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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 201–300 of 966
Determinants of Public Sector Managers' Intentions to Adopt AI in the Workplace
Majrashi, K. (2024) · International Journal of Public Administration in the Digital Age · doi:10.4018/ijpada.342849 · Cited by 22
López, C., Davidoff, A., Luco, F., Humeres, M., & Correa, T. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2356910 · Cited by 17
“Regarding trust in AI systems in particular, trust has also been studied as (1) human-like trust, which is related to perceptions of trans-parency, integrity, justice, fairness, and (2) functionality trust, associated with competence, performance, and reliability (Choung et al., 2022).”
Ronaghi, M. H., & Ronaghi, M. (2025) · Asian Education and Development Studies · doi:10.1108/aeds-08-2024-0182 · Cited by 14
Greussing, E., Guenther, L., Baram‐Tsabari, A., Dabran‐Zivan, S., Jonas, E., Klein-Avraham, I., Taddicken, M., Agergaard, T. E., Beets, B., Brossard, D., et al. (2025) · Journal of Science Communication · doi:10.22323/2.24020205 · Cited by 21
Self-disclosure to AI: People provide personal information to AI and humans equivalently
Merwin, E. R., Hagen, A. C., Keebler, J. R., & Forbes, C. E. (2025) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2025.100180 · Cited by 17
Human-centric AI governance: what the EU public values, what it really, really values
Wittmann, V., & Meynhardt, T. (2025) · Government Information Quarterly · doi:10.1016/j.giq.2025.102084 · Cited by 14
Modeling Student Acceptance of AI Technologies in Higher Education: A Hybrid SEM–ANN Approach
Saflor, C. S. (2025) · Future Internet · doi:10.3390/fi17120581 · Cited by 13
“Although prior studies emphasize the role of trust in technology adoption [4,5], the SEM results in this study indicate that trust did not significantly predict actual use.”
Empathetic Robot Judge, we Trust You
Watamura, E., Ioku, T., Mukai, T., & Yamamoto, M. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2232982 · Cited by 14
“Outside of the courtroom, growing evidence suggests that empathy increases trust in AI (e.g., Liu & Sundar, 2018; Pelau et al., 2021) and that trust promotes social acceptance (e.g., Choung et al., 2023; Huq et al., 2016; Schmidt et al., 2020).”
Abdulmunem, R. A. M., Jebreen, K., RADWAN, E. S., & Rebai, W. K. (2024) · Human Arenas · doi:10.1007/s42087-024-00399-1 · Cited by 21
Navigating human-AI dynamics: implications for organizational performance (SLR)
Khushk, A., Zhiying, L., Xu, Y., & Zhang, X. (2024) · International journal of organizational analysis · doi:10.1108/ijoa-04-2024-4456 · Cited by 18
Are the robots taking over? On AI and perceived existential risk
Hilliard, A., Kazim, E., & Ledain, S. (2024) · AI and Ethics · doi:10.1007/s43681-024-00600-9 · Cited by 17
“Indeed, a significant driver of distrust in AI is a lack of transparency [105, 106] and less than a third of the public can confidently explain how AI works [ 107].”
Sestino, A., Rizzo, C., Irgang, L., & Stehlíková, B. (2025) · Business Process Management Journal · doi:10.1108/bpmj-07-2024-0655 · Cited by 16
Chen, H., Chan, I. Y. S., Dong, Z., & Samuel, T.-A. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103555 · Cited by 15
Al-Asmari, A. S., Alruwaili, R. F., Alotaibi, R. F., Youssef, I. K., & ASKLANY, S. A. (2025) · PLoS ONE · doi:10.1371/journal.pone.0331003 · Cited by 12
“This familiarity in high-risk applications such as self-driving cars and AI-assisted diagnostics reduces anxiety and increases the likelihood of acceptance [15].”
Generative artificial intelligence use in the workplace: implications for management practice
Hernández-Tamurejo, Á., Bužinskienė, R., Barbosa, B., Miceikienė, A., & Saura, J. R. (2025) · Review of Managerial Science · doi:10.1007/s11846-025-00949-z · Cited by 17
“Furthermore, trust positively influences usage intention (Choung et al. 2022) and user engagement (Mostafa and Kasamani 2022).”
Zhang, J., Quoquab, F., Mohammad, J., & Li, Z. (2025) · Tourism Review · doi:10.1108/tr-07-2025-0719 · Cited by 12
Al‐Romeedy, B. S., & Alharethi, T. (2025) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer21010006 · Cited by 17
“Without sufficient trust, even highly sophisticated or contextually rich information may be dismissed, second-guessed, or subjected to extensive verification [67,68].”
Do We Trust Artificially Intelligent Assistants at Work? An Experimental Study
Cvetkovic, A., Savela, N., Latikka, R., & Oksanen, A. (2024) · Human Behavior and Emerging Technologies · doi:10.1155/2024/1602237 · Cited by 14
“A positive link between trust and intention to use and accept AI is found in many studies (e.g., Choung, David, and Ross [74] and Xiong et al. [75]), but there is also some evidence that higher trust in AI does not always lead to a higher intention to use it [76].”
BackgroundHauptman, A. I., Flathmann, C., & McNeese, N. J. (2024) · Applied Ergonomics · doi:10.1016/j.apergo.2024.104336 · Cited by 13
Digital Lyrebirds: Experimental Evidence That Voice-Based Deep Fakes Influence Trust
Schanke, S., Burtch, G., & Ray, G. (2024) · Management Science · doi:10.1287/mnsc.2022.03316 · Cited by 14
Exploring barriers to AI course assistant adoption: a mixed-methods study on student non-utilization
Hanshaw, G., & Sullivan, C. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00312-x · Cited by 22
“Moreover, trust has been shown to increase the perceived usefulness, which leads to more positive attitudes and a higher intention to use the technology [6].”
InfluentialArachchi, H. A. D. M., & Samarasinghe, G. (2025) · European Journal of Management Studies · doi:10.1108/ejms-05-2024-0047 · Cited by 20
Zhang, Y., Guo, J., Wang, Y., Li, S., Yang, Q., Zhang, J., & Lu, Z. (2025) · Systems · doi:10.3390/systems13100855 · Cited by 16
Factors Affecting Human-Generated AI Collaboration: Trust and Perceived Usefulness as Mediators
Chae, H.-S., & Yoon, C. (2025) · Information · doi:10.3390/info16100856 · Cited by 12
Framing Affects Support for the Development of Artificial Intelligence in the United States
Palm, R. I., Kingsland, J. T., & Bolsen, T. (2025) · Science Communication · doi:10.1177/10755470251317172 · Cited by 14
“Research that examines factors shaping public opinion about AI is critical because it allows us to anticipate potential public acceptance or resistance that, in turn, could affect its governance and the trajectory of its development (Brewer et al., 2022; Choung et al., 2023; Zhang & Dafoe, 2020).”
Wang, Y., Miao, H., Xiong, M., & Wang, Y. (2025) · Current Issues in Tourism · doi:10.1080/13683500.2025.2462188 · Cited by 12
“Similarly, Choung et al. (2023) demonstrated that trust significantly enhances users’ willingness to adopt AI technology.”
Singh, S., & Karamcheti, B. (2025) · Sustainable Futures · doi:10.1016/j.sftr.2025.100570 · Cited by 11
Lu, X., & Kim, J. (2025) · Behaviour and Information Technology · doi:10.1080/0144929x.2025.2483788 · Cited by 11
Zhang, Q., Wang, F., Liao, G., & Li, M. (2025) · Behavioral Sciences · doi:10.3390/bs15081064 · Cited by 14
“Employees are more likely to view AI as a partner and readily accept and cooperate with AI (Choung et al., 2023; Shao et al., 2024).”
Mirowska, A., & Arsenyan, J. (2025) · Journal of Organizational Behavior · doi:10.1002/job.70009 · Cited by 18
Assessing students’ intention to adopt generative artificial intelligence
Bouebdallah, N., & Youssef, W. A. B. (2025) · Journal of Accounting Education · doi:10.1016/j.jaccedu.2025.100984 · Cited by 16
What Shapes Learners’ Trust in AI? A Meta-Analytic Review of Its Antecedents and Consequences
Cui, Y., Zeng, M. L., Du, X. K., & He, W. M. (2025) · IEEE Access · doi:10.1109/access.2025.3611367 · Cited by 18
“Additionally, perceived usefulness and perceived ease of use reduce learners’ cognitive load associated with system operations and enhance expected learning benefits, further reinforcing recognition of AI system reliability and effectiveness [38], [83].”
Al-kfairy, M., Alalawi, M., Alrabaee, S., & Alfandi, O. (2025) · Computers and Education Open · doi:10.1016/j.caeo.2025.100292 · Cited by 10
Fatigued by uncertainties: Exploring the cognitive and emotional costs of generative AI usage
Yang, H. Y., Zeng, Y., Xing, H., & Hu, P. (2025) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2025.103010 · Cited by 16
Determinants of Emotion Recognition System Adoption: Empirical Evidence from Malaysia
Yamin, M. N. M., Aziz, K. A., Siang, T. G., & Aziz, N. A. A. (2023) · Applied Sciences · doi:10.3390/app132111854 · Cited by 12
Ayoub, D., Metawie, M., & Fakhry, M. (2024) · MSA- Management Sciences Journal/MSA-Management Sciences Journal · doi:10.21608/msamsj.2024.265212.1054 · Cited by 12
Liu, C. (2024) · Aslib Journal of Information Management · doi:10.1108/ajim-04-2024-0348 · Cited by 15
Towards Visual Analytics for Explainable AI in Industrial Applications
Kucher, K., Zohrevandi, E., & Westin, C. (2025) · Analytics · doi:10.3390/analytics4010007 · Cited by 14
“Human–AI collaboration presents several cognitive challenges for the end users who interact with it, notably the effect of trust and transparency on the adoption of AI tech-Analytics nology [12,13].”
Sustained use of generative AI for shopping: a PLS-ANN analysis
Foroughi, B., Ghobakhloo, M., Wen, J., & Fathi, M. (2025) · Service Industries Journal · doi:10.1080/02642069.2025.2485983 · Cited by 11
“Previous research has shown that trust is strongly associated with perceptions of usefulness and benefits in various digital and AI-driven contexts (Choung et al., 2023; Evans et al., 2024).”
Steerling, E., Svedberg, P., Nilsén, P., Siira, E., & Nygren, J. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1565080 · Cited by 13
“Previous empirical research on trust in AI have often applied and modi fi ed technology adoption models (37 – 41), such as the Technology Acceptance Model (TAM) (42) and the Uni fi ed Theory of Acceptance and Use of Technology (UTAUT) (43).”
The trust trifecta: How transparency, ethics, and benefits shape public confidence in government AI
Bian, X., Wang, B., & Yang, A. (2025) · Government Information Quarterly · doi:10.1016/j.giq.2025.102083 · Cited by 11
Chang, R.-C., Natalie, R., Xu, W., Yap, J. Z. F., & Guo, A. (2025) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3663547.3746319 · Cited by 21
“Although users may be drawn to an AI system’s human-like traits, sustained trust and long-term adoption are more strongly influenced by the system’s ability to perform reliably and meet expectations [27].”
Mohapatra, A., Sahoo, A. P., Tripathy, P., Matta, R., & Saxena, A. (2026) · Indian Journal of Finance · doi:10.17010/ijf/2026/v20i1/175903 · Cited by 9
Lu, Z., Wei, W., Qu, X., Mao, X.-L., Chen, D., & Chen, J. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.395 · Cited by 19
Zhang, X., Sun, Y., Yan, C., & Mao, H. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2323276 · Cited by 8
“As for the acceptance of AI technologies, an extended technology acceptance model (TAM) was built upon multidimensional factors including characteristics, intentions, and behaviors (Choung et al., 2023).”
Ethics and Transparency in Secure Web Model Generation
Sindiramutty, S. R., Prabagaran, K. R. V., Jhanjhi, N. Z., Murugesan, R. K., Malik, N. A., & Hussain, M. (2024) · Advances in information security, privacy, and ethics book series · doi:10.4018/979-8-3693-5415-5.ch012 · Cited by 12
Crowd-counting technology within the Smart City context: understanding, trust, and acceptance
Waclawek, T., Fiedler, A., Schütz, M., & Schütz, A. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1423837 · Cited by 7
Yang, T., & Lai, S. (2024) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-024-14838-4 · Cited by 9
Rahimi, A. R., & Mosalli, Z. (2025) · Smart Learning Environments · doi:10.1186/s40561-025-00368-3 · Cited by 18
“The same direct relationship between trust and AI-based platforms was also observed in a sample of US students (Choung et al., 2022).”
AI in the United Arab Emirates’ Media Sector: Balancing Efficiency and Cultural Integrity
Hassouni, A., & Mellor, N. (2025) · Journalism and Media · doi:10.3390/journalmedia6010031 · Cited by 9
Development and validation of the trust in AI scale (TAIS)
Wischnewski, M., Doebler, P., & Krämer, N. C. (2026) · Journal of Psychology and AI · doi:10.1080/29974100.2026.2715475 · Cited by 16
Between Innovation and Caution: How Consumers’ Risk Perception Shapes AI Product Decisions
Moon, W.-K., Wei, X., Overton, H. K., & Kim, J. K. (2025) · Journal of Current Issues & Research in Advertising · doi:10.1080/10641734.2025.2468474 · Cited by 9
“Examining how consumers view AI risks can provide insights into their willingness to adopt AI-based products (Choung, David, and Ross 2023).”
Lim, R. E., Kim, S., Abernathy, A. M., Shin, S., & Avance, R. (2025) · Journal of Marketing Communications · doi:10.1080/13527266.2025.2497259 · Cited by 11
Gökçearslan, Ş., Eşiyok, E., & Küçükergi̇n, K. G. (2025) · Journal of Computational Social Science · doi:10.1007/s42001-025-00387-7 · Cited by 14
“In general, Choung et al. [31] found that users’ trust in AI technology affected their PU.”
Liu, J., Zou, J., Zhang, J., & Teng, J. (2025) · Current Psychology · doi:10.1007/s12144-025-08116-z · Cited by 11
“Choung et al. ( 2023 ) explored the role of AI trust and reported that both human-like trust and functionality trust could play a role in AI-based voice assistants (Choung et al., 2023 ).”
Razzouki, M., Hammou, S. B., & Izenzal, M. (2025) · Cogent Education · doi:10.1080/2331186x.2025.2553829 · Cited by 13
“Therefore, trust in AI is particularly important as it affects the adoption and effective use of AI in HE (Choung et al., 2023).”
Kozan, K., Hur, J., Kim, I., & Barrett, A. J. (2025) · Education Sciences · doi:10.3390/educsci15091133 · Cited by 15
Rethinking Information Quality: How Trust in ChatGPT Shapes Destination Visit Intentions
Tedjakusuma, A. P., Liu, L.-W., Eunike, I. J., & Silalahi, A. D. K. (2025) · Tourism and Hospitality · doi:10.3390/tourhosp6040178 · Cited by 9
Mukhamedkarimova, D. F., & Umurkulova, M. M. (2025) · European Journal of Educational Research · doi:10.12973/eu-jer.14.4.1373 · Cited by 13
Balancing privacy and performance in healthcare: A federated learning framework for sensitive data
Tanveer, F., Iradat, F., Iqbal, W., AlSagri, H. S., Alhakbani, H. A., Ahmad, A., & Khan, F. A. (2025) · Digital Health · doi:10.1177/20552076251381769 · Cited by 9
YAPAY ZEKA KABUL ÖLÇEĞİ KISA FORMU’NUN PSİKOMETRİK ÖZELLİKLERİNİN İNCELENMESİ
Batuk, B., Aktu, Y., & Türk, N. (2025) · Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi · doi:10.35379/cusosbil.1695975 · Cited by 9
Grassini, S., Thorp, S. O., Cipriani, E., Mannari, V., & Sevic, A. (2025) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2025.100834 · Cited by 11
Matching generative AI word-of-mouth with product type: Impact on consumer adoption and trust
Ma, J., Zhang, D., Chen, C., & Du, H. S. (2025) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2025.104615 · Cited by 14
Investigating How Clinicians Form Trust in an AI-Based Mental Health Model: Qualitative Case Study
Kelly, A., Bhardwaj, N., Holmberg, T. T., van de Ven, P. W. J., Melia, R., Williams, J. E., Mathiasen, K., & Nielsen, A. S. (2025) · JMIR Human Factors · doi:10.2196/79658 · Cited by 12
“AI adoption depends on factors related to trust, such as system reliability, robust performance, and perceived competence, which is especially important in medical [4] and psychological decision making.”
Building Trust in Decision‐Support Artificial Intelligence: A Boundary Spanning Perspective
Cui, M., Li, W., & Kamoche, K. N. (2025) · Information Systems Journal · doi:10.1111/isj.70015 · Cited by 13
Li, X., Wang, C., & MaZisong (2026) · Telematics and Informatics Reports · doi:10.1016/j.teler.2026.100293 · Cited by 9
Sapru, A. (2026) · Technology in Society · doi:10.1016/j.techsoc.2026.103251 · Cited by 12
Providing feedback without verification: A hidden risk of trust in generative AI
Hu, P., Cao, C., & Li, D. (2026) · Technology in Society · doi:10.1016/j.techsoc.2026.103370 · Cited by 11
Oprea, S., & Bârã, A. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-02926-5 · Cited by 9
“Recently, the promise of these technologies in enhancing daily life and human capabilities has been acknowledged (Choung et al., 2023; Kopalle et al., 2022; Wang et al., 2023a; Yu and Helwig, 2022).”
Artificial intelligence (AI) in sales organizations: the role of trust
Rangarajan, D., Westphal, J., Habel, J., & Rutherford, B. N. (2026) · Journal of Personal Selling and Sales Management · doi:10.1080/08853134.2026.2614068 · Cited by 9
Rodríguez, C. G. (2024) · Studies in Psychology Estudios de Psicología · doi:10.1177/02109395241241386 · Cited by 7
“It is worth noting at this point that the discussion around AAIA should be conceptually distinguished from other aspects such as (mis)trust in AI (Gillath et al., 2021) or willingness to accept AI in different settings (Choung et al., 2023; Kelly et al., 2023).”
BackgroundInfluentialDorsch, J., & Deroy, O. D. (2025) · Scientific Reports · doi:10.1038/s41598-025-85558-2 · Cited by 10
“In our study, we employed a streamlined TAM based on the constructs identified by Davis 13 and further refined by Venkatesh and Davis 14 , as well as insights from Choung et al. 15 , to assess whether either the trust or reliability framework would predict changes in these variables.”
Yue, J., Wang, K., & Wang, C. (2025) · Journal of Asia Social Science Practice · doi:10.71411/jassp.2025.13 · Cited by 8
Molina, C. M., Pérez-Macías, N., Rey-Tienda, S., & López-Sanz, M. E. (2025) · Sustainable Technology and Entrepreneurship · doi:10.1016/j.stae.2025.100108 · Cited by 8
Fernández, M. A., Astobiza, A. M., & Lozano, R. O. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11306-7 · Cited by 10
“This is because “trust is a fundamental human mechanism that is required to cope with vulnerability, uncertainty, complexity, and ambiguity” (Choung et al. 2022 , 10).”
Gkanatsiou, M. A., Triantari, S., Tzartzas, G., Kotopoulos, T., & Gkanatsios, S. (2025) · Technologies · doi:10.3390/technologies13090396 · Cited by 13
“This supports existing research on rising concerns about data privacy in digital learning environments [103,104].”
Cui, W., & Mohib, M. (2025) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.70284 · Cited by 8
“The AI measurement scale was based on trust, ethical behavior, and functional efficiency, encompassing the idea that content designed by AI is credible and that intelligent technologies are effective [40, 46].”
Lee, Y.-H., & Yuan, C. W. (2025) · Telematics and Informatics · doi:10.1016/j.tele.2025.102339 · Cited by 9
Knowledge workers’ trust and reception of generative AI’s advice in complex tasks
Amrollahi, A., Yang, J., Fazal‐e‐Hasan, S. M., & Badreddine, B. (2026) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2026.103031 · Cited by 8
Güngören, Ö. C., Erdoğan, D. G., & Horzum, M. B. (2026) · BMC Psychology · doi:10.1186/s40359-026-03977-w · Cited by 12
“…a structured classification of studies examining AI acceptance across major dimensions: Recent studies highlight that while the Technology Acceptance Model (TAM) provides a useful framework for examining AI adoption in education [122], experimental findings indicate areas needing refinement [123].”
Amjad, A. I., & Sheikh, B. S. (2026) · European Journal of Education · doi:10.1111/ejed.70457 · Cited by 9
On the algorithmic unconscious: Can we humanize AI with psychoanalytic principles?
Nguyen, D., & Ho, T. M. (2025) · Subjectivity · doi:10.1057/s41286-025-00210-8 · Cited by 8
“Much like Crow 2.0, modern AI systems often encounter human skepticism and prejudice when they fail to conform to our established values, spirit, and lived experiences (Choung et al. 2022).”
InfluentialZhang, Q., & Liang Ma, A. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2366015 · Cited by 9
“On the other hand, from human-like trust perspective, future system/device/ service might should eloquently express its deep concern for users’ well-being and the sincere commitment to addressing their concerns, thereby embodying a profound sense of humanistic care (Choung et al., 2023).”
The Improvements in Significance of AI in Experiential and Medical Tourism
Barua, D. R., & Datta, S. (2024) · Advances in logistics, operations, and management science book series · doi:10.4018/979-8-3693-5578-7.ch012 · Cited by 7
Wachholz, F., Manno, S., Schlachter, D., Gamper, N., & Schnitzer, M. (2025) · BMC Research Notes · doi:10.1186/s13104-025-07172-9 · Cited by 7
“The TAM + Trust model was developed by [8] to include trust and its application to AI.”
Ode, E., Nana, R., Boro, I. O., & Ikyanyon, D. N. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100402 · Cited by 15
Zheng, H., Chen, L., Song, S., Ou, M., & Hansen, P. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2495115 · Cited by 8
“In this study, the willingness to rely on the platform is primarily influenced by users’ perceived usefulness of the information provided by AI technologies, such as chatbots, recommendation systems, or automated content curators on social media (Choung et al., 2023).”
Managing generative artificial intelligence in the daily lives of people with disabilities
Bagnato, G., Moreno, F. R., Giachino, C., & Ali, A. A. (2025) · Management Decision · doi:10.1108/md-10-2024-2471 · Cited by 7
Cao, B., Wen, C., & Zhang, R. (2025) · Journal of Broadcasting & Electronic Media · doi:10.1080/08838151.2025.2519730 · Cited by 8
“Trust plays a vital role in users’ acceptance and intention to use AI chatbots (Choung et al., 2023; Molina & Sundar, 2024).”
Zhang, X., & Hao, X.-L. (2025) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2025.124317 · Cited by 9
MBSE Co‐Pilot: A Research Roadmap
Zhang, W., Cockburn, C., Henshaw, M., Douglas, P., Palmer, P. J., Olivier‐Myall, J., & Ji, S. (2025) · Systems Engineering · doi:10.1002/sys.70011 · Cited by 7
“Trust in AI is an active area of study [26].”
Xu, S., Gong, Z. H., Li, Y., Zhao, Y., & Huang, S. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2560522 · Cited by 7
“Subsequent researchers have applied the interpersonal trust model to human-technology relationships and proposed a two-factor model encompassing cognition-based and affect-based trust (e.g., Choung et al., 2023; Glikson & Woolley, 2020; McAllister, 1995).”
Deng, J., Wu, F., & Qi, J. (2025) · Applied Sciences · doi:10.3390/app151910322 · Cited by 9
Adopting AR wayfinding in heritage tourism: extending the UTAUT in cultural contexts
Wang, F., Peng, X., Wang, Y., Fan, W., Wang, K., & Du, Y. (2025) · npj Heritage Science · doi:10.1038/s40494-025-02069-z · Cited by 10
“Perceivedtrustconstitutesafoundationalelementforthesustaineduse of AR technologies, as trust can stimulate users ’ agency and intentionality 45 .”
Measuring Social Trust in AI: How Institutions Shape the Usage Intention of AI‐Based Technologies
Amir, S., Luk, S. C. Y., Saha, S., Tsyrulneva, I., & Teo, M. T. L. (2025) · Human Behavior and Emerging Technologies · doi:10.1155/hbe2/4084384 · Cited by 10
An empirical study on the use behavior towards AI painting tools based on TAM3 model
Li, Y., Siek, H. L., & Guo, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-24405-w · Cited by 7
Alkaabi, A., Jabeen, F., Dwivedi, A., & Jose, S. (2025) · International Journal of Productivity and Performance Management · doi:10.1108/ijppm-06-2024-0369 · Cited by 11
Evaluating the Use of Generative AI Travel Assistants in Smart Tourism through Student Feedback
Banerjee, S. (2025) · Journal of Computers Mechanical and Management · doi:10.57159/jcmm.4.5.25231 · Cited by 7
“Extensions of this model show that trust and perceived risk strongly influence the intention to adopt AI systems [2, 3].”
Zhao, H., Yuan, B., Shen, X., Zhang, T., Xie, H., & Wu, S. (2026) · Service Industries Journal · doi:10.1080/02642069.2026.2620666 · Cited by 7
Ghasemaghaei, M. (2026) · Journal of Management Information Systems · doi:10.1080/07421222.2025.2602397 · Cited by 10
“Much of the IS and human-computer interaction (HCI) literature has focused on ability-based trust, examining how technical features (e.g., accuracy, explainability, error rates) influence user acceptance [14, 65].”
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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