Strategic Thought Transformation - The IIPM Think Tank
Strategic Thought Transformation - The IIPM Think Tank
Strategic Thought Transformation - The IIPM Think Tank
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E N T R Y S T R A T E G I E S<br />
try shows a discrete growth. Conclusively<br />
speaking, there is lack of continuity in the<br />
data collected. As a proof, example, the Indian<br />
Per Capita Income (in terms of Purchsing<br />
Power Parity) in 1999 was $2,149 (INR<br />
99,778). <strong>The</strong> same value when measured<br />
after four years (in 2003) stood at $2,892<br />
(INR 1,34,275). <strong>The</strong> change witnessed as<br />
a result of this time-interval amounts to<br />
INR 34,497 (at the current conversion rate<br />
of $1=INR 46.43). This demonstrates that<br />
if the standard time-interval during the<br />
study is taken as four years, then the data<br />
for per capita income collected is discrete<br />
with non-continuity prevalent. Now considering<br />
that the same data for the year<br />
2005 is $3,300 (INR 1,53,219), which essentially<br />
proves that even if the standard<br />
time-interval is altered to two years, the<br />
difference from the data collected in 2003<br />
would be INR 18,944. In simpler words,<br />
there inevitably exists discontinuity in per<br />
capita purchasing power when evaluated<br />
over regular intervals of time.<br />
Procedure<br />
<strong>The</strong> per capita income (PPP) and trip per<br />
capita of 44 countries were taken, with a<br />
requisite number of countries in all classified<br />
per capita income segments (low per<br />
capita income, medium per capita income<br />
and high per capita income). It is to be noted<br />
that for all analytical purposes, number<br />
of trips per capita was considered to be<br />
the dependent variable and the per capita<br />
income (in terms of PPP) was considered<br />
as the independent variable. This also<br />
runs well along the lines of our assumption<br />
(assumption 2), whereby we assume<br />
that with growth in per capita income, the<br />
dispensable income increases and hence<br />
the frequency of air travel (number of trips<br />
per capita) also increases. This would also<br />
sufficiently imply that a fall in per capita<br />
income would lower the number of trips<br />
per capita.<br />
Also to be taken in consideration is the<br />
fact that the number of trips per capita<br />
(which is the dependent variable) may depend<br />
upon a lot of other variables besides<br />
per capita income – like changing demographics,<br />
infrastructure and airport facilities,<br />
regional factors which may be legal,<br />
political, social or cultural – yet we will<br />
<strong>The</strong>re inevitably<br />
exists discontinuity<br />
in per capita<br />
purchasing power<br />
when evaluated<br />
over regular<br />
intervals of time<br />
assume that the economic factor has the<br />
single-largest dominant role to play in effecting<br />
the trip per capita and all other factors<br />
would hardly restrict such growth.<br />
<strong>The</strong> countries were chosen for the data<br />
list following a random sampling method<br />
among those for which the required data<br />
were available. <strong>The</strong> full list of countries and<br />
data involved is given in Table I (aside).<br />
From the graph, it can be easily understood<br />
that all attempts to paint a global<br />
demand scenario for the aviation industry,<br />
would yield a very haphazard pattern and<br />
therefore for conducting a REGRESSION<br />
ANALYSIS, we consider various trend lines<br />
that aid our process and therefore make<br />
the estimation model construction more<br />
accurate and precise.<br />
Linear trend line<br />
<strong>The</strong> linear trend line gives us an approximate<br />
equation of y=0.0665x and the R 2<br />
value for the trend line is just 0.3815,<br />
Hence we do not base our projections based<br />
on this simplistic model.<br />
Logarithmic trend line<br />
<strong>The</strong> logarithmic trend line is built with the<br />
equation y=1.0873ln(x)–1.7611 with an<br />
R 2 value of just 0.2922. Hence we also do<br />
not base our projections based on this simplistic<br />
model.<br />
Trinomial Trend line<br />
<strong>The</strong> trinomial trend line gives a curve formation<br />
with the equation of y=0.0001x 3 -<br />
0.0062x 2 +0.1563x+0.6525 and R 2 value<br />
of just 0.4469 which we discard as this<br />
trend line gives an R 2 value of just 0.5.<br />
Power Series<br />
<strong>The</strong> power series trend line is constructed<br />
with the equation: y=0.0007x 2.1239 with an<br />
R 2 value of 0.6383. <strong>The</strong>refore in comparison<br />
to all previous trend lines constructed,<br />
this is the most reliable as the R-squared<br />
value is greater than 0.5.<br />
Exponential Series trend line<br />
<strong>The</strong> exponential series trend line gives us<br />
the equation of y=0.0108e 0.1465x and an<br />
R 2 value of 0.6468 which is the highest<br />
so far. Hence we can easily conclude that<br />
this exponential series with the highest R 2<br />
value is thus the most reliable trend line<br />
that can be used for estimating the future<br />
demand generation in the sector. <strong>The</strong>refore,<br />
we take the exponential series trend<br />
line as the most reliable demand curve to<br />
estimate future demand.<br />
Dismissal of Linear, Logarithmic<br />
and Trinomial Trend lines<br />
Besides the low values of R 2 obtained in<br />
terms of the linear, logarithmic and the<br />
An <strong>IIPM</strong> Intelligence Unit Publication STRATEGIC INNOVATORS 15