Abstract

ABSTRACT Context: response surface analysis (RSA) is an approach that allows examining the extent to which combinations of two predictive variables relate to one outcome variable. The method is particularly interesting in cases where (in)congruence between the two predictive variables is a central consideration of the study. Objective: the purpose of this article is to provide a tutorial on applying RSA. Method: the method’s conceptual background and an illustrative example are provided so that the reader can understand some of the basic principles of the technique. This tutorial’s target audience is researchers who use mathematical modeling but are not yet familiar with the method. Results: the technique has the potential for application in various research questions in the field of Administration. Conclusions: besides providing a tutorial on how to use the investigated technique, the study demonstrates its relevance in the analysis of congruence and incongruence between the scores.

Highlights

  • Response surface analysis (RSA) is a technique that can provide a nuanced view of relationships between combinations of two predictor variables and an outcome variable by graphing the results of polynomial regression analyses in a three-dimensional space (Edwards & Parry, 1993)

  • We propose that when the buyer dependence (BD) is greater than the supplier dependence (SD) or vice versa, the satisfaction relationship (SAT) will be less than when the two support variables are in agreement

  • The purpose of this tutorial article is to help researchers use, objectively, the response surface analysis (RSA) method. In addition to this specific application, the RSA methodology can address a range of other issues and challenges in the most diverse areas of study

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Summary

INTRODUCTION

Response surface analysis (RSA) is a technique that can provide a nuanced view of relationships between combinations of two predictor variables and an outcome variable by graphing the results of polynomial regression analyses in a three-dimensional space (Edwards & Parry, 1993). In addition to overcoming traditional approaches to measuring the effects between parties, calculate the algebraic difference between dependencies, the average or the sum of these measures, or use spline scores For this reason, RSA provides a powerful alternative approach to test congruence hypothesis. The slope of the LOC shows the different levels of the outcome variable (for example, satisfaction) for an actor whose levels of the two predictor variables (for example, buyer dependence and supplier dependence) are necessarily the same across the continuum from minimum scores to maximum scores in both predictors In this sense, the perfect congruence of two variables is not reflected in a single combination of corresponding X and Y values, but in all combinations for which X is equal to Y. Polynomial regression can provide information on combinations of variables that go far beyond the information provided by traditional regression

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