Title:
Measuring Interaction:
An
Empirical Comparison of Three OLS Regression Models
Abstract: The capacity to correctly assess
the
existence of interaction is a high-value modelling capability among
researchers
of information systems (IS), especially those focussing on behavioural
paradigm
studies. Interaction is a notable aspect for the major theoretical
frameworks
of the IS field, particularly the adoption theories. Allowing for
crossover
effects in the Theory of Planned Behaviour resulted in improvements in
model
prediction (Taylor & Todd, 1995b). This study presents the trimmed
model,
which does not permit crossover effect relations among variables. In
complex
models, as mentioned by Pedhazur (1997), one variable may affect
another
variable indirectly through multiple paths. According to him, it stands
to
reason that indirect effects, through certain paths, may be more
meaningful
and/or stronger than others. The findings of this quantitative study
lead
one to conclude that crossover effect models are more capable of
showing
the interaction among models’ variables, as well as explaining the
highest
percentage of variation for a single dependent variable, in comparison
to
the full and trimmed models.
Authors: Ali Hussein
Saleh
Zolait, Ainin Sulaiman and Sharifah Faridah Syed Alwi