Applying big data analytics to support Kansei engineering for hotel service development
| DOI | https://doi.org/10.1108/DTA-05-2018-0048 |
| Date | 04 February 2019 |
| Published date | 04 February 2019 |
| Pages | 33-57 |
| Author | Mu-Chen Chen,Yu-Hsiang Hsiao,Kuo-Chien Chang,Ming-Ke Lin |
| Subject Matter | Library & information science,Librarianship/library management,Library technology,Information behaviour & retrieval,Metadata,Information & knowledge management,Information & communications technology,Internet |
Applying big data analytics to
support Kansei engineering for
hotel service development
Mu-Chen Chen
Department of Transportation and Logistics Management,
National Chiao Tung University, Taipei, Taiwan
Yu-Hsiang Hsiao
Department of Business Administration,
National Taipei University, New Taipei, Taiwan
Kuo-Chien Chang
Department of Leisure and Recreation Management,
Chihlee Institute of Technology, New Taipei, Taiwan, and
Ming-Ke Lin
Department of Transportation and Logistics Management,
National Chiao Tung University, Taipei, Taiwan
Abstract
Purpose –Leisure and tourism activities have proliferated and become important parts of modern life, and
the hotel industry plays a necessary role in the supply for and demand from consumers. The purpose of this
paper is to develop guidelines for hotel service development by applying a service development approach
integrating Kansei engineering and text mining.
Design/methodology/approach –The online reviews represent the voice of customers regarding the
products and services. Consumers’online comments might become a key factor for consumers choosing
hotels when planning their tourism itinerary. With the framework of Kansei engineering, this paper adopts
text mining to extract the sets of Kansei words and hotel service characteristics from the online contents as
well as the relationships among Kansei words, service characteristics and these two sets. The relationships
are generated by using link analysis, and then the guidelines for hotel service development are proposed
based on the obtained relationships.
Findings –The results of the present research can provide the hotel industry a comprehensive
understanding of hotels’customers opinions, and can offer specific advice on how to differentiate one’s
products and services from competitors’in order to improve customer satisfaction and increase hotels’
performance in the end. Finally, this study finds out the service development guidelines to meet customers’
requirements which can provide suggestions for hotel managers. The implications both for academic and
industry are also drawn based on the obtained results.
Originality/value –Now, in the internet era, consumers can comment on their hotel living experience
directly through the internet. The large amount of user-generated content (UGC) provided by consumers also
provides chances for the hospitality industry to understand consumers’opinions through online review
mining. The UGC with consumers’opinions to hotel services can be continuously collected and analyzed by
hoteliers. Therefore, this paper demonstrates how to apply the hybrid approach integrating Kansei
engineering and online review mining to hotel service development.
Keywords Online review, Service development, Text mining, Hotel, Big data analytics, Kansei engineering
Paper type Research paper
1. Introduction
In recent years, the importance of social media has noticeably increased, because a large
amount of information is posted on social media by users. With this trend, many companies
have begun to use social media as a platform for customer relationship management, and
they need to face the opportunities resulting from the huge amount of user-generated
content (UGC) created by customers (He et al., 2015). In contrast to conventional surveys or
Data Technologies and
Applications
Vol. 53 No. 1, 2019
pp. 33-57
© Emerald PublishingLimited
2514-9288
DOI 10.1108/DTA-05-2018-0048
Received 25 May 2018
Revised 18 September 2018
Accepted 20 October 2018
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/2514-9288.htm
33
Kansei
engineering for
hotel service
development
using Guest Comment Cards, such real-time internet-enabled data record people’s actual
behavior online, and offer lower tracking costs and the opportunity for earlier warning
(Kwok et al., 2017). The shopping of products and services has been transformed with the
advancement of social media, and customers’needs are better met since the characteristics
of products and services can be captured by observing online reviews (Salehan and Kim,
2016). Text mining can be applied to understand companies’products or services in their life
cycle (Fan and Gordon, 2014).
The rapid expansion of thetravel and tourism industry is driving the growth of the hotel
industry (WorldTravel and Tourism Council, 2016), duringwhich hotels are also adapting to
the different demands of consumers and developing different services and business models
(Xu and Li, 2016). Customers’experience and satisfaction have been always topics of interest
in the hospitalityindustry, and are essentialin securing customers’loyaltyand re-purchasing,
and in establishing a good reputation and enhancing the hotel’s revenue (Kuo et al., 2012).
Therefore,consumer satisfaction is key for a hotelto become better than competitors, through
understanding whether its consumers are satisfied with the products and services provided
by the hotel. Thisis the basis for proposing improvementstrategies to promote the qualityof
hotel services and products, creating a higher customer return rate (Xiang et al., 2015).
In the hotel industry, customer satisfaction is a complicated experience often defined as
the evaluation result between before and after the consumer purchase (Parasuraman et al.,
1985). Therefore, in order to better measure consumers’satisfaction with hotels, it is
important to use UGC, such as consumers’comments, to better understand consumers’
experience with the hotels (Xu and Li, 2016.), and finally, to reach ends such as increasing
the customer return rate and hotel revenue (Kuo et al., 2012). Nowadays, with the internet
growing so quickly (Permatasari et al., 2013), consumers can make public their comments on
and evaluations of providers of goods and services through the internet, so consumers’
online comments might have an influence and be a key factor for others when making their
consumption choices (Kwok et al., 2017; Mudambi and Schuff, 2010). Particularly, the hotel
market appears to be influenced most by online reviews compared to other tourism sectors
as the overwhelming majority of UGC travel website users refer to online reviews when
deciding where to stay (Dinçer and Alrawadieh, 2017). Online customer reviews are not only
an important source of acquiring product information (Huang et al., 2015) for customers but
also help businesses (e.g. the hotel industry) obtain critical insights into consumers’
attitudes (Dellarocas et al., 2007; Dinçer and Alrawadieh, 2017). These large amounts of UGC
can be used as a source for analysis to understand consumer behavior in the hospitality
industry. Accor hospitality, which operates more than 4,100 properties in more than
90 countries, monitors customers’opinions posted on travel websites to improve their
reputations and performance (Fan and Gordon, 2014).
Big data analytics (BDA) has been taken seriously by scholars in hospitality areas,
because text mining can convert a large amount of customers’comments collected from the
internet into useful information (Xiang et al., 2015; Kwok et al., 2017). Nevertheless, there is
still a lack of empirical studies using text mining to understand the relationship between the
hotel industry’s service characteristics and customers’Kansei feelings. Kansei is a Japanese
term that refers to perceptions, feelings, impressions and a certain affective presentation
(e.g. cute, lively or elegant). The term could be translated into English as “consumers’
psychological feeling and image”(Nagamachi, 1997, cf. Schütte et al., 2004, p. 216); that is,
Kansei words represent general customer impressions of and emotions toward particular
product or service concepts and can be extracted and translated into design characteristics
(Guo et al., 2014; Hartono, 2012). Using text mining is expected to save the excessive
burden of manual identification, and can not only improve analytical efficiency but
also enhance analytical accuracy, in order to establish a suitable method for hotel
service development.
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DTA
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