Evaluation of Factors Affecting Innovation Productivity by Pythagorean Fuzzy AHP Method

Yıl 2024, PRODUCTIVITY FOR INNOVATION, 89 - 106, 15.01.2024

Miraç Tuba Çelik , Aytaç Yıldız

https://doi.org/10.51551/verimlilik.1319522

Öz

Purpose: In this study, it is aimed to rank the factors affecting the innovation productivity of enterprises.
Methodology: The Pythagorean Fuzzy Analytical Hierarchy Process (AHP) method, which gives successful results in modelling uncertainty and uses Pythagorean fuzzy sets, is used to rank the factors affecting innovation productivity according to their importance.
Findings: In the application part of study firstly, the factors affecting the innovation productivity were determined and as a result of expert evaluations, the steps of the method were applied and the factors were ranked according to their importance. Finally, the most important factors were determined by performing a sensitivity analysis. When the results obtained from the study are examined, it has been determined that the factor of preparing the technology roadmap affects the innovation productivity the most, while the sector and market structure affect the innovation productivity the least among the determined factors.
Originality: It is the first study in the literature in which the factors affecting innovation productivity are determined and ranked according to their importance.

Anahtar Kelimeler

Innovation, Productivity, Innovation Productivity, Pythagorean Fuzzy AHP

İnovasyon Üretkenliğine Etki Eden Faktörlerin Pisagor Bulanık AHP Yöntemi İle Değerlendirilmesi

Öz

Amaç: Bu çalışmada, işletmelerin inovasyon verimliliğini etkileyen faktörlerin sıralanması amaçlanmaktadır.
Yöntem: Belirsizliğin modellenmesinde başarılı sonuçlar veren ve Pisagor bulanık kümelerini kullanan Pisagor Bulanık Analitik Hiyerarşi Süreci (AHP) yöntemi yenilik üretkenliğini etkileyen faktörlerin önem derecelerine göre sıralanmasında kullanılmıştır.
Bulgular: Çalışmanın uygulama kısmında öncelikle inovasyon verimliliğini etkileyen faktörler belirlenmiş ve uzman değerlendirmeleri sonucunda yöntemin adımları uygulanmış ve faktörler önem sırasına göre sıralanmıştır. Son olarak duyarlılık analizi yapılarak en önemli faktörler belirlenmiştir. Çalışmadan elde edilen sonuçlar incelendiğinde, belirlenen faktörler arasında inovasyon verimliliğini en çok teknoloji yol haritası hazırlama faktörünün etkilediği, en az ise sektör ve pazar yapısının inovasyon verimliliğini etkilediği tespit edilmiştir.
Özgünlük: İnovasyon üretkenliğine etki eden faktörlerin belirlendiği ve önem derecesine göre sıralandığı literatürdeki ilk çalışma özelliği göstermektedir.

Anahtar Kelimeler

İnovasyon, Verimlilik, İnovasyon Verimliliği, Pisagor Bulanık AHP

Kaynakça

• Akgemci, T., Öğüt, A. and Ay Tosun, M. (2005). “Küresel Rekabetin Sunduğu Fırsatlar ve Tehditler Bağlamında Kobi’lerde Stratejik Yenilik Yönetimi: SWOT Analizine Dayalı Kuramsal Bir Değerlendirme”, Sosyal Ekonomik Araştırmalar Dergisi, 5 (10), 139-156.
• Aksoy, E. and Demir, A.O. (2019). “Firmalarin Inovasyon Sürecini Etkileyen Unsurlar”, İstanbul Ticaret Üniversitesi Girişimcilik Dergisi, 3(5), 61-74.
• Aktaş, E. (2018). “İnovasyon Yönetimi Ve Işletmelerde Inovasyon Yönetimine Yönelik Bir Araştırma”, Yüksek Lisans Tezi, Okan Üniversitesi Sosyal Bilimler Enstitüsü, İstanbul.
• Asan, G. (2006). “Productivity Improvement Application in an Automotive Company”, Yüksek Lisans Tezi, Dokuz Eylül Üniversitesi Fen Bilimleri Enstitüsü, İzmir.
• Aslantaş, T. (2021). “İnovasyon Kapasitesini Değerlendirmeye Yönelik Bir Uygulama”, Gazi University Journal of Science Part A: Engineering and Innovation, 8(3), 339-360.
• Atanassov, K. (1986). “Intuitionistic Fuzzy Sets”, Fuzzy Sets and Systems, 20(1986), 87-96.
• Audretsch, D.B. and Belitski, M. (2020). “The Role of R&D and Knowledge Spillovers in Innovation and Productivity”, European Economic Review, 123, 103391.
• Ayyildiz, E. and Taskin Gumus, A. (2021). “Pythagorean Fuzzy AHP Based Risk Assessment Methodology for Hazardous Material Transportation: An Application in Istanbul”, Environmental Science and Pollution Research, 28, 35798-35810.
• Ayyildiz, E., Yildiz, A., Taskin, A., and Ozkan, C. (2023). “An Interval Valued Pythagorean Fuzzy AHP Integrated Quality Function Deployment Methodology for Hazelnut Production in Turkey”, Expert Systems with Applications, 120708.
• Başaran, Y., Aladağ, H. and Işık, Z. (2023). “Pythagorean Fuzzy AHP Based Dynamic Subcontractor Management Framework”, Buildings, 13(5), 1351.
• Bourgeois, Y. and LeBlanc, S. (2002). “Innovation in Atlantic Canada”, The Canadian Institute for Research on Regional Development”, Maritime Series, Canada.
• Bulut, M. and Özcan, E. (2023). “Ranking of Advertising Goals on Social Network Sites by Pythagorean Fuzzy Hierarchical Decision Making: Facebook”, Engineering Applications of Artificial Intelligence, 117, 105542.
• Celik, M.T. and Yildiz, A. (2022). “Evaluation of Green Innovation Criteria by Using Pythagorean Fuzzy AHP Method”, Journal of Engineering Research and Applied Science, 11(2), 2185-2193.
• Chesbrough, H. (2006). “Open Innovation: A New Paradigm for Understanding Industrial Innovation”, Open Innovation: Researching A New Paradigm, 400, 0-19.
• Chesbrough, H.W. and Appleyard, M.M. (2007). “Open Innovation and Strategy”, California Management Review, 50(1), 57-76.
• Ciocanel, A.B. and Pavelescu, F.M. (2015). “Innovation and Competitiveness in European Context”, Procedia Economics and Finance, 32, 728-737.
• Çalık, A. (2021). “A Novel Pythagorean fuzzy AHP and Fuzzy TOPSIS Methodology for Green Supplier Selection in the Industry 4.0 Era”, Soft Computing, 25(3), 2253-2265.
• Çetin, T. (2019). “Pazar Rekabeti Kapsamında Yöneticilerin Inovasyon Algısı Ile Seçilen Toplam Kalite Yönetimi Uygulamalarının Ürün Kalitesine Ve Inovasyonuna Etkisi”, Doktora Tezi, Kütahya Dumlupınar Üniversitesi Sosyal Bilimler Enstitüsü, Kütahya.
• Dahl, A. (2011). “The Idea-Driven Workforce Finding New Ways to Engage Employees in Innovation”, World American Management Association, 35-37.
• De, A.K., Chakraborty, D. and Biswas, A. (2022). “Literature Review on Type-2 Fuzzy Set Theory”, Soft Computing, 26(18), 9049-9068.
• Deshpande, Y., Sayre, T., Deshmukh, A., Shaji, D. and Bhosale, V. (2023). “A Pythagorean Fuzzy AHP Approach to Evaluate the Enablers of Healthcare Operations”, Advanced Engineering Optimization Through Intelligent Techniques: Select Proceedings of AEOTIT 2022, 347-357.
• Doğan, Ö. (2017). “Türkiye İmalat Sanayi Firmalarının Ar-Ge, Yenilik, İhracat ve Üretkenlikleri Arasındaki Dinamik İlişki”, Doktora Tezi, Ankara Üniversitesi Sosyal Bilimler Enstitüsü, Ankara.
• Ejegwa, P.A. (2021). “Generalized triparametric Correlation Coefficient for Pythagorean Fuzzy Sets with Application to MCDM Problems”, Granular Computing, 6(3), 557-566.
• Elçi, Ş. (2007), “İnovasyon Kalkınmanın ve Rekabetin Anahtarı”, Technopolis Group, Ankara Fagerberg, J. (2003). “The Dynamics of Technology, Growth and Trade: A Schumpeterian Perspective”, Centre for Technology, Innovation and Culture, University of Oslo, Working Paper, 25.
• Farooq, D. and Moslem, S. (2022). “Estimating Driver Behavior Measures Related to Traffic Safety by Investigating 2-Dimensional Uncertain Linguistic Data-A Pythagorean Fuzzy Analytic Hierarchy Process Approach”, Sustainability, 14(3), 1881.
• Feng, L., Zhao, W., Wang, J., Feng, J. and Guo, Y. (2023). “Combining Machine Learning with A Pharmaceutical Technology Roadmap to Analyze Technological Innovation Opportunities”, Computers & Industrial Engineering, 176, 108974.
• Gandon, F., Poggi, A., Rimassa, G. and Turci, P. (2002). “Multi-Agent Corporate Memory Management System”, Applied Artificial Intelligence, 16(9-10), 699-720.
• Gupta, P. (2007). “Firm Specific Measures of Innovation”, Illinois Institute of Technology, Chicago.
• Güler, E.Ö. and Kanber, S. (2011). “İnovasyon Aktivitelerinin İnovasyon Performansi Üzerine Etkileri: İmalat Sanayii Uygulamasi”, Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 20(1), 61-76.
• Güngör, P. (2011). “The Relationship between Reward Management System and Employee Performance with the Mediating Role of Motivation: A Quantitative Study on Global Banks”, Procedia Social and Behavioral Sciences, 24, 1510-1520.
• Hall, B.H. (2011). “Innovation and Productivity (No. W17178)”, National Bureau of Economic Research.
• Harris, R.G. (1999). “Determinants of Canadian Productivity Growth: Issues and Prospects.”, Industry Canada Conference on Canada in the 21st Century: A Time for Vision. Ottawa.
• Karasan, A., Ilbahar, E. and Kahraman, C. (2019). “A Novel Pythagorean Fuzzy AHP and Its Application to Landfill Site Selection Problem”, Soft Computing, 23, 10953-10968.
• Korkmaz, I.H., Taşkesen, A.C. and Cetinkaya, C. (2018). “İnovasyon Yönetimi Süreçlerini Etkileyen Faktörlerin Kahramanmaraş’ta Faaliyet Gösteren KOBI'ler Üzerinden Incelenmesi”, R&S-Research Studies Anatolia Journal, 1(2), 113-125.
• Kurt, Z.B. and Yıldız, A. (2020). “Fuzzy TOPSIS Based Decision Model for Evaluating and Prioritizing R&D/Innovation Projects, Ar-Ge/İnovasyon Projelerinin Değerlendirilmesi ve Önceliklendirilmesi İçin Fuzzy TOPSIS Tabanlı Karar Modeli”, Electronic Letters on Science and Engineering, 16(2), 93-107.
• Lahane, S. and Kant, R. (2021). “Evaluating the Circular Supply Chain Implementation Barriers Using Pythagorean Fuzzy AHP- DEMATEL Approach”, Cleaner Logistics and Supply Chain, 2, 100014.
• Lin, M., Chen, Y., and Chen, R. (2021). “Bibliometric Analysis on Pythagorean Fuzzy Sets During 2013-2020”, International Journal of Intelligent Computing and Cybernetics, 14(2), 104-121.
• Mairesse, J. and Mohnen, P. (2004). The importance of R&D for innovation: a reassessment using French survey data. The Journal of Technology Transfer, 30(1-2), 183-197.
• Megill, K.A. (2005). “Corporate Memory: Records and Information Management in The Knowledge Age”, KG Saur
• Mendel, M.E. (1983).” Improving Productivity and Effectiveness”, United States of America: Prentice Hall, Inc.
• Muhtar, A.C. (2022). “İnovasyon Performansına Etki Eden Faktörlerin Bulanık Bilişsel Haritalama Yöntemi ile Önceliklendirilmesi ve Telekomünikasyon Sektöründe Bulanık Çok Kriterli Karar Verme Yöntemleri ile Proje Seçimi”, Yüksek Lisans Tezi, İstanbul Teknik Üniversitesi Lisansüstü Eğitim Enstitüsü, İstanbul.
• OECD/EUROSTAT, (2005). “Oslo Manual- Guidelines for Collecting and Interpreting Innovation Data”, OECD Publications, Third edition, Paris.
• Oslo Guide (2005). “Principles for Collecting and Interpreting Innovation Data”, 3rd Edition, OECD/Eurostat Joint Publication.
• Özbek, A. and Yildiz, A. (2020). “Digital Supplier Selection for a Garment Business Using Interval Type-2 Fuzzy TOPSIS”, Textile and Apparel, 30(1), 61-72.
• Peng, X., and Yang, Y. (2016). “Fundamental Properties of Interval‐Valued Pythagorean Fuzzy Aggregation Operators” International Journal of Intelligent Systems, 31(5), 444-487.
• Ramos, A.G., Daim, T., Gaats, L., Hutmacher, D.W. and Hackenberger, D. (2022). “Technology Roadmap for the Development of a 3D Cell Culture Workstation for a Biomedical Industry Startup”, Technological Forecasting and Social Change, 174, 121213.
• Rao, S., Ahmad, A., Horsman, W. and Kaptein-Russell, P. (2001). “The Importance of Innovation for Productivity”, CSLS.
• Sarkar, B. and Biswas, A. (2021). “Pythagorean Fuzzy AHP-TOPSIS Integrated Approach for Transportation Management Through a New Distance Measure”, Soft Computing, 25(5), 4073-4089.
• Satıcı, S. (2021). “Ülkelerin Inovasyon Performansının CRITIC Temelli WASPAS Yöntemiyle Değerlendirilmesi”, Girişimcilik ve Kalkınma Dergisi, 16(2), 91-104.
• Saunila, M., Ukko, J., Rantala, T., Nasiri, M. and Rantanen, H. (2020). “Preceding Operational Capabilities as Antecedents for Productivity and Innovation Performance”, Journal of Business Economics, 90(4), 537-561.
• Seçilmiş, N. and Konu, A. (2019). “OECD Ülkelerinde Ar-Ge Teşvikleri ve Inovasyon Ilişkisi Üzerine Ampirik Bir Inceleme”, Kahramanmaraş Sütçü İmam Üniversitesi Sosyal Bilimler Dergisi, 16(2), 686-702.
• Shahzad, K., Lu, B. and Abdul, D. (2022). “Entrepreneur Barrier Analysis on Renewable Energy Promotion in the Context of Pakistan Using Pythagorean Fuzzy AHP Method”, Environmental Science and Pollution Research, 29(36), 54756-54768.
• Shahzad, K., Lu, B., Abdul, D., Safi, A., Umar, M. and Afridi, N. K. (2023). “Assessment of Biomass Energy Barriers Towards Sustainable Development: Application of Pythagorean Fuzzy AHP”, Geological Journal, 58(4), 1607-1622.
• Sivam, A., Dieguez, T., Ferreira, L.P. and Silva, F.J.G. (2019). “Key Settings for Successful Open Innovation Arena”, Journal of Computational Design and Engineering, 6(4), 507-515.
• Starr, M.K. (1978). “Operations Management”, U.S.A: Prentice-Hall, Inc
• Şengül, Ü., Eren, M. and Shiraz, S. E. (2012). “Bulanık AHP İle Belediyelerin Toplu Taşima Araç Seçimi”, Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 40, 143-165.
• Tainter, J.A. (1988). “The Collapse of Complex Societies”, Cambridge University Press, Cambridge.
• TİM Inosuit (2019-2022). “Etki Analizi”, https://tim.org.tr/files/downloads/inosuit/TIM_Inosuit_Programi.pdf (Erişim Tarihi: 15.06.2023).
• Tiwari, R. (2007). “The Early Phases of Innovation: Opportunities and Challenges in Public-Private Partnership”, Asia Pacific Tech Monitor, 24 (1), 32-37.
• Top, A. (2002). Verimlilik ve Üretkenlik Üzerine Düşünceler, Öneri Dergisi, 5 (17), 31-34.
• Xiao, F. and Ding, W. (2019). “Divergence Measure of Pythagorean Fuzzy Sets and its Application in Medical Diagnosis”, Applied Soft Computing, 79, 254-267.
• Yager, R.R. (2013). “Pythagorean Membership Grades in Multicriteria Decision Making”, IEEE Transactions on Fuzzy Systems, 22(4), 958-965.
• Yazıcı, E., Alakaş, H.M. and Eren, T. (2023). “Prioritizing of Sectors for Establishing a Sustainable Industrial Symbiosis Network with Pythagorean Fuzzy AHP-Pythagorean Fuzzy TOPSIS Method: A Case of Industrial Park in Ankara”, Environmental Science and Pollution Research, 30, 77875-77889.
• Yildiz, A. (2016). “Interval Type 2-Fuzzy TOPSIS and Fuzzy TOPSIS Method in Supplier Selection in Garment Industry/Metoda Fuzzy TOPSIS Interval Tip 2 Si Metoda Fuzzy TOPSIS În Selectarea Furnizorului Din Industria De Confectii”, Industria Textila, 67(5), 322.
• Yıldız, A. and Demir, Y. (2019). “Bulanik TOPSIS Yöntemiyle Türkiye’nin Yerli Otomobili İçin En Uygun Fabrika Yerinin Seçimi”, Business and Management Studies: An International Journal, 7(4),1427-1445.
• Yılmaz, O. (2020). “İnovasyon Yönetimi”, Gazi Kitabevi, Ankara.
• Yılmaz, Y.E. (2016). “Pazarlamada Süreç, Inovasyon Stratejileri ve Firma Performansı Ilişkisi”, Yüksek Lisans Tezi, Okan Üniversitesi Sosyal Bilimler Enstitüsü, İstanbul.
• Yucesan, M. and Gul, M. (2020). “Hospital Service Quality Evaluation: An Integrated Model Based on Pythagorean Fuzzy AHP and Fuzzy TOPSIS”, Soft Computing, 24(5), 3237-3255.
• Yucesan, M. and Kahraman, G. (2019). “Risk Evaluation and Prevention in Hydropower Plant Operations: A Model Based on Pythagorean Fuzzy AHP”, Energy Policy, 126, 343-351.
• Zadeh, L.A. (1965). “Fuzzy sets”, Information and control, 8(3), 338-353.
• Zhang, X. and Xu, Z. (2014). “Extension of TOPSIS to Multiple Criteria Decision Making with Pythagorean Fuzzy Sets”, International Journal of Intelligent Systems, 29(12), 1061-1078.
• Zhou, F. and Chen, T. Y. (2023). “A Hybrid Group Decision-Making Approach Involving Pythagorean Fuzzy Uncertainty for Green Supplier Selection”, International Journal of Production Economics, 261, 108875.
• Zimmermann, H.J. (2010). “Fuzzy Set Theory”, Wiley Interdisciplinary Reviews: Computational Statistics, 2(3), 317-332.