A review of data mining in knowledge management: applications/findings for transportation of small and medium enterprise
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A review of data mining in knowledge management: applications/ findings for transportation of small and medium enterprises Siti Aishah Mohd Selamat1 · Simant Prakoonwit1 · Wajid Khan1 Received: 15 October 2019 / Accepted: 24 March 2020 © Springer Nature Switzerland AG 2020
Abstract A core subfield of knowledge management (KM) and data mining (DM) constitutes an integral part of the knowledge discovery in database process. With the explosion of information in the new digital age, research studies in the DM and KM continue to heighten up in the business organisations, especially so, for the small and medium enterprises (SMEs). DM is crucial in supporting the KM application as it processes the data to useful knowledge and KM role next, is to manage these knowledge assets within the organisation systematically. At the comprehensive appraisal of the large enterprise in the transportation sector and the SMEs across various industries—it was gathered that there is limited research case study conducted on the application of DM–KM on the transportation SMEs in specific. From the extensive review of the case studies, it was uncovered that majority of the organisations are not leveraging on the use of tacit knowledge and that the SMEs are adopting a more traditional use of ICTs to its KM approach. In addition, despite DM–KM is being widely implemented—the case studies analysis reveals that there is a limitation in the presence of an integrated DM–KM assessment to evaluate the outcome of the DM–KM application. This paper concludes that there is a critical need for a novel DM–KM assessment plan template to evaluate and ensure that the knowledge created and implemented are usable and relevant, specifically for the SMEs in the transportation sector. Therefore, this research paper aims to carry out an in-depth review of data mining in knowledge management for SMEs in the transportation industry. Keywords Knowledge management · Data mining · SMEs · Transportation · Data mining models · Knowledge
1 Introduction In an information era, knowledge is deemed as the lifeblood of an organisation and that its survival is relatively dependent on it. Arising as a crucial business element, the utilisation of knowledge aids organisation to remain competitive in today’s volatile business environment— notably in the services industries [4]. The differentiating competitive factors of an organisation are in its intangible data assets. Therefore, managing knowledge is as critical in contrast to administering any other assets of the organisation. Knowledge management (KM) implementation creates effective and efficient pathways for organisation to
employ its intellectual assets [2]. Several KM approaches have been advocated in depositing information at work for organisations [27]. These approaches include instruments and methods that require an eminent level of resources in order to deliver the KM goals. The vast amount of data have been collected by many organisations. The organisational challenge is discovering valuable information in the larg
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