CHAID ALGORİTMASI İLE BİREYLERİN AKILLI TELEFON TERCİHLERİNİN STRATEJİK OLARAK İNCELENMESİ

Year 2023, Issue: 41, 124 - 145, 31.10.2023

Mehmet Polat

https://doi.org/10.18092/ulikidince.1311655

Abstract

Küresel akıllı telefon piyasası, yüksek düzeyde rekabetin olduğu en dinamik gelişen pazarlardan biridir. Akıllı telefon piyasasında yüksek teknoloji ürünlerinin yer alması, firmaları daha temkinli ve stratejik hareket etmeye zorlamaktadır. Firmaların başarılı olmaları, akıllı telefon kullanıcılarının taleplerini karşılama düzeylerine bağlıdır. Bu çalışmada, bireylerin akıllı telefon tercihlerinde hangi faktörlerden etkilendiği ve bu faktörlerin firma stratejilerine etki düzeyinin ne olabileceğinin tespit edilmesi amaçlanmıştır. Bu amaç doğrultusunda TRA2 Bölgesi’nde bir anket yapılmıştır. Anketlerden elde edilen veriler, CHAID algoritması kullanılarak değerlendirilmiştir. Çalışmada, akıllı telefon kullanıcılarının algılanan hizmet kalitesi bakımından en çok Apple’ı beğendikleri görülmüştür. Ayrıca beklenen hizmet kalitesi bakımından da en yüksek beklenti düzeyinin Apple markası üzerinde yoğunlaştığı gözlemlenmiştir. Diğer yandan ençok tercih edilen Xiaomi’nin en düşük marka sadakatine sahip olduğu tespit edilmiştr.

Keywords

Küresel Akıllı Telefon Piyasası, CHAID Algoritması, Akıllı Telefon, Apple

STRATEGIC ANALYSIS OF INDIVILDUALS’ SMARTPHONE PREFERENCES THROUGH CHAID ALGORITHM

Abstract

The global smartphone market is one of the most dynamically developing markets with high competition. The presence of high-tech products in the smartphone market forces companies to act more cautiously and strategically. The success of companies depends on their level of meeting the demands of smartphone users. To this end, this study aims to determine the factors that are affected by the smartphone preferences of individuals and the effect level of these factors on company strategies. For this purpose, in this study, a survey was conducted in the TRA2 Region located in the east of Turkey. The obtained data from the survey was evaluated using the CHAID algorithm. In the study, it was seen that smartphone users liked the Apple the most in terms of perceived service quality. In addition, it has been observed that the highest level of expectation in terms of expected service quality is concentrated on the Apple brand. On the other hand, the most preferred Xiaomi was found to have the lowest brand loyalty.

Keywords

The Global Smartphone Market, CHAID Algorithm, Smartphone, Apple

References

• Akin, M., Eyduran, E., & Reed, B. M. (2017). Use of RSM and CHAID Data Mining Algorithm for Predicting Mineral Nutrition of Hazelnut. Plant Cell, Tissue and Organ Culture (PCTOC), 128(2), 303–316. https://doi.org/10.1007/s11240-016-1110-6
• Antipov, E., & Pokryshevskaya, E. (2010). Applying CHAID for Logistic Regression Diagnostics and Classification Accuracy İmprovement. Journal of Targeting, Measurement and Analysis for Marketing, 18(2), 109–117. https://doi.org/10.1057/jt.2010.3
• Bilgin, G. (2021). Investigation of The Risk of Diabetes in Early Period Using Machine Learning Algorithms. Journal of Intelligent Systems: Theory and Applications, 4(1), 55–64. https://doi.org/10.38016/jista.877292
• Chien, C.-F., & Chen, L.-F. (2008). Data Mining to İmprove Personnel Selection and Enhance Human Capital: A Case Study in High-Technology Industry. Expert Systems with Applications, 34(1), 280–290. https://doi.org/10.1016/j.eswa.2006.09.003
• Chung, K. Y., Oh, S. Y., Kim, S. S., & Han, S. Y. (2004). Three Representative Market Segmentation Methodologies for Hotel Guest Room Customers. Tourism Management, 25(4), 429–441. https://doi.org/10.1016/S0261-5177(03)00115-8
• Counterpoint. (2022). Global Smartphone Revenue Hits Record $450 Billion in 2021; Apple Captures Highest Ever Share in Q4 2021. Retrieved January 16, 2023, from Counterpoint website: https://www.counterpointresearch.com/global-smartphone-revenue-hits-record-450-billion-2021-apple-captures-highest-ever-share-q4-2021/
• Del Rosario, P. M., & White, R. M. (2005). The Narcissistic Personality İnventory: Test–Retest Stability and İnternal Consistency. Personality and Individual Differences, 39(6), 1075–1081. https://doi.org/10.1016/j.paid.2005.08.001
• Doğan, R., Yavuz, M., Küçükdemirci, İ., & Eren, T. (2015). Öğrencilerde Akıllı Telefon Kullanımının Özellikleri Bakımından Oyun Teorisi İle Analiz Edilmesi. Aksaray Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 7(3), 67–76. Retrieved from http://aksarayiibd.aksaray.edu.tr/tr/download/article-file/209240
• Ercan, U., & Irmak, S. (2017). Karar Ağaçları Kullanılarak Türkiye Hanehalkı Zeytinyağı Tüketimi Görünümünün Belirlenmesi. International Journal of Management Economics and Business, 13(3), 0–0. https://doi.org/10.17130/ijmeb.2017331329
• Fan, Y., & Yang, C. (2020). Competition, Product Proliferation, and Welfare: A Study of the US Smartphone Market. American Economic Journal: Microeconomics, 12(2), 99–134. https://doi.org/10.1257/mic.20180182
• Filieri, R., & Lin, Z. (2017). The Role of Aesthetic, Cultural, Utilitarian and Branding Factors in Young Chinese Consumers’ Repurchase İntention of Smartphone Brands. Computers in Human Behavior, 67, 139–150. https://doi.org/10.1016/j.chb.2016.09.057
• Fortune Business Insights. (2022). Smartphone Market Size, Share | Growth Analysis Report [2029]. Retrieved March 16, 2023, from Fortune Business Insights website: https://www.fortunebusinessinsights.com/industry-reports/smartphone-market-100308
• Gallart-Camahort, V., Fiol, L. C., & García, J. S. (2023). Influence of The İnternet on Retailer’s Perceived Quality in the Generation of Retailer’s Brand Equity. Vision: The Journal of Business Perspective, 27(1), 33–47. https://doi.org/10.1177/0972262921992212
• Hébert, M., Collin-Vézina, D., Daigneault, I., Parent, N., & Tremblay, C. (2006). Factors Linked to Outcomes in Sexually Abused Girls: A Regression Tree Analysis. Comprehensive Psychiatry, 47(6), 443–455. https://doi.org/10.1016/j.comppsych.2006.02.008
• Kadirhanoğulları, İ. H., Konu Kadirhanoğulları, M., Kara, M. K., & Kumlay, A. (2021). Iğdır İl’inde Organik Gıda Bilgi Düzeyinin Belirlenmesi. Kahramanmaraş Sütçü İmam Üniversitesi Tarım ve Doğa Dergisi, 25(4), 882–889. https://doi.org/10.18016/ksutarimdoga.vi. 890284
• Karadas, K., & Kadırhanogullari, I. H. (2017). Predicting Honey Production Using Data Mining and Artificial Neural Network Algorithms in Apiculture. Pakistan Journal of Zoology, 49(5), 1611–1619. https://doi.org/10.17582/journal.pjz/2017.49.5.1611.1619
• Karlıdağ, S. (2017). Dijital Çağda Fikri Mülkiyet Hakları ve Akıllı Telefonların Patent Savaşları: Kim Kazanıyor Kim Kaybediyor. İletişim Kuram ve Araştırma Dergisi, 0(44), 348–368.
• Kass, G. V. (1980). An Exploratory Technique for Investigating Large Quantities of Categorical Data. Applied Statistics, 29(2), 119. https://doi.org/10.2307/2986296
• Kim, J., Lee, H., & Lee, J. (2020). Smartphone Preferences and Brand Loyalty: A Discrete Choice Model Reflecting the Reference Point And Peer Effect. Journal of Retailing and Consumer Services, 52, 101907. https://doi.org/10.1016/j.jretconser.2019.101907
• Kim, S. S., Timothy, D. J., & Hwang, J. (2011). Understanding Japanese Tourists’ Shopping Preferences Using the Decision Tree Analysis Method. Tourism Management, 32(3), 544–554. https://doi.org/10.1016/j.tourman.2010.04.008
• Liao, C. H., & Hsieh, I. Y. (2013). Determinants of Consumer’s Willingness to Purchase Gray-Market Smartphones. Journal of Business Ethics, 114(3), 409–424. https://doi.org/10.1007/s10551-012-1358-7
• Maheswari, V. (2015). A Study on Brand Preference of Mobile Phone Users in Chidambaram Town. International Journal of Information Research and Review, 2(2), 341–345.
• Nezoo. (2023). Top Countries/Markets by Smartphone Penetration & Users | Newzoo. Retrieved March 16, 2023, from https://newzoo.com/insights/rankings/top-countries-by-smartphone-penetration-and-users
• Nisbet, R., Miner, G., & Yale, K. (2018). Handbook of Statistical Analysis and Data Mining Applications. In Handbook of Statistical Analysis and Data Mining Applications. Elsevier. https://doi.org/10.1016/C2012-0-06451-4
• Park, J., & Han, S. H. (2013). Defining User Value: A Case Study of A Smartphone. International Journal of Industrial Ergonomics, 43(4), 274–282. https://doi.org/10.1016/j.ergon.2013.04.005
• Park, N., Kim, Y.-C., Shon, H. Y., & Shim, H. (2013). Factors Influencing Smartphone Use and Dependency in South Korea. Computers in Human Behavior, 29(4), 1763–1770. https://doi.org/10.1016/j.chb.2013.02.008
• Persaud, A., & Azhar, I. (2012). Innovative Mobile Marketing Via Smartphones: Are Consumers Ready? Marketing Intelligence and Planning, 30(4), 418–443. https://doi.org/10.1108/ 02634501211231883
• Polat, M., & Akan, Y. (2020). Akıllı Telefon Piyasasında Firmalar Arasındaki Rekabetin Stratejik Olarak İncelenmesi: Oyun Teorisi Kapsamında Uygulamalı Bir Çalışma. Iğd Üniv Sos Bil Der, 24, 677–699. Retrieved from https://dergipark.org.tr/tr/pub/igdirsosbilder/issue/66831/1045312
• Rajagopal. (2010). Conational Drivers Influencing Brand Preference Among Consumers. Journal of Transnational Management, 15(2), 186–211. https://doi.org/10.1080/15332667.2010.481255
• Rashidi, S., Ranjitkar, P., & Hadas, Y. (2014). Modeling Bus Dwell Time With Decision Tree-Based Methods. Transportation Research Record: Journal of the Transportation Research Board, 2418(1), 74–83. https://doi.org/10.3141/2418-09
• Salim, M. (2022). Post Purchase Regrets and Perceived Brand Image an Exploratory Study and Usage of Mobile Phone Users. Academy of Marketing Studies Journal, 26(5), 1–12.
• Şata, M. (2018). CHAID Analizi ve Lojistik Regresyon Analizi Sonuçlarının Karşılaştırılması. Dicle Üniversitesi Ziya Gökalp Eğitim Fakültesi Dergisi, (33), 48–56. https://doi.org/10.14582/DUZGEF.1876
• Shrestha, R., Kadel, R., & Mishra, B. K. (2023). A Two-Phase Confirmatory Factor Analysis and Structural Equation Modelling for Customer-Based Brand Equity Framework in The Smartphone Industry. Decision Analytics Journal, 8, 100306. https://doi.org/10.1016/j.dajour.2023.100306
• Statista. (2022). Smartphone Market Shares by Vendor 2009-2022. Retrieved March 16, 2023, from Statista website: https://www.statista.com/statistics/271496/global-market-share-held-by-smartphone-vendors-since-4th-quarter-2009/
• Tabassum, R., & Ahmed, S. (2020). Xiaomi Invades The Smartphone Market in India. DECISION, 47(2), 215–228. https://doi.org/10.1007/s40622-020-00242-w
• Varun Kiran, A. (2022). Prediction of Mobile Phone Price Class using Supervised Machine Learning Techniques. In International Journal of Innovative Science and Research Technology (Vol. 7). Retrieved from www.ijisrt.com248
• Yoo, B., & Donthu, N. (2001). Developing and Validating A Multidimensional Consumer-Based Brand Equity Scale. Journal of Business Research, 52(1), 1–14. https://doi.org/10.1016/S0148-2963(99)00098-3
• Yoo, J. (2020). The Effects of Perceived Quality of Augmented Reality in Mobile Commerce—An Application of the Information Systems Success Model. Informatics, 7(2), 14. https://doi.org/10.3390/informatics7020014