SavedAdd your journal

Journals / PressAcademia Procedia / 2017 / Cilt: 3 - Sayı: 1

COMPARISON OF AIRLINE DESTINATIONS IN TERMS OF ONLINE CONSUMER BEHAVIORS USING MULTI DIMENSIONAL SCALE ANALYSIS

Bahri Baran Kocak
Ozlem Atalik
Cem Burak Kocak
Pages
891–899
Publication Date
2017-06-30
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

By using multidimensional scaling analysis, the locations of Turkey and other destinations can be displayed and evaluated in two dimensional space.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:With digital media becoming an important position in mass communication, information is shared and spread to those who use this media.
📊
Key Findings & Data:This study aims to find out the location of each destination according to the share, retweet, likes and reasons for reccommendation of users by looking at the destinations that are shared by Turkish Airlines (THY) on Twitter in 2016.
💡
Conclusion & Impact:By using multidimensional scaling analysis, the locations of Turkey and other destinations can be displayed and evaluated in two dimensional space.
🔬Design:Research Article
📍Scope:2016 Verileri
⚙️Variables:Social media · E-Wom · Twitter

Abstract

With digital media becoming an important position in mass communication, information is shared and spread to those who use this media. Twitter in digital media is one of the most popular social media platforms that has come up with the combination of social network sites and blogs, with millions of members and the ability to share and spread information quickly. Sharing of information via electronic word of mouth (e-WOM) is one of the important issues that attract the attention of marketers. In this respect, this behavior of social media users is very important in sharing information by sharing, retweeting or liking of any posts on Twitter, as it is displayed by other users on their friends list. This study aims to find out the location of each destination according to the share, retweet, likes and reasons for reccommendation of users by looking at the destinations that are shared by Turkish Airlines (THY) on Twitter in 2016. The study will reveal how our country is in relation to other destinations and the tendency of Turks to other destinations to be seen in concrete terms. By using multidimensional scaling analysis, the locations of Turkey and other destinations can be displayed and evaluated in two dimensional space.

🔖

Cite This Article

Kocak, B. B., Atalik, O., Kocak, C. B. (2017). COMPARISON OF AIRLINE DESTINATIONS IN TERMS OF ONLINE CONSUMER BEHAVIORS USING MULTI DIMENSIONAL SCALE ANALYSIS. PressAcademia Procedia, Cilt: 3 - Sayı: 1, 891–899. https://ajindex.com/articles/842519

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (15)

  1. Asur, S ve Huberman, B. A. (2010, August). Predicting The Future With Social Media. In Web Intelligence And Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference On (Vol. 1, Ss. 492-499).
  2. IEEE. Berthon P. R., Pitt L ve Campbell C. (2008). When Customers Create The Ad. California Management Review. 50(4), 6–30.
  3. Borg, I ve Groenen, P. J. (2005). Modern Multidimensional Scaling: Theory And Asslications. Springer Science & Business Media. Boyd, D., Golder, S ve Lotan, G. (2010, January). Tweet, Tweet, Retweet: Conversational Aspects Of Retweeting On Twitter. In System Sciences (HICSS), 2010 43rd Hawaii International Conference On (Ss. 1-10).
  4. IEEE. Busing, F. M. T. A., Commandeur, J. J., Heiser, W. J., Bandilla, W ve Faulbaum, F. (1997). PROXSCAL: A Multidimensional Scaling Program For İndividual Differences Scaling With Constraints. Softstat, 97, 67-74.
  5. Bülbül, S ve Köse, A. (2010). Türkiye’de Bölgelerarası İç Göç Hareketlerinin Çok Boyutlu Ölçekleme Yöntemi İle İncelenmesi. İstanbul Üniversitesi İşletme Fakültesi Dergisi, 39(1), 75-94.
  6. Heinonen, K. (2011). Consumer Activity İn Social Media: Managerial Assroaches To Consumers' Social Media Behavior. Journal Of Consumer Behaviour, 10(6), 356-364.
  7. Kalaycı, Ş. (2005). SPSS Uygulamalı Çok Değişkenli İstatistiksel Teknikleri. Asil Yayın Dağıtım Ltd: İstanbul, Ss.379-396.
  8. Hair, J.F. Anderson, R.E., Tatham, R. L ve W. C. (1998).
  9. Black, Multivariate Data Analysis. Fifth Edition, Prentice-Hall Inc: USA. Ss.536-539.
  10. Kaplan, A. M ve Haenlein, M. (2010).
  11. Users Of The World, Unite! The Challenges And Ossortunities Of Social Media. Business Horizons, 53(1), 59-68.
  12. Köksal, Y ve ÖZDEMİR, Ş. (2013). Bir İletişim Aracı Olarak Sosyal Medya'nın Tutundurma Karması İçerisindeki Yeri Üzerine Bir İnceleme. Süleyman Demirel Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, 18(1). Kruskal, J. B. (1964). Multidimensional Scaling By Optimizing Goodness Of Fit To A Nonmetric Hypothesis. Psychometrika, 29(1), 1-27.
  13. Mangold, W. G ve Faulds, D. J. (2009). Social Media: The New Hybrid Element Of The Promotion Mix. Business Horizons, 52(4), 357-365.
  14. Oğuzlar, A. (2005b), “Çok Boyutlu Ölçekleme Analizi Yardımıyla Avrupa Birliği Üyeliğini Etkileyen Faktörlerin Konumlandırılması”, Uludağ Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, 24(1), S. 33–43.
  15. Tüzüntürk, S. (2009). Çok Boyutlu Ölçekleme Analizi: Suç İstatistikleri Üzerine Bir Uygulama. Uludağ Üniversitesi İktisadi Ve İdari Bilimler Fakültesi