Research Article
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A Novel Reviewer Evaluation Process and Decision Support System Proposal Based on Entropy and Clustering

Year 2025, Volume: 8 Issue: 4, 1034 - 1049, 15.07.2025

Seda Nur Budak , Kumru Didem Atalay , Mehmet Kabak , Tacettin Sercan Pekin

https://doi.org/10.34248/bsengineering.1608978

Abstract

The complexity and shortcomings of the evaluation processes of research and development (R&D) projects lead to problems of consistency and traceability in the evaluation of the projects. This study aims to develop an objective, consistent, and highly applicable decision support system by addressing the main challenges encountered in the evaluation processes of R&D projects in Türkiye. In this context, the literature on evaluation criteria was thoroughly reviewed, expert opinions were collected, the criteria were ranked according to their importance using the entropy method, and the most effective criteria for evaluation were identified. Consequently, the number of criteria was reduced from 247 to 96, thereby facilitating the evaluator’s task and forming a more effective evaluation set. The developed decision support system enables comprehensive analysis of projects and allows all stakeholders—from reviewers to executive board members—to conduct evaluations on a more transparent platform. Based on the reviewer scores obtained through the system, projects were classified into four main groups using the k-means clustering algorithm. This platform has accelerated evaluation processes and reduced the workload of decision-makers by 31% to 39%. The results demonstrate that more comprehensive and objective evaluations can be made in dimensions such as the innovative aspects of R&D projects, their economic and national contributions, project planning, and technical infrastructure. The newly developed system addresses the shortcomings of existing evaluation procedures and offers a more structured and holistic approach. It is envisioned that the system can be integrated into various R&D programs in the future to maximize the country’s R&D potential and innovation capacity.

Keywords

R&D project evaluation process Multi-criteria decision making Entropy K-means clustering Decision support system

Thanks

TÜBİTAK TEYDEB’e çalışmalarımıza olan katkı ve desteklerinden dolayı teşekkür ederiz.

References

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Entropi ve Kümeleme Tabanlı Yeni Bir Hakem Değerlendirme Süreci ve Karar Destek Sistemi Önerisi

Year 2025, Volume: 8 Issue: 4, 1034 - 1049, 15.07.2025

Seda Nur Budak , Kumru Didem Atalay , Mehmet Kabak , Tacettin Sercan Pekin

https://doi.org/10.34248/bsengineering.1608978

Abstract

Araştırma ve geliştirme (Ar-Ge) projelerinin değerlendirme süreçlerinin karmaşıklığı ve eksiklikleri, projelerin değerlendirilmesinde tutarlılık ve izlenebilirlik sorunlarına yol açmaktadır. Bu çalışma, Türkiye'deki Ar-Ge projelerinin değerlendirme süreçlerinde karşılaşılan temel sorunları ele alarak, nesnel, tutarlı ve uygulanabilirliği yüksek bir karar destek sistemi geliştirmeyi amaçlamaktadır. Bu bağlamda, projelerin değerlendirilmesinde kullanılan kriterler için literatür detaylı bir şekilde incelenmiş, uzmanların görüşleri alınmış, entropi yöntemi ile önem derecelerine göre sıralanmış ve değerlendirmede daha etkili olduğu görülen kriterler belirlenmiştir. Böylece, kriter sayısı 247'den 96'ya indirilmiş ve değerlendiriciye kolaylık sağlanarak daha etkili bir değerlendirme seti oluşturulmuştur. Geliştirilen karar destek sistemi, projelerin kapsamlı bir analizine olanak tanımakta ve hakemlerden yürütme kurulu üyelerine kadar tüm paydaşların daha şeffaf bir platform üzerinde değerlendirme yapmasını sağlamaktadır. Karar destek sistemi ile elde edilen hakem puanlarına bağlı olarak projeler, K-ortalama kümeleme algoritması ile dört ana gruba ayrılmıştır. Bu platform, değerlendirme süreçlerinin hızlandırılmasını ve karar vericilerin iş yükünün %31 ila %39 oranında azaltılmasını sağlamıştır. Elde edilen sonuçlar, Ar-Ge projelerinin yenilikçi yönleri, ekonomik ve ulusal katkıları, proje planları ve teknik altyapıları gibi boyutlarda daha kapsamlı ve objektif bir değerlendirme yapılmasını mümkün kılmıştır. Yeni sistem, mevcut değerlendirme süreçlerinin eksikliklerini gidererek daha yapılandırılmış ve bütüncül bir yaklaşım sunmaktadır. Geliştirilen bu sistem, gelecekte farklı Ar-Ge programlarına entegre edilerek ülkenin Ar-Ge potansiyelini ve inovasyon kapasitesini en üst seviyeye çıkarmayı hedeflemektedir.

Keywords

Ar-Ge projeleri değerlendirme süreci Çok kriterli karar verme Entropi K-ortalama kümeleme Karar destek sistemi

Thanks

TÜBİTAK TEYDEB’e çalışmalarımıza olan katkı ve desteklerinden dolayı teşekkür ederiz.

References

  • Archer NP, Ghasemzadeh F. 1999. An integrated framework for project portfolio selection, Int J Proj Manag, 17(4): pp: 207-216.
  • Arthur D, Vassilvitskii S. 2006. k-means++: The advantages of careful seeding, Stanford, pp: 56-59.
  • Bard JF, Balachandra R, Kaufmann PE. 1988. An interactive approach to R&D project selection and termination, IEEE Trans Eng Manag, 35(3): pp: 139-146.
  • Beaujon GJ, Marin SP, McDonald GC. 2001. Balancing and optimizing a portfolio of R&D projects, Nav Res Logist, 48(1): pp: 18-40.
  • Bhattacharyya R, Kumar P, Kar S. 2011. Fuzzy R&D portfolio selection of interdependent projects, Comput Math Appl, 62(10): pp: 3857-3870.
  • Bhattacharyya R. 2015. A grey theory based multiple attribute approach for R&D project portfolio selection, Fuzzy Inf Eng, 7(2): pp: 211-225.
  • Bitman WR, Sharif N. 2008. A conceptual framework for ranking R&D projects, IEEE Trans Eng Manag, 55(2): pp: 267-278.
  • Bornmann L. 2011. Scientific peer review, Annu Rev Inf Sci Technol, 45(1): pp: 197-245.
  • Cebeci Z, Cebeci C. 2020. A fast algorithm to initialize cluster centroids in fuzzy clustering applications, Information, 11(9): pp: 446.
  • Cebeci Z, Yildiz F. 2015. Comparison of k-means and fuzzy c-means algorithms on different cluster structures, J Agric Inform, 6(3): pp: 15-59.
  • Charnes A, Cooper WW, Rhodes E. 1978. Measuring the efficiency of decision making units, Eur J Oper Res, 2(6): pp: 429-444.
  • Cheng CH, Liou JJ, Chiu CY. 2017. A consistent fuzzy preference relations based ANP model for R&D project selection, Sustainability, 9(8): pp: 1352.
  • Conka T, Vayvay O, Sennaroglu B. 2008. A combined decision model for R&D project portfolio selection, Int J Bus Innov Res, 2(2): pp: 190-202.
  • Criscuolo P, Dahlander L, Grohsjean T, Salter A. 2017. Evaluating novelty: The role of panels in the selection of R&D projects, Acad Manag J, 60(2): pp: 433-460.
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  • de Souza DGB, dos Santos EA, Soma NY, da Silva CES. 2021. MCDM-based R&D project selection: A systematic literature review, Sustainability, 13(21): pp: 11626.
  • Demšar J, Zupan B, Leban G, Curk T. 2004. Orange: From experimental machine learning to interactive data mining, In: Knowledge Discovery in Databases: PKDD 2004, Pisa, Italy, 20 September 2004 - 24 September 2004, Springer Berlin Heidelberg, pp: 537-539.
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  • Forgy EW. 1965. Cluster analysis of multivariate data: efficiency versus interpretability of classifications, Biometrics, 21: pp: 768-769.
  • Ghazal TM. 2021. Performances of k-means clustering algorithm with different distance metrics, Intell Autom Soft Comput, 30(2): pp: 735-742.
  • Ghasemzadeh F, Archer NP. 2000. Project portfolio selection through decision support, Decis Support Syst, 29(1): pp: 73-88.
  • Ghorbani HR, Jolai F, Tavakkoli-Moghaddam R, Rabbani M. 2006. A comprehensive model for R&D project portfolio selection with zero-one linear goal-programming (research note), Int J Eng, 19(1): pp: 55-66.
  • Gustafsson J, Salo A. 2005. Contingent portfolio programming for the management of risky projects, Oper Res, 53(6): pp: 946-956.
  • Hassanzadeh F, Nemati H, Sun M. 2014. Robust optimization for interactive multiobjective programming with imprecise information applied to R&D project portfolio selection, Eur J Oper Res, 238(1): pp: 41-53.
  • Henig MI, Katz H. 1996. R&D project selection: A decision process approach, J Multi-Criteria Decis Anal, 5(3): pp: 169-177.
  • Henriksen AD, Traynor AJ. 1999. A practical R&D project-selection scoring tool, IEEE Trans Eng Manag, 46(2): pp: 158-170.
  • Hesarsorkh AH, Ashayeri J, Naeini AB. 2021. Pharmaceutical R&D project portfolio selection and scheduling under uncertainty: A robust possibilistic optimization approach, Comput Ind Eng, 155: pp: 107114.
  • Heyard R, Ott M, Salanti G, Egger M. 2022. Rethinking the funding line at the Swiss national science foundation: Bayesian ranking and lottery, Stat Public Policy, 9(1): pp: 110-121.
  • Heydari T, Seyed Hosseini SM, Makui A. 2016. Developing and solving a one-zero non-linear goal programming model to R&D portfolio project selection with interactions between projects, Int Bus Manag, 10(19): pp: 4516-4521.
  • Hsu YG, Tzeng GH, Shyu JZ. 2003. Fuzzy multiple criteria selection of government-sponsored frontier technology R&D projects, R&D Manag, 33(5): pp: 539-551.
  • Hünermund P, Czarnitzki D. 2019. Estimating the causal effect of R&D subsidies in a pan-European program, Res Policy, 48(1): pp: 115-124.
  • Isied M, Daneshvar S. 2024. A second chance for failed projects using data envelopment analysis based on project attractiveness factors, Mathematics, 12(23): pp: 10-15.
  • Imoto S, Yabuuchi Y, Watada J. 2008. Fuzzy regression model of R&D project evaluation, Appl Soft Comput, 8(3): pp: 1266-1273.
  • Jeng DJF, Huang KH. 2015. Strategic project portfolio selection for national research institutes, J Bus Res, 68(11): pp: 2305-2311.
  • Kahraman C, Çağrı Tolga A. 2008. Fuzzy multiattribute evaluation of R&D projects using a real options valuation model, Int J Intell Syst, 23(11): pp: 1153-1176.
  • Kaplan RS, Norton DP. 1992. The Balanced Scorecard: measures that drive performance, Harvard Business Review, January-February: pp: 71-79.
  • Karami A, Johansson R. 2013. Utilization of multi attribute decision making techniques to integrate automatic and manual ranking of options, J Inf Sci Eng, 30(2): pp: 519-534.
  • Karasakal E, Aker P. 2017. A multicriteria sorting approach based on data envelopment analysis for R&D project selection problem, Omega, 73: pp: 79-92.
  • Karaveg C, Thawesaengskulthai N, Chandrachai A. 2015. A combined technique using SEM and TOPSIS for the commercialization capability of R&D project evaluation, Decis Sci Lett, 4(3): pp: 379-396.
  • Ketchen DJ, Shook CL. 1996. The application of cluster analysis in strategic management research: an analysis and critique, Strateg Manag J, 17(6): pp: 441-458.
  • Kumar SS. 2004. AHP-based formal system for R&D project evaluation, Int J Proj Manag, 22(5): pp: 423-432.
  • Liberatore MJ. 1986. R&D project selection, Telemat Inform, 3(4): pp: 289-300.
  • Liberatore MJ. 1987. An extension of the analytic hierarchy process for industrial R&D project selection and resource allocation, IEEE Trans Eng Manag, (1): pp: 12-18.
  • Liberatore MJ. 1988. An expert support system for R&D project selection, Math Comput Model, 11: pp: 260-265.
  • Liberatore MJ, Titus GJ. 1983. The practice of management science in R&D project management, Manag Sci, 29(8): pp: 962-974.
  • Liu M, Yang WQS. 2023. Analysis of the advantages and disadvantages of four comprehensive evaluation methods, Front Bus Econ Manag, 9: pp: 162-167.
  • Madey GR, Dean BV. 1985. Strategic planning for investment in R&D using decision analysis and mathematical programming, IEEE Trans Eng Manag, (2): pp: 84-90.
  • MacQueen J. 1967. Some methods for classification and analysis of multivariate observations, In: Proc of the Fifth Berkeley Symp on Math Statist and Probab, 1(14): pp: 281-297.
  • Mardani A, Jusoh A, Zavadskas EK, Khalifah Z, Nor KM. 2015. Application of multiple-criteria decision-making techniques and approaches to evaluating of service quality: a systematic review of the literature, J Bus Econ Manag, 16(5): pp: 1034-1068.
  • Mavrotas G, Makryvelios E. 2021. Combining multiple criteria analysis, mathematical programming and Monte Carlo simulation to tackle uncertainty in R&D project portfolio selection: a case study from Greece, Eur J Oper Res, 291(2): pp: 794-806.
  • Mavrotas G, Makryvelios E. 2023. R&D project portfolio selection using the Iterative Trichotomic Approach in order to study how subjectivity of the weights is reflected in the selected projects of the final portfolio, Oper Res, 23(3): pp: 50-56.
  • Meade LM, Presley A. 2002. R&D project selection using the analytic network process, IEEE Trans Eng Manag, 49(1): pp: 59-66.
  • Miyajima S, Isshiki T, Kunugi M, Uesaka S. 2022. Can we predict successful market introduction using on-going R&D evaluation data?, fteval J Res Technol Policy Eval, (53): pp: 153-159.
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There are 79 citations in total.

Details

Primary Language Turkish
Subjects Decision Support and Group Support Systems, Multiple Criteria Decision Making, Industrial Engineering
Journal Section Research Articles
Authors

Seda Nur Budak 0000-0003-4188-9988

Kumru Didem Atalay 0000-0002-9021-3565

Mehmet Kabak 0000-0002-8576-5349

Tacettin Sercan Pekin 0000-0002-6416-5205

Publication Date July 15, 2025
Submission Date January 1, 2025
Acceptance Date May 14, 2025
Published in Issue Year 2025 Volume: 8 Issue: 4

Cite

APA Budak, S. N., Atalay, K. D., Kabak, M., Pekin, T. S. (2025). Entropi ve Kümeleme Tabanlı Yeni Bir Hakem Değerlendirme Süreci ve Karar Destek Sistemi Önerisi. Black Sea Journal of Engineering and Science, 8(4), 1034-1049. https://doi.org/10.34248/bsengineering.1608978
AMA Budak SN, Atalay KD, Kabak M, Pekin TS. Entropi ve Kümeleme Tabanlı Yeni Bir Hakem Değerlendirme Süreci ve Karar Destek Sistemi Önerisi. BSJ Eng. Sci. July 2025;8(4):1034-1049. doi:10.34248/bsengineering.1608978
Chicago Budak, Seda Nur, Kumru Didem Atalay, Mehmet Kabak, and Tacettin Sercan Pekin. “Entropi Ve Kümeleme Tabanlı Yeni Bir Hakem Değerlendirme Süreci Ve Karar Destek Sistemi Önerisi”. Black Sea Journal of Engineering and Science 8, no. 4 (July 2025): 1034-49. https://doi.org/10.34248/bsengineering.1608978.
EndNote Budak SN, Atalay KD, Kabak M, Pekin TS (July 1, 2025) Entropi ve Kümeleme Tabanlı Yeni Bir Hakem Değerlendirme Süreci ve Karar Destek Sistemi Önerisi. Black Sea Journal of Engineering and Science 8 4 1034–1049.
IEEE S. N. Budak, K. D. Atalay, M. Kabak, and T. S. Pekin, “Entropi ve Kümeleme Tabanlı Yeni Bir Hakem Değerlendirme Süreci ve Karar Destek Sistemi Önerisi”, BSJ Eng. Sci., vol. 8, no. 4, pp. 1034–1049, 2025, doi: 10.34248/bsengineering.1608978.
ISNAD Budak, Seda Nur et al. “Entropi Ve Kümeleme Tabanlı Yeni Bir Hakem Değerlendirme Süreci Ve Karar Destek Sistemi Önerisi”. Black Sea Journal of Engineering and Science 8/4 (July 2025), 1034-1049. https://doi.org/10.34248/bsengineering.1608978.
JAMA Budak SN, Atalay KD, Kabak M, Pekin TS. Entropi ve Kümeleme Tabanlı Yeni Bir Hakem Değerlendirme Süreci ve Karar Destek Sistemi Önerisi. BSJ Eng. Sci. 2025;8:1034–1049.
MLA Budak, Seda Nur et al. “Entropi Ve Kümeleme Tabanlı Yeni Bir Hakem Değerlendirme Süreci Ve Karar Destek Sistemi Önerisi”. Black Sea Journal of Engineering and Science, vol. 8, no. 4, 2025, pp. 1034-49, doi:10.34248/bsengineering.1608978.
Vancouver Budak SN, Atalay KD, Kabak M, Pekin TS. Entropi ve Kümeleme Tabanlı Yeni Bir Hakem Değerlendirme Süreci ve Karar Destek Sistemi Önerisi. BSJ Eng. Sci. 2025;8(4):1034-49.
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