Case study: The case of EAI facilitating knowledge management integration in local government domain

  • Authors:

  • Muhammad Mustafa Kamal

  • Affiliations:

  • Brunel Business School, Brunel University, Uxbridge, Middlesex UB8 3PH, UK

  • Venue:
  • International Journal of Information Management: The Journal for Information Professionals
  • Year:
  • 2011

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Abstract

Information Technology (IT) infrastructure integration and knowledge management (KM) share communal objectives e.g. to transform organisations into more effective and efficient, agile and innovative, and more responsive to market changes. Such an association when assimilates bona fide knowledge management philosophy, it offers the IT departments a headship opportunity for organisational transformation in correlation with the rest of the organisation. Despite more than a decade of active research and practice in this complex problem area, advocates still perceive that Local Government Authorities (LGAs) lack integrated IT infrastructures that have resulted in the generation of data inconsistencies and redundancies, inefficient knowledge exchange and reduction in service quality and delivery. In the recent years, several LGAs have implemented Enterprise Application Integration (EAI) solutions to integrate their IT infrastructure. However, on analysing the relevant research studies, it is noticeable that application of EAI has been practiced at a larger scale in the private domain but to limited scale in the public domain. The shortage of such research studies presents a knowledge gap that needs to be endorsed. This research adapts a Revised Model for Integration Layers (REAL). By adapting to this model, it is exemplified that EAI achieves integration at five layers namely: connectivity, transportation, transformation, process integration and knowledge integration. The methodology for validating this model included a qualitative analysis of data gathered from formal interviews, observations and archive documents guided by initial conceptual observations from the literature. The findings indicate that cases leading to data inconsistencies and replication can be prevented by integrating knowledge through EAI.