Selection of Social Networking Sites among Undergraduate Students with AHP-TOPSIS Model

Authors

  • Liew Kah Fai

Abstract

Social networks are well established nowadays as the whole world is accessible to the internet. Social networking site provides different features that allow users from around the world to communicate, associate, relate and interact with each other. Facebook, Twitter, Google Plus and Instagram are the most common social networking sites that have been introduced nowadays. The objective of this study is to determine the preferred social networking site among the undergraduate students as well as identify the priority of decision criteria in the selection of social networking sites with the proposed AHP-TOPSIS model. AHP-TOPSIS model is a hybrid of AHP model and TOPSIS model which is an effective tool to tackle the multi-criteria decision making problem. AHP model is responsible to determine the weights of the decision criteria whereas TOPSIS model is adopted to identify the ranking of the social networking sites as well as to select the most preferred social networking sites as the ultimate goal of this study. The results of this study show that Instagram is the most preferred social networking site, followed by Facebook, Twitter and lastly Google Plus. Moreover, privacy is the most important decision criterion in the selection of social networking sites. This study is significant because it helps to identify the most influential decision criterion and also the most favourable social networking site among the undergraduate students. This study can serve as a reference for those less favourable social networking sites to identify their potential improvements so that they can offer better services to their users in the future.

Keywords:

Decision Criteria, Ranking, Social Networking Sites, Undergraduate Students

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Published

2021-12-30

How to Cite

Liew Kah Fai. (2021). Selection of Social Networking Sites among Undergraduate Students with AHP-TOPSIS Model. Applied Mathematics and Computational Intelligence (AMCI), 10, 188–202. Retrieved from https://ejournal.unimap.edu.my/index.php/amci/article/view/165

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Articles