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Measuring the business success of enterprise systems projects

Jones, Richard (2016). Measuring the business success of enterprise systems projects. (Unpublished Doctoral thesis, City, University of London)

Abstract

Enterprise resource planning (ERP) systems are integrated application software packages that meet most of the information systems requirements of business organisations. ERP, or more simply enterprise systems (ES), have constituted the majority of investment in information technology by global businesses over the last two decades and have had a profound impact upon the way these businesses have been managed. Yet there is not a good understanding of how the business success, as opposed to the implementation project success, of enterprise systems projects can be evaluated. Of the two success concepts, extant literature places more emphasis upon project success rather than business success.

This research is directed at exploring the relationship between planned business success, generally included in ERP project business cases, and subsequent, empirical, post-implementation measures of business success. The study involved the interviewing of 20 key informants from both ERP adopting companies and ERP consulting firms to answer the research question of ‘how do businesses evaluate the business success, as opposed to the project implementation success, of enterprise systems?’

Using 10 a priori categories derived from the literature, 100 correlated categories were identified from interview data by use of a three stage coding process; 25 categories were selected from this larger group to identify relationships that were the most pertinent to the central research question.

The key findings of the research were that the strength of the ERP system business case was generally determined by three main categories of business driver; strategic business change, a lower cost business model and business survival. These categories of business driver then determined the criteria for business success applied to the project in post-implementation stages. Where lower cost business models, often involving shared service centres and outsourcing of these centralised functions, were the driver, the business case metrics were more likely to be used for measurement of business success. Otherwise there was generally either a dissociation of benefits estimates in business cases from subsequent success measurement or simply an absence of estimated benefits.

This framework for the evaluation of the business success of enterprise systems has advantages over the delivery of estimated, a priori, business benefits because:

(1) The assumptions underlying the initial estimates of benefits will generally be invalidated because of the changed business environment prevailing after the lengthy implementation of a systems project. This makes comparisons with empirical post-implementation measures of business success of reduced value. Further, measures of business success based upon delivered benefits assume a degree of causality between the new ERP system and business benefits. However, it is often difficult to disentangle benefits from new business processes enabled by the enterprise system from benefits derived from other business initiatives.

(2) Actual, realised business benefits of a new IT system are often not measured for organisational and behavioural reasons. For example, there may be a lack of continuity of project stakeholders over the implementation period. Or more simply, people are reluctant to study what are viewed as past and irreversible events.

(3) A final factor is the absence of accounting or other measurement systems to evaluate actual benefits, often the result of the replacement of legacy accounting systems used to estimate the initial planned benefits.

This research also adds considerably to current literature on the implementation of enterprise systems, which has generally studied project success rather than business success because of the relative ease of measurement of project implementation success.

Publication Type: Thesis (Doctoral)
Subjects: Q Science > QA Mathematics > QA76 Computer software
Departments: Bayes Business School
Doctoral Theses
Bayes Business School > Bayes Business School Doctoral Theses
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