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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
Items 501–600 of 966
Ding, H., Shen, Y., Chen, J., & Wang, P. (2026) · Information Processing & Management · doi:10.1016/j.ipm.2026.105046 · Cited by 4
Generative Ai Literacy: Key Antecedents and Outcomes Among Youth in Four CountriesPreprint
Stević, A., Hodzic, S., Matthes, J., Nanz, A., Binder, A., & Chan, M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5277061 · Cited by 3
Kelly, A., Bhardwaj, N., Holmberg, T. T., van de Ven, P. W. J., Melia, R., Williams, J. E., Mathiasen, K., & Nielsen, A. S. (2025) · doi:10.2196/preprints.79658 · Cited by 2
Huynh, T. N., Nguyen, L. T., & Trong, L. N. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.20.665788 · Cited by 2
“Recent research from IEEE indicates that AI attributes such as “perceived intelligence,” “perceived anthropomorphism,” and “perceived animacy” directly influence users’ beliefs and attitudes, thereby enhancing their intention to engage in socially sustainable activities (Choung et al., 2023).”
Mahy, T., & Li, H. (2026) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2026.2623216 · Cited by 2
“…attitudes, etc.), largely based on adapting scales, most notably from the Technology Acceptance Model (TAM) (Davis, 1989) (e.g., studies by Choung et al., 2023; Lai et al., 2023; Ma et al., 2025) and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003)…”
Local Explanations and Self-Explanations for Assessing Faithfulness in black-box LLMs
Fragkathoulas, C., & Chlapanis, O. S. (2024) · Hellenic Conference on Artificial Intelligence · doi:10.1145/3688671.3688775 · Cited by 1
Gen Z's Interest in Online Travel Agencies in Indonesia: A Modified UTAUT2
Leon, J., Simanjuntak, F., & Firmansyah, M. D. (2025) · Jurnal Teknologi Terapan G-Tech · doi:10.70609/gtech.v9i1.6191 · Cited by 1
Wu, Q. P., & Jin, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-88193-z · Cited by 1
“The results showed the ease of use and usefulness of the method 9 .”
Roch, N. (2025) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3708557.3716152 · Cited by 1
Hao, Y. A. (2024) · Journal of big data and computing. · doi:10.62517/jbdc.202401401 · Cited by 1
Asbach, S., Graf‐Vlachy, L., & Fügener, A. (2025) · Lecture notes in information systems and organisation · doi:10.1007/978-3-031-80119-8_17 · Cited by 1
Szafarski, D., Radowski, C.-F., & Jung, D. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-93418-6_13 · Cited by 1
Kaur, A., Jeyakumaran, T., Kåsen, A., Saatvedt, P. T., & Chen, W. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-93412-4_12 · Cited by 1
Influencing the Perception and Acceptance of AI-Generated Marketing Content as Part of SGD
Kubovics, M. (2025) · Journal of Lifestyle and SDGs Review · doi:10.47172/2965-730x.sdgsreview.v5.n06.pe06929 · Cited by 2
Ram, K. M., & Vijayan, P. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-5223-5_44 · Cited by 1
Empowering Trust: The Role of Adaptable Design in AI Systems
Staab, V., Hein, I., Ramrath, M., Schlüter, L., Stuckstätte, A., Hohn, M., Sieberg, P. M., & Liebherr, M. (2025) · IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA) · doi:10.1109/cogsima64436.2025.11079500 · Cited by 1
“Furthermore, as part of the Algorithm Acceptance Model, trust has been recognized as a key driver of technology adoption (e.g., [27]), with numerous studies also identifying it as a strong predictor of AI usage intentions [9], [28].”
Conrad, C., Nissen, A., Masoumi-Ravandi, K., Ramchandani, M., Braga, R. F., & Newman, A. J. (2025) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2025.100185 · Cited by 4
Brains, Bond, and Belief: What Sustains Gen Z’s Use of AI Assistants
Das, S. (2025) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2025.73420 · Cited by 1
Küçükaydın, M. A. (2025) · Psychology in the Schools · doi:10.1002/pits.70068 · Cited by 1
Al-kfairy, M., Al-Adaileh, A., & Mustafa, D. (2025) · International Conference on e-Education, e-Business, e-Management and e-Learning · doi:10.1109/ic4e65071.2025.11075399 · Cited by 1
“It serves as a key factor in reducing concerns related to privacy and security, thereby strengthening user confidence in AI voice assistants [17].”
Kotzian, P., Weißenberger, B. E., & Prinz, S. G. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2543988 · Cited by 1
“But as such a notion of trust is rather broad and thus has been criticized in existing research (e.g., Choung et al., 2023), we also capture procedural aspects of trust using the approach suggested by Bhattacherjee (2002), and survey participants ’ opinions on different dimensions of the…”
The combined effect of entropy and complexity: Human analysis of AI painting recognition ability
Ma, D., An, Q., & Li, A. (2025) · Systems and Soft Computing · doi:10.1016/j.sasc.2025.200378 · Cited by 2
Kane, D., Goodridge, W., Ahlstrom, L., Lawanto, O., Harper, M., & McCall, C. (2025) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--55915 · Cited by 1
Couraceiro, P., Paisana, M., & Vasconcelos, A. (2025) · Revista de Comunicación · doi:10.26441/rc24.2-2025-4049 · Cited by 1
AI as a Catalyst for Expanding CSR: The Case of Moroccan Entreprises
Anouar, H., & Kasbaoui, T. (2025) · International Conference on Circuit, Systems and Communication (ICCSC) · doi:10.1109/iccsc66714.2025.11135146 · Cited by 1
“Trust significantly influences the intention to use AI voice assistants, with perceived usefulness and attitude toward the technology acting as mediators [33].”
Lin, S.-K., & Chung, H.-C. (2025) · SAGE Open · doi:10.1177/21582440251377226 · Cited by 1
Non-experts’ Trust in XAI is Unreasonably High
Brdnik, S., Colakovic, I., & Karakatič, S. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-08333-3_9 · Cited by 1
MacLennan, H., Malka, S. C., & Queiroz, H. J. D. S. D. (2025) · SAM Advanced Management Journal · doi:10.1108/samamj-04-2025-0023 · Cited by 2
Ma, C., Yang, Z., & Gao, C. (2025) · International Conference on Economic Management and Big Data Application (ICEMBDA) · doi:10.1145/3770177.3770192 · Cited by 1
“One analysis points to the number of Chinese students having been dismissed from universities in the US due to academic cheating using AI[9].”
Explainable and Interactive Scientometrics with Large Language Models and Knowledge Graphs
Saarela, M., Correia, A., & Kärkkäinen, T. (2025) · International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) · doi:10.1109/ismsit67332.2025.11268107 · Cited by 2
“Nevertheless, issues such as limited trust in AI-generated content [50] and opacity in prompt engineering and system behavior [51] remain significant adoption barriers.”
Cultural Sensitivity and AI Adoption: Exploring the Willingness to Embrace AI-Generated Content
Chiravuri, A., AlQasim, B., AlHammadi, S., Ergashev, K., Alhmoudi, A., & Alqahtani, M. S. (2025) · Journal of Information & Knowledge Management · doi:10.1142/s0219649225501308 · Cited by 1
Unveiling the Impact of AI-Chatbot Attributes and Anthropomorphic Cues in E-Commerce
Hamedani, S. S., Manivannan, Y., Yoke, L. K., Ee, L. S., & Aslam, S. (2025) · Pakistan Journal of Commerce and Social Sciences · doi:10.64534/commer.2025.512 · Cited by 1
Han, Y., Yang, Q., Zou, X., Tian, P., Feng, Y., & Yao, T. (2025) · Frontiers of Engineering Management · doi:10.1007/s42524-025-4210-3 · Cited by 1
“However, existing research primarily focuses on the static antecedents of trust, such as AI’s transparency, reliability, and tangibility (Kyung and Kwon, 2022; Choung et al., 2023).”
Ben-Shahar, D., Carmeli, A., Sulganik, E., & Weiss, D. (2025) · AI & Society · doi:10.1007/s00146-025-02773-1 · Cited by 4
Alhasasneh, S., Shishan, F., & Balushi, W. A. (2025) · Journal of Business and Socio-economic Development · doi:10.1108/jbsed-07-2025-0295 · Cited by 1
OpenAI’s Sora in the advertising film production process in Thailand
Mahasri, C., Yang, F., Dangseam, T., & Kerdvibulvech, C. (2025) · AI and Ethics · doi:10.1007/s43681-025-00951-x · Cited by 1
“Thus, awareness of Sora among Thai advertising profes - sionals remains nascent but optimistic, grounded in the belief that AI can serve as a creative collaborator—not a replace - ment—if it enhances human productivity and imagination. and human-in-the-loop controls are present [ 7 , 8 ].”
Pfeuffer, C. U. (2025) · International Conference on Human-Agent Interaction (HAI) · doi:10.1145/3765766.3765868 · Cited by 1
“First, participants were informed about the features and functions of They then rated their trust (4 items [5]; 1 = strongly disagree to 5 = strongly agree) and (state) anxiety (4 items [26]; 1 = strongly disagree to 5 = strongly agree) regarding each AI product (represented by an icon), first,…”
InfluentialWillingness to buy at AI powered retail stores in Saudi Arabia: Empirical study
Alam, S. S. (2026) · Electronic Markets · doi:10.1007/s12525-025-00865-w · Cited by 1
“Favora-ble attitudes toward technology predispose consumers to view AI systems positively, linking general confidence in innovation with trust in specific AI applications (Choung et al., 2022; Montag et al., 2023).”
From Chalk to Code: Asymmetric Nexus Between Artificial Intelligence and Educational Expenditures
Ali, S., Nazar, R., & Anser, M. K. (2026) · Journal of the Knowledge Economy · doi:10.1007/s13132-025-03080-2 · Cited by 1
Wang, H., Lai, X., Huang, S., Dai, X., Zhao, X., & Wang, Y. (2026) · Behavioral Sciences · doi:10.3390/bs16010072 · Cited by 1
“Trust in AI can predict a positive attitude towards AI and perceived usefulness, which is related to a greater willingness to use it (Choung et al., 2023).”
Cai, Y., & Rau, P. P. (2026) · Applied Ergonomics · doi:10.1016/j.apergo.2026.104738 · Cited by 1
Halal tourism and ChatGPT: the effect of value co-creation
Alizadeh, H., Kashani, H. N., Masoumi, F., Yousefli, A., Namazi, Y., & Saberian, H. (2026) · Journal of Islamic marketing · doi:10.1108/jima-12-2024-0597 · Cited by 4
AI Technology Adoption: A Systematic Review of Influencing Factors Based on the TAM Framework
Waluyo, R., Hariguna, T., Saputra, D. I. S., Oktaviana, L. D., Berlilana, & Hani, N. (2025) · International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE) · doi:10.1109/icitisee68184.2025.11355121 · Cited by 2
“Perceived usefulness of AI and trust in the system are significant factors in influencing user intentions to adopt AI [17], and the integration of trust and perceived control can increase AI adoption in various industrial and educational sectors [18], [19].”
Towards Safe and Trustworthy Intelligent Marine Operations: New Challenges and Solutions
Wang, T., Sanguino, B., Li, G., & Zhang, H. (2025) · International Conference on Control, Mechatronics and Automation (ICCMA) · doi:10.1109/iccma67641.2025.11369512 · Cited by 1
“While trustworthy autonomous systems do not entirely align with requirements on trustworthy AI [22], they share common technology foundations in this study.”
Yapay Zekâya İlişkin Tutumlar Ölçeği: Geliştirme, geçerlik ve güvenirlik çalışması
Şahin, N. H., Batıgün, A. D., Yasak, Y., & Eyrikaya, E. (2026) · Klinik Psikoloji Dergisi · doi:10.57127/kpd.26024438.1786161 · Cited by 1
AI adoption and organizational change: insights from international and local perspectives
Dumitrache, V.-G. (2025) · International Conference of Management and Industrial Engineering · doi:10.56177/12icmie2025.155 · Cited by 1
Human-Digital Symbiosis and Green Intelligent Manufacturing
Chen, Y., & Xin, J. (2026) · Journal of Global Information Management · doi:10.4018/jgim.401375 · Cited by 1
Devi, L. D., & Arumugam, T. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1734151 · Cited by 1
“The technology acceptance model (TAM) is a commonly cited framework for understanding individuals’ acceptance behavior toward technology (Choung et al., 2023).”
Zhao, H., Yuan, B., Zhang, B., & Liao, Y. (2026) · Tourism Review · doi:10.1108/tr-07-2025-0740 · Cited by 2
A Multi-Year Survey of Use and Perceptions of Generative AI in Higher Education
Crescenzi, A., Choi, B., Huang, P.-P., Gautier, E., Little, R., & Pandya, S. (2026) · Conference on Human Information Interaction and Retrieval · doi:10.1145/3786304.3787867 · Cited by 3
“In the context of gen AI technologies, factors such as trust, privacy concerns, and personal innovative-ness have emerged as particularly salient [13, 25, 36].”
CLARA: AI-Mediated Facilitation for Enhancing Group Cognition and Cohesion in Remote Collaboration
Gunasekaran, T. S., Doosti, M. S., Gupta, K., Bai, H., Pai, Y. S., & Billinghurst, M. (2026) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3786325 · Cited by 4
Who listens to ChatGPT and when should they? A two-study examination of AI-assisted decision making
Grawitch, M. J., Winton, S. L., & Mudigonda, S. P. (2026) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2026.100283 · Cited by 3
Public Preferences with Justice Initiatives of Digital Public Services Platforms
Sui, Q., & Xiong, B. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2630069 · Cited by 1
“However, many scholars remain concerned about ethical and governance challenges in the relationship between public digital service platforms and citizens (Choung et al., 2023; Barredo Arrieta et al., 2020).”
Kusi, E., Gael, E. A., Afriyie, E., & Duku, S. G. (2026) · European Journal of Mathematics and Science Education · doi:10.12973/ejmse.7.1.55 · Cited by 2
Chellappa, V. (2026) · Safety Science · doi:10.1016/j.ssci.2026.107194 · Cited by 2
Almutairi, N. (2026) · Electronics · doi:10.3390/electronics15061305 · Cited by 1
Lecturers' Self-efficacy in the use of Artificial intelligence: TVET lecturers’ perspectives
Omeh, C. B. (2026) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2026.100294 · Cited by 1
Valenzuela, S., Bachmann, I., Borah, P., & Valdés, N. S. (2026) · Digital Journalism · doi:10.1080/21670811.2026.2649018 · Cited by 3
“From the perspective of the TAM, users’ acceptance of technology is also influenced by pre-existing attitudes (e.g., trust, perceived risk, and ethical or privacy concerns) (Choung, David, and Ross 2023; Venkatesh and Davis 2000).”
Chan, C. K. Y., & Tse, S. S.-K. (2026) · International Journal of Psychology · doi:10.1002/ijop.70207 · Cited by 1
Martínez, R. S., García-Castillo, F., Sánchez, C. L., & del Castillo, J. A. G. (2026) · Edutec Revista Electrónica de Tecnología Educativa · doi:10.21556/edutec.2026.95.4409 · Cited by 2
Digital risk governance and behavioural drivers of robo-advisor adoption in financial services
Ansari, Y., Bansal, R., Mishra, A. K., & Maurya, P. K. (2026) · Risk Governance and Control Financial Markets & Institutions · doi:10.22495/rgcv16i1p18 · Cited by 1
Mazari, N. (2025) · Moscow University Journalism Bulletin · doi:10.55959/msu.vestnik.journ.6.2025.5379 · Cited by 1
Chen, E. W., Liu, D., Yang, F., Tang, H., Wang, Y., & Luo, B. N. (2026) · Group Decision and Negotiation · doi:10.1007/s10726-026-09983-y · Cited by 1
“Employees evaluated GenAI usefulness using a 5-item scale from Choung et al. ( 2023 ), adapted to the context of GenAI (Cronbach’s α = 0.98).”
InfluentialAnagha, R. B., Sreejesh, S., & Sankaranarayanan, V. (2026) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2026.104841 · Cited by 1
The epistemologies of trust: conflicting worldviews in the “Trustworthy AI” discourse
Broniatowski, D. A., Joubin, A. A., Aaronson, S. A., Yang, Y. T., Sikka, N. K., Abroms, L. C., Zirikly, A., & Szajnfarber, Z. (2026) · AI and Ethics · doi:10.1007/s43681-026-01087-2 · Cited by 1
Lakshmi, K., Benny, V., & Sonny, M. (2026) · International Journal of Conflict Management · doi:10.1108/ijcma-12-2025-0410 · Cited by 1
Generative AI and decision autonomy: a framework of psychological and structural empowerment
Mrayyan, N. E., Hani, I. B., & Alawadi, S. (2026) · Journal of Decision System · doi:10.1080/12460125.2026.2657495 · Cited by 1
Kleine, A., Schaffernak, I., & Lermer, E. (2026) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2026.100310 · Cited by 2
Sevryugina, Y. V., Collins‐Thompson, K., & Walter, N. G. (2026) · AI in Education · doi:10.3390/aieduc2020014 · Cited by 2
“Overall, trust in AI and its providers is a key driver of AI adoption and usage (Glikson & Woolley, 2020; Choung et al., 2022; Choudhury & Shamszare, 2023; Lukyanenko et al., 2022).”
InfluentialExploring Teenagers’ Trust in AI Chatbots: An Empirical Study of Chinese Middle-School Students
Qiu, S., Lin, A., Wang, S., & Lan, X. (2025) · International Conference on Human-Engaged Computing (ICHEC) · doi:10.1145/3786995.3787019 · Cited by 4
“This foundational conceptualization has further spurred extensive research on user trust toward various technologies, such as robots [6, 23], voice assistants [11], and autonomous vehicles [26].”
Knowledge graphs and large language models for prompt-based scientometric inquiry
Correia, A., Saarela, M., & Kärkkäinen, T. (2026) · Information Processing & Management · doi:10.1016/j.ipm.2026.104882 · Cited by 3
Tran, H. P., Nguyen, T. H. T., Nguyen, C., Nguyễn, H. K., & Quach, C. K. (2026) · Journal of Promotion Management · doi:10.1080/10496491.2026.2668536 · Cited by 2
Using It but Not Trusting It: Technology Acceptance and Generative AI in European Fact-Checking
Dierickx, L., & van Dalen, A. (2026) · Emerging Media · doi:10.1177/27523543261449974 · Cited by 3
“…acceptance (Schmidt et al., 2020), either functioning as a core element that affects the intention to use technology by shaping perceptions of usefulness or as an external variable that in fl uences user acceptance and helps predict behavioral intentions (Choung et al., 2023; Kelly et al., 2023).”
Hu, W. W. (2026) · Studies in Higher Education · doi:10.1080/03075079.2026.2658738 · Cited by 1
Deilen, M., De-Juan-Vigaray, M. D., Runia, P. M., & Meroño, M. C. P. (2026) · Journal of Global Scholars of Marketing Science · doi:10.1080/21639159.2026.2660934 · Cited by 1
“In high-stakes domains such as banking, where information asymmetry, risk, and regulatory scrutiny are inherent, the significance of trust in AI-mediated services cannot be overstated (Choung et al., 2023; Hentzen et al., 2022).”
Lu, Z., Guo, J., Yuan, T., Zhang, Y., Yang, J., 杜郁萱, Chen, M., Xie, M., Xian, L., Cao, H., et al. (2026) · Behavioral Sciences · doi:10.3390/bs16060932 · Cited by 1
“The items for algorithm appreciation were adapted from AI-related emotion and attitude scales developed by Xie et al. (2025) and Choung et al. (2023).”
Hu, W. X., Gao, C. Y., Qin, Y. X., & Guo, H. Y. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1831505 · Cited by 1
“Research has found that users’ level of trust in GAI significantly influences their willingness to use it, with functional trust (such as system reliability) having a greater impact on usage intent than human-like trust (such as anthropomorphism) (Choung et al., 2023).”
Modelling Human–AI Trust in Sociotechnical Systems: A Systems Perspective on Organizational Change
Yahong, W., Ali, A., & Meiguang, Z. (2026) · Systems Research and Behavioral Science · doi:10.1002/sres.70101 · Cited by 1
Ou, M., Chen, L., & Zheng, H. (2026) · Journal of Broadcasting & Electronic Media · doi:10.1080/08838151.2026.2692544 · Cited by 1
Kotsis, K. Τ., & Kotsis, K. Τ. (2026) · Journal of Artificial Intelligence and Technological Development · doi:10.59324/jaitd.2026.2(4).02 · Cited by 1
Klemm, A., & Reintjes, C. (2026) · HMD Praxis der Wirtschaftsinformatik · doi:10.1365/s40702-026-01291-y · Cited by 1
Suk, J., & Zhang, Y. (2026) · Journal of Broadcasting & Electronic Media · doi:10.1080/08838151.2026.2694048 · Cited by 1
YILMAZ, C. (2026) · International Journal Of Eurasia Social Sciences · doi:10.70736/ijoess.2213 · Cited by 1
Negotiating creator identity: agency and ethical awareness in AI-assisted art education
Lee, H., Yu, W., & Lee, J. (2026) · Ai & Society · doi:10.1007/s00146-026-03187-3 · Cited by 3
“One of the most widely used theoretical frameworks in these discussions is the Technology Acceptance Model (TAM) (Davis 1989; Choung et al. 2023; Kelly et al. 2023).”
Wang, H., Zhao, B., Jiao, Y., Liang, C., & Zheng, G. (2026) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2026.124803 · Cited by 3
Balázs, L. (2026) · Social Sciences · doi:10.3390/socsci15070433 · Cited by 2
Liang, Q., & Ollier‐Malaterre, A. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2676759 · Cited by 1
Keravnos, N., & Eleftheriou, M. (2026) · Educational Point · doi:10.71176/edup/18914 · Cited by 1
“Trust in technology reflects perceptions of reliability, fairness, explainability, and transparency (Choung et al., 2023; Lipsou et al., 2026; Shin, 2021).”
Zhang, C. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1784354 · Cited by 4
“Prior studies have consistently reported strong reliability (Cronbach’s α = 0.920) and confirmed convergent and discriminant validity across diverse technological contexts (Choung et al., 2023).”
InfluentialRyu, S. (2026) · Journal of Research in Interactive Marketing · doi:10.1108/jrim-10-2025-0635 · Cited by 1
Chen, D., Lin, H.-P., & Chu, T.-H. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1846181 · Cited by 2
Shi, W., Ramzy, M. I., & Ali, S. K. S. (2026) · African Journal of Science Technology Innovation and Development · doi:10.1080/20421338.2026.2692491 · Cited by 2
Asif, M., Ersoy, A., Shahzad, M. F., Ahmad, M. B., & Ashfaq, M. (2026) · Tourism and Hospitality Research · doi:10.1177/14673584261474503 · Cited by 1
Ahatsi, E., Winkler, H., & Olanrewaju, O. A. (2026) · Discover Sustainability · doi:10.1007/s43621-026-04407-y · Cited by 1
“However, whereas Shin [66] positions trust primarily as a mediator between system characteristics and adoption, Choung et al., [15] portray trust as both an independent predictor and an antecedent of perceived usefulness, suggesting that trust influences adoption through multiple psychological…”
AI-based interviews and applicant perceptions: insights from China and Pakistan
Zhao, G., Liang, Q., Alam, F., & Hossain, M. B. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1886963 · Cited by 3
“” Trust in AI System: Trust in the AI system was measured using six items adapted from Choung et al. (2023), reflecting confidence in the AI’s capability to make fair, unbiased, and reliable recruitment decisions.”
InfluentialLoaiza-Ramírez, J. P., Reimer, T. O., Todd, P. M., Qian, X., Gazmeh, H., Hamim, O. F., & Ukkusuri, S. V. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2727258 · Cited by 1
Damaševičius, R. (2024) · Machine Learning and Knowledge Extraction · doi:10.3390/make6030081 · Cited by 1
“Like Choung’s foundational concept of trust in AI technologies [2], the study emphasizes the importance of trust in AI adoption, focusing on both capabilities and benevolence, reflecting the multi-faceted nature of trust delineated in prior research.”
BackgroundDigitalisierung der Arbeitswelten – die systemische Transformation verstehen. Ein Ausblick
Pfeiffer, S., Nicklich, M., & Schreyer, J. (2024) · Springer eBooks · doi:10.1007/978-3-658-44458-7_25 · Cited by 1
A Multi-Layered Trust Model in the Internet of Things Smart Home Ecosystem
Shuhaiber, A., & Mashal, I. (2024) · International Conference on Wireless Networks and Mobile Communications (WINCOM) · doi:10.1109/wincom62286.2024.10655530 · Cited by 1
“For instance, Choung et al. [30] reported that competence trust in AI systems has boosted users trust in AI systems.”
BackgroundRisk Assessment and Mitigation With Generative AI Models
Sindiramutty, S. R., Jhanjhi, N. Z., Akbar, R., Soomro, T. R., & Ghazanfar, M. A. (2024) · Advances in digital crime, forensics, and cyber terrorism book series · doi:10.4018/979-8-3693-8944-7.ch002 · Cited by 1
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).
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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