Ecosystem vulnerability assessment is paramount for local environmental stability and lasting economic progress. This study selects Hubei Province as the research area, applying multi-source spatiotemporal datasets spanning the period 2000–2023. A pressure–state–response (PSR) framework, incorporating 14 distinct indicators, was developed. The selection criteria for these indicators adhered to principles of scientific rigor, all-encompassing scope, statistical representativeness, and practical applicability. The chosen indicators effectively encompass natural, anthropogenic, and socio-economic drivers, aligning with the specific ecological attributes and key vulnerability factors pertinent to Hubei Province. The analytic network process (ANP) method and entropy weighting (EW) method were integrated to ascertain comprehensive weights, thereby computing the ecological vulnerability index (EVI). In the meantime, we analyzed temporal and spatial EVI shifts. Spatial autocorrelation analysis, the geodetic detector, the Theil–Sen median, the Mann–Kendall trend test, and the Grey–Markov model were employed to elucidate spatial distribution, driving factors, and future trends. Results indicate that Hubei Province exhibited mild ecological vulnerability from 2000 to 2023, but with a notable deteriorating trend: extreme vulnerability areas expanded from 0.34% to 0.94%, while moderate and severe vulnerability zones also increased. Eastern regions demonstrate elevated vulnerability, but they were lower in the west, correlating with human activity intensity. The global Moran’s I index ranged from 0.8579 to 0.8725, signifying a significant positive spatial correlation of ecological vulnerability, with the highly vulnerable areas concentrated in regions with intense human activities, while the less vulnerable areas are located in ecologically intact areas. Habitat quality index and carbon sinks emerged as key drivers, possibly stemming from the forest–wetland composite ecosystem’s high dependence on water conservation, biodiversity maintenance, and carbon storage functions. Future projections based on Grey–Markov models indicate that ecological fragility in Hubei Province will exhibit an upward trend, with ecological conservation pressures continuing to intensify. This research offers a preliminary reference basis of grounds for ecological zoning, as well as sustainable regional development in Hubei Province, while also providing a theoretical and practical framework for constructing an ecological security pattern within the Yangtze River Economic Belt (YREB) and facilitating ecological governance in analogous river basins globally, thereby contributing to regional sustainable development goals.

Supplier selection is a strategic decision in supply chain management as it directly affects product quality, operational efficiency, and delivery reliability. In manufacturing industries, carton packaging serves not only as product protection but also as a determinant of perceived quality and customer satisfaction. PT Toa Galva Industries has experienced inconsistencies in carton box quality, leading to disruptions in packaging and distribution processes. This issue highlights the need for a multi-criteria decision-making approach capable of capturing interdependencies among evaluation criteria. This study aims to develop a supplier evaluation and ranking framework for carton packaging using an integrated Analytic Network Process (ANP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach. ANP is employed to determine the priority weights of criteria and subcriteria while considering their interrelationships, whereas TOPSIS is applied to rank suppliers based on their closeness to the ideal solution. Data were collected through questionnaires and interviews with experienced procurement and production personnel. The results indicate that quality is the most influential criterion, followed by service, delivery, and cost, with quality consistency identified as the most critical subcriterion. The supplier ranking reveals that PT CTP achieves the highest preference value among the evaluated alternatives. These findings emphasize that quality stability outweighs cost considerations in carton packaging supplier selection. This study contributes practically by providing a structured and objective decision-making framework and academically by extending the application of ANP–TOPSIS in packaging supplier evaluation within manufacturing contexts.

CV Master Grafix, a small-to-medium enterprise (SME) engaged in product label printing, faces various operational challenges due to the dominance of manual processes in key business activities such as color data management, design approval, raw material procurement, and employee performance evaluation. These inefficiencies lead to service delays and decreased competitiveness in responding to the dynamic demands of B2B customers. This research aims to develop a strategic Information Systems and Information Technology (IS/IT) plan to improve the company's operational efficiency. This study adopts a qualitative descriptive approach using a case study method and applies the Ward and Peppard framework to formulate the IS/IT strategy. Data will be collected through observation, interviews, questionnaires, and document analysis. Strategic evaluations will be conducted using approaches such as Value Chain, SWOT, Gap Analysis, Critical Success Factors (CSF), and the McFarlan Strategic Grid. Meanwhile, technology initiative priorities will be determined using the Analytic Network Process (ANP) method based on the BOCR dimensions (Benefit, Opportunity, Cost, Risk). The outcome of this research is a strategic IS/IT planning document that includes an information technology strategy, an information systems business strategy, and an IS/IT management strategy, along with a prioritized portfolio of applications. This plan is intended to serve as a foundation for developing technology solutions that enhance operational efficiency and strengthen the competitiveness of CV Master Grafix in the B2B printing industry

Purpose This study aims to examine how the Committee of Sponsoring Organizations (COSO) framework can be adapted to a public-sector context (customs administration) to strengthen internal controls in bonded warehouse operations and bridge the gap between accounting theory and public-sector practice. The contributions translate COSO principles into measurable, causally ordered control criteria and ranked interventions. Design/methodology/approach This research integrated the decision-making trial and evaluation laboratory (DEMATEL) and analytic network process (ANP) approaches to construct a multi-criteria decision-making model for identifying the factors and strategies that influence the success of internal control in bonded warehouses. Findings The results that DEMATEL identifies control environment and operations as the system cause with the highest net influence, driving risk assessment, information and communication and monitoring. ANP ranks smuggling risk and cargo movement safety as the two most critical criteria, followed by audit/control mechanisms. The integrated ANP prioritizes an intelligent bonded management system, full-spectrum internet of things monitoring and rolling regulatory review. Practical implications The main practical implication of this study is the proposal of implementation strategies for customs to enhance internal controls in bonded warehouses and identify the critical success factors. Originality/value This study examines the integration of the five interconnect components of COSO (control environment, risk assessment, control activities, information and communication and monitoring) into public sector governance, thereby contributing to academic research in accounting and organizational change.

This study introduces a comprehensive multi-criteria decision-making framework that integrates the Best–Worst Method (BWM), fuzzy DEMATEL, the Analytic Network Process (ANP), and TOPSIS to prioritize green port electrification and operational enhancements. The model reflects complex trade-offs that shape decarbonization plans by asking experts about 20 important techno-economic, environmental, and organizational factors. The most important results show that emission abatement, fuel savings, and pollution reduction had the highest BWM weights. This shows that environmental goals are the most important. Fuzzy DEMATEL research showed that lifecycle replacement risk and labor preparedness were the main factors that affected tariff exposure, operational dependability, and digital integration results. ANP adjusted the weights of the criteria to take into consideration interdependencies, making economic risk and human capital the most important factors in decision-making. The TOPSIS rating found that a hybrid phased deployment option was the best choice for meeting goals for cost, emissions reduction, and operational readiness. It did better than both electric and traditional methods. These results show that the framework may combine expert knowledge, causal structure, and network feedback to make green port techniques more important. The concept goes beyond linear weighing by using cause-and-effect maps and feedback loops. This gives decision-makers a better understanding and more confidence when it comes to allocating resources. The results encourage a balanced growth of capital investments, environmental protection, and the ability of the workforce. This flexible strategy is helpful in gradually combining the renewables, tariff dynamics, and operational data to create strong, low-carbon marine logistics centers.

Evaluating shoreline vulnerability to oil spills using multi-criteria approaches: The case of Bushehr Province, Iran.

This study aims to analyze potential collaboration models between sharia fintech and Bank Pembiayaan Rakyat Syariah (BPRS) in Indonesia and to determine which model offers the highest strategic impact according to expert judgment. Using the Analytic Network Process (ANP), this research evaluates four alternative collaboration models identified through literature review and expert interviews. The first model is Sharia Fintech as a Technology Enabler and Referral Partner, where fintech provides prospective customer referrals to BPRS. The second model is Sharia Fintech as a Technology Enabler and Distribution Channel, functioning as a fund channeling intermediary between BPRS and customers. The third model positions fintech as a technology enabler for creditworthiness assessment, focusing solely on credit scoring activities. The fourth model is Sharia Fintech as a Sharia Peer-to-Peer (P2P) Financing Enabler, in which fintech provides a project marketplace for BPRS to fund, with financing flows conducted through the P2P platform. ANP results show that Model 4 is the most preferred option, while Model 3 is the least recommended. Managerially, BPRS should prioritize Model 4 and establish a joint coordination team with fintech under the supervision of the sharia fintech association.

Micro waqf banks in Indonesia are crucial in financing and providing business support. However, guidance on fostering job creation through entrepreneurship among their customers remains limited. Amid the current global economic slowdown and rising threats of employment termination in Indonesia, this study aims to analyse the priority strategies that micro waqf banks can adopt to promote job creation through entrepreneurship. This research employs a qualitative case study approach. Data were collected through questionnaires and in-depth interviews with supervisors, managers, and customers of micro waqf banks in Banten Province, Indonesia, selected via purposive sampling from El Manahij, Lan Taburo, and An Nawawi banks. Data validity was ensured through source triangulation, and descriptive analysis was conducted using the Analytical Network Process (ANP) technique with the Super Decisions software. The findings indicate that job creation is a key priority for entrepreneurship development within micro waqf banks, encompassing business assistance, financing, and mentoring. The study highlights the importance of strengthening mentoring, financing, and entrepreneurship training to enhance and expand job creation efforts within the micro waqf bank ecosystem. This research reinforces the pivotal role of micro waqf banks in supporting small, micro, and medium enterprises (SMEs) through these strategies. Policy implications suggest that government support should enhance mentoring, financing, and entrepreneurship training policies for micro waqf banks to maximise job creation. The study’s limitation lies in its focus on Banten Province, and future research is recommended to test the effectiveness of these strategies in other regions and with different mentoring models.

Most countries are expanding their programs for electric vehicles as part of an action to reduce greenhouse gas emissions and fossil fuel consumption. In Indonesia, EV development is significantly behind target. Indonesia has specific conditions regarding innovative products in the automotive industry. The majority of automotive users are economy users. Foreign-origin equipment manufacturers dominate the automotive industry, whereas local manufacturers are comparatively less competitive. This study examines the critical policies for EV adoption and the ecosystem in Indonesia. This analysis employs the analytic network process method based on the benefits, opportunities, costs, and risks model. The results show that, in both the short and long term, the top three priorities offer users greater attractiveness in terms of price and charging-station availability, including purchasing incentives, annual tax incentives, and incentives for charging infrastructure providers. In the short term, the following priorities focus on a producer-oriented policy, specifically co-production with local start-ups and EV share-targeted production. In the long term, the priority is to enhance the appeal of EVs by granting them driving privileges, which will continue through co-production with local start-ups to increase local content-based EV production.

Purpose Construction companies typically look at important factors affecting building performance in order to make long-term improvements. In order to assess and forecast the performance of green buildings (GBs), this study considers the interrelationships among the performance measures and their dynamic behavior over time when assessing building performance. This consideration fills the gap in literature, where performance metrics were measured mostly individually and as steady values. Design/methodology/approach Following a comprehensive assessment of the literature and conversations with subject matter experts, 15 performance measures were identified. Five are qualitative, and 10 are quantitative. The analytic network process was used to calculate the priority weights for these performance indices. A system dynamics (SD) model was developed to simulate the interdependency among these indices and their dynamics over time. A case study of a commercial building was used to validate the model applicability. Findings The findings of the model simulation show the impact of the interrelationships among the studied key performance indicators (KPIs). For example, while sustainability indices like energy and water exhibited delayed effects, schedule and productivity served as triggering indications with rapid implications. Multiple KPIs were affected by early-stage interventions, especially innovation, as shown by scenario analysis (S1–S4). Innovation improvements, for instance, enhanced cost, schedule and environmental performance in Scenario S4. These findings confirm the model’s ability to capture both short-term and long-term dynamics in GB projects. Originality/value The developed SD model facilitates the early detection of performance gaps and offers practical advice to improve sustainability results. The model allows users to explore “what-if” scenarios and assess the potential impact of managerial decisions on the success and sustainability of GB projects which maximize the opportunities of success of GB projects.

China’s prefabricated construction industry, despite its recent emergence, faces challenges such as limited standardization, poor integration, and low industrialization. In the Engineering, Procurement, and Construction (EPC) model for prefabricated buildings, numerous risks arise due to the long project lifecycle, complex technical requirements, and force majeure factors. These risks may cause substantial project losses if not effectively controlled. This study, from the perspective of general contractors, explores risk assessment for prefabricated building projects under the EPC model to promote construction practices and enhance project resilience. The objective is to help contractors mitigate risks and ensure the smooth and environmentally responsible implementation of prefabricated projects. Risks were identified through literature review and case analysis, and a statistical process refined them into a structured index. The Analytic Network Process (ANP) was applied to assign indicator weights, establishing a comprehensive risk evaluation model. The Gray System Theory was then employed to assess the risks in a case study from Southwest China, validating the effectiveness and applicability of the proposed model. This research provides a systematic approach and theoretical support for EPC-based risk assessment, offering guidance for risk management and contributing to development in the construction industry.

This study investigates the critical success factors (CSFs) that shape the adoption of Robotic Process Automation (RPA) in the digital transformation of small and medium-sized accounting service firms in Taiwan. As the accounting sector faces labor shortages, increasing process complexity, and growing demands for accuracy and efficiency, RPA has emerged as a strategic enabler of operational upgrading. Drawing on a comprehensive literature review and expert insights, this study integrates the Decision Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Process (ANP) to construct a multi-criteria decision-making (MCDM) framework for evaluating key determinants of RPA implementation. The DEMATEL results reveal that process reengineering and digital transformation maturity serve as major causal drivers influencing system readiness, human resources, and external environmental factors. Subsequent ANP analysis prioritizes information security, degree of digital transformation, process optimization, and process standardization as the most influential CSFs. The findings underscore that successful RPA adoption requires not only technological investment but also organizational restructuring, workforce capability enhancement, and systematic governance. This study provides accounting SMEs with an evidence-based decision framework for allocating resources, mitigating implementation risks, and formulating effective digital transformation strategies aligned with long-term competitiveness. JEL classification numbers: M15, M41, O33. Keywords: Robotic Process Automation (RPA), Digital Transformation, Multi-Criteria Decision Making (MCDM), SME Accounting Firms.

There is growing interest in how local communities could become more resilient to the adverse effects of disasters. Identifying baseline indicators and establishing a methodology for evaluating community disaster resilience (DR) at a finer scale (neighborhood) is of utmost importance. This article (1) adapts the Baseline Resilience Indicators for Communities (BRIC) framework to identify baseline resilience indicators, (2) utilizes the hybrid Factor Analysis and Analytic Network Process (F’ANP) model to assess DR, and (3) uses discriminant analysis to validate the applicability of the proposed methodology at the neighborhood level of the Tehran Metropolis. Guided and inspired by the BRIC framework, 35 baseline DR indicators are chosen from the literature. Using the F’ANP model, the seven extracted DR subdomains are reorganized to align with the BRIC structure. A composite DR index is computed and classified into five categories, using K-Means cluster analysis. The results are validated by discriminant analysis and spatial autocorrelation. Employing the BRIC-F’ANP model allows for the restructuring of identified disaster resilience subdomains to form the necessary resilience structure. The obtained results indicate that higher DR neighborhoods are concentrated in the northern parts of the city, whereas those with lower DR are clustered in the southern parts of the Tehran Metropolis. The adaptation of the BRIC F’ANP model has resulted in the development of a robust methodology for measuring DR at the neighborhood level that could be replicated for assessing the neighborhood DR in other cities.

This study aims to develop on internal performance evaluation model for sub-providers at PT XYZ using the Analytic Network Process (ANP) and Modified Customer Satisfaction Index (CSI), called the Internal Performance Index (IPI). The main problem addressed in the delay of sub-providers in handling customer complaints, which affects customer satisfaction and operational efficiency. ANP is applied to determine the weighted interdependent criteria, while IPI assesses efficiency, compliance, and internal reporting accuracy. Data were collected through interviews and questionnaires involving five experienced internal respondents. The analysis resulted in a ranking of sub-providers based on weighted indicators. This finding contributes to improving the objective and systematic partner evaluation process and provides strategic recommendations for management in selecting the most appropriate partners.

ABSTRACT The rapidly expanding global cold chain logistics (CCL) sector, which is crucial for economic development, faces a sustainability paradox due to increasing environmental costs. Although the number of studies on sustainable CCL practices has increased in recent years, research focusing on the critical strategic factors for the sustainable operation of the cold chain sector remains limited. This study aims to identify the driving mechanisms and critical factors that contribute to sustainability in CCL. An integrated approach combining the interpretive structural modelling (ISM) and analytic network process (ANP) is employed to develop a hierarchical relationship among the identified critical factors. The findings indicate that the policy environment is the most critical success factor, followed by the standardisation system and market demand, which are also critical success factors. This study offers policymakers critical insights to identify and prioritise strategic pathways essential for advancing sustainable CCL.

Interface layout design of agricultural harvester based on analytic network process and quantum particle swarm optimization

PT. X is a company that produces slippers. The company has many stakeholders which is faced with several obstacles. The company experienced problems with each component of the supply chain, both the process of procuring raw materials and related parties' suppliers, the company's internal production process, and delivery accuracy based on time and quantity related to the party customer. This research was conducted to measure the company's supply chain performance based on the Order Fulfillment matrix, Lead Time Order Fulfillment, and Cash to Cash Cycle Time as well as knowing proposed improvements from the measurement results to improve the company's supply chain performance. The method used to measure supply chain performance is supply Chain Operation Reference (SCOR) which is normalized with Snorm De Boerand weighting with Analytical Network Process using Software Super Decisions. The measurement results at the measurement matrix level are Order Fulfillment (82.31% -Good), Lead Time Order Fulfillment (67% -Average), and Cash to Cash Cycle Time (40% – Marginal). Meanwhile, the company's supply chain performance value was 64.07, This value shows that the company's supply chain performance is still in an average position. The company is recommended to carry out system improvements supply chain currently implemented using analysis to improve shopfloor and cost control management.

The present study investigates and prioritizes criteria for developing a green shipping corridor between Cai Mep–Thi Vai and Hai Phong ports in Vietnam. A fuzzy Analytic Hierarchy Process (fuzzy AHP) is first applied to screen and retains the ten most influential criteria out of an initial set of fourteen. Subsequently, a fuzzy Analytic Network Process (fuzzy ANP) is employed to capture interdependence among criteria and clusters through pairwise comparisons. Four clusters and ten criteria are structured across economic–infrastructure, environmental–climate, governance–social, and technological–operational dimensions that shape green corridor development. Expert judgements, expressed as triangular fuzzy numbers, are aggregated and transformed into a fuzzy ANP limit super matrix, from which stable criteria and cluster priorities are derived. The results indicate that Infrastructure Readiness (IR) and Environmental Compatibility (EC) are the most critical criteria, with ANP weights of 0.295 and 0.197, respectively, followed by Regulatory Compliance (RC = 0.140) and Economic Viability (EV = 0.089). The EconInfra cluster attains the highest priority (0.384), highlighting the need to strengthen port and hinterland infrastructure, while EnvClimate (0.257) emphasizes stringent environmental performance along the corridor. A comparison with fuzzy AHP weights indicates that neglecting interdependencies overestimates EC and underestimates IR and RC, whereas the network structure shifts emphasis toward infrastructure and regulatory–economic factors. The proposed fuzzy‑ANP framework offers a transparent decision-support tool for policymakers and port authorities planning green maritime corridors in Vietnam.

ABSTRACT This study employs a hybrid technique based on the Decision‐Making Trial Evaluation Laboratory (DEMATEL), Analytic Network Process (ANP), and Multiple Criteria Decision methods to investigate the causation and mutual influence strength among the barriers to the growth of electric vehicles in India. DEMATEL is used to discern between cause‐and‐effect barriers, while ANP ranks and prioritizes the various obstacles. This study gives critical insights into the linkages between these hurdles that will aid in the development of measures to promote the rise of electric cars. According to the findings, the barriers to electric car adoption include a lack of charging infrastructure, issues of fire safety, supply chain hurdles, range anxiety, and high cost of ownership. Generally, this study leads to a better understanding of the multidimensional nature of electric cars’ barriers and their interdependencies.

This study develops a spatial transit accessibility assessment method within a multicriteria decision-making framework. The method adopts the analytic network process to account for interdependence between criteria and across neighborhoods, and is applied to a case study in the Taipei metropolitan area in Taiwan. The assessment incorporates nine criteria across three perspectives: access to transit (population coverage of rail, bus, and public bike systems), service quality (frequency of rail and bus services and the number of public bike docks), and access by transit (opportunities to reach jobs, schools, and neighborhoods within a sixty-minute travel time). Criteria data were obtained from governmental open data platforms, official statistics, and Google Maps route planning, with 2021 as the base year. Expert surveys were conducted to determine the relative importance and interdependence of criteria. Spatial regression models and sensitivity analyses are conducted to evaluate how effectively the proposed accessibility measure explains variations in neighborhood-level transit mode share and to assess the robustness of the results. Results reveal significant spatial clustering of transit accessibility, confirming the influence of interdependence across neighborhoods. Moreover, the proposed method yields assessment results with greater explanatory power than those derived from single-criterion or independence-assuming approaches. These findings underscore the method’s utility for local authorities in identifying transport-disadvantaged neighborhoods and developing targeted accessibility improvement plans.