Advertisement

Improvement on the Efficiency of Technology Companies in Malaysia with Data Envelopment Analysis Model

  • Lam Weng HoeEmail author
  • Lam Weng Siew
  • Liew Kah Fai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10645)

Abstract

Efficiency evaluation is vital as it is able to determine the financial performance of the companies. Efficiency describes how well the companies in utilizing their inputs to generate outputs. The objective of this study is to propose a financial ratio based Data Envelopment Analysis (DEA) model to evaluate and compare the efficiency of listed technology companies in Malaysia for the period of 2011–2015. In DEA model, the efficiency is defined as the ratio of sum-weighted outputs to sum-weighted inputs. In this study, LINGO software is used to solve the DEA model. The results of this study indicate that ELSOFT, GTRONIC, KESM, MPI and VITROX are ranked as efficient technology companies in Malaysia. Besides that, the potential improvement for each inefficient company can be identified based on the benchmark efficient companies. This study is significant because it helps to identify the efficient technology companies which can serve as benchmarks to other inefficient companies for further improvement. Moreover, it is a pioneer study of proposing DEA model with financial ratio to evaluate and compare the efficiency of technology companies in Malaysia.

Keywords

Data Envelopment Analysis Technology company Linear programming model LINGO software 

Notes

Acknowledgements

The authors express gratitude to the research grant project number FRGS/1/2015/SG04/UTAR/02/3 for the support.

References

  1. 1.
    Sohn, S.Y., Moon, T.H.: Decision tree based on data envelopment analysis for effective technology commercialization. Expert Syst. Appl. 26(2), 279–284 (2004)CrossRefGoogle Scholar
  2. 2.
    Memon, M.A., Tahir, I.M.: Relative efficiency of manufacturing companies in Pakistan using data envelopment analysis. Int. J. Bus. Commer. 1(3), 10–27 (2011)Google Scholar
  3. 3.
    Řepková, I.: Banking efficiency determinants in the Czech banking sector. Procedia Econ. Financ. 23, 191–196 (2015)CrossRefGoogle Scholar
  4. 4.
    Charnes, A., Cooper, W.W., Rhodes, E.: Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2(6), 429–444 (1978)CrossRefzbMATHMathSciNetGoogle Scholar
  5. 5.
    Mohamad, N.H., Said, F.: Measuring the performance of 100 largest listed companies in Malaysia. Afr. J. Bus. Manag. 4(13), 3178–3190 (2010)MathSciNetGoogle Scholar
  6. 6.
    Sillah, B.M.S., Harrathi, N.: Bank efficiency analysis: Islamic banks versus conventional banks in the Gulf Cooperation Council countries 2006–2012. Int. J. Financ. Res. 6(4), 143–150 (2015)CrossRefGoogle Scholar
  7. 7.
    Mukta, M.: Efficiency of commercial banks in India: a DEA approach. Pertanika J. Soc. Sci. Humanit. 24(1), 151–170 (2016)Google Scholar
  8. 8.
    Lam, W.S., Liew, K.F., Lam, W.H.: An empirical comparison on the efficiency of healthcare companies in Malaysia with data envelopment analysis model. Int. J. Serv. Sci. Manag. Eng. 4(1), 1–5 (2017)Google Scholar
  9. 9.
    Bursa Malaysia, Company Announcements—Bursa Malaysia Market. http://www.bursamalaysia.com/market/listed-companies/company-announcements/#/?category=all. Accessed 15 May 2017
  10. 10.
    Ong, P.L., Kamil, A.A.: Data envelopment analysis for stocks selection on Bursa Malaysia. Arch. Appl. Sci. Res. 2(5), 11–35 (2010)Google Scholar
  11. 11.
    Dalfard, V.M., Sohrabian, A., Najafabadi, A.M., Alvani, J.: Performance evaluation and prioritization of leasing companies using the super efficiency data envelopment analysis model. Acta Polytechnica Hungarica 9(3), 183–194 (2012)Google Scholar
  12. 12.
    Mohamad, N.H., Said, F.: Using super-efficient DEA model to evaluate the business performance in Malaysia. World Appl. Sci. J. 17(9), 1167–1177 (2012)Google Scholar
  13. 13.
    Arsad, R., Abdullah, M.N., Alias, S.: A ranking efficiency unit by restrictions using DEA models. In: AIP Conference Proceedings, vol. 1635, no. 1, pp. 266–273 (2014)Google Scholar
  14. 14.
    Rahmani, I., Barati, B., Dalfard, V.M., Shirkouhi, H.: Nonparametric frontier analysis models for efficiency evaluation in insurance industry: a case study of Iranian insurance market. Neural Comput. Appl. 24(5), 1153–1161 (2014)CrossRefGoogle Scholar
  15. 15.
    Zamani, L., Beegam, R., Borzoian, S.: Portfolio selection using data envelopment analysis (DEA): a case of select Indian investment companies. Int. J. Curr. Res. Acad. Rev. 2(4), 50–55 (2014)Google Scholar
  16. 16.
    Price, J.E., Haddock, M.D., Brock, H.R.: College Accounting, 10th edn. Macmillan/McGraw-Hill, New York (1993)Google Scholar
  17. 17.
    Ablanedo-Rosas, J.H., Gao, H., Zheng, X., Alidaee, B., Wang, H.: A study of the relative efficiency of Chinese ports: a financial ratio-based data envelopment analysis approach. Expert Syst. 27(5), 349–362 (2010)CrossRefGoogle Scholar
  18. 18.
    Östring, P.: Profit-Focused Supplier Management. Am. Manag. Assoc. Int., United State (2003)Google Scholar
  19. 19.
    Fraser, L., Ormiston, A.: Understanding Financial Statements. Pearson Prentice Hall, Upper Saddle River (2004)Google Scholar
  20. 20.
    Ercan, M.K., Ban, U.: Financial Management. Fersa Publication, Gazi Copy Purchaser, Ankara (2005)Google Scholar
  21. 21.
    Akguc, O.: Financial Statement Analysis, 13th edn. Arayis Publication, Istanbul (2010)Google Scholar
  22. 22.
    Sofianopoulou, S.: Manufacturing cells efficiency evaluation using data envelopment analysis. J. Manuf. Technol. Manag. 17(2), 224–238 (2006)CrossRefGoogle Scholar
  23. 23.
    Parthiban, P., Zubar, H.A., Katakar, P.: Vendor selection problem: a multi-criteria approach based on strategic decisions. Int. J. Prod. Res. 51(5), 1535–1548 (2013)CrossRefGoogle Scholar
  24. 24.
    Martic, M.M., Novakovic, M.S., Baggia, A.: Data envelopment analysis - basic models and their utilization. Organizacija 42(2), 37–43 (2009)CrossRefGoogle Scholar
  25. 25.
    Lam, W.S., Lam, W.H.: Portfolio optimization for index tracking problem with mixed-integer programming model. J. Sci. Res. Dev. 2(10), 5–8 (2015)Google Scholar
  26. 26.
    Lam, W.S., Lam, W.H.: Mathematical modeling of enhanced index tracking with optimization model. J. Numer. Anal. Appl. Math. 1(1), 1–5 (2016)Google Scholar
  27. 27.
    Lam, W.H., Lam, W.S.: Mathematical modeling of risk in portfolio optimization with mean-extended Gini approach. SCIREA J. Math. 1(2), 190–196 (2016)Google Scholar
  28. 28.
    Lam, W.S., Jaaman, S.H., Ismail, H.: Enhanced index tracking in portfolio optimization. In: 2013 International Conference on Mathematical Sciences and Statistics, vol. 1557, pp. 469–472. AIP Publishing, New York (2013)Google Scholar
  29. 29.
    Lam, W.S., Jaaman, S.H., Ismail, H.: Index tracking modeling in portfolio optimization mixed integer linear programming. J. Appl. Sci. Agricult. 9(18), 47–50 (2014)Google Scholar
  30. 30.
    Lam, W.S., Jaaman, S.H., Lam, W.H.: A new enhanced index tracking model in portfolio optimization with sum weighted approach. In: 2016 4th International Conference on Mathematical Sciences, vol. 1830, pp. 1–7. AIP Publishing, New York (2017)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Lam Weng Hoe
    • 1
    • 2
    • 3
    Email author
  • Lam Weng Siew
    • 1
    • 2
    • 3
  • Liew Kah Fai
    • 1
    • 2
  1. 1.Department of Physical and Mathematical Science, Faculty of ScienceUniversiti Tunku Abdul RahmanKamparMalaysia
  2. 2.Centre for Mathematical SciencesUniversiti Tunku Abdul RahmanKamparMalaysia
  3. 3.Centre for Business and ManagementUniversiti Tunku Abdul RahmanKamparMalaysia

Personalised recommendations