Extent of Employee Turnover in Humanitarian Logistics: An Interpretive Structural Modelling Approach - Muhammad Khan; Arif Hussain; Sami Ullah; Hassan Khan; Junghan Bae (International Journal of Supply Chain Management, 2020)
Following a disaster, humanitarian logistics service providers (HLSP) noted that besides their challenging job they are also facing high employee turnover. The main purpose of this article is to extend a precise assessment platform and provide a theoretical basis for increasing the understanding of the turnover of staff in humanitarian logistics (HL). Based on the identified variables leading to turnover of employee, this paper analyzes these variables affecting turnover of an employee in HL, using interpretive structural modeling (ISM) approach to evolve a model of hierarchy and categorized the interrelationships among these variables. In line with research conducted previously, the study identified and updated 16imperativeemployee turnover variables out of 24 as a key performance evaluation of HL. These variables can be categorized into eight levels, which denote the driving power from higher to lower. The study findings indicate that not all variables to employee turnover in HL require the same level of concentration. Out of 16 variables, there is a group of eight variables that have high driving control and low reliance, these variables are of strategic importance and require maximum attention. Also, another group contains six variables they have a low driving power but high reliance, whereas the one variable is in the linkage category between lower and upper-level variables. This categorization will help relief agencies to distinguish between dependent and independent variables that are imperative for improving the issue of employee turnover in HL. This article is the first to discuss employee turnover using ISM in the context of HL. The developed framework herein provides a precise guideline for HL to enhance their performance, as well as to promote the efficient application of resources through employee retention. Key Words: Humanitarian logistics, Employee turnover, interpretive structural modeling