Role of bigdata analytics in improving drivers of omni-channel retailing for improving logistics experience

Purpose The objective of this research is to evaluate and rank the factors influencing omnichannel (OC) logistics, while also investigating the significant impact of big data analytics in improving these drivers of OC logistics. Design/methodology/approach Using exploratory sequential mixed method design, an in-person interview survey was conducted to identify and stratifies the enablers of OC retailing. These interviews were supplemented with a case study in an apparel firm to prioritise the enablers of OC logistics. Further, a survey was conducted to understand the role of big data analytics in improving drivers of OC logistics as well as the role of Individual capability and organisational capability in big data usage for omnichannel retailing. Findings Findings represent that information management is the most important driver followed by inventory management and network design for improving OC logistics. Further, significant relationship between big data analytics and drivers of omnichannel logistics has been reported. Practical implications This study identifies and classifies the drivers of OC retailing relating to their level of criticality in OC logistics which will assists practitioners to prioritise their tasks for the successful development of OC logistics. The study will also help practitioners to use BDA for developing the drivers of OC. Originality/value The study substantiates and adds to the BDA literature by emphasising the positive role of BDA in development of OC driver and highlighting the significant role of drivers of BDA in its usage.