Extracting Value from Social Big Data: Empirical Studies on Online Customer Reviews and Managerial Responses

The advance of information technology has significantly digitalized our economy and activities and brought people into a big data era. The flourishing of social media has enabled massive-scale user-generated information sharing, which makes social big data available. In the context of e-commerce, consumers face a higher level of uncertainty and greater risk when purchasing online. As a result, many users utilize online customer review (OCR), as a novel Information Systems (IS) artifact, to alleviate their perceived uncertainty and the risks that hamper their online purchasing decisions. OCRs can influence consumer attitude and business performance, which drives companies to proactively intervene in the OCRs through the use of the managerial response (MR) function. An enormous amount of social big data in the form of OCRs and MRs has been accumulated online so far and presents both opportunities and challenges to researchers and stakeholders to use them as a rich source of business insights. The objective of this dissertation is to offer new knowledge on the value of social big data in the form of OCRs and MRs for different stakeholders in the context of the tourism industry from three perspectives: the consumer perspective, the company perspective, and the industry perspective. Such new knowledge is derived from reflections on four previous papers that I had authored and co-authored. From the angle of consumers, Paper 1 synthesizes literature on the helpfulness of OCRs and provides an integrated understanding of the determinants of OCR helpfulness through a systematic literature review. Paper 2 analyzes big data of OCRs in light of the attribution theory to investigate the impact of review content structures on OCR helpfulness and to demonstrate the important moderating effects of the reviewer reputation and review sentiments. Paper 3 focuses on the impacts of the MR function on company performance, specifically utilizing Kano's theory of attractive quality to investigate the effect of dissipating benefits of IS service availability. This paper shows that whereas companies offering MRs gain constant advantages over those not employing the MR function, the ability of the MR function to improve business performance dissipates over time among the companies adopting it. Paper 4 quantifies the detrimental effect of air pollution on the revisit behaviors of foreign tourists by analyzing a large volume of OCRs, which introduced a novel approach to examining collective consumer behavior using social big data from the industry perspective. This dissertation contributes to the scholarly discussion of social big data in the form of OCRs and MRs and concretely demonstrates the value of social big data to various stakeholders. In addition, this work can benefit IS researchers and stakeholders striving to exploit phenomena connected to IS artifacts and consumer behavior in the big data era.