A Vector Space Approach for Measuring Relationality and Multidimensionality of Meaning in Large Text Collections.

In this article, we develop a methodological approach for organizational research regarding the construction of multidimensional and relational similarity measures by using the vector space model in natural language processing (NLP). Our vector space approach draws on the well-established premise in organizational research that texts provide a window into social reality and allow measuring theory-based constructs (e.g., organizations' self-representations). Using a vector space approach allows capturing the multidimensionality of these theory-based constructs and computing relational similarities between organizational entities (e.g., organizations, their members, and subunits) in social spaces and with their environments, such as the organization itself, industries, or countries. Thus, our methodological approach contributes to the recent trend in organizational research to use the potential inherent in big (textual) data by using NLP. In an example, we provide guidance for organizational scholars by illustrating how they can ensure validity when applying our methodological contribution in concrete research practice. [ABSTRACT FROM AUTHOR]