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Multi Criteria Decision Making Techniques in Management Information Systems Literature: A Systematic Review

Year 2021, Volume: 9 Issue: 1, 111 - 146, 28.04.2021
https://doi.org/10.22139/jobs.894997

Abstract

Aim: To provide a guidance evaluation to the academicians who will work in this field by determining the methods used in solving multi-criteria problems in the management information systems literature and their application areas and trends.

Method: The data set was taken from 107 academic journals in the field of MIS in the SCOPUS database. The data set was analyzed, coded and filtered with a systematic method before analysis. Various analyzes such as bibliometric, trend analysis, text mining, clustering were carried out on the data set.

Findings: Publications are categorized in 3 different groups (information systems related, other fields and method development) in the management information systems literature. It is seen that the problem solving approaches directly related to information systems has more importance in the literature. In addition, it has been determined that customer-oriented subject areas (web site design, service quality, decision support systems and writing selection) are more prominent in the literature. In recent years, it is thought that the field has shown a trend towards areas such as data mining and big data

Results: The results obtained from the systematic literature review are thought to be both guiding academicians who will work in the field and beneficial for academic journal managers in cope with the topic related issues. Although the MIS literature is open to MCDM publications in both information systems and other fields, there is a shift towards customer-oriented and modern information systems areas.

References

  • Bana e Costa, C. A., & Vansnick, J. C. (1997). The MACBETH approach: Basic ideas. In Proceedings of the International Conference on Methods and Applications of Multicriteria Decision Making (pp. 86-88).
  • Bhimani, H., Mention, A. L., & Barlatier, P. J. (2019). Social media and innovation: A systematic literature review and future research directions. Technological Forecasting and Social Change, 144, 251-269.
  • Bohanec, M., & Rajkovič, V. (1990). DEX: An expert system shell for decision support. Sistemica, 1(1), 145-157.
  • Brans, J. P., Vincke, P., & Mareschal, B. (1986). How to select and how to rank projects: The PROMETHEE method. European journal of operational research, 24(2), 228-238.
  • Brauers, W. K., & Zavadskas, E. K. (2006). The MOORA method and its application to privatization in a transition economy. Control and cybernetics, 35, 445-469.
  • Campos, M., & Krohling, R. A. (2016). Entropy-based bare bones particle swarm for dynamic constrained optimization. Knowledge-Based Systems, 97, 203-223.
  • Cao, D., Leung, L. C., & Law, J. S. (2008). Modifying inconsistent comparison matrix in analytic hierarchy process: A heuristic approach. Decision Support Systems, 44(4), 944-953.
  • Cegan, J. C., Filion, A. M., Keisler, J. M., & Linkov, I. (2017). Trends and applications of multi-criteria decision analysis in environmental sciences: literature review. Environment Systems and Decisions, 37(2), 123-133.
  • Churchman, C. W., & Ackoff, R. L. (1954). An approximate measure of value. Journal of the Operations Research Society of America, 2(2), 172-187.
  • de Almeida, A. T., Alencar, M. H., Garcez, T. V., & Ferreira, R. J. P. (2017). A systematic literature review of multicriteria and multi-objective models applied in risk management. IMA Journal of Management Mathematics, 28(2), 153-184.
  • Dong, Y., Zhang, G., Hong, W. C., & Xu, Y. (2010). Consensus models for AHP group decision making under row geometric mean prioritization method. Decision Support Systems, 49(3), 281-289.
  • Edwards, W. (1977). How to use multiattribute utility measurement for social decisionmaking. IEEE transactions on systems, man, and cybernetics, 7(5), 326-340.
  • Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society: Series A (General), 120(3), 253-281.
  • Figueira, J. R., Mousseau, V., & Roy, B. (2016). ELECTRE methods. In Multiple criteria decision analysis (155-185). Springer, New York, NY.
  • Gomes, L. F. A. M., & Lima, M. M. P. P. (1992). TODIM: Basics and application to multicriteria ranking of projects with environmental impacts. Foundations of Computing and Decision Sciences, 16(4), 113-127.
  • Govindan, K., Rajendran, S., Sarkis, J., & Murugesan, P. (2015). Multi criteria decision making approaches for green supplier evaluation and selection: a literature review. Journal of Cleaner Production, 98, 66-83.
  • Greco, S., Matarazzo, B., Slowinski, R., & Stefanowski, J. (2000, October). Variable consistency model of dominance-based rough sets approach. In International Conference on Rough Sets and Current Trends in Computing (170-181). Springer, Berlin, Heidelberg.
  • Hirsch, J. E. (2005). An index to quantify an individual's scientific research output. Proceedings of the National academy of Sciences, 102(46), 16569-16572.
  • Hwang, C. L., & Yoon, K. (1981). Methods for multiple attribute decision making. In Multiple attribute decision making (58-191). Springer, Berlin, Heidelberg.
  • Ju-Long, D. (1982). Control problems of grey systems. Systems & control letters, 1(5), 288-294.
  • Jun, W., Lingyu, T., Yuyan, L., & Peng, G. (2017). A weighted EMD-based prediction model based on TOPSIS and feed forward neural network for noised time series. Knowledge-Based Systems, 132, 167-178.
  • Kahraman, C., Onar, S. C., & Oztaysi, B. (2015). Fuzzy multicriteria decision-making: a literature review. International Journal of Computational Intelligence Systems, 8(4), 637-666.
  • 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.
  • Malczewski, J. (2006). GIS‐based multicriteria decision analysis: a survey of the literature. International Journal of Geographical Information Science, 20(7), 703-726.
  • Marttunen, M., Lienert, J., & Belton, V. (2017). Structuring problems for Multi-Criteria Decision Analysis in practice: A literature review of method combinations. European Journal of Operational Research, 263(1), 1-17.
  • Memari, A., Dargi, A., Jokar, M. R. A., Ahmad, R., & Rahim, A. R. A. (2019). Sustainable supplier selection: A multi-criteria intuitionistic fuzzy TOPSIS method. Journal of Manufacturing Systems, 50, 9-24.
  • Monti, D., Rizzo, G., & Morisio, M. (2020). A systematic literature review of multicriteria recommender systems. Artificial Intelligence Review, 1-42.
  • Opricovic, S. (1998). Multicriteria optimization of civil engineering systems. Faculty of Civil Engineering, Belgrade, 2(1), 5-21.
  • Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57.
  • Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15(3), 234-281.
  • Saaty, T. L. (1996). Decision making with dependence and feedback: The analytic network process (Vol. 4922). Pittsburgh: RWS publications.
  • Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3), 379-423.
  • Sharda, R., Delen, D., & Turban, E. (2020). Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support. Pearson.
  • Thomas, J., McNaught, J., & Ananiadou, S. (2011). Applications of text mining within systematic reviews. Research Synthesis Methods, 2(1), 1-14.
  • Warfield, J. N. (1974). Developing interconnection matrices in structural modeling. IEEE Transactions on Systems, Man, and Cybernetics, (1), 81-87.
  • Xu, X. (2001). The SIR method: A superiority and inferiority ranking method for multiple criteria decision making. European Journal of Operational Research, 131(3), 587-602.
  • Zavadskas, E. K., & Kaklauskas, A. (1996). Determination of an efficient contractor by using the new method of multicriteria assessment. In International Symposium for “The Organization and Management of Construction”. Shaping Theory and Practice (Vol. 2, 94-104).
  • Zavadskas, E. K., Turskis, Z., & Kildienė, S. (2014). State of art surveys of overviews on MCDM/MADM methods. Technological and Economic Development of Economy, 20(1), 165-179.
  • Zavadskas, E. K., Turskis, Z., Antucheviciene, J., & Zakarevicius, A. (2012). Optimization of weighted aggregated sum product assessment. Elektronika ir Elektrotechnika, 122(6), 3-6.
  • Zopounidis, C., & Doumpos, M. (2002a). Multicriteria classification and sorting methods: A literature review. European Journal of Operational Research, 138(2), 229-246.
  • Zopounidis, C., & Doumpos, M. (2002b). Multi‐criteria decision aid in financial decision making: methodologies and literature review. Journal of Multi‐Criteria Decision Analysis, 11(4‐5), 167-186.

Yönetim Bilişim Sistemleri Literatüründe Çok Kriterli Karar Verme Teknikleri: Sistematik bir İnceleme

Year 2021, Volume: 9 Issue: 1, 111 - 146, 28.04.2021
https://doi.org/10.22139/jobs.894997

Abstract

Amaç: Yönetim bilişim sistemleri literatüründe çok kriterli problemlerin çözümünde kullanılan yöntemleri ve bunların uygulama alanlarını ve trendlerini belirleyerek bu alanda çalışacak akademisyenlere yol gösterici bir değerlendirme sunmak

Yöntem: YBS alanında yayın yapan 107 adet SCOPUS indeksli derginin çok kriterli karar verme yayınlarının sistematik olarak toplanması, incelenmesi, kodlanması ve filtrelenmesi ile veri seti elde edilecek ve bu veri seti üzerinde çeşitli analitik yaklaşımlar ile (Bibliyometrik, trend analizi, metin madenciliği, kümeleme) kapsam, dergi ve kavram odaklı değerlendirmeler yapılması

Bulgular: Yönetim bilişim sistemleri literatüründe 3 farklı alanda (bilişim ile alakalı, diğer alanlar ve yöntem geliştirme) çok sayıda yayın yapılmaktadır. Bilişim ile direkt alakalı olan problemlerin çözümünün literatürde daha fazla değer gördüğü görülmektedir. Ayrıca müşteri odaklı konu alanlarının (web site tasarımı, hizmet kalitesi, karar destek sistemleri ve yazılım seçimi) literatürde daha ön planda olduğu belirlenmiştir. Son yıllarda ise alanın özellikle veri madenciliği ve büyük veri gibi alanlara doğru bir yönelim gösterdiği düşünülmektedir.

Sonuç: Sistematik inceleme sonucunda elde edilen sonuçların hem alanda çalışacak akademisyenlere yol gösterici olması, hem de akademik dergi yöneticileri için konu belirleme konusunda fayda sağlaması düşünülmektedir. YBS literatürünün hem bilişim ile ilgili hem de diğer alanlardaki ÇKKV yayınlarına açık olduğu görülmekle birlikte müşteri odaklı ve modern bilişim konularına doğru bir kayış söz konusudur.

References

  • Bana e Costa, C. A., & Vansnick, J. C. (1997). The MACBETH approach: Basic ideas. In Proceedings of the International Conference on Methods and Applications of Multicriteria Decision Making (pp. 86-88).
  • Bhimani, H., Mention, A. L., & Barlatier, P. J. (2019). Social media and innovation: A systematic literature review and future research directions. Technological Forecasting and Social Change, 144, 251-269.
  • Bohanec, M., & Rajkovič, V. (1990). DEX: An expert system shell for decision support. Sistemica, 1(1), 145-157.
  • Brans, J. P., Vincke, P., & Mareschal, B. (1986). How to select and how to rank projects: The PROMETHEE method. European journal of operational research, 24(2), 228-238.
  • Brauers, W. K., & Zavadskas, E. K. (2006). The MOORA method and its application to privatization in a transition economy. Control and cybernetics, 35, 445-469.
  • Campos, M., & Krohling, R. A. (2016). Entropy-based bare bones particle swarm for dynamic constrained optimization. Knowledge-Based Systems, 97, 203-223.
  • Cao, D., Leung, L. C., & Law, J. S. (2008). Modifying inconsistent comparison matrix in analytic hierarchy process: A heuristic approach. Decision Support Systems, 44(4), 944-953.
  • Cegan, J. C., Filion, A. M., Keisler, J. M., & Linkov, I. (2017). Trends and applications of multi-criteria decision analysis in environmental sciences: literature review. Environment Systems and Decisions, 37(2), 123-133.
  • Churchman, C. W., & Ackoff, R. L. (1954). An approximate measure of value. Journal of the Operations Research Society of America, 2(2), 172-187.
  • de Almeida, A. T., Alencar, M. H., Garcez, T. V., & Ferreira, R. J. P. (2017). A systematic literature review of multicriteria and multi-objective models applied in risk management. IMA Journal of Management Mathematics, 28(2), 153-184.
  • Dong, Y., Zhang, G., Hong, W. C., & Xu, Y. (2010). Consensus models for AHP group decision making under row geometric mean prioritization method. Decision Support Systems, 49(3), 281-289.
  • Edwards, W. (1977). How to use multiattribute utility measurement for social decisionmaking. IEEE transactions on systems, man, and cybernetics, 7(5), 326-340.
  • Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society: Series A (General), 120(3), 253-281.
  • Figueira, J. R., Mousseau, V., & Roy, B. (2016). ELECTRE methods. In Multiple criteria decision analysis (155-185). Springer, New York, NY.
  • Gomes, L. F. A. M., & Lima, M. M. P. P. (1992). TODIM: Basics and application to multicriteria ranking of projects with environmental impacts. Foundations of Computing and Decision Sciences, 16(4), 113-127.
  • Govindan, K., Rajendran, S., Sarkis, J., & Murugesan, P. (2015). Multi criteria decision making approaches for green supplier evaluation and selection: a literature review. Journal of Cleaner Production, 98, 66-83.
  • Greco, S., Matarazzo, B., Slowinski, R., & Stefanowski, J. (2000, October). Variable consistency model of dominance-based rough sets approach. In International Conference on Rough Sets and Current Trends in Computing (170-181). Springer, Berlin, Heidelberg.
  • Hirsch, J. E. (2005). An index to quantify an individual's scientific research output. Proceedings of the National academy of Sciences, 102(46), 16569-16572.
  • Hwang, C. L., & Yoon, K. (1981). Methods for multiple attribute decision making. In Multiple attribute decision making (58-191). Springer, Berlin, Heidelberg.
  • Ju-Long, D. (1982). Control problems of grey systems. Systems & control letters, 1(5), 288-294.
  • Jun, W., Lingyu, T., Yuyan, L., & Peng, G. (2017). A weighted EMD-based prediction model based on TOPSIS and feed forward neural network for noised time series. Knowledge-Based Systems, 132, 167-178.
  • Kahraman, C., Onar, S. C., & Oztaysi, B. (2015). Fuzzy multicriteria decision-making: a literature review. International Journal of Computational Intelligence Systems, 8(4), 637-666.
  • 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.
  • Malczewski, J. (2006). GIS‐based multicriteria decision analysis: a survey of the literature. International Journal of Geographical Information Science, 20(7), 703-726.
  • Marttunen, M., Lienert, J., & Belton, V. (2017). Structuring problems for Multi-Criteria Decision Analysis in practice: A literature review of method combinations. European Journal of Operational Research, 263(1), 1-17.
  • Memari, A., Dargi, A., Jokar, M. R. A., Ahmad, R., & Rahim, A. R. A. (2019). Sustainable supplier selection: A multi-criteria intuitionistic fuzzy TOPSIS method. Journal of Manufacturing Systems, 50, 9-24.
  • Monti, D., Rizzo, G., & Morisio, M. (2020). A systematic literature review of multicriteria recommender systems. Artificial Intelligence Review, 1-42.
  • Opricovic, S. (1998). Multicriteria optimization of civil engineering systems. Faculty of Civil Engineering, Belgrade, 2(1), 5-21.
  • Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57.
  • Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15(3), 234-281.
  • Saaty, T. L. (1996). Decision making with dependence and feedback: The analytic network process (Vol. 4922). Pittsburgh: RWS publications.
  • Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3), 379-423.
  • Sharda, R., Delen, D., & Turban, E. (2020). Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support. Pearson.
  • Thomas, J., McNaught, J., & Ananiadou, S. (2011). Applications of text mining within systematic reviews. Research Synthesis Methods, 2(1), 1-14.
  • Warfield, J. N. (1974). Developing interconnection matrices in structural modeling. IEEE Transactions on Systems, Man, and Cybernetics, (1), 81-87.
  • Xu, X. (2001). The SIR method: A superiority and inferiority ranking method for multiple criteria decision making. European Journal of Operational Research, 131(3), 587-602.
  • Zavadskas, E. K., & Kaklauskas, A. (1996). Determination of an efficient contractor by using the new method of multicriteria assessment. In International Symposium for “The Organization and Management of Construction”. Shaping Theory and Practice (Vol. 2, 94-104).
  • Zavadskas, E. K., Turskis, Z., & Kildienė, S. (2014). State of art surveys of overviews on MCDM/MADM methods. Technological and Economic Development of Economy, 20(1), 165-179.
  • Zavadskas, E. K., Turskis, Z., Antucheviciene, J., & Zakarevicius, A. (2012). Optimization of weighted aggregated sum product assessment. Elektronika ir Elektrotechnika, 122(6), 3-6.
  • Zopounidis, C., & Doumpos, M. (2002a). Multicriteria classification and sorting methods: A literature review. European Journal of Operational Research, 138(2), 229-246.
  • Zopounidis, C., & Doumpos, M. (2002b). Multi‐criteria decision aid in financial decision making: methodologies and literature review. Journal of Multi‐Criteria Decision Analysis, 11(4‐5), 167-186.
There are 41 citations in total.

Details

Primary Language Turkish
Subjects Business Administration
Journal Section Review Articles
Authors

Halil İbrahim Cebeci 0000-0001-5058-7741

Publication Date April 28, 2021
Submission Date March 11, 2021
Acceptance Date April 27, 2021
Published in Issue Year 2021 Volume: 9 Issue: 1

Cite

APA Cebeci, H. İ. (2021). Yönetim Bilişim Sistemleri Literatüründe Çok Kriterli Karar Verme Teknikleri: Sistematik bir İnceleme. İşletme Bilimi Dergisi, 9(1), 111-146. https://doi.org/10.22139/jobs.894997