Purpose Previous studies have predominantly examined the influence of supplier relationships on corporate green innovation from a static perspective, treating such relationships as stable over time, and lacked in-depth discussions on the internal mechanisms. This paper aims to focus on the relations from a dynamic perspective. Design/methodology/approach This study analyzes the effect of dynamic supplier changes on green innovation, using empirical data of Chinese A-share listed enterprises and ordinary least squares regression to test the research hypotheses. Findings This study draws the following conclusions: first, supplier changes can jeopardize the green innovation of enterprises. Second, operational risk mediates the relations between supplier change and green innovation. Third, market competition moderates the relations between supplier change and operational risk positively. In addition, further analysis of heterogeneous effects suggests that the effects of supplier changes on green innovation are more significant among non-state-owned firms, small firms and firms with dispersed shareholdings. Originality/value This study expands the current literature and offers guidance for firms to enhance their capacity for green innovation.

To enhance the operational performance of supply chains under the trends of globalization and customization, integrated multi-factory production and distribution has recently attracted increasing attention. This paper presents a novel integrated multi-factory production scheduling and vehicle routing problem. In this problem, a set of customer orders is first assigned to several distributed factories for production, each of which is arranged as a hybrid flow shop (HFS). Owing to the technical or physical aspects, factory eligibility is considered in the production stage, where some orders can only be processed in a subset of factories. The finished products are then delivered by capacitated vehicles, subject to customer time windows. As a combination of the distributed HFS scheduling problem and the vehicle routing problem, three types of decisions have to be made, namely factory allocation, job scheduling, and vehicle assignment and routing. Considering the NP-hardness of the studied problem, a hybrid algorithm that integrates a distribution estimation algorithm (EDA) with an adaptive large neighborhood search (ALNS) is developed to generate solutions. To improve the local search capability of this algorithm, Q-Learning is employed to dynamically determine the destroy-and-repair operators of ALNS. Computational results on both small-sized and large-sized test problems indicate the superiority of the proposed algorithm.

The door-to-door service industry has undergone rapid development, driven by the growing demand for convenient and personalized services in urban areas. Faced with diverse customer requirements, service providers need to integrate the decisions of workforce scheduling and service routing to deliver high-quality services to customers. This paper focuses on a novel workforce scheduling and routing problem with sequence constraints and alternative locations (WSRP-SCAL). In this problem, customers may request multi-stage indoor services in a specific order, each of which is performed by different employees. To offer customers greater flexibility and convenience, they are allowed to provide multiple alternative locations with different time windows to receive services. As a new variant of the workforce scheduling and routing problem (WSRP), WSRP-SCAL is NP-hard and difficult to solve. We present a hybrid algorithm (HVNS-SA) to minimize the total cost of workforce and routing. This algorithm integrates a variable neighborhood search (VNS) with a simulated annealing (SA) algorithm to generate workforce schedules and routing paths. Specifically, several problem-specific neighborhoods for shaking and local search are developed with consideration of sequence constraints and alternative locations. To further improve the solution quality, two strategies for handling infeasible solutions, namely the penalizing strategy and the recovery strategy, are designed. Computation results on both small-sized and large-sized WSRP-SCALs indicate the effectiveness and efficiency of the proposed HVNS-SA. In addition, a sensitivity analysis of key problem parameters is conducted to provide valuable managerial insights for door-to-door service providers.

Purpose-To establish an efficient distribution network, pharmaceutical retail companies have to address two major challenges. First, warehouse location decisions need to be integrated with daily transportation routes. Second, the multi-echelon structure of the pharmaceutical network and the large number of retail pharmacies further increase the difficulty of network design. This paper aims to address a two-echelon distribution location problem (2E-DLP) and provide pharmaceutical operations managers with an efficient algorithm for distribution network planning. Design/methodology/approach-A two-stage clustering-based algorithm (TSCBA) is proposed to solve the 2E-DLP. It first uses a weighted K-means clustering algorithm to generate potential locations for central procurement warehouses and regional distribution centers. Then, a greedy algorithm is applied to estimate the transportation cost between each regional distribution center and its retail pharmacies, which is used to compute the total cost of different location results. The TSCBA is highly efficient in evaluating location results, especially when handling numerous retail pharmacies. Findings-The performance of the proposed TSCBA is evaluated by different scale 2E-DLPs, which are generated based on the operational data from a leading pharmaceutical retail company in China. The computation results indicate that TSCBA is capable of striking a good balance between solution quality and computation cost. Originality/value-This paper proposes a novel TSCBA for solving the 2E-DLP in pharmaceutical retail logistics. The strong performance of TSCBA can be attributed to the effective hybridization of two fast algorithms for the warehouse location problem and the vehicle routing problem. The TSCBA has significant potential to optimize two-echelon and large-scale distribution networks for pharmaceutical retail companies.

IntroductionGreen restaurants minimize negative impacts on the environment through the implementation of green practices. Analyzing how urban and rural customers differ in their green consumption behaviors is necessary to get the whole society to support green eateries. This paper uses the theory of planned behavior with two extended predictors green innovation and anticipated regret, to explore whether there are differences in the factors that influence urban and rural residents' intention to patronize green restaurants.MethodsA questionnaire survey was conducted with 301 urban and 320 rural residents. The analysis was conducted using partial least squares structural equation modeling (PLS-SEM) with SmartPLS 3.0 and multigroup analysis is employed.ResultsThe findings reveal signi?cant differences between the impacts of green innovation on the attitudes, the impacts of green innovation on behavioral intention, the impacts of subjective norms on behavioral intention, and the indirect effects of green innovation on the behavioral intention via attitudes for rural and urban residents. However, the results do not support any differences between the impacts of the attitudes, perceived behavioral control, anticipated regret on the behavioral intention for rural and urban residents.DiscussionThis study has enriched the relevant literature on green restaurant patronage from the perspective of comparing rural and urban residents, and can be used as a reference for the managers in implementing targeted strategies tailored to different residents for promoting green restaurant.

The utilization of drones to conduct inspections on industrial electricity facilities, including large-sized wind turbines and power transmission towers, has recently received significant attention, mainly due to its potential to enhance inspection efficiency and save maintenance costs. Motivated by the advantages of drones for facility inspection, we present a novel station-based drone inspection problem (SDIP) for large-scale facilities. The objective of SDIP is to determine the locations of multiple homogeneous automatic battery swap stations (ABSSs) equipped with drones, assign facility inspection tasks to the ABSSs with operation duration constraints, and design drone inspection routes with battery capacity constraints, such that minimize the sum of fixed ABSS costs and drone travel costs. The SDIP can be regarded as a variant of the location-routing problem, which is NP-hard and difficult to solve optimally. To obtain the optimal solution of SDIP efficiently, we firstly formulate this problem into an arc based formulation and a route based formulation, and then develop a logic-based Benders decomposition (LBBD) algorithm to solve it. The SDIP is decomposed into a master problem (MP) and a set of subproblems (SPs). The MP is solved by a branch-and-cut (BC) procedure. Once a feasible integer solution is found, the linear relaxation of SPs are solved by a stabilized column generation to generate Benders cuts. If the cost of all the SPs’ optimal LP solutions plus the cost of the MP’s solution is less that current best cost, the SPs are exactly solved by a Branch-and-Price (BP) algorithm to generate the logic cuts. The numerical results on five scales of randomly generated instances validate the effectiveness of the LBBD algorithm. Specifically, the LBBD can solve all small- and middle-sized instances, and seven out of ten large-sized instances in 1000 s. Furthermore, we conduct a sensitivity analysis by varying the attributes of ABSSs and drones, and provide valuable managerial insights for large-scale facility inspection.

This paper empirically examines the impact of industrial robot application on corporate risk-taking value using data from Chinese A-share listed manufacturing companies, measured by the Sharpe ratio. The findings indicate that industrial robots significantly enhance corporate risk-taking value through information effects, innovation, and production optimisation. Heterogeneity tests reveal that firms with higher capital density, non-state ownership, lower financing constraints, and more government subsidies experience a more substantial increase in their risk-taking value. These insights contribute to understanding the evolution of enterprise risk decisions in industrial intelligence and provide theoretical references for policies promoting industrial development.

Purpose Digital technologies over time are becoming increasingly pervasive and relatively affordable, finding a large diffusion in Small and Medium Enterprises (SMEs) also for internationalization purposes. However, less is known about the specific mechanisms by which this can be achieved. Specifically, we focus on how SMEs can face the international environment, leveraging digital technologies and thanks to their intellectual capital (IC).Design/methodology/approach We analyze the relationship between digital technologies and the internationalization of SMEs, exploring the mediating role of IC in its three dimensions: human, relational and innovation capital, and assessing the possible moderating effects posed by international institutional conditions, specifically the Sino-US trade frictions. The relationships are tested using a sample of companies listed on China’s A-share Growth Enterprise Market (GEM) from 2010 to 2021.Findings Digital technologies help to internationalize SMEs. However, this positive relationship is affected (mediated) by the presence of an already consolidated IC. In addition, the institutional conditions of the international market, such as the Sino-US trade friction, moderate the components of IC differently. Specifically, the overall mediating effect of human and relational capital is boosted, while this does not happen for innovation capital.Originality/value First, this study contributes to the literature on organizational resilience, especially digital resilience, confirming its validity in the context of internationalization and, in particular, those processes adopted by SMEs. Second, we clarify the mechanisms through which digital technologies exert their impact on the process of internationalization and in particular the prominent necessity of having IC. Third, our conclusions enrich the understanding of how IC components react to turbulence in international markets.

Sluggish market demand can deteriorate the financial situation of a company and affect a shareholder’s decision to adopt environmental, social, and governance criteria (ESG). According to the socioemotional wealth theory, family firms place significant emphasis on sustainable development and long-term orientation, but this emphasis can be either internally or externally driven according to the type of involvement chosen by the owning family. Therefore, this study uses listed family firms to explore the relationship between different types of family involvement (i.e., family ownership and control, the influence of market competition, and the institutionalisation level of the environment in which a firm decides to pursue ESG criteria). We performed a multivariate regression analysis on a sample of 1,151 Chinese companies to test these relationships and found that both family ownership and control are positively related to ESG scores. Market competition negatively moderates the influence of both family ownership and control on the adoption of ESG criteria. Moreover, the influence of family control is negatively moderated by the institutional environment. Thus, types of family involvement seem to be relevant for the firm’s engagement with ESG criteria.

Nowadays, to promote ecotourism, governments have introduced a series of incentive policies. This study considers the impacts of government subsidy on ecotourism development and builds a three-party evolutionary game model involving the government, tourism enterprises and downstream consumers. The game strategies and behavior evolution of each party have been studied. Three conditional evolutionarily stable strategy points are obtained. The authors also investigate the final evolutionary results, influencing factors and optimization paths of various parties. Some interesting conclusions are drawn. First, in the introduction stage of ecotourism, government subsidies are effective at boosting ecotourism. However, when the market reaches stabilization, enterprises and consumers might not choose ecotourism even with government subsidies. Second, during the evolutionary process, the government subsidies probability exhibits two trends of an initial increase followed by a subsequent fall to zero, or ongoing subsidies. Finally, the initial state of the government, enterprises and consumers has a greater impact on the stability outcomes. When enterprises' and consumers' initial preferences for ecotourism grow, the government's willingness to subsidize diminishes significantly. Meanwhile, subsidies for consumers work better than those for enterprises.

This paper investigates an integrated optimization problem concerning berth allocation, quay crane assignment, and truck deployment in container terminals, with a specific focus on incorporating quay crane maintenance into the integrated models. A nonlinear integer programming model is proposed, and then a number of equivalent or relaxed models are developed to ease the model. To solve the model on large-scale instances, a SWO-GA is developed to provide a solution. Finally, a set of test instances are randomly generated to access the applicability of the proposed models and the efficiency of the algorithm. The results show that the SWO-GA can obtain a good solution with a small gap within a much shorter computation time than that of CPLEX. Quay crane maintenance significantly affects the operation plans and increases costs. The maintenance time step and the number of berths required for quay crane have a proportional impact on the total cost. It is also found that quay crane maintenance is of great concern for large-sized container terminals. The model and algorithm proposed in this paper provide important insights for operation managers.

This paper investigates the relationship between rural tourism experience and tourists' post-experience green consumption intention. This study is conducted with 345 respondents who have been to a rural destination for tourism purposes within the last five years. Results, employing Stimulus-Organism-Response (S-O-R) model, show that the dimensions of rural tourism experience (i.e. education, esthetic, entertainment, and escapism) positively affect memorable rural-based tourism experiences which also have a positive and significant influence on connectedness to nature. Connectedness to nature and environmental awareness both have statistically significant influences on tourists’ green consumption intentions later in life. The results indicate that better rural tourism experience can increase motivation for green consumption. Meanwhile, the results demonstrated the importance of memorable rural-based tourism experiences, connectedness to nature, and environmental awareness, which have been found to play full mediating roles in the lasting relation between rural tourism experience and green consumption.

In China's state-owned listed companies, there exists the type I agency problem primarily caused by owners' absence and insiders' control, as well as the type II agency problem of the infringement on the interests of small and medium-sized shareholders by the largest shareholders. Our research examines the relationship between directors appointed by non-state shareholders and the CEO turnover-performance sensitivity, so as to clarify whether directors appointed by non-state shareholders are more inclined to monitor the type I agency problem or the type II agency problem. Using the sample of state-owned listed companies from 2006 to 2016, we find that directors appointed by non-state shareholders are more likely to monitor the type II agency problem, as demonstrated by significantly reducing the CEO turnover-performance sensitivity. Our research also finds that directors appointed by non-state shareholders play more important role in reducing the CEO turnover-performance sensitivity when the company has a high degree of separation of ownership and control, operates in the non-regulated industry, and has a large number of following security analysts. Besides, we perform propensity score matching, instrumental variable regressions, placebo test and several robustness checks to address possible endogeneity concerns and measurement errors.

As an important part of the safe and stable operation of the power grid production emergency repair project, the investment scale level has been continuously improved. However, the current project budget preparation and calculation provisions used in the pre-preparation (settlement) calculation of e

Despite the continuous acceleration of global industrial informatization process, the manufacturing industry is still facing many problems, such as the lack of data in the machining process, the low utilization rate of energy and so on. This paper proposes an integrated framework based on Internet of things (IoT) and machine learning to realize the monitoring and collection of original real-time data, data association and achieve the trade-off optimization among energy consumption, processing time and surface roughness for the milling process in manufacturing system. This framework is composed of four functional modules, i.e. IoT-based processing data acquisition, milling experiment design, performance index prediction based on machine learning and performance index multi-objective optimization. The optimization results exhibit obvious advantages in energy consumption saving, processing time reduction and surface roughness improvement for the milling process.

Purpose From the perspective of the institution and internationalization speed, the article discusses the internal mechanism of cross-border e-commerce selection mode, as well as the moderating role of social networks as the intangible resource, and expand the theoretical system of corporate internationalization. Design/methodology/approach Based on the empirical data of 456 multinational e-commerce companies in five first-tier cities in China from 2016 to 2019, our research explores the selection mode of cross-border e-commerce. Findings The results show that (1) the institutional distance of the host country leads to the exit from cross-border e-commerce platforms in the international expansion of enterprises. (2) The difference in internationalization speed online and offline has become a mediated mechanism for the exit of cross-border e-commerce platforms due to the institutional distance of the host country. (3) The diversity and scale of offline social networks can weaken the impact of differences in internationalization speed on the exit from cross-border e-commerce platforms. (4) The resistance of companies expanding to countries with a weak institutional environment is greater than that experienced when expanding to countries with a strong one. Originality/value This study shows, for the first time, how to select expansion mode for cross-border e-commerce. And the paper also centers on the research of the impact of “social network”, a kind of intangible resource, on cross-border e-commerce platform adoption.

Although the current sampling inspection and quarantine efficiency, there are still prevention and control of security risks. Therefore, the existing process is reorganized and optimized, and a comprehensive quarantine strategy is proposed from a theoretical point of view. The results show safety an

Based on fully absorbing and learning from the relevant research results at home and abroad, this paper focuses on four aspects: the transfer of polluting industries in the central and western regions and residents' health problems. Firstly, the current pattern and development trend of regional pollution transfer in China; secondly, the economic effect of the transfer of polluting industries on the western undertaking areas; finally, from the micro and macro perspectives, the paper investigates the impact of the transfer of polluting industries on the individual health and health expenditure of residents in western China. The results show that the gap between industrial environmental and economic efficiency in the western region narrowed and gradually stabilised from 2008 to 2017. However, the gap between industrial environmental efficiency and economic efficiency in the western region widened from 2017 to 2020. The impact of environmental pollution on industrial environmental efficiency in the western provinces and regions has regional differences. Secondly, the present situation and trend of pollution control in western China are macroscopically investigated by comparing the output of general industrial solid waste and the investment in pollution control in different regions. Finally, this paper reveals the impact of the transfer of polluting industries on the health of residents in western China. Based on the characteristics of household category, age, and income of interviewees, the income growth effect of the transfer of polluting industries is less than the health loss caused by the transfer of pollution. It can be found that the transfer of polluting industries has a significant impact on the environment, economy, and the health of residents in western China.

To improve surgical services and hospital performance, collaborative operating room planning and scheduling across a network of hospitals has recently emerged as a new challenge in both healthcare industry and academic community. The paper considers a novel distributed operating room scheduling problem (DORSP), in which elective patients planned on a given day are scheduled for surgeries in the distributed operating rooms of collaborative hospitals. Since hospitals are different in terms of specialization and medical expertise, some complicated surgeries in this problem can only be performed in a subset of collaborative hospitals. Based on the similarities between healthcare delivery systems and production systems, DORSP is modelled as a distributed two-stage no-wait hybrid flow shop scheduling problem with factory eligibility. To deal with the NP-hardness of DORSP, an adaptivelearning-based genetic algorithm (ALBGA) is proposed to generate collaborative surgery schedules. In addition to traditional genetic operators, ALBGA also applies an adaptive learning operator to enhance the search ability by mimicking human learning behaviours. Computational results on both small-sized and large-sized test problems show that ALBGA is competitive among the compared algorithms.(c) 2022 Elsevier B.V. All rights reserved.