A Kansei Engineering Approach to Evaluate Consumer Perception on Social Media: A Case Study of Giant Manufacturing Company


Nowadays, social media marketing is becoming increasingly important issue for companies to gain website traffics or attention from their customers. The main purposes of applying social medias, in marketing is as a communications tool that makes the companies and their products accessible to the target customers as well as potential customers. However, most companies do not know how to design and manage their social media websites, resulting in poor word-of-mouth, slow sales growth, and reducing brand value. This paper develops a Kansei engineering methodology to help companies better understand how consumer perception on the social website influences consumer intension. The Facebook fan page of Giant Manufacturing Co., the world’s largest bicycle manufacturer, is served as the study subject. The questionnaire is designed based on Kansei words collected from different sources and the concept of experiential marketing used to define design elements. Principle component analysis is used to reduce the number of perception variables and then regression analysis is applied to determine the ranking order of perception variables that have impacts on corporate site traffics. In addition, Kano model is also employed to classify perception variables, providing more information to understand consumer conversion behavior from fan pages to corporate websites. The contribution of this research is to help firms better understand significant impacts of consumer perception for corporate fan pages on their website. The firm can apply the developed methodology to improve their fan page design and management, leading to better customer experience, higher conversion rate, and increased brand recognition.

Author Information
Ming-Bao Lin, National Chung-Hsing University, Taiwan
Juite Wang, National Chung-Hsing University, Taiwan

Paper Information
Conference: ACP2015
Stream: Linguistics

This paper is part of the ACP2015 Conference Proceedings (View)
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Posted by James Alexander Gordon