Search Results (118)

Value-added services (VASs) are widely used to incentivize user adoption in the platform economy. While considering the symmetry of cross-network externalities of a platform, i.e., suppliers and manufacturers exert balanced and mutually reinforcing influences on each other’s participation, this study develops a stylized game model to investigate platforms’ optimal bilateral user pricing decisions and VAS provision strategies, such as outsourcing to a third-party service provider (Model OS) or in-house provision (Model PS). Then, the platform’s and the third-party service provider’s optimal pricing decisions are derived, and the equilibrium results are compared. The findings demonstrate that a platform should implement Model PS when the outsourced VAS cost coefficient is sufficiently high or the outsourced VAS quality and cost coefficient are low concurrently. Only when the outsourced VAS quality is relatively high and cost coefficient is in a low range should a platform choose Model OS. Additionally, to address the problem of declines in supply chain members’ profits caused by investment in low-quality outsourced VASs (VAS utility provided by a third party exceeds the specific value 1.38), this study also proposes a feasible VAS cost-sharing contract (Model CS) to incentivize the third-party provider to provide investment in high-quality VASs. The contract design can achieve a “win-win” outcome when the sharing ratio is at a moderate rate (especially a range from 0.291 to 0.5) and the outsourced VAS cost coefficient meets suitable thresholds. Full article

Artificial Intelligence (AI) is rapidly transforming medical education by enabling adaptive tutoring, interactive simulation, diagnostic enhancement, and competency-based assessment. This narrative review explores how AI has influenced learning processes in undergraduate and postgraduate medical training, focusing on methodological rigor, educational impact, and implementation challenges. The literature reveals promising results: large language models can generate didactic content and foster academic writing; AI-driven simulations enhance decision-making, procedural skills, and interprofessional communication; and deep learning systems improve diagnostic accuracy in visually intensive tasks such as radiology and histology. Despite promising findings, the existing literature is methodologically heterogeneous. A minority of studies use controlled designs, while the majority focus on short-term effects or are confined to small, simulated cohorts. Critical limitations include algorithmic opacity, generalizability concerns, ethical risks (e.g., GDPR compliance, data bias), and infrastructural barriers, especially in low-resource contexts. Additionally, the unregulated use of AI may undermine critical thinking, foster cognitive outsourcing, and compromise pedagogical depth if not properly supervised. In conclusion, AI holds substantial potential to enhance medical education, but its integration requires methodological robustness, human oversight, and ethical safeguards. Future research should prioritize multicenter validation, longitudinal evaluation, and AI literacy for learners and educators to ensure responsible and sustainable adoption. Full article

This paper develops a bilevel game-theoretic model to analyze the strategic effects of tariffs and subsidies in a global supply chain involving a manufacturer and a third-party logistics (3PL) provider. The government, acting as a Stackelberg leader, sets fiscal instruments to maximize national welfare, while downstream supply chain participants respond by optimizing production, pricing, and logistics outsourcing decisions. The model is evaluated under three coordination structures—centralized, decentralized, and alliance-based—to examine how decision alignment influences policy effectiveness. Simulation results show that while tariffs negatively impact supply chain efficiency and profitability, well-designed subsidies can partially or fully offset these effects, particularly under centralized coordination. The model further reveals that policy outcomes are highly sensitive to the strategic power structure within the supply chain. This study advances the literature by integrating endogenous government behavior with logistics coordination and supply chain decision-making within a unified bilevel optimization framework. The findings offer actionable insights for both policymakers and global supply chain managers in designing robust fiscal and coordination strategies. Full article

Background: The growth of parcel volumes in urban areas, largely driven by e-commerce, has increased the complexity of pickup and delivery operations. To meet demands for cost efficiency, flexibility, and sustainability, CEP (Courier, Express, and Parcel) operators increasingly outsource segments of their last-mile networks. Methods: This study proposes a novel multidimensional cost model for outsourcing, integrating five key variables: transport unit type (parcel/pallet), service phase (pickup/delivery), vehicle category, powertrain type, and delivery point type. The model applies correction coefficients based on internal operational costs, further adjusted for location and service quality using a bonus/malus mechanism. Results: Each cost component is calculated independently, enabling full transparency and route-level cost tracking. A real-world case study was conducted using operational data from a CEP operator in Bosnia and Herzegovina. The model demonstrated improved accuracy and fairness in cost allocation, with measurable savings of up to 7% compared to existing fixed-price models. Conclusions: The proposed model supports data-driven outsourcing decisions, allows tailored cost structuring based on operational realities, and aligns with sustainable last-mile delivery strategies. It offers a scalable and adaptable tool for CEP operators seeking to enhance cost control and service efficiency in complex urban environments. Full article

This study proposes a balanced truck dispatching strategy for inter-terminal transportation (ITT) in large ports, incorporating proactive demand outsourcing to address stochastic and imbalanced ITT demand. A portion of ITT tasks are intentionally outsourced to third-party public trucks at a higher cost, so that self-owned trucks can be reserved for more critical tasks. The ITT system is modeled as a closed Jackson network, in which self-owned trucks circulate among terminals and routes. An optimization model is developed to determine the optimal proactive outsourcing ratios for origin–destination terminal pairs and the appropriate fleet size of self-owned trucks, aiming to minimize total transportation costs. Reactive outsourcing is also included to handle occasional truck shortages. A mean value analysis method is used to evaluate system performance with given decisions, and a differential evolution algorithm is employed for optimization. The case study of Shanghai Yangshan Port demonstrates that the proposed strategy reduces total system cost by 9.8% compared to reactive outsourcing. The results also highlight the importance of jointly optimizing outsourcing decisions and fleet size. This study provides theoretical insights and practical guidance for ITT system management under demand uncertainty. Full article

Local governments provide numerous services to their citizens. In doing so, they utilize two primary methods to deliver them: (1) producing them in-house with their own employees and equipment or (2) outsourcing them to external actors, which may take the form of other public agencies, for-profit firms, or non-profit organizations. In this entry, the authors review the logic of why local governments might choose one mechanism over another. The goal is to give readers a feel for the state of the academic literature in this regard. After reviewing basic concepts, such as the difference between the “provision” and “production” of services, the authors frame the discussion in terms of a variety of lenses used by scholars attempting to better understand the determinants of such decision-making. These include agency theory, transaction cost economics, and New Public Management. The authors also consider the role that management capacity plays in allowing cities to successfully deliver services to their constituents. Additionally, the authors offer a discussion regarding how local governments partner with non-profits in less formal ways than contracting to ensure their citizens have access to needed services. Finally, there is a review of the tradeoffs between efficiency and other values that should be accounted for when arranging service production. Full article

This study investigates how small and medium-sized enterprises (SMEs) can successfully transform into high-tech, high-value-added companies within the aviation industry, considering the latest manufacturing, certification, and quality technologies. We identified critical factors through a comprehensive literature review and expert interviews, then analyzed the relationships between these factors using two complementary methodologies: grey DEMATEL (Decision Making and Trial Evaluation Laboratory) and ISM (Interpretive Structural Modeling). Our approach employed grey numbers to address individual uncertainty and utilized the Aczel–Alsina function to integrate expert opinions while accounting for inter-expert disagreements. The research focused on traditional machinery manufacturers in Taiwan transitioning to aviation manufacturing, the findings being applicable to enterprises in other countries with similar environments where small and medium-sized enterprises are the main players. The results revealed three critical factors determining successful transformation: organizational culture and workforce quality, aviation certification protocols, and original equipment manufacturer (OEM) outsourcing policies and requirements. Based on these findings, the study provides strategic recommendations for government policymakers and business executives to facilitate the successful entry of traditional industrial enterprises into the aviation manufacturing sector. Full article

As environmental issues are of worldwide concern and consumers grow more concerned about the environment, green investments have emerged as a key factor in attracting consumers. To enhance consumer trust in enterprise investments in green and sustainable practices, blockchain technology, with its tamper-resistant and traceable characteristics, is being adopted by an increasing number of enterprises. However, the resulting spillover effect may lead to adverse consequences in a co-opetitive supply chain. This study examines a green supply chain comprising Brand O, a high brand value entity, and a contract manufacturer (CM) with lower brand value. The two parties collaborate through outsourced production while competing in the retail market. Three decision-making models were constructed, namely, without blockchain, Brand O adopting blockchain, and the CM adopting blockchain, and equilibrium solutions were derived to facilitate analysis. We find that Brand O tends not to introduce blockchain in order to avoid the loss of brand value and the spillover of consumer trust. The CM tends to introduce blockchain to enhance its products’ environmental impact and gain an exclusive competitive advantage, targeting the high-end market. These findings guide managers and practitioners in a co-opetitive green supply chain: high brand value retailers should cautiously evaluate blockchain’s impact, staying alert to risks hidden beneath benefits; upstream manufacturers can prioritize blockchain adoption for competitive advantage. Full article

Land Degradation Neutrality (LDN) is an ambitious initiative by the United Nations Convention to Combat Desertification (UNCCD) to tackle land degradation. Inspired by the “no net loss” concept, LDN seeks to counterbalance unavoidable land degradation—primarily driven by food systems—through targeted regenerative actions at multiple scales—such as regenerative agriculture or grazing practices that simultaneously support production and preserve land fertility. The objective is to ensure that degradation does not surpass the 2015 baseline. While the UNCCD’s Science–Policy Interface provides guidance and the LDN Target Setting Programme has led many countries to define baselines using agreed indicators (soil organic carbon, land use change, and primary productivity), concrete intervention strategies often remain poorly defined. Moreover, the voluntary nature of LDN has limited its effectiveness. A key shortcoming is the lack of integrated planning. LDN should function as a “Plan of Plans”—a coordinating framework to align policies across sectors and scales, reconciling conflicting agendas in areas such as food, energy, and water. To this end, we advocate for a systemic approach to uncover synergies, manage trade-offs, and guide decision-making in complex socio-ecological landscapes. Land degradation is intricately linked to issues such as food insecurity, land acquisitions, and transboundary water stress. Although LDN is implemented at the national level, its success also depends on accounting for global dynamics—particularly “LDN leaks”, where land degradation is outsourced through international trade in food and raw materials. In an increasingly complex world shaped by globalization, resource depletion, and unpredictable system dynamics, effective responses demand an integrated socio-ecological management approach. LDN is not simply a strategy to address desertification. It offers a comprehensive framework for sustainable resource management, enabling the balancing of trade-offs and the promotion of long-term resilience. Full article

Cloud computing is widely used by organizations and individuals to outsource computation and data storage. With the growing adoption of machine learning as a service (MLaaS), machine learning models are being increasingly deployed on cloud platforms. However, operating MLaaS on the cloud raises significant privacy concerns, particularly regarding the leakage of sensitive personal data and proprietary machine learning models. This paper proposes a privacy-preserving decision tree (PPDT) framework that enables secure predictions on sensitive inputs through homomorphic matrix multiplication within a three-party setting involving a data holder, a model holder, and an outsourced server. Additionally, we introduce a leaf node pruning (LNP) algorithm designed to identify and retain the most informative leaf nodes during prediction with a decision tree. Experimental results show that our approach reduces prediction computation time by approximately 85% compared to conventional protocols, without compromising prediction accuracy. Furthermore, the LNP algorithm alone achieves up to a 50% reduction in computation time compared to approaches that do not employ pruning. Full article

In the context of green economy, Closed-Loop Supply Chain (CLSC) competition is intensifying. This study aims to help companies operating in green supply chains determine optimal remanufacturing strategies when competing with other firms. We examine the decision-making problem of CLSCs in competitive environments facing multiple remanufacturing mode options. The research constructs a Prisoner’s dilemma model for dual CLSCs, where each chain has three strategic choices: independent remanufacturing, outsourced remanufacturing, and authorized remanufacturing. Employing Stackelberg game models, Nash equilibrium analysis, and numerical simulations, this study explores how remanufactured product unit saving costs affect remanufacturing mode decisions concerning competitive intensity and discount policies. The study then draws the following conclusions: (1) CLSCs prefer outsourced and authorized remanufacturing in competitive scenarios; (2) Remanufactured product discounting significantly influences CLSC remanufacturing decisions; (3) Competitors typically adopt conservative strategies by aligning decisions with rivals. These results provide practical guidance for CLSCs selecting remanufacturing approaches when facing substitute competition, contributing to more sustainable and competitive supply chain operations. Full article

Aiming at the problem of the low collaborative efficiency of outsourced processing of wallboard parts of a coating machine under a network collaborative manufacturing mode, this paper proposes a wallboard manufacturing resource composition optimization method based on the Improved Polar Bear Optimization (IPBO) algorithm. The processing process of the wallboard is analyzed, and the process-level splitting of the wallboard manufacturing task is completed; the required manufacturing resource service portfolio is determined, and the resource evaluation indicator system for key performance indicators of wallboard manufacturing resources is established; non-cooperative game decision-making is used to construct a wallboard manufacturing resource composition optimization model from two aspects, namely, quality indicators and flexibility indicators; an adaptive vision and mutation strategy is introduced to carry out the Polar Bear Optimization (PBO) algorithm. Finally, the improved algorithm is used to solve the wallboard manufacturing resource composition optimization model. The experimental results show that the IPBO algorithm reduces the average convergence time by 6.51% and the optimal convergence time by 9.26% compared with the suboptimal Dung Beetle Optimization (DBO) algorithm, and 65%–72% of the test points of the IPBO algorithm are more in line with the preference criteria of the Pareto frontier. Meanwhile, it demonstrates both validity and superiority in solving the problem of expanding the size of wallboards for coating machines. Full article

Background: Ensuring cost-efficient and high-quality processes for logistics tasks is a significant competitive factor for companies. This includes not only improving existing processes but also examining outsourcing opportunities. Current trends, such as the increasing variety of products, shorter product life cycles, and a dynamically changing economic environment, necessitate frequent reviews and, if needed, the reorganization of logistics activities. Methods: Modern digitalization technologies (e.g., digital twins, artificial intelligence, etc.) open new possibilities for (re)evaluating outsourcing decisions, such as improving process transparency and leveraging optimization opportunities. The currently applied solutions are fragmented and, in many cases, do not integrate digitalization technologies and standardized examination processes, necessitating the development of a new process development framework concept. The research follows an inductive–deductive methodology, combining practical industrial experience with a thorough literature review. Results: The framework presented in this study enables a faster and more efficient evaluation compared to previous approaches by incorporating the application of digitalization technologies. The validity of the developed concept is demonstrated through a case study. Conclusions: The findings highlight the importance of integrating digitalization technologies into logistics process development to enhance decision-making and efficiency. The proposed framework provides a structured approach that facilitates a more effective evaluation of outsourcing decisions and process improvements. Full article

Pesticide application has significantly aided global agriculture, but the overuse of pesticides also poses a threat to sustainable agriculture development in the future. The application of outsourcing services in the pesticide application process is a good way to promote pesticide reduction, but the actual effect is not satisfactory. The possible reason is ignorance of the regulatory role of supervision and land management scale. Based on the data of 1490 corn growers, this research investigates how outsourcing service application affects pesticide application intensity through the principal–agent theory through the instrumental variable method and examines the moderator effect of supervision and land management scale. The study found that: (1) Farmers who applied outsourcing services during pesticide application process constituted 15% of the farmers in the sample, and the average pesticide cost per 0.000667 km² was USD 7.59; (2) The use of outsourcing services in the pesticide application process can lower the pesticide application intensity; (3) The application of outsourcing services in the pesticide application process has received the regulatory role of supervision and land management scale in reducing the intensity of pesticide application. Specifically, supervision can play a positive regulatory role, and land management scale plays a negative regulatory role. The research is helpful to deepen the understanding of the correlation between outsourcing service adoption and pesticide application reduction and provide decision-making reference for the formulation and improvement of pesticide reduction-related policies. Full article

Using farm machinery plays a significant role in easing the issue of slowing growth of operating income among farm households in China. Drawing data from CFPS2018, this study adopts a multinomial endogenous switching regression (MESR) to analyze the factors influencing farm households’ choices regarding self-owned farm machinery and outsourced machinery services, as well as their subsequent impact on operating income. The results of the study show that the characteristics of the head of household, family, village, and region have a significant impact on the farm households’ selection of whether to use self-owned machinery or outsourced services. Furthermore, the exclusive use of self-owned farm machinery and the combined use of both self-owned and outsourced machinery substantially enhance farm households’ operating income. An additional analysis indicates that these two types of machinery are complementary, and their combined use generates a superimposed effect that further boosts income. These findings suggest that the combined use of self-owned and outsourced machinery is optimal for farm households who wish to expand their operating income. Full article