A Novel City Benchmarking Methodology: Multidimensional Managerial Insight

Yıl 2022, , 943 - 960, 30.08.2022

Ahmet Bahadır Şimşek , M. Edib Gürkan

https://doi.org/10.18506/anemon.1096732

Öz

Benchmarking is a managerial tool that enables decision-makers to make critical inferences about their organizations from different perspectives such as their strengths and weaknesses, priorities, past and future. Benchmarking cities receives considerable research interest mainly because of its potential benefits to managers in evaluating policies and making strategic decisions. Current research on city benchmarking focuses on identifying the benchmark factors and developing methods for measuring the benchmark scores. In other words, the existing methods aim to derive city benchmark scores by combining the weighted factors and compare cities based on their respective scores. However, policymakers tend to request more detailed information to guide their policies, rather than having a simple scoring. This study aims to fill this gap with a novel benchmarking approach. The proposed approach relies on the sensitivity analysis of the multi-criteria decision-making technique adopted in benchmarking, and offers decision-makers three main outputs for each city: (i) delivering a consensual ranking that is free of decision-maker bias, (ii) indicating priority areas under which require the least effort to achieve better ranking, and (iii) revealing the relative effects of the previous policy results and projecting the future ranking if the current policies remain same. The implementation of the proposed methodology is illustrated by a case study. The case study highlights that adopting the proposed methodology is promising since it provides insightful managerial information to decision-makers.

Anahtar Kelimeler

benchmarking, cities, multi criteria decision-making, sensitivity analysis

Yeni Bir Şehir Kıyaslama Metodolojisi: Çok Boyutlu Yönetimsel İçgörü

Öz

Kıyaslama, karar vericilerin kuruluşları hakkında güçlü ve zayıf yönleri, öncelikleri, geçmişi ve geleceği gibi farklı perspektiflerden kritik çıkarımlar yapmalarını sağlayan bir yönetim aracıdır. Şehirlerin kıyaslaması, yöneticilere politikaların değerlendirilmesi ve stratejik kararlar alınması açısından sağladığı faydalar nedeniyle bilim insanlarının ilgisini çekmektedir. Şehir kıyaslaması üzerine mevcut araştırmalar, kıyaslama faktörlerini belirlemeye ve kıyaslama skorlarını ölçmek için yöntemler geliştirmeye odaklanmıştır. Bir diğer ifadeyle, mevcut yöntemler, şehirleri karşılaştırmak için ağırlıklı faktörleri birleştirerek tek bir puan hesaplamayı amaçlamaktadır. Bununla birlikte, politika yapıcılar, politikalarına rehberlik etmek için basit bir puanlamaya sahip olmak yerine daha ayrıntılı bilgi talep etme eğilimindedir. Bu çalışmada önerilen kıyaslama yaklaşımı bu boşluğu doldurmayı amaçlamaktadır. Önerilen yaklaşım, kıyaslama için kabul edilen çok kriterli karar verme tekniğinin duyarlılık analizine dayanmaktadır ve karar vericiler her şehir için üç ana çıktı sunmaktadır: (i) karar verici yanlılığından uzak, uzlaşıya dayalı bir sıralama sunar, (ii) daha iyi sıralama elde etmek için minimum çaba gerektiren öncelikli alanları gösterir ve (iii) geçmişten bugüne uygulanan politikaların sonuçlarının göreceli etkilerini sergiler ve geçmişten bugüne uygulanan politikalar devam ettirilirse şehrin gelecekteki sıralamasını yansıtır. Metodolojinin uygulanışı bir vaka çalışması ile gösterilmektedir. Vaka çalışması, önerilen metodolojiyi uygulamanın karar vericiye önemli yönetimsel çıkarımlar sunması sebebiyle oldukça ümit verici olduğunu vurgulamaktadır.

Anahtar Kelimeler

kıyaslama, şehirler, çok kriterli karar verme, duyarlılık analizi

Kaynakça

• Abdel-Basset, M., & Mohamed, R. (2020). A novel plithogenic TOPSIS- CRITIC model for sustainable supply chain risk management. Journal of Cleaner Production, 247, 119586. https://doi.org/10.1016/j.jclepro.2019.119586
• Ács, Z. J., Autio, E., & Szerb, L. (2014). National Systems of Entrepreneurship: Measurement issues and policy implications. Research Policy, 43(3), 476–494. https://doi.org/10.1016/j.respol.2013.08.016
• Ahmad, S., Baiocchi, G., & Creutzig, F. (2015). CO2 Emissions from Direct Energy Use of Urban Households in India. Environmental Science and Technology, 49(19), 11312–11320. https://doi.org/10.1021/ES505814G/SUPPL_FILE/ES505814G_SI_001.PDF
• Audretsch, D. B., Belitski, M., & Desai, S. (2015). Entrepreneurship and economic development in cities. Annals of Regional Science, 55(1), 33–60. https://doi.org/10.1007/s00168-015-0685-x
• Audretsch, D. B., & Fritsch, M. (2002). Growth regimes over time and space. Regional Studies, 36(2), 113–124. https://doi.org/10.1080/00343400220121909
• Chalgham, M., Khatrouch, I., Masmoudi, M., Walha, O. C., & Dammak, A. (2019). Inpatient admission management using multiple criteria decision-making methods. Operations Research for Health Care, 23, 100173. https://doi.org/10.1016/j.orhc.2018.10.001
• Chen, P. (2020). Effects of the entropy weight on TOPSIS. Expert Systems with Applications, 114186. https://doi.org/10.1016/j.eswa.2020.114186
• Činčikaitė, R., & Meidute-Kavaliauskiene, I. (2021). An Integrated Competitiveness Assessment of the Baltic Capitals Based on the Principles of Sustainable Development. Sustainability 2021, Vol. 13, Page 3764, 13(7), 3764. https://doi.org/10.3390/SU13073764
• Du, Q., Wang, Y., Ren, F., Zhao, Z., Liu, H., Wu, C., Li, L., & Shen, Y. (2014). Measuring and Analysis of Urban Competitiveness of Chinese Provincial Capitals in 2010 under the Constraints of Major Function-Oriented Zoning Utilizing Spatial Analysis. Sustainability 2014, Vol. 6, Pages 3374-3399, 6(6), 3374–3399. https://doi.org/10.3390/SU6063374
• Gawlak, A., Matuszewska, M., Ptak, A., Carmona-Torres, M., Cobo-Cuenca, A. I., Laredo-Aguilera, A., Ángel, P., Román, L., Antonio, J., Montilla, P., & Pozuelo-Carrascosa, D. P. (2021). Inclusiveness of Urban Space and Tools for the Assessment of the Quality of Urban Life—A Critical Approach. International Journal of Environmental Research and Public Health 2021, Vol. 18, Page 4519, 18(9), 4519. https://doi.org/10.3390/IJERPH18094519
• Ghalehteimouri, K. J., Hatami, A., & Asadzadeh, H. (2020). Measuring the Quality of Life and City Competitiveness: A Methodological Framework for the Iranian Metropolis. Journal of Urban Culture Research, 21, 90–111. https://doi.org/10.14456/JUCR.2020.14
• Giffinger, R., Haindlmaier, G., & Kramar, H. (2010). The role of rankings in growing city competition. Urban Research & Practice, 3(3), 299–312. https://doi.org/10.1080/17535069.2010.524420
• González-García, S., Rama, M., Cortés, A., García-Guaita, F., Núñez, A., Louro, L. G., Moreira, M. T., & Feijoo, G. (2019). Embedding environmental, economic and social indicators in the evaluation of the sustainability of the municipalities of Galicia (northwest of Spain). Journal of Cleaner Production, 234, 27–42. https://doi.org/10.1016/J.JCLEPRO.2019.06.158
• Jadidi, O., Firouzi, F., Mohd Yusuff, R., & Zulkifli, N. (2008). TOPSIS and fuzzy multi-objective model integration for supplier selection problem. Journal of Achievements in Materials and Manufacturing Engineering of Achievements in Materials and Manufacturing Engineering, 31(2), 762–769. www.journalamme.org
• Jadidi, O., Sai Hong, T., Firouzi, F., & Yusuff, R. M. (2009). An optimal grey based approach based on topsis concepts for supplier selection problem. International Journal of Management Science and Engineering Management, 4(2), 104–117. https://doi.org/10.1080/17509653.2009.10671066
• Jahan, A., & Edwards, K. L. (2015). A state-of-the-art survey on the influence of normalization techniques in ranking: Improving the materials selection process in engineering design. Materials and Design, 65, 335–342. https://doi.org/10.1016/j.matdes.2014.09.022
• Jiang, Y., & Shen, J. (2010). Measuring the urban competitiveness of Chinese cities in 2000. Cities, 5(27), 307–314. https://doi.org/10.1016/J.CITIES.2010.02.004
• Johnston, A. (2011). The Economic Performance of UK Cities, 1995–2005: Driven by Knowledge-based Sectors or Consumption-based Sectors? European Planning Studies, 19(12), 2095–2108. https://doi.org/10.1080/09654313.2011.633821
• Kaklauskas, A., Zavadskas, E. K., Radzeviciene, A., Ubarte, I., Podviezko, A., Podvezko, V., Kuzminske, A., Banaitis, A., Binkyte, A., & Bucinskas, V. (2018). Quality of city life multiple criteria analysis. Cities, 72, 82–93. https://doi.org/10.1016/j.cities.2017.08.002
• Keirstead, J. (2013). Benchmarking urban energy efficiency in the UK. Energy Policy, 63, 575–587. https://doi.org/10.1016/J.ENPOL.2013.08.063
• Kim, G., Park, C. S., & Yoon, K. P. (1997). Identifying investment opportunities for advanced manufacturing systems with comparative-integrated performance measurement. International Journal of Production Economics, 50(1), 23–33. https://doi.org/10.1016/S0925-5273(97)00014-5
• Kitchin, R., Lauriault, T. P., & McArdle, G. (2015). Knowing and governing cities through urban indicators, city benchmarking and real-time dashboards. Regional Studies, Regional Science, 2(1), 6–28. https://doi.org/10.1080/21681376.2014.983149
• Kose, E., Vural, D., & Canbulut, G. (2020). The most livable city selection in Turkey with the grey relational analysis. Grey Systems, 10(4), 529–544. https://doi.org/10.1108/GS-04-2020-0042/FULL/XML
• Liu, X., Guo, H., Li, Y., Li, Y., & Pan, W. (2016). Measuring the urban competitiveness of Chinese cities based on multi-attribute decision making approach. International Journal of Sustainable Development, 19(4), 315–341. https://doi.org/10.1504/IJSD.2016.080505
• Luque-Marínez, T., & Muñoz-Leiva, F. (2005). City benchmarking: A methodological proposal referring specifically to Granada. Cities, 22(6), 411–423. https://doi.org/10.1016/j.cities.2005.07.008
• Milani, A. S., Shanian, A., Madoliat, R., & Nemes, J. A. (2005). The effect of normalization norms in multiple attribute decision making models: A case study in gear material selection. Structural and Multidisciplinary Optimization, 29(4), 312–318. https://doi.org/10.1007/s00158-004-0473-1
• Ozkaya, G., & Erdin, C. (2020). Evaluation of smart and sustainable cities through a hybrid MCDM approach based on ANP and TOPSIS technique. Heliyon, 6(10), e05052. https://doi.org/10.1016/j.heliyon.2020.e05052
• Papadopoulos, S., & Kontokosta, C. E. (2019). Grading buildings on energy performance using city benchmarking data. Applied Energy, 233–234, 244–253. https://doi.org/10.1016/J.APENERGY.2018.10.053
• Prakash, M., Shukla, R., Chakraborty, A., & Joshi, P. K. (2016). Multi-criteria approach to geographically visualize the quality of life in India. Http://Dx.Doi.Org/10.1080/13504509.2016.1141119, 23(6), 469–481. https://doi.org/10.1080/13504509.2016.1141119
• Sáez, L., Heras-Saizarbitoria, I., & Rodríguez-Núñez, E. (2020). Sustainable city rankings, benchmarking and indexes: Looking into the black box. Sustainable Cities and Society, 53, 101938. https://doi.org/10.1016/j.scs.2019.101938
• Sáez, L., & Periáñez, I. (2015). Benchmarking urban competitiveness in Europe to attract investment. Cities, 48, 76–85. https://doi.org/10.1016/j.cities.2015.06.002
• Sáez, L., Periáñez, I., & Heras-Saizarbitoria, I. (2017). Measuring urban competitiveness: ranking European large urban zones. Journal of Place Management and Development, 10(5), 479–496. https://doi.org/10.1108/JPMD-07-2017-0066/FULL/PDF
• Shih, H. S., Shyur, H. J., & Lee, E. S. (2007). An extension of TOPSIS for group decision making. Mathematical and Computer Modelling, 45(7–8), 801–813. https://doi.org/10.1016/j.mcm.2006.03.023
• Tang, J., Zhu, H. L., Liu, Z., Jia, F., & Zheng, X. X. (2019). Urban Sustainability Evaluation under the Modified TOPSIS Based on Grey Relational Analysis. International Journal of Environmental Research and Public Health, 16(2). https://doi.org/10.3390/IJERPH16020256
• Triantaphyllou, E., & Sánchez, A. (1997). A sensitivity analysis approach for some deterministic multi-criteria decision-making methods. Decision Sciences, 28(1), 151–194. https://doi.org/10.1111/j.1540-5915.1997.tb01306.x
• Vakilipour, S., Sadeghi-Niaraki, A., Ghodousi, M., & Choi, S. M. (2021). Comparison between Multi-Criteria Decision-Making Methods and Evaluating the Quality of Life at Different Spatial Levels. Sustainability 2021, Vol. 13, Page 4067, 13(7), 4067. https://doi.org/10.3390/SU13074067
• Vishwakarma, A., Kulshrestha, M., & Kulshreshtha, M. (2012). Efficiency evaluation of municipal solid waste management utilities in the urban cities of the state of Madhya Pradesh, India, using stochastic frontier analysis. Benchmarking, 19(3), 340–357. https://doi.org/10.1108/14635771211242996/FULL/XML
• Wang, X., Li, Z., Meng, H., & Wu, J. (2017). Identification of key energy efficiency drivers through global city benchmarking: A data driven approach. Applied Energy, C(190), 18–28. https://doi.org/10.1016/J.APENERGY.2016.12.111
• Zavadskas, E. K., Turskis, Z., & Tamosaitiene, J. (2008). Construction risk assessment of small scale objects by applying TOPSIS method with attributes values determined at intervals. Computer Modelling and New Technologies, 12(4), 38–43.