Skip to main content

Extension of Intelligence of Decision Support Systems: Manager Perspective

  • Conference paper
  • First Online:
Information Technology for Management: New Ideas and Real Solutions (ISM 2016, AITM 2016)

Abstract

The article presents an approach to extend the functionality and knowledge of Decision Support Systems to answer the requirements of managers of small and medium-sized enterprises (SMEs). It concerns two major aspects of the system, i.e. the interface that takes into account the level of knowledge of the manager, and the interpretation of economic and financial information using the built-in domain ontologies. The project is related to the design of smart decision support systems based on financial ontology and on the model of manager knowledge created by eye-tracking analysis. An experiment was carried out on real financial data extracted from the database of BINOCLE system, developed by Bilander Co. To create a model of manager knowledge, a number of financial analysts, experts and economists were invited to analyze the pre-defined financial reports. Their tasks were observed and analyzed by the eye-tracking system StudioTM, Tobii. The logs of the system as well as the financial ontology have been used to develop the intelligent interface of a Decision Support System.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    It should be emphasized that the selection of appropriate methods of analysis of the financial requirements of SME managers is necessary to determine the company's ability to continue its operations, and to define financial needs, budgets and capital resources of financing assets, signs of danger and risk of changes in the competitive position and trends in various business areas.

  2. 2.

    The management of long-term financial analysis conducted by managers of small businesses should include: a preliminary analysis of financial statements, ratio analysis, information on liquidity ratios, evaluation of static measures of liquidity, turnover ratios of inventories, receivables and payables analysis, analysis of the operating cycle in terms of the efficiency of indicators of capital engagement, productivity of assets and equity, and information on current and future income and expenses.

References

  1. Korczak, J., Dudycz, H., Dyczkowski, M.: Intelligent dashboard for SME managers. Architecture and functions. In: Ganzha, M., Maciaszek, L., Paprzycki, M. (eds.) Proceedings of the Federated Conference on Computer Science and Information Systems, pp. 1003–1007. Polskie Towarzystwo Informatyczne, IEEE Computer Society Press, Warsaw, Los Alamitos, (2012)

    Google Scholar 

  2. Samonas, M.: Financial Forecasting, Analysis and Modelling. Wiley, Chichester (2015)

    Book  Google Scholar 

  3. Power, D.J.: Decision Support Basics. Business Expert Press, LLC (2009)

    Book  Google Scholar 

  4. Power, D.J., Sharda, R., Burstein, F.: Decision Support System. Wiley Encyclopedia of Management, vol. 7. Management Information Systems (2015)

    Google Scholar 

  5. Dudycz, H.: The Topic Map as a Visual Representation of Economic Knowledge. Wrocław University of Economics, Wrocław (2013). (in Polish)

    Google Scholar 

  6. Aruldoss, M., Maladhy, D., Prasanna, Venkatesan V.: A framework for business intelligence application using ontological classification. Int. J. Eng. Sci. Technol. 3(2), 1213–1221 (2011)

    Google Scholar 

  7. Cheng, A., Lu, Y.-C., Sheu, C.: An ontology-based business intelligence application in financial knowledge management system. Expert Syst. Appl. 36(2), 3614–3622 (2009). part 2

    Article  Google Scholar 

  8. Neumayr, B., Schrefl, M., Linner, K.: Semantic cockpit: an ontology-driven, interactive business intelligence tool for comparative data analysis. In: Troyer, O., Medeiros, C.B., Billen, R., Hallot, P., Simitsis, A., Mingroot, H. (eds.) ER 2011. LNCS, vol. 6999, pp. 55–64. Springer, Heidelberg (2011). doi:10.1007/978-3-642-24574-9_9

    Chapter  Google Scholar 

  9. Pinto, F., Santos, M.F., Marques, A.: Ontology based data mining – a contribution to business intelligence. In: 10th WSEAS International Conference on Mathematics and Computers in Business and Economics (MCBE 2009), 23–25 March, Czech Republic, pp. 210–216, (2009)

    Google Scholar 

  10. Saggion, H., Funk, A., Maynard, D., Bontcheva, K.: Ontology-based information extraction for business intelligence. In: Aberer, K., et al. (eds.) ASWC/ISWC -2007. LNCS, vol. 4825, pp. 843–856. Springer, Heidelberg (2007). doi:10.1007/978-3-540-76298-0_61

    Chapter  Google Scholar 

  11. Sell, D., da Silva, D.C., Beppler, F.D., Napoli, M., Ghisi, F.B., Pacheco, R., Todesco, J.L.: SBI: a semantic framework to support business intelligence. In: Proceeding of the First International Workshop on Ontology-supported Business Intelligence, Article no. 11. ACM, New York (2008)

    Google Scholar 

  12. Korczak, J., Dudycz, H., Dyczkowski, M.: Design of financial knowledge in dashboard for SME managers. In: Ganzha, M., Maciaszek, L., Paprzycki, M. (eds.) Proceedings of the 2013 Federated Conference on Computer Science and Information Systems. Annals of Computer Science and Information Systems, vol. 1, pp. 1111–1118. Polskie Towarzystwo Informatyczne, IEEE Computer Society Press, Warsaw, Los Alamitos (2013)

    Google Scholar 

  13. Dyczkowski, M., Korczak, J., Dudycz, H.: Multi-criteria evaluation of the intelligent dashboard for SME managers based on scorecard framework. In: Ganzha, M., Maciaszek, L., Paprzycki, M. (eds.) Proceedings of the 2014 Federated Conference on Computer Science and Information Systems. Annals of Computer Science and Information Systems, New York City, vol. 2, pp. 1147–1155 (2014). doi:10.15439/2014F388

  14. Subramanyam, K.R., Wild, J.: Financial Statement Analysis. McGraw-Hill Education (2013)

    Google Scholar 

  15. Schenk, E., Guittard, C.: Towards a characterization of crowdsourcing practices. J. Innov. Econ. Manage. 7, 93–107 (2011)

    Article  Google Scholar 

  16. Hwang, H.J., Kwon, K., Im, C.H.: Neurofeedback-based motor imagery training for brain-computer interface (BCI). J. Neurosci. Methods 179, 150–156 (2009). Republic of Korea

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank the staff and the companies Bilander and Tobii for their support in developing the prototype. Thanks go also to the financial experts: Wojciech Hasik, Mariola Kotłowska and Wojciech Ostojski, and students of the Faculty of Management, Informatics and Finance of Wrocław University of Economics.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jerzy Korczak .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Korczak, J., Dudycz, H., Nita, B., Oleksyk, P., Kaźmierczak, A. (2017). Extension of Intelligence of Decision Support Systems: Manager Perspective. In: Ziemba, E. (eds) Information Technology for Management: New Ideas and Real Solutions. ISM AITM 2016 2016. Lecture Notes in Business Information Processing, vol 277. Springer, Cham. https://doi.org/10.1007/978-3-319-53076-5_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-53076-5_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53075-8

  • Online ISBN: 978-3-319-53076-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics