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Public Survey Instruments for Business Administration Using Social Network Analysis and Big Data

Author

Listed:
  • V.V. Kolmakov
  • L.N. Rudneva
  • Y.E. Thalassinos

Abstract

Purpose: The subject matter of this research is closely intertwined with the scientific discussion about the necessity of developing and implementing practice-oriented means of measuring social well-being taking into account the intensity of contacts between individuals. The aim of the research is to test the toolkit for analyzing social networks and to develop a research algorithm to identify sources of consolidation of public opinion and key agents of influence. The research methodology is based on postulates of sociology, graph theory, social network analysis and cluster analysis. Design/Methodology/Approach: The basis for the empirical research was provided by the data representing the reflection of social media users on the existing image of Russia and its activities in the Arctic, chosen as a model case. Findings: The algorithm allows to estimate the density and intensity of connections between actors, to trace the main channels of formation of public opinion and key agents of influence, to identify implicit patterns and trends, to relate information flows and events with current information causes and news stories for the subsequent formation of a "cleansed" image of the object under study and the key actors with whom this object is associated. Practical Implications: The work contributes to filling the existing gap in the scientific literature, caused by insufficient elaboration of the issues of applying the social network analysis to solve sociological problems. Originality/Value: The work contributes to filling the existing gap in the scientific literature formed as a result of insufficient development of practical issues of using analysis of social networks to solve sociological problems.

Suggested Citation

  • V.V. Kolmakov & L.N. Rudneva & Y.E. Thalassinos, 2020. "Public Survey Instruments for Business Administration Using Social Network Analysis and Big Data," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(2), pages 3-18.
  • Handle: RePEc:ers:ijebaa:v:viii:y:2020:i:2:p:3-18
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    References listed on IDEAS

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    4. Vladimir KOLMAKOV & Alexandra POLYAKOVA & Sergei POLYAKOV, 2019. "A Valuation Approach To The Russian Liberal Establishment Consolidation," REVISTA ADMINISTRATIE SI MANAGEMENT PUBLIC, Faculty of Administration and Public Management, Academy of Economic Studies, Bucharest, Romania, vol. 2019(32), pages 93-107, June.
    5. Eleftherios I. Thalassinos & Mike P. Hanias & Panayiotis G. Curtis & John E. Thalassinos, 2013. "Forecasting Financial Indices: The Baltic Dry Indices," International Journal of Maritime, Trade & Economic Issues (IJMTEI), International Journal of Maritime, Trade & Economic Issues (IJMTEI), vol. 0(1), pages 109-130.
    6. A.G. Polyakova & M.P. Loginov & A.I. Serebrennikova & E.I. Thalassinos, 2019. "Design of a Socio-economic Processes Monitoring System Based on Network Analysis and Big Data," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(1), pages 130-139.
    7. Manfred M. Fischer & Peter Nijkamp (ed.), 2014. "Handbook of Regional Science," Springer Books, Springer, edition 127, number 978-3-642-23430-9, March.
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    9. M. Hanias & P. Curtis & E. Thalassinos, 2012. "Time Series Prediction with Neural Networks for the Athens Stock Exchange Indicator," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 23-32.
    10. Matthew Pepper & Andrew Sense, 2014. "Local government service systems improvement: exposing the social networks," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 18(3), pages 873-890, August.
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    More about this item

    Keywords

    Sociological research; public opinion; big data; social space; social networks.;
    All these keywords.

    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • M10 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - General

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