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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 301–400 of 966
How physicians embrace AI: insights from technology acceptance and trust theories
ÇİÇİN, F. N., & Gürkan, G. Ç. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1722087 · Cited by 9
“These results are consistent with previous research (7, 9, 12, 13, 26, 39, 41–45).”
Oguntuase, O. J., Adu, O. B., & Obayori, O. S. (2023) · Frontiers in Sustainability · doi:10.3389/frsus.2023.1148001 · Cited by 7
VisionCam: A Comprehensive XAI Toolkit for Interpreting Image-Based Deep Learning Models
Abdullah, W., Tolba, A., Elmasry, A., & Mostafa, N. N. (2024) · Sustainable Machine Intelligence Journal · doi:10.61356/smij.2024.8290 · Cited by 7
“…addresses various aspects of interpretability, and addresses the scientific problem of bridging the gap between AI's predictive capabilities and its interpretability with human understanding, thus facilitating broader acceptance and deployment of AI technologies across diverse domains [8].”
BackgroundCEO AI orientation, human resources and green innovation: an attention-based view
Wang, X., & Wu, W. (2024) · Kybernetes · doi:10.1108/k-04-2024-0964 · Cited by 7
Lim, J. S., & Hong, N. (2025) · Health & New Media Research · doi:10.22720/hnmr.2025.00115 · Cited by 6
Gong, Y., Tang, X., & Peng, H. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1437915 · Cited by 8
“Researchers have identified various factors that influence trust in AI (see Kaya et al., 2024; Jackson et al., 2023; Bergner et al., 2023; Huang and Wang, 2023; Ismatullaev and Kim, 2024; Choung et al., 2023; Clegg et al., 2024; Usman et al., 2024).”
Dutta, P., Josan, P. K., Wong, R. K. W., Dunbar, B. J., Diaz‐Artiles, A., & Selva, D. (2025) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/15553434251338433 · Cited by 8
“One factor that is known to in fl uence the use of AI agents and their acceptance is trust (Choung et al., 2023; Kelly et al., 2023).”
Cleared for Takeoff - Artificial Intelligence Acceptance in Air Traffic Control
Schon, J., Bruder, C., Liebherr, M., & Theis, S. (2025) · Annual Symposium on Human-Computer Interaction for Work (CHIWORK) · doi:10.1145/3729176.3729185 · Cited by 7
“TT is central to TA [48, 90], influencing PU [13] and IU[39], but it also counts as a predictor for PU in AI-based voice systems [52].”
Li, B., Zhang, Z., Lowell, V. L., Wang, C., & Bonk, C. J. (2025) · System · doi:10.1016/j.system.2025.103793 · Cited by 7
A Validation of the Human-Generative Artificial Intelligence Trust Scale
Wang, P., Yin, K., Tian, M., Zheng, Y., Wu, H., Zhou, C. Q., Zhang, M., Ma, J., & Yuan, X. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2542881 · Cited by 7
“Researchers in the field of HCI generally consider trust in AI a positive factor (Choung et al., 2023), as it is believed that trust in AI can increase the willingness to collaborate with AI, facilitate cooperative behavior, and enable the more effective utilization of AI capabilities (Shin, 2021).”
Xu, X., Liu, J., Zheng, R., Lei, V. N.-L., & An, Q. (2025) · Acta Psychologica · doi:10.1016/j.actpsy.2025.105491 · Cited by 13
AI term aversion in career decision-making: contextual reactions to algorithmic labels
Chacon, A., & Larraín, M. (2025) · Journal of Decision System · doi:10.1080/12460125.2025.2599916 · Cited by 6
Li, Y., Liao, T.-H., Tsai, C.-H., & Wu, T. (2025) · Behavioral Sciences · doi:10.3390/bs16010013 · Cited by 6
“It reflects employees’ subjective judgments regarding whether AI can reliably execute tasks and support their work goals (Choung et al., 2023).”
Zhou, Y., Wang, Y., & Su, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-28532-2 · Cited by 6
“20,48,85 However, unlike earlier research, 48,94 this study indicates that PTR does not significantly impact IU.”
Shannaq, B., Almaqbali, S., Ali, O., & Marhoubi, A. H. (2026) · Studies in systems, decision and control · doi:10.1007/978-3-032-07220-7_36 · Cited by 6
Li, J., & Yuan, Q. (2026) · Journal of Knowledge Management · doi:10.1108/jkm-02-2025-0231 · Cited by 7
Public trust in AI: A dynamic social media view
Thaiduong, N. (2026) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2026.102457 · Cited by 8
Cheng, J., Khan, M., Dash, S., Costanzo, B., & Mueller, J. (2026) · Journal of Knowledge Management · doi:10.1108/jkm-05-2025-0707 · Cited by 6
Who uses general-purpose AI? A typology of ChatGPT early adopters
Gerling, C., Teubner, T., & Braesemann, F. (2026) · Electronic Markets · doi:10.1007/s12525-025-00853-0 · Cited by 11
“Studies have shown that functionality is a necessary but not sufficient condition for user acceptance of AI tools (Choung et al., 2022; Hamm et al., 2023; Wanner et al., 2022).”
When employees meet gen AI: The double-edged effects of AI attitude on creativity
Ma, L., Ge, L., Li, Y., & Wang, Y. (2026) · Technovation · doi:10.1016/j.technovation.2026.103495 · Cited by 6
Ahmed, S. Y., & Pardaev, J. (2025) · Decision Making Applications in Management and Engineering · doi:10.31181/dmame8220251484 · Cited by 6
Loefflad, C., & Großklags, J. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3658986 · Cited by 9
“While both trust in ADM systems [18, 20, 47], as well as perceived trustworthiness of ADM systems [64] has found considerable attention in the human-computer interaction literature, only few studies have centered on the perceived legitimacy of ADM systems [52, 84].”
BackgroundÉtica e Responsabilidade na Era da Inteligência Artificial: Um Survey com Estudantes de Computação
da Silva, M., Seixas, E. F. R., Ferro, M., Viterbo, J., Seixas, F. L., & Salgado, L. C. D. C. (2024) · Anais do Workshop sobre Educação em Computação (WEI) · doi:10.5753/wei.2024.3148 · Cited by 6
Gharbaoui, O. E., Boukhari, H. E., & Salmi, A. (2024) · TEM Journal · doi:10.18421/tem133-11 · Cited by 10
Yapay zekâya yönelik tutumun, kolaylığın, yenilikçiliğin ve güvenin satın alma niyeti üzerine etkisi
Korkmaz, İ. (2024) · Afyon Kocatepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi · doi:10.33707/akuiibfd.1522892 · Cited by 6
Student Adoption of AI Writing Tools
Malmous, M., & Zaidoune, S. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-7220-3.ch009 · Cited by 7
Beyond reality: examining consumer intention to embrace metaverse within hospitality
Azhar, M., Khan, S., Kirmani, M. D., Mustafa, F., & Khan, F. M. (2025) · Journal of Hospitality and Tourism Insights · doi:10.1108/jhti-10-2024-1089 · Cited by 6
Suspicious of AI ? Perceived autonomy and interdependence predict AI ‐related conspiracy beliefs
Zhao, Q., van Prooijen, J., Jiang, X., & Spadaro, G. (2025) · British Journal of Social Psychology · doi:10.1111/bjso.12883 · Cited by 6
“From both a theoretical and practical perspective, it is useful to understand what exact features the public perceives as threatening, how these perceptions may be attenuated, and what may drive support for AI (Choung et al., 2023; Kelly et al., 2023 ).”
Lin, H., Chen, Z., Wei, W., & Lu, H. (2025) · Lecture notes in educational technology · doi:10.1007/978-981-96-4952-5_7 · Cited by 6
Enhancing college students’ AI literacy through human-AI co-creation: a quantitative study
Wen, H., Lin, X., Liu, R., & Su, C. (2025) · Interactive Learning Environments · doi:10.1080/10494820.2025.2495733 · Cited by 7
Levine, M. (2025) · British Journal of Social Psychology · doi:10.1111/bjso.12896 · Cited by 5
“Researchers also explore barriers around perceptions of trust in technology (Choung et al., 2023), the threat technologies might play to traditional ways of operating (Carpenter et al., 2019) and the relative risks people imagine as a consequence of engaging with new technologies (Horst et al.,…”
Theresiawati, Hidayanto, A. N., Seta, H. B., Widyatmoko, A. M., Muhammad, M. A., Said, M. F. A., & Adifai, S. R. (2025) · Interactive Learning Environments · doi:10.1080/10494820.2025.2546630 · Cited by 8
“According to studies, when users perceive GenAI systems as trustworthy, they are more likely to adopt and use them consistently (Choung et al., 2022).”
Bashir, B., & MUHAMMAD, T. (2023) · Innovare Journal of Social Sciences · doi:10.22159/ijss.2023.v11i2.47349 · Cited by 10
An Empirical Study on the Muslims' Intention to Use Digital Waqf Innovation
(2023) · Journal of System and Management Sciences · doi:10.33168/jsms.2023.0608 · Cited by 6
Savellon, M., Baybayan, S., & Asiri, M. S. (2024) · Journal of Education and Academic Settings · doi:10.62596/t3wgsm55 · Cited by 7
“Additional data suggests that a heightened level of trust has a beneficial impact on the intention to use AI technology (Ayoub et al., 2024; Choung et al., 2022).”
BackgroundThe Effect of AI-generated Advice on Decision-Making in Personnel SelectionPreprint
Cecil, J., Lermer, E., Hudecek, M. F. C., Sauer, J., & Gaube, S. (2023) · doi:10.31219/osf.io/349xe · Cited by 6
Raees, M., Khan, V.-J., Lykourentzou, I., & Papangelis, K. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791467 · Cited by 5
“The concept of trust has evolved over the years, from trust in automation (TiA) [65, 87] to trust in modern-day AI assistance [34, 74, 89, 154].”
Dabran-Zivan, S., Klein-Avraham, I., & Baram-Tsabari, A. (2026) · PLOS One · doi:10.1371/journal.pone.0345300 · Cited by 6
“This lack of transparency underscores the centrality of trust when interacting with AI-driven decision-making processes [35].”
Breideband, T., Bush, J. B., Reitman, J. G., Rose, S., Weatherley, J., Penuel, W. R., & D’Mello, S. K. (2025) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-025-00509-2 · Cited by 5
Tam, L., Kim, S., & Gong, Y. (2025) · Media and Communication · doi:10.17645/mac.9534 · Cited by 5
Customer Immersiveness and Customer Retention
Srivastava, A., Singh, B. M., Singh, A., & Singh, A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-0340-6.ch023 · Cited by 5
Al‐Romeedy, B. S., Khairy, H. A., Jasim, T. A., & Baquero, A. (2025) · Advances in hospitality, tourism and the services industry (AHTSI) book series · doi:10.4018/979-8-3693-7447-4.ch010 · Cited by 5
Intuitive judgements towards artificial intelligence verdicts of moral transgressions
Liu, Y., & Moore, A. (2025) · British Journal of Social Psychology · doi:10.1111/bjso.12908 · Cited by 8
“…factors that enhance or undermine trust in human–machine interaction, devising measurement of trust in machines (Malle & Ullman, 2021; Ullman & Malle, 2018, 2019), comparing trust in human/AI (Liang & Newell, 2022) and exploring relationships between trust and other variables (Choung et al., 2023).”
How privacy calculus builds user engagement through trust in AI medical consultation
Luo, Y., Li, S., Ye, Q., & Zheng, J. (2025) · Electronic Markets · doi:10.1007/s12525-025-00795-7 · Cited by 7
“Some studies found that different dimensions of trust exert varying degrees of influence on user engagement, (Choung et al., 2023; Dennis et al., 2020).”
InfluentialExploring User Attitudes and Trust Toward ChatGPT as a Social Actor: A UTAUT-Based Analysis
Noh, H., Rim, H. B., & Lee, B. (2025) · SAGE Open · doi:10.1177/21582440251345896 · Cited by 6
“Embracing AI as a social actor hinges on establishing and nurturing trust (Choung et al., 2022).”
InfluentialAhmed, M. U., Barua, S., Begum, S., Jmoona, W., D’Cruze, R. S., Veyrié, A., Kebir, S., & Hurter, C. (2024) · Artificial Intelligence and Cloud Computing Conference (AICCC) · doi:10.1145/3719384.3719389 · Cited by 5
“A trust scale, e.g., the Mayer, Davis, and Schoorman Trust Scale score will measure the human-to-machine trust besides the traditional surveys and questionnaire-based approach [19] [20].”
When Generative AI Meets Abuse: What Are You Anxious About?
Song, Y., & Tan, H. (2025) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer20030215 · Cited by 5
Exploring Artificial Intelligence Adoption for E-Government: A Systematic Literature Review
Alorini, F. T., & Alkhalifah, A. (2025) · IEEE Access · doi:10.1109/access.2025.3618778 · Cited by 7
“In addition, perceived usefulness can increase users’ willingness to engage with AI systems [72].”
InfluentialMapping the Trust Terrain: LLMs in Software Engineering—Insights and Perspectives
Khati, D., Liu, Y., Palacio, D. N., Zhang, Y., & Poshyvanyk, D. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3771282 · Cited by 17
Mercuri, F., Quaglieri, L., Ramírez‐Hurtado, J. M., & Quattrociocchi, B. (2025) · European Journal of Innovation Management · doi:10.1108/ejim-03-2025-0359 · Cited by 9
Thi Thuy An, N. G. O., Chau, H. K. L., Tran, N. P. N., & Bui, C. T. (2025) · Acta Psychologica · doi:10.1016/j.actpsy.2025.106095 · Cited by 8
Banking on voice: AI attributes, technology perceptions, and trust in banking voicebot acceptance
Liew, T. W., Lim, C. T. M., Khan, M. T. I., & Tan, S.-M. (2025) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2025.100812 · Cited by 9
Generative AI and digital literacy: Unravelling user intentions through PLS-SEM and machine learning
Bahçekapılı, E., & Boztaş, G. D. (2025) · Journal of Information Science · doi:10.1177/01655515251396900 · Cited by 6
Attitudes Toward Artificial Intelligence in Higher Education: A Mixed-Methods Study
Ayçiçek, B. (2025) · Üniversite Araştırmaları Dergisi · doi:10.32329/uad.1770973 · Cited by 5
Goyanes, M., Utz, S., & de Zúñiga, H. G. (2026) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2026.100279 · Cited by 8
Kandala, R., Moharir, A. K., & Nayak, D. A. (2025) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw69685.2025.00156 · Cited by 5
“Clinicians cannot take professional responsibility for an AI’s diagnoses and recommendations without knowing the reasoning behind the output, and patients will not trust diagnoses from an algorithm they cannot understand [6]–[8].”
Employees’ Trust in AI and Innovative Behavior: A JD-R Model Perspective
Liu, C., Luo, J., Liao, Q., & Lu, J. (2026) · Behavioral Sciences · doi:10.3390/bs16030425 · Cited by 5
“In relevant studies on trust in AI, this construct is often identified as a critical predictor of employees’ AI usage (Choung et al., 2023) or effective collaboration with AI (Bedué & Fritzsche, 2022).”
Jain, P., Shajit, D., Maini, A., Shahin, Y., & Panda, A. (2026) · Cognitive Processing · doi:10.1007/s10339-026-01375-z · Cited by 10
User perspectives on AI explainability in aerospace manufacturing: a Card-Sorting study
Morandini, S., Fraboni, F., Hall, M., Quintana-Amate, S., & Pietrantoni, L. (2025) · Frontiers in Organizational Psychology · doi:10.3389/forgp.2025.1538438 · Cited by 5
Unveiling the influence of dialogic communication on AI-technology trust in e-commerce
Zhang, H., Khan, A. N., & Khan, N. A. (2025) · Aslib Journal of Information Management · doi:10.1108/ajim-05-2024-0420 · Cited by 6
Hughes, N. G. J., Jia, Y., Sujan, M. A., Lawton, T., Habli, I., & McDermid, J. A. (2024) · Applied Ergonomics · doi:10.1016/j.apergo.2024.104275 · Cited by 5
A Systematic Literature Review on Emotion Recognition System In Malaysia
Yamin, M. N. M., Aziz, K. A., Siang, T. G., & Aziz, N. A. A. (2023) · Proceedings of International Conference on Artificial Life and Robotics · doi:10.5954/icarob.2023.gs3-4 · Cited by 5
“With the addition of ERS as an emerging technology, it can enhance technology further by technology to understand human emotions and behavioural responses [9].”
BackgroundMethodInfluentialFrom Trust to Appropriate Reliance: Measurement Constructs in Human-AI Decision-Making
Raees, M., & Papangelis, K. (2026) · arXiv · doi:10.48550/arxiv.2604.23896 · Cited by 5
Oprea, S., & Bârã, A. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e40268 · Cited by 5
Law Enforcement and Legal Professionals’ Trust in Algorithms
Kennedy, R., Tiede, L. B., Austin, A., & Ismael, K. (2025) · Journal of law & empirical analysis. · doi:10.1177/2755323x251325594 · Cited by 6
“Previous studies have reached divergent conclusions about trust in algorithms among the general public as well as speci fi c groups (Choung et al., 2022; Mousavi Baigi et al., 2023).”
Enhancing Explainability, Robustness, and Autonomy: A Comprehensive Approach in Trustworthy AI
Ahmed, M. U., Begum, S., Barua, S., Masud, A. N., Flumeri, G. D., & Navarin, N. (2025) · IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx) · doi:10.1109/citrex64975.2025.10974944 · Cited by 4
“[49,50] will measure the human-to-machine trust besides the traditional surveys and questionnaire-based approach.”
Huang, X., Cui, Y., Jia, D., Ma, X., Wang, Z., & Cui, R. (2025) · Systems · doi:10.3390/systems13090829 · Cited by 6
“Unlike traditional computing technologies that rely on real-time user input, AI technologies possess a certain degree of autonomy [48], often bypassing significant portions of human decision-making processes.”
Liu, J., & Sun, Y. (2025) · IEEE Access · doi:10.1109/access.2025.3613754 · Cited by 4
“…perceived trust not only shapes users’ perceptions of system reliability and integrity, but also serves as a key mediating variable linking perceived quality with behavioral intentions, thereby directly influencing users’ acceptance and willingness to adopt the technology [63], [64], [65].”
Balancing Minds and Data: The Privacy Dilemma of LLMs and Anthropomorphism in LLMs
Meier, R. L. (2025) · Journal of Social Computing · doi:10.23919/jsc.2025.0014 · Cited by 7
Schrills, T., Franke, T., Hoesterey, S., & Roesler, E. (2025) · Behaviour and Information Technology · doi:10.1080/0144929x.2025.2553153 · Cited by 5
“The relevance of trust as a construct for HAII (Bolton 2022; Choung, David, and Ross 2023) and the fact that trust as an attitude can only be assessed through selfreported assessments is currently discussed (Brzowski and Nathan-Roberts 2019).”
Malin, C. D., Fleiß, J., Ortlieb, R., & Thalmann, S. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1671997 · Cited by 12
“Hence, an explanatory component is an essential design feature of (X) AI systems that shapes fairness perceptions (Hyesun et al., 2023).”
What Drives Hospitality Employees’ Trust in Service Robots?
Park, M., Andress, D., Chang, J. H., Lee, A., & Lee, C.-H. (2025) · Tourism and Hospitality · doi:10.3390/tourhosp6050231 · Cited by 5
“2025 , 6 , 231 2 of 24 2023), mobile banking (Arangarajan et al., 2024), military decision making (Schaefer et al., 2016), and AI-integrated systems (Choung et al., 2023; Davis, 1989).”
Pan, J., Shen, Y., He, C., & Shi, J. (2025) · Accident Analysis & Prevention · doi:10.1016/j.aap.2025.108365 · Cited by 4
Shin, I., Choung, H., & Choi, M. (2025) · Computers in Human Behavior · doi:10.1016/j.chb.2025.108897 · Cited by 5
Self-citationThe impact of DeepSeek’s perceived interactivity on medical students’ self-directed learning ability
Ju, Y., Li, J., Zhang, X., Wu, M., Pang, X., Li, Z., Wang, J., Li, J., Zhang, Y., & Dai, X. (2026) · Scientific Reports · doi:10.1038/s41598-025-33780-3 · Cited by 4
“Particularly in high-risk or specialized application scenarios (such as medical education), trust becomes a critical psychological boundary condition for technology adoption and sustained use 65,66 .”
Amin, M. R. M., Wider, W., Sivakumaran, V. M., Yang, L., & Asbi, A. (2026) · Journal of Islamic marketing · doi:10.1108/jima-02-2025-0103 · Cited by 10
Park, E. K. (2026) · Behaviour and Information Technology · doi:10.1080/0144929x.2026.2619983 · Cited by 7
Paik, J. E., Choung, H., & Yang, Q. (2026) · Health Communication · doi:10.1080/10410236.2026.2620497 · Cited by 4
“Trust in AI may involve elements such as benevolence and integrity, extending beyond accuracy and reliability alone (Choung et al., 2023).”
Self-citationResponsible generative AI in tourism: Conceptualization, measurement, and validation
Shen, P., Chen, Y., & Xiong, S. (2026) · Journal of Hospitality and Tourism Management · doi:10.1016/j.jhtm.2026.101441 · Cited by 7
Wang, H., & Tian, J. (2026) · Education and Information Technologies · doi:10.1007/s10639-026-13983-5 · Cited by 4
“GenAI has become an important tool for university students for tasks such as information retrieval, writing assistance, and study planning (Estrada-Araoz et al., 2025), and perceptions of GenAI attributes underpin their trust decisions (Choung et al., 2023).”
Nguyen, P., Watson, G. P., Barnes, D., Agrawal, S., Schuster, A. M., & Cotten, S. R. (2026) · Journal of Management & Organization · doi:10.1017/jmo.2026.10090 · Cited by 10
“Embedding explainability features and offering feedback loops can reduce concerns associated with unclear, black-box systems (Choung et al., 2023).”
InfluentialXie, Y., Song, Z., Wang, E., Li, P. D., & Wang, J. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1728867 · Cited by 7
“Consistent with our observations, Choung et al. indicated that technology trust fosters healthcare professionals’ intention to use AI systems by mitigating cognitive uncertainty (33).”
Cheng, Y., & Liu, H. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1822948 · Cited by 4
“It also explores the potential dual role of CA as both a driver and a constraint (Choung et al., 2023; Jiao and Cao, 2024).”
Generative AI for Elderly Well-being through the Computer as Social Actor Paradigm
Pramod, D., & Patil, K. P. (2024) · International Conference on Contemporary Computing · doi:10.1145/3675888.3676033 · Cited by 4
Engineering Safety and Ethical Challenges in 2045 Artificial Intelligence Singularity
Suo, J., Li, M., Guo, J., & Sun, Y. (2024) · Sustainability · doi:10.3390/su162310337 · Cited by 8
Technology Acceptance Model and Fintech: An Evidence from Italian Banking Industry
Raza, A., & Türsoy, T. (2024) · Revista Mexicana de Economía y Finanzas · doi:10.21919/remef.v20i1.993 · Cited by 11
“FinTech apps handle multidimensional data, making trust increasingly important (Choung et al., 2022).”
The Role of Trust in AI-Augmented Business Process Management Systems
Acitelli, G., Agostinelli, S., Casciani, A., & Marrella, A. (2025) · Lecture notes in business information processing · doi:10.1007/978-3-031-78666-2_1 · Cited by 4
Lee, S., Suh, S., & Chun, J.-S. (2025) · Asian Communication Research · doi:10.20879/acr.2025.22.002 · Cited by 5
“In this context, trust can be categorized as either human or mechanical (Choung et al., 2022).”
InfluentialPrivacy-Friendly and Trustworthy Technology for Society
Fedosov, A., Tamò‐Larrieux, A., Lutz, C., Fosch‐Villaronga, E., & Čartolovni, A. (2025) · Digital Society · doi:10.1007/s44206-025-00167-w · Cited by 4
“Unsurprisingly, both research on the role of trust in technology adoption (Bahmanziari et al., 2003; Choung et al., 2023) and research on privacy implications of such technologies (Davis et al., 1989; Fosch-Villaronga et al., 2020; Lutz et al., 2019, 2024 ) have significantly developed in recent…”
Ejaz, F., Sultana, S., Rashid, S., Aziz, M. A., Ejaz, S., & Hossain, M. B. (2025) · Innovations in Education and Teaching International · doi:10.1080/14703297.2025.2517295 · Cited by 6
“Choung et al. (2023) found that performance expectancy is a significant factor in developing initial trust related to technology.”
Wei, F., & Hwang, B. (2025) · Journal of Management in Engineering · doi:10.1061/jmenea.meeng-6921 · Cited by 5
Vranken, S., Kaňková, J., & Matthes, J. (2025) · Telematics and Informatics · doi:10.1016/j.tele.2025.102327 · Cited by 4
Examining demographic and psychographic factors in AI perception
Molnár, L., & Horváth, K. (2026) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2025.102368 · Cited by 5
Artificial Intelligence: Challenges and Opportunities for the Accounting Profession in Nigeria
Nkwede, M., & Aniuga, C. (2023) · African Journal of Politics and Administrative Studies · doi:10.4314/ajpas.v16i1.1 · Cited by 4
Artificial Intelligence and Mobile Computing: Role of AI in Ergonomics
Somaraju, P., Kulkarni, S. S., Duffy, V. G., & Kanade, S. G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-61066-0_16 · Cited by 4
Hägglund, S., Andtfolk, M., Rosenberg, S., Wingren, M., Andersson, S., & Nyholm, L. (2024) · Frontiers in Robotics and AI · doi:10.3389/frobt.2024.1332110 · Cited by 4
Value-Based Narratives Foster Trust in Scientists and Communication Behaviors
Yang, J. Z., Arpan, L. M., Ophir, Y., & Shah, P. (2025) · Science Communication · doi:10.1177/10755470251345234 · Cited by 4
“The ubiquity and impact of artificial intelligence (AI) in everyday life has necessitated social science research to understand public perceptions of and trust in this technology and its applications (Choung et al., 2023).”
Cruz, C. J., & Quinto, E. J. M. (2025) · International Conference on Education and Multimedia Technology (ICEMT) · doi:10.1145/3761843.3761918 · Cited by 4
“…finding aligns with other studies that state that challenges in verbal fluency still occur among users due to limited interactive dialogue opportunities due to factors that allows for review, revise, and rewrite outputs before submission, which is not always the case for speaking tasks [75], [77].”
From chatbots to conversational commerce: The role of AI in shaping consumer purchase decisions
Muliansyah, D., & Nurhidayah, R. (2024) · RIGGS Journal of Artificial Intelligence and Digital Business · doi:10.31004/riggs.v3i2.1068 · Cited by 4
Will Generation Z Use ChatGPT for Tourism Recommendations?
Suasapha, A. H. (2025) · Tourism and hospitality management · doi:10.20867/thm.31.3.11 · Cited by 5
“Notably, the expected influence of Perceived Ease of Use (EOU) on Generation Z’s attitude (ATT) to use ChatGPT for tourism recommendation was not supported (β = 0.086, p > 0.05), indicating that Hypothesis 2 is not supported.”
Influential
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.
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