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     Advance Journal of Food Science and Technology


Study of Consumers' Acceptance of Eco-friendly Green Food Hotels

Cheng-Ming Chang
Department of Information Management, Hwa Hsia University of Technology, Taipei 10000, Taiwan, China
Advance Journal of Food Science and Technology   2016  5:356-359
http://dx.doi.org/10.19026/ajfst.10.2082  |  © The Author(s) 2016
Received: May ‎16, ‎2015  |  Accepted: ‎July ‎2, ‎2015  |  Published: February 15, 2016

Abstract

This study is different from previous literature, as it uses AI technology, such as the artificial neural network and evolutionary computation techniques to analyze Taiwan's consumers' acceptance of eco-friendly green food hotels by sensor data mining, in order to know the characteristics of the consumption intentions of respondents. First, Grey Relational Analysis (GRA) is used to discuss the acceptance of various domains in the questionnaire and then the GRA performance values of the various domains are used for clustering analysis, in order to know the characteristics of the consumption intentions of different respondents. Finally, the evolutionary computation technique is combined with the artificial neural network to build an eco-friendly hotel acceptance detection model as reference for eco-friendly hotel operators. The findings show that, in the hotel operators' view, increasing the room rate of eco-friendly green food hotels can be accepted by consumers; the consumers agree with the subject of resource recovery; government bodies shall subsidize hotel operators in promoting eco-friendly green food hotels, thus, contributing to the promotion of eco-friendly green food hotels.

Keywords:

Clustering analysis, eco-friendly hotel, grey relational analysis, sensor data mining,


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Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2042-4876
ISSN (Print):   2042-4868
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