This dissertation investigates the existence and the impact of fairness in the context of
dynamically complex supply chains. Fairness can be defined as impartial and just treatment or
behavior without favoritism or discrimination. It means giving each person what they deserve.
Within economic research it is often modeled as a social preference known as inequity aversion
which states that fair-minded individuals dislike inequitable monetary outcomes. Several models
of inequity aversion have been developed and used to study and explain human behavior in
economic environments. These models however are limited in terms of their applicability to
economic environments with multiple decision-makers interacting repeatedly with each other over
an extended period of time. The first part of this dissertation identifies the most commonly
used models of inequity aversion in the extant literature in order to overcome their
limitations with regard to addressing dynamics. The extended models are then used in the second
part for a computer-based simulation study which investigates the effects of inequity-averse
decision-makers in the context of the Beer Distribution Game. The simulation results are used
to develop several hypotheses that are tested in an experimental study presented in the third
part. It is concluded that inequity aversion plays an important role in the experience of a
supply chain decision-maker even when there is only one human actor in the supply chain with
others being replaced by a computer algorithm. Furthermore contrary to the established
literature it is not the factory (i.e. the furthest upstream) stage that suffers the most in
the supply chain experiencing progressive order amplification. If the supply chain includes
fair-minded decision makers it is the distributor stage that suffers the most due to perceived
supply chain unfairness (being a result of its limited freedom to act and to influence supply
chain performance).
