The review, led by Xiaolin Wang of Peking University School and Hospital of Stomatology together with colleagues from the National Cancer Center in Beijing, was registered in PROSPERO and searched PubMed, Embase, Web of Science, and CINAHL from their inception through 7 September 2025. Two reviewers independently carried out study selection, data extraction, and quality assessment, and the team employed a best-fit framework approach: they mapped findings deductively onto the Unified Theory of Acceptance and Use of Technology, a widely used model of technology adoption, while also coding inductively for concepts that emerged from the data themselves. Eleven studies spanning multiple countries made the final cut, and confidence in the resulting findings was rated moderate to high using the GRADE-CERQual framework.

What emerged from the synthesis was not a single barrier or a single driver but five interrelated themes that together explain the fate of mHealth in head and neck cancer care. The first is perceived clinical value: patients, caregivers, and clinicians engage with digital tools when they can see a tangible benefit, whether that is earlier detection of complications, better symptom control, or more efficient communication with the care team. Where that value is ambiguous or invisible, enthusiasm evaporates quickly. The second theme concerns usability and burden, a factor that carries particular weight in this disease.

Head and neck cancer and its treatment impose a distinctive symptom burden. Radiation-based therapy commonly triggers oral mucositis, a painful inflammation of the mouth and throat lining, while surgery and radiotherapy frequently impair swallowing, speech, and shoulder function. Patients may struggle with dry mouth, neck stiffness, disfigurement, and profound fatigue. Against that backdrop, an app that demands lengthy data entry, precise touchscreen manipulation, or sustained visual attention can become yet another burden rather than a relief. The synthesis makes clear that usability is not a cosmetic concern for this population; it is a determinant of whether the technology is used at all.

The third theme identified by the reviewers is professional and social endorsement and support. Patients are far more likely to adopt and sustain use of a mobile health tool when clinicians actively recommend it, integrate it into consultations, and respond to the data it generates. Caregivers, who often shoulder much of the day-to-day care in head and neck cancer, act as both facilitators and gatekeepers, helping patients navigate the technology while also drawing support from it themselves. Social reinforcement, including encouragement from peers and family, further shapes whether a tool becomes part of daily routine or a forgotten icon on a home screen.

The fourth theme, system readiness and workflow integration, shifts the lens from the individual to the institution. Even a well-designed, clinically valuable app will falter if hospitals lack the infrastructure to receive its data, if responsibilities for responding to patient-reported symptoms are unclear, or if the tool sits awkwardly alongside existing electronic health records and clinical routines. The review underscores that mHealth adoption is not merely a patient-side decision but a system-side commitment, requiring organizational buy-in, training, and clear pathways for acting on the information the technology produces.

Perhaps the most striking insight of the synthesis is its fifth theme: needs and perceptions are not static. The cancer care trajectory, from diagnosis through intensive treatment into survivorship or palliative care, acts as a key moderator of engagement. A patient in the throes of radiotherapy may crave daily symptom check-ins and rapid clinical feedback, while the same person months later, in recovery, may value rehabilitation exercises, peer connection, or long-term surveillance instead. Tools designed for one phase of the journey often fail to flex to the next, and the review suggests that this mismatch between evolving needs and rigid technology is a major reason engagement decays over time.

The theoretical scaffolding of the study matters here. The Unified Theory of Acceptance and Use of Technology, and its later meta-analytic refinements, predict that performance expectancy, effort expectancy, social influence, and facilitating conditions jointly determine both the intention to use a technology and the behavior itself. By anchoring the synthesis in this framework while remaining open to emergent concepts, the researchers were able to translate abstract adoption theory into concrete, cancer-specific mechanisms. The result is a map that developers, clinicians, and health system planners can actually navigate: demonstrate value, minimize burden, mobilize professional and social champions, and engineer the system so that the digital tool has a genuine home in the workflow.

The stakes are considerable. Head and neck cancer accounts for a substantial share of the global cancer burden, with GLOBOCAN 2022 estimates documenting rising incidence and mortality across many regions, and the disease is notorious for treatment-related morbidity that persists for years. Prophylactic swallowing interventions, for instance, are known to improve outcomes but demand sustained patient effort at precisely the time when patients feel worst. Mobile platforms, including apps that coach swallowing exercises and wearable devices that track function, could close that gap, and prior studies of tools such as swallowing therapy applications have shown promising feasibility. But feasibility in a pilot study is not the same as sustained adoption in the messy reality of patients’ lives, which is exactly the terrain this synthesis illuminates.

The review also speaks to a broader conversation in digital health. The World Health Organization’s 2019 guideline on digital interventions for health system strengthening called for rigorous evaluation of whether such tools actually deliver benefit, and subsequent reviews across chronic disease and cancer survivorship have repeatedly found that user engagement is the weak link. By focusing specifically on the lived experiences of patients, caregivers, and clinicians in head and neck cancer, and by assessing confidence in each finding rather than simply aggregating quotes, the Chinese team has produced evidence that is both methodologically disciplined and clinically actionable. Their conclusion is measured but firm: acceptance and sustained use of mHealth in this field are shaped by a dynamic interplay among perceived value, usability, endorsement, and system readiness, with the trajectory of care moderating everything. For a field that has often treated app adoption as a one-time event, that reframing, engagement as a moving target that must be re-earned at every stage of the cancer journey, may prove to be the study’s most enduring contribution.

Subject of Research: Factors shaping acceptance and sustained use of mobile health technology in head and neck cancer care

Article Title: Experiences of mHealth use in head and neck cancer care: a thematic synthesis guided by the Unified Theory of Acceptance and Use of Technology

Article References: Wang, X., Zhou, N., Yang, Y., & Zhang, J. (2026). Experiences of mHealth use in head and neck cancer care: a thematic synthesis guided by the Unified Theory of Acceptance and Use of Technology. Supportive Care in Cancer, 34(10), Article 1015. https://doi.org/10.1007/s00520-026-11253-1

Image Credits: AI Generated

DOI: 10.1007/s00520-026-11253-1

Keywords: mHealth, head and neck cancer, UTAUT, qualitative evidence synthesis, digital health, symptom monitoring, swallowing rehabilitation, patient engagement, health informatics, supportive care, caregivers, technology acceptance

The research arrives at a pivotal moment for Bangladesh. Mobile financial services such as bKash and Nagad now process more than USD 20 billion in transactions every month, and smartphone coverage reached roughly 70 percent in 2023, creating an enormous trove of digital transaction data. Yet the banking sector remains hampered by persistent inefficiencies: slow loan processing, weak monitoring, and a non-performing loan ratio of 8.16 percent in 2023. While digital payments have flourished, the integration of FinTech into core loan management, including origination, tracking, and recovery, has lagged far behind, leaving a critical gap between the country’s payment revolution and its lending operations.

To bridge that gap, the research team extended the classic UTAUT framework, originally developed by Venkatesh and colleagues, by adding Loan Performance as a final endogenous variable. The original model posits that Performance Expectancy, the belief that technology improves job performance; Effort Expectancy, the perceived ease of use; and Social Influence, the pressure from peers, managers, and regulators, jointly shape Behavioral Intention to adopt a technology. Behavioral Intention and Facilitating Conditions, which cover infrastructure, training, and technical support, then drive actual Use Behavior. The researchers argue this original formulation fits institutional banking better than the consumer-oriented UTAUT2, because bank adoption decisions hinge on efficiency and performance rather than hedonic motivation or price value.

The data were collected through structured questionnaires administered both physically and electronically, targeting bank officials, loan officers, IT staff, and FinTech users across all major divisions of Bangladesh, including Dhaka, Chattogram, and Khulna. Of 440 initial surveys, 354 valid responses were retained, an 80.45 percent retention rate that comfortably exceeds methodological minimums for partial least squares structural equation modeling. Respondents were experienced and well qualified: nearly half had more than fifteen years in the industry, and most held graduate degrees or professional banking credentials. All constructs were measured on seven-point Likert scales adapted from validated prior instruments and refined through a pilot test with thirty respondents.

The statistical analysis, conducted with SmartPLS and verified with STATA, revealed a clear and statistically significant causal chain. Performance Expectancy exerted a strong effect on Behavioral Intention with a path coefficient of 0.381, while Effort Expectancy was the single most powerful adoption driver at 0.438, underscoring that in a market with uneven digital literacy, user-friendly systems matter enormously. Social Influence contributed a smaller but still significant 0.193. On the next stage of the chain, Facilitating Conditions dominated with a coefficient of 0.615, and Behavioral Intention added 0.380 in predicting actual Use Behavior. Most strikingly, Use Behavior predicted Loan Performance with a coefficient of 0.898, confirming that the benefits of FinTech materialize only through consistent, effective use rather than mere intention.

The explanatory power of the model was remarkable across every stage. Behavioral Intention accounted for 82.9 percent of the variance, Use Behavior for 88.8 percent, and Loan Performance for 80.7 percent, all well above the 0.75 threshold conventionally regarded as strong. Measurement quality checks were rigorous: indicator loadings, composite reliability values between 0.880 and 0.942, and average variance extracted figures above 0.50 confirmed convergent validity, while heterotrait-monotrait ratios all fell within the acceptable 0.90 limit. Harman’s single-factor test and full collinearity assessments indicated that common method bias was not a serious concern, and robustness checks re-estimating the model without high-collinearity items produced consistent results.

The theoretical contribution is substantial. Most previous UTAUT research stopped at intention or usage, and most FinTech performance studies examined macro-level indicators such as profitability without grounding them in adoption theory. By linking individual-level acceptance constructs to institutional loan outcomes through a behavior-to-performance pathway, the study converts UTAUT into a performance-oriented institutional model. The mediation analysis is particularly instructive: Behavioral Intention affects Loan Performance indirectly, only through Use Behavior, demonstrating that strategic financial gains require the transformation of positive attitudes into daily operational practice.

The findings also carry important cautions. The authors emphasize that FinTech adoption exposes banks and borrowers to significant risks, including phishing, identity theft, ransomware, fraudulent loan applications, and algorithmic bias that could disadvantage rural borrowers, women, and small enterprises with thin credit files. Data privacy, transparency in automated lending decisions, responsible affordability assessments, and accessible grievance mechanisms are essential to sustaining trust. Bangladesh’s substantial Islamic banking sector adds a further layer, since digital lending products must comply with Shariah principles and offer clear contractual terms. Regulatory foundations such as Bangladesh Bank’s ICT Security Guidelines, mobile financial services regulations, and updated electronic know-your-customer requirements provide a starting framework, but regulatory clarity for emerging tools like peer-to-peer lending and blockchain-based credit remains uncertain.

For policymakers and bank leaders, the practical implications are direct. Because Effort Expectancy proved so influential, banks should invest in intuitive, well-tested lending platforms and pair them with digital literacy programs for staff and customers. The dominance of Facilitating Conditions argues for sustained spending on IT infrastructure, reliable connectivity, and technical support, especially in rural branches, alongside cybersecurity measures such as multi-factor authentication, encryption, real-time fraud detection, and rapid incident response. Social Influence, though weaker, can be harnessed through leadership-led digital transformation targets, industry-wide standards, and visible success stories that normalize FinTech-based lending.

The study does have limitations that temper interpretation. It relies on self-reported, perceptual measures of loan performance, since bank-level financial data are confidential, and it treats all banks as operating under comparable conditions despite real differences between public, private, and specialized institutions. Its cross-sectional design cannot capture how FinTech benefits accumulate over time, and moderating factors such as organizational culture, customer trust, and regulatory constraints were not modeled explicitly. Future research using objective indicators such as non-performing loan ratios and recovery rates, along with longitudinal and moderated designs, would strengthen the evidence. Even so, the Bangladesh experience offers a compelling template for other developing economies: locally adapted digital innovation, when paired with infrastructure, training, and prudent regulation, can convert technology acceptance into faster, safer, and more inclusive lending.

Subject of Research: FinTech adoption and bank loan performance in Bangladesh using the UTAUT model

Article Title: The role of FinTech in enhancing bank loan performance in Bangladesh through the UTAUT model

Article References: The role of FinTech in enhancing bank loan performance in Bangladesh through the UTAUT model. (n.d.). https://doi.org/10.1007/s44282-026-00573-6

Image Credits: AI Generated

DOI: 10.1007/s44282-026-00573-6

Keywords: FinTech, Bangladesh, UTAUT, bank loan performance, digital lending, structural equation modeling, behavioral intention, use behavior, financial inclusion, cybersecurity, mobile financial services, emerging economies

The research, published in Smart Agricultural Technology, was led by Sabrina Gulab, Hanan Ishaque, and Guillaume Lhermie, who surveyed 587 commercial crop and livestock producers operating farms larger than 700 acres across the three Prairie provinces. After restricting the analysis to producers who had adopted at least one digital agricultural technology, or DAT, the final analytical sample comprised 392 operations. The team used a binary logit regression model to identify which factors were associated with what they call data-use intensity, defined as the degree to which producers integrate technology-generated data into day-to-day farm management decisions. Respondents were classified as making high use of data or low or no use, and the model estimated how behavioral, psychological, and institutional variables shifted the probability of falling into the high-use category.

The theoretical foundation of the study is an extension of two influential frameworks in technology acceptance research: the Technology Acceptance Model, or TAM, and the Unified Theory of Acceptance and Use of Technology, known as UTAUT. Both frameworks were originally designed to explain why people adopt technology in the first place, emphasizing constructs such as perceived usefulness, effort expectancy, and facilitating conditions. The authors argue that these models fall short in the post-adoption phase, where the relevant question is not whether a farmer expects a tool to perform, but whether the continuous stream of data it generates is perceived as actionable, trustworthy, and valuable for ongoing decisions. They systematically remapped UTAUT constructs to post-adoption equivalents: performance expectancy became the perceived usefulness of data insights, facilitating conditions became data handling support, and social influence was extended to capture emerging norms around data sharing and collective contribution to agricultural data ecosystems.

The descriptive findings alone are sobering for the digital agriculture industry. Only 40.3 percent of surveyed producers reported high use of data in farm decision-making, while 54 percent reported low use and 6 percent reported no use at all. In other words, a majority of farmers who had already paid for and installed digital technologies were not meaningfully integrating the resulting information into their management choices. Producers operating larger farms showed higher levels of data integration than those on smaller operations, a pattern the authors attribute to economies of scale, where data-driven management produces clearer efficiency gains on complex, resource-intensive enterprises.

The regression results sharpen the picture considerably. Perceived usefulness of data emerged as the single strongest predictor: a one-unit increase in the belief that data helps optimize resources and inputs was associated with a 20.4 percentage point higher probability of high data use. Openness and proactive behavior came next, with roughly a one-standard-deviation increase associated with a 14.9 percentage point rise in the probability of high data use. Producers who actively seek information about new technologies, experiment with unfamiliar tools, and attend workshops appear to sustain engagement with data long after the initial purchase. Altruism also played a meaningful role, with producers comfortable sharing farm data with providers, researchers, companies, and government institutions showing an 8.7 percentage point higher probability of high data use, consistent with the idea that contributing to a shared data ecosystem both reflects and reinforces deeper engagement with one’s own data.

On the negative side, risk perception significantly suppressed data engagement. Producers who felt the risks of digital technologies outweighed their benefits had an 8.1 percentage point lower probability of high data use. Notably, the study uncovered a moderating effect: the negative influence of risk perception was stronger among high adopters who used many technologies, suggesting that farmers with the most at stake operationally and financially are also the most sensitive to concerns about data privacy, system reliability, and technological risk. Lack of digital literacy was another substantial barrier, associated with a 9.8 percentage point lower probability of high data use. Drawing on cognitive load theory, the authors argue that when interpreting complex dashboards and agronomic recommendations exceeds a producer’s cognitive resources, the data simply never makes it into the decision-making process.

Perhaps the most counterintuitive finding concerns social support. Producers who frequently consulted neighbors when they ran into difficulties with their technologies were less likely to use data intensively, with each unit increase in reliance on peer consultation associated with a 4.3 percentage point decrease in the probability of high data use. The authors interpret this as evidence that dependency-oriented support may substitute for independent engagement: farmers with lower technological self-efficacy lean on neighbors rather than developing the autonomous problem-solving skills needed to work with data themselves. This challenges a long-standing assumption in agricultural extension literature that peer networks uniformly accelerate technology uptake, and it suggests that building independent digital competence may matter more than facilitating help-seeking.

Equally striking is what did not predict data use. Concerns around data governance, including privacy, ownership, and commercial exploitation of farm data, showed no statistically significant association with data-use intensity, nor did trust in technology providers or the lack of facilitating conditions such as technical support and system interoperability. These factors dominate the adoption literature and are frequently cited in policy debates, yet in this sample of commercially established producers they did not shape what happened after adoption. The authors propose two explanations: established commercial producers may already operate within sufficiently developed governance arrangements, or governance concerns may function primarily as barriers at the adoption stage rather than during ongoing data integration. Either way, the finding underscores a stage-based view of technology engagement in which different barriers operate at different points along the adoption continuum.

The implications extend beyond farm gates. High-quality, farm-level data are the raw material for the next generation of AI-enabled agriculture, feeding the predictive models and recommendation systems that promise precision forecasting and optimized input use. If most adopters leave their data unexamined, both current returns on technology investment and future innovation pipelines are constrained. The authors argue that policy should therefore prioritize behavioral and cognitive enablers alongside infrastructure spending: strengthening digital literacy, reducing perceived risk through experiential learning, and framing data sharing as a contribution to the collective resilience of the agricultural community. Crop producers, who showed a 29.3 percentage point lower probability of high data use than livestock producers, may deserve particular attention, since livestock systems tend to depend on continuous monitoring that makes data value more immediately visible. As governments pour money into rural broadband and digital agriculture subsidies, this study offers a clear warning: the bottleneck is no longer the technology itself, but the human capacity and motivation to turn its output into decisions.

Subject of Research: Post-adoption data use behavior among commercial crop and livestock producers using digital agricultural technologies in Western Canada.

Article Title: Beyond adoption: Understanding data use behavior in digital agriculture

Article References: Gulab, S., Ishaque, H., & Lhermie, G. (2026). Beyond adoption: Understanding data use behavior in digital agriculture. Smart Agricultural Technology, 15, Article 102546. https://doi.org/10.1016/j.atech.2026.102546

Image Credits: AI Generated

DOI: 10.1016/j.atech.2026.102546

Keywords: digital agriculture, data use behavior, precision agriculture, technology adoption, UTAUT, data governance, digital literacy, farm decision-making, risk perception, altruism, Western Canada, agricultural policy

The study, led by Agostinho Sousa Pinto of the Polytechnic Institute of Porto together with colleagues in Portugal, Spain and Bangladesh, surveyed 202 university students and analysed their responses using partial least squares structural equation modelling, or PLS-SEM. This statistical technique allows researchers to test how multiple latent factors, such as trust or performance expectancy, interact to shape an outcome like the intention to use a technology. The team built their model on the extended unified theory of acceptance and use of technology (UTAUT), a framework that has become the workhorse of technology adoption research since its introduction in the early 2000s.

What sets this work apart is the addition of geopolitical concern to the model. The construct captures worries about national security risks, the strategic implications of relying on a Chinese AI system, and the broader question of technological dependency. The results showed that these concerns significantly shaped students’ trust in DeepSeek and their privacy perceptions, and in doing so indirectly dampened adoption intentions. In other words, students did not simply ask whether the tool worked well; they also asked who built it, where their data might end up, and what it means for their country’s digital sovereignty to depend on a foreign power’s AI infrastructure.

Despite these anxieties, the overall picture was surprisingly favourable. Students rated DeepSeek positively for reasons that reflect the realities of studying in a developing economy: the model is free to use, shows an element of cultural alignment that Western tools often lack, and offers a way to reduce dependence on Western technology giants. The researchers describe DeepSeek’s approach as a form of frugal innovation — delivering capable AI at minimal cost — and argue that this model provides a blueprint for emerging economies seeking affordable access to frontier technology.

On the technical side, the analysis identified performance expectancy and facilitating conditions as the key drivers of adoption. Performance expectancy, the belief that the tool improves learning and research productivity, was a strong predictor of behavioural intention, while facilitating conditions — the availability of infrastructure and support, such as reliable internet access — also played a significant role. Effort expectancy, how easy students find the tool to use, contributed as well. By contrast, social influence, the pressure or encouragement from peers and instructors, and hedonic motivation, the sheer enjoyment of using the technology, had negligible effects. This suggests Bangladeshi students approach AI tools pragmatically rather than socially or recreationally.

The mediating role of trust deserves particular attention. The study found that geopolitical concerns did not simply add or subtract directly from adoption intentions; instead, they worked indirectly by eroding trust and amplifying privacy concerns. This pathway aligns with earlier research showing that trust is a critical mediator in AI acceptance, and it highlights how geopolitical sentiment can undermine even a technically capable and free product. For policymakers in the Global South, the implication is that promoting AI adoption is not only a matter of building infrastructure but also of addressing public anxieties about data flows and foreign dependency.

The timing of the research is significant. DeepSeek made global headlines with its efficient mixture-of-experts architecture and its reasoning-focused R1 model, demonstrating that frontier-level AI could be developed and deployed at a fraction of the cost of Western equivalents. For countries like Bangladesh, which sit outside the major AI powers and often cannot afford premium subscriptions to Western services, such models are attractive precisely because they lower the barrier to entry. The Bangladeshi press has even framed DeepSeek’s rise as a call for the country to invest in retaining its own AI talent rather than watching it emigrate.

At the same time, the study is candid about the risks. Security analysts have raised questions about data governance in Chinese AI services, and the researchers note that privacy risks remain real even when users view a tool favourably. Bangladesh’s internet infrastructure also presents a constraint: reliability and speed gaps can undermine facilitating conditions and therefore suppress adoption, no matter how capable the underlying model is. The authors argue that policymakers and AI developers must address both geopolitical sentiments and infrastructure shortfalls if adoption is to grow.

Broader lessons extend well beyond Bangladesh. As the supply of globally available AI models diversifies, adoption decisions in the Global South increasingly reflect a calculus of sovereignty, cost and cultural fit rather than a simple race to the most powerful model. The researchers emphasise the need for culturally congruent, sovereignty-sensitive AI tools — systems that respect local data concerns while remaining affordable. Their study is, to their knowledge, among the first to formally integrate geopolitical factors into an established AI adoption framework, opening a line of inquiry that seems likely to grow as AI becomes an arena of great-power competition.

For educators and developers watching the generative AI boom, the message is clear: in emerging markets, the winning formula combines genuine usefulness, minimal cost, dependable infrastructure and attention to the political anxieties that surround foreign technology. DeepSeek’s popularity among Bangladeshi students shows that even in a field dominated by Silicon Valley narratives, the Global South is charting its own course through the AI revolution — one weighed down by real concerns, but propelled by the promise of accessible intelligence for all.

The theoretical lineage of the framework used in the study is worth unpacking. UTAUT emerged from a synthesis of eight earlier acceptance models and was designed to explain a large share of the variance in behavioural intention across workplace technologies. Its later extensions added constructs such as hedonic motivation, price value and habit, reflecting the shift from mandatory enterprise systems to consumer-facing tools. The Bangladeshi study pushes this evolution further by treating geopolitics as a measurable latent variable rather than background noise, an approach that acknowledges how international relations now shape everyday software choices in ways the original model’s authors could not have anticipated.

The methodological choices also merit attention. Partial least squares structural equation modelling is particularly suited to exploratory research where a new construct is being introduced, because it places fewer demands on sample size and distributional assumptions than covariance-based approaches. The researchers followed established practice by assessing the measurement model for reliability and validity before testing the structural paths, drawing on widely cited criteria for convergent and discriminant validity. A sample of 202 students is modest but adequate for this technique, and the reliance on self-reported intentions means the findings describe attitudes rather than observed long-term behaviour, a limitation common to the adoption literature.

The technical backdrop to DeepSeek’s appeal lies in its architectural efficiency. The model family employs a mixture-of-experts design, in which only a subset of parameters activates for any given query, cutting computational cost substantially compared with dense models of similar capability. Its reasoning model demonstrated that reinforcement learning could elicit strong chain-of-thought performance, and independent evaluations have since tested such models on engineering tasks with encouraging results. For students in low-income settings, this efficiency translates directly into free or near-free access to tools that would otherwise sit behind subscription paywalls.

Bangladesh’s broader digital context helps explain the pattern of results. The country has invested heavily in digital government services and mobile connectivity, yet internet quality remains uneven, and the study’s emphasis on facilitating conditions echoes real infrastructure constraints documented in connectivity indices. Prior research on AI literacy among South Asian students, including library and information science cohorts in Bangladesh, India and Pakistan, has shown uneven familiarity with AI systems, suggesting that effort expectancy and support structures matter as much as raw capability. The pragmatic orientation of the students surveyed, with social influence playing little role, fits a picture of adoption driven by tangible academic need rather than fashion.

The geopolitical dimension resonates with precedents elsewhere in the region. Debates over the TikTok ban in India illustrated how digital sovereignty concerns can override consumer popularity, and public opinion research has documented shifting views of China and the United States among South Asian populations. The study’s finding that geopolitical sentiment operates through trust rather than directly suggests a subtle mechanism: users may continue to value a tool’s usefulness while quietly discounting their confidence in it, a state of ambivalence that could shift rapidly with news of data mishandling or regulatory change.

Future research could extend the framework in several directions. Longitudinal designs would reveal whether geopolitical concerns harden or soften as familiarity grows, and comparative studies across countries with different alignments could test whether the construct behaves consistently. Sampling instructors, administrators and policymakers alongside students would broaden the picture, and behavioural measures such as actual usage logs would strengthen inference. As AI models multiply and great-power competition intensifies, the integration of geopolitical constructs into adoption theory offers a template for understanding how the next generation of digital tools will be welcomed, resisted or renegotiated across the Global South.

Subject of Research: Adoption of the DeepSeek large language model among university students in Bangladesh, analysed with an extended UTAUT framework incorporating geopolitical concern.

Article Title: Exploring DeepSeek Adoption in Higher Education in Bangladesh: A UTAUT-Based Approach

Article References: Pinto, A. S., Abreu, A., Cota, M. P., Paiva, J., & Biswas, M. S. (2026). Exploring DeepSeek Adoption in Higher Education in Bangladesh: A UTAUT-Based Approach. Frontiers of Digital Education, 3(2), Article 16. https://doi.org/10.1007/s44366-026-0090-2

Image Credits: AI Generated

DOI: 10.1007/s44366-026-0090-2

Keywords: DeepSeek, generative AI, UTAUT, Bangladesh, higher education, geopolitical concern, PLS-SEM, technology adoption, digital sovereignty, Global South, trust and privacy, frugal innovation