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Students’ Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology
Open accessAbstract
AI-powered chat technology is an emerging topic worldwide, particularly in areas such as education, research, writing, publishing, and authorship. This study aims to explore the factors driving students' acceptance of ChatGPT in higher education. The study employs the unified theory of acceptance and use of technology (UTAUT2) theoretical model, with an extension of Personal innovativeness, to verify the Behavioral intention and Use behavior of ChatGPT by students. The study uses data from a sample of 503 Polish state university students. The PLS-SEM method is utilized to test the model. Results indicate that Habit has the most significant impact (0.339) on Behavioral intention, followed by Performance expectancy (0.260), and Hedonic motivation (0.187). Behavioral intention has the most significant effect (0.424) on Use behavior, followed by Habit (0.255) and Facilitating conditions (0.188). The model explains 72.8% of the Behavioral intention and 54.7% of the Use behavior variance. While the study is limited by the sample size and selection, it is expected to be a starting point for more research on ChatGPT-like technology in university education, given that this is a recently introduced technology.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This study applies an extended UTAUT2 model, adding Personal Innovativeness, to examine Polish university students’ acceptance and use of ChatGPT. It finds Habit, Performance expectancy, and Hedonic motivation as key drivers of Behavioral intention, while Behavioral intention, Habit, and Facilitating conditions drive Use behavior.
Key findings
- Habit had the strongest impact on Behavioral intention (0.339), followed by Performance expectancy (0.260) and Hedonic motivation (0.187); the model explains 72.8% of Behavioral intention variance.
- Behavioral intention had the strongest effect on Use behavior (0.424), followed by Habit (0.255) and Facilitating conditions (0.188); these explain 54.7% of Use behavior variance.
- Facilitating conditions did not significantly affect Behavioral intention (−0.023, not significant at p < .05), but did significantly affect Use behavior (0.188).
- Effort expectancy (0.079), Social influence (0.093), Price value (0.083), and Personal innovativeness (0.086) had positive but small effects on Behavioral intention, with f² below 0.02.
Methods
- The study used an extended UTAUT2 model with seven UTAUT2 constructs plus Personal innovativeness, measured with a seven-point Likert scale; Use behavior was measured on a seven-option scale from “never” to “several times a day.”
- Data came from 503 valid responses from students at the University of Economics in Katowice, Poland; 13,388 students were invited by email between 13 and 21 March 2023, and a pilot study used 36 students.
- Analysis used PLS-SEM via SmartPLS 4 with path weighting, 3000 iterations, and bootstrapping with 5000 samples; measurement model reliability, convergent validity, HTMT discriminant validity, R², and path coefficients were assessed.
Limitations
- The paper states the sample was limited to students from one state university in Poland, data were collected during a turbulent phase of AI development, academic-year timing or subject of study may affect use, and different study programs were not considered as a moderating variable.
- Evident from the text: the design is cross-sectional and single-institution, limiting generalizability; the study also intentionally did not incorporate age, gender, and experience as moderators.
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26 Own result · 11 Own method · 9 Not checked yet · 9 Own interpretation
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Results indicate that Habit has the most significant impact (0.339) on Behavioral intention, followed by Performance expectancy (0.260), and Hedonic motivation (0.187).”
- Also stated in: § Results · plain
“The primary observation reveals that HT has the most prominent impact (0.339) on BI, followed by PE (0.260), and HM (0.187), explaining 72.8% of the”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Abstract
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.
- Also stated in: § Results · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“SI (0.093), PI (0.086), PV (0.083), and Effort expectancy (0.079) have positive effect on Behavioral intention, however there is no f 2 effect size for these relationships.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Results
plaindirect- 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.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“SI (0.093), PI (0.086), PV (0.083), and Effort expectancy (0.079) have positive effect on Behavioral intention, however there is no f 2 effect size for these relationships.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Results
plaindirect- 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.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“With respect to”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Discussion
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.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Behavioral intention has the most significant effect (0.424) on Use behavior, followed by Habit (0.255) and Facilitating conditions (0.188).”
- Also stated in: § Results · plain
“Conversely, BI has the most significant effect (0.424) on UB, with HT (0.255) and FC (0.188) following suit.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Abstract
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.
- Also stated in: § Results · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“SI (0.093), PI (0.086), PV (0.083), and Effort expectancy (0.079) have positive effect on Behavioral intention, however there is no f 2 effect size for these relationships.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Results
plaindirect- 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.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In our study,”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Discussion
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.
A result the paper reports from its own data — 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 72.8% of the Behavioral intention and 54.7% of the Use behavior variance.”
- Also stated in: § Results · plain
“These three variables account for 54.7% of the”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Abstract
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.
- Also stated in: § Results · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“SI (0.093), PI (0.086), PV (0.083), and Effort expectancy (0.079) have positive effect on Behavioral intention, however there is no f 2 effect size for these relationships.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Results
plaindirect- 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.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Behavioral intention has the most significant effect (0.424) on Use behavior, followed by Habit (0.255) and Facilitating conditions (0.188).”
- Also stated in: § Results · plain
“Conversely, BI has the most significant effect (0.424) on UB, with HT (0.255) and FC (0.188) following suit.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Abstract
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.
- Also stated in: § Results · plain
- It may state two separate claims as one. 13
- Probably not a substantial claim. 18
- The quote only partly states this claim. 1
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“SI (0.093), PI (0.086), PV (0.083), and Effort expectancy (0.079) have positive effect on Behavioral intention, however there is no f 2 effect size for these relationships.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Results
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.
In the paper
“The reliability and validity criteria were met for each construct, and discriminant validity was confirmed (Hair et al., 2013).”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Sample Characteristics
What the sources say
- Not checked yetcites Hair et al., 2013 source
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
“After removing 3 responses with a variance of zero, 503 valid responses were collected, which is enough, according to a minimal sample size calculation.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Sample Characteristics
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: 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.
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
“To ensure unbiased data, the students were assured of the confidentiality, voluntary participation, and anonymity of their responses.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Sample Characteristics
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
“After removing 3 responses with a variance of zero, 503 valid responses were collected, which is enough, according to a minimal sample size calculation.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Sample Characteristics
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.
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
“Loadings are presented in Appendix Table 3 and exceed the lower bond except the FC4, which was removed from additional processing within the model and is not taken into account.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Results
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
“All measurement instruments have met the quality criteria (Table 1).”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Results
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.
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 finding could be since students have not fully experimented with ChatGPT and may have insufficient knowledge on how to use it.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Discussion
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: 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
“The questionnaire was circulated during a turbulent phase related to AI, with each month profoundly influencing the tool's evolution.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Limitations
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: 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
“UTAUT2 is a prominent theoretical model that aims to understand the factors that influence individuals' adoption and use of new technologies in organizational and personal contexts (Tamilmani et al., 2021).”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Methodology
What the sources say
- Not checked yetcites Tamilmani et al., 2021 source
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.
In the paper
“Additionally, Dwivedi et al. (2019) noted that previous research in this field typically utilized only a portion of the UTAUT2 model and frequently neglected moderators.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Methodology
What the sources say
- Not checked yetcites Dwivedi et al., 2019 source
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.
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
“Additionally, factors like specific times in the academic year, like assignment submissions or the specific subject of study, might affect how the tool is used.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Limitations
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. 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
“However, the sample was diverse in terms of study experience.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Limitations
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: 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
“While the study is limited by the sample size and selection, it is expected to be a starting point for more research on ChatGPT-like technology in university education, given that this is a recently introduced technology.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Abstract
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
“while others did not find this significance in areas like mobile learning (Nikolopoulou et al., 2020) and e-learning (Osei et al., 2022).”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Discussion
What the sources say
- Not checked yetcites Nikolopoulou et al., 2020
- Not checked yetcites Osei et al., 2022
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
“Even as a considerable volume of studies on AI in education emerged in the past years, signifying a booming interest, the actual effects of applications like ChatGPT on higher education institutions and stakeholders remain enigmatic (Zawacki-Richter et al., 2019).”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Introduction
What the sources say
- Not checked yetcites Zawacki-Richter et al., 2019 source
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
“Venkatesh et al. (2016) recommended using UTAUT2 as a fundamental model to establish hypotheses regarding the connections between proposed variables and user adoption of technology.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Methodology
What the sources say
- Not checked yetcites Venkatesh et al., 2016 source
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.
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 likelihood of adopting the AI-enabled chat is higher among early adopters who have a higher level of education and are less swayed by external factors.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Discussion
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.
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 demonstrates that using this technology in higher education requires very little effort and does not affect”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Discussion
boosted- 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.
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 is possible that different study programs, such as IT, business, management, and finance, have different levels of AI chat use.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Limitations
hedgedcorrelational- 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.
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
“We did not consider different study programs as a moderating variable.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Limitations
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
“Although the ChatGPT plus version with the latest GPT-4 language model is available for $20 per month, the difference in the ChatGPT's use, powered by the GPT-3.5 version, for everyday student tasks is unlikely to be noticeable.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Discussion
hedged- 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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
- Rests on an assumption: ChatGPT is currently free of charge during the research preview.
In the paper
“The original study by Venkatesh et al. (2012) did not provided how the actual use was measured.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Methodology
What the sources say
- Not checked yetcites Venkatesh et al., 2012 source
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
“With respect to”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Discussion
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. The quote only partly states this claim.
The paper relies on this without support.
Research into how ChatGPT is perceived and utilized by university students remains sparse.
“Though ChatGPT has swiftly gained popularity as a cutting-edge tool in natural language processing, research into how it's perceived and utilized by university students remains sparse.”
The paper relies on this without support.
The sample of students from the University of Economics in Katowice is representative of Polish state university students.
“The study uses data from a sample of 503 Polish state university students.”
“A total of 13,388 students from the University of Economics in Katowice, Poland, were invited to participate via email in the period of 13th to 21st March 2023.”
The paper relies on this without support.
The relationships estimated by PLS-SEM represent causal effects of the predictors on Behavioral intention and Use behavior.
“Our study argues that”
The paper relies on this without support.
ChatGPT is currently free of charge during the research preview.
“This may be because the use of ChatGPT is currently free of charge during the tool's research preview.”
The paper relies on this without support.
ChatGPT doesn't have adaptive capabilities and isn't tailored for academic use.
“It's pertinent to mention that ChatGPT doesn't have adaptive capabilities and isn't tailored for academic use, indicating a need for more research on its potential role in learning environments.”
The paper relies on this without support.
ChatGPT use in higher education is not yet explored.
“Since ChatGPT use in higher education is not yet explored, there are many recommendations for future studies.”
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?
In the citing paper
“This finding suggests that university students, in particular, value ChatGPT for its utility in completing academic tasks, such as writing assistance, research, and learning new concepts [1,7,20,65].”
From ChatGPT in Learning: Assessing Students’ Use Intentions through the Lens of Perceived Value and the Influence of AI Literacy (2024) · cites this paper as [7]
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 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
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Discussion
“This demonstrates that using this technology in higher education requires very little effort and does not affect "Behavioral intention".”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Conclusions
“| 2 | EE-> BI | 0.079 | 2.061 | 0.010 | Yes |”
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
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Discussion
“"Effort expectancy", "Social influence" and "Price value" have been found to have a positive and significant effect on "Behavioral intention", despite having f 2 values that are lower than 0.02.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Conclusions
“| 3 | SI-> BI | 0.093 | 2.880 | 0.021 | Yes |”
Citations
Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · cited 1×
“However, a marked improvement in AI’s capabilities, particularly in the realm of generative AI, became apparent in last decade (Adiwardana et al., 2020; Vaswani et al., 2017).”
The theory of planned behavior
Ajzen, I. (1991) · Organizational Behavior and Human Decision Processes · doi:10.1016/0749-5978(91)90020-t · Cited by 90,587
User Acceptance of Information Technology: Toward A Unified View1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003) · MIS Quarterly · doi:10.2307/30036540 · Cited by 45,035 · cited 6×
“One of the most prominent theories used to explain and predict technology acceptance is the “Unified Theory of Acceptance and Use of Technology” (UTAUT) (Venkatesh et al., 2003).”
InfluentialA new criterion for assessing discriminant validity in variance-based structural equation modeling
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-014-0403-8 · Cited by 38,459 · cited 2×
“If they are more distinct, a limit of 0.85 should be used (Henseler et al., 2015).”
Influential(1975) · PhilPapers (PhilPapers Foundation) · Cited by 36,737
Pls-Sem: indeed a silver bullet
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011) · Journal of Marketing Theory and Practice · doi:10.2753/mtp1069-6679190202 · Cited by 21,631 · cited 1×
“According to Hair et al. (2011), R 2 values can generally be interpreted as weak (0.25), moderate (0.50), or substantial (0.75).”
InfluentialA primer on partial least squares structural equation modeling (PLS-SEM)
Leguina, A. (2015) · International Journal of Research & Method in Education · doi:10.1080/1743727x.2015.1005806 · Cited by 18,921
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012) · MIS Quarterly · doi:10.2307/41410412 · Cited by 15,756 · cited 10×
“The original study by Venkatesh et al. (2012) did not provided how the actual use was measured.”
InfluentialChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · doi:10.35542/osf.io/5er8f · Cited by 7,552
ChatGPT for good? On opportunities and challenges of large language models for education
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · Learning and Individual Differences · doi:10.1016/j.lindif.2023.102274 · Cited by 7,326
Research works printed but not linked: 3
- Adiwardana, D., Luong, M.-T., So, D. R., Hall, J., Fiedel, N., Thoppilan, R., Yang, Z., Kulshreshtha, A., Nemade, G., Lu, Y., & Le, Q. V. (2020). Towards a human-like open-domain chatbot. http://arxiv.org/abs/2001.09977 · arXiv
- Ringle, C. M., Wende, S., & Becker, J.-M. (2022). SmartPLS 4. SmartPLS GmbH.
- Rudolph, J., Tan, S., & Tan, S. (2023). ChatGPT: Bullshit spewer or the end of traditional assessments in higher education? Journal of Applied Learning & Teaching, 6(1), 1–22.
Cited by
Sorted by citations
Items 401–473 of 473
Zhang, Y., Tian, Y., Lee, K. C. S., & Beh, W. F. (2026) · PLoS ONE · doi:10.1371/journal.pone.0346003
Herlambang, A. D., Nadia, D. M., Putra, W. H. N., & Hanggara, B. T. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.07.039
İyilikci, E. A., Yurt, H. M., Keysan, E. E., Ağaoğulları, N., Dallar, H., Güven, L. S., Özkan, M. S., Şahin, M., Günhan, Z., Baykuşak, E., et al. (2026) · Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi · doi:10.35379/cusosbil.1647141 · Cited by 1
Djunaedi, M. E., & Sundjaja, A. M. (2026) · International Conference on Intelligent Systems and Digital Applications (ISDA) · doi:10.1109/isda70544.2026.11606059
Hsieh, C., Bali, S., Romadhoni, M., Yen, A.-C., Chen, T.-C., & Andania, R. A. (2026) · Quality & Quantity · doi:10.1007/s11135-026-03015-2
Vo, D. H., Tran, G. H., Chau, L. L., Vo, T. V. T., & Lam, D. K. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7201043
Li, B., Qin, Q., Ao, Y., He, X., & (李雨黎), Y. L. (2026) · SAGE Open · doi:10.1177/21582440261468418
Wibowo, A., Narmaditya, B. S., Alkhudri, A. T., & Sahid, S. (2026) · Discover Education · doi:10.1007/s44217-026-02002-x
“Preliminary investigations (e.g., [43, 44]) identified a significant relationship between social influence and the extent of technology adoption.”
InfluentialLabrado-Antolín, M. (2026) · Journal of Information Technology Case and Application Research · doi:10.1080/15228053.2026.2711153
Arthur, F., Abraham, A. Y., & Inkoom, M. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-9484-8.ch015
AI-Powered Learning Through ChatGPT-4: Simplifying Complex Concepts with Creative Pedagogy
Allam, H., Dempere, J. M., Kumar, L., Alagure, Z., Davison, C. B., & Lazaros, E. J. (2026) · Advances in Science, Technology & Innovation/Advances in science, technology & innovation · doi:10.1007/978-3-032-24784-1_16
Li, W., Zhang, W., Li, M., & Zhang, Y. (2026) · Journal of Intelligence · doi:10.3390/jintelligence14080185
“First, many studies focus on acceptance, perceived usefulness, academic integrity, or performance outcomes, with less attention to psychological mechanisms linking GenAI use to students’ perceived creative intellectual functioning (Chan & Hu, 2023; Strzelecki, 2024; Tlili et al., 2023).”
SRILEKHA, K., Naik, J. R. K., & Vamsi, D. (2026) · International Journal of Technology & Emerging Research · doi:10.64823/ijter.2608003
“…to use chatbots; those who are early adopters, well educated, and technology students are more likely to adopt AI-enabled chatbot tools without being influenced by others; the study found that not yet widely used, and there is no strong social pressure for using the technology ChatGPT [47].”
InfluentialBeyond Tool Rationality: Contextualizing AR Picture Book Acceptance in Parent–Child Shared Reading
Zeng, Y., Wang, Y., Gong, Z., Li, X., Wang, Y., Xu, Y., & Li, Z. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2712813
Mellado, R., Cubillos, C., & Roncagliolo, S. (2026) · Behavioral Sciences · doi:10.3390/bs16081405
“…on cross-sectional designs in which acceptance is measured once and in connection with general-purpose academic activities such as retrieval, drafting, or summarizing, so that operational ease is presumed rather than observed under cognitive strain (Strzelecki, 2024; Vhatkar et al., 2024).”
A call for an interdisciplinary ethical foundation for GenAI governance in higher education
Uddın, M. M. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01999-2 · Cited by 1
“Studies grounded in acceptance-based frameworks such as UTAUT primarily explain students’ intentions to adopt GenAI through variables such as perceived usefulness, ease of use, and facilitating conditions [28, 35].”
Alamri, H. A. (2026) · Cognitive Processing · doi:10.1007/s10339-026-01389-7
“These findings extend prior research that has largely examined GenAI adoption or perceptions in isolation (Chan and Hu 2023 ; Strzelecki 2024 ), by showing that students’ reported learning benefits are more strongly related to their psychological readiness and cognitive engagement with GenAI than…”
InfluentialChristopoulos, A., Mystakidis, S., Perikos, I., Bourazeri, A., Michael, A., & Laakso, M. (2026) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2026.100378
Zhang, Y., Wang, Z., & Xu, H. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1935139
“Previous studies have consistently reported that students’ perceptions of GenAI usefulness and ease of use significantly predict their intention to adopt AI technologies in educational settings (Shahzad et al., 2025; Strzelecki, 2024; Zhao et al., 2024).”
Khlaif, Z. N., & Hamamra, B. (2026) · JMIR Medical Education · doi:10.2196/105172
Song, H., Hu, A., & Li, X. (2026) · Behavioral Sciences · doi:10.3390/bs16091460 · Cited by 1
“…on university students’ AI attitudes has largely adopted a variable-centered approach, emphasizing discrete acceptance-related factors such as perceived usefulness, ease of use, trust, anxiety, and behavioral intention (Chan & Hu, 2023; Budhathoki et al., 2024; Maheshwari, 2024; Strzelecki, 2024).”
Bekešienė, S. (2026) · Sustainability · doi:10.3390/su18178758
Wang, F., Huang, Y., Jiang, Q., & Chen, S. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2705666
Bui, D. K. (2026) · International Artificial Intelligence Technology Conference (AITC) · doi:10.1109/aitc70732.2026.11666499
“In higher education, ChatGPT has been studied as a tool that can influence students' acceptance of AI-supported learning, particularly when learners perceive it as useful, easy to use, and relevant to their academic needs [1].”
InfluentialApata, O. E., Awoyemi, I. D., Onalo, N., Sani, G., Oladipo, E. T., Saidu, G. O., & Olodude, I. O. (2026) · Culture · doi:10.3390/culture2040028
Nguyen, H. V., Le, N. K. N., Nguyen, T. H. T., Tran, N. M., Phu, G. T., Nguyen, N. T., & Nguyen, K. M. (2026) · China Finance Review International · doi:10.1108/cfri-07-2025-0470 · Cited by 1
Liu, Y., Lian, X., & Qi, X. (2026) · Education Sciences · doi:10.3390/educsci16091458
Kumar, V., Anand, A., Butler, R., & Bista, K. (2026) · AI in Education · doi:10.3390/aieduc2030031
“…examined students’ willingness to adopt AI technologies, perceptions of usefulness, and trust in AI-supported systems using theoretical frameworks such as the unified theory of acceptance and use of technology (UTAUT) and the technology acceptance model (TAM) (Chan & Zhou, 2023; Strzelecki, 2024).”
Oyebode, L. A., Obim, I. E., Okai, E. D. J., Abah, B. E., Nkang, E. E., Sunday, I. O., Opara, V. C., & Oladokun, B. D. (2026) · Research Square · doi:10.21203/rs.3.rs-10940455/v1
Hu, P., Rahman, M. K., Hossain, D., Piprani, A. Z., Feng, X., & Bhuiyan, M. A. (2026) · Interactive Learning Environments · doi:10.1080/10494820.2026.2721389
Mulyono, H., Suryoputro, G., Azizah, U. S. A., & Falah, Z. A. (2026) · SN Social Sciences · doi:10.1007/s43545-026-01716-x
“Recent study drawing on technology acceptance and behavioral-intention perspec - tives (e.g. Duong et al. 2023 ; Strzelecki 2024) has similarly shown that students’ attitudes toward AI are associated with how useful, manageable, and relevant they perceive AI technologies to be.”
Designing for AI literacy: psychological mechanisms and self-determination
Yu, S., Lin, Y., & Chen, C. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1937845
“Existing research on AIGC in education predominantly relies on the Technology Acceptance Model, focusing on external factors like perceived usefulness (Liu and Ma, 2024; Strzelecki, 2024a, 2024b).”
Hu, P. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1900190 · Cited by 1
“Models in the TAM and UTAUT traditions have established that trust, capability, and confidence each travel with use, but they typically enter these constructs as parallel antecedents of intention (Strzelecki, 2023; Tiwari et al., 2023; Ma et al., 2024).”
InfluentialMAIN DRIVERS OF CHATGPT ADOPTION BY ROMANIAN COMPUTER SCIENCE STUDENTS
Popescu, P. Ș., Mihăescu, M. C., & Pribeanu, C. (2026) · Problems of Education in the 21st Century · doi:10.33225/pec/26.84.646
Kalınkara, Y. (2026) · Educational Academic Research · doi:10.33418/education.1855067
Iqbal, N. (2026) · E-Learning and Digital Media · doi:10.1177/20427530261489797
AI Usefulness vs. Student's Concern in Vietnam
Tran, T. H. H., Luu, T. T. D., & Dao, D. L. (2026) · International Journal of Asian Business and Information Management · doi:10.4018/ijabim.422376
Modelo integrado de factores cognitivos, motivacionales y afectivos del uso académico tecnológico
Villanueva, O. A. C., & Vera, K. J. C. (2026) · Telos Revista de Estudios Interdisciplinarios en Ciencias Sociales · doi:10.36390/telos283.30
“En el contexto universitario, esta perspectiva adquiere especial relevancia, ya que el uso de las herramientas digitales no depende únicamente de sus beneficios académicos percibidos, sino también de la experiencia de uso que ofrecen (Strzelecki, 2024).”
Tan, W. H. E., Yeo, M. M. L., Toh, A. L. L., & Wansing, E. S. (2026) · Pedagogies An International Journal · doi:10.1080/1554480x.2026.2733074
Kim, S. Y., Bong, J. Y., Maxwell, D. W., & Oyarzun, B. A. (2026) · Educational Technology Research and Development · doi:10.1007/s11423-026-10713-z · Cited by 1
Vasileva, V., Reiser, T., & Jürjens, J. (2026) · Annals of Computer Science and Information Systems · doi:10.15439/2026f3410
Afrin, S., Ahmed, T., & Hasan, M. J. (2026) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2026.101358
The Determinants of Generative Artificial Intelligence Tools in Education: A Systematic Review
Alanazi, A. F., & Moaiad, Y. A. (2026) · Technology and Innovation Conference (TIC) · doi:10.1109/tic68483.2026.11702852
“The results of the study also sustain the UTAUT-based findings that performance, effort, and facilitating conditions are determinants of students’ acceptance of ChatGPT [22].”
Why Educators Use But Do Not Endorse Artificial Intelligence in Higher Education
Yu, J. H., Romero, P., Dunlap, M., & Warren, S. (2026) · Innovative Higher Education · doi:10.1007/s10755-026-09957-7
Risk Perception as a Moderating Variable in Generative AI Adoption Among Business School Students
Spilbergs, A. (2026) · WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS · doi:10.37394/23207.2026.23.134
Yang, Y. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1997877
“Much of the early research on generative AI in education has examined the learner side: student acceptance of ChatGPT, perceived benefits and risks, and the implications of chatbot use for academic integrity and learning quality (Chan and Hu, 2023; Chiu et al., 2023; Strzelecki, 2024).”
After Integration: A Framework for Designed AI Withdrawal in Higher Education
Anderson, J. E. (2026) · Innovative Higher Education · doi:10.1007/s10755-026-09963-9
Zhang, J. O., Wiryadisastra, M. F., Hadylaw, A. L., & Effendi, A. (2026) · International Conference on ICT for Smart Society (ICISS) · doi:10.1109/iciss71466.2026.11715443
“[8], [15] Hedonic Motivation (HM) (HM1) I enjoy using ChatGPT when I work on my essays.”
InfluentialAl‐Hattami, H. M., & AL-Sinawi, S. H. N. (2026) · The International Journal of Management Education · doi:10.1016/j.ijme.2026.101551
Adekoya, O. D., Ajonbadi, H. A., & Mordi, C. (2026) · British Journal of Educational Technology · doi:10.1111/bjet.70093
Galindo‐Domínguez, H., Campo, L., Delgado, N., & Urruzola, M. V. (2026) · Thinking Skills and Creativity · doi:10.1016/j.tsc.2026.102385
Mansoor, H. S., Sumardjoko, B., & Sutopo, A. (2026) · Open Praxis · doi:10.55982/openpraxis.18.4.1356
“Literature indicates that AI tools can increase the efficiency of learning, promote the quality of feedback and enhance English writing skills (Perkins, 2023; Song & Song, 2023; Strzelecki, 2024; Tiwari et al., 2024).”
der Merwe, N. V., Papageorgiou, E., Terblanche, W., & Lubbe, I. (2026) · Accounting Education · doi:10.1080/09639284.2026.2742947
Sun, C. (2026) · International Conference on Artificial Intelligence, Digital Media and Education · doi:10.1145/3839592.3839637
Doan, C. T. (2026) · Educational Media International · doi:10.1080/09523987.2026.2748545
Aldaabseh, T. K., Aljawarneh, N. M., Al-maaitah, T. A., & Fares, A. R. A. (2026) · Research Square · doi:10.21203/rs.3.rs-10974553/v1
Calle-Martínez, C., Silvera-Roig, M., Virto, N. R., & Paredes, M. (2026) · Creativity Studies · doi:10.3846/cs.2026.23543
Student use of generative AI and perceived institutional support across three linguistic contexts
Gusman, D., Aditya, D. S., Putri, D. W., DAMAYANTI, N., Nurmalina, N., & Bujdosó, G. (2026) · Discover Education · doi:10.1007/s44217-026-02325-9
“Meanwhile, the role of social influence in this approach varies across studies and settings [4, 6–16].”
InfluentialSuing, A., Gutierrez, J. M. R., Galarza-Ligña, V. N., & Coronado-Otavalo, X. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1948805
Ahmad, S. A., & Kamsin, I. F. (2026) · International Journal of Modern Education · doi:10.35631/ijmoe.831030
Doctoral researchers’ Generative AI use and research-integrity concerns: a cross-context study
Zhuang, Y. (2026) · Educational Studies · doi:10.1080/03055698.2026.2751067
Oswald, G. S., & Clegg, B. A. (2026) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/10711813261498709
Ong, A. V., Kadiyono, A. L., & Iskandarsyah, A. (2026) · Psikoborneo Jurnal Ilmiah Psikologi · doi:10.30872/whxvrh35
“Temuan ini sejalan dengan Strzelecki (2023), yang menjelaskan bahwa facilitating conditions lebih berkaitan dengan use behavior dibandingkan behavioral intention, serta Ratumanan dan Krismiyati (2025) yang menunjukkan peranan intensi dalam mendorong penggunaan AI.”
Wei, L., Zhang, S., & Heo, J. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1928967
Chao, Z., Li, D., & Tong, M. (2026) · Library & Information Science Research · doi:10.1016/j.lisr.2026.101428
Responsible GenAI Augmentation and Cognitive Agency in Higher Education
Sukprasert, A., & Phothong, L. (2026) · Sustainability · doi:10.3390/su182110831
Beyond AI skills: Understanding generative AI engagement in graduate business education
Fernández, R. C., & Lazarte-Aguirre, A. (2026) · The International Journal of Management Education · doi:10.1016/j.ijme.2026.101568
Dehouche, N., & Wei, X. (2026) · Heliyon · doi:10.1016/j.heliyon.2026.e45640
Fan, X., Cheng, A., & Wang, H. (2026) · International Conference on Intelligent Education and Computer Technology (IECT) · doi:10.1145/3845354.3845389
“This implies that AI-assisted learning is associated with students’ motivation and self-regulated learning [5].”
University students' use of GenAI in coursework writing: A qualitative study in Hong Kong
Pan, H., Kong, E. W. S., Lam, A. H. C., & Chiu, D. K. W. (2026) · The Journal of Academic Librarianship · doi:10.1016/j.acalib.2026.103362
Chen, C.-M., Li, J., 侯学新, & Li, X. (2026) · International Conference on Intelligent Education and Computer Technology (IECT) · doi:10.1145/3845354.3845535
“Bhattacherjee (2001) posits that higher satisfaction directly extends the system’s usage lifecycle [5]; empirical studies further confirm its necessity for continuance intention [13, 14].”
Wang, X., Xu, T., & Meng, X. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1913577
Understanding ChatGPT adoption in higher education: An affective-cognitive dual-pathway model
Pinho, C. S. B., & Lopes, J. M. (2026) · Industry and Higher Education · doi:10.1177/09504222261498352
Metadata
- Authors
- Strzelecki, A.
- Affiliations
- University of Economics in Katowice (PL)
- Corresponding author
- Strzelecki, Artur
- ORCID
- Strzelecki, Artur
- Venue
- Innovative Higher Education
- Published
- 2023-11-30
- Volume
- 49
- Issue
- 2
- Pages
- 223-245
- ISSN
- 0742-5627 (ISSN-L)
- 1573-1758
- OpenAlex
- W4389203481
- Cited by
- 477
- References
- 78
- Citations by year
- 2026: 195 · 2025: 179 · 2024: 103
- FWCI
- 121.61
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Innovative Higher Education (publishedVersion, cc-by)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Keywords
- ChatGPT, technology acceptance, UTAUT2, personal innovativeness, habit, behavioral intention, use behavior, performance expectancy, hedonic motivation, facilitating conditions, university students, artificial intelligence in higher education
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