Omega-international Journal of Management Science最新文献

Data resources, a fundamental component in the digital economy, play a vital role for businesses aiming to establish a lasting competitive edge. A company's data resources can uniquely influence the decisions surrounding products and services within the supply chain. The integrated dual-channel supply chain (DCSC) involves direct online channels utilized by manufacturing service providers alongside offline channels, such as physical retail stores, managed by sales service integrators. The DCSC is capable of performing second-stage data mining services in accordance with the sold products and customer services after the product sales are completed. Subsequently, data resource mining can be achieved through a cross-channel approach. As such, considering the exploration of cross-channel data resources, this study strives to formulate a structure for a dual-channel closed-loop supply chain. Furthermore, utilizing concepts from the Stackelberg and Nash equilibrium game theories, it delves into the analysis of pricing decisions and profit allocation. This examination encompasses distinct closed-loop supply chain configurations, viewed through the lenses of both centralized and decentralized decision-making approaches. In addition, the effects of cross-channel data mining and channel consumption preferences on supply chain decisions are analyzed, and the analysis is conducted in combination with numerical examples. As evidenced by the findings in this investigation, cross-channel data resource mining, consumer channel preference, and the data mining value conversion rate can prominently affect the formulation of pricing strategies and the distribution of profits in closed-loop supply chains. The potential value of data resources can lead to the generation of “external incentives” following the strategy of data resource mining. Furthermore, the data resource mining strategy is promising in stimulating the growth of the product and service markets. Finally, the overall profit of the supply chain is increased with the increase in the efficiency of data resource conversion. Enhancements in the efficacy of data resource conversion correspondingly lead to heightened overall profits within the supply chain.

For efficiency evaluation purposes, a multi-sector economy is always regarded as a “black box” in which the linkages among sectors are not considered. This leads to biased efficiency evaluation and failure to identify the sources of inefficiency. To solve these problems, this paper proposes a general Leontief optimization method for environmental performance estimation. The proposed method can assess the environmental benefits that result from the adjustment of input coefficients and environmental factor intensities. Moreover, this study proposes two models under different elasticity of input substitution assumptions. The difference in environmental efficiency between the two assumptions is defined here as the substitution efficiency. Further, a new Luenberger environmental productivity indicator is constructed that contains a new element reflecting the effect of input substitution. To demonstrate the new method, a numerical example is used to compare the estimation results between the general Leontief optimization method and the traditional data envelopment analysis method. Then, the environmental performance associated with CO₂ emissions and energy consumption in China is studied over two years, i.e., 2012 and 2017. By fully opening the “black box” of how to improve environmental efficiency, this study provides a reference with regard to cost-effective specific measures for policymakers in China.

Uncertainty and risk have become increasingly prominent issues in firm operating environments. Addressing uncertainties and risks brought by the historically disruptive COVID-19 pandemic crisis requires organizations to establish reliable, visible, and traceable information processing and sharing capabilities. Blockchain is a disruptive technology that can build capabilities to support operational resilience where uncertainty may manifest as risk or opportunity. Using an event study, we examine the COVID-19 pandemic relationship to stock returns for 217 blockchain-related firms—those that develop or apply blockchain technology—over five major pandemic life cycle periods. Our results indicate firms with blockchain capabilities have less mean negative stock price reaction—on average, a reduced 8.24 % shareholder value loss across crisis stages. Different blockchain application fields exhibit varied shareholder value effects—in general, production blockchain applications can help firms reduce more negative impact while financial blockchain applications show no significant mitigation effects. We further demonstrate how blockchain operational characteristics affect stock returns. Overall, blockchain technology provides effective risk buffer capabilities and can help manage shifting institutional fields—which the market views as building resilient capabilities resulting in competitive advantage to overcome known and unknown uncertainties and risks.

With the development of tourism, digital technology is increasingly being applied in the design of tourist routes. This study takes into account that tourists are experience-driven in tourism activities and hotel selections. In this study, the tourist trip design problem with hotel selection is formulated based on bi-objective optimization with total utility of the points of interest maximization and the average utility of the hotels maximization, and a three-step hybrid algorithm combined with discrete particle swarm optimization, an adaptive differential evolution with an optional external archive, and a local search is designed to identify the optimal route. To examine the performance of the designed algorithm, a numerical experiment was conducted. The results of Wilcoxon rank sum tests verified that the proposed algorithm performed distinctly better than extant approaches. Moreover, the results also indicate that the two main innovative mechanisms about initialization and hybrid evolution play a critical role in improving the algorithm's efficiency for the tourist trip design problem with hotel selection.

More robust and resilient health systems must safeguard populations and economies in search of a more sustainable future. Their complexity, alongside the crises they have been facing, postulates the need for appropriate performance assessment mechanisms to improve the access and quality of their services. Given the existence of multiple criteria regarding these dimensions, this work proposes a collaborative multi-criteria decision-aiding framework to categorise the performance of public hospitals in terms of the access and quality of their services according to a five-star rating. Besides, although the literature is keen on assuming criteria independence, the fact is that there are several potential benefits of considering interactions between criteria. However, there is not an unmitigated, nor consensual, understanding of this concept yet, nor applications to the health sector. Therefore, our framework adopts an extension of the Electre Tri-nC method to incorporate interactions between criteria. In the end, from a sample of 26 hospitals in Portugal between 2018 and 2021, the results show that the majority are assigned to “Poor” (“Two stars”) and “Average” (“Three stars”) categories over the considered 4-year period - something that increased over time. Ultimately, assuming criteria dependence reveals more realistic results in comparison to a criteria independence assumption. Furthermore, the number of doctors and nurses is found not to influence the performance categories attributed to each hospital. In the end, several robustness analyses prove the framework’s credibility.

In this paper, we introduce the notion of multicriteria-based directional distance determination for estimating the efficiency of decision units under different (and sometimes conflicting) interests and objectives. Different objectives are interpreted as different dimensions of a production system. Multidimensional industries, such as the Higher Education system, usually have clear preference structures and value judgments over teaching, research and service (outreach), leading to ranking differences for countries and organizations based on policy perspectives, production compensations, substitutions or complementation. We develop this framework based on the non-compensatory reasoning of outranking multicriteria methods to determine the most practical directional vector for the output expansion of Higher Education and support a multidimensional efficiency measurement. The numerical application using panel data illustrates policy-directed European countries based on the three dimensions of higher education. A similar understanding can be extended for determining directional efficiency for service units based on other multicriteria methods and heuristics.

With a growing emphasis on corporate social responsibility (SR), companies are increasingly engaging in SR disclosure activities to establish transparency and enhance consumer confidence in the SR efforts of their supply chains. In this study, we introduce a game-theoretic model that examines the dynamic interplay of SR investment and disclosure within a supply chain. In our model, the supplier decides the SR investment effort aimed at mitigating the product’s negative social impact, while the retailer can disclose the supplier’s investment effort to promote SR transparency. Two disclosure formats are considered: mandatory disclosure, where the retailer commits to disclosing the supplier’s social impact before any evaluations, and strategic disclosure, where the retailer makes the disclosure decision after evaluating the supplier’s effort. We investigate the implications of disclosure formats on investment effort, chain members’ profits, and consumer surplus. Our analysis surprisingly shows that strategic disclosure can yield a higher investment effort. We further demonstrate that (i) the retailer may voluntarily choose mandatory format even without regulations, (ii) mandatory format may decrease consumer surplus, (iii) mandatory format will never benefit the monopolistic supplier, and (iv) only strategic format can serve all parties’ interests simultaneously. These findings provide insights for the design of policies that promote SR behaviors and serve different stakeholders’ interests.

Online Freight Platforms (OFPs) are emerging and developing rapidly based on the Internet of Things and big data technologies. They provide professional logistics services by aggregating the scattered transportation capabilities of individual truck drivers. OFPs have become increasingly popular compared to Traditional Logistics Service Providers (TLSPs), and OFPs have integrated more than 20% of the truck drivers in China. In this paper, we study a manufacturer’s production, distribution, and transportation of a product in bulk to a downstream firm via two transportation channels: TLSP and OFP. The two channels offer different operational modes, service levels, and contract prices. We develop a game-theoretical model to analyze the strategic interactions among the manufacturer, transportation service provider, and downstream firm. Our study highlights the implications for the manufacturer’s logistics outsourcing strategy with OFP and TLSP operations. We find that when an OFP is available, differences in transportation service levels and prices significantly affect the equilibrium decisions of the manufacturer and TLSP. Surprisingly, the emergence of OFPs does not necessarily threaten TLSP operations. This study provides guidance to manufacturers about their selection of shipping outsourcing strategies and new guidance for TLSP operations considering the emergence of OFPs.

Unpredictable disruptions arising in railway operations can cause significant inconvenience for passengers, including missed connections or deviations from their travel plans. To address this issue, this paper proposes a service-oriented timetable rescheduling approach to assist dispatchers in managing major disruptions in a railway system that incorporates a seat reservation mechanism. The railway network is abstracted at a near-microscopic level, and the actual passenger service is explicitly modeled at the station platform. An integer linear programming (ILP) formulation is then constructed based on the space-time network, which considers train retiming, reordering, and local rerouting to minimize the adverse effects on passengers. A compact form of an incompatible set is formulated to describe the coupling connections of train headway and station capacity, to minimize weighted passengers' inconvenience. To overcome computational barriers and practical complexities, the paper proposes an Alternating Direction Method of Multipliers Heuristic, i.e., ADMM-H. The ADMM-H decomposes the original problem into a series of train-specific subproblems that are easy to solve. An importance-based heuristic sorting strategy is adopted while individually solving train-level path subproblems based on their duality and feasibility. The proposed methods were tested using numerical instances based on real-world data to verify their effectiveness and efficiency. The results showed that the service-oriented train rescheduling approach significantly improves the passenger experience during disruptions, with an improvement (up to 19.86 %) compared to traditional operation-oriented approaches. The ADMM-H algorithm can reduce the computational time (up to 8.41 %) and obtain a sub-optimal solution with a lower gap compared with the naive ADMM. The proposed approach and algorithm can be valuable for railway operators to mitigate the negative effects of disruptions and enhance passenger satisfaction.

We consider a real-world three-dimensional container loading problem in which the objective is to maximize the volume of the items packed into a single vehicle. While container loading problems have been extensively studied in the literature, our case study displays a set of item features (rotation, medium-sized dimensions, stackability, weak heterogeneity) that was not often considered together in previous research papers. In fact, we show that some of these features can be exploited in an innovative way to derive more effective exact algorithms. We first describe a compact integer programming model to solve the problem exactly together with a number of ad hoc reduction procedures and modeling tricks to enhance the empirical performance of the model. We then present a sequential approach where one generates item columns in a first stage and then solves a two-dimensional knapsack problem afterwards and show that each of the two components is $\mathrm{NP}$-hard. Thereafter, we introduce a set of exact algorithms based on Benders’ decomposition. We identify three ways to split the problem decisions (deciding if an item should be packed, in which column, in which $x$-coordinate, and in which $y$-coordinate) into the classical master-subproblem framework and we observe trough an extensive set of computational experiments on both real and randomly generated instances that not all decomposition methods are as competitive as the others. We conclude our work by showing how relevant extensions of the problem related to item fragility and customer visit order can be taken into account. Overall, this work aims at establishing a bridge between the theoretical field of two-dimensional packing problems and the more practical field of container loading problems.