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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 701–800 of 966
Do AI voices follow social nuances? The case of politeness and speech rate
Rabin, E., Elyoseph, Z., Israel‐Fishelson, R., Dali, A., & Nussinson, R. (2026) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2026.100256 · Cited by 1
“These qualities influence the acceptance of AI technologies, willingness to engage, and trust (Choung et al., 2023; Fan & Liu, 2025).”
Attitudes Toward Artificial Intelligence in Urban Ecuadorian Populations
Males-Villegas, M. S., Males-Villegas, A. D., Chiza, J. C., & Hurtado-Caina, J. S. (2026) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-032-10310-9_11
Bu, L., Hou, Y., Cui, B., Han, Y., Zhang, D., & Guan, J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6253743
Digital Recruitment Strategies: Exploring the Role of Technology in Modern Talent Acquisition
Akshera, R. V., Saranya, S. M., & Kani, R. M. (2026) · Thunderbird International Business Review · doi:10.1002/tie.70086
CONFIGURING TRUST IN GENERATIVE AI: INTEGRATING TRUST THEORY AND USER SEGMENTATIONPreprint
Akhmedova, A., Marimón, F., Mas‐Machuca, M., & Amat‐Lefort, N. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6278331
Hamali, S., Darmo, I. S., & Meliala, J. S. (2025) · International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) · doi:10.1109/isriti68345.2025.11393261
“People’s trust is derived from the system’s competence, integrity, and benevolence [20].”
Mine but Not Ours: How and When Perosnal AI Learning Triggers Knowledge HidingPreprint
Gao, F., Liu, W., Wang, M., & Wang, Y. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6304539
Trust in Artificial Intelligence and Its Acceptance in Croatian Society
Bara, M., Dorotić, J., & Ćosić, T. (2026) · Interdisciplinary Description of Complex Systems · doi:10.7906/indecs.24.1.4
How do consumers perceive virtual agents with behavioral realism? The role of loneliness and trust
Sun, B., & Chen, Y. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1676234
“Consistent with this view, numerous studies indicate that users are more likely to adopt virtual agents when a sufficient level of trust has been established (Chang and Park, 2024; Choung et al., 2023; Ho and Cheung, 2024; Kim et al., 2021; Le et al., 2023).”
Investigate Determinants and Configuration of Human AI Use: Combining PLS-SEM and fsQCA Analysis
Zhang, X., Dong, Q., Ma, L., Zhang, G., & Hao, F. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2631657
uulu, C. U., Pacheco, N. M. M., Joosten, J., Steinke, I., Kulyabin, M., Petridis, F., Michel, L., Bosch, J., & Olsson, H. H. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6368072
Zhao, P., Liao, X., & Wu, K. (2026) · International Journal of Applied Linguistics · doi:10.1111/ijal.70158
Working with AI: How team cohesion depends on perceiving the AI as a tool or a partner
Hoffmann, M., Wollstein, C., Zimber, F., & Tausch, A. (2026) · Research for People in Organizations · doi:10.5964/rpio.17157
Wang, H., Winkle, K., & Castellano, G. (2026) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3776734.3794552
Fang, H., Li, B., Zhou, Z., Li, M., Lai, H., Zheng, Y., Hu, B., & Chen, W. (2026) · PLoS ONE · doi:10.1371/journal.pone.0344501
“Source: Adapted from Choung et al. (2023) [31] and Nazrin et al. (2025) [22].”
Zhang, Y., Gu, P., Zhang, M., Ma, Z., Zhang, Y., Chen, L., & Xu, F. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2633211
The Social Aptitude of AI: Quantifying and Assessing Driving Automation-Pedestrian Interactions
Elahi, M. F., Jiang, J., Domeyer, J. E., & Tian, R. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2622003
Sebopelo, P., & Chukwuma, N. N. (2026) · Review of Artificial Intelligence in Education · doi:10.37497/rev.artif.intell.educ.v7ii.78
Cox, J. A., Ivory, B. E., Salmon, P. M., & Read, G. J. M. (2026) · AI & Society · doi:10.1007/s00146-026-02949-3
Nawahdah, M., Sawalha, H., & Jebara, H. (2026) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-95-4423-3_25
How AI transforms the workplace: implications for creativity, risk-taking and social innovation
Hassanzadeh, M., Arabkarami, S., Farivar, F., & Olfat, M. (2026) · Aslib Journal of Information Management · doi:10.1108/ajim-05-2025-0289 · Cited by 1
Wu, Q., Sun, J., & Hu, Y. (2026) · The Asia-Pacific Education Researcher · doi:10.1007/s40299-026-01103-3
“Researches by Choung et al. (2022) and Zhang et al. (2024 ) also verified the significant influence of usefulness in cultivating trust.”
Alshammari, M., & Gronfula, M. (2026) · IntechOpen eBooks · doi:10.5772/intechopen.1013167
From Usefulness to Trust: How AI Shapes Learning Attitudes in Higher Education
Jeranah, J., Kurniawan, N. C., & Ahmad, A. (2026) · International Journal of Contemporary Educational Research · doi:10.52380/ijcer.2026.13.1.876
“Choung et al. (2023) opined that high trust in students in the use of AI, has a great impact in influencing the intention to use.”
Yuan, T., Yang, M., Yu, D., & Rau, P. P. (2026) · Wiley eBooks · doi:10.1002/9781394266401.ch7
Shi, L., Xie, J., Yu, X., Zhang, B., Liu, X., & Han, X. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2645441
ŞAHİN, A., Arpacı, İ., Tatoğlu, E., & Sahin, A. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6508416
Chanthawong, A., Imjai, N., Nimtrakool, K., Usman, B., & Aujirapongpan, S. (2026) · Logistics · doi:10.3390/logistics10040075 · Cited by 1
“…based on the Technology Acceptance Model (TAM) typically examine perceived usefulness, perceived ease of use, and behavioral intention as key predictors of technology adoption [9–11], while more recent AI-related studies emphasize trust and system performance as determinants of acceptance [2,3,12].”
Digital Branding, Social Media and AI Integration
(2026) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83549-009-920261012
Linking Brand Authenticity and Automation: A Focus on Ethical Implications of AI in Retail Branding
Kanchi, P. A., Foroudi, P., & Palazzo, M. (2026) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83549-009-920261005 · Cited by 1
Butt, S., & Ceballos, H. G. (2026) · Law, governance and technology series · doi:10.1007/978-3-032-13063-1_6
Saoud, L. S., & Qureshi, O. A. (2026) · Lecture notes in civil engineering · doi:10.1007/978-3-032-12697-9_5
All's well that begins just: organizational justice as precursor of trust in AI-enabled HRM
Shukla, Y., Pandey, J., & Pereira, V. E. (2026) · Personnel Review · doi:10.1108/pr-05-2025-0508 · Cited by 5
Ling, T.-W., Peng, W., Meng, J., & Liu, J.-H. (2026) · CHI Extended Abstracts · doi:10.1145/3772363.3798608
Nahar, N., Tafsirun, U., Tanchangya, T., Siddiqi, K. O., Naher, K., Islam, N., & Hossan, M. A. (2026) · Research Square · doi:10.21203/rs.3.rs-9161042/v1
The Impact of AI Trustworthiness Labels on the Perception of AI Products
Pfeuffer, C. U. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790776
Yang, Y., Wang, Z., Ma, R., & Schaub, F. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790881
Unveiling the Bond: How Embodied Mind Perception Shapes Human-Robot Attachment
Chen, Y., Zheng, J., Jiang, N., & Ma, L. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2651372
Advancing sustainable construction: a trust-enhanced model of AI adoption and actual usePreprint
Regona, M., Yiğitcanlar, T., Hon, C. K. H., & Teo, M. M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6614001
Lan, O. L. V., Ly, U. H. T., Nhu, Q. N. N., & Thuy, N. T. T. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7200-6.ch003
Bassler, C. T., Reichel, P., & Spörrle, M. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2646417
Phan, P. (2026) · Accounting and Auditing Journal · doi:10.59006/vnfa-jaa.04202607
Lee, W.-J., Shin, S., & Shin, L. (2026) · Health Marketing Quarterly · doi:10.1080/07359683.2026.2663735
Jones-Jang, S. M., Kim, N., Chung, M., Choi, J., & Lee, S. Y. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2663075
Huang, R., Chen, Z., & Sun, Z. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2663074
How Autonomy and Trust Influence Patient Satisfaction Under Dynamic Dependencies
Stella, F., Sapienza, A., & Falcone, R. (2026) · Sci · doi:10.3390/sci8050101
Ünal, E., & Şahan, E. (2026) · Health Sciences Quarterly · doi:10.26900/hsq.3023
Kılıç, S. (2026) · Uluslararası Iktisadi ve Idari Incelemeler Dergisi · doi:10.18092/ulikidince.1849363
Khandelwal, R., & Kapoor, D. (2026) · Asian Education and Development Studies · doi:10.1108/aeds-06-2025-0269
Glikson, E., Asscher, O., & Harush, R. (2026) · Cross Cultural & Strategic Management · doi:10.1108/ccsm-09-2024-0230
Yun, C. H., & Teoh, A. P. (2025) · International Conference on Business Intelligence for Technology Innovation (ICBITI) · doi:10.1109/icbiti65527.2025.11500650
“[8] AITA2 I predict that I would use the AI-based e-Government system.”
InfluentialChen, Y., Kanchanapoom, K., & Deeprasert, J. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01352-7
“Existing research has distinguished between the concepts of cognitive trust and affective trust, and has also found that these two types of trust exert different mediating effects on users’ behavioral intentions toward AI systems [16, 84].”
Low, M. P., Wut, T. M., & Pok, W. F. (2026) · Human Systems Management · doi:10.1177/01672533261451847
Song, S., Shi, Y., Ge, X., Yu, H., Wang, W., & Tian, A. W. (2026) · The International Journal of Human Resource Management · doi:10.1080/09585192.2026.2670478
“Trust also transforms the human-intelligent machine relationship, leading employees to view intelligent machines as partners rather than mere tools (Choung et al., 2023; Warren & Hillas, 2020).”
InfluentialKara, I., ŞAHİNLER, Y., Koc, M. C., Şahin, M. Y., ŞEBİN, K., Talaghir, L.-G., Iconomescu, T. M., Nanu, L., Liușnea, C. Ș., & Manolache, G. M. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1830510
Досжан, Р., Glavee-Geo, R., Sabidullina, A., Zhanibekova, G. K., Baimukhanova, S., & Shaikh, A. A. (2026) · European Research on Management and Business Economics · doi:10.1016/j.iedeen.2026.100315
Kraus, F., & Burmann, C. (2026) · Springer eBooks · doi:10.1007/978-3-658-49371-4_11
Gonzales, G. G., Hallarte, D. K., Milano, M. L., Pasaje, S. J., Villaflor, M. G., & Gonzales, R. R. (2026) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2026.102921
Li, F., Zhang, L., & Roy, S. K. (2026) · Journalism and Media · doi:10.3390/journalmedia7020105
Regional cognitive pathways to intention to use AI: a structural equation modeling approach
Jang, Y., Jeong, T., & Suh, T. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1758998
“Choung et al. (2023) investigated the role and pathway characteristics of trust in shaping the intentions of university students and the general public to adopt AI voice assistants.”
The Use of Artificial Intelligence for Transforming E-Governance in Kerala
Joseph, M. K., & Madhav, N. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-19684-2_27
Sutrisno, J., Makarawung, R. J. N., Bernanda, D. Y., Kembau, A. S., Felix, A., & Dermawan, R. M. (2026) · International Conference on Current Research in Artificial Intelligence and Data Science (ICCRAIDS) · doi:10.1109/iccraids67816.2026.11519712 · Cited by 2
“Nonetheless, the usage of the aforementioned systems has been primarily based on user intentions to interact with the system in the first place [7].”
InfluentialBertacchini, A., & Carleo, S. (2026) · Springer series in design and innovation · doi:10.1007/978-3-032-13836-1_8
AI: Optimist or Skeptic? The Role of Trust in EU Institutions in Shaping Attitudes Toward AIPreprint
Castellacci, F., & D'Ecclesiis, E. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6835482
Hamidi, A., Salaripour, A., & Hesam, M. (2026) · SN Social Sciences · doi:10.1007/s43545-026-01506-5 · Cited by 1
The Role of Trust in the Educational Integration of Social Robots and Artificial Intelligence
Cirasa, C., Høgsdal, H., & Conti, D. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-6471-1.ch015
Wang, J., & Yu, X. H. (2026) · The International Journal of Press/Politics · doi:10.1177/19401612261451950
Erlebach, S. (2026) · Lecture notes in information systems and organisation · doi:10.1007/978-3-032-08486-6_13
On the Perceptions and Effectiveness of AI in News: AI as Journalists and Fact-checkersPreprint
Horne, B. D., & Craig, M. J. A. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6853622
Chatterjee, R. S., Singh, B., Chaudhuri, R., Chatterjee, S., Vrontis, D., & Foroudi, P. (2026) · Journal of Strategic Marketing · doi:10.1080/0965254x.2026.2662669
Minglie, H. E., & Song, Y. (2026) · European Journal of Education · doi:10.1111/ejed.70708
Moravec, V., Gavurová, B., & Rigelský, M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6866867
An, M., Lee, C., Kim, M., Lim, H., Wohn, D. Y., & Ahn, C. R. (2026) · Journal of Management in Engineering · doi:10.1061/jmenea.meeng-7288
Schulte, B. K., & Hattula, C. (2026) · Springer eBooks · doi:10.1007/978-3-658-51635-2_2
Kim, O., & Noh, G. Y. (2026) · Behaviour and Information Technology · doi:10.1080/0144929x.2026.2682278
Kouhsari, M., & Chen, J. (2026) · Journal of Computer Assisted Learning · doi:10.1002/jcal.70274
Gupta, P., & Chauhan, S. (2026) · Behaviour and Information Technology · doi:10.1080/0144929x.2026.2682279 · Cited by 2
Sun, Y., Liu, J., Liu, T., Wei, Z., & Shen, H. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1855345
Alhilfi, A. H. H., Mohammed, A. K., Jasim, Z. A., Almatwari, A. A. S., Hussein, S. A.-D. A., & Alyousuf, A. N. J. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7240-2.ch007
Investigating Employee Intentions to Adopt Service Robots in the Hotel Industry
Alnaemat, N. D. O., AlRamahi, S. K. S., & Aljasim, M. J. H. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7240-2.ch008
Pham, V. K., Giao, L. L. P., Huynh, A.-V., & Thi, T. D. P. (2026) · Journal of Decision System · doi:10.1080/12460125.2026.2684000
Le, H. T. P. M., Phan, M. T. N., Le, T. D. M., & Ha, T. M. (2026) · Journal of Travel & Tourism Marketing · doi:10.1080/10548408.2026.2679970 · Cited by 2
“Moreover, Choung et al. (2023) distinguished between two dimensions of trust in AI: functional trust and human-like trust, which could enrich the conceptualization of AIWOM adoption.”
Zhao, Q., Li, T., Li, S., Pan, Y., Wang, H., & Liao, C. (2026) · Acta Psychologica · doi:10.1016/j.actpsy.2026.107267
Qi, Z., Yan, Y., & Ren, P. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1804847
“This competence-based trust is crucial for promoting a sense of identification with AI technology (Choung et al., 2023).”
Zou, W., Qu, J. G., Yi, J., & Li, Z. (2026) · Mass Communication & Society · doi:10.1080/15205436.2026.2687866
Sun, M. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2683907
Neutral or Positive? Understanding Finance Professionals' Attitudes Toward AI Adoption
Hu, X., Veenstra, K., & Yoon, C. E. (2026) · Accounting and Finance · doi:10.1111/acfi.70247
Zhao, J. Y., & Shi, J. (2026) · Acta Psychologica · doi:10.1016/j.actpsy.2026.107295 · Cited by 1
Asghar, M. Z., Fabusoye, O. A., & Özbilen, F. M. (2026) · Discover Computing · doi:10.1007/s10791-026-10313-8
“Importantly, the present study also incorporates ethical considerations as part of the acceptance construct, reflecting the growing importance of ethical trust, transparency, and fairness in AI-related decision-making [35, 38].”
To Build or Not to Build? Factors that Lead to Non-Development or Abandonment of AI Systems
Chappidi, S., & Singh, J. P. (2026) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3805689.3806736 · Cited by 1
Abdi, A. S., & Abdullahi, A. M. (2026) · Discover Computing · doi:10.1007/s10791-026-10276-w
“First, because GenAI produces knowledge-like outputs, students must judge whether those outputs are reliable, transparent, and safe enough to use for academic work [62–68].”
Too Much is as Bad as Too Little: The Nonlinear Impact of AI Risk Perception on Trust in AI
Li, M., & Wang, J. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2673407
Wang, T. R., Yang, Y., & Zhou, J. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2686773 · Cited by 1
Wu, X., Akanbi, F., Men, L. R., Dang, T., He, H., & Youngs, K. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6984559
Understanding What Builds and Breaks Travelers' Trust in ChatGPT's Travel Recommendations
Khairallah, M. (2026) · International Symposium of Systems, Advanced Technologies and Knowledge (ISSATK) · doi:10.1109/issatk69667.2026.11566711
“Shin’s later work (2021, 2021) investigated how FATE, trust, user perceptions, and acceptance are interrelated which suggests that trust is pivotal in shaping positive perceptions and user experiences with AI technologies [52].”
Liu, Y.-L., Li, R., Mi, J., Dai, J., & Hu, B. (2026) · Telecommunications Policy · doi:10.1016/j.telpol.2026.103279
AI-Based Recruitment: An Integrative Framework for Human Resources Professionals’ Adoption
Gül, B., & Soyer, A. (2026) · Systems · doi:10.3390/systems14060713 · Cited by 3
Chen, W.-H. (2026) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2026.103095
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
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