Abstract
The surge of interest in big social data has led to growing demand for social media analytics (SMA). Having robust SMA can help firms create value and achieve competitive advantages. However, most firms do not always know how to embrace big social data to establish a path to value. This study addresses this key question to deepen our understanding of how different types of SMA can be applied to create value. Specifically, the findings show the significant uses of opinion mining or sentiment analysis, topic modeling, engagement analysis, predictive analysis, social network analysis, and trend analysis. Finally, the study provides directions for the challenges and opportunities of SMA to maximize value.Article Preview
Top2. Methods
In this study, a systematic literature review was conducted. Social media analytics research still being at its infancy, this methodology is appropriate for this exploratory study. We used an approach similar to the one used by Chai, Liu, & Ngai (2013) and Ngai, Moon, Riggins, & Yi (2008). The approach consists of developing a classification framework to organize all relevant articles identified through the literature review. Consistent with prior studies using a similar approach (Samuel Fosso Wamba, Akter, Edwards, Chopin, & Gnanzou, 2015; Ngai & Wat, 2002), this study uses peer-reviewed journal articles and seminal conference proceedings. Our classification framework has 6 dimensions through which a firm can create and/or co-create value via social media analytics: opinion mining or sentiment analysis, topic modeling, engagement analysis, predictive analysis, social network analysis and trend analysis. All these dimensions are discussed below. Table 1 shows a summary of social media analytics tools used by firms and the values across critical business functions.