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This paper studies whether social media personal branding (PB) improves a job candidate's labor market performance in the context of executive employment and compensation. We focus on executives employed by Standard & Poor's 500 constituent companies from 2010 to 2013 and evaluate their PB on social media by analyzing their Twitter accounts. To disentangle the effect of PB from that of personality traits, we exploit a (positive) shock to the effectiveness of PB caused by a series of technology upgrades by Twitter. Estimations from a two-sided matching model suggest that social media PB benefits executive candidates in job markets. This paper contributes to the literature by initiating the study of the emerging phenomenon of social media PB and testing its effect on job market performance. [ABSTRACT FROM AUTHOR]

This research methods article proposes a visual data analytics framework to enhance social media research using deep learning models. Drawing on the literature of information systems and marketing, complemented with data-driven methods, we propose a number of visual and textual content features including complexity, similarity, and consistency measures that can play important roles in the persuasiveness of social media content. We then employ state-of-the-art machine learning approaches such as deep learning and text mining to operationalize these new content features in a scalable and systematic manner. For the newly developed features, we validate them against human coders on Amazon Mechanical Turk. Furthermore, we conduct two case studies with a large social media dataset from Tumblr to show the effectiveness of the proposed content features. The first case study demonstrates that both theoretically motivated and data-driven features significantly improve the model’s power to predict the popularity of a post, and the second one highlights the relationships between content features and consumer evaluations of the corresponding posts. The proposed research framework illustrates how deep learning methods can enhance the analysis of unstructured visual and textual data for social media research. [ABSTRACT FROM AUTHOR]

Popular knowledge management platforms such as community-based question answering sites (CQAs) and electronic networks of practice (ENPs) rely on accurate quality assessment of user-contributed content to ensure effective knowledge creation and exchange. However, quality assessment is subjective by nature. Based on the politeness theory, we hypothesize that answers written more politely are more likely to be perceived as high quality answers by the question asker due to the low face threat. We first test our hypotheses through a random coefficient logit model with data obtained from Stack Exchange, a popular CQA platform. We then conduct a randomized experiment where we exogenously manipulate the politeness level of otherwise similar answers. Our analyses, based on both the Stack Exchange dataset and the randomized experiment, lend strong support to the existence of a politeness bias, which affects question askers’ subjective evaluation of answer quality. This study contributes to the literature in knowledge management, cognitive bias, and behavioral issues in information systems. [ABSTRACT FROM AUTHOR]

The article presents a study regarding identity management and online reputation trading. Topics include the importance of auditing in separating different types of agents, concerns regarding whether reputations are reliable when they are purchased rather than built originally, and the implications for establishing reliable online environments and promoting effective online interactions.

In this article, we propose a new data-analytic approach to measure firms’ dyadic business proximity. Specifically, our method analyzes the unstructured texts that describe firms’ businesses using the statistical learning technique of topic modeling, and constructs a novel business proximity measure based on the output. When compared with existent methods, our approach is scalable for large datasets and provides finer granularity on quantifying firms’ positions in the spaces of product, market, and technology. We then validate our business proximity measure in the context of industry intelligence and show the measure’s effectiveness in an empirical application of analyzing mergers and acquisitions in the U.S. high technology industry. Based on the research, we also build a cloud-based information system to facilitate competitive intelligence on the high technology industry. [ABSTRACT FROM AUTHOR]