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The study of critical service incidents has been extensively investigated in service marketing, however, very little attention has been paid to the role that these incidents have in the building, development and maintenance of marketing relationships. This thesis sought to address the deficiency in knowledge on the subject by exploring the critical service incident from a relational exchange perspective, with the members and management of a health and fitness club providing the central focus of the buyer-seller relationship. This investigation adopted a pragmatic philosophy in methodological design, arguing that the research had to be conducted within real time business constraints. Action research was used as an umbrella strategy for this investigation which incorporated a multi-method research design that consisted of; participant observation, in-depth interviews, a management group discussion and a telephone survey to investigate the questions of each research cycle. Whilst existing service marketing literature implies that positive and negative critical service incidents result in the bi-polar outcomes of buyer loyalty or switching respectively, the findings of this research demonstrated that the critical service incident may not be that critical to the development and maintenance of the relationship. The thesis makes an original contribution to knowledge by developing existing relationship marketing theory and making explicit the influence of the critical service incident on the buyer-seller relationship that is implied in service marketing literature. This thesis proposes that the critical service incident, produces buyer inferences, effects and consequences that result in a tendency toward loyalty or switching and that when the duration of the buyer-seller relationship is considered, these evaluative buyer judgements produce a phenomenon termed 'relationship elasticity'.

Evolutionary search algorithms (ESAs from now on) are iterative problem solvers developed with inspiration from neo-Darwinian survival of the fittest genes. This thesis looks at the core issues surrounding ESAs and is a step towards building a rational methodology for their effective use. Currently there is no such method of best practice rather the whole process of designing and using ESAs is seen as more of a black art than a tried and tested engineering tool. Consequently, many non-practitioners are sceptical of the worth of ESAs as a useful tool at all. Therefore the first task of the thesis is to layout the reasons, from computational theory, why ESAs can be a potentially powerful tool. In this context the theory of NP-completeness is introduced to ground the discussions throughout the thesis. Then a simple framework for describing ESAs is developed to form another cornerstone of these later discussions. From here there are two main themes of the thesis. The first theme is that the No Free Lunch result requires us to take a problem centric, as opposed to algorithm centric, perspective on ESA research. The second major theme is the argument that whole algorithms and traditional computer science problem classes are the wrong level of granularity for the focus of our research. Instead we should be researching empirical questions of search bias at the granularity of the components of search algorithms. Furthermore, we should be finding empirical evidence to demonstrate that our granularity of analytic class is such that one analytic class maps onto one search bias class. We will see that this can mean that we have to sub-divide our classic computer science problems classes into smaller sub-classes. The hope is that we can find analytic distinctions that will sub-divide the instances along lines that match the divisions between the various empirically discoverable search bias classes. The intention is to develop our knowledge until we get one analytic class to map into one empirical class. If we have strong empirical evidence to suggest that this has been achieved then we have good grounds on which to confidently use this knowledge to predict the effective search biases required for new problem instances. In the last two chapters of the thesis we demonstrated these ideas on various instances of the euclidean TSP problem class