ’ s general attitudes toward AI are linked to their acceptance and the use of AI in their daily lives (Choung et al., 2023; Gillespie et al., 2021; Kelly et al., 2023; Lancelot Miltgen et al., 2013; Schepman and Rodway, 2020; Schepman and Rodway, 2022).”
Maheshwari, G. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-12333-z · Cited by 216
“In the context of the technology acceptance model and its application to understand the usage of AI, trust perception indirectly influences the behavioural intention to use AI technology and its associated applications (Choung et al., 2023).”
Zhang, L., Anjum, M. A., & Wang, Y. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2184594 · Cited by 160
“…has called for the characteristics of human – system interactions to be examined where the trustee is a technological environment, such as an AI chat channel or a blockchain-enabled mutual aid environment (Bao et al., 2021; Chi et al., 2021; Choung et al., 2022; Shao, Zhang, Brown, et al., 2022).”
BackgroundFenwick, A., Molnár, G., & Frangos, P. (2024) · Discover Artificial Intelligence · doi:10.1007/s44163-024-00125-4 · Cited by 238
“Despite rapid developments in AI within organizations, its adoption remains challenging due to factors like AI-related fears (e.g., [39]), trust issues [40, 41], knowledge gaps (e.g., [27, 42]), and integration difficulties (e.g., [43]).”
The risks of using ChatGPT to obtain common safety-related information and advice
Oviedo-Trespalacios, O., Peden, A. E., Cole-Hunter, T., Costantini, A., Haghani, M., Rod, J. E., Kelly, S., Torkamaan, H., Tariq, A., David Albert Newton, J., et al. (2023) · Safety Science · doi:10.1016/j.ssci.2023.106244 · Cited by 148
Huang, A., Ozturk, A. B., Zhang, T. C., Velasco, E. D. L. M., & Haney, A. (2024) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2024.103693 · Cited by 168
Assessing AI adoption in developing country academia: A trust and privacy-augmented UTAUT framework
Rana, M. M., Siddiqee, M. S., Sakib, M. N., & Ahamed, M. R. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e37569 · Cited by 143
“The term “ Trust ” has been demarcated as the assurance user gain in the AI based learning system ’ s ability to provide an efficient and reliable service [95,98].”
BackgroundBećirović, S., Polz, E., & Tinkel, I. (2025) · Smart Learning Environments · doi:10.1186/s40561-025-00384-3 · Cited by 133
“Other studies (Choung et al., 2023; Kraus et al., 2020; Montag et al., 2023; Schepman & Rodway, 2023) found a positive association between students’ affirmative view of AI and their self-efficacy.”
Shata, A., & Hartley, K. (2025) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-025-00511-7 · Cited by 127
“For example, Choung et al. (2022) explored the role of trust in adopting AI voice assistant technology, and found trust significantly influenced both perceived usefulness of the AI voice assistant and users’ attitudes toward it, which subsequently elevated usage intention.”
InfluentialTanantong, T., & Wongras, P. (2024) · Systems · doi:10.3390/systems12010028 · Cited by 114
Evaluating privacy, security, and trust perceptions in conversational AI: A systematic review
Leschanowsky, A., Rech, S., Popp, B., & Bäckström, T. (2024) · Computers in Human Behavior · doi:10.1016/j.chb.2024.108344 · Cited by 155
Explainable AI improves task performance in human–AI collaboration
Senoner, J., Schallmoser, S., Kratzwald, B., Feuerriegel, S., & Netland, T. H. (2024) · Scientific Reports · doi:10.1038/s41598-024-82501-9 · Cited by 157
Mustofa, R. H., Kuncoro, T. G., Atmono, D., Hermawan, H. D., & Sukirman, S. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100379 · Cited by 187
Choung, H., David, P., & Ross, A. A. (2022) · AI & Society · doi:10.1007/s00146-022-01473-4 · Cited by 142
“These capabilities necessitate a new socio-technical understanding of trust that extends beyond mere functionality to human-like characteristics of the machine (Choung et al. 2022).”
BackgroundSelf-citationKauttonen, J., Rousi, R. A., & Alamäki, A. V. (2025) · Journal of Medical Internet Research · doi:10.2196/65567 · Cited by 133
“Trust is closely related to acceptance and intention to use different types of technology, as demonstrated by various studies [10-13].”
InfluentialExploring the influence of ChatGPT on tourism behavior using the technology acceptance model
Solomovich, L., & Abraham, V. (2024) · Tourism Review · doi:10.1108/tr-10-2023-0697 · Cited by 119
Nazaretsky, T., Mejia‐Domenzain, P., Swamy, V., Frej, J., & Käser, T. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100368 · Cited by 164
Consumer engagement with AI‐powered voice assistants: A behavioral reasoning perspective
Açikgöz, F., Perez‐Vega, R., Okumuş, F., & Stylos, N. D. (2023) · Psychology and Marketing · doi:10.1002/mar.21873 · Cited by 114
Zhan, E. S., Molina, M. D., Rheu, M., & Peng, W. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2261731 · Cited by 112
“…enhances decision-making accuracy and efficiency in different tasks, some people may fear that learning to work with AI requires a higher intelligence threshold (e.g., having a computer science degree) in organizations, which also elicits fear of lack of transparency (Choung et al., 2023).”
Shao, C., Nah, S., Makady, H., & McNealy, J. (2024) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2024.2331858 · Cited by 94
“If individuals believe that using AI is ethical, they are likely to view AI as a beneficial tool for addressing various challenges and improving tasks and processes (e.g., Choung et al., 2023).”
Towards Risk‐Free Trustworthy Artificial Intelligence: Significance and Requirements
Alzubaidi, L., Al-Sabaawi, A., Bai, J., Dukhan, A., Alkenani, A. H., Al‐Asadi, A., Alwzwazy, H. A., Manoufali, M., Fadhel, M. A., Albahri, A. S., et al. (2023) · International Journal of Intelligent Systems · doi:10.1155/2023/4459198 · Cited by 116
Is Everyone an Artist? A Study on User Experience of AI-Based Painting System
Xu, J., Zhang, X., Li, H., Yoo, C., & Pan, Y. (2023) · Applied Sciences · doi:10.3390/app13116496 · Cited by 97
“Moreover, Perceived Trust (PT) has a positive influence on both the PU and PEOU of users, a result that confirms previous scholars’ views [76,118] that trust is particularly”
BackgroundResultUnveiling trust in AI: the interplay of antecedents, consequences, and cultural dynamics
Dang, Q., & Li, G. (2025) · AI & Society · doi:10.1007/s00146-025-02477-6 · Cited by 135
“Studies consistently highlight ease of use as a crucial determinant of preliminary trust in AI (Choung et al. 2023; Liu and Tao 2022; Mostafa and Kasamani 2022; Pitardi and Marriott 2021; Xu et al. 2022).”
Kim, S., Khoreva, V., & Vaiman, V. (2024) · Human Resource Management · doi:10.1002/hrm.22268 · Cited by 134
Cheng, M., Li, X., & Xu, J. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph192013311 · Cited by 115
“In the context of AI-assisted diagnosis and treatment, HCT demonstrates that healthcare workers believe the suggestions, actions, and decisions provided by AI-assisted diagnosis and treatment are reliable [62,63].”
BackgroundShifting attitudes and trust in AI: Influences on organizational AI adoption
Daly, S. J., Wiewiora, A. M., & Hearn, G. (2025) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2025.124108 · Cited by 118
Kleizen, B., Van Dooren, W., Verhoest, K., & Tan, E. (2023) · Government Information Quarterly · doi:10.1016/j.giq.2023.101834 · Cited by 80
Convergence of artificial intelligence with social media: A bibliometric & qualitative analysis
Saheb, T., Sidaoui, M., & Schmarzo, B. (2024) · Telematics and Informatics Reports · doi:10.1016/j.teler.2024.100146 · Cited by 104
Feldman-Maggor, Y., Cukurova, M., Kent, C., & Alexandron, G. (2025) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-025-00486-6 · Cited by 97
“…of AI in Education, primarily examined through the lens of acceptance of technology recommendations among teachers (e.g., Antonietti et al., 2022; Cukurova et al., 2023; Feldman-Maggor et al., 2024) and learners (e.g., Choung et al., 2023; Conijn et al., 2023; Kizilcec, 2016; Maheshwari, 2023).”
The AI humanness: how perceived personality builds trust and continuous usage intention
Hsieh, S. H., & Lee, C. T. (2024) · Journal of Product & Brand Management · doi:10.1108/jpbm-10-2023-4797 · Cited by 79
Bui, H. T., Filimonau, V., & Sezerel, H. (2024) · Journal of Destination Marketing & Management · doi:10.1016/j.jdmm.2024.100956 · Cited by 95
Wang, F., Li, N., Cheung, A. C. K., & Wong, G. K. W. (2025) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-025-00547-9 · Cited by 69
“The new collaborative relationship requires learners to develop appropriate trust in GenAI to ensure its appropriate use throughout the learning process, given that trust is a central component in human-AI interaction (Choi et al., 2023; Choung et al., 2023; Jacovi et al., 2021).”
InfluentialGen-AI integration in higher education: Predicting intentions using SEM-ANN approach
Jain, K., & Raghuram, J. N. V. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12506-4 · Cited by 89
“(Choung et al., 2023).”
One AI Does Not Fit All: A Cluster Analysis of the Laypeople’s Perception of AI Roles
Kim, T., Molina, M. D., Rheu, M. (., Zhan, E. S., & Peng, W. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581340 · Cited by 68
“trust as an antecedent of attitude [35].”
More trust or more risk? User acceptance of artificial intelligence virtual assistant
Xiong, Y., Shi, Y., Pu, Q., & Liu, N. (2023) · Human Factors and Ergonomics in Manufacturing & Service Industries · doi:10.1002/hfm.21020 · Cited by 76
“Recent study demonstrated that trust had significant effects on perceived usefulness, attitude, and the intention to use voice assistants (Choung et al., 2023).”
BackgroundMethodResultInfluentialAI Knowledge: Improving AI Delegation through Human Enablement
Pinski, M., Adam, M., & Benlian, A. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580794 · Cited by 62
“Human beliefs and attitudes about AI are crucial factors in facilitating human-AI collaboration [25].”
BackgroundLopes, J. M., Silva, L., & Massano‐Cardoso, I. (2024) · Behavioral Sciences · doi:10.3390/bs14070616 · Cited by 85
“AI technology is constantly evolving, which affects the dynamics of society’s general behavior, specifically consumers’ behavior.”
BackgroundWang, D., Bian, C., & Chen, G. (2024) · British Journal of Educational Technology · doi:10.1111/bjet.13466 · Cited by 77
What kind of trust does AI deserve, if any?
Alvarado, R. (2023) · AI and Ethics · doi:10.1007/s43681-022-00224-x · Cited by 60
Choung, H., Seberger, J. S., & David, P. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2266244 · Cited by 73
“Such influence raises concerns about bias, social justice, ethics, and human autonomy (Choung et al., 2022a; Krafft et al., 2022), tempering the promise of ADM with skepticism that is grounded in the fundamental belief that humans are better at making complex, context-sensitive, and nuanced human…”
InfluentialSelf-citationLearning to Prompt in the Classroom to Understand AI Limits: A Pilot Study
Theophilou, E., Koyutürk, C., Yavari, M., Bursic, S., Donabauer, G., Telari, A., Testa, A., Boiano, R., Hernandez-Leo, D., Ruskov, M., et al. (2023) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-47546-7_33 · Cited by 58
Anthropomorphization of AI: Opportunities and Risks
Deshpande, A., Rajpurohit, T., Narasimhan, K., & Kalyan, A. (2023) · Proceedings of the Natural Legal Language Processing Workshop 2023 · doi:10.18653/v1/2023.nllp-1.1 · Cited by 57
Topsakal, Y. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2426044 · Cited by 69
“This body of research highlights the importance of cultivating consumer trust (Choung et al., 2023) and familiarity with AI in order to enhance market acceptance and drive the success of AI-assisted innovations (Horowitz et al., 2023).”
Zhang, X., Chen, A. L., Piao, X., Yu, M., Zhang, Y., & Zhang, L. (2024) · Acta Psychologica · doi:10.1016/j.actpsy.2024.104501 · Cited by 79
Li, Y., Wu, B., Huang, Y., Liu, J., Wu, J., & Luan, S. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2356909 · Cited by 61
“…a pivotal role in shaping actual behavior toward specific targets, and a positive perception of quality can predict favorable behavioral intentions, such as adopting technology (Choung et al., 2023), purchasing products (Rheu et al., 2021), and engaging in collaboration (Leeman & Reynolds, 2012).”
Aldraiweesh, A., & Alturki, U. (2025) · IEEE Access · doi:10.1109/access.2025.3534099 · Cited by 78
“As educational institutions worldwide open on the journey of AI integration, understanding the essential factors that shape acceptance becomes crucial for successful implementation [45], [46].”
Artificial intelligence, workers, and future of work skills
Bankins, S., Hu, X., & Yuan, Y. E. (2024) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2024.101828 · Cited by 96
“In terms of upsides, workers will generally have better experiences of using AI when they trust it [31], when they perceive good system quality [32], and when they are skilled to use it effectively [33].”
BackgroundBhatnagr, P., & Rajesh, A. (2024) · Management Decision · doi:10.1108/md-07-2023-1145 · Cited by 63
Yang, S., Krause, N. M., Bao, L., Calice, M. N., Newman, T. P., Scheufele, D. A., Xenos, M. A., & Brossard, D. (2023) · Journalism & Mass Communication Quarterly · doi:10.1177/10776990231190868 · Cited by 52
“For example, research has explored how trust in AI-based conversational agents )Choung et al., 2022( and news algorithms )Shin, Zaid, et al., 2022( shapes acceptance of the technology or related user behavior.”
Đerić, E., Frank, D., & Milković, M. (2025) · Information · doi:10.3390/info16070622 · Cited by 60
Oyekunle, D. O. T., Matthew, U. O., Preston, D., & Boohene, D. (2024) · Journal of Computer and Communications · doi:10.4236/jcc.2024.126006 · Cited by 51
Starke, G., Gille, F., Termine, A., Aquino, Y. S. J., Chavarriaga, R., Ferrario, A., Hastings, J., Jongsma, K. R., Kellmeyer, P., Kulynych, B., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/56306 · Cited by 62
“As recent empirical work using path analysis has highlighted, trust in AI-based technologies seems to have a significant effect on users’intention to use such systems [30].”
Su, J., Wang, Y., Liu, H., Zhang, Z., Wang, Z., & Li, Z. (2025) · Scientific Reports · doi:10.1038/s41598-025-01897-0 · Cited by 68
“Choung et al. 75 expanded upon the TAM by demonstrating that trust positively predicts perceived usefulness.”
What is critical for human-centered AI at work? – Toward an interdisciplinary theory
Mazarakis, A., Bernhard-Skala, C., Braun, M., & Peters, I. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1257057 · Cited by 64
Choung, H., David, P., & Ling, T.-W. (2024) · Technology in Society · doi:10.1016/j.techsoc.2024.102721 · Cited by 61
Self-citation(Over)Trusting AI Recommendations: How System and Person Variables Affect Dimensions of Complacency
Harbarth, L., Gößwein, E., Bodemer, D., & Schnaubert, L. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2301250 · Cited by 57
“Regarding interaction with an AI system, Choung et al. (2021) show that users who evaluate a system as useful and easy to use, report a higher intention to use it.”
Pan, Z., Xie, Z., Liu, T., & Xia, T. (2024) · Systems · doi:10.3390/systems12050176 · Cited by 63
The transformative impact of AI on knowledge management processes
Nakash, M., & Bolisani, E. (2025) · Business Process Management Journal · doi:10.1108/bpmj-11-2024-1137 · Cited by 39
Toward the dynamic relationship between AI transparency and trust in AI: A case study on ChatGPT
Lee, C., & Cha, K. (2025) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2024.2405266 · Cited by 53
“Accessibility, effort (Lee & Cha, 2023; Meske et al., 2022). in the context of transparency and interpersonal trust, in that technology lacks volition and trust in technology is knowledge-based (Choung et al., 2023; Jian et al., 2000; Malle & Ullman, 2021; McKnight et al., 2011).”
Li, J., & Lv, C. (2024) · PLoS ONE · doi:10.1371/journal.pone.0308267 · Cited by 55
Koenig, P. D. (2023) · AI & Society · doi:10.1007/s00146-024-01987-z · Cited by 56
“Notably, contributions extending the TAM have pointed out its limitations for studying AI and stressed the need to integrate trust (Acharya and Mek-ker 2022; Vorm and Combs 2022; Bel and Coeugnet 2023; Chen et al. 2023; Choung et al. 2023)—a factor central to the delegation model.”
Huynh, M.-T., & Aichner, T. (2025) · AI & Society · doi:10.1007/s00146-025-02378-8 · Cited by 69
Exploring Motivators for Trust in the Dichotomy of Human—AI Trust Dynamics
Gerlich, M. (2024) · Social Sciences · doi:10.3390/socsci13050251 · Cited by 46
“Extending the TAM model, Choung et al. (2022) found that trust positively predicts PU.”
Wang, S. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.107971 · Cited by 43
Liu, Y., & Sun, X. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.108006 · Cited by 46
Kang, S., Choi, Y., & Kim, B. (2024) · Social Sciences · doi:10.3390/socsci13090475 · Cited by 58
“After all, as Choung et al. (2023) argued, trust is an important factor that increases users’ use intention and attitude to accept AI-based services.”
Wang, G., Obrenovic, B., Gu, X., & Godinić, D. (2025) · Current Psychology · doi:10.1007/s12144-025-07357-2 · Cited by 52
“Trust has previously been proven to be an antecedent to technology adaptation and diffusion (Choung et al., 2023b; Montag et al., 2023; Söllner et al., 2016 ).”
Trust and Trustworthiness in AI
Durán, J. M., & Pozzi, G. (2025) · Philosophy & Technology · doi:10.1007/s13347-025-00843-2 · Cited by 47
“Similar to interpersonal trust, the users’ trust in AI systems is foundational for their acceptance and successful integration into relevant social practices (Choung et al., 2022).”
Zhou, Z., Li, J., Zhang, Z., Yu, J., & Duh, H. B. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2370635 · Cited by 47
“Previous studies suggest that trust significantly affects users’ intentions to use AI and is also associated with other perceptions (Choung et al., 2023; Sison et al., 2023).”
Koning, B., & Voorveld, H. A. M. (2025) · Journal of Interactive Advertising · doi:10.1080/15252019.2025.2554149 · Cited by 70
Chat or cheat? Academic dishonesty, risk perceptions, and ChatGPT usage in higher education students
Ortiz-Bonnin, S., & Blahopoulou, J. (2025) · Social Psychology of Education · doi:10.1007/s11218-025-10080-2 · Cited by 80
“Intention to use ChatGPT (Choung et al., 2023 ) was measured by asking users if they intend to continue using ChatGPT.”
Zhou, Q., Yang, L., Tang, Y., Yang, J., Zhou, W., Guan, W., Yan, L., & Liu, Y. (2025) · BMC Nursing · doi:10.1186/s12912-025-03406-0 · Cited by 40
Khoso, A. K., Honggang, W., & Darazi, M. A. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-06362-x · Cited by 51
Cecil, J., Lermer, E., Hudecek, M. F. C., Sauer, J., & Gaube, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-60220-5 · Cited by 37
“Research has shown that acceptance and use of AI-enabled systems increase when individuals trust the system’s predictions, which will most likely increase when people regularly interact with these systems and perceive it as useful 23 .”
See Widely, Think Wisely: Toward Designing a Generative Multi-agent System to Burst Filter Bubbles
Zhang, Y., Sun, J., Feng, L., Yao, C., Fan, M., Zhang, L., Wang, Q., Geng, X., & Rui, Y. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642545 · Cited by 60
Zhao, X., You, W., Zheng, Z., Shi, S., Lu, Y., & Sun, L. (2025) · Behavioral Sciences · doi:10.3390/bs15030337 · Cited by 47
“…studies have analyzed the correlation between trust and acceptance through traditional paradigms, such as the Technology Acceptance Model (TAM) (Choung et al., 2023) and the Unified Theory of Acceptance and Use of Technology (UTUAT) (Roh et al., 2023; Abadie et al., 2024), they cannot further…”
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
“Although the governance mechanisms related to AI systems and their ubiquity are emergent, there is growing consensus on the principles of ethical AI (Choung et al., 2022).”
Self-citationKaraarslan, D., Kahraman, A., & Ergın, E. (2024) · Journal of Pediatric Nursing · doi:10.1016/j.pedn.2024.04.031 · Cited by 40
Kozak, J., & Fel, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-80305-5 · Cited by 43
“…and understanding AI 25,26 , anthropomorphism, the attribution of human characteristics to AI 27,28 , students’ striving for independence in the process of education 29 , belief in the technical reliability of AI 27 , and appreciating the functionality, usefulness 30 , and innovativeness of AI 23 .”
Generative AI Characteristics, User Motivations, and Usage Intention
Kim, D., Kim, S., Kim, S., & Lee, B. H. (2024) · Journal of Computer Information Systems · doi:10.1080/08874417.2024.2442438 · Cited by 48
Using Generative AI in nursing education: Students’ perceptions
Khlaif, Z. N., Salameh, N., Ajouz, M., Mousa, A., Itmazi, J. A., Alwawi, A. A., & Alkaissi, A. (2025) · BMC Medical Education · doi:10.1186/s12909-025-07416-z · Cited by 54
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
“Recent extensions of the Technology Acceptance Model (TAM) emphasize that trust is central in reducing uncertainty and perceived risk, both of which are barriers to adoption of emerging technologies [108 – 111].”
The Mediating Effect of AI Trust on AI Self-Efficacy and Attitude Toward AI of College Students
Obenza, B. N., Baguio, J. S. I. E., Bardago, K. M. W., Granado, L. B., Loreco, K. C. A., Matugas, L. P., Talaboc, D. J., Zayas, R. K. D. D., Caballo, J. H. S., & Caangay, R. B. R. (2023) · International Journal of Metaverse · doi:10.54536/ijm.v2i1.2286 · Cited by 49
“This finding resonates with Choung and Ross (2022), who asserted that trust serves as a precursor to positive attitudes, subsequently impacting usage intentions.”
BackgroundMethodInfluentialVuori, N., Burkhard, B., & Pitkäranta, L. (2025) · Journal of Management Studies · doi:10.1111/joms.13177 · Cited by 54
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
The effectiveness of explainable AI on human factors in trust models
Cheung, J. C. T., & Ho, S. S. (2025) · Scientific Reports · doi:10.1038/s41598-025-04189-9 · Cited by 36
“Naturally, a proliferation of research has emerged in the trust in AI scholarship, examining its effects 40,41 and determinants 42–44 .”
Huo, W., Zhang, Z., Qu, J., Yan, J., Yan, S., Yan, J., & Shi, B. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2176985 · Cited by 41
“Hence, trust can play an important role when consumers face complex and uncertain AI-enabled systems (Choung et al., 2022).”
BackgroundHo, S. S., & Cheung, J. C. T. (2024) · Technology in Society · doi:10.1016/j.techsoc.2024.102533 · Cited by 44
Liu, J. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2544006 · Cited by 43
“Therefore, when people perceive AI technologies as easy to use, they are more likely to trust them and view them as useful (Choung et al., 2023).”
Factors for Patient Trust and Acceptance of Medical Artificial Intelligence
Bracic, A., Spector‐Bagdady, K., Towle, S., Zhang, R., James, C. A., & Price, W. N., II (2026) · JAMA Network Open · doi:10.1001/jamanetworkopen.2026.0815 · Cited by 43
Li, H. (2023) · Personality and Individual Differences · doi:10.1016/j.paid.2023.112401 · Cited by 37
Nyaaba, M., Kyeremeh, P., Majialuwe, E. K., Owusu-Fordjour, C., Asebiga, E., & A-ingkonge, B. (2024) · Journal of AI · doi:10.61969/jai.1400867 · Cited by 37
“This aligns with the concerns raised about the impact of GAI on the originality of research as well (Choung et al., 2023; Mosha & Laizer, 2021).”
BackgroundArtificial intelligence for collective intelligence: a national-scale research strategy
Bullock, S., Ajmeri, N., Batty, M., Black, M. M., Cartlidge, J., Challen, R., Chen, C., Jing, C., Condell, J. V., Danon, L., et al. (2024) · The Knowledge Engineering Review · doi:10.1017/s0269888924000110 · Cited by 34
“…data from human experiments can inform a series of design iterations, focussing on accessibility, usability, explainability, adaptability and trust (Choung et al ., 2023), drawing upon long-standing approaches to defining and measuring trust in automation (e.g., Lee & See, 2004), with all being key…”
Almheiri, H. M., Ahmad, S. Z., Khalid, K., & Ngah, A. H. (2024) · Journal of Systems and Information Technology · doi:10.1108/jsit-10-2022-0239 · Cited by 33
Miedema, E., Waschull, S., & Emmanouilidis, C. (2025) · Computers in Industry · doi:10.1016/j.compind.2025.104409 · Cited by 41
The future is now? Consumers' paradoxical expectations of human-like service robots
Zhu, T., Lin, Z., & Liu, X. (2023) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.122830 · Cited by 33
Riedmann-Streitz, C., Streitz, N., Antona, M., Marcus, A., Margetis, G., Ntoa, S., Rau, P. P., & Rosenzweig, E. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2325175 · Cited by 40
“Trust in smart and AI-enabled environments constitutes an area of scholarly discourse currently in the foreground, having an impact in the overall acceptance of AI technologies (Choung et al., 2023).”
Metadata
- Authors
- Choung, H., David, P., & Ross, A.
- Affiliations
- Michigan State University (US)
- Corresponding author
- Choung, Hyesun
- Venue
- International Journal of Human–Computer Interaction
- Publisher
- Informa UK Limited
- Published
- 2022-04-20
- Volume
- 39
- Issue
- 9
- Pages
- 1727-1739
- ISSN
- 1044-7318 (ISSN-L)
- 1532-7590
- OpenAlex
- W4221014470
- Semantic Scholar
- f281ad6573ed8ea7fb3116a3747b0e80ef522ea9
- Cited by
- 963
- References
- 62
- Citations by year
- 2026: 402 · 2025: 360 · 2024: 142 · 2023: 54 · 2022: 5
- FWCI
- 117.34
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- International Journal of Human-Computer Interaction (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Social Sciences › Human Factors and Ergonomics › Human-Automation Interaction and Safety
- Keywords
- trust in AI, technology acceptance, intention to use, voice assistants, perceived usefulness, perceived ease of use, attitudes toward technology, trustworthy AI, technology acceptance model
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.
A language model drew the claims from the paper on 2026-10-06, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-06 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
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