This document summarizes a study that used association rule mining to analyze the ethnic restaurant selection patterns of US tourists in Hong Kong. The study analyzed over 4,000 online restaurant reviews from US Yelp users to identify common associations between different ethnic restaurant types. The results found that US tourists most frequently visited Cantonese and Japanese restaurants. Several association rules also indicated that US tourists who reviewed certain ethnic restaurants like Japanese, Asian fusion, or Taiwanese were less likely to review Cantonese restaurants. The study aims to provide insights for the tourism industry on how to better market different ethnic cuisines to US visitors.

1. ENTER 2018 Research Track Slide Number 1
Ethnic Restaurant Selection Patterns of U.S. Tourists
in Hong Kong: An Application of Association Rule
Mining
Munhyang (Moon) Oha
, Irene Cheng Chu Chana
,
Fuad Mehraliyeva
a
The Hong Kong Polytechnic University, Hong Kong S.A.R., China
moon.oh@connect.polyu.hk, irene.cc.chan@connect.polyu.hk,
fuad.mehraliyev@connect.polyu.hk
http://www.polyu.edu.hk

2. ENTER 2018 Research Track Slide Number 2

3. ENTER 2018 Research Track Slide Number 3
Introduction
Food consumption
•Manifests national identity
and culture (Kim & Kim, 2017)
•Motivates travel (Enright &
Newton, 2005)
•Enhances tourist experience
(Kivela & Crotts, 2006)
•Occupies a large portion of
tourists’ spending (HKTB, 2016)

4. ENTER 2018 Research Track Slide Number 4
Introduction
• Purchases decisions are not independent (Solnet et al., 2016)
Are choices of ethnic restaurants associated with
each other? If so, how?

5. ENTER 2018 Research Track Slide Number 5
Introduction
• Long-haul vs. Short-haul tourists
• Focus on restaurant selection patterns among U.S.
tourists  the largest source market visiting HK (HKTB, 2016)
Objectives:
1.To identify the pattern of ethnic restaurant selection
using data from U.S. tourists
2.To demonstrate the usefulness of association rule
mining in providing useful suggestions to industry
practitioners

6. ENTER 2018 Research Track Slide Number 6
Literature Review
• Ethnic food: food which represents the cultural
characteristics of a particular ethnic group (Kim & Kim, 2017)
• Ethic restaurant: any full-service restaurant that serves
food of a specific country or region (Ting et al., 2016)
• Tourists’ restaurant selection:
– Food quality, atmosphere, and service quality affect restaurant
choices (Clemes et al., 2013)
– Selection criteria are different for different type of restaurants
(e.g., upscale vs. fast-food restaurants)
– Sometimes random decisions were made (Cohen & Avieli, 2004)

7. ENTER 2018 Research Track Slide Number 7
Literature Review
• Variety-seeking behavior in food
consumption: tourists may more
likely to try variety of food when
they are traveling
• Food neophobia: a person’s
unease towards unfamiliar food,
and his/her tendency to avoid it
(Baarrena & Sánchez, 2013)

8. ENTER 2018 Research Track Slide Number 8
Literature Review
• Association rules mining
– To identify correlations among items, e.g., beers and nuts in
supermarket
(1) Support: frequency of occurrence for a particular rule
(2) Confidence: the probability of Y’s existence given X’s existence
in the same basket
(3) Lift: the probability of co-existence of X and Y in the same
basket, given that X and Y are independent

9. ENTER 2018 Research Track Slide Number 9
Literature Review
• Association rules mining in tourism
– Destination choice (Li et al., 2010)
– Hotel preference attributes (Emel et al., 2007)
– Tourist attractions (Versichele et al., 2014)
– Food consumption?

10. ENTER 2018 Research Track Slide Number 10
Method
• Online reviews on ethnic restaurants as proxy for restaurant selection
• Yelp.com (most popular among U.S. tourists)
• Five pieces of information were extracted:
1. Restaurant name
2. Restaurant type
3. Reviewers’ name
4. Reviewers’ place of origin (only U.S. tourists were retained)
5. Date of reviews being posted
• 4,411 entries, 596 restaurants in HK

11. ENTER 2018 Research Track Slide Number 11
Method
• Classification of ethnic
restaurant
– Ethnicity
– Date of review (in the same
trip): 18 days
– Place of origin to confirm
unique identity of each
reviewer
– 3152 online review baskets
• Data analysis: Rstudio
• Support = 1%
• Confidence = 10%
Adapted from: Okumus, Okumus, and McKercher, 2007
Category Sub-category
Chinese Cantonese
Pekinese
Shanghainese
Other Chinese (Northeastern Chinese,
Shandong, Shanxi, Chiu Chow, Szechuan)
Other Asian Japanese
Taiwanese
Korean
Asian fusion
Other Asian (Indian, Indonesian, Singaporean,
Malaysian, Thai, Vietnamese)
Western French
Italian
American (U.S., Mexican)
Spanish
Other Western (British, German, International)

12. ENTER 2018 Research Track Slide Number 12
Results
• Frequency analysis

13. ENTER 2018 Research Track Slide Number 13
Results
• Association rules mining
No. Rules Support
(%)
Confidence
(%)
Lift
Antecedent

Consequent
1
2
3
4
5
6
7
{ }
{ }
Japanese
Asian Fusion
Shanghainese
Taiwanese
American
Cantonese
Japanese
Cantonese
Cantonese
Cantonese
Cantonese
Cantonese
62.13
11.39
2.92
2.19
2.06
1.55
1.14
62.13
11.39
25.63
27.27
35.91
26.20
18.75
1.00
1.00
0.44
0.42
0.58
0.41
0.30Notes: minimum support threshold=1%, minimum confidence threshold=10%
When U.S. tourists post comments on Japanese restaurants, Asian Fusion
restaurants, Shanghainese restaurants, Taiwanese restaurants, and American
restaurants, they are less likely to post reviews on Cantonese restaurants.

14. ENTER 2018 Research Track Slide Number 14
Discussions & Implications
• American tourists show substantial preference to local
food (Cantonese), followed by Japanese and Asian Fusion
cuisines
• U.S. tourists may consider Japanese, Asian Fusion,
Shanghainese, and Taiwanese cuisines as competitive
substitutes for Cantonese ones
• Put more effort in highlighting the unique features of
Cantonese cuisine to stimulate interests

15. ENTER 2018 Research Track Slide Number 15
Limitation
• Use of online review data to represent restaurant selection and
preference
• Future research:
– address the methodological limitation in this study
– “why?” (test the two explanations: food neophobia and variety seeking)

16. ENTER 2018 Research Track Slide Number 16
Thank You!