Theorising Fuzzy Set Analysis a Complementary Approach to Net-effect Models

Ernest F. Mabonesho


Net-effect models assume that independent variables have a standalone impact on depended variables! As such the focus of net effect models is to examine the relationship between independent variables (causals) and dependent variables (outcome). I argue that this is not always true, independent variables may synergistically work together to bring impacts on a dependent variable, this allows researchers to examine if the independent variables are necessary or sufficient for an outcome of interest (dependent variable) to occur. This paper adopted a descriptive approach, I reviewed the literature on set-theoretic approach to understand how fuzzy set analysis can be viewed as a complementary approach to net-effect models in accounting and finance research. I note that Fuzzy set analysis has qualities that allow researchers to examine the necessity and sufficiency of independent variables to impacting dependent variables, this allows research to complement relationship studies with necessity and sufficiency studies. In addition, I note that the fuzzy set analysis allows researchers to identify core and supporting conditions for influencing an outcome of interest, this can complement examination of variables which have significant impact in the outcome of interest. In these contexts, I conclude that fuzzy set analysis complements examination of relationships and correlations between independent and dependent variables through examination of necessary and sufficient condition for an outcome of interest. This paper acknowledges that, although the proposed approach may lead to improved quality of the findings, the approach may suffer from subjectivity problems, especially when establishing the three benchmarks for scaling the original variables to fuzzy sets. It is suggested that substantial knowledge of the variables is highly required when determining the three benchmarks.

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