A Proposal for a Standard With Innovation Management System

Ioannis Akritidis and Alexandros Kakouris
Where 0 ≤ a , β ≤ 1 constants which define Kolb’s learning style. Thus, the term Lu represents a
“myopic” view of the market evidence due to the learning style.
A description of the four learning styles of Kolb and their match with the learning environment of
entrepreneurial courses can be found in Kakouris (2009). In brief, diverging style learners are
imaginative and emotional and tend to see concrete situations from many points of view; assimilating
style learners organize information into logical forms and are more interested in theorising than in
applying; converging style learners tend to find solutions to problems and to cope with technical tasks;
while accommodating style learners tend to try different experiments to solve a problem and they
involve themselves in new experiences.
An example for different style information can be the following. Consider an entrepreneur who runs a
business in the clothing sector. He/she may choose to observe data and facts: (a) from the clothing
market in his country or (b) from another sector in his country (e.g. technology market). Parameters
(a) facilitate convergent thinking (i.e. problem solving) whilst parameters (b) facilitate the diverging
one (i.e. examine alternative views). An entrepreneur with convergent learning style is expected to
underestimate parameters (b) over (a) whilst another one with divergent style is expected to do the
opposite. In this case, if market signals rise in the convergent domain, the former entrepreneur will
derive the promising results earlier.
Including the learning style in the Minniti and Bygrave model, the equation with the probability term
becomes:
( Lu Y p )
p = p + α −
t+
1 t t t t t
(3)
t 1 t t with δ the rate at which the step size falls off. p is the vector that describes
the entrepreneur’s confidence levels according to his point of view that depends not only on his/her
experiences but from his/her learning style too. The final form of Equation (3) becomes:
δ
where α = / ( S + Y )
( p ) α ( p ) .
p + = p + α f + ϕ
(4)
t 1 t t t t t t
The weighted average expected payoff, f ( p )
f ( p)
= Σ j L(
j)
ψ ( j)
[ u(
j)
− p]
p(
j)
. (5)
α , is now dependent on the vector L:
and the random vector ϕ t , which represents noise, is defined as:
t
( p ) = Lu − Y p − f ( p )
ϕ (6)
t
t
t
t
t
t
t
Using the iterative method of Equations (1) to (6), we simulate the evolution of entrepreneurial
learning from experience for different learning styles in the next section.
3. Simulation results
Let us consider the example of Figure 1. We suppose that an entrepreneur examines 80 different
options which can be attributed to the four learning style features of Kolb (e.g. 20 options for each
learning style as shown in Figure 1). Hence, options and respective market observations 1–20
correspond to the diverging learning style, 20–40 to the assimilative one, 40–60 to the converging one
and 60–80 to the accommodative one.
The initial self-confidence levels (i.e. the distribution of probability amongst alternative options) of the
entrepreneur are shown with the solid curve. For the present example, we chose normal distributions
of confidence levels as the simplest ones. It is shown that the entrepreneur promotes option 10 with a
confidence level of ~8% which is a divergent thinking parameter. On the other hand, market evidence
shown by the dashed curve maximises for option 50 (~8%) which is related to convergent thinking.
Running the evolutionary model we aim to find when the entrepreneur will realise and promote his 50 th
3
t