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This study investigates whether a firm that has experienced an environmental accident (EA) is less likely to conduct a product recall. If true, it would indicate that EAs tempt firms to hide operational problems that need to be revealed. The logic is that both events are operational failures that damage a firm's reputation and share price. Following an EA, a firm may avoid a discretionary product recall to avoid providing additional evidence of operational incapability and social irresponsibility and thereby triggering amplified reputational and market penalties.

The dataset is compiled from several public and private sources and includes 4,355 product recalls, 153 EAs and 120 firms from the industries that often recall products, including automotive, pharma, medical device, food and consumer products. The study timeframe is 2002–2013. Empirical models are evaluated using hazard modeling.

Results show that EAs reduce the probability of a product recall by 32%, on average. Effect sizes are larger when accidents are more frequent or more severe and when recalls are less severe. Through post hoc analyses, the study finds support for the proposed mechanism that firms avoid recalls due to reputational concerns, provides evidence that EAs can have a lengthy impact on recall behavior, and shows that firms are more likely to avoid recalls managed by the CPSC and NHTSA than recalls managed by the FDA.

Prior studies in operations management (OM) have not examined the impact of one negative event on another. This study finds that EAs tempt firms to hide operational problems that need to be revealed. While recalling fewer defective products is of concern to consumers and regulators, should EAs influence a broader set of discretionary operational decisions, such as closing/relocating a production facility, outsourcing production or conducting a layoff, study implications increase significantly.

The total defence (TD) concept constitutes a joint endeavour between the military forces and civil defence structures within a TD state. Logistics is essential for such joint collaboration to work; however, the mismatch between military and civil defence logistics structures poses challenges for such joint collaboration. The purpose of this paper is to identify logistics concept areas within the TD framework that allow for military and civil defence collaborations from a logistics operations perspective.

Pattern-matching analysis is used to compare patterns found in the investigated case with those prescribed from the literature and predicted to occur. The study seeks to identify logistics concepts within TD from the literature and from the events describing the Swedish response to the Covid-19 pandemic. Pattern matching thus allows for the reconciliation of logistics concepts from the literature to descriptions of how the response was handled, albeit under a TD framework.

Findings show quite distinct foci between the theoretical and observational realms in terms of logistics applications. While the theoretical realm identifies four main logistics concepts, the observational realm identifies five logistics conceptual themes. This goes on to show an incongruence between the military and civil parts of the TD.

This study provides basis for further research into the applications and management of logistics activity within TD and emergency response.

Logistics applications within TD have not, until now, received much attention in the literature. Given this knowledge gap, this study is of original value.

The theory of managerial discretion and the direct insights it provides in the understanding of the varying impact strategic and operational actions have on organizational change and business fortunes is an area of research potential underexplored in the UK. This study aims to establish whether the measurement of managerial discretion is constant between the two similar societal corporate frameworks of the UK and the USA listed markets.

The extant managerial discretion ranking model, established in the USA, is empirically assessed for its validity and effectiveness across a sample of high- and low-discretion companies from the FTSE 350.

Using accounting measures, a clear and significant difference is established between UK high and low managerial discretion entities. The results prove to be significant in enabling the differential comparative analysis of the institutional characteristics of corporates.

To the best of the authors’ knowledge, no study of this nature has been conducted previously in the UK context. While the original model developed in the USA is now several decades old, the UK results reflect similar industry rankings as found originally in the USA, subject to some differences considered to be a result of the changing nature of global business since the 1990s. This study opens a new seam of novel research, which has the potential to uncover, at a granular level, the differential mores and character of management ethics, styles and practices in such issues as organizational change, corporate culture, governance and social responsibility.

The literature suggests that increasing the intensity of compensation incentives for corporate venture capital (CVC) managers can contribute to successful exits of direct CVCs. This study explores the impact of compensation incentives on the successful exits of indirect CVCs under different geographical distances between parent companies and indirect CVC managers.

The authors observed the compensation terms of CVC managers through investment announcements made by listed companies and used a probit regression model to test the hypotheses from a sample of 241 investment events with indirect CVCs in China.

The results show that if parent companies are geographically close to the managers of indirect CVCs, increasing the intensity of compensation incentives for managers will help the successful exit of indirect CVCs. However, if parent companies are not geographically close to indirect CVC managers, increasing the intensity of compensation incentives for managers will not promote the successful exit of indirect CVCs.

This study contributes significantly to the CVC literature. First, it sharpens our understanding of the differences in operational mechanisms between direct and indirect CVCs. Second, we find that the threshold returns of indirect CVC managers are non-negligible compensation incentives. Finally, the empirical evidence supports that in indirect CVC investments, the geographical distance between parent companies and managers is concerning because it affects whether compensation incentives contribute to the successful exit of indirect CVCs.

The primary aim of this study is to provide actionable guidance for augmenting profitability in photovoltaic power plant investments within Iran’s solar energy sector. By emphasizing prudent capital management and strategic investment decisions, our research seeks to assist emerging businesses in attaining sustained success in this domain.

This study presents a comprehensive approach to refined decision-making in Iran’s solar energy sector. Our methodology integrates the best-worst method, ArcGIS software for site selection, and the TOPSIS method for decision-making, aiming to enhance precision and reliability.

Our research has identified ten promising regions suitable for photovoltaic power plant installations in Iran. Leveraging the TOPSIS method, we have made optimal selections among these alternatives. Furthermore, our exhaustive cost analysis, incorporating factors like land prices, system maintenance, revenue estimation, and various financial scenarios, has yielded insights into project cost-effectiveness.

By filling a notable gap in the literature regarding optimal site selection and investment strategies for photovoltaic power plants in Iran, our research contributes to the sustainable development of solar energy infrastructure. Through a thorough literature review and the development of a novel methodology, we offer valuable guidance for businesses and investors seeking success in Iran’s solar energy sector. Our study represents a significant advancement by introducing a novel methodology that integrates the best-worst method, ArcGIS software, and the TOPSIS method for site selection and investment analysis. These findings furnish valuable guidance for businesses seeking success in the solar energy sector, thereby contributing to the sustainable development of renewable energy infrastructure in Iran and beyond.

This study aims to investigate the feasibility of proposed microgrid (MG) that comprises photovoltaic, wind turbines, battery energy storage and diesel generator to supply a residential building in Grindelwald which is chosen as the test location.

Three operational configurations were used to run the proposed MG. In the first configuration, the electric energy can be vended and procured utterly between the main-grid and MG. In the second configuration, the energy trade was performed within 15 kWh as the maximum allowable limit of energy to purchase and sell. In the third configuration, the system performance in the stand-alone operation mode was investigated. A whale optimization technique is used to determine the optimal size of MG in all proposed configurations. The cost of energy (COE) and other measures are used to evaluate the system performance.

The obtained results revealed that the first configuration is the most beneficial with COE of 0.253$/KWh and reliable 100%. Furthermore, the whale optimization algorithm is sufficiently feasible as compared to other techniques to apply in the applications of MG.

The value of the proposed research is to investigate to what extend the integration between MG and main-grid is beneficial economically and technically. As opposed to previous research studies that have focused predominantly only on the optimal size of MG.

Home is a place/system/product that becomes increasingly occupied with various tasks used to be performed in workplaces. However, the knowledge of the relationship between residential physical environments and occupant experience is limited, especially when considering the effect of indoor plants (IPs) and climate zones. To address the gap, this study conducted a questionnaire survey in three cities across different regions in China.

Based on User Experience and Customer Satisfaction Index theory, following the research paradigm, a total of 627 valid samples were collected and analyzed in a stepwise statistical analysis, including descriptive statistics, reliability and validity test, correlation test and region comparison, then the model of PROCESS was adopted to examine the hypotheses that are given based on the former studies.

The results showed that residential physical environments have a significant effect on occupant satisfaction (OS) in all regions, as well as OS on occupant performance. However, regional differences were found that OS is a complete mediator in the Middle region, while a partial mediator in the North and South. A slight moderating effect of IPs was also found in the region of South. Nevertheless, both the number of plants and plant types have a significant moderating effect on the mechanism.

Besides combining two theories and confirming the mechanism in the residential physical environment, it is also the first study to consider the moderating effects of IPs and climate zones, providing potential empirical support for not only design and management stages but also facing global challenges of working at home and climate changes.

To achieve the Sustainable Development Goals (SDGs) in the era of Logistics 4.0, machine learning (ML) techniques and simulations have emerged as highly optimized tools. This study examines the operational dynamics of a supply chain (SC) in Vietnam as a case study utilizing an ML simulation approach.

A robust fuel consumption estimation model is constructed by leveraging multiple linear regression (MLR) and artificial neural network (ANN). Subsequently, the proposed model is seamlessly integrated into a cutting-edge SC simulation framework.

This paper provides valuable insights and actionable recommendations, empowering SC practitioners to optimize operational efficiencies and fostering an avenue for further scholarly investigations and advancements in this field.

This study introduces a novel approach assessing sustainable SC performance by utilizing both traditional regression and ML models to estimate transportation costs, which are then inputted into the discrete event simulation (DES) model.

The purpose of this study is to formulate an algorithm designed to discern the optimal routes for efficient municipal solid waste (MSW) collection.

The research method is simulation. The proposed algorithm combines heuristics derived from the constructive genetic algorithm (CGA) and tabu search (TS). The algorithm is applied in a municipality located at Southern Brazil, with 40,000 inhabitants, circa.

The implementation achieved a remarkable 25.44% reduction in daily mileage of the vehicles, resulting in savings of 150.80 km/month and 1,809.60 km/year. Additionally, it reduced greenhouse gas emissions (including fossil CO₂, CH₄, N₂O, total CO₂e and biogenic CO₂) by an average of 26.15%. Moreover, it saved 39 min of daily working time.

Further research should thoroughly analyze the feasibility of decision-making regarding planning, scheduling and scaling municipal services using digital technology.

The municipality now has a tool to improve public management, mainly related with municipal solid waste. The municipality reduced the cost of public management of municipal solid waste, redirecting funds to other priorities, such as public health and education.

The study integrates MSW collection service with an online platform based on Google MapsTM. The advantages of employing geographical information systems are agility, low cost, adaptation to changes and accuracy.