Dergiler / Üçüncü Sektör Sosyal Ekonomi / 2019 / Cilt: 54 - Sayı: 3

An Early Warning Model for Turbulence Management by Using Analytic Hierarchy Process

Sayfa
1399–1413
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

It can be said that knowing the turbulence degree of the business environment, in which it operates, provides an opportunity to manage the turbulence.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:The business environment is one of the main factors that affect its activities thereby the existence of the business is influenced, however; it has a dynamic structure which includes different conditions and situations.
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Temel Bulgular & Veri:In the results of this study, it is seen that the turbulence degree of the business environment is % 72, 24 and this degree can be considered as a high level according to the method which was used in this study.
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Sonuç & Katkı:It can be said that knowing the turbulence degree of the business environment, in which it operates, provides an opportunity to manage the turbulence.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

Özet

The business environment is one of the main factors that affect its activities thereby the existence of the business is influenced, however; it has a dynamic structure which includes different conditions and situations. Turbulence conditions and situations, one of the features of the business environment, has an impact on the functions and actions of the business. It is a vital obligation for business activities to be continued and not to be interrupted in the conditions of the business turbulence. This should be provided by the business. Therefore, it is important to predict possible conditions or situations of turbulence in terms of maintaining the lifetime of the business. The aim of this study is to develop an early earning model for predicting turbulence conditions that a business can encounter in a macro and sectoral environment. In this study turbulence evaluation criteria and turbulence factors - related to the external environment of business are identified. The external environment factors of business are utilized according to criteria which determine turbulence conditions and situations. In the final phase of the study, turbulence degree of the business environment is determined. In this project Analytic Hierarchy Process that is a multiple criteria decision making technique was used. In the results of this study, it is seen that the turbulence degree of the business environment is % 72, 24 and this degree can be considered as a high level according to the method which was used in this study. When the outcomes of the study are evaluated, it can be observed that the turbulence degree of a business environment can be identified with the model recommended in this study. It can be said that knowing the turbulence degree of the business environment, in which it operates, provides an opportunity to manage the turbulence.

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Bu Makaleye Atıf Yap

KÖMÜRCÜ, K., GÖKDENİZ, İ. (2019). An Early Warning Model for Turbulence Management by Using Analytic Hierarchy Process. Üçüncü Sektör Sosyal Ekonomi, Cilt: 54 - Sayı: 3, 1399–1413. https://ajindex.com/articles/320118

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Kaynakça (14)

  1. Acharya, V., Sharma, S. K., & Gupta, S. K. (2018). Analyzing the factors in industrial automation using analytic hierarchy process. Computers and Electrical Engineering, 71, 877-886.
  2. Barrows, E., & Neely, A. (2012). Managing Performance in Turbulent Times. Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Brodnick, R., & Gryskiewicz, S. (2018). Using Positive Turbulence for Planning and Change. Planning for Higher Education Journal, 46 (4), 27-40.
  3. Cameron, S. K., Kim, M. U., & Whetten, A. D. (1987). Organizational effects of decline and turbulence. Administrative Science Quarterly, 32, 222–240.
  4. Ciao, B. (2012). Knowledge-Based Changes in Turbulent Environments: Categories and Effects on Value Creation. Strategic Change, 21, 23–40.
  5. Expert Choice. (2000).
  6. Expert choice, Analytical hierarchy process (AHP) Software, Version 9.5. Expert Choice. Pittsburgh. Glassman, A. M., Zell, D., & Duron, S. (2015). Thinking Strategically in Turbulent Times: An Inside View of Strategy Making. New York, USA. Gökdeniz, İ., Kartal, C., & Kömürcü, K. (2017). Strategic Assessment based on 7S McKinsey Model for a Business by Using Analytic Network Process (ANP). International Journal of Academic Research in Business and Social Sciences, 7 (6), 342-353.
  7. Ivanco, M., Hou, G., & Michaeli, J. (2017). Sensitivity analysis method to address user disparities in the analytic hierarchy process. Expert Systems With Applications, 90, 111-126.
  8. Koçel, T. (2005). İşletme Yöneticiliği (10. b.). İstanbul:
  9. Arıkan. Lynch, R. (2012). Strategic management (6 b.). England: Pearson Education Limited. McCann, J. E., & Selsky, J. (1984). Hyperturbulence and the emergency of type 5 environments. Academy of Management Review, 3, 460–470.
  10. Melton, E. K. (2017). Testing Turbulence: Exploring the Determinants of Managerial Networking. Public Organization Review, 17, 19–37.
  11. Metaxas, I. N., & Koulouriotis, D. E. (2014). A theoretical study of the relation between TQM, assessment and sustainable business excellence. Total Quality Management, 25 ( 5), 494–510.
  12. Pfeffer, J., & Salancik, R. G. (2003). The external control of organizations a resource dependence perspective. Stanford, California: Stanford University Press.
  13. Podgorski, D. (2015). Measuring operational performance of OSH management system – A demonstration of AHP-based selection of leading key performance indicators. Safety Science, 73, 146-166.
  14. Rosca, I. G., & Moldoveanu, G. (2009). Management in Turbulent Conditions. Journal of Economi