Dealing with Interpretability Issues in Predicting Firm Growth: Factor Analysis Approach

Abstract

Background: Alongside the theoretical progress made in understanding the factors that influence firm growth, many methodological challenges are yet to be overcome. Authors point to the notion of interpretability of growth prediction models as an important prerequisite for further advancement of the field as well as enhancement of models’ practical values. Objectives: The objective of this study is to demonstrate the application of factor analysis for the purpose of increasing overall interpretability of the logistic regression model. The comprehensive nature of the growth phenomenon implies propensity of input data to be mutually correlated. In such situations, growth prediction models can demonstrate adequate predictability and accuracy, but still lack the clarity and theoretical soundness in their structure. Methods/Approach: The paper juxtaposes two prediction models: the first one is built using solely the logistic regression procedure, while the second one includes factor analysis prior to development of a logistic regression model. Results: Factor analysis enables researchers to mitigate inconsistencies and misalignments with a theoretical background in growth prediction models. Conclusions: Incorporating factor analysis as a step preceding the building of a regression model allows researchers to lessen model interpretability issues and create a model that is easier to understand, explain and apply in real-life business situations.

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