No abstract

Merchandise return policies (MRPs) have long been an important area of interest for operations and supply chain management researchers, who have identified some key advantages and disadvantages of offering comprehensive and convenient return policies. However, there is a lack of rigorous scholarship on one key dimension of MRPs—return time leniency. Understood as the amount of time that buyers have within which to return purchased items, return time leniency is often considered one of the leading indicators of a retailer's MRP friendliness. In the current study, we extend prior work on MRPs by introducing a signaling theory perspective to return time leniency. This allows us to develop a more nuanced interpretation of the underlying economics associated with extending the window of time during which returns are accepted. We do so by positioning our study in a retail context where such issues are highly important to consumers—online retail.

We propose that CEO dismissal can change the strategic decision-making of CEOs at competing firms. Competitor CEOs will experience an increase in job insecurity, which motivates them to refrain from strategic risk taking. We also identify two key boundary conditions that shape the influence of CEO dismissal on competitor CEOs' risk taking. We test our ideas on a sample of CEO dismissals among S&P 1500 firms using a novel synthetic control method approach to matching. We also test the underlying theoretical mechanism using a complementary experiment on top executives. Taken together, these studies advance CEO dismissal research by investigating the spillover effect of CEO dismissal on competitor CEOs' behaviors. Managerial Summary: The position of CEO is more volatile today than ever. When it comes to pulling the trigger on CEO dismissal, companies have increasingly twitchy fingers. Therefore, it seems important to ask: when a company fires their CEO, what happens at all the other companies in the industry? We suggest the CEOs at those companies will start worrying about their job. This fear affects their strategic decision-All authors contributed equally to this study.

Supply chain managers have yet to solve the conundrum of profitably distributing and selling to the poorest consumers. Most prevailing methods of addressing this problem take one of two contrasting approaches—that is, (1) price subsidization or (2) benefits/cash transfers. The former has been heavily studied in the literature with the consensus being that it is highly inefficient and prone to leaks. We investigate the viability of the latter by focusing on how branching out to reach the poorest customers impacts the performance of banks. Results indicate that the impacts of this approach are deleterious, thereby questioning its commercial scalability. Therefore, we argue that this approach may also have only limited potential in terms of being an effective, large‐scale solution to the problem of access for the poor. Instead, a third approach to achieve scalable Bottom of the Pyramid growth and development needs to be considered—cultivating partnerships through joint distribution.

Sarcopenia can cause various senile diseases and is a major factor associated with the quality of life in old age. To diagnose, assess, and monitor muscle loss in daily life, 10 sarcopenia and 10 normal subjects were selected using lean mass index and grip strength, and their gait signals obtained from inertial sensor-based gait devices were analyzed. Given that the inertial sensor can measure the acceleration and angular velocity, it is highly useful in the kinematic analysis of walking. This study detected spatial-temporal parameters used in clinical practice and descriptive statistical parameters for all seven gait phases for detailed analyses. To increase the accuracy of sarcopenia identification, we used Shapley Additive explanations to select important parameters that facilitated high classification accuracy. Support vector machines (SVM), random forest, and multilayer perceptron are classification methods that require traditional feature extraction, whereas deep learning methods use raw data as input to identify sarcopenia. As a result, the input that used the descriptive statistical parameters for the seven gait phases obtained higher accuracy. The knowledge-based gait parameter detection was more accurate in identifying sarcopenia than automatic feature selection using deep learning. The highest accuracy of 95% was achieved using an SVM model with 20 descriptive statistical parameters. Our results indicate that sarcopenia can be monitored with a wearable device in daily life.