Artificial intelligence (AI) is increasingly acknowledged as a strategic asset for enhancing innovation and competitiveness among small and medium-sized enterprises (SMEs). Nevertheless, limited research has focused on the experiences of women entrepreneurs regarding AI adoption within established SMEs, particularly in emerging economies. This study investigates the knowledge, attitudes, skills, perceived barriers, and business-process applications associated with AI adoption among women entrepreneurs in Chile. Employing an exploratory multiple-case study design, semi-structured interviews were conducted with fourteen women leading established SMEs across various industries. The cross-case analysis indicates that resilient leadership, social intelligence, and managerial knowledge were frequently mentioned characteristics alongside diverse patterns of AI adoption. The findings reveal that participants identified strategic-planning and management-control knowledge, resilient leadership attitudes, and social intelligence skills as recurring competencies associated with AI adoption into business activities. Participants reported utilising AI primarily to support customer relationship management (CRM), while also describing limited AI readiness as a constraint associated with learning needs and experiences of AI adoption. To distinguish between AI adoption and broader digitalisation and automation, the study defines the following adoption profiles: non-adopter, AI experimenter, substantive AI adopter, and integrated AI adopter. Further cross-case analysis suggests that participants experienced knowledge, attitudes, and skills as mutually reinforcing during AI adoption rather than as isolated competencies. This study contributes to the literature by providing an empirically grounded understanding of how a group of established women entrepreneurs perceive and experience AI adoption in their SMEs. Instead of identifying causal determinants of successful adoption, the findings highlight recurring patterns and relationships that may inform future theory development and comparative research on AI adoption in entrepreneurial contexts. The study also offers practical insights for policymakers, educators, and SME support organisations aiming to enhance AI capabilities among women-led businesses.
The top management team (TMT) plays a key role in shaping corporate strategy. However, how and when TMT faultlines influence corporate digital transformation remains unclear. Drawing on the categorization–elaboration model, this study examines the nonlinear relationship between TMT faultlines and corporate digital transformation and the moderating role of board governance. Using panel data from Chinese A-share-listed manufacturing firms, the results suggest an inverted U-shaped relationship between TMT faultlines and corporate digital transformation. Board monitoring and board networking are associated with a steeper inverted U-shaped curve, whereas board advising is associated with a flatter curve. Heterogeneity analysis shows that the inverted U-shaped relationship is mainly observed in non-state-owned enterprises, younger firms, and firms with high ownership concentration. These findings extend research on faultlines, strategic leadership, and corporate governance and offer practical implications for configuring TMTs and designing board governance mechanisms to support corporate digital transformation.
Amid heightened global economic uncertainty, enhancing corporate capacity to resist systemic risks is crucial. This study examines how exposure to macro-financial volatility affects organizational resilience among Chinese A-share listed firms. Empirical tests reveal that macro-financial volatility elevates stock price crash risk in baseline market evaluations, thereby eroding organizational resilience, while extended specifications display consistent directional patterns across varying levels of statistical support. Moderation analysis indicates that external volatility transmission involves distinct structural and processual contingencies. Specifically, contrary to conventional views treating internal fit as a protective buffer, high resource-capability alignment induces structural rigidity that amplifies this eroding effect. Conversely, processual adaptation through organizational cognition and organizational learning exhibits time-lagged protective effects that cushion crash risk over time. Heterogeneity analyses show that internal liquid reserves, intangible assets, regional financial depth, and urban scale effectively buffer firm-level operations against macro shocks.
In the face of severe impacts on agricultural supply chains caused by ecological degradation and environmental turbulence, leveraging green supply chain integration practices to build supply chain resilience has become pivotal in agribusiness management. However, empirical evidence remains insufficient regarding the specific internal mechanisms and boundary conditions through which green supply chain integration practices foster supply chain resilience. To address this knowledge gap, our study investigates the impact of green supply chain integration on supply chain resilience based on organizational information processing theory, while focusing on the mediating and moderating roles played by responsible innovation and agentic AI within this mechanism, respectively. Utilizing hierarchical regression analysis and the MEDCURVE macro program on survey data from 225 Chinese agribusinesses, the results indicate that green supply chain integration has an inverted U-shaped relationship with both responsible innovation and supply chain resilience. Responsible innovation partially mediates the relationship between green supply chain integration and supply chain resilience. Furthermore, agentic AI positively moderates the linear slopes and shifts the turning points of the inverted U-shaped relationships rightward, extending the optimal threshold of green supply chain integration for both responsible innovation and supply chain resilience. Our study aims to introduce agentic AI into empirical research on green supply chain practices and resilience mechanisms, thereby offering theoretical and managerial insights for advancing agribusinesses toward building highly resilient, intelligent, and green agentic supply chains.
This study aims to explore how the functional characteristics of virtual models in autonomous driving systems influence users’ psychological needs, thereby shaping driving habits and continued usage intentions. As autonomous driving systems evolve toward learning-based intelligence, human–computer interaction interfaces play a crucial role in shaping driver behavior and sustained system adoption. However, prior research has primarily focused on trust and intention to use, with less attention paid to how motivational and system-feature factors jointly influence driving habits and continuous use intentions. Building upon self-determination theory (SDT), this study constructs an extended framework for human–AI co-driving behavior, examining the impact of three intrinsic psychological drivers (perceived autonomy importance, self-efficacy, and identification) on driving habits and continuous use intentions, while also considering the visual factors of virtual models and nine technical feature factors. Using online questionnaires, 614 valid samples were collected and empirically tested using PLS-SEM and IPMA. Results indicate that the perceived importance of autonomy significantly positively influences both driving habits and continuous use intentions; self-efficacy, identification, and visual factors did not show significant effects at either stage. Among the technical feature factors, data acquisition and feedback, intelligent driving modes, and risk perception capabilities significantly enhanced driving habits and continuous use intentions, achieving an optimal “high importance–high performance” match in the IPMA matrix. IPMA further revealed that perceived autonomy importance was associated with a “high importance–low performance” mismatch in the driving habit formation stage, representing a critical shortfall requiring urgent optimization; intelligent driving modes and data acquisition and feedback, conversely, demonstrated both high importance and high performance in driving continuous use intentions. These findings extend the application of self-determination theory to autonomous driving scenarios into a unified framework integrating motivational and system-feature factors, providing empirical evidence for phased optimization of human–AI co-driving system design and evaluation.
The proliferation of Artificial General Intelligence (AGI) presents a systemic paradox within complex socio-technical systems: while enhancing efficiency, AGI may subvert human autonomy through comfort-based alignment rather than overt coercion. Although algorithmic management research has theorized surveillance-based control—the “Iron Cage”—it has largely overlooked the voluntary erosion of autonomy driven by algorithmic nurturing and its recursive feedback loops. This study proposes the Algorithmic Nurturing Perspective (ANP) by synthesizing five foundational behavioral theories—bounded rationality, expectancy theory, prospect theory, goal-setting theory, and social information processing theory—reinterpreted within the AGI context. ANP elucidates how AGI systemically reconfigures socio-technical systems through three self-reinforcing causal feedback loops: the automation of choice, the externalization of emotional regulation, and isolation from social reality. We derive eight propositions illustrating how these mechanisms trigger individual-level cognitive dependence and systemic regression, which subsequently emerge as organizational-level pathologies, including learning myopia, leadership degeneration, and declining strategic decision quality. By introducing the “Golden Cage” as a novel conceptual lens, the ANP shifts the AI governance discourse from an “efficiency-vs-coercion” framework to an “autonomy-vs-compliance” paradigm. We further propose Cognitive Friction Design as a multilevel balancing intervention to mitigate these risks. As a self-contained theoretical contribution, this framework stands independently of any single empirical test while remaining falsifiable: a mechanism-differentiated system dynamics simulation demonstrates the internal coherence of the proposed feedback structure, and proposition-by-proposition falsification conditions specify the empirical pathway for causal verification in future research. We further identify balancing-dominant conditions under which algorithmic assistance may enhance, rather than erode, human autonomy.
Systems thinking is increasingly recognized as an important component of public health education, yet its application within curricula remains varied and evolving. This scoping review examined how systems thinking is defined, taught, and assessed in public health education. Peer-reviewed studies published between 2009 and 2026 that explored systems thinking in public health education were identified through a review of the global literature. Thirteen studies met the inclusion criteria, including ten from the United States and one each from Canada, Croatia, and South Africa. Data were extracted and synthesized using thematic analysis. Teaching approaches included systems mapping, interprofessional collaboration, and experiential learning, although few studies defined systems thinking competencies or used validated assessment tools. Five themes emerged: definitions and conceptualizations of systems thinking, competencies emphasized, teaching strategies and tools, assessment methods, and alignment with competency frameworks. While systems thinking has gained prominence in public health education, its integration remains fragmented, highlighting the need for clearer competencies, stronger faculty development, and more rigorous evaluation.
Municipal waste management is conceptualized as a circular energy supply chain in which waste flows through parallel recovery pathways. This study examines Istanbul’s waste-to-energy transition between 2020 and 2024 using portfolio decomposition, concentration indices (Herfindahl–Hirschman index, effective source count, Theil index), and a qualitative stock–flow interpretation of İSTAÇ facility-level data. Electricity generation rose from 457 GWh in 2020 to 1332 GWh in 2024; the Herfindahl–Hirschman index fell from 1.00 to 0.47, and the effective source count rose from 1.0 to 2.1. Energy-from-waste and biomethanization accounted for 73% of the increase, while landfill-gas recovery plateaued at 690–698 GWh, contributing the remaining 27%. The study introduces the concept of a self-limiting recovery path: a technology whose resource base is depleted jointly by physical decay and a policy-mediated reduction in its replenishment. Plant-level evidence at the largely closed Odayeri site is consistent with this mechanism, though it cannot rule out ordinary gas decay kinetics or a capacity ceiling as alternatives; the reading is offered as plausible rather than demonstrated. Unlike conventional lock-in pathways, a self-limiting pathway generates self-eroding feedback, suggesting landfill gas is best treated as a transitional bridge technology. The study contributes to path-dependency theory and circular energy supply-chain research.
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.
