A methodology for strategic diagnosis of business corruption behaviours using network analysis

Dana Nedea and Ioan Petrișor
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Dana Nedea: Faculty of Economics and Business Administration, West University of Timișoara, Timișoara, Romania
Ioan Petrișor: Faculty of Economics and Business Administration, West University of Timișoara, Timișoara, Romania

Journal of Administrative and Business Studies, 2019, vol. 5, issue 6, 323-329

Abstract: Business corruption behaviours directly impact the business environment and the way in which strategic diagnosis is conducted at organizational level. Thus, the diagnosis of these business corruption cases becomes highly important in the process of strategic decisions. The methodology we use is based on networks analysis. We propose a Strategic Diagnosis Based on Networks Analysis (SDBN) which incorporates modularity and clustering coefficient measurements. These measurements allow for a cross-diagnosis of business corruption behaviours. A large quantity of event data from mass-media was extracted from the Global Database of Events, Language, and Tone regarding European Union business corruption, over the year of 2019. The data encompasses countries and organisations & institutions as the nodes, and the number of times that > and > appear together in our search, as the edges of the networks. These networks are then located in our proposed SDBN model, and are separated in concentration or diversification strategies, weakly connected or highly connected. The distinctiveness of our research consists of involving the theory of network analysis in the process of strategic diagnosis. The results reveal the specific business corruption behaviours conducted by actors involved in business corruption cases, with the limits being given by the usage of big-data and the magnitude of corruption behaviours. Future research will intend to apply the proposed SDBN to a larger sample of data and to different perspectives.

Keywords: Business corruption; Behaviour; Network analysis; GDELT (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:apb:jabsss:2019:p:323-329

DOI: 10.20474/jabs-5.6.3

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