Polynomial Regression and Response Surface Methodology: Theoretical Non-Linearity, Tutorial and Applications for Information Systems Research

  • Darshana Sedera Swinburne University of Technology
  • Maura Atapattu University of Queensland
Keywords: Quantitative analysis, Polynomial regression, Response surface methodology, Non-linearity

Abstract

Information systems (IS) studies regularly assume linearity of the variables and often disregard the potential non-linear theoretical interrelationships among the variables. The application of polynomial regression and response surface methodology can observe such non-linear theoretical assumptions among variables. This methodology enables to examine the extent to which two predictor variables relate to an outcome variable simultaneously. This paper utilizes the expectation confirmation theory as an example and provides a methodological commentary that illustrates a step-wise process for conducting a polynomial regression and response surface methodology.
Published
2019-09-23
How to Cite
Sedera, D., & Atapattu, M. (2019). Polynomial Regression and Response Surface Methodology: Theoretical Non-Linearity, Tutorial and Applications for Information Systems Research. Australasian Journal of Information Systems, 23. https://doi.org/10.3127/ajis.v23i0.1966
Section
Research Note