The method behind the study is as revealing as its findings. The authors used VOSviewer, a widely adopted bibliometric mapping tool, to construct keyword co-occurrence networks, run thematic clustering, and generate time-weighted overlay maps. These techniques allow researchers to visualize how scientific terms cluster together and how those clusters evolve over time. In the early years of the study window, the dominant keywords were unmistakably computational: deep learning, classification, and algorithmic approaches filled the maps. The literature of that era reads like a computer science conference, with psychology often relegated to a supporting role as a source of data rather than a partner in design.

Then came the inflection point. According to the overlay analysis, after 2023 the thematic center of gravity shifted toward human-centered concepts: large language models, digital empathy, therapeutic alliance, and personalization surged in prominence. This is not merely a change in fashionable vocabulary. The therapeutic alliance — the collaborative bond between therapist and client — is one of the most robust predictors of psychotherapy outcomes, established in decades of meta-analytic research. Its appearance as a central theme in AI research signals that developers and scholars are no longer asking whether machines can process clinical language, but whether they can participate in the relational work that makes therapy effective.

To interpret this shift, the authors turned to Everett Rogers’ Diffusion of Innovations model, a classic framework describing how new technologies spread through communities. In Rogers’ schema, adoption proceeds through stages: innovators and early adopters experiment first, followed by the early majority, late majority, and laggards. The bibliometric evidence suggests that AI in psychology has crossed a critical threshold. The early phase, dominated by technical experimentation and proof-of-concept studies, corresponds to the innovator and early adopter stages. The post-2023 explosion of work on empathy, personalization, and therapeutic relationships marks the field’s entry into the early majority stage, where the question is no longer whether the technology works but how it should be integrated into everyday practice.

The geographical distribution of the literature tells its own story. Publication output was concentrated overwhelmingly in China and the United States, with the United Kingdom, Germany, and Australia forming a second tier. This concentration mirrors broader patterns in global AI research, where the two superpowers dominate output and impact. Notably, prior research cited in the study suggests that China and the United States produce more impactful AI research when they collaborate rather than compete — a finding with real implications for mental health technology, where shared standards and cross-cultural validation could determine whether AI tools work for patients everywhere or only for some.

Perhaps the most consequential finding concerns the direction of research intensity itself. The analysis shows a clear movement away from fully automated systems and toward hybrid models in which AI augments rather than replaces human practitioners. The authors describe this as a positioning of artificial intelligence around the axis of enhancement rather than replacement. It is a reframing with enormous stakes. Surveys of the field have repeatedly asked whether AI could replace psychotherapists, and the new evidence suggests the literature has largely answered: not replace, but extend. AI systems that draft empathic responses, flag risk, personalize interventions, or train novice counselors appear to be valued precisely because they keep humans in the loop.

The empirical work behind this shift lends it credibility. Studies cited in the bibliometric map include findings that ChatGPT outperformed humans on certain emotional awareness evaluations, that human-AI collaboration produced more empathic conversations in text-based peer-to-peer mental health support, and that large language models can help upskill novice counselors through simulated practice and feedback. Other research has compared responses from human therapists and LLM-based chatbots to assess therapeutic communication quality, and integrative reviews have begun asking whether a genuinely digital therapeutic alliance exists at all. Together, these studies form the intellectual scaffolding of the post-2023 thematic cluster that the bibliometric analysis identifies.

The timing matters. The World Health Organization’s 2022 world mental health report documented a staggering global gap between need and available care, with millions unable to access any form of treatment. Conversational agents and AI-based interventions have long been promoted as a scalable answer to that shortage, and meta-analyses of AI-based conversational agents for mental health and well-being have shown promising, if still preliminary, results. The bibliometric evidence suggests the field is now grappling seriously with the conditions under which such tools can deliver not just information but genuine relational benefit — and with the ethical questions, including bias in AI mental health applications, that scale-up inevitably raises.

For clinicians, researchers, and technology developers, the study offers a roadmap of where the field has been and where it is heading. The interdisciplinary structure it documents — spanning psychology, computer science, psychiatry, and engineering — reflects a maturing consensus that no single discipline can build trustworthy mental health AI alone. The transition from algorithmic dominance to human-centered themes suggests that the next wave of innovation will be judged not by benchmark accuracy alone but by whether systems can sustain something resembling a working alliance, adapt to individual users, and enhance the capacities of human caregivers rather than simulating them wholesale.

The decade from 2015 to 2025 began with machines learning to classify and ended with researchers asking machines to care. Whether digital empathy can ever be more than a well-engineered imitation remains an open and contested question. But the bibliometric record is unambiguous about the direction of travel: the AI revolution in psychology has moved from the laboratory to the consulting room, from replacement to enhancement, and from algorithms to empathy — and the early majority is now paying attention.

Subject of Research: Bibliometric mapping of artificial intelligence research trends in psychology and mental health from 2015 to 2025

Article Title: From algorithms to empathy: a bibliometric analysis of the AI revolution in psychology and related fields (2015–2025)

Article References: Kusci, İ., & Bakır, V. (2026). From algorithms to empathy: a bibliometric analysis of the AI revolution in psychology and related fields (2015–2025). Current Psychology, 45(18), Article 1510. https://doi.org/10.1007/s12144-026-10070-3

Image Credits: AI Generated

DOI: 10.1007/s12144-026-10070-3

Keywords: artificial intelligence, psychology, bibliometric analysis, large language models, digital empathy, therapeutic alliance, mental health, ChatGPT, diffusion of innovations, psychotherapy, hybrid models, digital mental health

E-governance, as a field of study, sits at the intersection of public administration theory, information systems research, and political science. It asks not merely whether a government has a portal or an app, but how digital tools alter the relationship between the state and the citizen, how bureaucratic incentives shift when services are automated, and how accountability functions when decisions are mediated by software. The book under review positions itself across both theory and application, a pairing that the review treats as one of its notable strengths, since many works in the field tend to privilege one side of that divide at the expense of the other.

One of the intellectual anchors visible in the review’s framing is the technology acceptance model, originally developed by F. D. Davis in 1989. The model, among the most cited frameworks in information systems research, holds that two perceptions largely determine whether users adopt a new technology: perceived usefulness, the belief that the tool will improve performance, and perceived ease of use, the belief that operating it will be free of excessive effort. Applied to government services, the model raises an uncomfortable question for digital transformation programs. A citizen who finds an online tax portal confusing, or a civil servant who sees no efficiency gain in a new workflow system, will quietly abandon the technology no matter how ambitious the policy announcement behind it was.

The review’s reference list also points to the role of trust, drawing on work by Gefen and Straub on trust and the technology acceptance model in online shopping, and on McKnight, Choudhury, and Kacmar’s 2002 typology of trust measures for e-commerce. That lineage matters because the stakes differ sharply between buying a book online and interacting with a state. Trust in an e-commerce platform concerns whether a transaction will complete as promised. Trust in e-governance concerns whether personal data will be protected, whether an algorithmic decision can be contested, and whether the digital interface is a genuine channel of the state or a barrier placed in front of it. Scholars in the field have long argued that without institution-based trust, the adoption curves of government technology flatten regardless of usability.

A third theoretical pillar is Everett Rogers’s diffusion of innovations, in its fifth edition from 2003, which also appears among the works cited in the review. Rogers’s framework describes how innovations spread through social systems over time, carried first by innovators and early adopters and only later reaching the early and late majorities, with the laggards arriving last, if at all. Transposed to the public sector, the framework helps explain why digital government initiatives so often produce uneven outcomes. A national identity system may be embraced in capital cities while failing to penetrate rural regions, not because the technology is deficient but because the diffusion process depends on communication channels, social networks, and the perceived compatibility of the innovation with existing values and practices.

The review connects these classic frameworks to contemporary developments in the journal’s own pages. Among the works it cites is a 2024 study by Menke, Gomes, and Xavier on the impacts of AI-based anti-corruption audits on risk aversion in decision-making, using Brazil’s ALICE tool as a case study. ALICE applies artificial intelligence to detect anomalies in public procurement, one of the most corruption-prone areas of government activity. The study’s findings on how such audits change the behavior of officials, making them more risk-averse in their decisions, illustrate a theme that runs through the modern e-governance literature: technology does not merely make administration faster, it reshapes the incentives and even the psychology of the people inside the bureaucracy.

Another cited contribution, W. Potipiroon’s 2024 analysis of the TikTok ban votes in the United States House of Representatives, pushes the discussion beyond administration and into technopolitics, the terrain where governments regulate, restrict, and weaponize the same digital platforms that citizens use daily. The inclusion of such work in the review’s frame signals that e-governance can no longer be understood as a one-way story of states adopting tools. States are also actors in a contested digital ecosystem, weighing national security against platform economies, and their choices feed back into public trust in every government technology initiative.

The journal hosting the review, Global Public Policy and Governance, is published by Springer Nature under the exclusive license of the Institute for Global Public Policy at Fudan University, and its scope spans e-governance, public administration theory, science and technology governance, and technopolitics. That breadth is deliberate. The questions raised by Trotta’s book, and by Zhao’s assessment of it, do not respect the boundary between domestic administration and international affairs. A data localization rule in one country affects cloud providers on other continents. An identity platform designed for service delivery can become an instrument of surveillance. The field’s journals increasingly treat these as a single, connected problem set.

What makes the review timely, beyond its scholarly content, is the trajectory of the field it surveys. Since the book’s 2018 publication, governments worldwide have deployed artificial intelligence for everything from fraud detection to chatbot citizen services, while simultaneously confronting the governance failures those deployments can produce, including biased algorithms, opaque automated decisions, and data breaches. The theoretical toolkit highlighted in the review, spanning acceptance models, trust typologies, and diffusion theory, was largely built for an era of websites and portals. Whether those frameworks can carry the weight of generative AI in the public sector is now one of the field’s open questions, and reviews like this one serve as checkpoints on how well the existing literature has prepared researchers for that transition.

For readers outside academia, the practical takeaway is that the success of digital government is not primarily a technical achievement. It is a behavioral and institutional one, resting on whether citizens find digital services useful and easy, whether they trust the institutions behind the interfaces, and whether innovations diffuse through society rather than stalling among early adopters. The review of Advances in E-governance, published as Global Public Policy and Governance Volume 5, pages 82 to 86, consolidates a body of theory that has quietly shaped how governments around the world build their digital faces, and it underscores that the hardest problems in e-governance are the oldest ones in public administration: trust, legitimacy, and the enduring challenge of making institutions work for the people they serve.

Subject of Research: A scholarly review of a book on the theory and application of e-governance technological initiatives

Article Title: Trotta, A. M. (2018). Advances in E-governance: Theory and application of technological initiatives. Routledge

Article References: Zhao, B. (2025). Trotta, A. M. (2018). Advances in E-governance: Theory and application of technological initiatives. Routledge. Global Public Policy and Governance, 5(1), 82-86. https://doi.org/10.1007/s43508-025-00111-1

Image Credits: AI Generated

DOI: 10.1007/s43508-025-00111-1

Keywords: e-governance, public administration, digital government, technology acceptance model, trust, diffusion of innovations, artificial intelligence, anti-corruption, technopolitics, book review, public policy, digital transformation

The research, conducted by Moon Hee Yu and Song Yi Lee of the Department of Counseling and Coaching at Dongguk University in Seoul, set out to capture the subjective viewpoints of practicing counselors rather than to measure their knowledge or test their attitudes on a scale. The team turned to Q methodology, a research technique developed in the 1930s by physicist and psychologist William Stephenson that is specifically designed to map patterns of subjectivity. Unlike a conventional survey, which asks everyone the same fixed questions, Q methodology asks participants to rank-order a set of statements according to how much they agree with them, from their own standpoint. The resulting rankings are then compared statistically to reveal clusters of people who share a coherent point of view.

In this study, thirty practicing counselors in South Korea sorted forty statements concerning the integration of neuroscience into counseling. The statements covered a wide terrain: the professional relevance of neuroscience, its practical applicability in the therapy room, the training it would require, and its implications for the identity of the counseling profession itself. Participants physically arranged the statements in a quasi-normal distribution, forcing them to make relative judgments rather than simply endorsing everything they liked. The researchers then applied principal component analysis with varimax rotation, a statistical procedure that identifies the main dimensions along which the sortings vary, using the KADE desktop application built for Q methodological work.

The analysis converged on four distinct viewpoints, and the labels the researchers gave them are evocative in themselves. The first was called Open and Proactive. Counselors in this group embraced neuroscience enthusiastically, seeing it as a source of professional enrichment and scientific legitimacy. They positioned brain-based knowledge as something to be actively sought out, integrated into case conceptualization, and used to strengthen the credibility of counseling in an era when clients and institutions increasingly expect evidence grounded in biology. For these practitioners, the question was not whether to adopt neuroscience but how quickly and how deeply.

The second viewpoint, Cautious and System-Oriented, revealed a very different calculus. These counselors did not reject neuroscience, but they insisted that any adoption must be filtered through institutional and systemic realities. They worried about training infrastructure, about whether graduate curricula could realistically accommodate rigorous neuroscience content, and about the risk of superficial or misapplied brain talk spreading through the profession. Their stance reflects a familiar tension in professional fields: enthusiasm for new knowledge collides with the practical machinery of accreditation standards, coursework, supervision, and continuing education. Notably, the study’s reference list includes the standards of the Council for Accreditation of Counseling and Related Educational Programs, suggesting that the authors situate these concerns within ongoing international debates about what counselor education should contain.

The third group was labeled Delimiting and Conditional Acceptance, and its members drew explicit boundaries around what neuroscience is allowed to do. They accepted that brain science has value, but only within carefully circumscribed limits, and they resisted any suggestion that neural explanations should displace the relational, humanistic core of counseling. This position echoes a long-standing scholarly argument, represented in the literature by work asking what neuroscience can and cannot answer. For these counselors, the danger lies in reductionism: the idea that a client’s grief, trauma, or anxiety could be adequately described in terms of circuits and neurotransmitters rather than meaning, relationship, and lived experience. Their acceptance was real but conditional, granted only where neuroscience serves the therapeutic work without colonizing it.

The fourth viewpoint, simply called Uncertain, may be the most consequential for the profession’s future. Counselors in this cluster expressed genuine ambivalence, torn between curiosity and doubt. They were unsure whether neuroscience was relevant to their daily work, unsure whether they had the background to evaluate its claims, and unsure what adopting it would mean for their professional standing. The researchers connect this pattern to the psychological literature on intolerance of uncertainty, which shows that ambiguity about new demands can heighten negative emotional states and paralyze decision-making. In a profession where confidence and self-efficacy shape the quality of clinical work, a substantial group of practitioners sitting in unresolved uncertainty is not a trivial finding.

What makes the study’s conclusion genuinely important is its refusal to interpret these four viewpoints as a simple spectrum from acceptance to resistance. The authors argue instead that each viewpoint represents a distinct configuration of interpretive framing, cognitive and affective readiness, professional identity, and institutional conditions. In other words, whether a counselor embraces brain science depends not just on what they know, but on how they make sense of it, how they feel about the uncertainty it introduces, who they understand themselves to be as professionals, and what their training systems and workplaces make possible. This framing draws on established theories of professional identity formation and on diffusion-of-innovations research, which has long shown that the adoption of new practices is a social and interpretive process, not a purely rational calculation of usefulness.

The practical implications are significant. If counselors occupy four different interpretive worlds, then a one-size-fits-all training program in neuroscience is likely to fail most of them. The authors argue for differentiated support: enriched, proactive training for those already eager to integrate brain science; systemic, curriculum-level planning for those focused on institutional feasibility; carefully bounded guidance that respects the limits of neuroscience for those wary of reductionism; and patient, confidence-building education for those still uncertain. This resonates with prior research, including surveys showing that many counselors receive little formal training in neuroscience-informed competencies, and with recommendations for dedicated graduate courses that translate brain science into clinically usable concepts rather than leaving practitioners to navigate the primary literature alone.

There are, of course, limits to what a single Q methodological study of thirty counselors in South Korea can establish. Q methodology yields substantive, pattern-based generalizations about the viewpoints that exist, not statistical estimates of how many counselors hold each one, and the findings are shaped by the forty statements the researchers composed and the specific professional context of Korean counseling, a field that has been actively debating regulation and professionalization. Still, the study offers something rare: a rigorous, empathetic map of how a profession actually thinks about a scientific frontier. It suggests that the future of neuroscience in counseling will not be decided by the persuasiveness of brain images, but by whether the field can meet counselors where they stand, in four very different places at once, and give each of them a credible path forward.

Subject of Research: Counselors' subjective viewpoints on the adoption of neuroscience in counseling practice

Article Title: Making sense of neuroscience in counseling: counselors’ subjective views on its adoption

Article References: Yu, M. H., & Lee, S. Y. (2026). Making sense of neuroscience in counseling: counselors’ subjective views on its adoption. Current Psychology, 45(19), Article 1581. https://doi.org/10.1007/s12144-026-10053-4

Image Credits: AI Generated

DOI: 10.1007/s12144-026-10053-4

Keywords: neuroscience, counseling, Q methodology, professional identity, counselor training, Current Psychology, South Korea, psychological resistance, diffusion of innovations, mental health, counselor perceptions, interdisciplinary integration

The research team, led by Koorosh Azizi of Tennessee State University and The University of Texas at Austin, together with Margaret Garcia of Arizona State University, Dev Niyogi and R. Patrick Bixler of The University of Texas at Austin, set out to explain why policy support for green stormwater infrastructure has not translated into the collective impact that planners hoped for. Individual household decisions matter, but the hydrological benefits of decentralized stormwater control only materialize at scale, when large fractions of a watershed’s impervious surface are treated. That means the crucial question for cities is not whether a few enthusiastic early adopters will install a rain garden, but whether adoption will diffuse through neighborhoods in a self-sustaining wave.

To answer it, the team constructed one of the most empirically grounded simulations of green infrastructure uptake to date. The model represents individual households as agents, each endowed with socio-demographic attributes drawn from census data, parcel attributes from municipal GIS records, and behavioral traits calibrated against a de-identified household survey. The agents form social networks, exchange influence through peer effects, and update internal psychological states, including awareness of green stormwater practices, personal norms, attitudes toward adoption, and social capital, the web of trust and reciprocity that shapes how neighbors persuade one another. Adoption occurs when a household’s intention crosses an individual threshold, a formulation rooted in established behavioral theory, including the theory of planned behavior and social-cognitive frameworks of observational learning.

What makes the model technically distinctive is its layered experimental design. The researchers staged mechanisms incrementally, from a static baseline in which nothing changes, through layers that separately activate feasibility and affordability constraints, social capital dynamics, and behavioral channels for awareness, norms and attitudes, up to a fully coupled configuration in which gains in one psychological variable carry over into others within each simulation step. Each layer thereby isolates a candidate engine of diffusion, and the city of Austin, divided into its ten council districts, serves as the empirical laboratory. Over 120 simulated time steps, the model tracks how many households adopt two broad classes of practices: structural installations such as rain gardens and permeable surfaces, and lower-cost rain-catching measures such as barrels and cisterns.

The central finding is stark: no single factor is sufficient to trigger widespread adoption. Visibility alone, in the form of neighbors’ visible installations, generates curiosity but not commitment if residents cannot afford or physically manage the conversion. Capacity alone, even when subsidies make practices affordable, produces only scattered adoption if households never see the practice working next door or receive no social encouragement. And social capital without concrete, visible examples and feasible options can reinforce skepticism as easily as enthusiasm. Sustained diffusion emerges only when the three mechanisms operate together, creating a feedback loop in which visible adoption builds social proof, social proof lowers the perceived risk of conversion, and capacity converts intention into installed infrastructure that becomes visible in turn.

Because neighborhoods differ in their endowments of these three ingredients, their adoption trajectories diverge into qualitatively distinct patterns. Some districts exhibit rapid takeoff, with adoption accelerating once a critical mass of installations and social connections is reached. Others show delayed diffusion, where early progress is slow but momentum eventually builds as awareness and networks mature. Still others display episodic growth, alternating bursts of uptake with plateaus, or persistent stagnation, in which adoption never escapes its initial low level. The citywide average, the authors show, masks this heterogeneity almost completely, meaning that aggregate statistics can conceal neighborhoods that need entirely different interventions.

The model also reveals that the dominant psychological mechanism driving adoption shifts over time and across space. In some districts, adoption is sustained chiefly by growing awareness; in others, personal norms, attitudes or social capital take turns leading, sometimes within the same neighborhood as the diffusion process matures. This shifting dominance has practical consequences: a one-size-fits-all campaign that emphasizes environmental messaging may work in one district while doing little in another where the binding constraint is affordability or the absence of visible examples. Diagnostic tools like this model, the authors argue, can help cities identify neighborhood-specific barriers and target resources accordingly, whether through demonstration projects, cost-sharing programs, or community-network building.

The theoretical contribution is equally significant. Diffusion-of-innovations research has long recognized the roles of observability, compatibility and social networks, and studies of rooftop solar adoption have documented peer effects that cluster installations spatially. But green stormwater infrastructure poses a harder test, because it is what the authors characterize as a complex contagion: a behavior whose benefits are collective, whose adoption requires repeated social reinforcement, and whose costs fall on private parcels. By embedding behavioral dynamics, physical feasibility and social network structure in a single spatially explicit framework, the study provides a generative explanation of how those ingredients combine, and it connects the stormwater literature to broader debates about infrastructure inequality and the legacy effects of segregation on urban green space.

The implications for practice are direct. Cities seeking to accelerate green stormwater infrastructure, whether to reduce pluvial flooding, cut pollutant loads, or meet regulatory requirements, should stop asking which single lever works best and start asking whether the three conditions are jointly present. Where visibility is low, demonstration installations and open-garden events can seed social proof. Where capacity is the constraint, reverse auctions, rebates and streamlined permitting can lower the feasibility barrier, an approach consistent with earlier household incentive studies. Where social capital is thin, partnerships with community organizations may be needed to build the trust through which peer influence travels. The Austin model, whose data and code are openly available through Zenodo and GitHub, offers planners a template for testing these strategies in silico before deploying them in the watershed, a step the authors suggest could meaningfully shorten the long wait for green infrastructure to deliver its promised resilience at city scale.

Subject of Research: Agent-based modeling of the social and structural drivers of household adoption of green stormwater infrastructure in cities

Article Title: Sustaining green stormwater infrastructure adoption in cities

Article References: Azizi, K., Garcia, M., Niyogi, D., & Bixler, R. P. (2026). Sustaining green stormwater infrastructure adoption in cities. Nature Cities, 3(10), 965-977. https://doi.org/10.1038/s44284-026-00520-1

Image Credits: AI Generated

DOI: 10.1038/s44284-026-00520-1

Keywords: green stormwater infrastructure, agent-based model, urban resilience, rain gardens, social capital, diffusion of innovations, Austin Texas, nature-based solutions, urban flooding, household adoption, stormwater management, neighborhood inequality

The research team, led by Edward Afum Banahene and Enoch Kwame Tham-Agyekum of the Kwame Nkrumah University of Science and Technology, with collaborators at the University of Pretoria, Mwalimu Julius K. Nyerere University of Agriculture and Technology, the University of Cape Coast, and the Malawi University of Science and Technology, set out to answer a deceptively simple question: which communication channels actually move farmers from awareness to action? Climate variability in Ghana—erratic rainfall, prolonged dry spells, and progressive soil degradation—continues to undermine agricultural productivity and food security. Climate-resilient agricultural practices, ranging from drought-tolerant and early-maturing crop varieties to intercropping, conservation tillage, agroforestry, organic manure application, rainwater harvesting, cover cropping, and the use of weather forecasts, have been promoted for years. Yet adoption remains low and inconsistent, despite growing policy support.

The researchers argue that the bottleneck is not the existence of technologies but the communication mechanisms that carry knowledge of them. Farmers must be aware of a practice, understand how it works, and trust the source before they will implement it. Drawing on Diffusion of Innovations theory and the framework of Agricultural Knowledge and Information Systems, the team compared multiple pathways within a single analytical framework: face-to-face extension, community radio, digital platforms and social media, mobile-based services, cooperatives and farmer organizations, peer learning networks, farmer field schools, demonstration plots, agricultural shows, participatory video, community theater, and village knowledge-sharing platforms. Crucially, they combined farmers’ perceptions of trust, accessibility, credibility, and convenience with measurable behavioral outcomes, and used multivariate statistics to control for socio-economic factors such as education, gender, farm size, income, and extension contact.

Methodologically, the study employed a descriptive, quantitative design. From a population of roughly 40,000 smallholder farmers in the municipality, a sample of 396 was drawn using Yamane’s formula at a 95 percent confidence level and a 5 percent margin of error. Two-stage sampling first purposively identified communities cultivating climate-sensitive crops, then randomly selected farmers from lists supplied by the Ministry of Agriculture and farmer-based organizations. Structured questionnaires with Likert-scale items were administered face-to-face in December 2025, with reliability confirmed by Cronbach’s alpha values between 0.78 and 0.81 across the constructs of accessibility, relevance, effectiveness, and trust. Ethical clearance was granted by KNUST’s Humanities and Social Sciences Research Ethics Committee, and participation was voluntary and anonymized.

The results paint a vivid picture of a communication landscape in which social embeddedness trumps broadcast reach. Cooperatives and farmer organizations emerged as the most frequently used pathway, with a mean usage score of 3.92 on a five-point scale, followed closely by community radio (3.77), digital platforms and social media (3.73), peer learning networks (3.73), and farmer-to-farmer communication (3.60). But frequency of use is not the same as influence. Spearman rank correlation analysis revealed that peer learning networks and farmer-to-farmer communication exhibited the strongest positive and statistically significant associations with nearly every climate-resilient practice measured. Peer learning networks correlated at 0.756 with intercropping, 0.714 with conservation tillage, 0.523 with organic manure application, and 0.479 with the use of climate information. Farmer-to-farmer communication showed similarly strong associations with intercropping (0.630), conservation tillage (0.606), mulching (0.565), and cover cropping (0.440).

These correlations, the authors argue, reflect the mechanics of social learning. Peer exchange allows direct observation of outcomes, experimentation, and feedback, reducing the uncertainty that surrounds new practices. Trust, shared norms, and cumulative experience shape decision-making in ways that one-way broadcasts cannot. Climate-resilient farming, in this view, is not a solitary undertaking but a collective one, embedded in social systems. Extension strategies that institutionalize peer-based learning—lead farmers, farmer champions, group-based experimentation—are therefore more likely to achieve sustainable and scalable resilience than top-down information delivery.

The study’s regression analysis added a socio-economic dimension. Farmer group membership significantly predicted the use of agricultural shows and exhibitions (β = 0.315, p < 0.05), community theater (β = 0.338, p < 0.05), and participatory video (β = 0.423, p < 0.05), suggesting that organized groups act as gateways to collective, experiential communication formats. Engagement in off-farm income activities significantly predicted farmer-to-farmer communication (β = 0.309, p < 0.05), indicating that time-constrained farmers gravitate toward fast, trusted peer exchanges rather than formal training sessions. Intriguingly, education, age, gender, farm size, and income were largely insignificant predictors of pathway use, hinting that in this setting, social dynamics matter more than individual human capital. A possible substitution effect also surfaced: farmers with frequent extension contact showed marginally lower use of community radio and digital media, suggesting imperfect integration between formal and informal information systems.

Not every finding flattered conventional wisdom. Farmer field schools and demonstration plots—long staples of agricultural extension—displayed significant negative correlations with adoption, including −0.327 for intercropping and −0.297 for conservation tillage. The authors attribute this to poor contextualization, infrequent delivery, didactic teaching styles, and time lags before benefits materialize. NGO and donor-funded groups showed weak negative effects, and climate adaptation networks were largely ineffective, likely lacking visibility, structure, and sustained engagement. Community radio, digital and social media, and participatory video showed low or insignificant correlations with actual adoption, reinforcing the argument that while mass media excels at awareness creation, it lacks the interactivity and specificity required for behavioral change. Mobile-based services occupied a middle ground, with moderate but consistent positive correlations, particularly with drought-tolerant varieties (0.161) and climate information use (0.212), positioning them as valuable tools for the knowledge and early decision stages of adoption.

Perception data reinforced the hierarchy of trust. Extension officers were the most trusted information actors (mean 4.25), followed by farmer networks and cooperatives (3.84), while trust in mobile applications (3.02) and social media (3.22) remained moderate—evidence that exposure does not automatically breed confidence. Farmers rated information clarity highly (4.05) and peer-to-peer interaction as especially effective for knowledge transfer (4.07), but field demonstrations (2.01) and climate adaptation networks (1.96) scored lowest, pointing to weak institutional embedding rather than intrinsic ineffectiveness. The study also identified barriers to adoption using Kendall’s coefficient of concordance, which confirmed significant consensus among respondents. Risk aversion and fear of yield loss ranked as the most critical constraint (mean rank 3.66), followed by lack of access to accurate and timely climate information, low education and technical knowledge, labor and financial constraints, distrust in information sources, and land tenure insecurity. Notably, language barriers ranked least significant, suggesting that most information in Techiman is already delivered in familiar local dialects.

The implications extend well beyond Ghana. The authors recommend scaling peer learning platforms, integrating timely and localized climate information into channels farmers already trust, developing visual, stepwise communication materials, providing financial and labor support mechanisms such as collective labor schemes and staggered input repayment, and delivering advice through credible local actors including opinion leaders and farmer representatives. They caution that the cross-sectional design limits causal inference and that self-reported data may carry recall and social desirability bias. Still, the central message is clear and potentially transformative for adaptation policy across Africa: psychological and informational barriers outweigh structural ones, and the most powerful communication technology in climate-smart agriculture may not be a smartphone or a radio transmitter at all, but the trusted voice of a fellow farmer standing at the edge of a demonstration field, describing what actually worked.

Subject of Research: The relationship between communication pathways and smallholder farmers' adoption of climate-resilient agricultural practices in Techiman Municipality, Ghana.

Article Title: Talking climate, growing change: communication pathways and farmer adoption of resilient practices in Techiman, Ghana

Article References: Banahene, E. A., Tham-Agyekum, E. K., Ndosi, J., Ankuyi, F., & Nyengere, J. (2026). Talking climate, growing change: communication pathways and farmer adoption of resilient practices in Techiman, Ghana. Journal of Environmental Studies and Sciences. https://doi.org/10.1007/s13412-026-01136-6

Image Credits: AI Generated

DOI: 10.1007/s13412-026-01136-6

Keywords: climate-resilient agriculture, communication pathways, smallholder farmers, peer learning, Ghana, adoption, agricultural extension, climate information, farmer organizations, diffusion of innovations, Techiman, risk aversion

The study, published in the journal Discover Informatics, examined eight Ghanaian universities with operational institutional repositories, including the University of Ghana, Kwame Nkrumah University of Science and Technology, the University of Cape Coast, the University of Education Winneba, the University for Development Studies, Ashesi University, the Ghana Institute of Journalism, and the University of Professional Studies Accra. Led by De-Graft Johnson Dei and Karim Awudu, the research team surveyed 120 respondents drawn from ICT personnel, repository managers, academic librarians, systems librarians, and faculty members, then followed up with in-depth interviews with twelve senior experts. The design was a sequential explanatory mixed-methods approach, meaning the survey results shaped the questions asked in later interviews, and the two strands of evidence were triangulated during interpretation.

The headline finding is sobering. Forty percent of respondents said their institutions had no digital rights management mechanism in place at all. Access control was the most commonly implemented practice, appearing at 44.2 percent of institutions, while advanced techniques such as digital watermarking and encryption were implemented at only 9.2 percent. Usage tracking, copyright auditing, and automated licensing enforcement were similarly rare. Interview participants described repositories that upload content without defined rights policies, verify permissions manually, and have no idea who downloads what or where it ends up. One participant at the University for Development Studies put it bluntly: access matters more than protection. Another observed that if content is on the internet, people assume it is free to use, a presumption that puts author rights at risk.

Against this backdrop, blockchain technology has been proposed as a transformative tool. A blockchain is a distributed ledger in which transactions are recorded in cryptographically verified, time-stamped blocks linked to their predecessors, maintained across a network of nodes rather than a single central authority. Because no single party controls the record, entries cannot be quietly altered, and every change leaves a permanent, auditable trail. For digital rights management, this architecture offers several distinct functions: authorship verification through immutable timestamps and digital signatures, provenance tracking across the scholarly lifecycle, content integrity through cryptographic verification, and automated rights enforcement through smart contracts, which are self-executing programs that apply licensing terms without continuous administrative intervention.

The researchers grounded their analysis in two complementary theoretical lenses. The Technology-Organization-Environment framework, developed by Tornatzky and Fleischer, examines how technological features, organizational capacity, and environmental conditions such as national policy shape whether institutions adopt an innovation. Diffusion of Innovations theory, articulated by Everett Rogers, explains how individual stakeholders perceive new ideas through the attributes of relative advantage, compatibility, complexity, trialability, and observability. Together, the frameworks allowed the team to assess both the structural readiness of Ghanaian universities and the attitudes of the people who would actually operate blockchain-enabled systems.

Awareness of blockchain turned out to be uneven. Overall, 62.5 percent of respondents had heard of the technology, but mean perceived understanding was low at 2.31 on a five-point scale. ICT personnel were far ahead, with 86.7 percent awareness and a mean understanding score of 3.82, while faculty members lagged at 33.3 percent awareness and a mean of 1.74. Interviews revealed persistent conceptual confusion, with some participants equating blockchain entirely with Bitcoin and others describing it as futuristic but unproven in a library context. Several respondents expressed a genuine appetite for training, noting that interest exists but workshops and webinars do not. According to Diffusion of Innovations theory, these conditions mark an early adoption stage characterized by low observability and high perceived complexity, yet also a latent receptiveness that targeted demonstration could convert into adoption.

Institutional readiness fared worse. The overall preparedness score was 2.25, rated as poor, and only 18.3 percent of respondents believed their infrastructure could support blockchain. A striking 81.7 percent said their digital library strategies made no mention of the technology. Funding was the dominant constraint, cited by 74.2 percent, followed by technical complexity at 74.2 percent, shortage of qualified staff at 72.5 percent, and absence of legal and regulatory frameworks at 68.3 percent. Participants described server capacity too limited to host current systems, ICT plans with no blockchain reference whatsoever, and donor projects that fund basic digital libraries but not cutting-edge infrastructure. In northern Ghana, unreliable internet and electricity compounded the problem, cited by 54.2 percent of respondents.

Yet the appetite for blockchain’s benefits was remarkably strong. Tamper-proof authorship verification topped the list, endorsed by 78.3 percent of respondents with a mean rating of 4.21, followed by improved content authenticity and integrity at 74.2 percent and transparent audit trails at 71.7 percent. Smart contract automation of copyright enforcement drew support from 66.7 percent, blockchain timestamping from 61.7 percent, and decentralized data control from 63.3 percent. Librarians interviewed for the study described repeated incidents of downloaded content being renamed and shared without credit, and saw in blockchain a way to safeguard the uniqueness of their researchers’ work. Others noted that precise access tracking could transform impact monitoring, and that smart contracts could enforce Creative Commons licenses without manual policing.

On strategy, stakeholders favored cautious, low-risk pathways. Staff training and capacity building was the most endorsed approach at 79.2 percent, followed by pilot blockchain repository modules at 76.7 percent and phased implementation at 74.2 percent. Open-source platforms such as Hyperledger attracted 72.5 percent support, reflecting a preference for affordable, flexible tools over commercial systems. Multidisciplinary task forces spanning librarians, attorneys, and IT professionals, donor and government funding, and regional consortia all received strong ratings, while alignment with national education policy goals ranked lowest at 60.8 percent, exposing a strategic disconnect between institutional action and frameworks set by the Ministry of Education and the Ghana Tertiary Education Commission.

The study concludes that blockchain-enabled digital rights management in Ghanaian repositories is a promising but still emerging innovation, transformative in potential yet constrained by infrastructure, funding, expertise, and policy. The authors recommend phased implementation, pilot projects beginning perhaps with postgraduate theses, open-source tools, interdisciplinary collaboration, and sustained investment in digital infrastructure and human capacity. They also caution that the findings rest on self-reported perceptions from eight universities and do not evaluate operational blockchain systems, leaving room for future work on prototypes, longitudinal adoption studies, and integration with technologies such as big data analytics and large language models. For now, the message is clear: Ghana’s repositories hold valuable scholarship that is largely unprotected, and while blockchain is no silver bullet, stakeholders across the country’s universities are ready to explore whether a decentralized ledger can finally give their research the trust and security it deserves.

Subject of Research: Blockchain adoption for digital rights management in Ghanaian university institutional repositories

Article Title: Evaluating blockchain adoption for digital rights management in institutional repositories

Article References: Johnson Dei, D.-G., & Awudu, K. (2026). Evaluating blockchain adoption for digital rights management in institutional repositories. Discover Informatics, 1(1), Article 5. https://doi.org/10.1007/s44564-026-00008-z

Image Credits: AI Generated

DOI: 10.1007/s44564-026-00008-z

Keywords: blockchain technology, digital rights management, institutional repositories, Ghana, higher education, scholarly communication, smart contracts, copyright protection, digital preservation, open access, TOE framework, Diffusion of Innovations