The relationship between servicescapes and customer post purchase behaviour
thesisposted on 2017-12-06, 00:00 authored by Anthony PerroneAnthony Perrone
There has been extensive research undertaken in the area of service quality and the strategies that encourage customers to repurchase a product. In some service settings, the servicescape, the physical environment in which a customer and service provider meet can have a significant effect on what a customer experiences and the subsequent perceptions and behaviour of that customer. Anything a customer sees, feels, touches, hears and smells can have an impact on a firm’s success or failure. This research reports on an empirical study of the potential significance of the inclusion of the ‘servicescape’ in a model relating customer service quality with customer behavioural outcomes in a service setting. The research addressed the question: How does the retail servicescape relate to customer’s post purchase perceptions and future behaviour? This research contributes to the marketing literature by developing a service quality model of future customer behaviour. This model espouses a more longitudinal conceptualisation of the retail servicescape by recognising the multidimensional nature of the services industry, with the addition of the servicescape service quality (SSSQ) variable with other service quality variables. This adds a significant element to improve the predictive effect, and provides empirical evidence, on succeeding customer perceptions and behaviours. This research develops and tests a new theoretical framework, the proposed ‘Model of Future Customer Behaviour’. The model provides service providers with a means of evaluating the relationship between the retail establishment and their consumers, which will be statistically linked to the model’s three variables of service performance, as measured by three independent variables (product delivery, functional service quality and servicescape service quality). Post purchase perceptions are measured by two mediating variables (customer satisfaction and relationship strength) linked to the outcome, future customer behaviour as measured by two dependent variables (retention intention and word-of-mouth behaviour). The origin of the service quality framework was based on the seminal conceptualisation of service quality advanced by Grönroos (1990, 1982) whose theory postulated that service quality was the result of customer perceptions based on the interaction that took place during customer service delivery (functional service quality) and product delivery (technical service quality). The combining of those two constructs and the addition of servicescape service quality provide an informed measure of service quality. This research was conducted within a positivism ontology, (how does the servicescape in retail affect customer service retention intentions and word-of-mouth?) and epistemology, (do customers actually consider environment as a key factor regarding repurchase intentions?). As this research was grounded in a positivism philosophy, structural equation modelling was considered appropriate as the relevant method of statistical analysis. Five hypotheses were tested in coffee shops in Melbourne. This research was conducted using a two stage ethodological approach. Stage 1 was qualitative and exploratory in nature by conducting focus groups and personal interviews. This process combined with the review of the literature provided a basis for the construction of the test instrument in Stage 2. During Stage 2, the quantitative stage, a survey was carried out in ten coffee shops providing the required data to address the research question and hypotheses. A total of 500 usable surveys, which were stratified by obtaining 50 responses from patrons of those ten different coffee shops, were completed enabling the data to be analysed using structural equation modelling as the main statistical technique along with SPSS 15.0 and AMOS 7.0 software. Exploratory factor analysis and confirmatory factor analysis were conducted on four models as first step processes prior to analysing the research data using structural equation modelling. The data obtained during Stage 2 was used to test the competing models developed for this research. This process included assessing the original theoretical and structured models to find the most parsimonious fit model. In analysing the researches four models steps were taken to evaluate and determine which model had the best fit indices and highest R squared value. This process resulted in the identification of the model of best fit, highest R squared value and parsimony. As the initial theoretical framework outlined it was established that from a theoretical perspective the servicescape variable would be presented as one construct and was presented as a single construct in developing the hypotheses. Through the literature reviewed and as the variable is defined, Section 1.2, some researchers (Wagner 2000; Bitner 1992 and Baker 1987) intimated that there were more than one servicescape component. Factor analysis testing revealed that the servicescape construct did, in fact, split into three distinct components: servicescape service quality facilities (SF), servicescape service quality atmosphere (SAT) and servicescape service quality appearance (SAP) and are presented as such in the model. Tests were undertaken to evaluate if the servicescape variables did play a significant role in the predictive powers of the model. The tests revealed that the model with the servicescape did in fact produce more significant results than the model without the servicescape; customer satisfaction (model with SSSQ R2 = 0.719; without SSSQ R2 = 0.676); relationship strength (model with SSSQ R2 = 0.642; without SSSQ R2 = 0.606); retention intentions (model with SSSQ R2 = 0.651; without SSSQ R2 = 0.643); and, word-of-mouth (model with SSSQ R2 = 0.433; without SSSQ R2 = 0.427). The results of the tests on the theoretical model and the most parsimonious model therefore indicated that the servicescape variables did improve and had a positive effect on the model’s predictive ability. Overall, the research identified that of the five hypotheses developed four were supported, concluding that a mixture of direct and indirect relationships has lead to outcomes of retention intentions and word-of-mouth providing support for the relationships of a Model of Future Customer Behaviour.