Polynomial Regression and Response Surface Methodology: Theoretical Non-Linearity, Tutorial and Applications for Information Systems Research
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...
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doaj-0d527903344d4b789c79ce01b26cf5af2021-08-02T09:01:23ZengAustralasian Association for Information SystemsAustralasian Journal of Information Systems1449-86181449-86182019-09-0123010.3127/ajis.v23i0.1966761Polynomial Regression and Response Surface Methodology: Theoretical Non-Linearity, Tutorial and Applications for Information Systems ResearchDarshana Sedera0Maura Atapattu1Swinburne University of TechnologyUniversity of QueenslandInformation 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.https://journal.acs.org.au/index.php/ajis/article/view/1966Quantitative analysisPolynomial regressionResponse surface methodologyNon-linearity |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Darshana Sedera Maura Atapattu |
spellingShingle |
Darshana Sedera Maura Atapattu Polynomial Regression and Response Surface Methodology: Theoretical Non-Linearity, Tutorial and Applications for Information Systems Research Australasian Journal of Information Systems Quantitative analysis Polynomial regression Response surface methodology Non-linearity |
author_facet |
Darshana Sedera Maura Atapattu |
author_sort |
Darshana Sedera |
title |
Polynomial Regression and Response Surface Methodology: Theoretical Non-Linearity, Tutorial and Applications for Information Systems Research |
title_short |
Polynomial Regression and Response Surface Methodology: Theoretical Non-Linearity, Tutorial and Applications for Information Systems Research |
title_full |
Polynomial Regression and Response Surface Methodology: Theoretical Non-Linearity, Tutorial and Applications for Information Systems Research |
title_fullStr |
Polynomial Regression and Response Surface Methodology: Theoretical Non-Linearity, Tutorial and Applications for Information Systems Research |
title_full_unstemmed |
Polynomial Regression and Response Surface Methodology: Theoretical Non-Linearity, Tutorial and Applications for Information Systems Research |
title_sort |
polynomial regression and response surface methodology: theoretical non-linearity, tutorial and applications for information systems research |
publisher |
Australasian Association for Information Systems |
series |
Australasian Journal of Information Systems |
issn |
1449-8618 1449-8618 |
publishDate |
2019-09-01 |
description |
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. |
topic |
Quantitative analysis Polynomial regression Response surface methodology Non-linearity |
url |
https://journal.acs.org.au/index.php/ajis/article/view/1966 |
work_keys_str_mv |
AT darshanasedera polynomialregressionandresponsesurfacemethodologytheoreticalnonlinearitytutorialandapplicationsforinformationsystemsresearch AT mauraatapattu polynomialregressionandresponsesurfacemethodologytheoreticalnonlinearitytutorialandapplicationsforinformationsystemsresearch |
_version_ |
1721236461326434304 |