Discussions
The results of the foregoing analyses have been quite revealing. It is
not surprising that the results of the empirical analysis agree largely
with the findings from the literature review process. While the use of
geometric mean to unify all performance parameter proxies showed no
significant difference between itself and the a prioriexpectations or the theses researchers’ pre-study perceptive conclusions
indicated in the analysed theses, using 10% level of significance
(β (30) = .278, t (30) =
.695, R2 = .0772, p > .10),
all the individual surrogate variables excepting ROE returned
significant figures for t at the 10% significant level.
Buttressing the t statistics findings with
F statistics, the geometric mean unified variable result
was also not significant both at the 5% and 10% levels of significance
(Adj. R2 = .0442, F (1,
28) = 2.426, p > .05), as against the test for the
individual performance parameter surrogates which returned overall
significant difference in relationship with the analysed theses
pre-study expectations (Adj. R2 = .0291,F (4, 25) = 22.598, p < .05).
In other words, what the results of the various analysis is telling us
is that the theses researchers’ pre-conceived conclusions were no
different from the results obtained using the geometric mean unified
dependent variables. Looking at the figures on table 3, it will be
interesting to note that while the geometric mean unified performance
variable maintained a positive relationship with the theses a
priori expectations, one out of the four surrogate variables (ROA)
showed strong negative relationship indicating that its individual
influence inhibits the positive pull of the other variables, thereby
distorting logic of joint conclusion. This trait was also exhibited on
table 2 (summary statistics) which showed all the figures of the unified
variable as positive but three of the individual surrogate variables
returned negative minimum figures.