Generating Business Intelligence Through Social Media Analytics: Measuring Brand Personality with Consumer-, Employee-, and Firm-Generated Content

• 成果类型: Article
• 署名作者: Hu, Yuheng; Xu, Anbang; Hong, Yili; Gal, David; Sinha, Vibha; Akkiraju, Rama
• 署名单位: University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; International Business Machines (IBM); IBM USA; International Business Machines (IBM); IBM USA; Arizona State University; Arizona State University-Tempe; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; Northwestern University
• 刊物名称: JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
• ISSN/ISSBN: 0742-1222
• DOI: 10.1080/07421222.2019.1628908
• 发表日期: 2019
• 页码: 893-930
• 关键词: impact; self; user; BEHAVIOR; INFORMATION; selection; IDENTITY; QUALITY; common; CONSEQUENCES

摘要

Social media platforms provide an enormous public repository of textual data from which valuable information can be extracted. We show that firms can extract business intelligence from social media data bearing on an important business application, measuring brand personality. Specifically, we develop a text analytics framework that integrates different distinct sources of social media data generated by consumers, employees, and firms, to measure brand personality. Based on Elastic-Net regression analyses of a large corpus of social media data, including self-descriptions of 1,996,214 consumers who followed the sample of brands on social media, 312,400 employee reviews of the brands' firms, and 680,056 brand official tweets, we develop a brand personality model that achieves prediction accuracy as high as 0.78. Among key insights, we find that the profile of individuals who choose to associate with brands on social media is an important predictor of brand personality; this provides the first real-world evidence for a consumer identity-brand personality link. We also identify a link between an organization's internal corporate environment as perceived by employees and brand personality as judged by consumers. We further illuminate the practical implication of our predictive model by building a cloud-based information system that allows managers and analysts to explore and track personality of their own brands and their competitors' brands.