An Integrated Information Systems Framework for Enhancing Reverse Logistics Performance in Supply Chains

Abstract

The main objective of the current study is to establish a well-rounded Information System (IS) integrated framework which can enhance reverse logistics (RL) performance in supply chains. Reverse logistics is a vital domain in supply chains and of late has been gaining a lot of researcher s and practitioner s attention. The organization s emphasis has gradually shifted from the forward supply chain to the reverse chain because of its ability to generate competitive benefits. Although information and communication technologies supposedly have a positive effect on performance, literature has reported a mixed-bag effect. Hence, it becomes vital to study the role of information technology (specifically information systems) in enhancing RL performance by establishing a well-rounded framework. With RL being affected by other factors as well such as formalised RL processes, government regulations, frequency of product returns, strategic management focus and resource commitment to RL, it becomes important to analyze their effects in the reverse supply chain context as well. This study, through an extant review of supply chain literature, identified a set of IS and non-IS constructs which lead to an increase in RL performance. After identifying the constructs, possible relationships have been established between the variables and an IS-integrated conceptual framework has been proposed along with the corresponding hypotheses. A questionnaire was developed using the existing scales from the supply chain literature and was circulated to respondents in industries where reverse logistic operations are prevalent. The sampling technique used was a combination of convenience and snowball sampling and the prospective respondents were top / middle management executives in the supply chain / logistics / operations / purchasing / IT / IS domain. The survey instrument was pilot tested in two phases to establish reliability and content validity.