Learning to Be an Entrepreneur: Evidence from a Technology Enhanced Learning Tool - 2nd International Workshop on Evidence-Based Technology Enhanced Learning - page 68

whether it is enough to face the loss). In both cases the assets and the equity base are
reduced by the loss. When equity base is enough to face the loss, the firm survives.
In this case the software skips the “decrease debt” input field, and it activates the
“decrease equity” field. When the loss is higher than the equity base, the decisions
on debt and equity reduction are both skipped and the prompt jumps to the bailout
section before going to the next period production choice.

Implementing the Evidence Based Learning. The forecasting service as well as
demand trends are built perturbing a regular benchmark pattern with random noises.
The standardized percentage deviation of perturbations follow a probability distribu-
tion chosen by the researcher so that both the accuracy of forecasts and the volatility
of demand can be exogenously tuned. Standard deviations of demand and forecast
perturbations are crucial in determining the degree of riskiness in the virtual corpo-
rate environment. By changing these parameters, the instructor can make users to
experience situations ranging from easily manageable to highly turbulent. The user
can learn how to modify the strategy s/he uses to improve her/his performances by
repeating the exercise with unchanged settings. Furthermore, a valuable educative
task can be achieved by comparing the best strategies found for different operational
contexts.
Some evidence on the learning process implemented by using the software are
reported in the following section.

3 Evidence

We report hereafter some results of exercises we have performed. The first exercise
is characterized by a very reliable forecasting service: the value supplied is always
equal to the level of demand which will be received by the firm. At first glance,
the exercise seems to be trivial because the experimental subjects know in advance
the level of demand and consequently they adapt the production capacity precisely.
This apparently trivial exercise has a hidden mechanism: the leverage effect. As dis-
cussed above, in the proposed setting debt is cheaper (1% interest rate) than equity
(the entrepreneur must pay 5% to shareholders). Equity is a buffer against losses,
but with perfect forecast one cannot suffer losses. Taking all this in mind, the best
performing strategy is a 100% debt liability structure. In this context, experimental
subjects prove to be fast in learning to use the supplied forecast. However, hetero-
geneity in learning the leverage effect is present. In the exercises we have performed,
the experimental subjects were allowed to repeat the exercise as many times as they
wanted with the aim of improving their score. 2
The results obtained from our activity concerning the perfect forecast context are
reported in tables 1 and 2 which reports the final scores of the subjects.

2 For a survey of the literature on the learning to forecast “ability” of economic agents and in
order to gain more insights on this topic see [3], [4], [1].