Search Results (48)

Exploitation alliances struggle to maintain long-term stability, adapt to changing conditions, and deliver lasting value. Although contractual governance is widely recognized as crucial to exploitation alliance performance, the role of specific clauses remains unclear, especially in turbulent environments. This study examined how contractual clauses interact and affect exploitation alliance performance. Clause-level data collected from 110 senior managers involved in Chinese residential joint ventures (JVs), which are typical examples of exploitation alliances, were analyzed using partial least squares structural equation modeling (PLS-SEM). Results indicate that constitutional clauses, such as decision-making mechanisms and the alliance manager, promote the implementation of input–output controls, which in turn boost operational efficiency, the core of sustainable performance in exploitation alliance. Input–output controls both mediate and moderate the effect of constitutional clauses on risk controls. Conversely, clauses on division of labor and risk control have no direct effect on operational efficiency. This study identifies a three-tier contractual hierarchy and shows that operational efficiency arises mainly from performance-oriented clauses, rather than protective safeguards. These insights offer a micro-foundational view on designing contracts to foster resilient, value-creating collaborations in repeated, resource-intensive ventures. Full article

This study introduces the Hybrid Hydrological Forecast System (HHFS), a dual-stage, data-driven framework for monthly streamflow forecasting at the Santa Branca outlet in the upper Paraíba do Sul River Basin, Brazil. The system combines two nonlinear regressors, Multi-Layer Perceptron (MLP) and extreme Gradient Boosting (XGB), calibrated through a structured four-step evolutionary procedure in GA₁ (hydrological weighting, dual-regime Ridge fusion, rolling bias correction, and monthly mean–variance adjustment) and a hydro-adaptive probabilistic optimization in GA₂. SHAP-based analysis provides physical interpretability of the learned relations. The regressive stage (GA₁) generates a bias-corrected and climatologically consistent central forecast. After the full four-step optimization, GA₁ achieves robust generalization skill during the independent test period (2020–2023), yielding NSE = 0.77 ± 0.05, KGE = 0.85 ± 0.05, R² = 0.77 ± 0.05, and RMSE = 20.2 ± 3.1 m³ s⁻¹, representing a major improvement over raw MLP/XGB outputs (NSE ≈ 0.5). Time-series, scatter, and seasonal diagnostics confirm accurate reproduction of wet- and dry-season dynamics, absence of low-frequency drift, and preservation of seasonal variance. The probabilistic stage (GA₂) constructs a hydro-adaptive prediction interval whose width (max-min streamflow) and asymmetry evolve with seasonal hydrological regimes. The optimized configuration achieves comparative coverage COV = 0.86 ± 0.00, hit rate p = 0.96 ± 0.04, and relative width r = 2.40 ± 0.15, correctly expanding uncertainty during wet-season peaks and contracting during dry-season recessions. SHAP analysis reveals a coherent predictor hierarchy dominated by streamflow persistence, precipitation structure, temperature extremes, and evapotranspiration, jointly explaining most of the predictive variance. By combining regressive precision, probabilistic realism, and interpretability within a unified evolutionary architecture, the HHFS provides a transparent, physically grounded, and operationally robust tool for reservoir management, drought monitoring, and hydro-climatic early-warning systems in data-limited regions. Full article

This study investigates how strategic foresight can enhance FinTech governance and policy resilience in emerging economies, using Kuwait as an illustrative case. It aims to identify which foresight interventions should be prioritized across alternative futures to strengthen innovation, security, and institutional adaptability within the digital finance ecosystem. A scenario-based Multi-Criteria Decision Analysis (MCDA) framework is applied, combining the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Expert evaluations were conducted to assess five foresight interventions against eight policy and performance criteria across three plausible scenarios: Optimistic Growth, Status Quo, and Crisis and Contraction. Sensitivity analyses were performed to validate the stability of intervention rankings. The results reveal distinct priorities under each scenario: SME-oriented digital finance platforms and talent development dominate under growth and stability, while cybersecurity investment becomes paramount during crisis conditions. Regulatory fast-tracking maintains a consistent, moderate influence across all contexts. These outcomes underscore the need for adaptive, context-sensitive policy design that accommodates uncertainty. The framework provides policymakers with a structured approach to align FinTech strategies with long-term national visions such as Kuwait’s Vision 2035, while offering transferable insights for other emerging economies. The study’s originality lies in integrating strategic foresight and MCDA for FinTech governance—a methodological and practical contribution to foresight-informed policymaking. Full article

Motivated by the policy urgency of China’s dual-carbon goals and the practical obstacle that official input–output (IO) and MRIO tables are sparse and non-consecutive, this study investigates how to generate credible, mechanism-aware provincial–sector forecasts of carbon footprints and embodied transfers for Western China—a region with pronounced structural heterogeneity. We develop a regionalized forecasting pipeline that fuses balance-constrained MRIO completion (RAS–CE) with a Whale-optimized Grey Neural Network (WOA–GNN), bridging the data gap (2007–2017 reconstruction) and delivering 2018–2030 projections at province–sector resolution. The novelty lies in integrating RAS–CE with a meta-heuristic grey learner and layering explainable network analytics—Grey Relational Analysis (GRA) for factor ranking, complex-network measures with QAP regressions for driver identification, and SHAP for post hoc interpretation—so forecasts are not only accurate but also actionable. Empirically, (i) energy mix/intensity and output scale are the dominant amplifiers of footprints, while technology upgrading (process efficiency, electrification) is the most robust mitigator; (ii) a structural sectoral hierarchy persists—S2 (non-metallic minerals) remains clinker/heat-intensive, S3 (general/special equipment) operates as a mid-chain hub, and S6/S7 (electrical machinery/instruments) maintain lower, more controllable intensities as the grid decarbonizes; (iii) by 2030, the embodied carbon network becomes denser and more centralized, with Sichuan–Chongqing–Guizhou–Guangxi forming high-betweenness corridors; and (iv) QAP/SHAP converge on geographic contiguity (D) and economic differentials (E) as the strongest positive drivers (openness Z and technology gaps T secondary; energy-mix differentials F weakly dampening). Policy-wise, the framework points to green-power contracting and trading for hubs, deep retrofits in S2/S3 (low-clinker binders, waste-heat recovery, efficient drives, targeted CCUS), technology diffusion to lagging provinces, and corridor-level governance—demonstrating why the RAS–CE + WOA–GNN coupling is both necessary and impactful for data-constrained regional carbon planning. Full article

To address the challenges of multiple cost-influencing factors, high risks, and difficult control in highway construction projects, this study conducts a cost risk assessment based on the full-process perspective of project owners. Considering the long duration and distinct phases of highway construction projects, the study employs a literature-based statistical method to identify the factors influencing cost risks and establishes an evaluation index system for cost risk factors throughout the entire construction process. Based on questionnaire surveys, the study applies the Analytic Hierarchy Process (AHP) to calculate the initial weights of the cost risk factors. Then, the improved entropy weight method is used to compute the correction coefficients for the initial weights and determine the final weight of each influencing factor. By integrating the results from AHP, the comprehensive weights of all factors are obtained, thereby identifying the key factors affecting cost risks throughout the entire highway construction process. Additionally, cost risk prevention and control measures are proposed. The research findings indicate that among the 42 evaluation factors, the ten factors with the greatest impact on project cost risks are project positioning changes, price inflation, unclear or erroneous contract terms, lack of supervision by design units, delayed compensation payments, collusion in bidding (including bid-rigging and cover bidding), lack of coordination among different departments leading to schedule risks, construction claims risks, risks associated with bidding methods, and financing risks. These ten key factors are analyzed in detail, and corresponding risk prevention and control measures are proposed. Full article

Examples from urban infrastructure in the United States show that high-level policy reports focused on investment neglect performance improvement, as well as changes in society and technology. A study methodology using systems approaches, institutional analysis, and examples from US situations was used to probe causes and remedies of this policy shortcoming. A conceptual systems model of services and the Maslow hierarchy of needs identified essential services spanning water, energy, transit, and streets management. Drinking water services have greater clarity and were selected to assess actor roles, responsibilities, and actions. The institutional analysis and development framework was used to organize the actors, settings, norms, incentives, rules, and action arenas. Data from the drinking water sector indicated that infrastructure policy reports mix issues and obscure significant impacts on specific sectors. They assume a static view and do not consider transformations in social contracts, alternative technologies, and service delivery methods. Without policy reform, public trust in government services will diminish, but political and administrative realities constrain rational and comprehensive approaches. The drinking water social contract is unlikely to change, but partnerships can incentivize reforms like performance assessment and agency accreditation. Development of a road map for urban infrastructure policy reform will require research by task forces of leading-edge practitioners within categorical arenas like drinking water, electric power, transit, and public works. Full article

Government-invested Engineering, Procurement, and Construction (EPC) projects often encounter challenges, such as ambiguous value-added pathways and undefined key driving mechanisms, which impede efficiency improvements during implementation. To systematically elucidate the value-added pathways and core driving mechanisms in these projects, this study identified and distilled 20 critical influencing factors across four dimensions—contract cost, organization, technology, and environment—through a combination of a literature review, case analysis, and a questionnaire survey yielding 68 valid responses. Employing a DEMATEL–TAISM–MICMAC hybrid model, the research conducted an in-depth analysis: the DEMATEL method quantified the interdependencies among factors and identified key causal elements; a TAISM-directed topological hierarchy diagram was constructed to clearly delineate the hierarchical transmission pathways; and the MICMAC model was utilized for driver–dependency analysis, classifying factor roles and providing cross-validation from three analytical perspectives. The results indicate that S₁₂ (collaborative participation in early planning and design phases) exhibits the highest causal influence and serves as the core driving factor, while S₁ (detailed and explicit contractual clauses) and S₁₂ are positioned at the root level of the hierarchical model, functioning as foundational independent factors that regulate the entire system. The value-added pathways are characterized by a hierarchical transmission logic of “root level → transitional level → direct level”. Based on these findings, the study proposes a system optimization strategy of “strengthening the root level, optimizing the transitional level, and safeguarding the direct level”, thereby offering both theoretical insights and practical guidance for enhancing the value-added efficiency of government-invested EPC projects. Full article

Risk management is crucial in engineering projects, especially in highly complex environments like petrochemical plants producing polyvinyl chloride (PVC). This study proposes a tailored risk management model, using analytic hierarchy process (AHP) and linear regression analysis, alongside MS Excel and IBM SPSS^® version 23, to identify, assess, and prioritize key risks. Surveys and interviews revealed seven management factors (budget, schedule, safety, productivity, contracting, quality, and environment) and 18 critical risks, including design errors and procurement delays. The model quantifies risk impacts, provides a regression equation for risk classification, and supports effective mitigation strategies. Based on this model, decision-making can be facilitated for the implementation of effective mitigation strategies. It also promotes continuous improvement, optimizing economic resources and minimizing environmental impacts, addressing a research gap in Colombia’s petrochemical sector and paving the way for broader industrial applications. Full article

Corruption in public procurement remains a challenge to good governance, especially in developing nations. Blockchain technology has been espoused as a new paradigm for achieving sustainable public procurement practices for effective service delivery and, by extension, promoting sustainable development. Given the potential of blockchain technology, its implementation has been slow in developing countries. Additionally, there is an inadequate decision support framework to prioritize corruption-prone stages of the public procurement cycle for strategic blockchain integration at the most critical corruption-prone stages of the public procurement cycle given the scarce resources available in developing countries. Therefore, we employed a matured theory that is the principal-agent theory to identify key agency problems related to public procurement in developing countries. An interview with 25 experts and a thorough review of Ghana’s Auditor General produced seven public procurement cycle stages. Further, a survey was designed for experts and stakeholders to prioritize the identified procurement stages under the agency problems through the Analytic Hierarchy Process (AHP). Our results revealed that tender evaluation was the most critical stage susceptible to corruption, followed by contract management and procurement planning in the public procurement stages. Additionally, for the relative importance of the criteria, information asymmetry was ranked first, followed by moral hazard, and then adverse selection. This study offers a targeted framework for blockchain deployment in public procurement from an African country perspective. The outcome of this study provides insights for policymakers and procurement practitioners to know the most critical stages of public procurement stages and leverage blockchain technology given the scarcity of resources in developing countries to aid sustainable public procurement. The proposed blockchain framework can enhance service delivery, citizens’ trust, and international donor confidence in partnership and funding for public procurement projects in developing countries. Full article

Taipei mass rapid transit (MRT), operational since 1996, serves up to two million passengers daily. Equipment malfunctions pose a safety risk, making the dual goals of cost reduction and safety a significant challenge. Recently, outsourcing non-core technical tasks has emerged as an effective cost-control strategy, allowing resource allocation to employee salaries and operational efficiency. This study uses the analytic hierarchy process (AHP) and fuzzy analytic hierarchy process (FAHP) to prioritize outsourcing for electromechanical equipment. It incorporates analysis from the outsourcing literature, historical data, and ISO documents from Taipei MRT. The research included interviews and surveys with seven senior managers, using software to analyze the outsourcing priorities of four key systems: electrical and fire safety, environmental air conditioning, escalators and elevators, and power supply. It suggests prioritizing environmental air conditioning, followed by power supply systems, escalators and elevators, and electrical and fire safety systems. Additionally, this study employed the FAHP and the technique for order of preference by similarity to ideal solution (TOPSIS) for the rigorous evaluation and monitoring of vendor selection to ensure quality service and effective contract execution. By comparing technical expertise, problem-solving capabilities, certifications, response times, and contractual performance, this study identified the most suitable vendors. It concludes with recommendations for Taipei MRT to enhance maintenance quality and reduce costs. Full article

This paper introduces an innovative cooperative model for energy efficiency retrofitting that incorporates carbon emission rights, addressing critical financial constraints in Energy Performance Contracting (EPC). By employing the fuzzy analytic hierarchy process (F-AHP) to evaluate risk assessment indicators and stakeholder contributions and utilizing the enhanced Shapley method for equitable benefit distribution, the model demonstrates significant improvements in financing and efficiency for energy conservation projects. The findings are as follows: (1) the energy efficiency retrofit model, which integrates carbon emission rights, effectively alleviates the financial constraints and fosters energy conservation and emission reduction in guaranteed-savings EPC projects; (2) the enhanced Shapley method is deemed appropriate for the equitable distribution of energy-saving benefits among stakeholders; (3) when compared with the traditional model and the benefit allocation-absent carbon rights, the energy-saving benefits of the energy efficiency retrofit model incorporating carbon emission rights are higher in individual and overall terms. The findings of this study offer a viable solution to financing challenges faced by stakeholders in such projects and delineate a pragmatic approach for enterprises to enhance energy efficiency and reduce emissions. Full article

This study used a technology–organization–environment (TOE) framework as the primary analytical tool to explore the burgeoning capabilities of blockchain technology in the area of 5G ceramic antenna development. A causal loop diagram (CLD) analysis is used to further clarify the complex dynamics and feedback mechanisms, and the impact of blockchain on the design, production, and deployment phases of ceramic antennas, which play a pivotal role in the development of 5G communications, is studied. We found that blockchain’s unique features, including its immutable ledger and decentralized architecture, have the potential to significantly improve the transparency, security, and efficiency of the ceramic antenna manufacturing process. Technology (T), organization (O), and environment (E) were used as the top factors, and the subfactors of TOE were selected and analyzed using the Analytic Hierarchy Process (AHP) by CLD. The AHP analysis was used to evaluate the relative importance of various internal and external factors affecting the adoption of blockchain technology. The integration of the TOE framework with AHP and CLD provides a comprehensive analytical tool that enhances the understanding of the complex dynamics in the 5G ceramic antenna manufacturing process. This methodological approach not only clarifies the interactions between technological, organizational, and environmental factors but also facilitates strategic decision-making through a structured evaluation of these factors. The AHP analysis showed that technical factors are the most important in the TOE analysis of 5G ceramic antenna manufacturing, with a weight of 0.427, which indicates the important role of technical factors in the development of ceramic antenna production. In addition, environmental and organizational factors were given weights of 0.302 and 0.271, respectively, confirming the importance of technological innovation and internal process optimization. In the subfactor of Technology (T), ‘Blockchain Technology’ has the highest ranking among the subfactors, with a global weight value of 0.129, emphasizing the importance of blockchain technology. This study explored the technical and organizational complexities of introducing blockchain technology into the 5G ceramic antenna manufacturing industry and, through an in-depth investigation of the potential benefits of such integration, it aims to propose new approaches to improve quality control and manufacturing efficiency. The research findings aim to contribute to the sustainable growth of the telecommunications industry by providing strategic recommendations for the application of blockchain technology in the production of 5G ceramic antennas. Full article

This study explores the architectural framework of a value-driven token economy on a blockchain content platform and critically evaluates the relationship between blockchain’s decentralization and sustainable economic practices. The existing literature often glorifies the rapid market expansion of cryptocurrencies but overlooks how underlying blockchain technology can fundamentally enhance content platforms through a more structured user engagement and equitable reward system. This study proposes a new token economy architecture by adopting the triple-bottom -line (TBL) framework and validates its practicality and effectiveness through an analytic-hierarchy-process (AHP) survey of industry experts. The study shows that the most influential factor in a successful token economy is not profit maximization but fostering a user-centric community where engagement and empowerment are prioritized. This shift can be expected to combine blockchain technology with meaningful economic innovation by challenging traditional profit-driven business models and refocusing on sustainability and user value. Full article

This study pioneers the enhancement of 5G antenna manufacturing efficiency and reliability by integrating blockchain and smart contract technologies, supported by an in-depth Analytic Hierarchy Process (AHP) analysis. At the heart of our innovation lies the blockchain-based SER-M (B-SER-M) model, which delineates ‘Subject’, ‘Environment’, and ‘Resources’ as crucial factors in the manufacturing process. Our refined AHP analysis reveals ‘Subject’ as the paramount factor, with a pivotal influence weight of 0.465, underscoring its significance in elevating production efficiency and reliability. The integration of blockchain technology facilitates impeccable record-keeping and tracking at each production stage, thereby bolstering data integrity and enhancing traceability. Furthermore, the incorporation of smart contracts streamlines operations by automating processes, enabling the rapid identification and resolution of issues. These technological advancements not only significantly elevate manufacturing efficiency but also markedly improve reliability and quality control across antenna production. The enhanced results of our study demonstrate the formidable potential of integrating cutting-edge technologies in manufacturing, presenting a solid model for sustaining industry competitiveness in an increasingly digital and interconnected realm. Our contributions lay the groundwork for transformative advancements in manufacturing practices, setting a new benchmark for the integration of blockchain and smart contract technologies in enhancing 5G antenna production efficiency and reliability. Full article

Contracting parties in construction projects confront significant challenges due to changes. This is an inherent industry characteristic. Managing changes properly with the help of a taxonomy encompassing many of the causes of changes can have a longitudinal and positive effect on project performance, knowledge management, and stakeholder management. However, studies to date have failed to propose an in-depth taxonomy for change causes in construction projects. Therefore, a taxonomy for change causes that can be applied to different construction projects has been developed. First, a systematic literature review and desk study sessions were conducted to identify the initial list of the taxonomy components. Six case studies were then analyzed to reveal the change causes of these cases. Based on the extracted change causes from the literature review and case studies, a taxonomy was developed by conducting focus group discussions with six experts. In the next step, the applicability and validity of the refined taxonomy were evaluated through face-to-face interviews. As a result, a taxonomy with a three-level hierarchy was proposed. This taxonomy is divided into three levels with 13 main categories, 50 subcategories, and 52 change causes. The proposed taxonomy is expected to contribute to practice by reducing the frequency of changes through proactive management of potential changes and standardizing knowledge management practices for managing change. Full article