The Technology Acceptance Model (TAM) (Davis et al., 1989) examines user adoption behaviors toward computational systems through dual cognitive mechanisms: perceived ease of use and perceived usefulness. Similarly, Rogers’ (1962) diffusion of innovations (DOI) theory explicates the societal adoption process of novel technologies, ideas, products, and services. Rogers (2003) identified five critical factors influencing innovation adoption—relative advantage, complexity, compatibility, trialability, and observability—while acknowledging that certain factors may impede acceptance and lead to adoption failure.

While generative AI has demonstrated ease of access, simplicity of use, and efficient outputs, the paradoxes of use and dependency, multi-information and accuracy, and limited access that accompany its development in education have challenged traditional models of acceptance (Lim et al., 2023). However, extended models such as the unified theory of acceptance and use of technology (UTAUT) and TAM3 incorporate more factors explored in addition to the characteristics of the technology itself, such as gender, age, and previous experience of use (Jin et al., 2025). However, factors affecting acceptance, such as use anxiety and personal fear of technology, addressed in TAM3 have yet to be specifically discussed for GAI technology (Avcı, 2024). Therefore, the present study was conducted to explore the process of individuals’ acceptance of GAI technology.

Hesitancy in decision-making manifests as a form of behavioral postponement and delay of individual action (Bussink-Voorend et al., 2022). In the context of GAI technology, hesitancy involves not only delayed acceptance or skepticism about the foundational use of GAI techniques but also selective adoption of the content and form of GAI outputs, as well as delayed use or avoidance of potentially risky GAI techniques.

The phenomenon of vaccine adoption hesitancy, operationalized as delayed adoption behaviors or nonacceptance decisions that persist despite healthcare service accessibility, has been systematically investigated in immunization behavioral studies. Building upon this conceptual foundation, contemporary health behavior models frame resistance as a continuum of behavioral uncertainty, a sociocognitive process demonstrating temporal–spatial contingency and situational variability across demographic groups (Bedford et al., 2018).

Research on technology adoption and diffusion indicates that rapid advancements in AI technology do not necessarily cultivate enthusiastic adoption; instead, users often exhibit cautious or resistant attitudes (Hinks, 2020). Different categories of GAI tools, particularly those designed for entertainment versus professional applications, have generated varying market responses (Wach et al., 2023). Within the student population, while the utility of GAI technology prevents wholesale rejection, individual subjective factors significantly influence adoption patterns. Therefore, characterizing higher education students’ complex relationship with GAI technology as hesitancy more accurately reflects the nuanced reality of their engagement with these emerging tools.

In the technological domain, stress-inducing elements frequently manifest as technological threats, which are particularly evident in the functionality and autonomy of GAI systems (Clauson, 2022). Perceived technological threat encompasses both the perceived severity of potential risks and their anticipated negative impacts.

A substantial corpus of literature exists on the development of AI technology and the potential risks associated with the advancement of GAI technology. First, the iterative process of technological updating facilitated by GAI technology, improvements in productivity, adjustments in the current position of humans regarding labor, and the corresponding employment crisis have led to significant changes in the labor market (Kandoth and Kushe Shekhar, 2022). Therefore, in the AI era, eliminating technological threats to human jobs has become more important than ever. Second, the technology gap created by the fact that few people control such technology has widened the wealth gap between individuals who have access to this technology and everyone else (Kitsara, 2022). As an emerging and rapidly evolving technological innovation, GAI technology has intensified public discourse that critically examines both its transformative potential and associated societal risks. While theoretical frameworks persistently highlight systemic vulnerabilities inherent in its developmental trajectory, empirical investigations are progressively quantifying user perceptions through measurable impact assessments of algorithmic systems. This dual analytical approach combines philosophical contemplation of technological evolution with data-driven evaluations of human–algorithm interactions, fostering a comprehensive understanding of GAI’s tangible implications across operational contexts.

Clauson (2022) divided perceived technological threat into numerous dimensions that are more convenient to measure, including four main categories: substitution (which refers to situations in which GAI technology replaces humans’ jobs, tasks, lifestyles, and core experiences), difficulty (which pertains to the difficulty of learning to use GAI technology), burden (which refers to the time and energy consumed through the use of GAI technology), and control (which includes issues such as the violation of personal privacy through monitoring).

The influence of GAI technology on the university student population is becoming evident, particularly with respect to learning styles and outcome outputs, as well as the prospect of highly intelligent future career paths, which are closely intertwined with two significant challenges (Holmes and Tuomi, 2022). From the perspective of human instincts, humans perceive clues related to threats and then react to them through avoidance or rejection; this process represents a basic behavioral mechanism aimed at maximizing biological survival (Luchkina and Bolshakov, 2019). Moreover, empirical studies on vaccines have demonstrated that hesitant behavior arises because users have some degree of apprehension about vaccination (Bedford et al., 2018). In this study, while university students perceived the usefulness and ease of use of GAI technology, they nevertheless exhibited considerable apprehension regarding the threat posed by GAI technology, which in turn led to their hesitancy in utilizing the technology rather than clear acceptance or rejection. On the basis of the preceding discussion, this study posits Hypothesis 1:

Perceived avoidability is defined as the likelihood that an individual can avoid a threat by implementing specific safeguards (Liang and Xue, 2009). Traditional technology threat avoidance theory (TTAT) posits that perceived avoidability is a protective assessment strategy after a user perceives a threat (Cao et al., 2021). In addition, protection motivation theory (PMT) posits that following stimulation by the external environment, individuals will assess both the perceived threat and their personal response to it. These individuals will subsequently engage in specific actions aimed at self-protection (Floyd et al., 2000). Perceived technological threat represents the assessment of the threat itself, whereas perceived avoidability signifies the evaluation of individuals’ personal capacity to confront technological threats. It specifically denotes the probability that an individual currently possesses the ability to avoid a perceived technological threat (Alam et al., 2024). Higher perceived technological threats necessitate the development of more extensive coping skills to facilitate resolution. Therefore, when a technological threat to an individual is perceived to be high, the relative perception of avoidability is likely to decline. In instances where technology is perceived as a threat that is challenging to avoid, individuals may engage in self-protective behaviors that delay or refuse the use of technology for reasons related to personal development and well-being. Therefore, this study proposes Hypotheses 2, 3, and 4.

Fear of GAI is a negative emotion evoked after an individual perceives threats from the development of GAI technology (Gorman, 2008). Public apprehensions surrounding GAI technology predominantly stem not from technological artifacts per se but rather from the sociotechnical externalities of algorithmic advancement. These societal repercussions manifest across temporal dimensions, encompassing both contemporary workforce displacement anxieties and prospective implications of labor market paradigm shifts. The core concern lies in the algorithmic displacement phenomenon, wherein intelligent systems progressively automate cognitive tasks traditionally within human domains, fundamentally reconfiguring employment ecosystems. The fear-as-acquired-drive model (Hovland et al., 1953) posits that fear or emotional tension motivates individuals to react defensively when they perceive a threat. When individuals perceive a threat to GAI technology, fear and apprehension about the development of GAI technology will drive them to reduce the use of GAI technology and avoid the damage that the development of the technology may cause to the individual. Sutton’s (1982) meta-analysis established a causal linkage between threat cognition and affective arousal, demonstrating that amplified risk appraisals evoke psychophysiological fear responses that proportionally intensify behavioral intentions to adopt safeguarding measures. Extending this theoretical framework to GAI technological contexts, an increase in the threat perception of GAI technology may result in an elevated level of fear among students. Consequently, an increased level of fear may lead to a greater reluctance to utilize and indirectly safeguard oneself from the technology. Therefore, this study proposes Hypotheses 5, 6, and 7.

Within the theoretical architecture of protection motivation theory, fear emerges as a dynamic psychosocial phenomenon operationalized through threat appraisal-coping appraisal interactions (Floyd et al., 2000; Alam et al., 2024). Those who perceive GAI threats will undertake a personal assessment of the threat, whereby the presence of threats that cannot be avoided on the basis of the individual’s abilities and social circumstances will elicit anxiety or fear. This fear will prompt individuals to engage in avoidance behaviors that are beneficial to them. It can be reasonably deduced that a reluctance to utilize GAI may be prompted by the emotion of fear, which may arise when an individual perceives that threats posed by the technology cannot be avoided. H8 was subsequently formulated.

Building upon foundational work in artificial intelligence education (Long and Magerko, 2020), AI literacy has been conceptualized as the ability to critically assess and operationally deploy AI technologies, thereby enhancing collaborative workflows and productive outcomes. This investigation synthesizes contemporary scholarly perspectives, integrating Ng et al.’s (2021) framework to propose a structured definition aligned with technological advancements. Specifically, AI literacy encompasses four interconnected dimensions: recognizing and understanding AI, using and applying AI, evaluating and creating AI, and understanding the ethical implications of AI.

From the perspective of AI-for-social-good, Hermann (2022) posited that AI literacy can assist individuals in more effectively addressing and assessing the challenges posed by the mass production changes brought about by AI. Higher levels of AI literacy serve to mitigate the adverse effects of the technology while simultaneously enabling individuals to capitalize on the opportunities presented by AI developments. Furthermore, higher AI literacy can motivate more people or institutions to take the initiative to establish measures that balance people and technology to increase the service potential of AI and minimize its negative impacts (Carolus et al., 2023). Thus, AI literacy has a moderating effect on students’ GAI technology use, and higher AI literacy can attenuate the overall relationship between technology threat and hesitation to use. This is evidenced by the observation that as AI literacy increases, individuals perceive a reduction in the threat posed by AI, an increase in the perceived avoidability of negative consequences, a decrease in fear, and a corresponding reduction in hesitation to use. Thus, this research proposes Hypotheses 9 and 10.

Figure 1 illustrates the idea underlying this study and the specific model.

This study employed measurement items drawn from well-established previous studies to enhance the validity of this research. For this study, two doctoral students majoring in Communication Studies and proficient in English translated the survey questionnaire into Chinese. Their work was compared to create the preliminary version of the questionnaire, which was pre-tested on 50 college students. After the pretest, the questionnaire was revised to correct any inaccuracies, resulting in the final version. The key variables included in the analysis were operationalized as follows:

As presented in Table 1, the reliability analysis established measurement consistency across all eight primary latent variables, with Cronbach’s alpha coefficients (α) ranging from 0.82 to 0.915, exceeding the recommended benchmark of 0.7 for social science research. Furthermore, composite reliability estimates ranged between 0.869 and 0.934, surpassing the 0.70 benchmark for construct reliability (Hair et al., 2020). These results collectively confirm adequate internal consistency across all the measurement scales.

The measurement model’s convergent validity was rigorously evaluated through contemporary psychometric protocols (Hair et al., 2020), employing two complementary diagnostic metrics: standardized measurement loadings and average variance extracted (AVE) indices. All standardized factor loadings exceeded 0.7, indicating strong indicator reliability, as shown in Table 1. Convergent validity was substantiated by AVE indices surpassing the psychometric benchmark of 0.50 (range: 0.571–0.764), fulfilling essential requirements for latent construct measurement precision (Hair et al., 2020). Discriminant validity evaluation employed the heterotrait–monotrait (HTMT) ratio methodology, with all cross-construct correlation estimates remaining under the stringent cutoff value of 0.85 in Table 2 (Hair et al., 2020). These combined consequences substantiate the measurement model’s psychometric adequacy, confirming distinct construct operationalization without evidence of cross-dimensional contamination.

For the formative measurement evaluation, multicollinearity was first assessed through variance inflation factor (VIF) analysis. As summarized in Table 3, all the VIF values for indicators of the formative construct (perceived technological threat) remained substantially below the critical threshold of 5 (Hair et al., 2020), effectively ruling out multicollinearity concerns. Subsequent bootstrapping analysis with 5,000 subsamples was used to evaluate the statistical significance of the outer weights, which represent standardized regression coefficients indicating each indicator’s relative contribution to the formative construct (Becker et al., 2023). The results in Table 3 demonstrate significant outer weights for all four formative indicators (p < 0.01), confirming their substantive relevance to the higher-order construct.

This sequential validation process, encompassing collinearity diagnostics, outer weight significance testing, and threshold compliance verification, satisfies the recommended criteria for formative measurement model evaluation (Becker et al., 2023; Hair et al., 2020). The collective evidence substantiates both the psychometric adequacy of the measurement framework and the appropriateness of proceeding to structural model interpretation.

As shown in Table 4, the model demonstrated a strong global fit with an SRMR value of 0.044, substantially below the recommended threshold of 0.08 (Hair et al., 2020). The explanatory power analysis revealed moderate predictive efficacy across the following endogenous constructs: perceived avoidability (R² = 0.244), fear of general artificial intelligence (GAI) (R² = 0.276), and use hesitancy (R² = 0.262). Effect size evaluation through Cohen’s f² indicated substantial relationships, with perceived technological threat demonstrating large effects on perceived avoidability (f² = 0.276) and fear of GAI (f² = 0.236), alongside a medium effect on use hesitancy.

The model’s predictive ability was further substantiated through blindfolding procedures, yielding positive Q² values for all endogenous constructs (Table 4), thereby confirming its predictive relevance (Hair et al., 2020). Bootstrap analysis (5,000 subsamples) of path coefficients revealed statistically significant structural relationships (p < 0.01) across all hypothesized paths. Collectively, these metrics confirm the structural model’s robustness in explaining theoretical relationships while maintaining appropriate parsimony.

The concluding phase of structural model assessment involves hypothesis verification through examination of critical statistical parameters. As documented in Table 5 and visually represented in Figure 3, the analytical outcomes demonstrate path coefficient magnitudes accompanied by their respective significance indicators (t statistics and p values), precision estimates (95% confidence intervals), and ultimate hypothesis validation conclusions.

As expected, H1 to H6 are supported. Specifically, perceived technology threat is positively associated with GAI technology use hesitancy (β = 0.356, p < 0.001) and fear of GAI (β = 0.468, p < 0.001) but negatively related to perceived avoidability (β = −0.463, p < 0.001), thus supporting H1, H2, and H5. Perceived avoidability is found to be negatively associated with GAI technology use hesitancy (β = −0.132, p < 0.001), supporting H3, and fear of GAI is positively related to GAI technology use hesitancy (β = 0.128, p < 0.001), thus supporting H6.

In addition, the mediating effects of perceived avoidability and fear of GAI are also important for this study. Table 5 also shows the effects of mediation. The mediating effect of perceived avoidability between perceived technology threat and use hesitancy is significant [β = 0.061, CI = (0.028, 0.095)], thus supporting H4. The mediating effect of fear of GAI between perceived technology threat and use hesitancy is also significant [β = 0.060, CI = (0.027, 0.093)], supporting H7. Perceived avoidability and fear of GAI play a chain mediating role between perceived technology threat and use hesitancy, which is significant [β = 0.006, CI = (0.001, 0.012)], thus supporting H8. Moreover, the analysis results related to the control variables reveal no significant relationships with use hesitancy, perceived avoidability or fear of GAI.

In H9 and H10, this study assumed that AI literacy would moderate the path between perceived technological threat and perceived avoidability and the path between perceived technological threat and fear of GAI. First, we added the interaction term for perceived technological threat and AI literacy to the structural model. According to the results in Table 5, the path coefficient from the interaction term of perceived technological threat and AI literacy is significant (β = 0.07, p < 0.05), and the path between perceived technological threat and perceived avoidability is moderated by AI literacy. However, perceived technological threat is negatively related to perceived avoidability; thus, AI literacy does not enhance the relationship between perceived technological threat and perceived avoidability but rather weakens it. As a result, H9 is not supported. In addition, the moderating effects of AI literacy on perceived technological threat and fear of GAI are not significant (β = −0.058, p = 0.108). Therefore, H10 is not supported. The results are presented in Figure 4.

This study plots predicted perceived technological threat against perceived avoidability separately at both low and high levels of AI literacy (Figure 5). The results reveal that the relationship between perceived technological threat and perceived avoidability is weaker among participants who present high levels of AI literacy (b_simple = −0.83, p < 0.001) than among participants who present low levels of AI literacy (b_simple = −0.90, p < 0.001). Specifically, the impact of perceived technological threat on perceived avoidability is attenuated by an individual’s AI literacy; the impact of perceived technological threat on perceived avoidability diminishes as an individual’s AI literacy increases.

This study enriches the traditional TAM and DOI theory, thus enabling them to be applied to new technologies. In response to the new technological revolution, the audience not only pays attention to the advantages of the technology itself but also worries about the threats caused by such technology. Although the audience may benefit greatly from the new technology, people remain concerned about it. Furthermore, this study extends the notion of behavioral intention in the traditional TAM by exploring the notion of technology use hesitancy. Owing to the rapid development of AI technology, the audience’s values or social environment may change at any time, and their attitudes toward AI technology may similarly shift (Demirgüneş, 2018). It is difficult to describe the ambivalence of the current audience in terms of the simple acceptance or rejection of a given technology. Our focus on hesitation reflects the audience’s possible psychological experiences over a relatively long period.

Furthermore, this study posits that targeted enhancement of professional AI literacy, in conjunction with the cultivation of general knowledge competencies among students in higher education, can assist students in mitigating their perception of threats in technological threat assessment and enhancing their capacity to respond to such threats in a strategic and evasive manner. Furthermore, it can mitigate the effects of fear and emotional avoidance decisions resulting from a lack of competence. In the context of university education, the establishment of relatively sound rules concerning the use of AI technology can reduce students’ abuse of and bias toward technology (Pradana et al., 2023). Guiding students to view the development and use of AI technology positively can mitigate the negative emotions they experience with respect to the use of technology. Moreover, industry norms regarding the use of AI should be clarified, and ways of addressing the threat caused by using AI technology proactively should be proposed, thereby effectively mitigating the negative emotions experienced by the masses about new technologies and helping promote the use of AI technology.

There are several limitations of this study. First, we cannot make long-term predictions because AI technology is changing at a rapid pace. Moreover, we employed a cross-sectional design in this research, and a longitudinal design or experimental design is needed to confirm the causal relationships among the variables reported here. Finally, this study primarily focuses on the potential threats that GAI technology poses to individuals, mainly discussing the negative impacts that accompany its development. It does not extensively discuss the positive impacts of GAI development.

Future research should focus on the rapid development of GAI technology. More attention has been given to the technical pressures that people face in the process of using GAI. A discussion of how to avoid or address these negative effects when applying the technical advantages offered by GAI to all walks of life is thus of great practical value.