Authors

1. Nachiket Talwar

Affiliation

• Student, Department of Computer Science and Engineering, Vellore Institute of Technology, Vellore

Abstract

Abstract
Purpose
The purpose of this paper is to present a method of data analysis that can analyze both linear and non-linear associations between independent and dependent variables. The program is developed to help researchers examine the behaviors of Asian consumers to help firms understand the factors influencing their buying behavior.
Design/methodology/approach
The ANN program was developed using Python. It is an end-to-end program that runs all the statistical tests to confirm data suitability for ANN modeling and calculate the relative importance of each identified independent variable.
Findings
This study presents the program and the reasoning why ANN as a method of data analysis is useful. The program was developed as a part of an internship to help researchers analyze data. As a result, this program's value comes from the fact that it has been used for data analysis in many studies, some of which have already been published in top-tier refereed international journals.
Research limitations/implications
The scope of the study is limited to arguing the usefulness of ANN for behavioral researchers and presenting the program. Although studies using this program for data analysis have been published, no data was collected and analyzed in this study.
The objective of the study was to simply present this useful program that can help researchers to analyze non-linear and linear data which does not meet the multivariate requirements of (a)normality, (b) multicollinearity, (c) linearity, and (d) homoscedasticity.
Originality/value
This study makes an original contribution by presenting a program in a way that is easy to reproduce for researchers with different backgrounds to analyze the data. The objective of writing this program was to help researchers from Asia examine complex associations between the variables that impact consumers' decisions. However, the program can be used for analyzing associations in different contexts.

Keywords

Artificial neural network; dependent variable; independent variable; Python; structural equation modeling

References

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