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 study rests on two theoretical pillars. The first is the Technology Acceptance Model, developed by Fred Davis in 1986, which holds that people adopt a technology when they judge it useful and easy to use. In this framing, Nigerian students embrace AI because they perceive it as an instrument that improves access to information, simplifies difficult concepts, and supports learning activities, and that acceptance then shapes how they interact with resources, seek information, and complete tasks. The second lens, Barry Zimmerman’s Self-Regulated Learning theory from 1989, treats students as active agents who plan, monitor, and evaluate their own learning. Crucially, the theory predicts that AI’s impact depends on regulation: used deliberately to clarify concepts and gather tailored resources, it can support autonomy; used as a substitute for cognitive effort, it can quietly hollow out the learning it appears to accelerate.

Methodologically, the research is exploratory and deliberately small. One federal university was purposively chosen from each geopolitical zone: the University of Ibadan in the South-West, the University of Nigeria, Nsukka in the South-East, the University of Port Harcourt in the South-South, the University of Maiduguri in the North-East, the University of Abuja in the North-Central, and Ahmadu Bello University in the North-West. From each institution, one male and one female undergraduate were selected, yielding twelve participants aged 20 to 29 who already used AI tools for academic work. Semi-structured in-depth interviews lasting roughly 40 to 45 minutes were conducted by telephone or Zoom, audio-recorded with consent, transcribed verbatim, and coded inductively with NVivo 12 software. The interview guide was vetted by experts in qualitative research and educational technology and pilot-tested before the main data collection, with two professors independently reviewing the coding to strengthen analytical rigour.

One of the study’s most striking findings concerns where awareness begins. Not one participant credited formal university instruction as their gateway to AI. Instead, they described encountering the technology through social media platforms such as YouTube and Twitter, through classmates who demonstrated tools in action, and, less often, through lecturers who recommended AI when students struggled with course content. One male student recalled stumbling onto a YouTube platform two years earlier that framed AI as a way of easing academic stress; a female student described a Twitter conversation that transformed her view of AI from a threat into a learning aid. This informal diffusion matters because awareness acquired through social media and peer networks shapes expectations before any classroom policy does. The finding echoes earlier Nigerian surveys in which peers, friends, and social media far outstripped official channels as sources of first exposure to tools like ChatGPT, suggesting universities arrive late to a conversation already well underway among students.

Once adopted, AI visibly restructured four dimensions of academic behaviour. Students reported dramatically faster access to materials, with some describing the library as nearly obsolete for routine information needs. One participant explained that AI lets him skip less important details and extract the main points of a course in seconds, replacing weeks of textbook reading. Second, assignment completion became markedly quicker and less effortful, easing deadline pressure across disciplines. Third, and more positively, the technology facilitated genuinely independent study and research, with students exploring topics beyond lecture content and at their own pace, free from the scheduling constraints of lecturer availability. Fourth, participants prized AI’s ability to tailor explanations, prompting chatbots to rephrase complex concepts at their preferred level of comprehension and to answer follow-up questions in ways standard textbooks cannot. Taken together, these accounts describe a shift from hunting for information toward evaluating and interrogating material that arrives pre-synthesised.

Yet the same interviews surfaced a darker ledger. Students described AI-generated information that was inaccurate, outdated, or misleadingly confident, sometimes distorting concepts they did not yet understand well enough to catch the errors. One participant reported receiving a misinterpretation of an idea she had queried; another described outdated facts and content errors in an AI-assisted assignment, concluding that students should never take AI answers at face value. A second cluster of problems involved AI literacy itself: several students admitted that their early prompts produced poor results simply because they lacked the skills to formulate effective queries or assess outputs. A third was infrastructural. Unreliable campus Wi-Fi and unstable connectivity repeatedly disrupted access to AI platforms, with one student noting the network was usable mainly at night, raising equity concerns for students who cannot afford private data plans.

The most sobering theme, however, was overdependence. Participants volunteered candid admissions that habitual AI use had dulled their willingness to think independently. One female student said she now felt lazy thinking on her own, having used AI for assignments, term papers, and exam preparation. A male participant went further, describing AI as a substitute for his thinking mind that had slowed his cognitive strength and prompting him to declare it time to regulate his own use. These confessions align with broader experimental evidence that heavy reliance on tools like ChatGPT can reduce students’ mental effort, and with the self-regulated learning framework’s warning that AI becomes harmful precisely when it replaces, rather than scaffolds, the cognitive work of learning. The study frames this not as an inherent property of the technology but as a failure of regulation, one that universities can address.

Participants themselves proposed remedies, and the study organises them into four strategies. First, students should be trained to critically verify AI outputs against credible academic sources before citing or using them, sharpening analytical habits rather than eroding them. Second, institutions should provide formal training on ethical AI use, covering plagiarism, bias, attribution, and academic integrity, so students stop perceiving AI as an ultimate source of learning that shortchanges individual effort. Third, AI literacy should be embedded directly into the university curriculum, with several participants arguing it should be compulsory, so that every graduate learns prompt formulation, output evaluation, and the technology’s limits. Fourth, governments and universities must invest in reliable digital infrastructure, including stable electricity and internet connectivity, because AI-supported learning remains inequitable without it.

The practical implications reach into assessment design as well. The finding that AI lets students finish assignments with little effort suggests that traditional take-home essays are increasingly poor measures of learning. The study points toward process-based assignments, in-class activities, oral defences, and reflective components that require students to critique and justify AI-assisted responses rather than submit them unexamined. Lecturers, identified as gatekeepers whose recommendations confer academic legitimacy on AI tools, are urged to move beyond banning or blessing the technology and instead model legitimate uses, such as brainstorming, clarification, and feedback, while holding students accountable for argumentation and final decisions. Peer networks, already the dominant diffusion channel, could be harnessed through student-led workshops and AI learning communities to spread responsible practice rather than shortcuts.

The author is careful to note the limits of what twelve interviews can establish: the findings are exploratory insights rather than generalisable conclusions, and future work should include larger samples, educators’ perspectives, and longitudinal or mixed-methods designs tracking how AI-mediated learning evolves. Still, the study’s significance lies in its refusal to reduce the AI question to adoption statistics. Across Germany, the Nordic countries, the United States, Ghana, South Africa, and Kenya, surveys show students flooding into AI use, but this Nigerian investigation reveals the behavioural mechanics underneath: informal awareness channels precede formal policy, efficiency gains coexist with cognitive offloading, and the educational value of AI ultimately hinges on each student’s capacity to regulate their own engagement. In that sense, the study’s central message travels well beyond Nigeria’s borders. The technology is not inherently a tutor or a crutch; it becomes whichever one the user’s self-discipline, and their institution’s readiness, allows it to be.

Subject of Research: The influence of artificial intelligence use on the academic learning behaviour of undergraduate students in Nigeria

Article Title: Artificial intelligence use and its influence on academic learning behaviour among undergraduate students in Nigeria

Article References: Chigozie, N. G. (2026). Artificial intelligence use and its influence on academic learning behaviour among undergraduate students in Nigeria. Discover Education, 5(1), Article 1007. https://doi.org/10.1007/s44217-026-02210-5

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02210-5

Keywords: artificial intelligence, higher education, undergraduate students, Nigeria, learning behaviour, ChatGPT, AI literacy, self-regulated learning, technology acceptance model, academic integrity, qualitative research, digital divide

The research, published in Discover Psychology, was led by Riketa Parmar and Vipul Patel of Ganpat University’s V. M. Patel College of Management Studies, together with Mruga H. Mehta and Jigneshkumar P. Desai of Parul University. The team set out to build and test what they call the Green Banking Consumer Adoption Model for India, or GB-CAM-India. Rather than relying on a single behavioural theory, the model deliberately stitches together three of the most widely used frameworks in consumer and environmental psychology: the Technology Acceptance Model, the Theory of Planned Behaviour, and the Value-Belief-Norm theory. To these it adds constructs tailored to the Indian market, including perceived trust, perceived risk and customer satisfaction.

The logic behind the integration is worth unpacking. The Technology Acceptance Model, developed originally to explain how people come to use new technologies, focuses on two perceptions: that a system is useful and that it is easy to use. The Theory of Planned Behaviour adds the role of attitudes, subjective norms, meaning the social pressure we feel from people around us, and perceived behavioural control, our sense that we are capable of performing the behaviour. The Value-Belief-Norm theory, rooted in environmental sociology, traces pro-environmental action back through a chain of personal values, ecological beliefs and the activation of moral norms. Green banking sits at the intersection of all three: it is a technology, a planned consumer behaviour and an environmentally significant act at once, which is precisely why the authors argued that no single theory could capture the whole picture.

To test the model empirically, the researchers collected primary survey data from 760 respondents spread across five regions of Gujarat, a western Indian state with a large and diverse banking population. For each theoretical construct, they computed composite, or summed, scale scores and then subjected the data to a battery of statistical checks. Exploratory factor analysis was used to confirm that the survey items clustered into the intended dimensions, and reliability analysis confirmed internal consistency. The psychometric credentials of the measures were strong: the Kaiser-Meyer-Olkin measure of sampling adequacy reached 0.906, a value well above the conventional 0.6 threshold, every composite reliability value was at least 0.829, and all average variance extracted values were at least 0.545, calculated from the standardised loadings produced by the factor analysis. In plain terms, the survey instruments measured what they claimed to measure, and they did so consistently.

With the constructs validated, the team turned to multiple regression to test eight hypotheses linking the predictors to green banking adoption behaviour. The model performed respectably, explaining 46.8 percent of the variance in adoption behaviour, with an F-statistic of 82.51 on 8 and 751 degrees of freedom and a p-value below 0.001. Customer satisfaction dominated the results, with a standardised beta of 0.464, far ahead of any other predictor. Perceived ease of use came second at 0.193, followed by social influence at 0.083, both also significant at the 0.001 level. Perceived risk worked in the opposite direction, exerting a significant negative effect with a beta of minus 0.085 and a p-value of 0.002. The message for banks is that making green services feel safe, simple and satisfying matters more than preaching their environmental virtues.

One of the most technically interesting parts of the study concerns how satisfaction connects upstream perceptions to downstream behaviour. Using bootstrapped indirect-effect tests with 5,000 resamples and bias-corrected 95 percent confidence intervals, the researchers found that customer satisfaction carries significant indirect effects from perceived usefulness, perceived trust and environmental attitude through to adoption behaviour. Crucially, the corresponding direct paths from those three constructs to adoption were statistically nonsignificant. That pattern means satisfaction does not merely supplement these influences; it is the channel through which they operate. The authors are careful with terminology here, characterising these as indirect-only effects rather than partial mediation, a distinction that matters for how future researchers model the pathway.

The study also probed how the satisfaction-adoption relationship varies across the population. Income level emerged as a significant moderator of that path: higher-income consumers showed reduced sensitivity to satisfaction signals when deciding whether to adopt green banking. In other words, for wealthier customers, a pleasant service experience moves the adoption needle less than it does for lower-income consumers, a finding with clear implications for how banks segment their green marketing. The researchers additionally observed that mean adoption scores differed significantly across Gujarat’s five regions, with an F-statistic of 9.88 on 4 and 755 degrees of freedom and a p-value below 0.001. They are careful to note, however, that this is a group-difference finding rather than a formally tested regional moderation effect, an honest caveat that reflects the exploratory, composite-score design of the analysis.

The practical implications ripple outward from these statistics. For banks, the results suggest that investments in service quality, complaint resolution and overall customer experience may do more to accelerate green banking uptake than awareness campaigns alone, because satisfaction is the conduit through which usefulness, trust and environmental attitudes are converted into action. Reducing perceived risk, whether fear of fraud in digital channels or uncertainty about green products, should also pay dividends, given its significant negative coefficient. For policymakers working under the Reserve Bank of India’s Green Finance Framework, the regional differences in adoption scores across Gujarat hint that a one-size-fits-all national rollout may underperform, and that income-sensitive design could matter, since wealthier consumers respond differently to satisfaction cues than lower-income ones.

The authors are equally candid about the limits of their approach. Because the analysis relied on composite scores, exploratory factor analysis and multiple regression rather than a full structural equation model, the causal architecture of GB-CAM-India remains a proposal awaiting confirmatory testing. They explicitly flag directions for future SEM-based research that could estimate the full path model with latent variables, test the regional moderation hypothesis formally, and extend the sample beyond Gujarat to other Indian states. The survey design is also cross-sectional, so the direction of influence, while theoretically motivated, is inferred rather than observed over time.

Even with those caveats, the study lands at a propitious moment. As India’s financial sector aligns itself with sustainable development goals, understanding the psychology of the consumer becomes as important as the engineering of the green products themselves. What this research demonstrates is that the road to sustainable finance in India runs through the everyday experience of the bank customer: a useful app that is trusted, a service that satisfies, and a social environment that quietly nudges people toward greener choices. If satisfaction is the engine of green banking adoption, then the banks that win India’s sustainable finance transition may be those that treat environmental ambition and customer experience as a single, inseparable project.

Subject of Research: Consumer adoption of green banking services in India, modelled through an integrated TAM-TPB-VBN framework

Article Title: An integrated TAM–TPB–VBN framework with empirical validation from Gujarat for green banking consumer adoption in the Indian market

Article References: Parmar, R., Patel, V., Mehta, M. H., & Desai, J. P. (2026). An integrated TAM–TPB–VBN framework with empirical validation from Gujarat for green banking consumer adoption in the Indian market. Discover Psychology. https://doi.org/10.1007/s44202-026-00883-5

Image Credits: AI Generated

DOI: 10.1007/s44202-026-00883-5

Keywords: green banking, consumer adoption, Technology Acceptance Model, Theory of Planned Behaviour, Value-Belief-Norm theory, India, Gujarat, customer satisfaction, sustainable finance, perceived risk, social influence, survey research

Menstrual cups are bell-shaped devices, typically made of medical-grade silicone, that are inserted into the vagina to collect rather than absorb menstrual blood. A single cup can be worn for up to twelve hours, then emptied, cleaned and reinserted, and with proper care a cup can last for as long as a decade. Compared with disposable pads and tampons, the economics are striking. One systematic review cited in the study calculated that if a woman used twelve pads per cycle, switching to a cup would represent roughly 0.4 percent of the waste and 5 percent of the cost; against twelve tampons per cycle, the cup would account for about 7 percent of the cost and 6 percent of the plastic waste. Research in Gujarat found that the average annual cost of sanitary pads is roughly twenty times that of a menstrual cup. Yet despite these advantages, usage among reproductive-age women in India has been reported at only around 5 percent.

The research team, led by Melisa Rudolph Menezes of Manipal Health Enterprises Limited, together with Swathi K S and Brayal D Souza of the Prasanna School of Public Health at Manipal Academy of Higher Education and Pallavi Upadhyaya of T A Pai Management Institute, grounded their investigation in three established behavioural frameworks: the Theory of Planned Behaviour, the Technology Acceptance Model and the Health Belief Model. This integrated approach allowed them to capture social influence, usability perceptions and health-related beliefs within a single model. The Theory of Planned Behaviour contributes the roles of attitude and subjective norms; the Technology Acceptance Model contributes perceived ease of use; and the Health Belief Model contributes safety perceptions, cost considerations and awareness as a cue to action. Because menstrual cups are inserted rather than worn externally, the researchers also hypothesised that safety perception would mediate the relationship between attitude and behavioural intention.

Data were collected through a cross-sectional online survey distributed via Google Forms and social media platforms, with voluntary participation and informed consent. The questionnaire contained 28 items measuring seven constructs: awareness, perceived ease of use, price value, attitude, safety perception, subjective norms and behavioural intention, all rated on five-point Likert scales. A priori power analysis using G*Power indicated a minimum sample of 103 for a medium effect size, so the achieved sample of 282 was comfortably adequate. Analyses were performed in SPSS for descriptive statistics and SmartPLS version 4 for structural equation modelling, following a two-step procedure that first validated the measurement model and then tested the structural paths.

The measurement model passed standard checks. Outer loadings exceeded the 0.7 threshold, Cronbach’s alpha and composite reliability met internal consistency criteria, average variance extracted surpassed 0.5 for convergent validity, and all heterotrait-monotrait ratios fell below 0.90, confirming discriminant validity. Variance inflation factors were all below 5, ruling out problematic multicollinearity. Most respondents were aged 18 to 30, about 91 percent had education at or above the 12th-standard level, roughly 30 percent were employed, and 73.4 percent were single, reflecting a largely young, educated, digitally connected sample.

The structural model delivered clear and statistically significant findings. Awareness, perceived ease of use and price value all significantly shaped attitudes towards menstrual cups, with price value showing the strongest path coefficient into attitude at 0.312. In turn, attitude, safety perception and subjective norms all significantly predicted behavioural intention. The coefficient of determination for behavioural intention was 0.424, indicating moderate predictive validity. Most strikingly, safety perception carried the highest path coefficient of any construct at 0.411, making it the single most powerful driver of intention in the model. All seven hypotheses were supported at the 5 percent significance level.

The mediation analysis added a crucial nuance. Without safety perception in the model, the direct effect of attitude on intention was 0.505; with the mediator included, it dropped to 0.246, an absorption of 0.259. The indirect effect through safety perception was 0.2558, and the variance accounted for statistic came to 0.5097, falling within the 20 to 80 percent range that indicates partial mediation. In practical terms, this means that even women who hold favourable attitudes towards menstrual cups may hesitate to adopt them unless they also believe the product is safe for their bodies. Safety beliefs about hygiene, insertion, sterilisation and leakage act as a cognitive filter through which positive attitudes must pass before converting into intention.

These findings align with, and help reconcile, a mixed international literature. Studies in Taiwan using the Theory of Planned Behaviour found attitude to be the largest predictor of cup intention, while research in the Philippines reported that subjective norms did not significantly influence intention, and work in Nepal suggested that positive peer exposure could increase adoption. The Indian results suggest that in a context where menstruation remains stigmatised and misconceptions about internal products persist, social endorsement matters, but it cannot fully override personal safety and comfort concerns. Prior Indian studies have reported that more than 80 percent of women in some populations are aware of menstrual cups and nearly half would be willing to try one if available, yet actual use remains low, evidence that knowledge alone is insufficient to change behaviour.

The authors argue that their results point to targeted, actionable interventions rather than generic promotion. To address safety perceptions and ease of use, policymakers and marketers could develop instructional workshops and video demonstrations on proper cup use and hygiene. Given the influence of subjective norms, peer-led campaigns and community ambassadors could normalise the product, while digital campaigns on popular social media platforms highlighting health benefits and cost-effectiveness could raise awareness. The study is not without limitations: its cross-sectional design cannot establish causality, convenience sampling through online channels may introduce self-selection bias and limit generalisability to women with less internet access or digital literacy, and measured intentions may not translate into actual behaviour where product availability, peer influence or personal comfort intervene. Even so, the research provides public health experts, policymakers and marketers with an evidence-based map of the psychological levers, above all safety, that must be pulled to make sustainable menstrual health choices genuinely accessible in India.

Subject of Research: Behavioral intentions of Indian women to adopt menstrual cups as sustainable menstrual hygiene products

Article Title: Shifting Preferences: Assessing Women’s Behavioral Intentions to Adopt Menstrual Cups

Article References: Menezes, M. R., K S, S., Upadhyaya, P., & Souza, B. D. (2026). Shifting Preferences: Assessing Women’s Behavioral Intentions to Adopt Menstrual Cups. Trends in Psychology. https://doi.org/10.1007/s43076-026-00533-8

Image Credits: AI Generated

DOI: 10.1007/s43076-026-00533-8

Keywords: menstrual cups, menstrual hygiene management, behavioral intentions, Theory of Planned Behaviour, Technology Acceptance Model, Health Belief Model, safety perception, PLS-SEM, India, sustainable menstrual products, consumer behavior, women's health

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