A novel approach for institutionalization analysis based on fuzzy cognitive maps

Year 2018, Volume: 22 Issue: 2, 557 - 571, 01.04.2018

Enes Furkan Erkan , Özer Uygun , Alper Kiraz

https://doi.org/10.16984/saufenbilder.330835

Cited By: 1

Abstract

Nowadays, it becomes very important to know the level of
institutionalization and as a result what improvements they can make for
organizations. Though there are many conceptual studies of institutionalization
in the literature, there is no study based on numerical methods that can
provide a foresight about institutionalization. In this paper, a new model has
been proposed by determining concepts that are effective on institutionalization
from literature and expert opinions. Firstly, the relationships between the
concepts are taken from the experts linguistically. Linguistic expressions are
converted to numerical values using the center of gravity method (COG) used in fuzzy
logic applications. Then, three different scenarios were investigated by using
the Fuzzy Cognitive Maps (FCMs) algorithm and the future states of the concepts
were determined and interpreted. In the first scenario, an organization with
poorly managed organizational concepts was considered. The institutionalization
tendency in this organization has reached to 0,027 value which is the
estimation calculated by FCM algorithm in the future. The second scenario and
the third scenario represents a midlevel and good organization respectively.
Institutionalization tendency values were 0.97 for the second and third
scenarios. However, when the number of iterations representing the time period
is examined, it is seen that the organization thought in the third scenario has
reached this value before 9 iterations. This is because the organization in the
third scenario is well managed in the current situation. With the developed
model, the most effective concepts on institutionalization were also
identified. It has been determined that the most important concepts affecting
institutionalization are process management, information management and
strategic management. Compared to the literature, the results seem to be
consistent.

Keywords

Fuzzy cognitive maps, institutionalization, excellence models

Kurumsallaşma analizi için bulanık bilişsel haritalar temelli yeni bir yaklaşım

Abstract

Kurumsallaşma, organizasyondaki
yöneticilere ve çalışanlara bağlı olmadan, tüm süreçlerin şeffaf ve sistematik
olarak yürütülmesini ifade eder.
Kurumsallaşmanın mükemmeliyeti organizasyonun misyon, vizyon ve stratejik
hedeflerine paralel yönde seyreden ticari faaliyetlerle sağlanabilir.
Kurumsallaşmanın organizasyon içerisinde benimsenememesi organizasyonların uzun
süreli yaşam döngüsünü engellemektedir. Bu nedenle, organizasyonların
kurumsallaşma seviyelerini takip edebileceği ve bu takip sonucunda hangi
iyileştirmeleri yapabilecekleri konusu çok önemli hale gelmektedir. Literatürde
kurumsallaşmanın kavramsal olarak incelendiği birçok çalışma olmasına rağmen
ileriye yönelik bir öngörü elde edilebilen sayısal yöntemlere dayalı bir
çalışmaya rastlanmamıştır. Bu çalışmada, kurumsallaşma üzerinde etkili olan
konseptler literatür ve uzman görüşleriyle belirlenerek yeni bir model
önerilmiştir. Öncelikle uzmanlardan konseptler arasındaki ilişkiler dilsel
olarak alınmıştır. Dilsel ifadeler, bulanık mantık uygulamalarında kullanılan
ağırlık merkezi yöntemiyle sayısal değerlere dönüştürülmüştür. Daha sonra,
Bulanık Bilişsel Haritalar(BBH) algoritması kullanılarak 3 farklı senaryo
incelenmiş ve konseptlerin gelecekteki durumları tespit edilip, yorumlanmıştır.
Geliştirilen model ile aynı zamanda kurumsallaşma üzerindeki en etkili
konseptler ve geleceğe yönelik öngörüler de belirlenmiştir.  

Keywords

Bulanık bilişsel haritalar, kurumsallaşma

References

• L. Broom and P. Selznick, Sociology: A Text with Adapted Readings. Row, Peterson, 1955.
• A. D. May, A. Lotfi, C. Langensiepen, K. Lee, and G. Acampora, “Human Emotional Understanding for Empathetic Companion Robots,” in Advances in Computational Intelligence Systems, Springer, Cham, 2017, pp. 277–285.
• A. Nikas and H. Doukas, “Developing Robust Climate Policies: A Fuzzy Cognitive Map Approach,” in Robustness Analysis in Decision Aiding, Optimization, and Analytics, M. Doumpos, C. Zopounidis, and E. Grigoroudis, Eds. Springer International Publishing, 2016, pp. 239–263.
• A. Amirkhani, E. I. Papageorgiou, A. Mohseni, and M. R. Mosavi, “A review of fuzzy cognitive maps in medicine: Taxonomy, methods, and applications,” Comput. Methods Programs Biomed., vol. 142, pp. 129–145, Apr. 2017.
• R. Romero-Córdoba, J. A. Olivas, F. P. Romero, F. Alonso-Gonzalez, and J. Serrano-Guerrero, “An Application of Fuzzy Prototypes to the Diagnosis and Treatment of Fuzzy Diseases,” Int. J. Intell. Syst., vol. 32, no. 2, pp. 194–210, Feb. 2017.
• D. T. Sarabai and K. Arthi, “Efficient Breast Cancer Classification Using Improved Fuzzy Cognitive Maps with Csonn,” Int. J. Appl. Eng. Res., vol. 11, no. 4, pp. 2478–2485, 2016.
• E. I. Papageorgiou, J. Subramanian, A. Karmegam, and N. Papandrianos, “A risk management model for familial breast cancer: A new application using Fuzzy Cognitive Map method,” Comput. Methods Programs Biomed., vol. 122, no. 2, pp. 123–135, Nov. 2015.
• J. Subramanian, A. Karmegam, E. Papageorgiou, N. Papandrianos, and A. Vasukie, “An integrated breast cancer risk assessment and management model based on fuzzy cognitive maps,” Comput. Methods Programs Biomed., vol. 118, no. 3, pp. 280–297, Mar. 2015.
• C. T. Chen and Y. T. Chiu, “A study of fuzzy cognitive map model with dynamic adjustment method for the interaction weights,” in 2016 International Conference on Advanced Materials for Science and Engineering (ICAMSE), 2016, pp. 699–702.
• D. M. Case and C. D. Stylios, “Fuzzy Cognitive Map to model project management problems,” in 2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS), 2016, pp. 1–6.
• M. I. F. Ribeiro, F. A. F. Ferreira, M. S. Jalali, and I. Meidutė-Kavaliauskienė, “A fuzzy knowledge-based framework for risk assessment of residential real estate investments,” Technol. Econ. Dev. Econ., vol. 23, no. 1, pp. 140–156, Jan. 2017.
• F. A. F. Ferreira, J. J. M. Ferreira, C. I. M. A. S. Fernandes, I. Meidutė-Kavaliauskienė, and M. S. Jalali, “Enhancing knowledge and strategic planning of bank customer loyalty using fuzzy cognitive maps,” Technol. Econ. Dev. Econ., pp. 1–17, Feb. 2017.
• P. P. Groumpos, “Modelling Business and Management Systems Using Fuzzy Cognitive Maps: A Critical Overview,” IFAC-Pap., vol. 48, no. 24, pp. 207–212, Jan. 2015.
• P. Cano Marchal, J. G. Garcia, and J. G. Ortega, “Application of Fuzzy Cognitive Maps and Run-to-Run Control to a Decision Support System for Global Set-Point Determination,” IEEE Trans. Syst. Man Cybern. Syst., pp. 1–12, 2017.
• G. P. Peter, A. P. Antigoni, and G. P. Vasileios, “A New Mathematical Modelling Approach for Viticulture and Winemaking Using Fuzzy Cognitive Maps,” IFAC-Pap., vol. 48, no. 24, pp. 15–20, Jan. 2015.
• F. C. A. Pacilly, J. C. J. Groot, G. J. Hofstede, B. F. Schaap, and E. T. L. van Bueren, “Analysing potato late blight control as a social-ecological system using fuzzy cognitive mapping,” Agron. Sustain. Dev., vol. 36, no. 2, p. 35, Jun. 2016.
• J. M. Vasslides and O. P. Jensen, “Fuzzy cognitive mapping in support of integrated ecosystem assessments: Developing a shared conceptual model among stakeholders,” J. Environ. Manage., vol. 166, pp. 348–356, Jan. 2016.
• R. Natarajan, J. Subramanian, and E. I. Papageorgiou, “Hybrid learning of fuzzy cognitive maps for sugarcane yield classification,” Comput. Electron. Agric., vol. 127, pp. 147–157, Sep. 2016.
• I. Mustapha, B. M. Ali, A. Sali, M. F. A. Rasid, and H. Mohamad, “An energy efficient Reinforcement Learning based Cooperative Channel Sensing for Cognitive Radio Sensor Networks,” Pervasive Mob. Comput., vol. 35, pp. 165–184, Feb. 2017.
• J. Kim, M. Han, Y. Lee, and Y. Park, “Futuristic data-driven scenario building: Incorporating text mining and fuzzy association rule mining into fuzzy cognitive map,” Expert Syst. Appl., vol. 57, pp. 311–323, Sep. 2016.
• M. Amer, T. U. Daim, and A. Jetter, “Technology roadmap through fuzzy cognitive map-based scenarios: the case of wind energy sector of a developing country,” Technol. Anal. Strateg. Manag., vol. 28, no. 2, pp. 131–155, Feb. 2016.
• G. Kyriakarakos, A. I. Dounis, K. G. Arvanitis, and G. Papadakis, “Design of a Fuzzy Cognitive Maps variable-load energy management system for autonomous PV-reverse osmosis desalination systems: A simulation survey,” Appl. Energy, vol. 187, pp. 575–584, Feb. 2017.
• V. Çoban and S. Ç. Onar, “Modelling Solar Energy Usage with Fuzzy Cognitive Maps,” in Intelligence Systems in Environmental Management: Theory and Applications, C. Kahraman and İ. U. Sari, Eds. Springer International Publishing, 2017, pp. 159–187.
• T. C. Kahveci, “The institutionalization and the enterprise modeling in the manufacturing firms,” Sakarya Üniversitesi, 2007.
• P. Selznick, “Institutionalism ‘Old’ and ‘New,’” Adm. Sci. Q., vol. 41, no. 2, p. 270, Jun. 1996.
• E. Karpuzoğlu, “Aile Şirketlerinin Sürekliliğinde Kurumsallaşma,” Istanb. Kültür Üniversitesi, vol. 1, pp. 42–53, 2004.
• J. R. Kimberly, “Issues in the Creation of Organizations: Initiation, Innovation, and Institutionalization,” Acad. Manage. J., vol. 22, no. 3, pp. 437–457, Sep. 1979.
• İ. Fındıkçı, Aile Şirketleri. 2014.
• B. Kosko, “Fuzzy cognitive maps,” vol. 24, pp. 65–75, 1986.
• R. Axelrod, Ed., Structure of Decision: The Cognitive Maps of Political Elites. Princeton University Press, 1976.
• A. K. Tsadiras, “Comparing the inference capabilities of binary, trivalent and sigmoid fuzzy cognitive maps,” Inf. Sci., vol. 178, no. 20, pp. 3880–3894, Oct. 2008.
• E. I. Papageorgiou, A. T. Markinos, and T. A. Gemtos, “Fuzzy cognitive map based approach for predicting yield in cotton crop production as a basis for decision support system in precision agriculture application,” Appl. Soft Comput., vol. 11, no. 4, pp. 3643–3657, Jun. 2011.
• Z. Sen, Fuzzy Logic and Hydrological Modeling. CRC Press, 2009.
• P. P. Groumpos, “Fuzzy cognitive maps: Basic theories and their application to complex systems,” in Fuzzy cognitive maps, Springer, 2010, pp. 1–22.
• P. Chytas, M. Glykas, and G. Valiris, “Software reliability modelling using fuzzy cognitive maps,” in Fuzzy Cognitive Maps, Springer, 2010, pp. 217–230.
• G. Caruso, C. Scartascini, and M. Tommasi, “Are we all playing the same game? The economic effects of constitutions depend on the degree of institutionalization,” Eur. J. Polit. Econ., vol. 38, pp. 212–228, Jun. 2015.
• D. Safina, “Favouritism and Nepotism in an Organization: Causes and Effects,” Procedia Econ. Finance, vol. 23, pp. 630–634, Jan. 2015.
• Ö. Uygun, T. Canvar Kahveci, H. Taşkın, and B. Piriştine, “Readiness assessment model for institutionalization of SMEs using fuzzy hybrid MCDM techniques,” Comput. Ind. Eng., vol. 88, pp. 217–228, Oct. 2015.
• E. F. Erkan, “Bulanık bilişsel haritalama yöntemiyle kurumsallaşma düzeyinin analizi,” Sakarya Üniversitesi, 2017.