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ÜNİVERSİTELERDE BİLGİ SİSTEMİ SEÇİM KRİTERLERİNİN SWARA YÖNTEMİ İLE AĞIRLIKLANDIRILMASI: AMPİRİK BİR ÇALIŞMA

Yıl 2018, Cilt 6, Sayı 1, 59 - 85, 20.04.2018
https://doi.org/10.22139/jobs.379695

Öz

Amaç: Bu çalışmada üniversitelerde satın alınması ve kullanılması planlanan bilgi sistemleri alternatifleri arasından en iyisinin seçilmesi için çeşitli seçim kriterlerinin önem derecelerine göre ağırlıklandırılmaları amaçlanmaktadır.

Yöntem: Değerlendirme kriterlerinin ağırlıklandırılmaları için Adım Adım Ağırlık Değerlendirme Oran Analizi (SWARA) yöntemi kullanılmıştır.

Bulgular: Üniversitelerin beklentilerini karşılayan bilgi sisteminin seçimi için kullanılan kriterler arasında en yüksek önem derecesine sahip kriterin “Memnuniyet” kriteri olduğu ve en düşük öneme sahip kriterin ise “Bilgi Süreçleri” olduğu sonucuna ulaşılmıştır. “Fonksiyonellik” ve “Maliyet”  sırasıyla ikinci ve üçüncü öneme sahip kriterler olarak bulunmuştur.

Sonuç: Üniversitelerde haberleşme, bilgi üretme, bilgiyi paylaşma, işbirliği yapma vb. gibi hayati önem taşıyan süreçleri kolaylaştıran bilgi sistemlerinin seçim süreci yönetilirken göz önüne alınması gereken hususlar hiyerarşik sıraya göre aydınlatılmıştır.

Kaynakça

  • Abu-sarhan, Z. (2011). Application Of Analytic Hierarchy Process ( AHP ) In The Evaluation and Selection Of an Information System Reengineering Projects. International Journal of Computer Science and Network Security, 11(1), 172–177.
  • Aghdaie, M. H., Zolfani, S. H., & Zavadskas, E. K. (2014). Synergies of data mining and multiple attribute decision making. Procedia-Social and Behavioral Sciences, 110, 767-776.
  • Aghdaie, M. H., Zolfani, S. H., & Zavadskas, E. K. (2014). Synergies of data mining and multiple attribute decision making. Procedia-Social and Behavioral Sciences, 110, 767-776.
  • Argote, L., & Ingram, P. (2000). Knowledge Transfer: A Basis for Competitive Advantage in Firms. Organizational Behavior and Human Decision Processes, 82(1), 150–169. http://doi.org/10.1006/obhd.2000.2893
  • Alimardani, M., Hashemkhani Zolfani, S., Aghdaie, M. H., & Tamošaitienė, J. (2013). A novel hybrid SWARA and VIKOR methodology for supplier selection in an agile environment. Technological and Economic Development of Economy, 19(3), 533-548.
  • Ayyıldız, E., & Demirci, E. (2018). Türkiye’de Yer Alan Şehirlerin Yaşam Kalitelerinin SWARA Entegreli TOPSIS Yöntemi ile Belirlenmesi. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi.30. 67-87.
  • Chang Lee, K., Lee, S., & Kang, I. (2005). KMPI: measuring knowledge management performance. Information & Management. http://doi.org/10.1016/j.im.2004.02.003
  • Çakır, E., & Akel, G. (2017). EVALUATION OF SERVICE QUALITY OF HOTEL AND HOLIDAY RESERVATION WEB SITES IN TURKEY BY INTEGRATED SWARA-GRAY RELATIONSHIP ANALYSIS METHOD. PressAcademia Procedia, 3(1), 81-95.
  • ÇAKIR, E. (2017). Kriter Ağırlıklarının SWARA–Copeland Yöntemi ile Belirlenmesi: Bir Üretim İşletmesinde Uygulama. Adnan Menderes Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 4(3), 42-56
  • ÇAKIR, E., & KARABIYIK, B. K. (2017). Bütünleşik SWARA-COPRAS Yöntemi Kullanarak Bulut Depolama Hizmet Sağlayıcılarının Değerlendirilmesi. Bilişim Teknolojileri Dergisi, 10(4), 417-434.
  • Darr, E. D., & Kurtzberg, T. R. (2000). An Investigation of Partner Similarity Dimensions on Knowledge Transfer. Organizational Behavior and Human Decision Processes, 82(1), 28–44. http://doi.org/10.1006/obhd.2000.2885
  • Dehnavi, A., Aghdam, I. N., Pradhan, B., & Varzandeh, M. H. M. (2015). A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran. Catena, 135, 122-148.
  • Droege, S. B., & Hoobler, J. M. (2003). Employee Turnover And Tacit Knowledge Diffusion: A Network Perspective. JOURNAL OF MANAGERIAL ISSUS, 15(1), 50–64. Erdem, İ. (2013). Yöneylem Araştırması ve WinQSB Uygulamaları. Ankara: Seçkin Yayıncılık.
  • Ernst, D., & Kim, L. (2002). Global production networks, knowledge diffusion, and local capability formation. Research Policy, 31(8–9), 1417–1429. http://doi.org/10.1016/S0048-7333(02)00072-0
  • Fasanghari, M., & Roudsari, F. H. (2008). Optimized ICT Project Selection Utilizing Fuzzy System. World Applied Sciences, 4(1), 44–49.
  • Ghorshi Nezhad, M. R., Zolfani, S. H., Moztarzadeh, F., Zavadskas, E. K., & Bahrami, M. (2015). Planning the priority of high tech industries based on SWARA-WASPAS methodology: The case of the nanotechnology industry in Iran. Economic research-Ekonomska istraživanja, 28(1), 1111-1137.
  • Guimaraes, T., & McKeen, J. D. (1988). Organizational bias in the selection of MIS projects. Omega, 16(4), 297–307. http://doi.org/10.1016/0305-0483(88)90066-7 Gupta, S., Woodside, A., Dubelaar, C., & Bradmore, D. (2009). Diffusing knowledge-based core competencies for leveraging innovation strategies: Modelling outsourcing to knowledge process organizations (KPOs) in pharmaceutical networks. Industrial Marketing Management, 38(2), 219–227. http://doi.org/10.1016/j.indmarman.2008.12.010
  • Hasan Aghdaie, M., Hashemkhani Zolfani, S., & Zavadskas, E. K. (2013). Decision making in machine tool selection: An integrated approach with SWARA and COPRAS-G methods. Engineering Economics, 24(1), 5-17.
  • Hashemkhani Zolfani, S., & Bahrami, M. (2014). Investment prioritizing in high tech industries based on SWARA-COPRAS approach. Technological and Economic Development of Economy, 20(3), 534-553.
  • Hasselbring, W. (2000). Information system integration. Communications of the ACM, 43(6), 32–38. http://doi.org/10.1145/336460.336472
  • He, W., Qiao, Q., & Wei, K. K. (2009). Social relationship and its role in knowledge management systems usage. Information and Management, 46(3), 175–180. http://doi.org/10.1016/j.im.2007.11.005
  • Heidary Dahooie, J., Beheshti Jazan Abadi, E., Vanaki, A. S., & Firoozfar, H. R. (2018). Competency‐based IT personnel selection using a hybrid SWARA and ARAS‐G methodology. Human Factors and Ergonomics in Manufacturing & Service Industries
  • Hillier, F. S., & Lieberman, G. J. (2001). Introduction to Operational Research. New York: McGraw-Hill.
  • Huang, J. (2008). Combining entropy weight and TOPSIS method for information system selection. In Proceedings of the IEEE International Conference on Automation and Logistics, ICAL 2008 (pp. 1281–1284). http://doi.org/10.1109/ICAL.2008.4636483
  • Işık, A., & Adalı, E. (2016). A comparative study for the agricultural tractor selection problem. Decision Science Letters, 5(4), 569-580.
  • Jiang, J. J., & Klein, G. (1999). Information system project-selection criteria variations within strategic classes. IEEE Transactions on Engineering Management, 46(2), 171–176. http://doi.org/10.1109/17.759145
  • Juodagalvienė, B., Turskis, Z., Šaparauskas, J., & Endriukaitytė, A. (2017). Integrated multi-criteria evaluation of house’s plan shape based on the EDAS and SWARA methods. Engineering Structures and Technologies, 9(3), 117-125.
  • Karabasevic, D., Stanujkic, D., Urosevic, S., & Maksimovic, M. (2015). Selection of candidates in the mining industry based on the application of the SWARA and the MULTIMOORA methods. Acta Montanistica Slovaca, 20(2).
  • Karabašević, D., Stanujkić, D., Urošević, S., & Maksimović, M. (2016). An approach to personnel selection based on Swara and Waspas methods. BizInfo (Blace) Journal of Economics, Management and Informatics, 7(1), 1-11.
  • Karabasevic, D., Paunkovic, J., & Stanujkic, D. (2016). Ranking of companies according to the indicators of corporate social responsibility based on SWARA and ARAS methods. Serbian Journal of Management, 11(1), 43-53
  • Kayrak, M. (2007). BİLİŞİM SİSTEMLERİ STRATEJİSİNİN ÖNEMİ VE SAYIŞTAY DENEYİMİ. SAYIŞTAY DERGİSİ ●, 65, 199–208.
  • Keršuliene, V., Zavadskas, E. K., & Turskis, Z. (2010). Selection of Rational Dispute Resolution Method by Applying New Step-Wise Weight Assessment Ratio Analysis (Swara). Journal of Business Economics and Management, 11(2), 243–258.
  • Keršulienė, V., & Turskis, Z. (2011). Integrated fuzzy multiple criteria decision making model for architect selection. Technological and Economic Development of Economy, 17(4), 645-666.
  • Kim, I., Shin, S., Choi, Y., Manh Thang, N., Ramos, E., & Hwang, W.-J. (2009). Development of a Project Selection Method on Information System Using ANP and Fuzzy Logic. World Academy of Science, Engineering and Technology, 29(5), 411–416.
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WEIGHTING UNIVERSITY INFORMATION SYSTEM SELECTION CRITERIA BY SWARA METHOD: AN EMPIRICAL STUDY

Yıl 2018, Cilt 6, Sayı 1, 59 - 85, 20.04.2018
https://doi.org/10.22139/jobs.379695

Öz

Aim: It is aimed to weight various information system selection criteria according to their importance levels in order to select the best one among the alternatives which is planned to be purchased and used by universities.

Method: The Step-by-Step Weighted Ratio Analysis (SWARA) method was used to weight the evaluation criteria.

Findings: It was concluded that the criterion with the highest degree of importance is the "Satisfaction" and the lowest criterion is the "Information Processes" among the criteria used for selecting the information system that meets the expectations of the universities. "Functionality" and "Cost" were found to have the second and third important criteria respectively.

Results: Aspects that must be considered while managing selection process of information systems that facilitates some vital process in universities such as communication, information generation, sharing of information, cooperation, etc. were highlighted in a hierarchical order.

Kaynakça

  • Abu-sarhan, Z. (2011). Application Of Analytic Hierarchy Process ( AHP ) In The Evaluation and Selection Of an Information System Reengineering Projects. International Journal of Computer Science and Network Security, 11(1), 172–177.
  • Aghdaie, M. H., Zolfani, S. H., & Zavadskas, E. K. (2014). Synergies of data mining and multiple attribute decision making. Procedia-Social and Behavioral Sciences, 110, 767-776.
  • Aghdaie, M. H., Zolfani, S. H., & Zavadskas, E. K. (2014). Synergies of data mining and multiple attribute decision making. Procedia-Social and Behavioral Sciences, 110, 767-776.
  • Argote, L., & Ingram, P. (2000). Knowledge Transfer: A Basis for Competitive Advantage in Firms. Organizational Behavior and Human Decision Processes, 82(1), 150–169. http://doi.org/10.1006/obhd.2000.2893
  • Alimardani, M., Hashemkhani Zolfani, S., Aghdaie, M. H., & Tamošaitienė, J. (2013). A novel hybrid SWARA and VIKOR methodology for supplier selection in an agile environment. Technological and Economic Development of Economy, 19(3), 533-548.
  • Ayyıldız, E., & Demirci, E. (2018). Türkiye’de Yer Alan Şehirlerin Yaşam Kalitelerinin SWARA Entegreli TOPSIS Yöntemi ile Belirlenmesi. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi.30. 67-87.
  • Chang Lee, K., Lee, S., & Kang, I. (2005). KMPI: measuring knowledge management performance. Information & Management. http://doi.org/10.1016/j.im.2004.02.003
  • Çakır, E., & Akel, G. (2017). EVALUATION OF SERVICE QUALITY OF HOTEL AND HOLIDAY RESERVATION WEB SITES IN TURKEY BY INTEGRATED SWARA-GRAY RELATIONSHIP ANALYSIS METHOD. PressAcademia Procedia, 3(1), 81-95.
  • ÇAKIR, E. (2017). Kriter Ağırlıklarının SWARA–Copeland Yöntemi ile Belirlenmesi: Bir Üretim İşletmesinde Uygulama. Adnan Menderes Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 4(3), 42-56
  • ÇAKIR, E., & KARABIYIK, B. K. (2017). Bütünleşik SWARA-COPRAS Yöntemi Kullanarak Bulut Depolama Hizmet Sağlayıcılarının Değerlendirilmesi. Bilişim Teknolojileri Dergisi, 10(4), 417-434.
  • Darr, E. D., & Kurtzberg, T. R. (2000). An Investigation of Partner Similarity Dimensions on Knowledge Transfer. Organizational Behavior and Human Decision Processes, 82(1), 28–44. http://doi.org/10.1006/obhd.2000.2885
  • Dehnavi, A., Aghdam, I. N., Pradhan, B., & Varzandeh, M. H. M. (2015). A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran. Catena, 135, 122-148.
  • Droege, S. B., & Hoobler, J. M. (2003). Employee Turnover And Tacit Knowledge Diffusion: A Network Perspective. JOURNAL OF MANAGERIAL ISSUS, 15(1), 50–64. Erdem, İ. (2013). Yöneylem Araştırması ve WinQSB Uygulamaları. Ankara: Seçkin Yayıncılık.
  • Ernst, D., & Kim, L. (2002). Global production networks, knowledge diffusion, and local capability formation. Research Policy, 31(8–9), 1417–1429. http://doi.org/10.1016/S0048-7333(02)00072-0
  • Fasanghari, M., & Roudsari, F. H. (2008). Optimized ICT Project Selection Utilizing Fuzzy System. World Applied Sciences, 4(1), 44–49.
  • Ghorshi Nezhad, M. R., Zolfani, S. H., Moztarzadeh, F., Zavadskas, E. K., & Bahrami, M. (2015). Planning the priority of high tech industries based on SWARA-WASPAS methodology: The case of the nanotechnology industry in Iran. Economic research-Ekonomska istraživanja, 28(1), 1111-1137.
  • Guimaraes, T., & McKeen, J. D. (1988). Organizational bias in the selection of MIS projects. Omega, 16(4), 297–307. http://doi.org/10.1016/0305-0483(88)90066-7 Gupta, S., Woodside, A., Dubelaar, C., & Bradmore, D. (2009). Diffusing knowledge-based core competencies for leveraging innovation strategies: Modelling outsourcing to knowledge process organizations (KPOs) in pharmaceutical networks. Industrial Marketing Management, 38(2), 219–227. http://doi.org/10.1016/j.indmarman.2008.12.010
  • Hasan Aghdaie, M., Hashemkhani Zolfani, S., & Zavadskas, E. K. (2013). Decision making in machine tool selection: An integrated approach with SWARA and COPRAS-G methods. Engineering Economics, 24(1), 5-17.
  • Hashemkhani Zolfani, S., & Bahrami, M. (2014). Investment prioritizing in high tech industries based on SWARA-COPRAS approach. Technological and Economic Development of Economy, 20(3), 534-553.
  • Hasselbring, W. (2000). Information system integration. Communications of the ACM, 43(6), 32–38. http://doi.org/10.1145/336460.336472
  • He, W., Qiao, Q., & Wei, K. K. (2009). Social relationship and its role in knowledge management systems usage. Information and Management, 46(3), 175–180. http://doi.org/10.1016/j.im.2007.11.005
  • Heidary Dahooie, J., Beheshti Jazan Abadi, E., Vanaki, A. S., & Firoozfar, H. R. (2018). Competency‐based IT personnel selection using a hybrid SWARA and ARAS‐G methodology. Human Factors and Ergonomics in Manufacturing & Service Industries
  • Hillier, F. S., & Lieberman, G. J. (2001). Introduction to Operational Research. New York: McGraw-Hill.
  • Huang, J. (2008). Combining entropy weight and TOPSIS method for information system selection. In Proceedings of the IEEE International Conference on Automation and Logistics, ICAL 2008 (pp. 1281–1284). http://doi.org/10.1109/ICAL.2008.4636483
  • Işık, A., & Adalı, E. (2016). A comparative study for the agricultural tractor selection problem. Decision Science Letters, 5(4), 569-580.
  • Jiang, J. J., & Klein, G. (1999). Information system project-selection criteria variations within strategic classes. IEEE Transactions on Engineering Management, 46(2), 171–176. http://doi.org/10.1109/17.759145
  • Juodagalvienė, B., Turskis, Z., Šaparauskas, J., & Endriukaitytė, A. (2017). Integrated multi-criteria evaluation of house’s plan shape based on the EDAS and SWARA methods. Engineering Structures and Technologies, 9(3), 117-125.
  • Karabasevic, D., Stanujkic, D., Urosevic, S., & Maksimovic, M. (2015). Selection of candidates in the mining industry based on the application of the SWARA and the MULTIMOORA methods. Acta Montanistica Slovaca, 20(2).
  • Karabašević, D., Stanujkić, D., Urošević, S., & Maksimović, M. (2016). An approach to personnel selection based on Swara and Waspas methods. BizInfo (Blace) Journal of Economics, Management and Informatics, 7(1), 1-11.
  • Karabasevic, D., Paunkovic, J., & Stanujkic, D. (2016). Ranking of companies according to the indicators of corporate social responsibility based on SWARA and ARAS methods. Serbian Journal of Management, 11(1), 43-53
  • Kayrak, M. (2007). BİLİŞİM SİSTEMLERİ STRATEJİSİNİN ÖNEMİ VE SAYIŞTAY DENEYİMİ. SAYIŞTAY DERGİSİ ●, 65, 199–208.
  • Keršuliene, V., Zavadskas, E. K., & Turskis, Z. (2010). Selection of Rational Dispute Resolution Method by Applying New Step-Wise Weight Assessment Ratio Analysis (Swara). Journal of Business Economics and Management, 11(2), 243–258.
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Ayrıntılar

Birincil Dil Türkçe
Konular Sosyal, İşletme
Yayınlanma Tarihi 6/1
Bölüm Özgün Makaleler
Yazarlar

Büşra KUTLU KARABIYIK (Sorumlu Yazar)
0000-0002-6691-2921
Türkiye


Mehmet Erdemir GÜNDOĞMUŞ Bu kişi benim
0000-0001-7789-8304
Türkiye

Yayımlanma Tarihi 20 Nisan 2018
Başvuru Tarihi 16 Ocak 2018
Kabul Tarihi 5 Nisan 2018
Yayınlandığı Sayı Yıl 2018, Cilt 6, Sayı 1

Kaynak Göster

APA Kutlu Karabıyık, B. & Gündoğmuş, M. E. (2018). ÜNİVERSİTELERDE BİLGİ SİSTEMİ SEÇİM KRİTERLERİNİN SWARA YÖNTEMİ İLE AĞIRLIKLANDIRILMASI: AMPİRİK BİR ÇALIŞMA . İşletme Bilimi Dergisi , 6 (1) , 59-85 . DOI: 10.22139/jobs.379695

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