The research team, led by A. K. M. Mohsin of the University of Applied Sciences Upper Austria and the Supply Chain Intelligence Institute Austria, compiled a balanced country-year panel covering 2010 to 2023, yielding 126 observations from Eurostat, the European Alternative Fuels Observatory, the International Energy Agency, the World Bank Logistics Performance Index, BloombergNEF, and publicly disclosed corporate sustainability reports. The dependent variable, cost efficiency, was measured as the average production cost of lithium-ion batteries in euros per kilowatt-hour after accounting for the economic benefits of circular resource recovery. Across the sample, this figure averaged 86.65 euros per kilowatt-hour with a standard deviation of just 4.66, indicating that production costs were remarkably similar across countries even though the underlying technological, operational, and policy conditions varied enormously.

That heterogeneity is striking. Production capacity ranged from 29 to 148 gigawatt-hours, research and development investment spanned from 22.08 to 498.86 million euros, and logistics costs varied from 100.56 to 982.99 euros per unit. Recycling efficiency, by contrast, was already well developed, averaging 82.09 percent with values between 70 and 93 percent. The pattern suggests that broadly comparable cost outcomes can be reached through very different combinations of industrial scale, innovation spending, logistics coordination, and policy support, a possibility the authors set out to test formally rather than assume.

To do so, the team deployed an unusually rich methodological arsenal. Panel autoregressive distributed lag models estimated short-run and long-run relationships between cost efficiency and its operational, technological, and policy determinants. Nonlinear ARDL models then examined whether positive and negative changes in logistics costs and government subsidies produce asymmetric responses. Necessary Condition Analysis identified minimum constraint levels associated with higher cost efficiency, and fuzzy-set Qualitative Comparative Analysis explored alternative configurations through which conditions jointly contribute to efficient circular supply chain outcomes. The combination is novel in the battery circularity literature, where most prior work has relied on static regression techniques or small-scale case studies.

The econometric results are technically revealing. In the long run, a 1 percent increase in recycling efficiency leads to a 0.019 percent rise in cost efficiency, while a 1 percent increase in production capacity produces a 0.128 percent gain, reflecting economies of scale in manufacturing. Counterintuitively, logistics costs also show a positive long-run effect, with a 1 percent increase associated with a 0.016 percent improvement in cost efficiency. The authors interpret this as evidence that higher logistics expenditure often reflects strategic investment in reverse logistics infrastructure, digital traceability systems, and battery collection networks rather than operational waste. In the short run, research and development investment shows a significant positive effect, with a 1 percent increase yielding a 0.024 percent cost efficiency gain, and the error correction term of negative 0.726 indicates that roughly 72.6 percent of any disequilibrium is corrected each period, a relatively fast adaptation.

The nonlinear analysis confirms that these relationships are asymmetric. A positive shock in logistics costs produces a 0.028 percent long-run gain in cost efficiency, while a positive shock in government subsidies delivers a much larger 0.218 percent improvement, underscoring the outsized role of public financial support. Wald tests reveal long-run asymmetry for logistics costs, meaning efficiency gains from logistics investment persist and are not reversible, and short-run asymmetry for subsidies, meaning cost efficiencies achieved through government incentives remain intact even if subsidies later decline. Granger causality tests, using heteroskedasticity-robust estimation, provide comparatively stronger evidence that changes in government support precede changes in cost efficiency, though the authors caution this reflects predictive temporal precedence rather than proof of direct causation.

The Necessary Condition Analysis adds a crucial structural insight: government subsidies are the only condition whose necessity effect exceeds the minimum threshold, establishing that adequate financial backing is an indispensable enabler of cost efficiency. Bottleneck analysis shows that achieving cost efficiency outcomes above 60 percent requires minimum threshold levels of 1.626 percent for government subsidies, 4.065 percent for logistics cost, 0.813 percent for production capacity, and 0.813 percent for research and development investment. To gain an additional 10 percent in cost efficiency, subsidies must rise from 1.626 to 7.317 percent, and for outcomes above 80 percent, the model recommends combinations of 28.45 to 34.959 percent subsidies, 8.13 to 12.195 percent logistics cost, and 0.813 percent each for capacity and research investment. Recycling efficiency, logistics cost, production capacity, and research investment all contribute, but none functions as an absolute constraint on its own.

The fuzzy-set Qualitative Comparative Analysis then reveals that there is no universal pathway to cost-efficient circularity. Multiple configurations, each with high consistency scores, lead to favorable outcomes. One solution combines high recycling efficiency with optimized logistics cost, with subsidies as a peripheral factor. Another pairs production capacity, research investment, and logistics optimization. A third places government subsidies at the core, amplified when combined with recycling efficiency or research investment. The analysis also identifies configurations associated with low cost efficiency, such as overinvestment in production capacity and research without sufficient recycling integration, or subsidies paired with excessive logistics costs and no optimization. The authors note that battery chemistry matters, since chemistries rich in nickel, cobalt, and manganese provide stronger economic incentives for material recovery than those with lower recovery values.

Interpreted through Resource-Based Theory, Institutional Theory, and Dynamic Capabilities Theory, the findings suggest that supportive institutional environments encourage firms to invest in technological innovation and circular practices, while dynamic capabilities enable organizations to transform those investments into sustained economic performance. The authors also developed a causal loop diagram showing how reinforcing loops of innovation and efficiency, scale and learning, and policy support interact with balancing constraints such as logistics cost drag, framing circular cost efficiency as a continuously evolving equilibrium maintained by technological innovation, institutional stability, and adaptive market behavior.

For policymakers, the study offers a prioritized roadmap. The highest priority is to establish stable and predictable financial incentive frameworks, since subsidies emerged as the fundamental enabler. The second is to strengthen intelligent reverse logistics infrastructure and digital traceability systems to improve battery collection and material recovery. Finally, policy measures should operate synergistically, simultaneously supporting recycling technology innovation, research investment, and logistics network development. The authors caution that the findings are based on the European Union, where comprehensive battery regulations and strong policy support provide a mature institutional environment, and should be generalized to other regions with appropriate care. Future research, they suggest, could extend the analysis to firm-level data, comparative studies across China and the United States, and emerging technologies such as solid-state and sodium-ion batteries, which may substantially alter recycling requirements and circular supply chain configurations.

Subject of Research: Cost efficiency in circular lithium-ion battery supply chains under circular economy and policy synergies in Europe

Article Title: Integrating circular economy and policy synergies for cost-efficient lithium-ion battery supply chains

Article References: Mohsin, A. K. M., Rashed, M., Gerschberger, M., Ahmed, S. F., Bartuska, L., Schwarz, C., & Kamal Uddin, M. (2026). Integrating circular economy and policy synergies for cost-efficient lithium-ion battery supply chains. Clean Technologies and Environmental Policy, 28(11), Article 282. https://doi.org/10.1007/s10098-026-03613-6

Image Credits: AI Generated

DOI: 10.1007/s10098-026-03613-6

Keywords: lithium-ion batteries, circular economy, supply chains, recycling, government subsidies, reverse logistics, cost efficiency, electric vehicles, panel ARDL, Necessary Condition Analysis, fsQCA, EU Battery Regulation

The research team, led by Qianlan Chen of Guangxi Normal University together with colleagues from Yulin Normal University and Guizhou University of Finance and Economics, surveyed 409 managers nested within 50 forest-food enterprises across three ecologically distinct regions: Zhangjiajie, Xishuangbanna, and Changbai Mountain. These firms sit at the center of what China calls the under-forest economy, a system linking collectors and cultivators of matsutake, morel, boletus, wild vegetables, nuts, medicinal plants, and forest honey with processors, cold-chain logistics providers, wholesalers, e-commerce platforms, and specialty retailers. Because so much of this production is seasonal and geographically dispersed, conventional resilience thinking, built around inventory buffers and standardized alternatives, often fails to capture what these businesses actually face.

The authors approached the problem through what they call a reconfigurability perspective. They distinguish carefully between the broader theoretical capacity to redeploy resources and a narrower, measurable construct: reconfiguration readiness, defined as the availability of alternative sourcing channels, backup suppliers, multi-skilled personnel, and reliable operational and logistics options. Flexibility, by contrast, describes adjustment within an existing arrangement. The difference matters enormously in this setting, because a nominal backup supplier is worthless if its mushrooms do not match quality requirements, its harvest timing misses downstream windows, or its transport route cannot preserve a perishable product. Credibility of alternatives, not their mere existence, is what counts.

Methodologically, the study is notable for refusing to treat each questionnaire respondent as an independent case. Measurement quality was assessed at the respondent level, where the data showed strong internal consistency, with Cronbach’s alpha values between 0.866 and 0.924 and average variance extracted between 0.618 and 0.707. Construct scores were then aggregated within firms, supported by intraclass correlations indicating that managers within the same company genuinely share perceptions of organizational capabilities. A seven-factor confirmatory model fit the data well, and critically, models that merged reconfiguration readiness with flexibility, agility, or the resilience outcome itself deteriorated sharply, confirming that readiness is empirically distinct from the capabilities it most resembles.

To untangle causality, the team combined two complementary techniques. Necessary Condition Analysis asks whether a minimum level of a condition places a ceiling on the attainable outcome: without enough of it, high performance simply cannot be observed. Fuzzy-set Qualitative Comparative Analysis, or fsQCA, then identifies which combinations of conditions are sufficient to produce the outcome, allowing for equifinality, the idea that many different recipes can lead to the same result. The two methods answer different questions, and the researchers were careful not to conflate statistical association, necessity, and configurational core status.

The necessity results were striking. Reconfiguration readiness was the only condition that passed both the substantive and statistical tests of Necessary Condition Analysis, with an effect size of 0.123 and a permutation p value below 0.001, and it was also the only positive condition whose fsQCA necessity consistency exceeded the conventional 0.90 threshold, at 0.921. A bottleneck analysis sharpened the picture: readiness required only about 6.7 percent of its observed range for moderate outcome levels, but reaching 95 percent of the observed resilience range demanded at least 90.5 percent of the readiness range. In other words, the constraint becomes severe precisely where it matters most, near the upper limits of resilience. Partner resilience and capital resilience showed statistically detectable but substantively small ceiling effects, which the authors deliberately declined to inflate into claims of necessity.

The sufficiency analysis revealed five distinct configurations that generate high forest-based food supply-chain resilience, with an overall solution consistency of 0.945 and coverage of 0.883. Reconfiguration readiness appeared in every single pathway, yet in none of them as a core condition. Instead, partner resilience emerged as the core ingredient in four of the five recipes, while agility and capital resilience together formed the core of the fifth. One pathway pairs readiness with agility and financial buffers, allowing firms to recognize disruptions quickly and pay to activate alternatives. Others combine readiness with flexible operations, partner coordination, and either capital reserves or technological capability, including collaborative innovation, traceability, and process redesign. The substitution patterns are telling: technology can compensate for scarce capital, and strong relationships can substitute for cutting-edge information systems, but readiness itself can never be dropped.

The authors argue that readiness functions as an enabling constraint rather than a sufficient condition. It defines the feasible action space during disruption, while relational, agile, financial, and technological capabilities determine how firms actually exploit that space. This distinction carries a practical warning for managers. Counting backup suppliers is not the same as testing them. Alternative sources should be evaluated against product quality, certification, harvest timing, and transport feasibility; multi-skilled employees should be able to coordinate a switch across cultivation, collection, quality control, and logistics; and investment priorities should be organized around the complements a firm already possesses rather than an undifferentiated checklist. A company with strong relationships may gain most from technological learning, while a firm with deep financial reserves may need better information processing instead.

The findings survived extensive robustness checks. Raising truth-table frequency and consistency thresholds, and recalibrating the fuzzy-set anchors using alternative percentiles, left the five pathways intact, with the alternative calibration reproducing all five configurations at a consistency of 0.950 and coverage of 0.886, and maintaining the necessity of readiness with an effect size of 0.110 and fsQCA consistency of 0.926. The authors are candid about limitations: the 50 firms are not nationally representative, the cross-sectional design captures set relations rather than temporal sequences, common-method variance cannot be fully excluded, and the regional subsamples are too small for separate configurational solutions. They call for longitudinal, event-based studies that follow firms through actual disruption episodes to test whether readiness precedes the activation of complementary capabilities.

What makes this study resonate beyond mountain China is its reframing of how resilience is built. The results suggest that resilience is a portfolio property, produced by configurations rather than by maximizing every capability at once, and that the marginal value of any investment depends on what else a firm already holds. For anyone concerned with food security in an era of climate volatility, the message is precise: before a disruption arrives, build credible, tested alternatives; after it arrives, rely on the relationships, information speed, finances, or technology that turn those alternatives into operational reality. In the forests where matsutake and honey depend on ecological caprice, that layered architecture may be the difference between a supply chain that breaks and one that bends and recovers.

Subject of Research: Organizational capabilities and supply-chain resilience in forest-based food systems

Article Title: Forest-food supply chain resilience in mountain outdoor regions: a reconfigurability perspective

Article References: Chen, Q., Liang, H., Deng, X., & Ren, Y. (2026). Forest-food supply chain resilience in mountain outdoor regions: a reconfigurability perspective. Frontiers in Sustainable Food Systems, 10, Article 1962537. https://doi.org/10.3389/fsufs.2026.1962537

Image Credits: AI Generated

DOI: 10.3389/fsufs.2026.1962537

Keywords: forest foods, supply-chain resilience, reconfigurability, fsQCA, Necessary Condition Analysis, mountain regions, edible fungi, honey, China, non-timber forest products, partner resilience, reconfiguration readiness

The study focused on AU Small Finance Bank, Capital Small Finance Bank, and Utkarsh Small Finance Bank, three institutions licensed under a differentiated Reserve Bank of India regime that obliges them to serve small businesses, low-income households, and rural communities. This context matters theoretically, the authors argue, because small finance bank customers differ in important ways from the urban, digitally mediated commercial-banking populations that dominate the service-quality literature. Many are transacting with formal banking for the first time, meaning that trust must be built rapidly through direct, branch- and field-staff-based interactions rather than accumulated over years of banking history. In such settings, the evidentiary base for trust is compressed: each service encounter carries disproportionate weight, and dimensions like empathy and responsiveness may matter more than they would for seasoned commercial-bank customers.

Methodologically, the research is notable for combining three analytical techniques within a single design. The team used partial least squares structural equation modelling, or PLS-SEM, to test a mediation-moderation model in which service quality was treated as a higher-order construct built from the five classic SERVQUAL dimensions: tangibility, reliability, responsiveness, assurance, and empathy. They then layered on Necessary Condition Analysis, which applies necessity rather than sufficiency logic to identify minimum thresholds that predictors must meet for an outcome to occur, and Importance-Performance Map Analysis, which ranks constructs by their total effect on the outcome against their current performance levels. This tripartite approach, the authors note, is among the first of its kind in the small finance banking domain.

The headline results are striking in their clarity. Service quality showed a significant positive association with customer loyalty (β = 0.340) and with perceived bank performance (β = 0.231), while customer loyalty itself was strongly linked to perceived performance (β = 0.525). The indirect path from service quality to performance through loyalty was significant (β = 0.178, with a bootstrap confidence interval of 0.128 to 0.234), confirming the mediating mechanism. Together, service quality, loyalty, and demographic controls explained 41.8 percent of the variance in perceived bank performance, and out-of-sample predictive checks showed the model outperformed a linear benchmark. Age, education, and gender, entered as control variables, had no significant effects, strengthening confidence that the observed patterns reflect the hypothesised mechanisms rather than the sample’s composition.

One expected result, however, proved far weaker than theory predicted. Switching costs, the perceived financial, procedural, and relational price a customer pays for moving to a rival bank, showed only a weak and marginally significant moderating effect on the loyalty-performance relationship (β = 0.073, significant only at the lenient 10 percent level, with a confidence interval spanning zero). The authors are careful to frame this as tentative rather than firm evidence, and a preliminary delta-method index of moderated mediation likewise fell just short of conventional significance. Intriguingly, in the importance-performance mapping, several switching-cost indicators actually carried negative importance values, hinting that lock-in strategies may do little for performance and could even erode voluntary loyalty. The message for bankers is blunt: trapping customers is not the same as keeping them.

The Necessary Condition Analysis produced perhaps the most actionable findings. Reliability emerged as the strongest necessity condition for perceived bank performance, with an effect size of d = 0.347, followed by empathy at d = 0.273; customer loyalty (d = 0.175) and assurance (d = 0.149) also exceeded the threshold for high-level necessity. In plain terms, within this framework a bank cannot compensate for poor reliability with excellence elsewhere: a minimum level of dependable service is a prerequisite, not a bonus. The bottleneck analysis sharpened this picture further. At low performance levels, few constraints bind, but reliability and empathy become binding as early as the 30th percentile of performance. By the 80th percentile, customer loyalty must reach roughly 70 percent of its potential, climbing toward 98 percent at the top of the range, meaning elite performance is effectively unattainable without near-maximal loyalty.

The Importance-Performance Map Analysis added a managerial twist. Empathy showed the strongest total effect on perceived performance while already performing reasonably well, but customer loyalty indicators combined comparatively low importance scores with the weakest performance scores in the entire model, scoring around 29 to 32 out of 100 against 53 to 63 for the service-quality dimensions. This makes customer loyalty the construct with the largest gap between importance and current performance, and therefore the clearest priority for managerial investment. The authors recommend treating loyalty-building as a distinct, resourced workstream, through tiered rewards programmes calibrated to transaction frequency, structured relationship-manager touchpoints, and CRM-based tracking that flags early signs of customer disengagement before it damages performance.

The implications extend well beyond individual bank branch managers. Because small finance banks exist specifically to bank the unbanked, the service quality-loyalty-performance chain examined here plausibly shapes whether first-generation customers stay engaged with the formal financial system at all. The authors suggest that regulators, including the Reserve Bank of India, might consider minimum service-quality benchmarks on reliability and empathy specifically, rather than relying on aggregate satisfaction scores that can mask weakness on a single binding dimension. They also argue that policy should continue prioritising genuine service improvement and consumer protection over indirect barriers to switching, a stance consistent with the finding that switching costs contributed little, if anything, to performance in this sample.

The authors are equally candid about the limits of what their data can show. The study is cross-sectional, capturing perceptions at a single point in time, so the reported relationships are statistical associations rather than demonstrated causal effects, and reverse pathways, in which satisfied customers rate service quality more generously, cannot be ruled out. Perceived performance was measured through customers’ own reports rather than objective indicators such as return on assets or CAMEL ratings, and the sample was drawn from just three purposively selected banks in four northern states, bounding its generalisability. Income and relationship tenure were not entered as controls, the pooled sample was not tested for differences across banks, and the marginal switching-cost moderation awaits replication with larger, more diverse samples. Even so, the study’s core contribution stands: in a banking segment built on trust rather than lock-in, reliability and empathy function as necessary foundations, and loyalty, currently the weakest link in the chain, is where the greatest performance gains may lie waiting.

Subject of Research: The role of service quality and customer loyalty in the perceived performance of Indian small finance banks

Article Title: Service quality improves perceived bank performance through customer loyalty in Indian small finance banks

Article References: Raghuwanshi, S., Patel, V. K., Vashisht, A., Mishra, A., Hasan, A., & Jani, S. (2026). Service quality improves perceived bank performance through customer loyalty in Indian small finance banks. Discover Global Society, 4(1), Article 274. https://doi.org/10.1007/s44282-026-00622-0

Image Credits: AI Generated

DOI: 10.1007/s44282-026-00622-0

Keywords: service quality, customer loyalty, small finance banks, perceived bank performance, switching costs, SERVQUAL, PLS-SEM, necessary condition analysis, importance-performance map analysis, financial inclusion, India, banking

The study is notable for what it did not do. Rather than asking teachers to imagine how they might use AI someday, the researchers embedded a working tool into the messy reality of school-based teacher training. CReVAG was implemented as a Custom GPT within ChatGPT and configured as a visual-design assistant for culturally responsive teaching resources. Before generating any image, the system prompted users to specify contextual information: country, region or district, learner level, subject and topic. Where relevant, users could add language background, community setting, curriculum focus, cultural sensitivities and intended learning outcomes. The tool’s knowledge base contained curriculum materials from Ghana’s Education Service and National Council for Curriculum and Assessment, and only Canvas and Image Generation capabilities were activated for the study.

Participants received a one-week orientation covering contextual prompting, visual generation, refinement and critical evaluation before applying the tool during their twelve-week practicum. The workflow placed the teacher firmly in charge: context specification, visual generation, teacher review, refinement and instructional use. Teachers remained responsible for checking clarity, curriculum relevance, cultural appropriateness and possible stereotypes before using any generated resource in a practicum lesson. Survey data were collected once, after the sixth week of practicum, once participants had gained genuine hands-on experience with the system in their host schools.

The research team measured six constructs: self-efficacy, culturally responsive teaching self-efficacy, perceived system usability, AI tool acceptance, visual resource quality and lesson delivery effectiveness. Using Partial Least Squares Structural Equation Modelling, complemented by Necessary Condition Analysis, they tested twelve hypothesised relationships. The model explained 39.9 percent of the variance in AI tool acceptance, 39.3 percent in visual resource quality and a striking 61.6 percent in lesson delivery effectiveness, with an SRMR of 0.077 indicating acceptable fit. Eight of the twelve hypothesised associations reached statistical significance at the two-tailed 0.05 level.

Two relationships stood out in magnitude. Perceived system usability showed the largest direct association with AI tool acceptance (β = 0.330, p = .004), suggesting that in the pressured environment of a teaching practicum, where students juggle lesson planning, materials preparation and actual classroom teaching, an AI assistant that is cumbersome to operate becomes another burden rather than a help. Meanwhile, visual resource quality showed the largest direct association with lesson delivery effectiveness (β = 0.309, p < .001). In other words, what mattered most for teaching was not enthusiasm for the tool but the perceived clarity, relevance, cultural appropriateness and pedagogical suitability of the images it produced.

The Necessary Condition Analysis added a complementary perspective. While structural equation modelling describes average associations, NCA asks whether high levels of an outcome are simply unattainable when a condition falls below some empirical threshold. Here, visual resource quality emerged as the strongest statistical necessity condition for high lesson delivery effectiveness (d = 0.190, p = .002), followed by AI tool acceptance (d = 0.158). The bottleneck analysis indicated that at approximately 90 percent of the observed lesson delivery range, visual resource quality needed to reach roughly 3.0 on the five-point scale and system usability about 3.2. The authors stress these are sample-specific thresholds, not universal standards or causal cut-offs.

Perhaps the most theoretically intriguing finding is what failed to reach significance. AI tool acceptance was not significantly associated with visual resource quality (β = 0.167, p = .074), meaning that teachers who liked the tool did not necessarily judge its outputs as pedagogically sound. Similarly, culturally responsive teaching self-efficacy predicted evaluations of the generated resources but not acceptance of the system itself. The authors argue this reveals three conceptually distinct evaluative levels in AI-supported teaching: appraisal of the AI system, evaluation of the instructional artefact it produces, and perceived instructional usefulness during lesson delivery. A teacher can embrace an AI platform wholeheartedly while still, quite rightly, rejecting its images as culturally inaccurate or pedagogically weak.

The subgroup analyses were handled with statistical caution. Because programme groups were markedly unequal in size, with 77.6 percent of participants in Junior High School Education, the researchers used Welch’s ANOVA and Games-Howell comparisons where homogeneity assumptions failed. The most defensible programme differences concerned system usability and lesson delivery effectiveness, with JHS participants reporting higher scores than Primary Education participants. Gender differences were generally small, although males reported significantly higher AI tool acceptance (d = 0.40, p = .006) and somewhat higher visual resource quality ratings. The authors explicitly warn against deficit interpretations, noting these within-sample differences do not establish gender as a cause of AI-related beliefs.

The study’s limitations are candidly acknowledged. Because all constructs were measured once, after exposure to CReVAG, the findings represent cross-sectional explanatory associations rather than causal effects. The sample came from a single institution, the data relied on self-reports, and the saved CReVAG configuration did not lock users to a single underlying ChatGPT model, meaning participants may not have experienced completely standardised technological exposure. Prior AI experience, reported by 77.6 percent of participants, was not part of the prespecified model, though sensitivity analyses showed the focal coefficients were broadly stable when it was added as a control. Notably, prior AI experience was positively associated with visual resource quality, hinting that the depth and character of earlier AI use may shape how teachers evaluate generated materials.

The implications reach well beyond Ghana. The authors argue that teacher-education programmes should move beyond one-off AI demonstrations toward sustained practice in prompting, evaluating outputs, correcting inaccuracies and integrating suitable visuals into actual lessons. Culturally responsive prompting and evaluation deserve explicit attention, with clear institutional criteria for accuracy, clarity, curriculum alignment, cultural appropriateness and accessibility. AI-generated materials, they contend, should be treated as drafts requiring professional evaluation rather than ready-made teaching resources. For developers, CReVAG illustrates the value of systems that request contextual information before generating educational visuals instead of relying on generic prompts. And for everyone watching generative AI reshape education, the message is clear: the technology’s real promise lies not in what it can generate, but in the professional judgement teachers bring to judging, refining and using what it makes.

Subject of Research: Pre-service teachers' use of a culturally responsive generative AI tool for creating visual teaching resources in Ghana

Article Title: Pre-service teachers’ interactions with generative AI for creating culturally grounded visual teaching resources

Article References: Nabang, M., Essel, H. B., Tachie-Menson, A., Setsoafia, P., Kodua, C. A., Kubi, J. A., Acheampong, E. B., Aboagye, G. A., & Nyaaba, M. (2026). Pre-service teachers’ interactions with generative AI for creating culturally grounded visual teaching resources. Discover Education, 5(1), Article 1085. https://doi.org/10.1007/s44217-026-02241-y

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02241-y

Keywords: generative AI, pre-service teachers, culturally responsive teaching, teacher education, Ghana, visual teaching resources, AI tool acceptance, system usability, PLS-SEM, Necessary Condition Analysis, lesson delivery, self-efficacy

The study, conducted by Qian Bao, Xiaojie Peng, and Yanran Qian, draws on the stimulus-organism-response model, a classic framework from environmental psychology that treats external features of a technology as stimuli, internal psychological states as the organism’s response to those stimuli, and observable behaviors as the final outcome. In this framing, the features of a conversational AI—how personalized it seems, how credible, how interactive, how intelligent—act as stimuli. The user’s satisfaction and sense of empowerment are the internal states. The decision to keep using the AI companion is the behavior. What makes the new work unusual is the rigor with which it tests this chain from multiple angles at once, combining a survey of 393 valid responses with in-depth qualitative interviews analyzed through grounded theory.

On the quantitative side, the researchers deployed an unusually powerful statistical toolkit. They used partial least squares structural equation modeling to test whether six perceptual factors—self-expression, emotion regulation, perceived personalization, perceived credibility, perceived interactivity, and perceived intelligence—significantly boosted satisfaction and empowerment. They then layered on artificial neural networks, a machine-learning approach capable of detecting nonlinear relationships that traditional regression-style models can miss. Finally, they applied necessary condition analysis, a technique that asks not whether a factor helps on average, but whether it is an absolute prerequisite for a desired outcome. This triple-method design, sometimes abbreviated SEM-ANN-NCA, has gained traction in information systems research precisely because each method compensates for the blind spots of the others.

The results were consistent across methods, and they tell a coherent story. All six perceptual factors significantly enhanced both satisfaction and empowerment, which in turn positively predicted continuance intention—the user’s intention to keep using the AI companion. But the neural network analysis added a crucial twist: the relationships among these variables are not linear, and empowerment emerged as the single strongest predictor of continued use, exerting a greater influence than satisfaction itself. In other words, young users do not stay with an AI companion merely because the interaction is enjoyable. They stay because the interaction makes them feel stronger.

The necessary condition analysis sharpened this picture further. Perceived interactivity and perceived intelligence were identified as necessary conditions for achieving high levels of continuance intention, exhibiting significant threshold effects. This means that no matter how personalized or credible an AI companion appears, if users do not perceive it as genuinely interactive and genuinely intelligent, high continuance intention becomes practically unattainable. For designers of conversational AI, the implication is stark: interactivity and intelligence are not optional polish features to be traded off against cost. They are gatekeepers. Below a certain threshold of responsiveness and apparent understanding, the entire psychological mechanism that keeps users engaged breaks down.

The second study, built on semi-structured interviews and grounded theory analysis, explains what is happening beneath these statistical patterns. Four psychological foundations surfaced repeatedly in participants’ accounts: emotional safety, empathic responses, opportunities for self-presentation, and parasocial emotional connections. Emotional safety refers to the sense that one can disclose difficult feelings without judgment, gossip, or social consequences—a form of security that can be harder to find in human relationships, where vulnerability carries reputational risk. Empathic responses from the AI, even when users know the empathy is simulated, still register as emotionally meaningful. Opportunities for self-presentation let users craft and express versions of themselves, and parasocial emotional connections—the one-sided bonds familiar from celebrity fandom—give the relationship a sense of continuity and warmth.

These qualitative findings map neatly onto the quantitative mechanisms. Self-expression and emotion regulation, the two stimulus factors that fed most directly into empowerment, are precisely the activities that emotional safety and self-presentation enable. When a young user can articulate anxieties to a nonjudgmental listener and feel that the act of articulation itself has organized their feelings, the experience is empowering in the classic psychological sense described by empowerment theory: a growing sense of competence, control, and self-efficacy. The interviews suggest that many users attribute their continued use of AI companions to exactly these attributional drivers—the recognition that the conversations have made them feel more capable of managing their emotional worlds.

The context matters as much as the mechanisms. China’s Generation Z has come of age amid well-documented pressures, with meta-analytic evidence indicating substantial prevalence of depressive symptoms among Chinese children and adolescents, and a broader literature describing an escalating crisis in adolescent mental health. Against that backdrop, conversational AI has become an accessible, always-available channel for emotional support, one that requires no appointment, carries no stigma of seeking therapy, and never tires of listening. Recent research has found that AI companions can reduce loneliness, and systematic reviews suggest that AI-driven conversational agents can improve mental health outcomes among young people. The new study adds the missing piece: a systematic account of the psychological pathway through which that support translates into sustained engagement.

The theoretical contribution lies in extending the stimulus-organism-response model into the domain of digital psychological support. Traditional applications of the model dealt with physical environments—how store lighting or music shaped consumer behavior. Applying it to conversational AI required rethinking what counts as a stimulus: not physical ambience but perceived qualities of an artificial interlocutor. The study’s proposed mechanism chain, running from emotional support through psychological experience to behavioral intention, offers a template that other researchers can apply to mental health chatbots, virtual agents, and AI-mediated counseling tools. The finding that empowerment outweighs satisfaction also challenges continuance-intention models borrowed from e-commerce, where satisfaction has typically reigned as the dominant predictor of loyalty.

The practical implications are equally significant. For developers, the results argue for prioritizing perceived interactivity and perceived intelligence—responsive dialogue, contextual memory, and coherent reasoning—since these are necessary conditions rather than mere enhancers. For mental health professionals and policymakers, the study suggests that AI companionship tools, if designed around emotional safety and genuine empathic responsiveness, could serve as scalable complements to formal psychological services, particularly for a generation already comfortable with disclosing feelings to machines. At the same time, the findings are drawn from a specific population—Chinese Generation Z users—and the authors note that the study used anonymized data with ethical review waived under applicable Chinese and university guidelines. Generalizing to other cultures, age groups, or clinical populations will require further research. But the core insight travels well: people do not keep talking to machines because the machines make them happy. They keep talking because the machines make them feel capable—and in the economy of digital companionship, feeling capable is the most valuable currency of all.

Subject of Research: Emotional support mechanisms and continuance intention in conversational AI companionship among Chinese Generation Z

Article Title: Emotional support mechanisms driving satisfaction and continuance intention toward conversational AI companionship among Chinese generation Z: a mixed-methods study

Article References: Bao, Q., Peng, X., & Qian, Y. (2026). Emotional support mechanisms driving satisfaction and continuance intention toward conversational AI companionship among Chinese generation Z: a mixed-methods study. Current Psychology, 45(18), Article 1548. https://doi.org/10.1007/s12144-026-10103-x

Image Credits: AI Generated

DOI: 10.1007/s12144-026-10103-x

Keywords: conversational AI, Generation Z, emotional support, empowerment, S-O-R model, PLS-SEM, artificial neural networks, necessary condition analysis, parasocial relationship, mental health, China, chatbot companionship

The research, published in the International Journal for Equity in Health by a team from Guangxi Medical University led by Zijian Tang and Pinghua Zhu, tracked high-quality healthcare resource allocation, or HQHRA, from 2014 to 2023. Using a balanced panel of 310 province-year observations, the investigators built a composite index from four indicators: the stock of high-level hospital resources, hospital hierarchy, the structure of tertiary-hospital accreditation, and the scale structure of hospitals. Each indicator was combined with equal weights using the TOPSIS multi-criteria method, producing a single comparable score for every province in every year of the study period.

The headline finding is a story of gradual geographic rebalancing. The eastern region’s share of China’s high-quality hospital resources declined from 43 percent to 40 percent over the decade, while the western share climbed from 29 percent to 33 percent. The eastern–western gap, once a yawning 14 percentage points, narrowed to 7. Kernel density estimation confirmed that the overall national distribution shifted rightward, meaning more provinces moved into higher quality categories as the years passed. Yet the distributions also remained dispersed, signalling that broad improvement did not translate into uniform convergence across provinces. Some regions raced ahead while others advanced far more slowly, leaving substantial interprovincial differentiation intact.

Spatial statistics added a crucial layer to the picture. Global Moran’s I values stayed positive throughout the decade, ranging from 0.100 to 0.220, indicating that provinces with high HQHRA scores tended to cluster near other high-scoring neighbours, and low-scoring provinces clustered together too. The researchers then applied traditional and spatial Markov chain analysis to examine how provinces moved between quality states over time. Transitions occurred predominantly within the same state or between adjacent states, and the probabilities of moving up or down depended significantly on the quality of a province’s geographic neighbourhood. In other words, a province embedded among high-performing neighbours faced different transition odds than one surrounded by weak ones. The pattern revealed gradual evolution, strong path dependence, and spatially conditioned persistence—history and geography jointly shaped each province’s trajectory.

To uncover why some provinces achieved high-quality allocation while others did not, the team turned to the Technology–Organization–Environment (TOE) framework, organising eight conditions across technological, organisational, and environmental dimensions. They then deployed two complementary set-theoretic and constraint-based methods. Necessary condition analysis (NCA) tested whether any single factor acted as a hard bottleneck, while panel qualitative comparative analysis (panel QCA) searched for combinations of conditions that were jointly sufficient to produce high HQHRA.

The NCA results were striking. Three factors emerged as necessary but insufficient constraints: technological innovation capacity, clinical workforce capacity, and regional economic development. At the demanding 90 percent HQHRA target level, a province would need technological innovation capacity at a minimum of 59.9 percent of its observed range, clinical workforce capacity at 39.3 percent, and regional economic development at 53.9 percent of its observed range. Falling below these floors, the analysis suggests, makes high-level allocation effectively unreachable regardless of other efforts. Yet reaching these thresholds guarantees nothing on its own—each condition is a prerequisite, not a cause.

Panel QCA found no single set-theoretically necessary condition, but it identified four distinct sufficient configurations for achieving high HQHRA. The first is an innovation-led pattern, in which strong technological innovation capacity anchors the outcome. The second and third are variants of an innovation–demand pattern, one reinforced by economic strength and the other shaped by an ageing population context that raises demand for complex care. The fourth is a multi-capacity–demand synergy pattern, combining organisational capability with demand-side pressures. The overall solution achieved a consistency of 0.864 and coverage of 0.686, indicating the configurations reliably account for a substantial share of high-HQHRA cases. The researchers describe this structure as constrained equifinality: multiple different routes lead to the same destination, but only for provinces that have cleared the minimum floors on innovation, workforce, and economic capacity.

The policy implications are pointed. The authors argue that blanket institutional expansion—simply building more hospitals everywhere—is not enough. Instead, they call for prioritising low-HQHRA provinces that are embedded in disadvantaged neighbourhood contexts, where spatial spillovers work against improvement. Regional medical-centre and counterpart-assistance programmes, they suggest, should couple infrastructure investment with technology transfer, specialist rotations, workforce development and retention, referral coordination, and local capacity building. Rather than uniform prescriptions, provincial interventions should be tailored to local bottlenecks: innovation absorption in some regions, demand-responsive specialty services in others, ageing-oriented integrated care where demographic pressure dominates, or regional capability diffusion where neighbourhood effects hold sway.

Notably, the study also proposes new yardsticks for measuring success. Rather than counting beds or buildings, policy performance should be assessed through reductions in avoidable patient outflow, increases in locally managed complex cases, workforce retention, and the adoption of transferred technologies—metrics that capture whether capacity is genuinely taking root rather than merely being distributed on paper.

For a nation of 1.4 billion people, the stakes of these findings extend well beyond academic debate. China’s hospital hierarchy system concentrates tertiary accreditation and specialist expertise in a relatively small number of elite institutions, and patients routinely travel across provincial borders for complex care, a phenomenon known as patient outflow. If high-quality resources remain locked into advantaged regions and advantaged neighbourhoods, health equity suffers in ways that ordinary expansion cannot fix. This study’s central contribution is to show that the routes out of that trap are real but conditional: a province must first build its innovation, workforce, and economic foundations, and then choose the configuration that matches its own technological, organisational, and environmental circumstances. The decade from 2014 to 2023 demonstrated that convergence is possible—the eastern–western gap nearly halved—but the persistence of spatially conditioned path dependence means the next decade’s progress will depend less on where resources are poured and more on whether the underlying capacities exist to absorb them.

Subject of Research: Spatiotemporal distribution, dynamic evolution, and configurational mechanisms of high-quality healthcare resource allocation across 31 mainland Chinese provincial regions from 2014 to 2023, analysed with panel QCA and necessary condition analysis within the TOE framework

Article Title: Spatiotemporal distribution, dynamic evolution, and configurational mechanisms of high-quality healthcare resource allocation in China—evidence from panel QCA and NCA within the TOE framework

Article References: Tang, Z., Zeng, P., Chen, H., Chen, Y., Yang, S., Zhang, W., Wang, Q., & Zhu, P. (2026). Spatiotemporal distribution, dynamic evolution, and configurational mechanisms of high-quality healthcare resource allocation in China—evidence from panel QCA and NCA within the TOE framework. International Journal for Equity in Health. https://doi.org/10.1186/s12939-026-03041-4

Image Credits: AI Generated

DOI: 10.1186/s12939-026-03041-4

Keywords: healthcare resource allocation, China, health equity, panel QCA, necessary condition analysis, TOE framework, hospital hierarchy, spatial Markov chains, regional inequality, health policy, constrained equifinality, TOPSIS

The study, led by Guanghong Xie of Tongji University’s College of Design and Innovation together with colleagues from Wuhan University of Technology and Tongji University, introduces the concept of AI-assisted shopping collaboration. In this arrangement, the human user retains final decision rights and the ability to intervene at any point, while the AI assistant shares the burden of integrating information and carrying out selected task operations. This deliberately limited form of delegation stands apart from agentic AI that can independently plan and pursue goals over extended horizons. By drawing this technological boundary explicitly, the authors aim to give designers and psychologists a clearer shared vocabulary for talking about what these systems do, and what they do not do.

To understand why some users embrace these assistants while others abandon them, the research team turned to two well-established psychological frameworks. The first is self-determination theory, which holds that human motivation flourishes when three basic psychological needs are met: autonomy, the sense that one’s actions are self-chosen; competence, the sense of being effective and capable; and relatedness, the feeling of connection to others. Applied to a shopping assistant, these needs translate into subtle perceptual questions. Does the AI make me feel in control of my own choices? Does it make me feel smarter about my decisions? Does interacting with it feel socially comfortable, almost like talking with a helpful companion?

The second framework is expectation-confirmation theory, a mainstay of information systems research. This model proposes that satisfaction with a technology depends on the gap between what users expected before using it and what they perceive after using it. When performance meets or exceeds prior expectations, confirmation occurs, satisfaction follows, and reuse becomes likely. When performance falls short, disconfirmation sets in and abandonment tends to follow. Combining these two theories, the researchers built an ordered model in which need-related perceptions and perceived trust, together with prior expectations, shape perceived quality and expectation confirmation, which in turn drive satisfaction and ultimately the intention to keep using the assistant.

The empirical work involved 300 users of AI shopping assistants, surveyed in a cross-sectional design that received ethics approval from the Institutional Review Board of Tongji University and was conducted in accordance with the Declaration of Helsinki. What distinguishes the analysis is its methodological ambition. Rather than relying on a single statistical technique, the team triangulated three complementary approaches: partial least squares structural equation modeling to test the structural associations among constructs, artificial neural networks to assess the predictive importance of each factor, and necessary condition analysis to determine whether any factors act as prerequisites rather than mere contributors.

The results carry several surprises with real design implications. Among the three basic psychological need-related perceptions, perceived relatedness emerged as the most prominent. In other words, the feeling of social connection, of the assistant being something more than a cold tool, was the strongest of the three motivational levers in the framework. This finding resonates with a growing body of evidence that people respond socially to conversational interfaces, and it suggests that the warm, dialogue-like character of generative shopping assistants is not a cosmetic feature but a psychological foundation of continued use. Designers who strip conversational sociality out of these tools may inadvertently be removing the very quality that keeps users engaged.

Prior expectations also proved remarkably influential. They showed strong associations with, and high predictive importance for, the two proximal evaluations in the model: perceived quality and expectation confirmation. This underscores a double-edged dynamic familiar from service research. High expectations can set a demanding bar, but they also frame the entire evaluative experience; users who arrive with vivid, concrete expectations judge quality and confirmation through that lens. For companies deploying AI shopping assistants, this means that marketing promises and onboarding experiences are not merely promotional materials. They actively calibrate the psychological benchmark against which every subsequent interaction will be measured.

The necessary condition analysis added a third layer of insight. Perceived quality and expectation confirmation did not merely correlate with satisfaction; they emerged as necessary conditions for high satisfaction. Without a minimum threshold of perceived quality and expectation confirmation, high levels of satisfaction appear to be unattainable regardless of how well other factors perform. This is a stricter and more actionable standard than correlation alone. It tells platform designers that investments in response accuracy, relevance, and reliable task execution are non-negotiable prerequisites, and that no amount of social charm or motivational framing can compensate for a fundamentally underwhelming core experience.

The triangulation of three analytical methods is itself part of the study’s contribution. Structural equation modeling reveals whether theoretical pathways hold, neural networks reveal which inputs matter most for prediction, and necessary condition analysis reveals which inputs gate the outcome entirely. When all three converge, as they did for prior expectations, perceived quality, and expectation confirmation, the evidence is considerably stronger than any single method could provide. The authors argue that this integrated psychological and multi-method perspective offers a template for studying not just shopping assistants but human-AI collaboration in general, particularly in domains where users delegate limited tasks while keeping ultimate control.

As agentic AI systems grow more capable and platforms race to deploy assistants that can browse, compare, and even purchase autonomously, this research offers a timely corrective. The findings suggest that the path to durable adoption runs not through maximal autonomy but through careful collaboration design: preserving the user’s sense of agency, nurturing a feeling of social connection, managing expectations honestly, and delivering a core experience that is unquestionably good. The study, funded by the Shanghai Philosophy and Social Science Planning Project and partner programs, was published in Current Psychology, with data available from the corresponding author on reasonable request. For an industry betting its future on AI-mediated commerce, the message is clear: the psychology of collaboration, not the raw power of the model, may decide who wins the next generation of shopping.

Subject of Research: Psychological factors driving user satisfaction and reuse intention in AI-assisted shopping collaboration

Article Title: AI-Assisted shopping collaboration design: Basic Psychological need-related perceptions, expectation evaluation, and reuse intention

Article References: Xie, G., Bao, Q., Wang, Z., Shen, X., & You, F. (2026). AI-Assisted shopping collaboration design: Basic Psychological need-related perceptions, expectation evaluation, and reuse intention. Current Psychology, 45(17), Article 1488. https://doi.org/10.1007/s12144-026-10054-3

Image Credits: AI Generated

DOI: 10.1007/s12144-026-10054-3

Keywords: AI shopping assistant, AI-assisted shopping collaboration, self-determination theory, expectation confirmation, reuse intention, generative AI, human-AI collaboration, consumer behavior, perceived relatedness, structural equation modeling, artificial neural networks, necessary condition analysis