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Three studies address whether people can experience collective guilt for present-day behavior that harms ingroup members in the future. In a correlational study, collective guilt mediated the relationship between perceived harm to future ingroup members on willingness to engage in behaviors that mitigate global warming. An experimental study extended these results by showing similar effects for actual behavior and pro-environmental attitudes. A final experiment extended the other studies by showing similar effects on political conservativism, particularly when global warming was seen as having only a minor impact. These studies suggest that shifting the emphasis from collective for past harm to collective guilt for future harm has important implications for research on collective emotions and pro-environmental behavior.

AbstractNegotiations between an end product manufacturer and a parts supplier often revolve around two main issues: the supplier's price and the length of time the manufacturer is contractually held to its order quantity, commonly termed the "commitment time frame." Because actual demand is unknown, the specification of the commitment time frame determines how the demand risk is shared among the members of the supply chain. Casual observation indicates that most manufacturers prefer to delay commitments as long as possible while suppliers prefer early commitments. In this paper, we investigate whether these goals are always in the firm's best interest. In particular, we find that the manufacturer may sometimes be better off with a contract that requires an early commitment to its order quantity, before the supplier commits resources and the supplier may sometimes be better off with a delayed commitment. We also find that the preferred commitment time frame depends upon which member of the supply chain has the power to set their exchange price. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2003

"The practices of revenue management and pricing analytics have transformed the transportation and hospitality industries, and are increasingly important in industries as diverse as retail, telecommunications, banking, health care and manufacturing. Segmentation, Revenue Management and Pricing Analytics guides students and professionals on how to identify and exploit revenue management and pricing opportunities in different business contexts. Bodea and Ferguson introduce concepts and quantitative methods for improving profit through capacity allocation and pricing. Whereas most marketing textbooks cover more traditional, qualitative methods for determining customer segments and prices, this book uses historical sales data with mathematical optimization to make those decisions. With hands-on practice and a fundamental understanding of some of the most common analytical models, readers will be able to make smarter business decisions and higher profits"--

Closed-loop supply chain activities such as remanufacturing, recycling, dismantling for spare parts, and reverse logistics have helped many companies tap into different revenue streams by finding secondary markets for their products, all while reducing their overall carbon footprint. This book discusses various closed-loop supply chain processes.

AbstractThe COVID‐19 pandemic has undoubtedly affected every corner of the world in 2020. It has also emphasized the importance of managing supply chain risk and developing an agile supply chain. Supply chain management instructors will likely want to introduce risk management concepts and strategies into their courses to raise their students' sensitivity to the impact of supply chain disruptions. Unfortunately there is currently a dearth of coverage of supply chain disruptions and risk management in the major supply chain management text books as well as in the broader library of supply chain management pedagogical research publications. To help to close this gap in the literature for instructors, we have developed a reading and set of related discussion questions and exercises that detail the causes of the shortage in toilet paper during the pandemic and short‐ and long‐term strategies to make the supply chain more resilient in the future. By focusing on a disruption students likely experienced first‐hand, they can develop a more nuanced understanding of the complexity and interconnectedness of supply chain functions in providing products and services to customers.

ABSTRACTFood banks are humanitarian aid organizations that collect, organize, and deliver food to nonprofit member agencies and also to individuals to help alleviate the society's hunger problem. The supply chain of food banks is characterized by private sector companies, individual donors and governmental agencies providing monetary support and food inventory on the supply side, and the member agencies such as food pantries, soup kitchens, shelters, and volunteers delivering support on the demand side. Within the purview of this supply chain, food banks strive to improve their performance, which is commonly measured in terms of the amount of food delivered to the communities in need. Food banks rely on managerial talent that is relatively more constrained than the private sector and also use a voluntary workforce for attaining their performance goals. Considering the unique yet pertinent role of human assets in food banks to manage their supply chain integration initiatives, we employ an intellectual capital framework to analyze the human, organizational, and social capital antecedents of supply chain integration in food banks. Specifically, we suggest that intellectual capital drives supply chain integration in humanitarian organizations. Moreover, we propose specific relationships between the dimensions of intellectual capital. A carefully crafted survey is used to inform our results. The results indicate that human capital significantly impacts social capital, which, in turn, drives all supply chain integration dimensions. We discuss the implications of our findings for managing intellectual capital in the not‐for‐profit sector and offer directions for future research.

"Pricing analytics uses historical sales data with mathematical optimization to set and update prices offered through various channels in order to maximize profit. With this outstanding contribution to this subject, you will learn just how to identify and exploit pricing opportunities in different business contexts. Each chapter looks at pricing from an economist's viewpoint beginning with the basic concept of pricing analytics and what type of data are needed to use this powerful science; the common assumptions regarding the customer population's willingness-to-pay are discussed along with the price-response functions that result from these assumptions; examples from several industries and organizations; dynamic pricing, with a special emphasis on the most common application--markdown pricing; the new field of customized pricing analytics, where a firm responds to a request-for-bids or request-for-proposals with a customized price response; and the relevant aspects of behavioral science to pricing. Additional examples include the asymmetry of joy/pain that customers feel in response to price decreases/increases"--Provided by publisher.

AbstractGovernments in many jurisdictions are taking measures to promote the use of electric vehicles. As part of this goal, it is crucial to provide a sufficient number of charging stations to alleviate drivers' anxieties associated with the range of the vehicle. The goal of this research is to help governments develop vehicle charging networks for public use via the application of multistage stochastic integer programming model that determines both the locations and capacities of charging facilities over finite planning horizons. The logit choice model is used to estimate drivers' choices of nearby charging stations. Moreover, we characterize the charging demand as a function of the charging station quantity to reflect the range anxiety of consumers. The objective of the model is to minimize the expected total cost of installing and operating the charging facilities. An approximation algorithm, a heuristic algorithm, and a branch‐and‐price algorithm are designed to solve the model. We conduct numerical experiments to test the efficiency of these algorithms. Importantly, each algorithm has advantages over the CPLEX MIP solver. Finally, the City of Oakville in Ontario, Canada, is used to demonstrate the effectiveness of this model.