A framework for valuing the quality of customer information
AffiliationScience, Information Systems
Document TypePhD thesis
CitationsHill, G. (2009). A framework for valuing the quality of customer information. PhD thesis, Science, Information Systems, The University of Melbourne.
Access StatusOpen Access
© 2009 Dr. Gregory Hill
This thesis addresses a widespread, significant and persistent problem in Information Systems practice: under-investment in the quality of customer information. Many organisations require clear financial models in order to undertake investments in their information systems and related processes. However, there are no widely accepted approaches to rigorously articulating the costs and benefits of potential quality improvements to customer information. This can result in poor quality customer information which impacts on wider organisational goals. To address this problem , I develop and evaluate a framework for producing financial models of the costs and benefits of customer information quality interventions. These models can be used to select and prioritise from multiple candidate interventions across various customer processes and information resources, and to build a business case for the organisation to make the investment. The research process involved: The adoption of Design Science as a suitable research approach, underpinned by a Critical Realist philosophy. A review of scholarly research in the Information Systems sub-discipline of Information Quality focusing on measurement and valuation, along with topics from relevant reference disciplines in economics and applied mathematics. A series of semi-structured context interviews with practitioners (including analysts, managers and executives) in a number of industries, examining specifically information quality measurement, valuation and investment. A conceptual study using the knowledge from the reference disciplines to design a framework incorporating models, measures and methods to address these practitioner requirements. A simulation study to evaluate and refine the framework by applying synthetic information quality deficiencies to real-world customer data sets and decision process in a controlled fashion. An evaluation of the framework based on a number of published criteria recommended by scholars to establish that the framework is a purposeful, innovative and generic solution to the problem at hand.
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