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Fuzzy Multi Criteria Group Decision for Determining the Relative Importance of Critical Success Factors in Accounting Information Systems

Year 2021, Volume: 17 Issue: 37, 4473 - 4486, 31.05.2021
https://doi.org/10.26466/opus.869767

Abstract

An effective Accounting Information System (AIS) will support the effectiveness of management functions in an enterprise. So, the aim of this research is to construct a prioritization-based taxonomy of critical success factors using fuzzy multi criteria group decision method: Fuzzy Group Analytical Hierarchy Process (FGAHP). In order to identify critical success factors of AIS, firstly, a deep literature survey was done. Then, a group of academic experts has been chosen from the accounting branch for pairwise comparison of critical success factors of AIS. After then, these chosen accounting experts evaluate the relative significance of critical success factors of the AIS which were defined from the literature. The results of the FGAHP analysis show that the most important success criteria of an AIS is the technological factors with weight 54%, and the external factors are the least important main success criteria for an AIS with weight 9%. Also, FGAHP evaluation of accounting experts decisions about sub criteria indicate that "security of knowledge” is the most important sub criteria with the weight 19%, “software supporting systems” with weight 14% is the second important one, and. “R&D activities and improvement” criteria with weight 8% is the third important sub criteria.

References

  • Acar, D. and Özçelik, H.(2011). Critical success factors that affect the quality of ınformation which is produced by accounting ınformation systems. The Journal of Accounting and Finance, 49, 10-23.
  • Bahadır, S.K., Çebi,S., Kahraman, C. and Kaloğlu, F.(2013). Developing a smart clothing system for blinds based on ınformation axiom. International Journal of Computational Intelligence Systems, 6(2), 279-292.
  • Bullen, V.C. and Rockard, J.F.(1981). A primer on critical success factors, center for ınformation system research working paper (1220-81). Sloan School of Management, Massachussets Institute of Technology.
  • Büyüközkan, G. and Feyzioğlu, O.(2004). A fuzzy-logic-based decision- making approach for new product development. Int. J. Production Economics, 90, 27–45.
  • Chang, D.Y.(1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649-655.
  • Çukacı, Y.C.(2005). The evaluation of information as an economic value in terms of business and general economy. Eastern Anatolia Region Studies, 3(3), 11-19.
  • Demirel, T., Çetin-Demirel, N. and Kahraman, C.(2008). Fuzzy analytical hierarchy process and ıts application. In Fuzzy Multi-Criteria Decision Making: Theory and Applications with Recent Developments. (Ed. C. Kahraman), Springer.
  • Dinç, E. and Abdioğlu, H.(2009). Relationships between corporate governance and accounting ınformation systems on businesses: An empirical research on ISE– 100 Companies, Balıkesir University The Journal of Social Sciences Institute, 12(21), 157-184.
  • Ergin, H.(1997). Strategic management accounting. Kütahya: Nadir Publishing House.
  • Jiao, F.(2016). Risk assessment and control for accounting ınformation system based on fuzzy analytic hierarchy process. Eighth International Conference on Measuring Technology and Mechatronics Automation, 11-12 March 2016, Makao China, 580-583.
  • Kahraman, C.(2018). A brief literature review on fuzzy AHP. International Journal of the Analytic Hierarchy Process, 10(2), 293 -297.
  • Karagül, A.A.(2005). Information management, corporate source planning and accounting education ın the perspective of accounting ınformation systems. XXIV. Accounting Education Symposium Proceedings Book, 59-87.
  • Karahan, M.(2020). The relationship between pricing strategies and accounting ınformation quality in tourism enterprises. International Journal of Society Researches, 15(26), 4664-4685.
  • Keskin, D.F.(2020). A fuzzy ıntegrated approach for resilient supply chain network design problem. Süleyman Demirel University Journal of Vizyoner, 11(28), 770-789.
  • Lin, C.H.(2020). Optimal Core Operation in Supply Chain Finance Ecosystem by Integrating the Fuzzy Algorithm and Hierarchical Framework, International Journal of Computational Intelligence Systems, 13(1), 259–274.
  • Meixner, O.(2009). Fuzzy AHP group decision analysis and ıts application for the evaluation of energy sources. Vienna, Austria: Institute of Marketing and Innovation.
  • Mizrahi,R., Tektaş, K., and Karakul-Kayahan, A. (2020). A. Fuzzy AHP based prioritization and taxonomy of ınformation quality factors in accounting ınformation systems. In Intelligent and Fuzzy Techniques: Smart and Innovative Solutions. Proceedings of the INFUS 2020 Conference, Istanbul, Turkey, July 21–23, Springer, (2020), 1407-1414.
  • Rockard, J.F.(1979). Chief executives define their own data needs. Harward Business Review, 57(2), 81.
  • Rockard, J.F.(1982). The changing role of the ınformation systems executive: a critical success factors perspective. Sloan Management Review, 24(1), 1-33.
  • Saaty, T.L.(1995). Decision making for leaders. the analytic hierarchy process for decisions in a complex World. Pittsburgh: RWS Publications.
  • Sagedhi, A., Azar, A., and Rad, R.S.(2012). Developing a fuzzy group AHP model for prioritizing the factors affecting success of high-tech SME's in Iran: A Case Study. Procedia - Social and Behavioral Sciences, 62, 957 – 961.
  • Xu, H., Koronios, A. and Brown, N.(2002). Managing data quality in accounting ınformation systems. Idea Group Inc.
  • Yager, R.R.(1981). A procedure for ordering fuzzy subsets of the unit ınterval. Information Sciences, 24 (2), 143- 161.

Bulanık Çok Kriterli Grup Kararı Verme Yöntemi ile Muhasebe Bilgi Sisteminde Kritik Başarı Faktörlerinin Göreceli Öneminin Belirlenmesi

Year 2021, Volume: 17 Issue: 37, 4473 - 4486, 31.05.2021
https://doi.org/10.26466/opus.869767

Abstract

Etkili bir Muhasebe Bilgi Sistemi (MBS), bir kuruluştaki yönetim işlevlerinin etkinliğini destekleyecektir. Dolayısıyla, bu araştırmanın amacı, bulanık çok kriterli grup karar yöntemi: Bulanık Grup Analitik Hiyerarşi Süreci (FGAHP) kullanarak kritik başarı faktörlerinin önceliklendirmeye dayalı bir taksonomisini oluşturmaktır. MBS'nin kritik başarı faktörlerini belirlemek için ilk olarak kapsamlı bir literatür taraması yapılmıştır. Ardından, MBS'nin kritik başarı faktörlerinin ikili olarak karşılaştırılması için muhasebe anabilim dalından bir grup akademik uzman seçilmiştir. Daha sonra, seçilen bu muhasebe uzmanları, literatürden tanımlanan MBS'nin kritik başarı faktörlerinin göreceli önemini değerlendirmişlerdir. FGAHP analizinin sonuçları, bir MBS'nin en önemli ana başarı kriterinin % 54 ağırlık ile teknolojik faktörler olduğunu ve % 9 ağırlık ile bir MBS için dış faktörlerin en az önemli ana başarı kriterleri olduğunu göstermektedir. Ayrıca, muhasebe uzmanlarının alt kriterlere ilişkin kararlarının FGAHP yöntemiyle değerlendirilmesine göre, %19 ağırlık ile "bilgi güvenliği" en önemli alt kriter, %14 ağırlık ile "yazılım destek sistemleri" ikinci ve "Ar-Ge faaliyetleri" ise %8 ağırlık ile üçüncü önemli alt kriterdir.

References

  • Acar, D. and Özçelik, H.(2011). Critical success factors that affect the quality of ınformation which is produced by accounting ınformation systems. The Journal of Accounting and Finance, 49, 10-23.
  • Bahadır, S.K., Çebi,S., Kahraman, C. and Kaloğlu, F.(2013). Developing a smart clothing system for blinds based on ınformation axiom. International Journal of Computational Intelligence Systems, 6(2), 279-292.
  • Bullen, V.C. and Rockard, J.F.(1981). A primer on critical success factors, center for ınformation system research working paper (1220-81). Sloan School of Management, Massachussets Institute of Technology.
  • Büyüközkan, G. and Feyzioğlu, O.(2004). A fuzzy-logic-based decision- making approach for new product development. Int. J. Production Economics, 90, 27–45.
  • Chang, D.Y.(1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649-655.
  • Çukacı, Y.C.(2005). The evaluation of information as an economic value in terms of business and general economy. Eastern Anatolia Region Studies, 3(3), 11-19.
  • Demirel, T., Çetin-Demirel, N. and Kahraman, C.(2008). Fuzzy analytical hierarchy process and ıts application. In Fuzzy Multi-Criteria Decision Making: Theory and Applications with Recent Developments. (Ed. C. Kahraman), Springer.
  • Dinç, E. and Abdioğlu, H.(2009). Relationships between corporate governance and accounting ınformation systems on businesses: An empirical research on ISE– 100 Companies, Balıkesir University The Journal of Social Sciences Institute, 12(21), 157-184.
  • Ergin, H.(1997). Strategic management accounting. Kütahya: Nadir Publishing House.
  • Jiao, F.(2016). Risk assessment and control for accounting ınformation system based on fuzzy analytic hierarchy process. Eighth International Conference on Measuring Technology and Mechatronics Automation, 11-12 March 2016, Makao China, 580-583.
  • Kahraman, C.(2018). A brief literature review on fuzzy AHP. International Journal of the Analytic Hierarchy Process, 10(2), 293 -297.
  • Karagül, A.A.(2005). Information management, corporate source planning and accounting education ın the perspective of accounting ınformation systems. XXIV. Accounting Education Symposium Proceedings Book, 59-87.
  • Karahan, M.(2020). The relationship between pricing strategies and accounting ınformation quality in tourism enterprises. International Journal of Society Researches, 15(26), 4664-4685.
  • Keskin, D.F.(2020). A fuzzy ıntegrated approach for resilient supply chain network design problem. Süleyman Demirel University Journal of Vizyoner, 11(28), 770-789.
  • Lin, C.H.(2020). Optimal Core Operation in Supply Chain Finance Ecosystem by Integrating the Fuzzy Algorithm and Hierarchical Framework, International Journal of Computational Intelligence Systems, 13(1), 259–274.
  • Meixner, O.(2009). Fuzzy AHP group decision analysis and ıts application for the evaluation of energy sources. Vienna, Austria: Institute of Marketing and Innovation.
  • Mizrahi,R., Tektaş, K., and Karakul-Kayahan, A. (2020). A. Fuzzy AHP based prioritization and taxonomy of ınformation quality factors in accounting ınformation systems. In Intelligent and Fuzzy Techniques: Smart and Innovative Solutions. Proceedings of the INFUS 2020 Conference, Istanbul, Turkey, July 21–23, Springer, (2020), 1407-1414.
  • Rockard, J.F.(1979). Chief executives define their own data needs. Harward Business Review, 57(2), 81.
  • Rockard, J.F.(1982). The changing role of the ınformation systems executive: a critical success factors perspective. Sloan Management Review, 24(1), 1-33.
  • Saaty, T.L.(1995). Decision making for leaders. the analytic hierarchy process for decisions in a complex World. Pittsburgh: RWS Publications.
  • Sagedhi, A., Azar, A., and Rad, R.S.(2012). Developing a fuzzy group AHP model for prioritizing the factors affecting success of high-tech SME's in Iran: A Case Study. Procedia - Social and Behavioral Sciences, 62, 957 – 961.
  • Xu, H., Koronios, A. and Brown, N.(2002). Managing data quality in accounting ınformation systems. Idea Group Inc.
  • Yager, R.R.(1981). A procedure for ordering fuzzy subsets of the unit ınterval. Information Sciences, 24 (2), 143- 161.
There are 23 citations in total.

Details

Primary Language English
Subjects Operation
Journal Section Articles
Authors

Rozi Mizrahi 0000-0001-7173-4456

Berna Tektaş 0000-0002-0379-5916

Aygülen Kayahan Karakul 0000-0002-8310-1709

Publication Date May 31, 2021
Acceptance Date March 18, 2021
Published in Issue Year 2021 Volume: 17 Issue: 37

Cite

APA Mizrahi, R., Tektaş, B., & Kayahan Karakul, A. (2021). Fuzzy Multi Criteria Group Decision for Determining the Relative Importance of Critical Success Factors in Accounting Information Systems. OPUS International Journal of Society Researches, 17(37), 4473-4486. https://doi.org/10.26466/opus.869767