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Knowledge prioritisation for ERP implementation success: perspectives of clients and implementation partners in UK industries

JAYAWICKRAMA, Uchitha and Liu, Shaofeng and Hudson Smith, Melanie (2017) Knowledge prioritisation for ERP implementation success: perspectives of clients and implementation partners in UK industries. Industrial Management & Data Systems. (In Press)

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Abstract or description

Purpose:
Knowledge management (KM) is crucial for Enterprise Resource Planning (ERP) systems implementation in real industrial environments, but this is a highly demanding task. The primary purpose of this study 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 (AHP) 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; four 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.

Item Type: Article
Faculty: School of Computing and Digital Technologies > Computing
Depositing User: Uchitha JAYAWICKRAMA
Date Deposited: 17 May 2017 12:55
Last Modified: 18 May 2017 13:35
URI: http://eprints.staffs.ac.uk/id/eprint/3077

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