The Role of Big Data Predictive Analytics and Radio Frequency Identification in the Pharmaceutical Industry

Abstract

Increasing supply chain performance (SCP) has always been a common phenomenon in the history of organizations. Recent technological advancements [including, radio frequency identification (RFID) in combination with big data predictive analytics (BDPA)] have been able to significantly leverage SCP. In this respect, there is an urgent need for acceptance of RFID technology as well as BDPA to be empirically investigated in order to better understand the enhancement of SCP. Therefore, this paper aims at establishing and empirically investigating the relationship between BDPA acceptance, RFID acceptance, and SCP, respectively. The population of this paper comprises the pharmaceutical logistics industry in China. The sample was obtained from employees through the use of a simple random sampling method. Data were collected through an adapted questionnaire. SmartPLS 3.0 software was used for structural equation modeling analysis. Partial least square-structural equation modeling (PLS–SEM), the value of path coefficient, was found to be 0.700. This showed a direct positive effect of BDPA acceptance on SCP. Moreover, RFID acceptance was also tested through the “variance accounted for” method of mediation. Results showed a partial mediation of 57% between BDPA and SCP. Theoretically, this paper fills the knowledge gap by finding relationships between BDPA and SCP via a mediation role of RFID technology. Practically, it would be beneficial for supply chain managers in the pharmaceutical industry to adopt and implement BDPA and RFID technology so as to enhance SCP.

Keywords

• Big data predictive analytics
• Internet of medical Things (IoMT)
• radio frequency identification
• pharmaceutical logistic
• supply chain management
• supply chain performance