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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 401–500 of 966
Using AI in My Disputes? Clients' Perception and Acceptance of Using AI in Mediation
Choi, Y. (2025) · Conflict Resolution Quarterly · doi:10.1002/crq.21483 · Cited by 3
Students’ Attitudes Towards Artificial Intelligence and Their Digital Competence
Baum, A., & Trzcińska-Król, M. (2024) · The New Educational Review · doi:10.15804/tner.2024.si.5.09 · Cited by 5
Bryant, M. J., Wilde, S. J., Coles, G., Sorwar, G., & Bui, V. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2531289 · Cited by 3
“…been delineated as a complex multidimensional concept encompassing numerous domains, including personal, business, organizational, governmental, and community relationships (Akrout et al., 2016; Choung et al., 2023; Esmaeilzadeh, 2020; Hahn et al., 2016; Rousseau et al., 1998; Wang & Lin, 2016).”
InfluentialOmori, M., Basnayake, P., Keogh, L. A., Frazer, H. M. L., Kunicki, K., & Lippey, J. (2025) · Health Expectations · doi:10.1111/hex.70415 · Cited by 4
Krogh, M., Nielsen, M. G., Petersen, G. B., Jensen, M. S. A., Jensen, M. B., Vorum, H., Bruun, N. H., & Kristensen, J. K. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1610114 · Cited by 4
Trust in the Machine: How Empathy and Exposure Shape Human Engagement with Social Chatbots
Mrabet, J., Moussa, N., Mansour, R., & Alhendi, E. (2025) · International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) · doi:10.1109/acdsa65407.2025.11165924 · Cited by 3
Li, Z., Lyu, M., Li, L., & Huang, J. (2025) · Information Communication & Society · doi:10.1080/1369118x.2025.2565359 · Cited by 3
“…risk of users, which serve as the constitutive elements of technological literacy, exert a notably significant influence on users’ attitudes towards the utilization of AI and their corresponding behavioral intentions (Choung et al., 2023; Fu et al., 2024; Hidalgo et al., 2021; Xiong et al., 2024).”
The Influence of Framing, Domain and Task Type on Trust in AI
Guidi, S., Reina, L., Currò, F., Cipriani, E., Grassini, S., & Parlangeli, O. (2025) · European Conference on Cognitive Ergonomics · doi:10.1145/3746175.3747196 · Cited by 3
Rukadikar, A., & Khandelwal, K. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00526-z · Cited by 3
“Many researchers have confirmed TAM and AI adoption; trust in technology is a key predictor of technology usage [15, 35, 71].”
Newen, C., Müller, E., & Newen, A. (2025) · Topoi · doi:10.1007/s11245-025-10287-0 · Cited by 6
Zhong, C., & Wang, Y. (2025) · Behavioral Sciences · doi:10.3390/bs15121635 · Cited by 7
Zhang, Y. L., Liu, Z., Liu, T., Xie, J., & Chao, M. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2580549 · Cited by 6
“The findings indicate a positive correlation between trust in AI and the intention to engage with AI, suggesting that when adolescents perceive AI as trustworthy and beneficial, they are more inclined to develop a stronger intention to use it (Choung et al., 2022).”
Apaolaza, V., Paredes, M. R., Hartmann, P., & Eletxigerra, A. (2025) · Food Quality and Preference · doi:10.1016/j.foodqual.2025.105841 · Cited by 3
The influence of cultural-psychological factors on trust and adoption of autonomous vehicles
Zhang, X., & Cui, H. (2026) · Transportation Research Part F Traffic Psychology and Behaviour · doi:10.1016/j.trf.2026.103545 · Cited by 3
Schirmer, M., Fußwinkel, S., Mütze‐Niewöhner, S., Nitsch, V., & von Nitzsch, R. (2026) · Group Decision and Negotiation · doi:10.1007/s10726-026-09966-z · Cited by 4
“Individuals who already have experience using AI in decision-making tend to show more (initial) trust in these systems (cf. Choung et al. 2023; Glikson and Wool-ley 2020).”
Zhang, L., Mao, Y., & Cheang, P. Y. S. (2026) · Information and Computer Security · doi:10.1108/ics-10-2025-0432 · Cited by 4
Wang, Z., Wang, K., Park, S., & Yan, J. (2026) · Current Psychology · doi:10.1007/s12144-025-08966-7 · Cited by 5
“Moreover, when AI systems possess anthropomorphic features, users were more inclined to attribute intentions and motivations to them, thereby fostering a tendency toward trust (Choung et al., 2023; Glikson & Woolley, 2020; Lankton et al., 2015; Maehigashi et al., 2022; Waytz et al., 2014).”
Kim, M., & Lee, S.-M. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2636249 · Cited by 4
Bhatt, K. N., AlAshry, M. S., Kohli, S., Kabia, S. K., & Seabra, C. (2026) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2026.103058 · Cited by 3
Ibrahim, F., Telle, N.-T., Lauckner, M., Karl, J. A., & Daseking, M. (2026) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2026.101003 · Cited by 4
Liang, M. (2026) · Cogent Business & Management · doi:10.1080/23311975.2026.2658868 · Cited by 7
Klein-Avraham, I., & Baram‐Tsabari, A. (2026) · Public Understanding of Science · doi:10.1177/09636625261442388 · Cited by 4
“They are positively associated with trust in AI, and function as a key factor in the adoption of AI technologies (Bergdahl et al., 2023; Choung et al., 2023).”
Vijayananth, M., & Saravanabhavan, N. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1867266 · Cited by 8
“Investors with greater competency are more likely to understand algorithmic insights and use them appropriately, which enhances the impact of trust on ESG investment decisions (Choung et al., 2023).”
InfluentialZi, Y., 梁舒乔, Zhang, M., Liu, X., & Liu, B. (2026) · Sustainability · doi:10.3390/su18136701 · Cited by 4
Cui, K., Li, J., He, H., Wang, C., Zou, B., & Du, Y. (2026) · Journal of Research on Technology in Education · doi:10.1080/15391523.2026.2700717 · Cited by 3
The Impact of AI Trust Violation on Trustworthiness: An Empirical Study Based on AI Chatbots
Wu, L., Xiang, P., Chen, Y., Li, J., Cao, Z., & Qi, J. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-5803-6_27 · Cited by 3
Lin, Y.-H., Liang, C.-C., Chou, Y.-L., Lin, C.-S., Chen, K.-L., Pan, L., Cheng, K.-Y., & Ke, C. (2024) · Diagnostics · doi:10.3390/diagnostics14192126 · Cited by 3
Hu, F., & Lee, K. (2025) · Journal of Asian Scientific Research · doi:10.55493/5003.v15i1.5272 · Cited by 3
“Choung, et al. [29] and their research team's survey revealed that trust plays a positive role in meeting users' needs when using artificial intelligence and positively influences user attitude [30]. demonstrated a positive correlation between trust and the intention to use Alipay.”
Warintarawej, P., Thongsri, N., Seksan, J., & Marchoo, W. (2025) · Cogent Business & Management · doi:10.1080/23311975.2025.2460627 · Cited by 3
“…is the key factor determining the intention to use new technologies, such as the trust in chatgPt (choudhury & shamszare, 2023) and aI technologies (choung et al., 2023). nonetheless, it was interesting that mIce knowledge had no significant effect on the intention to use the mIce city system. the…”
Perceptions of Financial AI Assistants and Intentions Toward AI-Assisted Financial Products
Hung, N. T., & Vu, Q. T. (2025) · International Journal of Asian Business and Information Management · doi:10.4018/ijabim.371629 · Cited by 5
“The theory on the associations among perceptions, trust, and intentions supported this hypothesis (Choung et al., 2023; Shin, 2021).”
AI in the Public Eye: Decoding Perception of Generative AI Through Natural Language Processing
Jeong, H., & Sung, M. (2025) · Asian Communication Research · doi:10.20879/acr.2025.22.006 · Cited by 3
Noh, H., & Lee, B. (2025) · Communication Quarterly · doi:10.1080/01463373.2025.2537027 · Cited by 3
“Trust in AI’s professional competence and reliability mediates the relationship between message framing and intention to use the system (Choung et al., 2023; Kim & Song, 2023).”
Torturing the algorithm: Everyday sadism predicts acceptance of anthropomorphic AI
Li, H., & Cao, Y. (2025) · Personality and Individual Differences · doi:10.1016/j.paid.2025.113448 · Cited by 3
Yang, Q., Han, Y., Cheng, Y., Tian, P., Zou, X.-Q., & Yao, T. (2026) · Computers & industrial engineering · doi:10.1016/j.cie.2025.111697 · Cited by 2
Shrestha, S., & Yi, J. (2026) · Journal of Mathematics Teacher Education · doi:10.1007/s10857-026-09747-0 · Cited by 10
Artificial Intelligence Integration in e-Government: Insights from the Korean Case
Yun, C. H., Teoh, A. P., & Khaw, T. Y. (2024) · IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA) · doi:10.1109/eebda60612.2024.10485972 · Cited by 3
“In this study, SI focuses on Korea-specific factors such as governmental leadership and political aspects [5, 7] and PT considers both human-like elements and functional aspects [9, 10].”
BackgroundMethodInfluentialAI Integration in E-Commerce Wishlists
Behare, N., Behare, S. N., & Waghulkar, S. (2024) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/979-8-3693-4195-7.ch003 · Cited by 3
Quid pro Quo: Information disclosure for AI feedback in Human-AI collaboration
Cvetković, I., Grashoff, I., Jovančević, A., & Bittner, E. A. C. (2025) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2025.100137 · Cited by 4
Questioning AI: How Racial Identity Shapes the Perceptions of Algorithmic BiasPreprint
Kim, S., Lee, J., & Oh, P. (2023) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/afcdn · Cited by 6
Garcia, M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5242823 · Cited by 3
Role of Emotional Experience in AI Voice Assistant User Experience in Voice Shopping
Wang, X., Liu, Y., Wuji, S., & Luo, R. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-57850-2_14 · Cited by 2
Impact of artificial intelligence (AI) on industrial design for architect design purposes
Vasiliu, R. E. (2024) · MATEC Web of Conferences · doi:10.1051/matecconf/202439619001 · Cited by 2
“Issues of fairness and some possible gaps or constraints in the datasets may negatively influence the design of future products or services [8].”
BackgroundDo You Trust AI? Examining AI Trustworthiness Perceptions Among the General Public
Batut, A., Prudhomme, L., van Sambeek, M., & Chen, W. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-60611-3_2 · Cited by 2
Artificial intelligence (AI): Theoretical framework and events industry application in sports venues
Dorey, A. (2024) · Marketing · doi:10.5937/mkng2403163d · Cited by 2
AI Teacherbots: the lure driving migration from offline to online learning
Shi, Y., Li, W., & He, F. (2025) · Current Psychology · doi:10.1007/s12144-025-07888-8 · Cited by 2
“The construct, attitude toward AI technologies, was measured by four items (Choung et al., 2023 ).”
Song, Q., & Bai, Z. (2025) · Applied Sciences · doi:10.3390/app15116033 · Cited by 2
Ethical AI governance, trust and deepfake regulation in the UAE’s media landscape
Heluey, N. (2025) · Journal of Digital Media & Policy · doi:10.1386/jdmp_00173_1 · Cited by 2
Saidkhujaev, U. (2025) · Academia Open · doi:10.21070/acopen.10.2025.11734 · Cited by 2
Examining the Role of AI Information-Sharing on Trust in Human-AI Teams
Schelble, B. G., Filer, G., Flathmann, C., & Madathil, K. C. (2025) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/10711813251367726 · Cited by 2
Evaluation and incident prevention in an enterprise AI assistant
Maharaj, A. V., Arbour, D. T., Lee, D., Bhattacharya, U., Rao, A. B., Zane, A. V., Feller, A., Qian, K., Rahman, S., & Li, Y. (2025) · AI Magazine · doi:10.1002/aaai.70028 · Cited by 2
Rahman, M. M., Alshakhsi, S. A. A., Babiker, A., Yankouskaya, A., Liebherr, M., & Ali, R. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-06164-5_25 · Cited by 3
From algorithms to access: Role of artificial intelligence in revolutionizing financial inclusion
Tian, Y., Zhu, D., Nazar, R., Ali, S., & Mirza, A. (2025) · Technology in Society · doi:10.1016/j.techsoc.2025.103098 · Cited by 2
Trust and AI Adoption for Mobile Learning in Higher Education: Evidence from Tanzanian Universities
Mushi, R. M. (2025) · Advances in Mobile Learning Educational Research · doi:10.25082/amler.2025.02.014 · Cited by 2
Novel Cognitive Trust Model in Travel Chatbots
Dubey, A. D., & Meena, R. (2025) · NMIMS Management Review · doi:10.1177/09711023251379994 · Cited by 2
“Repeated interaction increases cognitive trust and shapes individual behavior and users are more inclined to use chatbots (Li & Zhang, 2023), leading to repeat usage and satisfaction (Shahzad et al., 2024) and purchase intention through technology usage (Mkedder et al., 2024).”
Yoon, J., Zajac, P., Pococke, L., Jicol, A., Clarke, C., O'Neill, E. J., Petrini, K., Lutteroth, C., & Jicol, C. (2025) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2025.3616748 · Cited by 3
“There are numerous potential applications of HAI robots in healthcare and here we focus on the simulation of a typical initial contact with the healthcare system as initial trust can shape future use [11].”
InfluentialСеменовна, Р. А., & Воронин, А. Н. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2580550 · Cited by 2
“We expected trust in GenAI to correlate strongly with technology acceptance (Davis, 1989), consistent with evidence that functionality-driven trust predicts AI adoption (Choung et al., 2023).”
InfluentialTrust in AI: Evidence of Trust-Supporting Mechanisms from 17 Countries
Lockey, S., & Gillespie, N. A. (2025) · Springer eBooks · doi:10.1007/978-3-032-01940-0_10 · Cited by 4
When citizens meet the chatbot: Evidence from a survey vignette experiment in Estonia
Alishani, A., & Homburg, V. M. F. (2025) · Public Policy and Administration · doi:10.1177/09520767251404286 · Cited by 2
Generational differences in AI adoption among fashion curation platform users
Jeong, D., & Kim, Y.-S. (2025) · Fashion and Textiles · doi:10.1186/s40691-025-00448-5 · Cited by 2
“…fit characteristics and core TAM beliefs remain central to consumer evaluations, with usefulness and ease of use shaping downstream satisfaction and adoption intentions (Choung et al., 2022; Ding et al., 2022; Divitiis et al., 2022; Mu et al., 2024; Pillai & Sivathanu, 2020; Vorm & Combs, 2022).”
Synergic Thinking: A New Era of Human-AI Decision-Making
Krishna, M. S. R., Singh, T. M., Karuna, D., Divya, R., & Shuaib, M. (2025) · Springer eBooks · doi:10.1007/978-3-031-89424-4_10 · Cited by 2
Trustability and trustworthiness: conceptual foundations and the case of AI
Jannel, R., & Tallant, J. (2025) · AI and Ethics · doi:10.1007/s43681-025-00839-w · Cited by 2
“In a number of places, Ryan [ 27 : 1, 2, 4, 5, 6, 13, 14] notes that AI does not have the ‘capacity to be trusted’.”
Lipowski, M., & Lipowska, I. (2025) · Central European Management Journal · doi:10.1108/cemj-07-2024-0212 · Cited by 2
Joo, K. (2025) · Current Issues in Tourism · doi:10.1080/13683500.2025.2608924 · Cited by 3
Tariq, M. Z., Song, J., & Xie, Y. (2025) · The International Review of Retail Distribution and Consumer Research · doi:10.1080/09593969.2025.2609857 · Cited by 2
Guidi, S., Reina, L., Currò, F., Cipriani, E., Grassini, S., & Parlangeli, O. (2026) · Behaviour and Information Technology · doi:10.1080/0144929x.2025.2606832 · Cited by 2
Validity and Trustworthiness of AI-Generated Information in an Online Nutrition Course
Briggs, M., Archibald, A., Connors, P., & Heap, T. (2026) · Journal of Hospitality & Tourism Education · doi:10.1080/10963758.2026.2619164 · Cited by 2
Zhao, J. (2026) · Current Psychology · doi:10.1007/s12144-025-08631-z · Cited by 2
“Trust acts as a critical boundary condition, where perceived reliability of technology directly moderates the effect of PU (Choung et al., 2022).”
Aghajani, M. S., Hatami, J., & Toofaninejad, E. (2026) · Teaching and Learning in Medicine · doi:10.1080/10401334.2025.2608190 · Cited by 2
Schneider, E. J., Kyriakopoulos, V., Seo, Y., Hong, N., Lee, M. J., & Lim, J. S. (2026) · Corporate Reputation Review · doi:10.1057/s41299-026-00254-9 · Cited by 3
Engagement in LLM chatbot-supported learning: The pivotal roles of GenAI competency and emotion
Huang, X., & Zhang, S. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100559 · Cited by 2
Yu, A., Ma, Y., Xu, G., Zhou, W., Gao, A., & Trainin, G. (2026) · Acta Psychologica · doi:10.1016/j.actpsy.2026.106451 · Cited by 2
Chen, B., & Ahmed, S. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2623213 · Cited by 2
“Three items adapted from an existing study (Choung et al., 2023) were used to measure intention to use from “1” ¼ strongly disagree to “5” ¼ strongly agree.”
Wang, X., Guo, L., Xu, L., Guan, L., Ge, P., & Gao, T. (2026) · Ergonomics · doi:10.1080/00140139.2026.2621032 · Cited by 2
Yoon, Y., & Lee, O.-K. D. (2026) · Information Systems Frontiers · doi:10.1007/s10796-026-10702-w · Cited by 2
“Recent studies have started to investigate the interaction process between humans and AIVAs by viewing them as social partners (Choung et al., 2023; Sharpe & Ciriello, 2024; Song et al., 2022; Xu & Li, 2024 ).”
Bashir, M., Rahim, N., El–Sappagh, S., El-Serafy, O. A., & Abuhmed, T. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114291 · Cited by 2
Do emotions matter in AI? The mediating role of emotional response between perceived risk and trust
Babiker, A., Almourad, M. B., Alshakhsi, S. A. A., Liebherr, M., & Ali, R. (2026) · AI & Society · doi:10.1007/s00146-026-02929-7 · Cited by 3
Balancing utopian and dystopian paradigms: trust, values, and AI literacy in generative AI adoption
Kim, D., Bak, S., Kim, S., Lee, B. H., Lee, Y., & Park, S. (2026) · Asian Journal of Communication · doi:10.1080/01292986.2026.2639672 · Cited by 2
“Choung et al. (2023) found that trust in AI positively influences both PU and PEOU, which in turn indirectly enhance BI.”
InfluentialLiu, J., Sun, Y., & Xiao, K. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1781974 · Cited by 5
“…shapes users’ evaluations of the accuracy and stability of system outputs, but also serves as an important psychological link between perceived quality and behavioral intention, playing a central role in technology acceptance processes (Afroogh et al., 2024; Choung et al., 2023; Hu et al., 2025).”
Nagvanshi, S., Akhtar, M. J., Kumar, V., & Singh, T. (2025) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83662-314-420251020 · Cited by 3
Factors influencing user adoption of AI painting tools based on the AIDUA model
Li, Y., Siek, H. L., & Guo, J. (2026) · Scientific Reports · doi:10.1038/s41598-026-48130-0 · Cited by 2
Zhang, T., Qiu, J., & Xu, Z. (2026) · Acta Psychologica · doi:10.1016/j.actpsy.2026.106907 · Cited by 3
Saoud, L. S., & Qureshi, O. A. (2026) · Strategic Business Research · doi:10.1016/j.sbr.2026.100157 · Cited by 2
Exploring the role of trust and satisfaction in shaping AI-driven tourist behaviour
Tien, M. B., Nguyen, V. K., & Natoli, R. (2026) · Journal of Hospitality and Tourism Insights · doi:10.1108/jhti-04-2025-0507 · Cited by 2
The big six model of AI trust: a unifying framework and 20-item scale for knowledge creation
Dickens, J. M., Uvet, H., Bauman, A., & Kwon, H. Y. (2026) · Journal of Knowledge Management · doi:10.1108/jkm-02-2025-0222 · Cited by 2
Pum, M., Heng, K., & Sok, S. (2026) · Open Praxis · doi:10.55982/openpraxis.18.3.1047 · Cited by 2
Shishakly, R., Khattak, M. N., & Abukhait, R. (2026) · Current Psychology · doi:10.1007/s12144-026-09739-6 · Cited by 2
“Similarly, Choung et al. ( 2022) emphasize the pivotal role of trust in shaping positive attitudes toward technology use.”
Factors Affecting Continuance Intention of M-Government
Alshammari, T. (2023) · International Journal of E-Business Research · doi:10.4018/ijebr.326550 · Cited by 2
“Choung et al. (2022) defined T as believing in the trusted party’s competence, benevolence, and integrity.”
BackgroundArtificial Intelligence and Robotic Adoption by Customers in the Global Hotel Sector
Ndhlovu, E., & Thwala, K. C. (2024) · Springer eBooks · doi:10.1007/978-3-031-63077-4_2 · Cited by 2
Vertrauen als Motor des KI-Wertschöpfungszyklus
Glinz, D. (2024) · Springer eBooks · doi:10.1007/978-3-658-43816-6_4 · Cited by 2
Jafari, H., Naghshineh, N., Alvarado, O., Keshavarz, H., & Lund, B. D. (2024) · InfoScience Trends · doi:10.61186/ist.202401.01.17 · Cited by 4
Feng, L., & Yu, X. D. (2024) · Thermal Science and Engineering Progress · doi:10.1016/j.tsep.2024.103085 · Cited by 2
RetractedSTRIFE: A Socio-Technical Framework for Threat Modeling of Artificial Intelligence Systems
Parthasarathy, R., Rangarajan, A., Ghatak, S., & Bingi, P. (2025) · International Journal of Intelligent Information Technologies · doi:10.4018/ijiit.370967 · Cited by 2
How does the adoption of ChatGPT influence soccer betting? A reasoned action perspective
Dinç, H., Phantumabamrung, P., Çevik, H., Gürbüz, B., Yeşiltepe, B. S., Argan, M., & Zhang, J. J. (2025) · International Journal of Sports Marketing and Sponsorship · doi:10.1108/ijsms-09-2024-0254 · Cited by 2
The digital solution: how artificial intelligence is revolutionizing energy poverty alleviation
Tianyi, Z., Zhou, Q., Ali, S., Nazar, R., & Anser, M. K. (2025) · Environment Development and Sustainability · doi:10.1007/s10668-025-06552-2 · Cited by 2
LLM anthropomorphization: balancing ethics and business value
Reusens, M., & Baesens, B. (2025) · Journal of Business Analytics · doi:10.1080/2573234x.2025.2551951 · Cited by 2
“Users tend to trust LLMs more when they are anthropomorphized, regardless of the accuracy of their outputs (Abercrombie et al., 2023; Chiesurin et al., 2023; Hyesun Choung et al., 2023).”
Artificial intelligence and behavioural finance biases: improving Lebanese investors decision-making
Chammaa, C., & Haddad, L. (2025) · Journal of Decision System · doi:10.1080/12460125.2025.2537076 · Cited by 3
“Choung et al. (2022) introduced the concept of trust in AI, which affects these two elements.”
Akoğlu, H. E. (2025) · Avrasya Spor Bilimleri ve Eğitim Dergisi · doi:10.47778/ejsse.1735558 · Cited by 2
Welcome to the Brave New World: Lay Definitions of AI at Work and in Daily Life
Li, W., Lu, S., Xu, S., & Zheng, X. (2025) · Social Science Computer Review · doi:10.1177/08944393251382233 · Cited by 3
“For example, Choung et al. (2023) found that trust in AI increased individuals ’ perceptions about the usefulness of AI and thereby more positive attitudes toward AI and Yang et al. (2023) showed that greater trust predicted lower risks, higher bene fi ts, and more favorable views.”
A Human-Machine Trust Model and Trust Calibration Method for Decision-Aid Systems
Zhu, X., Liang, L., Lu, J., Zhao, Y., & Kang, Y. (2025) · Chinese Control Conference (CCC) · doi:10.23919/ccc64809.2025.11178628 · Cited by 2
“When using a decision-aid system, the level of trust that humans have in it is of great importance and will affect the overall performance of the system [5][6].”
Influence of Ethical AI Practices on Gig Workers' Trust and Psychological Well-being
Shanthi, K., & Suganya, V. (2025) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83608-298-920251005 · Cited by 2
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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