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Sentiment analysis is a text analysis method that is developed for systematically detecting, identifying, or extracting the emotional intent of words to infer if the text expresses a positive or negative tone. Although this novel method has opened an exciting new avenue for organizational research – mainly due to the abundantly available text data in organizations and the well-developed sentiment analysis techniques, it has also posed a serious challenge to many organizational researchers. This chapter aims to introduce the sentiment analysis method in the text mining area to the organizational research community. In this chapter, the authors first briefly discuss the central role of sentiment in organizational research and then introduce the traditional and modern approaches to sentiment analysis. The authors further delineate research paradigms for text analysis research, advocating the iterative research paradigm (cf., inductive and deductive research paradigms) that is more suitable for text mining research, and also introduce the analytical procedures for sentiment analysis with three stages – discovery, measurement, and inference. More importantly, the authors highlight both the dictionary-based and machine learning (ML) approaches in the measurement stage, with special coverage on deep learning and word embedding techniques as the latest breakthroughs in sentiment and text analyses. Lastly, the authors provide two illustrative examples to demonstrate the applications of sentiment analysis in organizational research. It is the authors’ hope that this chapter – by providing these practical guidelines – will help facilitate more applications of this novel method in organizational research in the future.

Increased emphasis on offering quality education underscores the need for developing a rigorous process for assessing academic programs in higher education. In this chapter, we develop a practical and rigorous framework for comprehensive assessment of academic programs. This framework generates in-depth communication between the academic departments and the university administration. It provides a useful tool for advancing the university mission, setting priorities, allocating resources, and identifying future areas of potential growth. This data-driven framework covers a wide range of qualitative and quantitative variables. To ensure a smooth and efficient implementation of the assessment process we present the critical stages in the development of a successful program assessment framework − from determining the assessment criteria, establishing the organizational climate, appointing the assessment committee, preparing program self-studies, to collecting and analyzing data. We present real examples from the author’s home institution to illustrate and support the reader’s understanding of the framework.

The purpose of this chapter is to review the accumulated research on a strategically important intangible asset – organizational reputation – and articulate promising research pathways forward. To do so, I first provide definitional clarity by comparing reputation to the related constructs of status, celebrity, legitimacy, and social approval and highlight the codifiable, cumulative, and beneficial nature of reputation. I then discuss three developments in reputation literature: (1) conceptualizing reputation as a multidimensional, rather than generalized, construct; (2) theorizing about reputational malleability rather than its path dependence; and (3) focusing on the costs of a high reputation in addition to its benefits. Based on these developments and the increasing role of social media in affecting and reflecting stakeholder perceptions, I discuss three pathways for future reputation research. Specifically, I focus on the decrease in credibility of powerful intermediaries and increase in stakeholder empowerment, conceptualization of reputation as a flow rather than a stock, and the role of strategic reticence. My hope is that this chapter will stimulate conceptual and empirical work on the role of reputation in the complex and dynamic era of social media.