This research explores the online purchase decision-making behaviour of consumers by introducing a comprehensive approach that covers two different viewpoints: a) individual-level behaviour and b) market-level behaviour. Individual-level behaviour enhances our understanding of how purchase decision-making processes unfold and whether they differ for different individuals. Drawing from decision analysis and consumer behaviour literature, four segments of online consumers are introduced based on two individual factors: decision making style and knowledge of the product. Archetypal behaviour of each segment is identified addressing variations in the process and process outcome for different groups. In addition, market-level behaviour investigates the actual behaviour of consumers in relation with different retailers in the market; it is based on the aggregated behaviour of 60,000 individuals. Not only behaviour in a particular website but also cross-visiting behaviour of consumers comparing multiple retailers is examined. For this purpose, a multi-level mixed-method approach is designed. Video recording sessions, think-aloud method, interviews and questionnaires are used to capture the dynamic decision-making process, segment consumers and measure the outcome of the process at individual level. Business process modeling approach and an adaptation of path configuration method are selected for modelling the process. Data from an Internet panel data provider, comScore, is analyzed to explore the market-behaviour of consumers visiting multiple retailers. A set of measurement frameworks, that have been developed to fully exploit the research potential of Internet panel data, are designed for this research. Two sectors of banking and mobile network providers are selected; this research methodology enables a much more detailed evaluation of online behaviour and can be applied in other consumer markets.A conceptual model of online purchase decision making is proposed synthesizing theory from three disciplines: consumer behaviour, decision analysis and Information Systems. This model is able to explain the complexities and dynamic nature of real-life decision-making processes. The results of individual-level analysis show that the synthesized model has an enhanced descriptive power. Purchase decision-making processes in the two sectors appear to be highly complex with a large number of iterations, being more unstructured in banking sector. The process is found to be influenced by the both individual characteristics and each segment exhibits a certain typology of behaviour. Behaviour in terms of the way stages are performed is identical across the two sectors; whereas it differs in relation to intensity of decision-making cycles, duration of the process and the process outcome, being a function of product/ market characteristics.The findings of market-level analysis revealed that banking websites are preliminary visited for using online banking services; despite the high portion of visitors, the intensity of research in these websites is low. On the contrary, mobile network providers attract a higher portion of consumers with purchase intentions and enjoy more intensive research. Consumers have a small consideration set in both sectors; and consider certain banks/providers rather than using the accessibility of all alterative on the Internet. It is evident that comparison sites play an important role in both markets affecting the behaviour of online consumers. Finally, the research stresses the use of the Internet as a complementary channel offering specific benefits in each sector.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:570264 |
| Date | January 2013 |
| Creators | Karimi, Sahar |
| Contributors | Holland, Christopher; Papamichail, Nadia |
| Publisher | University of Manchester |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://www.research.manchester.ac.uk/portal/en/theses/a-purchase-decisionmaking-process-model-of-online-consumers-and-its-influential-factora-cross-sector-analysis(702ce943-3925-4b84-b99f-d170d3b8e386).html |
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