Trust and Information Sharing in Supply Chains

Drawing on behavioral research, we construct a multi‐period model with which to examine the role of trust and other social characteristics in a supply chain. Specifically, we focus on trust building in the context of a salesperson who acts as a representative of a manufacturer and shares demand forecast information with a retailer. The actions of the salesperson affect both her immediate economic gain and her future credibility as determined by retailer's trust. Our analysis reveals that, in such environments, although salespersons of widely varying types (e.g., honest, self‐serving, benevolent, loyal) lie some extent about their forecast information, they tend to be trusted in long relationships, provided their forecasting accuracy is higher than that of the retailer. Furthermore, while the presence of a salesperson can improve the profits of both the retailer and manufacturer, there are cost structures under which the manufacturer is better off without a salesperson. Finally, we make the general observation that the appropriate salesperson compensation scheme depends on her social characteristics, and the specific observation that when the salesperson cares for the retailer, the linear compensation scheme commonly suggested in the literature as the optimal compensation scheme for the salesperson is no longer optimal.     

Analyzing factors contributing to COVID-19 mortality in the United States using artificial intelligence techniques

Having started since late 2019, COVID-19 has spread through far many nations around the globe. Not being known profoundly, the novel virus of the Coronaviruses family has already caused more than half a million deaths and put the lives of many more people in danger. Policymakers have implemented preventive measures to curb the outbreak of the virus, and health practitioners along with epidemiologists have pointed out many social and hygienic factors associated with the virus incidence and mortality. However, a clearer vision of how the various factors cited hitherto can affect total death in different communities is yet to be analyzed. This study has put this issue forward. Applying artificial intelligence techniques, the relationship between COVID-19 death toll and determinants mentioned as strongly influential in earlier studies was investigated. In the first stage, employing Best-Worst Method, the weight of the primer contributing factor, effectiveness of strategies, was estimated. Then, using an integrated Best-Worst Method–local linear neuro-fuzzy–adaptive neuro-fuzzy inference system approach, the relationship between COVID-19 mortality rate and all factors namely effectiveness of strategies, age pyramid, health system status, and community health status was elucidated more specifically.     

Investment Areas to Enhance Public Employee Resilience during the Coronavirus Disease 2019 (COVID-19): Evidence from Iran

Public Organization Review - This study aims to investigate the areas of investment that the human resources management of the Iranian public sector has invested in for improving employees'...