Use of “Knowledge House”, DWS, DMS and DSS methodology by completing a competitors' analysis in the railway sector

Vaidas Gaidelys and Stasys Dailydka

Journal of Business Economics and Management, 2016, vol. 17, issue 6, 1022-1051

Abstract: In completing a competitors’ analysis in the railway sector by using the “Knowledge House” method, there is frequently a problem of data and information accessibility. The quality of primary information has direct influence on the quality of analytical conclusions. One more condition for the qualitative application of this method is the intellectual capital and experience of the analyst. One should note that in this regard we face another problem, that of selection of proper personnel, on the qualification of whom depends the accuracy of the evaluation and final results, on the basis of which strategic decisions are taken. The main aim of the paper is to assess the opportunities for applications of competitive intelligence methods in the railway sector. The study is using “Knowledge House”, DWS, DMS, DSS methodologies.Having analysed the scientific works the direct scientific sources of information, which are oriented to the application of the methods of competitive intelligence to the railway sector, have not been identified. The paper is absolutely original in that until now the competitive intelligence techniques have not been applied for the railway sector companies.Considering the fact that foreign companies, which compete for freighting at the international level, are regarded as the main competitors of the railway sector, the use of the methods of the competitive intelligence becomes more important while fighting for the part of the market. The competitive intelligence methods and their application to the railway sector companies are little studied. In accordance with application of the relevant methods in other sectors, it can be assumed that these innovative approaches could have a positive impact on the competitiveness of companies in the railway sector and their income.

Date: 2016
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DOI: 10.3846/16111699.2016.1251963

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