Inclusive heritage tourism has received growing scholarly attention, but there is little empirical research about the role of experience design in the creation of value among visually-impaired tourists. This work assessment evaluates the effects of gamification and multidimensional immersive senses on perceived value through experience quality in the context of heritage tourism in Shanxi, China. Using the Stimulus, Organism, and Response (S-O-R) theoretical framework, the gamification and multisensory immersion are presented as the types of experiences as stimulus, the experience (experiential quality) as the mediator, and perceived value as the response outcome. A quantitative cross section survey was distributed to the visually impaired tourists who have visited at least one heritage tourism site in Shanxi during the past twelve months. A total of 278 valid responses were analyzed by a partial least squares structural equation modelling (PLS-SEM). Results show that both gamification and multisensory immersion make a significant contribution to the eminence of the experiential quality. Experiential quality in turn becomes the strongest predictor of perceived value. In addition, gamification has a large direct impact on perceived value, while multisensory immersion has a direct impact on the perceived value via experiential quality. These findings therefore suggest that value on inclusive heritage tourism is not only dependent on the presence of interactive or sensory features, but rather on the degree in which these are integrated into an integral, accessible and meaningful experience. The present study enhances the use of the S-O-R framework into an accessibility-sensitive heritage context and emphasizes the primary role of experience design leading to effective implementation of meaningful participation, value creation for visually-impaired tourists.
This study investigates the impact of financial distress on earnings management among Thailand-listed companies during the COVID-19 recovery period (2021-2023). Using 1,323 firm-year observations, this study compared the Modified Jones Model and the Yoon Model to determine the most appropriate framework for emerging markets. The results demonstrate that the Yoon Model provides superior explanatory power for the Thai context. Furthermore, empirical evidence reveals that financial distress significantly and positively influences earnings management. This suggests that distressed firms utilize discretionary accruals to project a favorable financial image to investors and creditors, as well as to enhance their ability to attract new investment, suggesting that earnings management serves as a mechanism employed by executives to obscure underlying financial problems.
The study shows how blockchain security is used to assess the performance and reliability of smart Waqf sukuk in Islamic philanthropic finance. It lurks on how the distinguishing characteristics of block chain i.e., decentralization, immutability, cryptographic integrity and consensus mechanisms can be used to elevate the degree of transparency, trust and operational efficiency of the digital sukuk constructions hinged on the principles of Waqf. Another question that is established in the study is whether the cybersecurity technologies play a moderating role in improving the resilience and stability of the smart Waqf sukuk systems regarding the advanced authentication systems, intrusion detection software and artificially intelligence-based threat detection systems. It is implied to base on a quantitative research methodology and apply structural equation modeling as the tool of assessing the interdependence of blockchain security and cybersecurity technologies, as well as smart Waqf sukuk performance. It is predicted that the study will prove the close relationship between the increased blockchain security and reliability, transparency and effectiveness of smart Waqf sukuk platforms. It is also expected that cybersecurity technologies will improve this relationship by minimizing the vulnerabilities, enhancing the system resilience and enhancing the protection of the digital resources and transactions. The anticipated conclusions will also lead to the creation of secure Islamic financial instruments by demonstrating that the combination of strong blockchain security with modern cybersecurity systems will increase the trust of the stakeholders, maintain the integrity of assets and help sustainably manage Waqf resources. Apart from the regulatory implications the paper has raised to the regulators in order to move forward with the digital governance of Waqf and improve smart sukuk ecosystems that are more secure and reliable, it also gave regulatory suggestions to Islamic financial institutions and technology developers. This study is conceptual and analytical in nature, and the data presented are representative examples to illustrate the concept being presented rather than actual empirical results. The findings suggest that the hypotheses are partially supported, as the blockchain security dimensions statistically significantly affect the Smart Waqf Sukuk, but some of the hypothesized relationships (Consensus Mechanisms and Cybersecurity Technologies) are opposite to the reality, and the moderating effect of cybersecurity technologies is only statistically significant for the relationship between Consensus Mechanisms.
Improving student success and supporting evidence-based decision-making in higher education depend on early detection of academically at-risk students. An Explainable Artificial Intelligence (XAI) framework is proposed in this paper for multimodal student performance prediction to support institutional decision-making at Prince Sattam bin Abdulaziz University (PSAU). The framework was developed and evaluated using a publicly available educational dataset collected from a higher education institution, integrating data from the Student Information System (SIS), Moodle Learning Management System (LMS), and the eDify mobile learning application. The proposed framework combines comprehensive data preprocessing, Mutual Information-based feature selection, Optuna-based hyperparameter optimization, and an optimized CatBoost classifier. Using repeated stratified 5-fold cross-validation, the model's performance was assessed and compared with statistical learning methods, conventional machine learning algorithms, homogeneous ensemble methods, and heterogeneous ensemble approaches. The proposed model demonstrated superior predictive efficacy, attaining an Accuracy of 92.0%, Precision of 92.7%, Recall of 97.9%, F1-score of 95.2%, ROC-AUC of 0.913, PR-AUC of 0.974, Balanced Accuracy of 0.824, and Matthews Correlation Coefficient of 0.726, significantly exceeding all rival models according to the Wilcoxon signed-rank test (p < 0.001). Mutual Information feature selection further improved predictive performance while reducing model complexity. Explainability was achieved through CatBoost feature importance and SHAP analyses, which consistently identified assessment outcomes and online learning engagement variables as the most influential predictors. The proposed framework provides a transferable blueprint for implementing explainable early-warning systems at Prince Sattam bin Abdulaziz University and other higher education institutions to support timely interventions, personalized learning support, and evidence-based institutional decision-making.
The Environmental Phillips Curve (EPC) is understood as an effort to highlight the relationship between environmental quality and unemployment. research testing the relationship variations in the spatial influence of unemployment, regional income, human development, electricity consumption, population growth and pandemic-year dummy on environmental degradation in Indonesia within the EPC concept framework. This study uses the Geographically Weighted Panel Regression method. The GWPR method is used to show variations in the influence between independent and dependent variables across the characteristics of each province in Indonesia. GWPR test results, the variables of unemployment, human development, and electricity consumption can reduce environmental degradation in Indonesia, with varying levels of significance across provinces. Regional income and population growth increases environmental degradation in Indonesia. This indicates that the EPC concept has been proven in Indonesia but varies by province, depending on each province's economic structure. The findings of this study are that the EPC is proven in regions whose economies rely on the extractive sector, mining, manufacturing industry, transportation, and tourism. These findings imply that employment and environmental policies should be tailored to regional economic structures, promoting green employment in extractive regions and accelerating low-carbon industrial transformation in manufacturing- and service-based provinces.
The purpose of this investigation was to analyze the effect of financial structure and fruit-fly control on the development of Small and Medium Enterprises (SMEs) of citrus in the Central Jungle of Peru. Using a quantitative design and a balanced sample of 54 observations, the analysis estimates complementary linear models with interaction terms and restricted cubic spline specifications to capture direct, synergistic, and nonlinear effects. The results of the baseline estimate show that both financial structure and control of fruit flies have a positive and significant impact on the business growth of SMEs of citrus. Additionally, the interaction between financial structure and phytosanitary control is also positive and significant. This means that the return to better financing is greater when there is stronger phytosanitary management, in addition to the fact that effective pest control is more productive when there are stable and diversified financial resources. The flexible spline estimates of the equations also show that the relationships between the variables under study are not constant along the space of the variables. While certain characteristics of citrus enterprises remain invariant, others change as enterprises ascend the financial and technology gradient. Future sustainable growth of citrus SMEs will depend upon enhanced rural finance and phytosanitary capabilities currently lacking in many countries. An empirical analysis based on an integrated conceptual framework that draws together crop protection, rural finance and managerial capability is developed to elucidate the impact of these factors on farm and enterprise performance within the agricultural development context.
As more and more AI-powered tools and platforms are adopted in organizations to automate routine tasks, support decision-making, and improve efficiency, adoption is often lopsided, with employees embracing the system while also wondering whether it can effectively perform work-critical tasks. This study examines how organizations adopt AI platforms by applying the Unified Theory of Acceptance and Use of Technology (UTAUT) in the context of user experience (UX) and competence trust, two AI-salient concepts. Competence trust is the extent to which employees believe an AI platform will reliably produce accurate, dependable, and work-relevant outputs. An exploratory sequential mixed-methods design was employed to generate and confirm inductive analysis-level explanations of trust formation and acceptance, grounded in insights gained through observation. The first phase involves semi-structured interviews with 15–25 organizational users. In this phase, the study maps the path from the UX stage to trust or distrust and acceptance, identifies important incidents that affect people's confidence in the platform, and gathers users’ trust-related language to help fine-tune constructs and measurement criteria. In Phase 2, a survey instrument is developed from issues identified in Phase 1 and established scales. CFA and SEM test a longer UTAUT model in which factors affecting performance expectancy, effort expectancy, social influence, and facilitating conditions are used to quantify levels of competence, trust and behavioral intention to use the AI platform. Where the sample size allows, multi-group comparisons can be made by user intensity or job function. Phase 3 combines qualitative themes and quantitative path findings through a combined display to extract converging data, identify contradictions, and provide expanded interpretations, ultimately deriving actionable suggestions that will be applied. Contribution of the study. The study contributes theoretically by framing competence trust as an integral mechanism linking UX to adoption within a broader UTAUT framework, and by providing a better explanation of why perceiving usefulness and ease of use alone may be inadequate in AI settings. Moreover, it has practical design and governance implications for organizations to foster sustainable adoption by incorporating UX features that signal reliability (e.g., stability, clear guidance, robust error handling) and by reinforcing social and organizational support to enhance trust.
Underage driving remains a persistent road safety problem in developing countries, where legal regulations often coexist with permissive social norms and limited transportation alternatives. This study examines the psychological mechanisms underlying parents’ protective behavior in preventing underage driving by applying the Protection Motivation Theory (PMT) framework. A quantitative survey was conducted involving 600 respondents (300 parents and 300 students) from Malang, East Java, and Bangkalan, Madura, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings demonstrate that both threat appraisal and coping appraisal positively and significantly influence behavioral intention and Protective Behavior, while behavioral intention also exerts a significant positive effect on actual protective behavior. Among all constructs, coping appraisal exhibits the strongest direct and indirect influence, indicating that parents’ confidence in their ability to prevent underage driving and their belief in the effectiveness of preventive actions are more influential than risk awareness alone. Behavioral intention further serves as a significant partial mediator linking cognitive appraisal to protective behavior. These findings suggest that preventing underage driving requires more than increasing awareness of traffic risks; it also depends on strengthening parents’ capacity to translate risk perceptions into consistent protective actions within complex social environments. The study extends the application of PMT to road safety in a developing-country context by integrating cognitive and contextual perspectives, providing evidence that effective interventions should combine psychological empowerment, family engagement, institutional support, and traffic law enforcement.
This study develops a data-driven model to examine the determinants of tourist satisfaction and revisit intention by integrating social media analytics and service quality factors within a digital tourism context. The rapid growth of big data generated through social media platforms has transformed how tourists perceive destinations, evaluate experiences, and make revisit decisions. However, limited studies have incorporated social media-driven variables into structural behavioral models in tourism research. This research proposes a comprehensive framework that includes brand image based on local wisdom, product quality, social media analytics, attitude, experience, and perceived risk as key predictors of tourist satisfaction and revisit intention. Data were collected from 160 domestic and international tourists visiting Belimbing Tourism Village, Bali, using a purposive sampling technique. The proposed model was analyzed using Structural Equation Modeling with Partial Least Squares (SEM-PLS), which is well-suited for predictive and complex data-driven analysis. The findings reveal that social media analytics, brand image, and product quality have significant positive effects on both tourist satisfaction and revisit intention, highlighting the critical role of digital engagement and online content in shaping tourist behavior. Attitude significantly influences satisfaction but does not directly affect revisit intention, while experience and perceived risk show no significant effects. Satisfaction is found to be a strong predictor of revisit intention and partially mediates the relationships between key antecedents and behavioral outcomes. This study contributes to the literature by integrating big data perspectives into tourism behavior modeling and demonstrates how social media-derived insights can enhance predictive accuracy in tourism analytics. Practically, the results suggest that tourism managers should leverage social media data, digital engagement metrics, and data-driven marketing strategies to strengthen destination competitiveness and foster sustainable revisit behavior.
The purpose of this study is to examine the influence of green word-of-mouth and environmental knowledge on environmental concern, perceived behavioral control, and green purchase intention, as reinforced by the Tri Hita Karana philosophy regarding Gringsing woven fabric in Bali. This study employed PLS-based Structural Equation Modeling (SEM) with 230 samples collected from consumers intending to purchase Gringsing woven fabric throughout Bali. On the other hand, environmental concern was found to partially mediate the effect of green word of mouth on green purchase intention, while perceived behavioral control partially mediated the effect of environmental knowledge on green purchase intention. Furthermore, the Tri Hita Karana philosophy was found to act as a pure moderator that strengthens the effects of environmental concern and perceived behavioral control on green purchase intention. These results imply the importance of integrating the Tri Hita Karana philosophy in optimizing green marketing strategies for Gringsing woven fabric. Therefore, it is recommended that businesses and the government develop educational narratives about the ecological value of Gringsing woven fabric based on the Tri Hita Karana values to enhance consumer confidence and green purchase intention.
This study aims to analyze the influence of national culture, internal control systems, transparency, and e-procurement on fraud prevention in public procurement within the Regional Government of Riau Province, Indonesia. The study also examines the moderating role of e-procurement in strengthening the relationship between organizational factors and fraud prevention. This study uses a mixed methods approach. The quantitative phase involved distributing structured questionnaires to 487 respondents consisting of Budget Users, Commitment Making Officers, Technical Implementation Officers, and members of the Procurement Service Unit. Data were analyzed using SmartPLS 4 to examine the structural relationships and moderating role of e-procurement. The qualitative phase was conducted through semi-structured interviews to triangulate the quantitative results. This study provides deeper insight into the influence of national culture, internal control systems, and transparency on fraud prevention with e-procurement as a moderating variable. The results show that national culture, internal control systems, and transparency have a positive and significant effect on fraud prevention. However, e-procurement and its moderating effects on those relationships are not significant. Triangulated responses confirm that integrity, internal oversight, and transparency are more effective deterrents than technology-based controls. This study is limited to the context of a regional government and may not capture variations across different administrative levels or cultural environments. Future research could integrate behavioral or qualitative perspectives to explore how technological adoption interacts with ethical culture. The findings suggest that strengthening internal culture, ethical integrity, and control systems is more crucial than relying solely on technological instruments such as e-procurement. Continuous evaluation, digital literacy improvement, and human integrity development are essential for effective fraud prevention. This research contributes to the literature by integrating cultural, institutional, and technological perspectives in fraud prevention, highlighting that internal organizational integrity and transparency remain the primary determinants of clean governance in public procurement.
This study develops a decision-oriented model of repeat patronage among Generation Z visitors to shopping malls in Bali by examining how two controllable managerial inputs, retail environment quality and experiential retail strategy, influence future patronage choice directly and indirectly through memorable retail experience. Survey data were obtained from 210 respondents aged 17–27 years who had visited a mall in Bali during the previous three months. The model was estimated using Partial Least Squares Structural Equation Modeling (PLS-SEM) with 5,000 bootstrap subsamples. Retail environment quality significantly increased repeat patronage intention (β = 0.312) and memorable retail experience (β = 0.398), while experiential retail strategy significantly increased repeat patronage intention (β = 0.289) and memorable retail experience (β = 0.421). Memorable retail experience was the strongest proximal predictor of repeat patronage intention (β = 0.356) and significantly mediated both strategic pathways. The model explained 71.4% of the variance in repeat patronage intention. The comparative effects indicate that environmental quality and experiential strategy function as complementary decision levers, while memory formation provides the internal mechanism through which both levers strengthen future patronage choice. The findings offer a practical decision architecture for balancing baseline environmental quality, experiential differentiation, and memory-forming engagement under limited managerial resources.
This study examines the relationship between tourism expansion and regional economic development across the thirteen administrative regions of Saudi Arabia over the period 2014–2024, a decade that spans the launch of Vision 2030 and the structural reorientation of the Kingdom away from hydrocarbon dependence. Drawing on export-base theory, endogenous growth theory, and the tourism-led growth hypothesis, a balanced panel is analyzed using a panel autoregressive distributed lag (ARDL) error-correction framework complemented by fixed-effects estimators with Driscoll–Kraay standard errors. Tourism expansion is proxied by tourism receipts, tourist arrivals, hotel capacity, and tourism employment share, while regional economic development is captured by regional gross domestic product per capita and regional employment growth. Infrastructure quality and human capital are specified as moderating conditions, and foreign direct investment, government capital expenditure, inflation, population size, and trade openness enter as controls. Panel unit-root tests confirm that the series are integrated of order one, and residual-based cointegration tests establish a stable long-run equilibrium. The results indicate that tourism expansion exerts a positive and statistically significant effect on regional economic development in both the short run and the long run, that the effect is amplified where infrastructure quality and human capital endowments are stronger, and that the estimated relationships are robust to alternative dependent variables, alternative tourism proxies, two-way fixed effects, and cross-sectionally consistent standard errors. The error-correction term is negative and significant, implying rapid adjustment toward equilibrium after short-run shocks. The findings furnish evidence-based guidance for the spatially balanced diversification agenda embedded in Vision 2030 and underscore the complementarity between tourism promotion and investment in enabling infrastructure and skills.
This study investigates the challenges of manpower planning and optimization for operational businesses functioning across multiple locations, where fluctuating customer demand and varying workforce capacities create significant planning complexities. To address these issues, several forecasting techniques were applied to predict order volumes for a three-month period, forming the analytical basis for a comprehensive workforce planning model. Based on forecasting results, a mathematical model was developed to determine the optimal number of workers required to meet dynamic demand levels. The model incorporates key operational parameters and automatically adjusts to changes in demand, workforce availability, and processing times, ensuring flexibility and long-term applicability. The analysis revealed recurring overstaffing and understaffing, highlighting the importance of aligning staffing levels with forecasted demand to reduce labour inefficiencies. To support decision-making, a dynamic and interactive planning interface was designed to integrate forecasting outputs, staffing requirements, and other performance indicators. The interface provides real-time visibility into workforce status, enabling identification of overstaffed, understaffed, and optimal staffing conditions. It also visualizes demand patterns in various perspectives, such as by hour, shift, month, and location. The model and the interface were validated through five scenario-based tests, demonstrating their responsiveness to abrupt changes such as seasonal peaks, peak hours, order picking time, and sudden fluctuations in order volume. Overall, the integrated forecasting and modelling interface offers a practical, data-driven solution for enhancing manpower planning and operational efficiency in multi-location business environments.
The rapid development of artificial intelligence is driving human resource management to transform from its traditional administrative support function into an intelligent strategic system capable of strengthening an organization's core capabilities. A new generation of agentic artificial intelligence (Agentic AI) human resource systems with independent adaptive capabilities has gradually been implemented in enterprise application scenarios. Currently, various types of AI-enabled human resource systems have become widespread in the market, but both academic and industry communities still lack empirical research on the internal mechanisms through which these agentic AI human resource systems impact organizational performance. This study is conducted specifically to fill this gap. This study adopts a quantitative research design. Using SmartPLS 4 software, it analyzes cross-sectional survey data through partial least squares structural equation modeling (PLS-SEM). Its core variables include the independent variable agentic AI human resource system, the mediating variables data-driven decision-making and workforce adaptability, and the dependent variable organizational performance. This study relies on dynamic capabilities theory, information processing theory, and the knowledge-based view as its core theoretical supports. Empirical findings show that the core independent variable has a significant direct positive effect on the dependent variable, and can also produce a significant indirect positive effect transmitted through two mediating variables. Among these mediators, employee adaptability exerts a stronger impact on organizational performance than data-driven decision-making. In subsequent work, this paper will sort out the study's theoretical contributions, put forward practical suggestions for corporate practice, and clarify potential directions for future research expansion.
The continuous decline of the Dead Sea water level represents one of the most critical environmental challenges in the Middle East, primarily caused by reduced inflow from the Jordan River basin and increased regional water consumption. Predicting the long-term evolution of the Dead Sea is therefore essential for evaluating the potential effectiveness of large-scale restoration initiatives such as the Red Sea-Dead Sea (RSDS) conveyance project. In this study, a stochastic simulation framework is proposed to model and predict the long-term dynamics of the Dead Sea water level under multiple inflow scenarios. The framework integrates probabilistic increment generation, recursive hydrological updating, and stability-aware monitoring within a unified computational procedure. Annual water-level increments are generated using statistically fitted probability distributions derived from historical observations, enabling the model to capture the inherent variability and uncertainty of natural hydrological processes. The system state is iteratively updated across the simulation horizon, while a stability index is used to evaluate convergence behavior and detect potential equilibrium conditions. Five representative RSDS scenarios are investigated, including baseline recovery, moderate inflow, accelerated recharge, threshold-stabilized inflow, and high-variability environmental conditions. Simulation results indicate that under the baseline scenario the Dead Sea level increases gradually from approximately −434 m to about −60 m within 500 simulation steps, whereas accelerated recharge conditions can raise the level beyond 200 m due to higher early-stage inflow rates. The threshold-stabilized scenario reveals regime transitions in increment dynamics that lead to a near-equilibrium state, while the high-variability scenario exhibits strong stochastic fluctuations with annual increments ranging between 0.4 and 2.2 m. Overall, the results demonstrate that the proposed framework effectively captures diverse hydrological behaviors and provides a flexible and computationally efficient tool for long-term forecasting and policy evaluation of Dead Sea restoration strategies.
The swift evolution of digital technology necessitated transformation within broadcasting entities. The use of new technologies became crucial in enhancing the performance of broadcasting professionals. Generative Artificial Intelligence (GAI) offers the opportunity to elevate the creation of broadcasting content, the efficiency, and the effectiveness of broadcasting operations. This study investigated the impact of GAI on digital professional performance in the Jordanian broadcasting sector and examined the mediating role of Big Data Analytics (BDA). A quantitative approach was used, and data were collected through an online structured questionnaire with a sample size of 215 employees working in different broadcasting institutions in Jordan. Data were analyzed using SPSS Version 29 and AMOS Version 29. The proposed hypotheses were examined using descriptive statistics, Confirmatory Factor Analysis (CFA), and Structural Equation Modelling (SEM). Bootstrapping techniques were used to determine the significance of the results. The findings of the study demonstrated that GAI positively impacts BDA and Digital Professional Performance, and BDA positively impacts Digital Professional Performance and partially mediates the relationship between GAI and Digital Professional Performance. The study makes a contribution to the theoretical and empirical literature on Jordanian Broadcasting Institutions by providing research evidence that demonstrates the need to align Artificial Intelligence technologies and analytics to enhance the professional activities of broadcasting institutions.
This study aims to model the strategic decision-making process of fashion Small and Medium-Sized Enterprises (SMEs) in Indonesia in transforming bonding social capital into a market-based advantage, as reflected in marketing performance. By integrating Social Capital Theory and the Dynamic Capabilities Perspective within a decision science framework, this study investigates how managerial decisions to develop brand resonance capability function as a structural conversion mechanism. An empirical quantitative approach was employed using survey data collected from 265 key decision-makers (owners and managers) of fashion SMEs located in Indonesia's creative textile industry clusters, including Bandung, Surakarta, Pekalongan, and Yogyakarta. Data was analyzed using Variance-Based Structural Equation Modeling (PLS-SEM) using SmartPLS. The findings reveal that reliance on bonding social capital alone does not directly generate significant improvements in marketing performance. However, the structural model demonstrates a full mediation effect, indicating that internal social capital must be strategically transformed through investments in brand resonance capability. This study contributes to the decision science literature by proposing a strategic choice architecture that bridges inward-looking relational assets and externally oriented competitive advantage.
This study examines the implementation of local labor absorption policy in the garment industry in Bantul Regency, Indonesia. Although labor-intensive industries are expected to generate employment opportunities for local communities, policy outcomes often depend on implementation quality, workforce readiness, institutional coordination, and employment sustainability. Using a mixed-methods approach, this study combines qualitative evidence from interviews and document analysis with econometric validation based on survey data from 420 garment workers. The qualitative findings show that local labor absorption policy has facilitated more open recruitment channels, pre-placement orientation, and institutional support for workforce preparation. However, implementation remains constrained by weak cross-sector coordination, limited worker readiness, high labor turnover, and mismatches between worker expectations and factory conditions. The Ordered Probit results indicate that orientation participation, vocational training, skill readiness, and coordination quality significantly improve perceived implementation effectiveness, while labor turnover perception reduces it. The Probit model further shows that orientation participation, training, job satisfaction, skill readiness, and age increase the probability of worker retention, whereas distance to the factory significantly reduces retention. Robustness checks using Ordered Logit confirm the stability of the main findings. The study concludes that local labor absorption policy should not be understood merely as a recruitment mechanism, but as a broader governance process involving worker preparation, institutional coordination, labor market matching, and employment retention. Policy efforts should prioritize standardized orientation programs, stronger vocational training, improved inter-agency coordination, better workplace conditions, and transport accessibility to enhance sustainable local employment outcomes.
This study aims to formulate a Multiobjective Multistage optimization model for land use change problems within a green economy framework. Land use allocation is formulated as a sequential decision-making process, where the decision variables denote the area allocated to each land use type at every stage. A dynamic programming structure is constructed by defining stages as the sequential determination of land allocations, states as the remaining land capacity, and a transition function that updates the state based on decisions made at the previous stage, thereby establishing a recursive relationship consistent with Bellman’s principle of optimality to obtain optimal solutions. The model features two objective functions, namely the maximization of economic benefits as measured by Gross Regional Domestic Product (GRDP) contributions and the minimization of net carbon emissions calculated from land use carbon stock coefficients. This formulation explicitly incorporates green economy principles via a trade-off that reconciles economic growth with environmental sustainability. The proposed approach combines Artificial Neural Networks (ANN) to estimate spatial transition probabilities from historical data and environmental predictor variables, which are subsequently incorporated as spatial constraint parameters in the optimization model, and the Non-dominated Sorting Dynamic Programming (NSDP) algorithm to solve the Multiobjective Multistage problem and generate a set of Pareto optimal solutions. This study has produced a set of Pareto optimal solutions for land allocation through a dynamic programming model that evaluates economic and environmental trade-off at each decision stage, thereby providing a data-driven mathematical framework to support sustainable land-use planning within the context of a green economy.
