Social media big data: a diary study of ten pharmaceutical firms

Abstract

Purpose: The goal of the research was to demonstrate how firms can use social media big data, to make strategic business decisions, through the lens of Resource Based Theory (RBT) and Dynamic Capability Theory (DCT), that could lead to a sustained competitive advantage. In and of its own, big data, does not constitute a competitive advantage. It may hold value for the firm, but lacks rarity, inimitability, and is not substitutable (Braganza, et al. 2017; Mata, Fuerst and Barney, 1995; Delmonte, 2003). It is in the analysis of this data, through RBT and DCT, that will turn the information into useful business intelligence (Amit and Schoemaker, 1993; Barney, 1991; 1995; Marr, 2015; Gupta and George, 2016; Kurtmollaiev, et al., 2018). Most importantly, firms must constantly reconfigure their resources in line with the dynamic business environment to ensure superior performance (Teece, Pisano and Shuen, 1997; Helfat, et al., 2007; Teece, 2014; 2018). Method: In this study, a qualitative approach was used to examine the RBT (Value, Rarity, Inimitability and Non-Substitutable - VRIN Framework) and DCT, to describe and understand the relevant theories and to build upon the quantitative results. While a quantitative approach was used to analyse the social media sentiment as depicted by Social Mention metrics. A novel technique, Chernoff Faces, was used to analyse and visualize the data (de Vos, Strydom, Fouche and Delport, 2011). Results and Findings: The research results show that, while the 10 firms in the study all have a presence on social media, it is on selective platforms. The content that is posted, is on very specific topics (Narayan, 2017; Cornejo, 2018). The Chernoff Faces indicate that the firms' Social Mention metrics, over the 30 day period, was at low values. Since strength of social mention is depicted by the face line, the thin, long, generally sad looking faces implies that more than 70 percent of the firms' social media strength over the study period, was weak. Conclusion: The literature indicates that the true value of big data and big data analytics can only be realised if firms make sound business decisions and act upon it swiftly.