Impact of HRM policies on quality assurance under requirement volatility

Purpose – Requirements of a project are found to change in various ways during the course of the same. Studies have investigated the effect of requirement volatility on different project parameters like effort, schedule, quality, etc. However, these studies have not looked into how different “patterns” of requirement volatility influence project quality; and which intervention strategies could be effective under the circumstances. This paper aims to address this issue. Design/methodology/approach – The “system dynamics” approach has been used for carrying out the research. Based on a recent finding, we implemented different resource management policies on a validated software process model on waterfall systems development life cycle. Subsequently, we examined the efficacies of these resource management policies on project quality under requirement volatility. Findings – Results indicate variations in quality metrics like error generation, error detection, and quality assurance effort across experimental scenarios as different patterns of requirement volatility and resource management policies impact the software project dynamics in different ways. Research limitations/implications – In absence of any imposed schedule penalty, the extent of variations in project parameters across the policy choices was not very significant. The results are also expected to differ depending upon the project development environment. Practical implications – Findings are expected to assist project managers in deciding on the workforce augmentation plan that would favorably satisfy both the organization's objectives as well as the users' quality requirements under requirement volatility. Originality/value – In present day context of shorter time to market and stringent quality requirements; meeting quality targets become difficult especially in scenarios where requirement volatility is a norm. This paper provides a dynamic view of the phenomenon of how quality gets affected; and explores the efficacy of different resource management strategies in improving quality under the experimental scenarios.