Organizational researchers collect and generate text data. Since text data are abundant and likely to generate knowledge, they often reveal aspects of and therewith contribute to our understanding of organizations. As compared with more reductionistic quantitative approaches, text captures richer context and may reveal the nuances of individuals and organizations. Analyzing huge amounts of text from varied textual sources may eliminate bias, permit triangulation, and enhances the validity of the outcomes. Moreover, the analysis of text which come from myriad of sources are expected to generate knowledge about jobs and people (e.g., what people need to know to be able to perform their jobs) that are necessary to serve HR practices of recruitment, training, and development.
For this dissertation I developed models and/or analyzed large text data. The primary goal is to extract information and produce deeper insights about jobs and people. This dissertation addresses research questions relevant to organizational research using text analytics and furnishing new analytical strategies that can be useful for job analysis, career, and HR research in general. The insights have implications not only for making practical HR decisions but also for advancing knowledge in the HR field (e.g., understanding career path and physical job mobility).
In summary, our contributions are the following: create models that are of interest primarily to job analysis and career researchers, derive knowledge from the models that can be useful for theory validation and building, and demonstrate the practicability of the models to job recommendation and recruitment.