SOSYAL MEDYA PLATFORMLARININ SEÇİMİ: YENİ BİR HİBRİT ÇOK KRİTERLİ KARAR VERME YAKLAŞIMI

Yıl 2023, Cilt: 34 Sayı: 2, 243 - 275, 31.08.2023

Selen Avcı , Melike Kübra Ekiz Bozdemir , Atakan Alkan

https://doi.org/10.46465/endustrimuhendisligi.1259667

Öz

Sosyal Medya Platformları (SMP), bireylerin ve toplulukların birbirleriyle video, fotoğraf vb. paylaştığı, fikir ve bilgi alışverişinde bulunduğu, tartıştığı, işbirliği yaptığı platformlardır. Milyonlarca kullanıcı bu platformlar aracılığıyla çevrimiçi ortamda birbirleriyle etkileşim kurarak birbirlerinin davranışlarını, tutumlarını, alışkanlıklarını önemli ölçüde etkileyebilirler. SMP'ler, bireysel kullanıcıların yanı sıra kuruluşlar tarafından da reklam, satış, müşteri ilişkileri yönetimi vb. birçok alanda kullanılmaktadır. Bu nedenle, SMP seçimi önemli bir Çok Kriterli Karar Verme (ÇKKV) problemi olarak ele alınabilir. Bu çalışmada, SMP'lerin seçimi ve sıralamasında SMP’leri aktif şekilde kullanan ve SMP’lerde üretilen içeriklerden fazlasıyla etkilenen yaş grubu olarak lisans öğrencilerinin görüşleri dikkate alınmıştır. Ayrıca hem farklı ÇKKV yöntemlerinin hem de kriterlere atanan ağırlıkların seçim ve sıralamaya etkisini analiz edebilmek amacıyla Faktör Analizi ve Analitik Ağ Analizi (FA+ANP), Faktör Analizi ve Karmaşık Nisbi Değerlendirme (FA +COPRAS), Analitik Ağ Analizi ve Karmaşık Nisbi Değerlendirme (ANP+COPRAS), Faktör Analizi ve Gri İlişkisel Analiz (FA+GRA) ile Analitik Ağ Analizi ve Gri İlişkisel Analiz (ANP+GRA) olmak üzere 5 farklı yaklaşım kullanılmıştır. Böylece daha çok tercih edilen SMP'ler ve SMP seçiminde etkili olan kriterler değerlendirilmiştir. Çalışmanın, yeni SMP fikirleri veya var olan SMP’lere yeni özellikler eklenmesi konusunda yol gösterici olacağı düşünülmektedir.

Anahtar Kelimeler

Çok Kriterli Karar Verme (ÇKKV), ANP, COPRAS, GRA, Sosyal Medya Platformu (SMP) Seçimi

SELECTION OF SOCIAL MEDIA PLATFORMS: A NEW HYBRID MULTI-CRITERIA DECISION MAKING APPROACH

Öz

Social Media Platforms (SMPs) are highly interactive platforms where individuals and communities share, discuss and collaborate ideas, information, videos, photos etc. to each other. In these platforms have millions of online users which interact with each other and significantly affect each other's behaviour, attitude, and habit etc. SMPs are used many areas to advertising, client relations, tourism, journey, and many others by means of users or organizations. Therefore, selection of SMPs is an important Multi Criteria Decision-Making (MCDM) problem. In this study, we have handled the selection and ranking of SMPs from the perspective of undergraduate students, who are one of more active and affected age groups of them. In addition, we aimed to analyse the effect of both the MCDM methods, and the weights assigned to the criteria on the selection and ranking with 5 different approaches which are called Factor Analysis and Analytical Network Analysis (FA+ANP), FA and Complex Proportional Assessment (FA +COPRAS), ANP+COPRAS, FA and Grey Relations Analysis (FA+GRA), and ANP+GRA. Thus, we have evaluated more important criteria and more preferred SMPs with these approaches. In addition, we can emphasize that our study will guide policy makers for updating or adding new features to SMPs.

Anahtar Kelimeler

Multi-Criteria Decision Making (MCDM), ANP, COPRAS, GRA, Selection of Social Media Platforms (SMPs)

Kaynakça

• Ackora-Prah, J., Owusu, R., & Haabilla, K. (2018). An optimal media selection model for a company in Ghana. Journal of Advances in Mathematics and Computer Science, 28(6), 1-13. Doi: https://doi.org/10.9734/ JAMCS/2018/43441
• Bozdemir, M. K. E., & Alkan, A. (2022). Selection of social media platforms using fuzzy promethee method with different scenario types. Journal of Engineering Studies and Research, 28(4), 41-50. Doi: https://doi.org/10.29081/ jesr.v28i4.005
• Cao, Y., Ajjan, H., & Hong, P. (2013). Using social media applications for educational outcomes in college teaching: A structural equation analysis. British Journal of Educational Technology, 44(4), 581-593. Doi: https://doi.org/10.1111/bjet.12066
• Celli, F., Di Lascio, F.M.L., Magnani, M., Pacelli, B., Rossi, L. (2010). Social network data and practices: the case of friendfeed. Chai, SK., Salerno, J.J., Mabry, P.L. (Ed.) Advances in Social Computing. SBP 2010. Lecture Notes in Computer Science, (s. 346-353). Berlin: Springer. Doi: https://doi.org/10.1007/978-3-642-12079-4_43
• Cetinkaya, L. (2017). The impact of WhatsApp use on success in education process. International Review of Research in Open and Distributed Learning, 18(7), 60-74. Doi: https://doi.org/10.19173/irrodl.v18i7.3279
• Tok, G., Görentaş, I., & Avcı, S. (2021). How do Turkish students of higher education perceive Syrians: the case of Kocaeli. International Journal of Afro-Eurasian Studies, 6(12), 32-53. Retrieved from: https://dergipark.org.tr/en/ pub/ijar/issue/64457/912651
• Chang, Y., Tang, L., Inagaki, Y., & Liu, Y. (2014). What is tumblr: A statistical overview and comparison. ACM SIGKDD explorations newsletter, 16(1), 21-29. Doi: https://doi.org/10.1145/2674026.2674030
• Chen, S., Zhang, H., Lin, M., & Lv, S. (2011). Comparision of microblogging service between Sina Weibo and Twitter. Proceedings of 2011 International Conference on Computer Science and Network Technology, 2259-2263, Harbin, China. Doi: https://doi.org/10.1109/ICCSNT.2011.6182424
• Constantinides, E., & Stagno, M. (2012). Higher Education Marketing: A Study on the Impact of Social Media on Study Selection and University Choice. International Journal of Technology and Educational Marketing, 2(1), 41-58. Doi: https://doi.org/10.4018/ijtem.2012010104
• Erol, I., Oztel, A., Searcy, C., & Medeni, T. (2023). Selecting the most suitable blockchain platform: A case study on the healthcare industry using a novel rough MCDM framework. Technological Forecasting and Social Change, 186, 122132. Doi: https://doi.org/10.1016/j.techfore.2022.122132
• Gao, F., & Zhang, Y. (2013). Analysis of WeChat on iPhone. Proceedings of 2nd International Symposium on Computer, Communication, Control, and Automation (3CA), 278-281, Dordrecht, Atlantis. Doi: https://doi.org/10.2991/3ca-13.2013.69
• Gensler, S., Völckner, F., Liu-Thompkins, Y., & Wiertz, C. (2013). Managing brands in the social media environment. Journal of interactive marketing, 27(4), 242-256. Doi: https://doi.org/10.1016/j.intmar.2013.09.0
• Gilbert, E., Bakhshi, S., Chang, S., & Terveen, L. (2013). "I need to try this"? a statistical overview of pinterest. Proceedings of the SIGCHI conference on human factors in computing systems, 2427-2436, New York, USA. Doi: https://doi.org/10.1145/2470654.2481336
• Hall, C., & Zarro, M. (2012). Social curation on the website Pinterest. com. Proceedings of the American Society for Information Science and Technology, 49(1), 1-9. Doi: https://doi.org/10.1002/meet.14504901189
• Hanum, S., Sucahyo, Y. G., & Gandhi, A. (2021). Communication media rankings to support socialization at PPATK. Paper presented at the meeting of the 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT) Surabaya, Indonesia.Doi: https://doi.org/10.1109/EIConCIT50028.2021.9431868
• Zolfani, S. H., & Bahrami, M. (2014). Investment prioritizing in high tech industries based on SWARA-COPRAS approach. Technological and Economic Development of Economy, 20(3), 534-553. Doi: https://doi.org/10.3846/20294913.2014.881435
• Hayes, T., Ruschman, D., & Walker, M. (2009). Social Networking as an Admission Tool: A Case Study in Success. Journal of Marketing for Higher Education, 19, 109–124. Doi: https://doi.org/10.1080/08841240903423042
• Hui, G., & Bifeng, S. (2009). Study on effectiveness evaluation of weapon systems based on grey relational analysis and TOPSIS. Journal of Systems Engineering and Electronics, 20(1), 106-111. Retrieved from: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6074622
• Hung, Y. H., Huang, T. L., Hsieh, J. C., Tsuei, H., Cheng, C. C., & Tzeng, G. H. (2012). Online reputation management for improving marketing by using a hybrid MCDM model. Knowledge-Based Systems, 35, 87-93. Doi: https://doi.org/10.1016/j.knosys.2012.03.004
• Javan, H. T., Khanlari, A., Motamedi, O., & Mokhtari, H. (2018). A hybrid advertising media selection model using AHP and fuzzy-based GA decision making. Neural Computing and Applications, 29(1), 1153-1167. Doi: https://doi.org/10.1007/s00521-016-2517-z
• Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business Horizons, 53(1), 59-68. Doi: https://doi.org/10.1016/j.bushor.2009.09.003
• Kaplan, A. M., & Haenlein, M. (2012). Social media: back to the roots and back to the future. Journal of Systems and Information Technology, 14(2), 101-104. Doi: https://doi.org/10.1108/13287261211232126
• Kaur, S. (2016). Social media marketing. Asian Journal of Multidimensional Research (AJMR), 5(4), 6-12. Retrieved from: https://www.indianjournals.com/ijor.aspx?target=ijor:ajmr&volume=5&issue=4&article=002
• Li, C., & Bernoff, G. (2008). Winning in a World Transformed by Social Technologies. Massachusetts: Harvard Business.
• Lin, C.-L., Shih, Y.-H., Tzeng, G.-H., & Yu, H.-C. (2016). A service selection model for digital music service platforms using a hybrid MCDM approach. Applied Soft Computing, 48, 385-403. Doi: https://doi.org/10.1016/ j.asoc.2016.05.035
• Lindqvist, J., Cranshaw, J., Wiese, J., Hong, J., & Zimmerman, J. (2011). I'm the mayor of my house: examining why people use foursquare-a social-driven location sharing application. Proceedings Of The SIGCHI Conference On Human Factors İn Computing Systems, 2409-2418. New York, USA. Doi: https://doi.org/10.1145/1978942.1979295
• Luna, S., & Pennock, M. J. (2018). Social media applications and emergency management: A literature review and research agenda. International journal of disaster risk reduction, 27, 565-577. Doi: https://doi.org/10.1016/j.ijdrr.2018.01.006
• Muruganantham, A., & Gandhi, M. (2016). Discovering and Ranking Influential Users in Social Media Networks Using Multi-Criteria Decision Making (MCDM) Methods. Indian J Sci Technol, 9(32), 1-11. Doi: : https://doi.org/10.17485/ijst/2016/v9i32/95171
• Oralhan, B. (2019). Determining Criteria Weights That Affect Social Media Platform Selection with Fuzzy DEMATEL Approach. IBAD Journal of Social Sciences, Special Issue, 408-420. Doi: https://doi.org/10.21733/ibad.615528
• Picazo-Vela, S., Gutiérrez-Martínez, I., & Luna-Reyes, L. F. (2012). Understanding risks, benefits, and strategic alternatives of social media applications in the public sector. Government Information Quarterly, 29(4), 504-511. Doi: https://doi.org/10.1016/j.giq.2012.07.002
• Podvezko, V. (2011). The comparative analysis of MCDA methods SAW and COPRAS. Engineering Economics, 22(2), 134-146. Doi: http://dx.doi.org/10.5755/j01.ee.22.2.310
• Saaty, T. L. (1990). How to make a decision: the analytic hierarchy process. European Journal of Operational Research, 48(1), 9-26. Doi: https://doi.org/10.1016/0377-2217(90)90057-I
• Saaty, T. L. (2001). The Analytic Network Process. Pittsburgh, PA: RWS Publications.
• Saaty, T. L. (2008). Relative measurement and its generalization in decision making why pairwise comparisons are central in mathematics for the measurement of intangible factors the analytic hierarchy/network process. RACSAM-Revista de la Real Academia de Ciencias Exactas, Fisicas y Naturales. Serie A. Matematicas, 102, 251-318. Doi: https://doi.org/10.1007/BF03191825.
• Salleh, S., Ismail, S. H., & Abdullah, H. (2021). Optimization Model of Media Selection through Integer Programming. International Journal of Applied Business and Information Systems, 4(2), 142-146. Doi: https://doi.org/10.31763/ijabis.v4i2.447
• Saravanakumar, M., & SuganthaLakshmi, T. (2012). Social media marketing. Life Science Journal, 9(4), 4444-4451. Retrieved from: http://www.lifesciencesite.com/lsj/life0904/670_13061life0904_4444_4451.pdf
• Shabbir, M. S., Ghazi, M. S., & Mehmood, A. R. (2016). Impact of social media applications on small business entrepreneurs. Arabian Journal of Business and Management Review, 6(3), 203-205. Doi: https://doi.org/10.4172/2223-5833.1000203
• Sharma, M., & Joshi, S. (2019). Optimal Media Mix for IEC Campaigns Using Fuzzy Linguistic Genetic Algorithm: A Study of Swachh Bharat Abhiyan (Clean India Mission). Journal of Operations and Strategic Planning, 2(1), 1-21. Doi: https://doi.org/10.1177/2516600X19857357
• Sipahi, S., & Timor, M. (2010). The analytic hierarchy process and analytic network process: an overview of applications. Management Decision, 48(5), 705-808. Doi: https://doi.org/10.1108/00251741011043920
• Sudipa, I. G., Astria, C., Irnanda, K. F., Windarto, A. P., Daulay, N. K., Suharso, W., & Wijaya, H. O. (2020). Application of MCDM using PROMETHEE II Technique in the Case of Social Media Selection for Online Businesses. Paper presented of the meeting of the IOP Conference Series: Materials Science and Engineering. Semarang, Indonesia. Doi: https://doi.org/10.1088/1757-899X/ 835/1/012059
• Thelwall, M. (2009). MySpace comments. Online Information Review. 33(1), 58-76. Doi: https://doi.org/10.1108/14684520910944391
• Wu, L.-C., Chang , K.-L., Chuang, T.-L., Chen, Y.-S., & Tsai, J.-F. (2022). Identification of Applicable YouTubers for Hotels: A Case Study of Integrated Hybrid MCDM Model. Sustainability, 14(18), 11494. Doi: https://doi.org/ 10.3390/su141811494
• Wu, L. C., Chang, K. L., & Liao, S. K. (2020). A hybrid MCDM model to select optimal hosts of variety shows in the social media era. Symmetry, 12(1), 125. Doi: https://doi.org/10.3390/sym12010125
• Zavadskas, E. K., & Kaklauskas, A. (1996). Multicriteria Evaluation of Building (Pastatų sistemotechninis įvertinimas). Vilnius: Technika.