As electric vehicles scale up, battery recycling has become critical to the circular economy. Yet ESG adoption in battery recycling is not merely an internal managerial choice but is also shaped by green social expectations and social comparison among firms. This study examines how these two forces affect recycling and pricing under non-adoption, partial adoption, and full adoption. We develop a dynamic game model in which green social expectations generate goodwill premiums and losses, while comparison-induced pride and guilt feed back into goodwill accumulation and strategic decisions. We find that green social expectations strengthen recycling incentives and reshape market demand through goodwill effects. Interestingly, guilt exerts a stronger effect than pride in social comparison, encouraging firms to increase recycling effort and accumulate higher goodwill. We also find that full ESG adoption helps firms build stable goodwill advantages, ease price competition, and improve long-term profitability. By contrast, partial adoption may trigger free-riding and strategic divergence. A key theoretical contribution is the joint integration of green social expectations, social comparison, and ESG adoption within a unified dynamic framework, with goodwill linking behavioral responses to firms’ recycling and pricing decisions. Managerially, firms should align ESG practices with continuous recycling effort, while regulators should improve the transparency and comparability of ESG performance to facilitate coordinated adoption.
Urban parking search remains a major source of congestion, travel delay, and unnecessary emissions, especially in areas where free on-street parking is not instrumented by dedicated infrastructure. This article investigates an infrastructure-less cooperative parking-guidance approach in which participating vehicles contribute lightweight parking-related evidence that is aggregated into shared heatmaps. The proposed model formalises parking-event semantics, heatmap representation, evidence updates, and a score-based parking-selection strategy that balances parking opportunity against walking distance. A SUMO-based simulation campaign evaluates the model under different user-preference parameters, system-adoption rates, and levels of initially available information. The results show that shared parking evidence can reduce search time under the evaluated conditions, including scenarios with partial adoption, while also highlighting the influence of user preferences and cold-start information availability on system performance. To examine the implementation feasibility of the information-generation mechanism assumed by the model, the article additionally presents a compact proof-of-concept pipeline based on a Bluetooth Low Energy beacon, a smartphone application, and a lightweight back-end service. Functional testing demonstrates that parking-related state-change events can be generated, transmitted, stored, spatially aggregated, and visualised without dedicated roadside parking sensors. This prototype serves only as an implementation-feasibility demonstration and does not constitute field validation of the guidance strategy or establish real-world accuracy, latency, reliability, or deployment readiness.
Artificial intelligence is becoming deeply embedded in higher education, yet how the characteristics of AI-based e-learning systems relate to students’ perceived learning effectiveness and satisfaction remains insufficiently understood. This study examines the relationships of AI Functionality Compatibility, AI Instructional Process Coverage, and AI-Assisted Learning Cognitive Usability with Perceived Online Learning Effectiveness and Online Learning Satisfaction. Data from 384 students at Chinese universities were analyzed using a two-stage approach combining partial least squares structural equation modeling and artificial neural networks (PLS-SEM-ANN). The results showed that all three system characteristics were positively associated with perceived learning effectiveness, with instructional process coverage showing the strongest relationship. Cognitive usability also had a significant direct association with learning satisfaction, whereas functionality compatibility and instructional process coverage showed significant indirect effects through perceived learning effectiveness. The findings reveal an outcome-oriented pattern in which perceived learning effectiveness occupies a central position between system characteristics and satisfaction. This study extends understanding of AI-supported learning systems by emphasizing the alignment of technical functions with pedagogical processes and learners’ cognitive needs. It also provides practical guidance for universities and developers seeking to better align the design and evaluation of AI-based e-learning systems with learners’ instructional and cognitive needs.
Frequent light-to-moderate rainfall may influence metro demand differently across rainfall stages and station ridership rhythms. Using automatic fare collection data from 80 Hangzhou Metro stations and station-matched hourly meteorological observations, this study examines weekday entry-ridership associations with rainfall onset, continuation, and the first post-rain hour. Three station ridership-rhythm types were identified from non-rain weekday entry–exit profiles: destination-oriented, balanced bidirectional, and origin-oriented. A Bayesian hierarchical negative binomial model estimated stage-specific associations while accounting for station-hour baseline ridership, hourly temperature, peak periods, and station- and date-level heterogeneity. During off-peak periods, rainfall onset and continuation were associated with 4.95% and 6.82% reductions in expected entry ridership at balanced bidirectional stations. Continuation associations were more negative in the primary model, but this difference was substantially attenuated after accounting for cumulative precipitation and elapsed episode duration. Origin-oriented stations generally showed more negative onset and continuation associations, while negative first-post-rain associations were observed across all three station types. These findings demonstrate that routine rainfall responses vary across both event stages and station ridership rhythms, supporting more stage-specific and station-sensitive passenger-flow monitoring.
Hydrogen innovation requires coordination across scientific research, engineering development, infrastructure deployment, market application, and public governance. However, heterogeneous incentives can destabilize collaborative participation even under strong policy support. This study examines how science-push and market-pull shape collaborative innovation in hydrogen technology. We develop a tripartite evolutionary game involving enterprises, universities, research institutions, and government under bounded rationality. The model incorporates collaborative returns and proportional downside-risk allocation, participation costs, spillover-based outside payoffs, market demand sensitivity, technological-foresight capability, and government governance strategies. The analytical results and numerical scenario illustrations identify three equilibrium configurations: independent R&D, one-sided participation, and full bilateral participation. Their occurrence depends on collaborative returns, participation costs, downside-risk penalties, external driving intensity, and governance conditions. Under matched driving intensity, the relative effectiveness of science-push and market-pull is actor-specific and determined by the balance among collaborative returns, costs, and outside opportunities. These findings clarify the strategic conditions shaping participation in hydrogen innovation systems and provide scenario-contingent implications for benefit allocation, risk management, and collaborative governance.
Digital behavior change tools increasingly integrate constructs from multiple theoretical traditions, yet there is limited cross-domain understanding of how these frameworks are combined and which outcomes they are used to explain. This scoping review therefore aimed to map integrated theoretical frameworks used in digital IT-based behavior change tools from 1999 to 2025, focusing on theory combinations, outcome types, intervention contexts, and research gaps. Following PRISMA-ScR and a registered protocol (PROSPERO CRD42022285741), searches across six databases identified 62 eligible studies. Twenty-nine theories and models were identified, with Self-Determination Theory (35%) and the Theory of Planned Behavior (29%) being the most prevalent. Behavior-change-oriented frameworks were more commonly associated with target behavioral outcomes, whereas technology-adoption-oriented frameworks primarily explained intention, acceptance, continuance, and technology use. Health and fitness interventions dominated the evidence base (44%), followed by online learning (23%) and mobile commerce (11%). Long-term follow-up and explicit theory-to-behavior-change-technique mapping remained limited. Overall, the review provides a 25-year cross-domain synthesis of theoretical integration in digital behavior change tools and highlights the need for clearer theory–intervention alignment, longer-term evaluation, and broader application across underrepresented digital contexts.
Airports handle the final stage of the arriving passenger’s journey at the baggage claim hall, one of the least instrumented terminal operations. At most airports, arriving bags are discharged onto a shared recirculating carousel, a passive, first-come process that lengthens passenger waiting, congests the claim hall, and gives operators no per-party measure of service. This paper formalizes inbound claim as a measurable, controllable terminal service, termed Baggage-Claim-as-a-Service (BCaaS), in which the passenger party rather than the individual bag is the unit of service and the complete bag set is delivered against an observable delivery-time target. The paradigm is evaluated through a per-party, cluster-based reclaim architecture that consolidates each party’s bags, routes the cluster through a multi-level sortation network, releases the complete set at an authenticated collection point, and records per-party telemetry for service monitoring. Using a discrete-event simulation calibrated to King Khalid International Airport Terminal 5 and benchmarked against a conventional carousel across 27 scenarios, per-party reclaim reduces availability-based passenger, cluster, and flight delivery times by 38%, 51%, and 27% on average, largest under light load. The results show that per-party reclaim makes inbound terminal logistics observable and supports operator service-level monitoring, more sustainable urban mobility, and future field validation.
The transformation of land-border ports from transit channels into regional hubs is increasingly seen as a way to support inclusive growth in frontier regions. Yet port expansion does not automatically improve local livelihoods. Whether port-led development is associated with income gains for rural residents in border counties depends on how cross-jurisdictional public goods are supplied, coordinated, and absorbed. Drawing on collective-action theory and regional public-goods theory, this study develops a two-tier analytical framework that distinguishes regional synergy from county response. Using panel data for seven counties along the China–Vietnam border in Guangxi from 2008 to 2023 and supplementary field research, we apply two-way fixed-effects models, control-function checks, mediation tests, and port-size heterogeneity analysis. Three main findings emerge. First, regional synergy and county response, proxied by Region_BPEDI and Dev_BPEDI, are both positively associated with the logarithm of rural per-capita disposable income in border counties. A one-standard-deviation comparison shows that the estimated association of Region_BPEDI is about 2.5 times that of Dev_BPEDI. Second, industrial-structure upgrading shows the strongest mediating pattern, while infrastructure connectivity and financial development depend more on cross-tier coordination. Third, large ports such as Dongxing and Pingxiang are better positioned to benefit from regional synergy, whereas medium and small ports rely more on niche-based differentiation. The results suggest that hub-oriented border transformation may benefit from multi-level institutional collaboration, place-based industrial matching, and tailored financial instruments. Field evidence also indicates that smart customs clearance, low-carbon logistics, and cross-border environmental coordination are becoming important sustainability dimensions of regional synergy. The study provides a diagnostic framework for evaluating border-port reforms along the Belt and Road and ASEAN economic corridors.
Knowledge translation (KT) involves the co-creation, exchange and dissemination of knowledge to increase evidence-informed public health. Increasingly, there is a demand to strengthen evidence-informed public health, and with this, a need to understand how to support KT efforts within public health systems. Whilst a variety of KT frameworks have been developed, frameworks and studies that provide insight into KT initiatives that operate across the individual, organisational and system levels are lacking. This paper presents the development of a KT framework used by a government-funded sexual health and blood-borne virus capacity-building partnership for research and evaluation in an Australian jurisdiction. The approaches taken and practical insights into operationalising a collaborative approach to KT are described. The process of developing the framework included inputs from the literature, a needs assessment survey, and stakeholder consultation. The final framework is composed of a conceptual model and a suite of KT strategies that reflect the dynamic and complex nature of KT in public health. The framework illustrates the broader influences of KT, key strategies, and guiding principles. Findings have relevance for service providers, researchers and policymakers (including funders) seeking ways to engage in KT at the individual, organisational and system levels.
Post-disaster emergency logistics is often organized according to prespecified response periods, although operational relief tasks may be completed earlier or later than those calendar boundaries. This study develops a task-completion-triggered multi-stage resource-allocation mechanism (TCMS) for a two-echelon emergency logistics network. Effective first-contact coverage and cumulative forward deployment generate three sequential operational stages—rapid coverage, intensive deployment, and recovery support—through a one-period state-update rule. The operational stage determines the priority regime used to select among feasible allocation schedules. The mechanism is evaluated within a common direct-decision rolling-horizon mixed-integer linear programming (MILP) framework. At each decision epoch, policy-neutral cost-service candidates are generated using total cost and cumulative raw weighted backlog, whereas the current operational stage supplies stage-dependent priority coefficients for selecting the implemented action. TCMS and a fixed-stage baseline therefore differ in the source of their operational stage, while a stage-free rolling-horizon benchmark retains a neutral priority regime. Computational experiments include formulation validation, Base and Medium rolling-horizon policy comparisons, a Large static MILP reference, and trigger-parameter and stage-priority-coefficient sensitivity analyses. The results show that endogenous stage timing changes the selected cost-service operating point, with effects varying across instances. Sensitivity analysis further shows that transition timing and allocation outcomes can vary with trigger settings and stage-priority calibration. TCMS is particularly suited to settings in which transition timing cannot be specified reliably in advance, and managers require an observable basis for adjusting allocation priorities as relief operations progress.
This study examines StudyFAB in Schweinfurt, Germany, as a case of inner-city revitalization through a comparative analysis of third space initiatives. In response to declining pedestrian traffic and rising vacancies linked to online shopping, it investigates StudyFAB’s impact on foot traffic, perceived quality of stay, and user engagement, compared with Utopiastadt Wuppertal and CityLAB Berlin. Using a mixed-methods design, the study combines pedestrian counts, PHINEO impact metrics, usage data, and a survey of N = 93 participants (33 StudyFAB users, 30 aware non-users, and 30 non-users). The findings show that StudyFAB did not significantly increase overall city-wide pedestrian frequency, but statistical analysis indicates strong positive associations and behavioural tendencies within its target groups, including more frequent city centre visits, improved perceptions of the city image, high user satisfaction, and measurable participation. Compared with the broader neighborhood and governance approaches of Utopiastadt and CityLAB, StudyFAB demonstrates a focused but effective contribution to urban revitalization. The results suggest that third spaces can serve as complementary tools for urban transformation when social, cultural, educational, and commercial functions are integrated. Future research should examine long-term scalability, sustainable funding, and transfer effects across projects.
Expanding offshore wind energy introduces complex marine pollution liability challenges for specific emerging markets like Taiwan. Traditional static regulations often trigger administrative conflicts and struggle to adapt to dynamic stakeholder behaviors. This study develops an integrated socio-technical model combining evolutionary game theory (EGT) and system dynamics (SD) to analyze the strategic co-evolution between regulators and developers. The model is strictly calibrated using empirical data from Taiwan’s offshore wind sector to simulate various scenarios. Phase plane simulations reveal an “excess-liability paradox”. Crucially, the mathematical stability analysis identifies the intangible value of corporate reputation as the structural prerequisite for system convergence. Statutory penalties serve effectively as a complementary deterrent signal, but long-term stability requires normative shifts. We propose combining high statutory penalty deterrence with ESG-linked reputation mechanisms, providing a fundamental theoretical reference for emerging offshore wind markets facing similar initial regulatory and economic trade-offs.
Digital transformation in higher education institutions requires approaches that recognize the diversity of organizational contexts and the varying priorities expressed by academic units. This study analyzes digital transformation objectives identified in the institutional development plans of six faculties and schools at the Universidad Nacional Autónoma de México using a document-based frequency prioritization approach. The study examines how institutional plans articulate digital transformation objectives in relation to organizational characteristics, management structures, and available resources, adopting a systems perspective to account for the interactions among these organizational conditions. A key contribution of this research is the use of institutional planning documents as an analytical basis for identifying seven core dimensions that characterize digital transformation initiatives in the university-analyzed context, providing a framework for understanding how academic units define and organize their transformation priorities. The proposed frequency-based prioritization approach operationalizes this comparison by calculating the relative weight of each dimension based on the number of technological objectives classified within it. The findings show that digital transformation priorities vary according to institutional autonomy, management arrangements, disciplinary profiles, existing capabilities, and available resources. In large and diverse universities characterized by decentralization and complex organizational structures, digital transformation does not follow a single pathway; instead, it develops through context-dependent strategies shaped by the specific conditions and needs of each academic unit.
Green innovation is essential to the low-carbon transition, but aggregate patent counts may conceal changes in the technological quality of corporate innovation. Using this observation as a starting point, this study asks how economic policy uncertainty (EPU) affects green innovation and its quality structure among Chinese heavy-polluting firms. Using panel data on A-share listed firms from 2010 to 2023, we distinguish green invention patents from green utility model patents and estimate two-way fixed-effects models. We find that EPU significantly reduces substantive green innovation and weakens the relative position of green invention patents, even though strategic green innovation does not decline correspondingly. These patterns hold across alternative patent measures, quality indicators, EPU specifications, and placebo tests. EPU is also associated with tighter financing constraints, higher cash holdings, and weaker bank credit access, and among these three responses, financing constraints offer the most robust independent explanation. Taken together, the results suggest that policy uncertainty can alter the composition of green innovation even when visible patenting activity is maintained, underscoring the importance of stable policy expectations and sustained financing for substantive green innovation.
Artificial intelligence (AI), especially generative AI, is becoming deeply embedded in healthcare, making doctor–AI collaborative diagnosis a common mode of medical decision-making. This development raises a critical question about responsibility attribution: When people perceive that a diagnostic error has occurred, how does AI involvement shape patients’ and observers’ judgments of the doctor’s responsibility? Drawing on responsibility attribution theory, we examine this question across five studies—one event-related potential (ERP) experiment and four scenario experiments. We find that when a diagnostic error is perceived, doctor–AI collaborative diagnosis (vs. doctor-only diagnosis) reduces perceived doctor responsibility by increasing perceived shared agency. This responsibility-reducing effect is weaker when the doctor rejects correct AI advice than when the doctor accepts incorrect AI advice. Theoretically, our findings show that responsibility attribution in human–AI collaboration involves two stages: agent identification and responsibility allocation. This account extends responsibility attribution theory to human–AI collaboration and identifies perceived shared agency as a key psychological mechanism underlying responsibility judgments in these settings. Practically, the findings can inform technology deployment, responsibility communication, and governance mechanisms in hospitals, AI firms, and regulatory agencies.
Public health emergencies often unfold over extended periods and place sustained pressure on community-level governance, particularly when demands for supplies, information, medical access, and daily support exceed routine service capacity. This study examines how community response organizations develop adaptive capacity under such conditions, drawing on a Shanghai community during the 2022 COVID-19 lockdown. Grounded theory was used to analyze semi-structured interviews, field observations, archival documents, and online community records and to identify the process through which fragmented resident mutual aid developed into organized collaboration. The grounded analysis identifies four linked stages of organizational evolution: capacity building, structural emergence, institutional consolidation, and experience accumulation. Building on these findings, a system dynamics model formalizes selected causal relationships and explores the feedback interactions among demand pressure, material reserves, information gaps, panic, volunteer mobilization, organizational division of labor, institutional support, and trust. The analysis illustrates how, within the proposed mechanism, resident participation may be activated through social capital, stabilized through institutional coordination and rule support, and sustained through feedback linking service performance, information reliability, trust, and continued participation. The study provides a feedback-based explanation of how community response capacity develops under prolonged emergency pressure and clarifies how informal resident action and formal governance arrangements can become mutually reinforcing. These findings contribute to research on community emergency adaptation and offer implications for grassroots preparedness, volunteer coordination, and collaborative governance.
This study proposes a systematic methodology integrating Bidirectional Encoder Representations from Transformers (BERT)-based topic modeling (BERTopic) with the ISO/IEC 25010:2011 product quality model to evaluate and prioritize software quality characteristics in cryptocurrency wallet applications using user review data. Using 68,790 preprocessed reviews, BERTopic identified 15 major topics representing user-reported quality concerns. The proposed approach combines topic frequency with user dissatisfaction to provide clear and prioritized insights for software quality improvement. Specifically, topic frequency is logarithmically transformed to reduce the disproportionate influence of highly frequent topics, while dissatisfaction is derived from the average star rating associated with each topic. These measures are combined to calculate topic-level weighted values, which are subsequently mapped to the corresponding ISO/IEC 25010:2011 quality characteristics. Because a single topic may be associated with multiple quality characteristics, fractional allocation is applied to distribute its weighted value across the mapped characteristics and avoid duplicated contributions. The results identify Security as the highest-priority quality characteristic, followed by Performance Efficiency and Reliability. A sensitivity analysis comparing fractional and full allocation further shows that the allocation rule can materially affect the resulting priority structure. These findings establish clear priorities for software quality improvement and highlight the importance of accounting for multi-label topic mappings when aggregating user feedback. Overall, the proposed framework provides a transparent and user-centered approach for transforming large-scale review data into structured software quality priorities.
Earthquake rescue priorities are often determined from incomplete evidence that is partly shared across information channels. Under such conditions, an apparently plausible mean score may conceal substantial directional conflict. This study investigates whether that conflict can be carried forward into the action boundaries themselves. We develop the Conflict-Driven Action Boundary Generation Model (CABGM), which links composite-conflict diagnosis to a constructed association proxy, normalized action propensities, and feedback-sensitive three-way decision boundaries. The structural coefficients are screened against prespecified sign, normalization, boundedness, and boundary-feasibility constraints and are treated as theory-constrained operating settings rather than estimates fitted to rescue outcomes. For the retrospective empirical application, we reconstructed ten named settlement-scale units from public records using a prespecified five-level documentary coding protocol, a fixed source hierarchy, and a fixed evidence cutoff before applying the frozen model specification. We also implemented a scalar-score adaptation of adaptive-consensus logic and a reliability-informed Dirichlet comparator for channel-weight uncertainty. Both comparators are transparent adaptations to the present 10×4 score matrix rather than exact reproductions of the original linguistic or event-network models. In the public-record audit, all five methods yielded identical rank-concordance statistics: a Spearman correlation of 0.912, Kendall’s tau-b of 0.839, a p-value of 0.0016 from an exact two-sided permutation test, and 100% top-four overlap. The fixed-threshold conflict gate, opinion-distance update, adaptive-consensus adaptation, and CABGM each produced an acceptance/deferment/rejection split of 6/4/0, whereas the Bayesian weight-uncertainty comparator produced a 4/6/0 split. CABGM offers a new methodological option for emergency group decision-making by integrating composite-conflict diagnosis, a constructed association proxy, normalized action propensities, and feedback-sensitive three-way decision boundaries within a unified framework. Compared with fixed-boundary and consensus-contraction approaches, the model makes the transmission of diagnosed conflict into boundary adjustment explicit and traceable, while distinguishing boundary adaptation from score smoothing. It therefore provides a transparent mechanism for preserving unresolved or threshold-adjacent alternatives for further verification before definitive action is taken.
Electric vertical takeoff and landing (eVTOL) represents a novel mode of transportation emerging within the low-altitude economy framework, exhibiting extensive development prospects. As a widely adopted incentive policy, government subsidy constitutes a crucial method for supporting the development of this strategic emerging industry. This paper constructs a differential game model of a supply chain composed of a manufacturer and retailer capable of simultaneously producing and selling eVTOL, considering three scenarios: centralized decision-making and decentralized decision-making with or without cost-sharing. Based on optimal control and differential game theory, the decision-making processes of supply chain members are investigated, and equilibrium strategies under different scenarios are compared and analyzed, with the model’s validity confirmed through numerical simulations. The findings indicate that the subsidy policy exerts a positive impact on the eVTOL supply chain: as the subsidy rates increase, supply chain members are better resourced to invest in endurance and marketing, thereby fostering eVTOL development. Concurrently, a reduction in wholesale and retail prices is observed, rendering eVTOL more affordable and of higher quality for consumers. The competitive structure of the upstream market does not alter this fundamental conclusion. The optimal endurance effort, marketing effort, eVTOL brand goodwill, and demand under the centralized decision-making model are higher than those under the decentralized one. In a certain feasible region, the two-way cost-sharing contract enables the eVTOL supply chain to achieve Pareto improvement, but it does not reach the level of centralized decision-making. This research expands the application of differential game theory in the field of the low-altitude economy, providing a scientific basis for the government to formulate policies and enterprises to distribute products.
Large language models (LLMs) are increasingly relevant to Business Process Management (BPM), particularly when process knowledge is dispersed across documents, conversations, and other unstructured sources. Their probabilistic outputs, however, raise questions about validation, traceability, and accountability. This paper develops a lifecycle-based conceptual framework for allocating and governing LLM use across the six stages of the BPM lifecycle. The framework separates generative interpretation from formal, empirical, and expert validation. It comprises five interdependent layers and six operational principles, implemented through a stage-risk-validation matrix, a principle-intensity map, and four evaluation dimensions. Governance requirements increase as outputs approach live execution or decisions that are difficult to reverse, with controls aligned with the NIST AI Risk Management Framework, the EU AI Act, and the GDPR. A customer complaint-handling scenario demonstrates how the framework can be applied. An illustrative stress test using the BPI Challenge 2017 event log and ten independent LLM generations instantiates the validation layer under information-asymmetric conditions. Although all generated models were structurally valid, the event log revealed incomplete activity coverage and control-flow mismatch. This illustrates the value of an external referent but does not establish comparative performance or a general difference in error detectability between LLM-generated and process-mining artefacts. The framework therefore positions LLMs as tools for turning unstructured information into preliminary process knowledge, while established BPM methods and human expertise remain responsible for validating consequential outputs.
