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Knowledge prioritisation for ERP implementation success Perspectives of clients & implementation partners in UK industries

Smith, Melanie Hudson; Jayawickrama, Uchitha; Liu, Shaofeng


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Dr Mel Smith
Associate Professor in Strat & Ops Mngt

Uchitha Jayawickrama

Shaofeng Liu


Purpose - Knowledge management is crucial for enterprise resource planning (ERP) systems implementation in real industrial environments, but this is a highly demanding task. The purpose of this paper is to examine the effectiveness of knowledge identification, categorisation and prioritisation that contributes to achieving ERP implementation success. Design/methodology/approach - This study adopts a mixed methods approach; a qualitative phase to identify and categorise knowledge types and sub-Types; conducting in-depth interviews with ERP clients and implementation partners; plus a quantitative phase to prioritise knowledge types and sub-Types based on their contribution to achieving ERP success for business performance improvement. An analytic hierarchy process-based questionnaire was used to collect empirical data for the quantitative phase. Findings - This study has been able to identify, categorise and rank various types of ERP-related knowledge based on in-depth interviews and survey responses from both ERP clients and implementation partners. In total, 4 knowledge types and 21 sub-Types were ranked based on their contribution to achieving ERP success; 4 variables of information quality, systems quality, individual impact and organisational impact were used to measure ERP success. Originality/value - The empirical findings demonstrate exactly what kinds of knowledge need to be managed, enabling knowledge prioritisation when a client organisation or an implementation partner steps into an ERP implementation, in a real industrial environment.


Smith, M. H., Jayawickrama, U., & Liu, S. (2017). Knowledge prioritisation for ERP implementation success Perspectives of clients & implementation partners in UK industries. Industrial Management and Data Systems, 117(7), 1521-1546.

Journal Article Type Review
Acceptance Date Apr 12, 2017
Online Publication Date Jul 3, 2017
Publication Date Jul 3, 2017
Deposit Date Apr 12, 2017
Publicly Available Date Jul 4, 2018
Journal Industrial Management and Data Systems
Print ISSN 0263-5577
Publisher Emerald
Peer Reviewed Peer Reviewed
Volume 117
Issue 7
Pages 1521-1546
Public URL
Publisher URL


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