The research, published in Discover Education, was led by Godswill Agu of Abia State University together with colleagues in Nigeria, Poland, and Spain. The team surveyed 213 final-year undergraduate marketing students drawn from Nigerian higher education institutions, focusing deliberately on students in their final year for three reasons: they have experienced the full breadth of their curriculum, they face cognitively demanding tasks such as dissertations and strategic campaign design where the temptation and utility of generative AI are greatest, and they are on the verge of entering a labor market where AI literacy is increasingly tied to employability. Data were collected over two weeks in October 2024 through a structured online questionnaire, and the researchers excluded eleven respondents who reported no prior awareness of ChatGPT, since awareness was a prerequisite for meaningfully reporting adoption intentions.

At the heart of the study lies a classic psychological framework: the Technology Acceptance Model, or TAM, first formulated by Fred Davis in the 1980s. TAM holds that two beliefs govern whether people embrace a new technology: perceived usefulness, the sense that the tool will improve performance, and perceived ease of use, the belief that operating it will require minimal effort. These beliefs shape attitudes, and attitudes in turn shape behavioral intention, the strongest proximal predictor of actual use. The model has endured for nearly four decades, but its authors and successors have long argued that its explanatory power can be enhanced by adding context-specific variables. Generative AI, the Nigerian team reasoned, is precisely the kind of technology that demands such an extension.

Unlike the relatively stable information systems of the pre-internet era, ChatGPT is interactive, adaptive, and demands active engagement. Users must craft effective prompts, critically evaluate AI-generated output for errors and bias, and integrate that output into academic work. The researchers therefore folded two additional constructs into the model. The first was awareness, drawn from Everett Rogers’s diffusion of innovations theory, which treats awareness as the opening stage of the innovation decision process: without knowing what a technology is and what it can do, people cannot form sensible judgments about its value. The second was perceived self-efficacy, rooted in Albert Bandura’s Self-Efficacy Theory, which captures a person’s confidence in their own ability to perform the tasks a technology requires. Crucially, self-efficacy is conceptually distinct from ease of use: the former is a judgment about the person, the latter about the system.

The analysis employed partial least squares structural equation modelling, a statistical technique well suited to exploratory models in emerging technology contexts, run in SmartPLS with bootstrapping on 10,000 subsamples. The sample skewed young and female: 57.28 percent of participants were women, and 88.26 percent were under 25 years old. The measurement scales, adapted from previously validated instruments and each measured with four items on five-point Likert scales, passed standard tests of reliability and validity, including factor loadings above the required threshold, satisfactory Cronbach’s alpha and composite reliability values, and convergent validity confirmed by average variance extracted figures exceeding the accepted minimum. Checks for common method bias, including Harman’s single-factor test and a full collinearity assessment, came back clean.

The results were dramatic. The extended model explained 81.6 percent of the variance in students’ attitudes toward ChatGPT and 80.1 percent of the variance in their intention to adopt it, figures the authors note are consistent with or higher than those reported in comparable studies elsewhere. Awareness emerged as the foundational driver, significantly boosting perceived usefulness, perceived ease of use, perceived self-efficacy, attitude, and intention alike. Its strongest single effect was on perceived ease of use, followed closely by self-efficacy and usefulness. Perceived usefulness significantly predicted both attitude and intention, and self-efficacy did the same, confirming that students who believed in their own capability to use ChatGPT effectively were markedly more positive about the tool and more determined to integrate it into their studies.

The surprise came with perceived ease of use. Although its path coefficients were positive, they failed to reach statistical significance for either attitude or intention. In other words, how easy students found ChatGPT to operate had no reliable bearing on whether they planned to use it. The authors offer a compelling explanation: for a generation of digitally fluent students, ease of use has become a baseline expectation rather than a differentiator. Moreover, the real challenge posed by ChatGPT is not technical but cognitive. The interface is simple; the harder skills involve formulating prompts, judging the reliability of outputs, and weaving AI content into rigorous academic work. Students, the study suggests, perceive the challenge of ChatGPT as skill-based rather than usability-based, which is precisely why self-efficacy, not ease of use, emerged as the significant determinant of adoption.

The study’s strongest single pathway ran from attitude to intention, with a path coefficient of 0.895. The authors caution that this very high value should be interpreted carefully, but they demonstrate that discriminant validity between the attitude and intention constructs held, with a heterotrait-monotrait ratio well within accepted bounds, meaning the effect is not an artifact of construct overlap. Notably, the finding challenges Davis’s original assertion that attitude is a weak predictor of intention, suggesting that for generative AI among technology-oriented students, attitudes function as a robust engine of adoption. It also aligns with prior work showing that as users grow familiar with AI and perceive its benefits, their attitudes shift from negative or merely instrumental to genuinely positive.

For educators and policymakers, the practical implications are pointed. Simply giving students access to generative AI tools may accomplish little if they lack the cognitive preparation to use them well. The authors argue that institutions should embed ChatGPT in structured learning activities that demonstrate its relevance to concrete marketing tasks, such as campaign development, while offering guided practice, prompt-design training, and explicit evaluation criteria to build student confidence. Clear ethical guidelines could reduce uncertainty and misuse. The authors also highlight that marketing students, as future shapers of consumer perceptions, may carry their attitudes toward AI well beyond the classroom, making mentoring and training in ethical and strategic AI use particularly consequential for this discipline.

The study has limits, as the authors acknowledge. It examined only final-year marketing students in a single country, limiting generalizability to other fields, levels, and national contexts, and its exclusively quantitative design cannot capture the texture of students’ lived experiences with the technology. Future work could test alternative frameworks such as the Unified Theory of Acceptance and Use of Technology, explore moderators like prior AI experience, digital literacy, or task complexity, and include the perspectives of administrators and policymakers. Still, the core message lands with force: in the age of generative AI, the decisive question is no longer whether a tool is easy to use, but whether people understand it and believe in their own ability to master it.

Subject of Research: Determinants of ChatGPT adoption among Nigerian undergraduate marketing students using an extended Technology Acceptance Model

Article Title: Extending the technology acceptance model with awareness and self-efficacy in predicting ChatGPT adoption among Nigerian marketing students

Article References: Agu, G., Obinna, E.-U. C., Okereafor, G. E., Omotosho, T. D., Chiana, C. A., Onyeokoro, S. C., Njoku, P. O., Okpara, G. S., & Margaça, C. (2026). Extending the technology acceptance model with awareness and self-efficacy in predicting ChatGPT adoption among Nigerian marketing students. Discover Education, 5(1), Article 1006. https://doi.org/10.1007/s44217-026-02152-y

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02152-y

Keywords: ChatGPT, generative AI, technology acceptance model, self-efficacy, awareness, higher education, Nigeria, marketing students, PLS-SEM, AI adoption, perceived usefulness, educational technology

The research addresses a distinctive commercial and psychological puzzle. Online paid courses, or OPCs, require consumers to evaluate two things simultaneously: the educational value of the content and the financial commitment needed to access it. Unlike general e-commerce, where the product is tangible and often returnable, a course is an intangible service whose quality can only be judged after purchase. Unlike free online learning, it carries real monetary risk. The authors argue that this dual burden of value assessment and financial exposure makes the purchase decision for paid digital education a unique behavioral case that existing single-theory models struggle to explain fully on their own.

To capture that complexity, the researchers built their framework from three established theoretical traditions. The technology acceptance model, originally formulated by Fred Davis in the 1980s, holds that adoption of a technology depends chiefly on perceived usefulness, the belief that the system will improve performance, and perceived ease of use, the belief that using it will be free of excessive effort. The theory of planned behavior, developed by Icek Ajzen, adds that behavior is predicted by intention, which is shaped by attitude toward the behavior, subjective norms, the perceived social pressure from important others, and perceived behavioral control. Trust was incorporated because digital transactions inherently involve uncertainty, and prior work in e-commerce has shown that trust shapes how consumers evaluate and adopt online services.

Methodologically, the study followed a quantitative design. Data were collected from 200 consumers using a structured questionnaire, and the proposed model was tested with structural equation modeling, a statistical technique that allows researchers to estimate networks of relationships among latent constructs that cannot be observed directly, such as usefulness or trust, while accounting for measurement error. Structural equation modeling is particularly well suited to this kind of theory integration because it evaluates whether the overall pattern of covariances in the data is consistent with the hypothesized causal structure, rather than testing each relationship in isolation.

The results are striking for what they confirm and, perhaps even more so, for what they contradict. Perceived usefulness, perceived ease of use and subjective norm all exerted positive and significant effects on both consumers’ attitudes toward online paid courses and their purchase intentions. In other words, when Bangladeshi consumers believe a course will genuinely improve their skills, when the platform is easy to navigate, and when family, friends or professional peers endorse the purchase, they both feel more positively about the courses and are more likely to buy them. This alignment between technology-related beliefs, social influence and intention suggests that the core engines of adoption in this market are practical and social rather than abstract.

The surprises emerge in the trust and attitude pathways. Trust significantly improved consumers’ attitudes toward online paid courses, confirming its theorized role as a belief that softens skepticism about online transactions. However, neither trust nor attitude translated directly into purchase intention. This finding runs against the intuitive assumption, common in e-commerce research, that favorable attitudes and trust reliably convert into buying behavior. In the Bangladeshi paid-course context, it appears that a consumer can trust a platform and hold a positive view of online education yet still hesitate to pay, with the decision hinging more directly on perceived usefulness, ease of use and social endorsement than on emotional or relational factors.

One plausible interpretation, consistent with the authors’ framing, is that in a developing economy where discretionary spending on education competes with many household priorities, the purchase decision for digital learning is treated as a calculated investment rather than an expression of sentiment. Consumers may weigh the expected performance gains from a course, the friction involved in accessing it, and the opinions of their social circle far more heavily than generalized feelings of warmth or confidence toward the seller. Trust, in this account, does its work earlier in the psychological chain, shaping whether a consumer views the category favorably at all, while the final intention to pay is governed by instrumental and normative judgments.

The findings carry substantial practical implications for e-learning providers operating in Bangladesh and in comparable emerging markets. First, improving platform usability is not cosmetic; perceived ease of use had a significant effect on both attitude and intention, meaning that intuitive interfaces, smooth enrollment processes, reliable video delivery and responsive support can directly move the bottom line. Second, providers should work to enhance the perceived value of their courses, since perceived usefulness was among the strongest levers in the model. Concrete demonstrations of skill outcomes, career relevance, certificates with labor-market recognition and transparent curricula can strengthen the belief that a course will genuinely improve performance. Third, although trust did not directly drive intention, it still shaped attitudes, and positive attitudes remain part of the adoption pathway, so visible credentials, secure payment systems, clear refund policies and credible instructor profiles retain strategic value.

The significance of subjective norm deserves particular attention from marketers in this market. Social influence, meaning the encouragement of friends, family, colleagues and peers, significantly affected both attitude and purchase intention. In a collectivist cultural context such as Bangladesh, where purchasing decisions are often discussed within families and social networks, word-of-mouth recommendations, testimonials from relatable peers, referral incentives and community-building around courses may be among the most cost-effective growth strategies available to course providers. The study suggests that a satisfied learner is not merely a repeat customer but a node in a social network that transmits adoption pressure to others.

Beyond its commercial relevance, the study extends academic theory by demonstrating that the integrated TAM-TPB-trust framework applies to the digital education context, a domain that sits awkwardly between pure technology adoption and consumer e-commerce. The finding that technology-related beliefs and attitudinal factors dominate while trust operates indirectly refines the relative weighting of constructs in this framework for paid-learning settings. As Bangladesh’s online education sector matures, the authors argue that improving usability, enhancing perceived value and establishing confidence will together promote course adoption and support the sustainable development of a market that has become a critical channel for skills formation in a rapidly digitizing economy. The research was self-funded by the authors and received ethics approval from the Research Management Committee of the Department of Marketing at Patuakhali Science and Technology University, with informed consent obtained from all survey participants.

The study’s sampling approach deserves brief explanation. Snowball sampling, a technique formalized in the social sciences in the early 1960s, relies on initial participants to recruit others from their own networks, making it a pragmatic choice when no comprehensive sampling frame of online course consumers exists. While this limits the statistical generalizability of the estimates to the broader Bangladeshi population, it is a common and accepted strategy for exploratory studies of emerging consumer segments, and the authors note that the underlying data will be made available on reasonable request to support scrutiny and replication.

The choice of structural equation modeling also connects the study to a substantial methodological literature. Researchers working with latent variable models typically distinguish between covariance-based and partial least squares approaches, and contemporary guidance emphasizes that the selection between them should follow considerations of model complexity, sample size and the explanatory versus predictive goals of the analysis. By situating their work within these established conventions, the authors align the study with a body of information systems research stretching back to foundational work on trust and technology acceptance in online shopping, which first demonstrated that trust and the classic acceptance beliefs operate as related but distinct drivers of consumer behavior.

The findings also resonate with recent research on the economics of paid online learning. Prior work on massive open online courses has shown that payment itself changes user engagement, with paying learners behaving differently from free participants, which underscores why purchase intention in this domain is a meaningful outcome in its own right rather than a proxy for adoption. Other scholarship has documented how pandemic-era policy responses accelerated the democratization of online learning, expanding access across socioeconomic groups and creating the demand conditions that markets like Bangladesh are now experiencing.

For theory, the indirect role of trust observed here echoes earlier integrated models in e-commerce, where trust frequently shapes attitudes and reduces perceived risk without always exerting a direct effect on intention. The Bangladeshi results suggest that in low-trust or high-uncertainty payment environments, trust may function primarily as a gatekeeping belief that determines whether consumers engage with the category at all. Future research could test whether these pathways hold across other paid digital education markets in South Asia, and whether constructs such as perceived behavioral control or price fairness, which the present framework did not center, add explanatory power as the market matures.

Subject of Research: Consumer purchase intention toward online paid courses in Bangladesh using an integrated technology acceptance, planned behavior and trust framework

Article Title: Predicting purchase intention towards online paid courses in Bangladesh: an integrated TAM-TPB-trust framework

Article References: Dipta, A. D., & Hossain, A. (2026). Predicting purchase intention towards online paid courses in Bangladesh: an integrated TAM-TPB-trust framework. SN Social Sciences, 6(9), Article 422. https://doi.org/10.1007/s43545-026-01715-y

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01715-y

Keywords: online paid courses, purchase intention, Bangladesh, technology acceptance model, theory of planned behavior, trust, e-learning, structural equation modeling, consumer behavior, digital education, perceived usefulness, subjective norm