An investigation of the factors influencing cost system functionality using decision trees, support vector machines and logistic regression

Purpose - The paper aims to identify and critically analyze the factors influencing cost system functionality (CSF) using several machine learning techniques including decision trees, support vector machines and logistic regression. Design/methodology/approach - The study used a self-administered survey method to collect the necessary data from companies conducting business in Turkey. Several prediction models are developed and tested; a series of sensitivity analyses is performed on the developed prediction models to assess the ranked importance of factors/variables. Findings - Certain factors/variables influence CSF much more than others. The findings of the study suggest that utilization of management accounting practices require a functional cost system, which is supported by a comprehensive cost data management process (i.e. acquisition, storage and utilization). Research limitations/implications - The underlying data were collected using a questionnaire survey; thus, it is subjective which reflects the perceptions of the respondents. Ideally, it is expected to reflect the objective of the practices of the firms. Second, the authors have measured CSF it on a “Yes” or “No” basis which does not allow survey respondents reply in between them; thus, it might have limited the choices of the respondents. Third, the Likert scales adopted in the measurement of the other constructs might be limiting the answers of the respondents. Practical implications - Information technology plays a very important role for the success of CSF practices. That is, successful implementation of a functional cost system relies heavily on a fully integrated information infrastructure capable of constantly feeding CSF with accurate, relevant and timely data. Originality/value - In addition to providing evidence regarding the factors underlying CSF based on a broad range of industries interesting finding, this study also illustrates the viability of machine learning methods as a research framework to critically analyze domain specific data.