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Customer perceived benefits and loyalty programme effectiveness in the financial services industryFourie, Sonja January 2018 (has links)
The effectiveness of loyalty programmes continues to be questioned, especially as their cost to firms increase together with their adoption rate across industries worldwide. Given the divergent industry specific findings predominantly focusing on the retail and airline industries, and the lack of previous consideration of important moderating variables type and timing of rewards, this study extended the research to service industries, investigating the effects of customer perceived benefits on loyalty programme effectiveness in terms of both attitudinal and behavioural loyalty.
Hypotheses established the extent to which reward design elements (customer perceived benefits and type and timing of rewards) develop customer relationships (perceived relationship investment and brand relationship quality) which are market-based assets driving future revenue for the firm, and resulted in customer loyalty in the financial services industry. A quantitative methodology and survey approach was adopted with a randomly selected stratified sample of respondents. The results supported the validity and reliability of the construct measures and a satisfactory adjusted SEM model fit.
The study provided industry-specific outcomes, indicating that social (integration with customer values), exploratory (exposure and access to relevant and timeous knowledge), monetary (financial value) and entertainment benefits drive customer loyalty in the financial services industry, with timing of rewards having no moderating impact and type of reward only impactful for consumers that prefer indirect (non-financial) exploratory and entertainment benefits. Importantly, the benefit of recognition was found not to have a significant influence. The study further supported divergent reward design elements as antecedents of customer loyalty across industries, as a result of the divergent nature of customer relationships between industries. Limitations of the research were consideration of customer characteristics, segments, and the relationship between attitudinal and behavioural loyalty.
The study’s theoretical contribution provides for a more comprehensive conceptual model of loyalty programme effectiveness, leveraging customer relationships which are grounded in market-based asset theory, as well as an empirical analysis of previously untested relationships between important variables. The research also confirms the requirement for industry-specific design elements for effective loyalty programmes. For practitioners, the findings provide guidance on design elements of an effective programme within the financial services industry. / Thesis (PhD)--University of Pretoria, 2018. / Gordon Institute of Business Science (GIBS) / PhD / Unrestricted
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Measuring and Influencing Sequential Joint Agent BehavioursRaffensperger, Peter Abraham January 2013 (has links)
Algorithmically designed reward functions can influence groups of learning agents toward measurable desired sequential joint behaviours. Influencing learning agents toward desirable behaviours is non-trivial due to the difficulties of assigning credit for global success to the deserving agents and of inducing coordination. Quantifying joint behaviours lets us identify global success by ranking some behaviours as more desirable than others. We propose a real-valued metric for turn-taking, demonstrating how to measure one sequential joint behaviour. We describe how to identify the presence of turn-taking in simulation results and we calculate the quantity of turn-taking that could be observed between independent random agents. We demonstrate our turn-taking metric by reinterpreting previous work on turn-taking in emergent communication and by analysing a recorded human conversation. Given a metric, we can explore the space of reward functions and identify those reward functions that result in global success in groups of learning agents. We describe 'medium access games' as a model for human and machine communication and we present simulation results for an extensive range of reward functions for pairs of Q-learning agents. We use the Nash equilibria of medium access games to develop predictors for determining which reward functions result in turn-taking. Having demonstrated the predictive power of Nash equilibria for turn-taking in medium access games, we focus on synthesis of reward functions for stochastic games that result in arbitrary desirable Nash equilibria. Our method constructs a reward function such that a particular joint behaviour is the unique Nash equilibrium of a stochastic game, provided that such a reward function exists. This method builds on techniques for designing rewards for Markov decision processes and for normal form games. We explain our reward design methods in detail and formally prove that they are correct.
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