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

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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Business Systems Research Journal de Gruyter

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

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Publisher
de Gruyter
Copyright
Copyright © 2016 by the
ISSN
1847-9375
eISSN
1847-9375
DOI
10.1515/bsrj-2016-0010
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Business Systems Research Journal – de Gruyter

Published: Sep 1, 2016

References

  • Measuring organizational growth : Issues consequences and guidelines of management No
    Weinzimmer
  • Institutional effects on the evolution of the size distribution of firms No
    Henrekson
  • Growth intentions and actual growth : The impact of entrepreneurial choice of enterprising Culture No
    Kolvereid
  • A paradigm of entrepreneurship : Entrepreneurial management management No
    Stevenson
  • Predicting an applicant status using principal component discriminant and logistic regression analysis Mathematics And Statistics Invention No
    Suleiman
  • A quantitative content analysis of the characteristics of rapid - growth firms and their founders of business venturing No
    Barringer
  • The conceptualisation of growth are employment and turnover interchangeable criteria ? of entrepreneurship No pp
    Janssen
  • Are we comparing apples with apples or apples with oranges ? Appropriateness of knowledge accumulation across growth studies Theory and Practice No
    Shepherd
  • Applied multivariate data analysis : volume II : Categorical and Multivariate Methods Springer Media
    Jobson
  • Research on Distinguish the Accounting Information Distortion Based on the Principle Components - logistic Regression Model of Security and Its Applications No
    Li
  • Simple means to improve the interpretability of regression coefficients in Ecology and Evolution No
    Schielzeth
  • Studies of discriminant analysis and logistic regression model application in credit risk for China s listed companies and Engineering No
    Zhu
  • Study on financial predicament forecast of listed company in China of Kunming University
    Sheng
  • A multidimensional model of venture growth of management No
    Baum
  • Empirical Comparison among Different Predicting Models of Financial Failure for China s Listed Corporations of Shanxi Finance and Economics University
    Kehong
  • Predicting rapid - growth SMEs through a reversal of credit - scoring principles of Entrepreneurship and Small Business No
    Sampagnaro