Investigating the role of knowledge management in driving the development of an effective business process architecture
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Abstract
Business Process Architecture (BPA) modelling methods are not dynamic and flexible enough to effectively respond to changes. This may create a barrier that contributes to a lack of knowledge and learning capabilities which can affect the BPA regarding its support for a sustainable competitive advantage in an organisation. New business challenges are driving business enterprises to adopt Knowledge Management (KM) as one means of making a positive difference to their performance and competitiveness. However, shortcomings still remain in utilising knowledge management in business processes where efforts were mostly directed towards the integration of knowledge management with business process management but not including BPAs. The idea of applying KM as a memory to be timely retrieved and updated as needed is no longer sufficient. The resource-based view suggests a number of key factors to be investigated and taken into consideration during the development of knowledge management systems. These key factors are known as Knowledge Management Enablers (KMEs). KMEs are crucial for representing KM and understanding how knowledge is created, shared and disseminated. They are also essential to identify available assets and resources, and to clarify how organisational capabilities are created and utilised.This research is aimed at investigating the role of the knowledge management enablers in the development of an effective process architecture. An effective process architecture needs to be dynamic and supports a sustainable competitive advantage in an organisation. Identifying the KMEs, selecting an appropriate BPA method, aligning these KMEs with this method as well as undertaking a critical evaluation of this alignment are the main objectives set for this research. In order to accomplish the research aim and objectives, a resource-based and semantic-enriched framework, namely the KMEOntoBPA has been designed using KMEs to drive the process of BPA development. Organisational structure, culture, information technology, leadership, knowledge context and business repository have been selected as representatives of the KMEs. The object-based BPA modelling, specifically the semantically enriched Riva BPA (srBPA) method, has been adopted in order to embrace the knowledge resources generated by KMEs and utilise them in the derivation and re-configuration of its constitutional elements. These knowledge resources are employed as business objects. They are considered as Candidate Essential Business Entities (CEBEs) in the Riva method, that characterise or represent a form of business of an organisation. The Design Science Research Methodology (DSRM) is used to guide the research phases with an emphasis on the design and development, demonstration and evaluation of the research framework. The KMEOntoBPA has been demonstrated using sufficient and representative core banking case studies of the Treasury, Deposits and Financing. These case studies have been applied to the DSRM iterations beginning with the Treasury as the 1st case study, followed by the Deposits and the Financing case studies.The results have revealed that KMEs utilisation provides an agile generation of representative CEBEs and their corresponding Riva BPA elements, which reflect the real business in each of the core banking business studies. This research also demonstrated the semantic Riva BPA method as an appropriate object-based method that is well aligned with KMEs in exploiting knowledge resources for the development of a dynamic BPA with reference to robustness and learning capabilities. In addition to these results, the research framework, i.e, the KMEOntoBPA has shown an understanding of the flow of knowledge in the bank and has provided several possible advantages such as the accuracy of service delivery and the improvement of the financial control. It also supports the sources of sustainable competitive advantage (SCA): technical capabilities, core competences and social capital.Finally, a number of significant contributions and artefacts have been attained. For example, there is the aKMEOnt which is the abstract ontology that utilises six KMEs in this research to investigate the effectiveness of using such KMEs in driving the development of the BPA. These contributions along with the research results provide a guide to future research directions such as using the aKMEOnt in the development of different business process modelling and deriving the Enterprise Information Architecture (EIA) and Service Oriented Architecture (SOA)