Analysis of the effect of social media in tourism sector in frame of uses and gratifications theory
2019
0 views
0 downloads
Advisor: Doç. Dr. Zümrüt Ecevit Satı
Abstract (EN)
Tourism sector has an important position in terms of countries. This sector, where investment potential continuously develops, is a source of income for the country's economy with its positive effects. Therefore, significant costs are incurred for the promotion of the country and tourism places.With the development of social media, it can be said that promotional activities are directed towards social media platforms. In this context, the use of social media activities in our study were examined within the framework of uses and satisfaction theory. Th eaim of this study is to investigate the effects of social media usage in tourism sector within theframe work of the theory of uses and gratifications.Our study was designed with a questionnaire method, one of the quantitative research methods. In this context, as a result of study on local tourists, social media activities proved to be an important promotional activity in tourism. The study was conducted with SPSS-24 program. Keywords:Tourismsector, social media, uses and satisfaction theory
Author
Dr. Reza Ahmadı
Institution
How to Cite
Reza Ahmadı (Master Thesis). Analysis of the effect of social media in tourism sector in frame of uses and gratifications theory, 2019, İstanbul University.
Keywords
License
Tüm Hakları Saklıdır
This work is shared under the specified license terms.
More theses from İstanbul University
- In the covid 19 pandemic of female employees at a university hospital attitudes and affecting factors in nutrition of 9 months-6 years old children(2022)
- The perception of the right-wing movements in Turkey as to the 27 May Coup: 1960-1980(2020)
- Economic and social life in the Ottoman Empire according to the 1890 year's news of La Turquie Newspaper(2022)
- Land regime in the Umayyads period(2022)
- Merkel hücreli karsinomda tanısal ve prognostik belirteçler(2022)
- Use of machine learning methods in classification of respiratory system diseases(2021)