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Content Analysis of TripAdvisor Reviews on Restaurants: A Case Study of Macau

Simon Leia &

Rob Lawb

aInstitute for Tourism Studies, MacaubThe Hong Kong Polytechnic University, Hong Kong

simonlei@ift.edu.mo

rob.law@polyu.edu.hk

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Agenda

• Introduction• Literature Review• Methodology• Findings & Discussions• Conclusion

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IntroductionWeb 2.0 & Social Media

• Web 2.0– Has four distinct features “interactivity, real-time

user control, social participation (sharing), and user-generated content” (Laudon & Laudon, 2014)

• Social media– “a group of Internet-based applications that build

on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of User Generated Content” (Kaplan & Haenlein (2010)

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IntroductionUser Generated Content

• UGC– Texts– Photos– Videos

• Travellers– Increasingly rely on trip planning– Tend to share more

Post-trip or post-consumption experiences(or even during a trip)

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IntroductionMacau

• Macau– Gambling paradise, ‘Las Vegas of Asia’– Has rich and diversified culture but not

sufficient to stand out– Little can be offered to non-gambling residents

and tourists

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IntroductionResearch Objectives

1) Environmental scanning of eWOM, as ‘health check’;

2) More expensive restaurants (i.e. fine dining) do a better job in higher customer satisfaction?

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Literature ReviewCustomer Satisfaction and Word of Mouth

• High customer satisfaction positive word of mouth, high customer loyalty

• Low customer satisfaction negative word of mouth, low customer loyalty

• eWOM

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Literature ReviewWeb 2.0 Applications

Ranking Name Home-based Features

1 Zagat New York City, USA 30-point scale ratings

2 Yelp San Francisco, USA 5-point scale ratings

3 Urbanspoon Seattle, USA ‘Like’ or ‘dislike’ ratings

4 Gayot Los Angeles, USA Ratings by experts

5 Dine.com California, USA 5-point scale ratings

Name Uniform Resource Locator (URL) Description

FoodIdea http://www.foodidea.net Macau local; in Chinese

OpenRice http://www.openrice.com/ Asian-based; in Chinese and English

TripAdvisor http://www.tripadvisor.com Worldwide; English and other languages

Hong Kong Tatler Dining http://hk.dining.asiatatler.com Hong Kong and Macau local; fine dining only; in English

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Literature ReviewTripAdvisor.com

Name Use Rationale

TripAdvisor Key secondary data source English; local and international presence; large

number of reviews

FoodIdea Not intended Chinese only; local presence only

OpenRice Not intended Chinese only; local and regional presence

A trustworthy secondary data source

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Literature ReviewResearch Gaps

• Classification of neutral comments

Count positive comments

Count negative comments

Count neutral comments

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MethodologyData Collection

Price Description

$ Fast food, self service

$$ Casual, sit-down service

$$$ More formal dining

$$$$ Most expensive, special occasion

Cut-off date: 1 March 2014(i.e. prior to this date)

English reviews only(i.e. non-English reviews ignored)

614 online reviews from

22 restaurants

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MethodologyData Analysis

Some comments can be mapped into to more than one attributes. For instance, the comment ‘The food served is consistently good and the prices are reasonable’ is translated into a ‘+1’ in Positive Food and a ‘+1’ in Positive Value.

Direct quotes are used from TripAdvisor.

A review is subdivided into sentences, marked by a full stop. Those sentences are known as comments.

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MethodologyValidity Checks

• A second proof-reader

• Localised knowledge– Websites– Persons

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MethodologyClassifications

Category Subcategory Rationale

Food Meals, snacks, wines, desserts,

variety of choices, portion size,

safety, temperature, smell

No comprehensive items

could be adopted to suit all

comments

Service Attitude, efficiency, helpfulness, host-

guest interaction

(King, 1995)

Value Prices on food and wine; comparative

prices; discounts and compliments

(Haemoon, 2000)

Atmosphere Ambiance, interior décor, view,

lights, music, TV, spatial layout,

surrounding environment, special

events, performers

(Bitner, 1992; Su, 2011)

Other Unclassified; miscellaneous (Law & Cheung, 2010)

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Findings & DiscussionsFrequency counts (1)

Categories Positive Comments Negative Comments Neutral Comments

Frequency Percentage Frequency Percentage Frequency Percentage

$ 208 82% 31 12% 16 6%

Food 93 90% 8 8% 2 2%

Service 48 98% 1 2% 0 0%

Value 14 74% 5 26% 0 0%

Atmosphere 30 97% 1 3% 0 0%

Other 23 43% 16 30% 14 26%

$$ 652 75% 58 7% 161 18%

Food 339 79% 21 5% 71 16%

Service 123 84% 10 7% 13 9%

Value 61 65% 9 10% 24 25%

Atmosphere 116 83% 7 5% 17 12%

Other 13 22% 11 18% 36 60%

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Findings & DiscussionsFrequency counts (2)

Categories Positive Comments Negative Comments Neutral Comments

  Frequency Percentage Frequency Percentage Frequency Percentage

$$$   390 82% 35 7% 51 11%

  Food 155 86% 18 10% 7 4%

  Service 100 88% 8 7% 6 5%

  Value 21 46% 8 17% 17 37%

  Atmosphere 70 93% 0 0% 5 7%

  Other 44 72% 1 2% 16 26%

$$$$ 580 69% 119 14% 141 17%

  Food 279 81% 29 8% 37 11%

  Service 142 71% 39 20% 19 10%

  Value 41 36% 22 19% 50 44%

  Atmosphere 88 81% 12 11% 9 8%

  Other 30 41% 17 23% 26 36%

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Conclusions• High-priced restaurants do not necessarily yield high

customer satisfaction;

• Small cafes in Macau can survive and do reasonably well in terms of customer satisfaction. But they are too small in size;

• Product quality, uniqueness, and knowledgeable and passionate restaurateurs are the key factors to success;

• At present, customers can still find restaurants that provide good food and service at reasonable prices

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ConclusionsLimitations

• Review process is manual, lengthy, and mechanical;

• Human counting errors may occur;

• Somewhat difficult to draw a line between positive and neutral, or between negative and neutral

• Did not distinguish first-time reviewers from senior contributors (i.e. same weight)

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ConclusionsFuture Researches

1) The perceived destination image of Macau as revealed from social media sites (e.g. TripAdvisor);

2) Content analysis on user-generated photos and videos taken in a destination

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Thank you!