Fuzzy Clustering Approach for Marketing Recycled Products of Tabriz Municipality Waste Management Organization

Authors

  • Vahid Saeid Nahaei * Center Municipality Building, Tabriz Municipality, Tabriz, Iran
  • Farzad Naziri-Oskuei Center Municipality Building, Tabriz Municipality, Tabriz, Iran

DOI:

https://doi.org/10.59615/ijime.1.1.25

DOR:

https://dorl.net/dor/20.1001.1.2783378.2021.1.1.4.4

Keywords:

Fuzzy Clustering, Marketing, Tabriz Municipality, Waste Management Organization

Abstract

The main concern of municipalities is the realization of sustainable revenues. Organizations affiliated with municipalities should play a role in generating revenue by defining specialized tasks while assisting municipal tasks. Tabriz Municipality Waste Management Organization seeks to achieve this by defining its strategies and goals. The organization has implemented various projects to generate revenue from recycled products. Poor planning and failure to fully outsource are among the obstacles of this organization. Therefore, marketing of recycled products is an important project. Lack of careful planning in this regard, marketing costs and weakness of private sector investment projects are the most important obstacles facing the organization. This article has determined the degree of homogeneity of waste organization projects in the marketing of recycled products with a fuzzy clustering approach and according to the opinions of experts. The results show that some of the organization's projects lack value. Instead, some projects, such as the construction of a recycling town with a variety of recycled products, renewable energy recycling, and plastic recycling with a variety of products, have similar features in the product mix marketing element, and this can reduce marketing costs and Focus on such projects.

Downloads

Download data is not yet available.

References

• Akcay, D., & Okkay, I. (2017). REAL TIME MARKETING IMPLEMENTATIONS: EXAMPLE OF KADIKÖY MUNICIPALITY. Turkish journal of design, art and communication, 7(1), 99-109, doi: 10.7456/10701100/009.

• Alliedmarketresearch. (2021), Waste Management Market by Type (Municipal Waste, Industrial Waste and Hazardous Waste) and Service (Collection and Disposable): Global Opportunity Analysis and Industry Forecast, 2021–2030, from https://www.alliedmarketresearch.com/waste-management-market.

• Arunachalam, D., & Kumar, N. (2018). Benefit-based consumer segmentation and performance evaluation of clustering approaches: An evidence of data-driven decision-making. Expert Systems with Applications, 111, 11-34. https://doi.org/10.1016/j.eswa.2018.03.007

• Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & geosciences, 10(2-3), 191-203. https://doi.org/10.1016/0098-3004(84)90020-7.

• Camara, R. C., Cuzzocrea, A., Grasso, G. M., Leung, C. K., Powell, S. B., Souza, J., & Tang, B. (2018, July). Fuzzy logic-based data analytics on predicting the effect of hurricanes on the stock market. In 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1-8). IEEE, doi: 10.1109/FUZZ-IEEE.2018.8491523.

• Chiang, W. Y. (2018). Applying data mining for online CRM marketing strategy: An empirical case of coffee shop industry in Taiwan. British Food Journal, doi: https://doi.org/10.1108/BFJ-02-2017-0075.

• D’Urso, P., Disegna, M., Massari, R., & Prayag, G. (2015). Bagged fuzzy clustering for fuzzy data: An application to a tourism market. Knowledge-Based Systems, 73, 335-346. https://doi.org/10.1016/j.knosys.2014.10.015.

• France, S. L., & Ghose, S. (2019). Marketing analytics: Methods, practice, implementation, and links to other fields. Expert Systems with Applications, 119, 456-475. https://doi.org/10.1016/j.eswa.2018.11.002.

• Gabrielli, L., Giuffrida, S., & Trovato, M. R. (2017). Gaps and overlaps of urban housing sub-market: hard clustering and fuzzy clustering approaches. In Appraisal: from theory to practice (pp. 203-219). Springer, Cham, doi: 10.1007/978-3-319-49676-4_15.

• Gandhmal, D. P., & Kumar, K. (2019). Systematic analysis and review of stock market prediction techniques. Computer Science Review, 34, 100190. https://doi.org/10.1016/j.cosrev.2019.08.001.

• He, H., & Harris, L. (2020). The impact of Covid-19 pandemic on corporate social responsibility and marketing philosophy. Journal of business research, 116, 176-182. https://doi.org/10.1007/s43039-020-00016-3.

• Hsu, T. H., Chu, K. M., & Chan, H. C. (2000, May). The fuzzy clustering on market segment. In Ninth IEEE International Conference on Fuzzy Systems. FUZZ-IEEE 2000 (Cat. No. 00CH37063) (Vol. 2, pp. 621-626). IEEE, doi: 10.1109/FUZZY.2000.839064.

• Kamthania, D., Pawa, A., & Madhavan, S. S. (2018). Market segmentation analysis and visualization using K-mode clustering algorithm for e-commerce business. Journal of computing and information technology, 26(1), 57-68. https://doi.org/10.20532/cit.2018.1003863.

• Li, D. X., Dong, H., & Jin, X. (2017). Model for evaluating the enterprise marketing capability with picture fuzzy information. Journal of Intelligent & Fuzzy Systems, 33(6), 3255-3263, doi: 10.3233/JIFS-161741.

• Lundmark, L. (2006). Mobility, migration and seasonal tourism employment: Evidence from Swedish mountain municipalities. Scandinavian Journal of Hospitality and Tourism, 6(3), 197-213, https://doi.org/10.1080/15022250600866282.

• Mahdiraji, H. A., Hafeez, K., Kord, H., & Kamardi, A. A. (2020). Analysing the voice of customers by a hybrid fuzzy decision-making approach in a developing country's automotive market. Management Decision, https://doi.org/10.1108/MD-12-2019-1732.

• Mahdiraji, H. A., Kazimieras Zavadskas, E., Kazeminia, A., & Abbasi Kamardi, A. (2019). Marketing strategies evaluation based on big data analysis: a CLUSTERING-MCDM approach. Economic research-Ekonomska istraživanja, 32(1), 2882-2892, https://doi.org/10.1080/1331677X.2019.1658534.

• Nahaei, V. S., & Bahrami, M. (2021). Uncertainty analysis of business components in Iran with fuzzy systems: By comparing hypermarkets and Net markets. International Journal of Innovation in Management, Economics and Social Sciences, 1(1), 45-55, https://doi.org/10.52547/ijimes.1.1.45.

• Nahaei, V. S., Novin, M. H., & Khaligh, M. A. (2021). Fuzzy clustering of investment projects in Tabriz Municipality Waste Management Organization with ecological approach. International Journal of Innovation in Management, Economics and Social Sciences, 1(2), 28-42, https://doi.org/10.52547/ijimes.1.2.28.

• Nahaei, V. S., Novin, M. H., & Khaligh, M. A. (2021). Review and prioritization of investment projects in the Waste Management organization of Tabriz Municipality with a Rough Sets Theory approach. International Journal of Innovation in Management, Economics and Social Sciences, 1(3), 46-57, https://doi.org/10.52547/ijimes.1.3.46.

• Pappas, I. O., & Woodside, A. G. (2021). Fuzzy-set Qualitative Comparative Analysis (fsQCA): Guidelines for research practice in Information Systems and marketing. International Journal of Information Management, 58, 102310, https://doi.org/10.1016/j.ijinfomgt.2021.102310.

• Parviznejad, P. S., & Akhavan, A. N. (2021). Impact of the Tourism Industry Scenarios in Urban Economy:(Case Study Tabriz). International Journal of Innovation in Management, Economics and Social Sciences, 1(1), 1-15, https://doi.org/10.52547/ijimes.1.1.1.

• Parviznejad, P. S., & Bahrami, M. (2021). Uncertainty analysis of tourism components in Tabriz. International Journal of Innovation in Management, Economics and Social Sciences, 1(3), 1-14, https://doi.org/10.52547/ijimes.1.3.1.

• Podapati, S., Lundberg, L., Skold, L., Rosander, O., & Sidorova, J. (2017, September). Fuzzy recommendations in marketing campaigns. In European Conference on Advances in Databases and Information Systems (pp. 246-256). Springer, Cham, doi: 10.1007/978-3-319-67162-8_24.

• Sengupta, D., & Agrahari, S. (Eds.). (2017). Modelling trends in solid and hazardous waste management. Springer Singapore, doi: 10.1007/978-981-10-2410-8_2.

• Sharma, A., Bhuriya, D., & Singh, U. (2017, April). Survey of stock market prediction using machine learning approach. In 2017 international conference of electronics, communication and aerospace technology (ICECA) (Vol. 2, pp. 506-509). IEEE, doi: 10.1109/ICECA.2017.8212715.

• Sigindi, T. (2018). Consumers, Businesses, and Governments During an Economic Crisis: A Marketing Perspective. In Managerial Strategies for Business Sustainability During Turbulent Times (pp. 208-222). IGI Global, doi: 10.4018/978-1-5225-2716-9.ch011.

• Youssefi, H., Nahaei, V., & Nematian, J. (2011). A new method for modeling system dynamics by fuzzy logic: Modeling of research and development in the national system of innovation. Journal of Mathematics and Computer Science, 2(1), 88-99, http://dx.doi.org/10.22436/jmcs.002.01.10.

• Zhong, X., & Enke, D. (2017). A comprehensive cluster and classification mining procedure for daily stock market return forecasting. Neurocomputing, 267, 152-168, https://doi.org/10.1016/j.neucom.2017.06.010.

Downloads

Published

2021-10-24

How to Cite

Saeid Nahaei, V., & Naziri-Oskuei, F. . (2021). Fuzzy Clustering Approach for Marketing Recycled Products of Tabriz Municipality Waste Management Organization. International Journal of Innovation in Marketing Elements, 1(1), 25–36. https://doi.org/10.59615/ijime.1.1.25

Issue

Section

Original Research