Search Results (677)

 Purpose: The research explores the impact of change management practices—leadership support, employee involvement, and regulatory compliance —on the practice of sustainable healthcare in Saudi Arabia. Operational efficiency is treated not as a management practice but as a key outcome of effective change management. The research also examines patient readiness as a mediator influencing awareness, participation, and satisfaction. Design/methodology/approach: The study used a quantitative Saudi Arabian healthcare consumer survey. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to analyze change management, patient readiness, and sustainable healthcare relations adoption. Findings: Findings indicate that change management plays a strong role in increasing patient adoption (β = 0.322; p = 0.083), but with large effects on awareness (β = 0.873; p < 0.001), engagement (β = 0.841; p < 0.001), and satisfaction (β = 0.881; p < 0.001), as adoption reflected through awareness, engagement, and satisfaction. Patient readiness as a mediator was significant with strong effects between change management and adoption (β = 0.571; p < 0.001). Originality/value: This research expands the Theory of Planned Behavior (TPB) by synthesizing it with strategic change management to predict patient readiness as a mediator of long-term adoption of healthcare in the Arab environment. Patient readiness is hypothecated as an observable behavioral construct to mediate organizational change practices—leadership, communication, and regulation—with individual adoption outcomes. The research provides theoretical and practical contributions for evidence-based healthcare policy and patient-led healthcare revolution. In addition, the study conforms with the United Nations Sustainable Development Goals (SDGs) including SDG 3 (Gsssssssood Health and Well-being), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 12 (Responsible Consumption and Production), and shows how effective change management not only assists national healthcare reforms but also global sustainability goals. Full article

Human behavior is shaped by a pervasive motive to align with others, manifesting across a wide range of tendencies—from motor synchrony and emotional contagion to convergence in beliefs and choices. Existing accounts explain how alignment arises through predictive coding and observation–execution mechanisms, but they do not address how it is regulated in a manner that considers when alignment is adaptive and with whom it should occur. We propose a goal-directed model of social alignment that integrates computational and neural levels of analysis, to enhance our understanding of alignment as a context-sensitive decision process rather than a reflexive social tendency. Computationally, alignment is formalized as a prediction-error minimization process over the gap between self and other, augmented by a meta-learning layer in which the learning rate is adaptively tuned according to the inferred value of aligning versus maintaining independence. Assessments of the traits and mental states of self and other serve as key inputs to this regulatory function. Neurally, higher-order representations of these inputs are carried by the mentalizing network (dmPFC, TPJ), which exerts top-down control through the executive control network (dlPFC, rIFG) to enhance or inhibit alignment tendencies generated by observation–execution (mirror) circuitry. By reframing alignment as a form of social decision-making under uncertainty, the model specifies both the computations and neural circuits that integrate contextual cues to arbitrate when and with whom to align. It yields testable predictions across developmental, comparative, cognitive, and neurophysiological domains, and provides a unified framework for understanding the adaptive functions of social alignment, such as strategic social learning, as well as its maladaptive outcomes, including groupthink and false information cascades. Full article

This conceptual article introduces Perceived Cognitive Assistance (PCA)—a novel psychological construct capturing how interactive support from Large Language Models (LLMs) alters investors’ perception of their cognitive capacity to execute complex trading strategies. PCA formalizes a behavioral shift: LLM-empowered retail investors may transition from intuitive heuristics to institutional-grade strategies—sometimes without adequate comprehension. This empowerment–distortion duality forms the theoretical contribution’s core. To empirically validate this model, this article outlines a five-step research agenda including psychological diagnostics, trading behavior analysis, market efficiency tests, and a Behavioral Shift Index (BSI). One agenda component—a dual-agent simulation framework—enables causal benchmarking in post-LLM environments. This simulation includes two contributions: (1) the Virtual Trader, a cognitively degraded benchmark approximating bounded human reasoning, and (2) the Digital Persona, a psychologically emulated agent grounded in behaviorally plausible logic. These components offer methods for isolating LLM assistance’s cognitive uplift and evaluating behavioral implications under controlled conditions. This article contributes by specifying a testable link from established decision frameworks (Theory of Planned Behavior, Technology Acceptance Model, and Risk-as-Feelings) to two estimators: a moderated regression for individual decisions (Equation (1)) and a composite Behavioral Shift Index derived from trading logs (Equation (2)). We state directional, falsifiable predictions for the regression coefficients and for index dynamics, and we outline an identification and robustness plan—versioned, time-locked, and auditable—to be executed in the subsequent empirical phase. The result is a clear operational pathway from theory to measurement and testing, prior to empirical implementation. No empirical results are reported here; the contribution is the operational, falsifiable architecture and its implementation plan, to be executed in a separate preregistered study. Full article

The environmental protection tax (EPT) is a vital means for China to promote sustainable development. However, its impact on corporate green innovation is controversial. Utilizing the data from Chinese A-share industrial listed companies from 2013 to 2022 and the difference-in-differences (DID) model, this study examines the impact of the EPT on corporate green innovation. The results indicate that the EPT can promote corporate green innovation, which is robust across various tests. Furthermore, the EPT fosters corporate green innovation mainly by stimulating companies to increase research and development (R&D) investment. The heterogeneity analysis demonstrates that the EPT promotes green innovation only in large-scale companies, non-state-owned companies, and eastern companies. The further analysis suggests that the green innovation brought by the EPT could improve corporate economic performance. Moreover, the EPT promotes both corporate substantive innovation and strategic innovation. That is, the EPT could enhance the quality of green innovation whilst also inducing strategic behavior. This study could provide profound insights to facilitate green transitions in emerging market countries like China. Full article

With the growing emphasis on sustainable development, organizations and government agencies are increasingly incorporating environmental, social, and governance (ESG) factors into their strategic agendas. However, previous research has primarily examined ESG performance, stakeholder engagement, and financial outcomes in isolation, overlooking the systemic role of employee perceptions and psychological responses. To address this shortcoming, this study integrated social identity theory and social exchange theory to explain how ESG practices influence green innovation behavior through organizational pride. Furthermore, drawing on organizational climate theory, we explored the moderating role of innovation climate in this relationship. We used structural equation modeling (SEM) to analyze data from 346 employees across diverse Chinese companies, enabling us to capture the overall structure of the relationship rather than isolated causal relationships. Our results show that all three dimensions of ESG practices significantly enhance organizational pride, which in turn stimulates green innovation, highlighting the indirect, systemic relationship between ESG and innovation outcomes. Organizational climate is an important contextual variable influencing both individual behavior and organizational performance. When organizations have a favorable innovation climate, employees are more likely to translate their pride into concrete innovative behaviors. While the direct impact of ESG (S) and ESG (G) on green innovation has not been confirmed, the mediating role of organizational pride and the moderating role of innovation climate highlight the dynamic interplay between psychological and organizational subsystems. This study conceptualizes ESG practices, organizational pride, and innovation climate as interconnected subsystems within a broader organizational system, providing a systems-based perspective for sustainability research. It advances theoretical understanding of how sustainability initiatives spread through psychological and organizational mechanisms and offers practical insights for policymakers and decision makers seeking to promote long-term green innovation. Full article

This research examines how artificial intelligence is reshaping business and labor structures through a sustainability lens. Drawing on survey data from 127 organizations and 14 case studies, we quantify workforce impacts while exposing methodological limitations in current projections. Our analysis reveals implementation variations of 37% across industries and 41% higher user adoption rates for hybrid governance approaches versus centralized models. The evidence supports a three-dimensional strategic framework for sustainable organizational development: comprehensive upskilling fostering behavioral change (2.7× higher implementation success), distributed innovation enabling cross-functional ideation (3.1× more identified use cases), and strategic integration aligning systems across departments (explaining 31% of implementation success variance). Organizations deploying all three dimensions achieved a 74% AI initiative success rate versus 12% for those using none. Implementation barriers include regulatory uncertainty, organizational resistance, and ethical considerations, with data infrastructure maturity (β = 0.32), executive sponsorship (β = 0.29), and change readiness (β = 0.26) explaining 58% of implementation success variance. Our findings indicate that sustainable adaptation capacity—not merely technological investment—determines which organizations successfully navigate this transformation while maintaining long-term organizational viability, workforce resilience, and contribution to broader sustainable development goals. Full article

Background: The growing burden of chronic illnesses calls for integrated and sustainable models of Primary Healthcare (PHC) that emphasize health promotion and patient-centered care. Interprofessional collaboration between General Practitioners (GPs) and Family and Community Nurses (FCNs) is a strategic approach to enhancing continuity of care and supporting individuals in adopting healthy behaviors across the trajectory of chronic conditions. This study aims to explore the experiences and perspectives of GPs and FCNs in Italy, with the goal of identifying the barriers, enablers, and transformative dynamics that can inform future PHC models. Methods: A qualitative study was conducted with four focus groups with 21 participants (8 GPs and 13 FCNs) from three Italian regions, carried out between March and November 2023. Data were analyzed using Reflexive Thematic Analysis (RTA) following Braun and Clarke’s framework. Ethical approval was obtained from the University of Parma (Protocol No. 0266537—21 October 2022). Results: Four themes and sixteen subthemes were identified: (1) barriers to effective collaboration (role ambiguity, limited time, structural misalignments); (2) facilitators of collaboration (openness, mutual recognition, shared goals); (3) team-building processes (phases of trust development, shared values, reflective problem-solving); and (4) transformation of work practices (improved patient outcomes, flexible methodologies, integrated care strategies). Conclusions: Interprofessional collaboration between GPs and FCNs enhances the capacity of PHC to address the complex needs of people with chronic conditions. Aligning relational, organizational, and structural factors is essential for sustainable, health-promoting care models. Tailored training, protected time, and shared spaces are critical to foster teamwork, promote patient empowerment, and ensure continuity of care in chronic illness management. Full article

This study examines and evaluates key performance indicators (KPIs) that impact user engagement on social media platforms, with a primary focus on fashion retail within seasonal tourism contexts. The primary objective is to determine which engagement metrics most accurately predict user interaction levels and to enhance strategic decision-making in digital marketing. Using a dataset of 2500 Facebook photos and videos from a women’s retail store, collected between 2016 and 2024, the study employs descriptive analysis and predictive modeling. Three KPIs—such as 3 s video views, reach from organic posts, and other clicks—are examined for their impact on user engagement. The posts are categorized into engagement levels, and classification models, including Random Forests (RF), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), and Naïve Bayes (NB), are evaluated. Results show that short video views and post reach are key predictors of user engagement. With XGBoost achieving a classification accuracy of 94.73%, the models perform effectively, and Cronbach’s alpha analysis confirms the consistency among the variables selected. The findings underscore the significance of KPI analysis in social media strategy and illustrate the value of data mining techniques in uncovering user behavior patterns that offer practical insights for optimizing digital marketing efforts. Full article

Agricultural systems are complex social–ecological systems shaped by interactions among diverse stakeholders including governments, enterprises, farmers, consumers, and financial institutions. To examine policy-driven sustainability transitions, this study focuses on three principal actors—government regulatory agencies, agricultural enterprises, and farmers—whose strategic interactions critically determine transition outcomes. The aim is to drive agricultural systems toward sustainability in China. This study develops a three-party evolutionary game model involving the government, enterprises, and farmers to explore how policy-driven incentives influence sustainable development practices. The model incorporates both static and dynamic reward–punishment mechanisms, calibrated with empirical data, to examine behavioral dynamics across stakeholders. The results indicate that fluctuations in enterprise and government engagement contribute to instability in agricultural sustainability transitions. While static reward mechanisms mitigate peak fluctuations, they are insufficient to fully stabilize enterprise commitment, with actors oscillating between sustainable and conventional agricultural practices. Linear dynamic reward mechanisms offer partial stabilization but lack the capacity to maintain long-run Nash equilibrium. In contrast, nonlinear dynamic mechanisms effectively align stakeholder incentives, fostering a stable and enduring shift toward sustainable agricultural systems. This study underscores the importance of tailored dynamic strategies to build resilient agricultural systems with integrated sustainability objectives. Full article

The central purpose of this study is to develop and validate an advanced numerical model capable of simulating the vibrational behavior of the operator’s seat in a tractor-type agricultural vehicle designed for operation in protected horticultural environments, such as vegetable greenhouses. The three-dimensional (3D) model of the seat was created using SolidWorks 2023, while its dynamic response was investigated through Finite Element Analysis (FEA) in Altair SimSolid, enabling a detailed evaluation of the natural vibration modes within the 0–80 Hz frequency range. Within this interval, eight significant natural frequencies were identified and correlated with the real structural behavior of the seat assembly. For experimental validation, direct time-domain measurements were performed at a constant speed of 5 km/h on an uneven, grass-covered dirt track within the research infrastructure of INMA Bucharest, using the TE-0 self-propelled electric tractor prototype. At the operator’s seat level, vibration data were collected considering the average anthropometric characteristics of a homogeneous group of subjects representative of typical tractor operators. The sample of participating operators, consisting exclusively of males aged between 27 and 50 years, was selected to ensure representative anthropometric characteristics and ergonomic consistency for typical agricultural tractor operators. Triaxial accelerometer sensors (NexGen Ergonomics, Pointe-Claire, Canada, and Biometrics Ltd., Gwent, UK) were strategically positioned on the seat cushion and backrest to record accelerations along the X, Y, and Z spatial axes. The recorded acceleration data were processed and converted into the frequency domain using Fast Fourier Transform (FFT), allowing the assessment of vibration transmissibility and resonance amplification between the floor and seat. The combined numerical–experimental approach provided high-fidelity validation of the seat’s dynamic model, confirming the structural modes most responsible for vibration transmission in the 4–8 Hz range—a critical sensitivity band for human comfort and health as established in previous studies on whole-body vibration exposure. Beyond validating the model, this integrated methodology offers a predictive framework for assessing different seat suspension configurations under controlled conditions, reducing experimental costs and enabling optimization of ergonomic design before physical prototyping. The correlation between FEA-based modal results and field measurements allows a deeper understanding of vibration propagation mechanisms within the operator–seat system, supporting efforts to mitigate whole-body vibration exposure and improve long-term operator safety in horticultural mechanization. Full article

Urbanization and the growing scarcity of surface land resources have highlighted the strategic importance of underground space as a critical component of sustainable urban infrastructure. This study presents a multi-objective optimization framework for underground infrastructure planning around transit hubs, aligning with the principles of Transit-Oriented Development (TOD). By integrating an agent-based model (ABM) with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and incorporating the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), the framework forms a unified evaluation and optimization tool that accounts for user behavior while addressing competing objectives, including minimizing evacuation time and functional conflicts, maximizing functional efficiency, and reducing layout deviations. Using Qingdaobei Railway Station in China as a case study, the method yields notable improvements: a 15% reduction in evacuation time, a 16% increase in development benefits, and a more balanced spatial configuration. Beyond technical gains, the study also discusses station planning and governance under the TOD policy context, highlighting how integrated layouts can alleviate congestion, strengthen functional synergy, and support sustainable urban development. Full article

This study analyzes how blockchain technology can be interpreted through an economic perspective, viewing network nodes as rational agents whose strategic behavior affects the efficiency and sustainability of decentralized systems. Using a multi-player non-cooperative game with complete but imperfect information, we model validators’ decisions in voting-based consensus mechanisms and compare alternative incentive configurations through simulation results. The analysis shows how variations in reward schemes influence validators’ behavior and consensus reliability. Extending the framework to Decentralized Autonomous Organizations (DAOs), the study explores how blockchain-based incentives can enhance participation, accountability, and decentralized governance. The findings highlight that incentive design plays a decisive role in aligning individual motivations with collective goals, ensuring both network integrity and long-term sustainability. Overall, this study connects economic theory with blockchain governance, extending its relevance to business and organizational contexts beyond cryptocurrencies. Full article

Traditional cyber attack-defense strategies have traditionally focused solely on the attacker and defender, while neglecting the role of government-led system administrators. To address strategic selection challenges in cyber warfare, this study employs an evolutionary game theory framework to construct a tripartite game model involving cyber attackers, defenders, and system administrators. The replicator dynamic equation is utilized for stability analysis of behavioral strategies across stakeholders, with Lyapunov theory applied to evaluate the equilibrium points of pure strategies within the system. MATLAB (2021a) simulations were conducted to validate theoretical findings. Experimental results demonstrate that the model achieves evolutionary stability under various scenarios, yielding optimal defense strategies that provide theoretical support for addressing cybersecurity challenges. Full article

Triaxial electrospinning was used to fabricate fiber membranes composed of polycaprolactone (PCL), poly(lactic-co-glycolide) (PLGA), and gelatin (GT), designed as carriers for curcumin (Cur) delivery. Here, synthetic polyesters acted as core and shell layers, while GT formed the middle layer containing Cur at varying concentrations. This paper aimed to demonstrate the effect of a shell layer by rearranging the core and shell layers on the kinetics of model drug delivery. In vitro release results indicated the shell layer considerably affected the release behavior, reducing the initial burst release by up to 28% in triaxial fibers compared to coaxial fibers in PLGA-shell forms. The release kinetics were interpreted using the Gallagher–Corrigan model. The membranes were also evaluated for their morphological properties. PLGA-shell-layered triaxial fibers exhibited pore sizes up to approximately 11 µm, small enough to prevent cell migration, while providing higher permeability. The surface wettability analysis of the developed fibers showed that all forms exhibited hydrophilic properties. Furthermore, the cytocompatibility of the fiber membranes was confirmed with the relative cell viability of over 80%. Triaxial fibers with different shell layers displayed similar release trends, yet fibers with the PLGA shell layer demonstrated more favorable performance, attributed to its layer configuration. These findings suggest that the strategic positioning of polymers in triaxial electrospun membranes could be pivotal in optimizing drug delivery systems. Full article

Digital mining has been evolving in recent years under the Industry 4.0 paradigm. In this sense, technological tools such as sensors aid the management and operation of mining projects, reducing the risk of accidents, increasing productivity, and promoting business sustainability. DT (Digital Twin) is a technological tool that enables the integration of various Industry 4.0 technologies to create a virtual model of a real, physical entity, allowing for the study and analysis of the model’s behavior through real-time data collection. A digital twin of an underground mine is a real-time, virtual replica of an actual mine. It is like an extremely detailed “simulator” that uses data from sensors, machines, and personnel to accurately reflect what is happening in the mine at that very moment. Some of the functionalities of an underground mining DT include (i) accurate geometry of the real physical asset, (ii) real-time monitoring capability, (iii) anomaly prediction capability, (iv) scenario simulation, (v) lifecycle management to reduce costs, and (vi) a support system for smart and proactive decision-making. A digital twin of an underground mine offers transformative benefits, such as real-time operational optimization, improved safety through risk simulation, strategic planning with predictive scenarios, and cost reduction through predictive maintenance. However, its implementation faces significant challenges, including the high technical complexity of integrating diverse data, the high initial cost, organizational resistance to change, a shortage of skilled personnel, and the lack of a comprehensive, multi-layered conceptual framework for an underground mine digital twin. To overcome these barriers and gaps, this paper proposes a strategy that includes defining an advanced, multi-layered conceptual framework for the digital twin. Simultaneously, it advocates for fostering a culture of change through continuous training, establishing partnerships with specialized experts, and investing in robust sensor and connectivity infrastructure to ensure reliable, real-time data flow that feeds the digital twin. Finally, validation of the advanced multi-layered conceptual framework for digital twins of underground mines is carried out through a questionnaire administered to a panel of experts. Full article