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Tourism Management 33 (2012) 731e740

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Tourism Management

journal homepage: www.elsevier .com/locate/ tourman

A behavioral analysis of web sharers and browsers in Hong Kong using targetedassociation rule mining

Jia Ronga,1, Huy Quan Vua,2, Rob Lawb,*, Gang Lia,3

a School of Information Technology, Deakin University, Vic 3125, Australiab School of Hotel & Tourism Management, Hong Kong Polytechnic University, Kowloon, Hong Kong

a r t i c l e i n f o

Article history:Received 13 May 2011Accepted 17 August 2011

Keywords:SharersBrowsersElectronic word-of-mouthAssociation rulesMachine learningData miningHong KongOutbound tourism

* Corresponding author. Tel.: þ852 3400 2181; fax:E-mail addresses: catjackie@gmail.com (J. Rong), h

hmroblaw@polyu.edu.hk (R. Law), gang.li@deakin.edu1 Tel.: þ61 3 925 17711.2 Tel.: þ61 4324 11359.3 Tel.: þ61 3 9251 7434; fax: þ61 3 9251 7604.

0261-5177/$ e see front matter � 2011 Elsevier Ltd.doi:10.1016/j.tourman.2011.08.006

a b s t r a c t

With the widespread use of Internet technology, electronic word-of-mouth [eWOM] communicationthrough online reviews of products and services has a strong influence on consumer behavior andpreferences. Although prior research efforts have attempted to investigate the behavior of usersregarding the sharing of personal experiences and browsing the experiences of others online, it remainsa challenge for business managers to incorporate eWOM effects into their business planning anddecision-making processes effectively. Applying a newly proposed association rule mining technique,this study investigates eWOM in the context of the tourism industry using an outbound domestictourism data set that was recently collected in Hong Kong. The complete profiles and the relations ofonline experience sharers and travel website browsers are explored. The empirical results are useful inhelping tourism managers to define new target customers and to plan more effective marketingstrategies.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

As one of the largest industries in the world, tourism playsa significant role in the global economy. Over the past decade, thecontribution of tourism has risen dramatically. The World Traveland Tourism Council [WTTC] commented that, in 2009, travel andtourism were directly and indirectly responsible for 9.4% of globalGDP and employed over 235 million employees worldwide(Baumgarten & Kent, 2010). With hundreds of millions of peopletraveling around the world every year, the tourism industry is oneof the most rapidly growing industries (WTI, 2010). Substantialtourism resources provide more opportunities to develop inclusiveservices that feature various combinations of such sectors astransportation, shopping, accommodation, and catering. Therefore,a better understanding of customer preferences and behavior isa key factor in effective planning and decision making, which arecrucial for the success of tourism businesses.

þ852 2362 9362.qvu@deakin.edu.au (H.Q. Vu),.au (G. Li).

All rights reserved.

Customer behavior and preferences are influenced by informa-tion from tourism practitioners and other consumers, the latter ofwhich is known as word-of-mouth [WOM]. WOM is defined asinformal communication between senders and receivers to shareinformation regarding products and services (Liao et al., 2010).Nowadays, such a way of communication has been changed due tothe growth in information technology, in particular, Internet tech-nology. People are now able to collect and disseminate informationvia a variety of platforms including e-mail, blogs, forums, onlinecommunities, and review sites. Information is shared among notonly friends and relatives but also strangers, as communicationnetworks are extended through the involvement of increasingnumbers of contributors and audiences (Cheng & Zhou, 2010b).Such sharing is referred to as electronic WOM, or eWOM. Hereafter,we use the term WOM to refer to word-of-mouth in the offlineenvironment and eWOM indicates the WOM in the online envi-ronment. In 2010, Cheng and Zhou (2010a) confirmed the signifi-cant effect of eWOM on consumer purchase decisions, and pointedout this offers extraordinary marketing opportunities as well aschallenges.

Because of the crucial role played by eWOM in the tourismindustry, researchers have investigated many different aspects ofthis form of information sharing. For example, Litvin, Goldsmith,and Pan (2008) found that eWOM is the most important sourceof information when consumers are making their decision to

J. Rong et al. / Tourism Management 33 (2012) 731e740732

purchase hospitality and tourism products, implying that eWOMcould be a promising cost-effective means of marketing in thisgrowing industry. In another study, Vermeulen and Seegers (2009)revealed the effectiveness of eWOM in improving the awareness oftourism products such as hotel services; while Ye, Law, and Gu(2009) as well as Ye, Law, Gu, and Chen (2010) demonstrateda strong relationship between consumer reviews and the businessperformance of hotels (specifically room sales), which suggests thathotel managers need to pay more attention to online reviews oftheir hotels.

Unfortunately, tourism managers who want to make use ofeWOM effects to identify potential target customers for strategicplanning and decision making still face two significant barriers,which are outlined as follows.

Profiles of sharers and browsers. The effective identification oftarget customers in tourism requires that complete profiles becompiled of, and for, those who share travel information andthose who seek such information on travel websites. However,because of the enormous volume of communication on theWorldWideWeb, the scope of eWOM is huge. No existing studyhas been able to construct complete user profiles that couldsatisfy entirely such demand.Relation between sharers and browsers. The relationship betweentravel experience for sharers and travel website browsers playsa vital role in eWOM effects because online communication canhappen in two ways. For example, a person can be a browser ofone website but also a sharer for another. What is shared candepend on what is read. Questions including “who shares andbrowses”, “who neither shares nor browses”, “who shares butdoes not browse”, and “who browses but does not share” remainunanswered.

Data mining has emerged as a useful method in helping busi-ness managers to achieve the goals of customer relationshipmanagement [CRM]. With the support of technology, it is possibleto turn customer data into profiling information, such as customervalue, customer targeting information, customer rating, andbehavior tracking, which is accessible by businesses (Wang,Rygielski, & Yen, 2002). Zhang and Chen (2007) applied a segmen-tation technique to a customer data base, which can help compa-nies to increase revenue and align cost to serve with customervalue. Hoontrakul and Sahadev (2008) employed the K-meansclustering technique to analyze data, which were collected viaa web-based intermediary and customer profiling, to help onlinetravel industries in modifying their business strategies. Travelreviews have become valuable to both travelers and practitionersfor various decision-making and planning processes. However,users are unable to read all available information, of which there isan overwhelming amount, on the Internet. Applying a range ofmachine-learning algorithms (e.g., Naive Bayes, Support VectorMachine, character-based n-gram), Ye, Zhang, and Law (2009)created a suggestion system for online booking in China whichhelps to analyze feelings and opinions in thousands of reviews,which cannot be done by humans (Zheng & Ye, 2009). To forecasttourism demand, Law (2000) applied a back propagation neuralnetwork (BPNN) and achieved promising results. In another study,Chena andWang (2007) showed that the genetic algorithm supportvector regression (GA-SVR) technique is more reliable than is theBPNN in forecasting. However, because of the complexity of eWOM,the creation of profiles through competitive analysis of existingdata for the efficient prediction and analysis of future events is stilla challenging task.

The aim of this study is to further explore the topic of eWOM intourism using an outbound Hong Kong tourism data set collected in

2010. Byapplying anewlyproposeddatamining technique, theoverallprofiles of sharers andbrowsers are constructed.Next, the associationsbetween the two groups are made explicit. For convenience, we usethe term “sharers” to refer to travelers who share their travel experi-ences online and the term “browsers” to refer to travelerswho browsetravel information online. Sharers are also alternatively referred to assenders, posters, or reviewers, and browsers as receivers or readers.The findings can help tourism managers to define new targetcustomers and to develop more effective marketing strategies. It ispossible to exploit eWOM effects and to use this informal type ofcommunication as an influential marketing tool, which has the dualbenefit of being both low cost and high speed.

The rest of the paper is organized as follows. The next sectionpresents a literature review of the existing eWOM research. Themethodology section provides an overview of association rulemining, a major data mining technique, and explains how it isincorporated into a new algorithm for more efficient performance.The section after that describes the processes of data collection andanalysis. The results section reports and analyzes the findings basedon a set of raw outbound tourism data. The last section summarizesthis study and offers suggestion for future research.

2. Literature review

In this section, the way that both WOM and eWOM can affectbusiness performance, namely, through its impact on consumerbehavior and decision making, is discussed. Then, the factors thatcan influence the effect of WOM and eWOM are analyzed. Anoverview of the existing studies on sharers and browsers and theirstudy limitations concludes the section.

2.1. Impact of eWOM on consumer behavior

The impact of eWOM on consumer behavior has been widelyinvestigated to provide guidance to business managers in theirdecision making and development of marketing strategies. Forinstance, Huang and Yang (2010) studied the effect of book reviewsin terms of quantity and content on reader borrowing intention.They found that a large number of reviews and richness of content(features assessed included sentence length, lexical richness, andthe use of personal pronouns) gave readers a favorable impressionof a book, which increased the level of their intention to read it.Regarding product reviews, WOM was found to account for about10% of the variation in consumer expectations about movies (Moul,2007), while online reviews of customer care experiences werefound to influence the choice of brand and company amongconsumersmaking purchasing decisions (Karakaya & Barnes, 2010).The effect of eWOM in supporting decision making in fundinvestment such as the share market has also been documented(Li & Liu, 2010; Uncles, East, & Lomax, 2010).

Although both eWOM and WOM influence various aspects ofconsumer behavior, their effects vary with consumer satisfactionor dissatisfaction with products and services. A good experiencecan lead to a positive review of a product; whereas a bad one canresult in a negative review. In measuring the persuasiveness ofeWOM, Zhang, Craciun, and Shin (2010) found that positivereviews are more effective than negative ones if the evaluatedproducts are associated with promotion consumption goals, andvice versa if the evaluated products are associated with preven-tion consumption goals. Recently, Xue and Zhou (2011) showedthat among Chinese consumers, negative reviews are more likelyto be trusted and shared; whereas positive ones appear to helpincrease the level of brand interest and purchase intention. Insome cases, positive reviews have a stronger effect than havenegative ones. East, Hammond, and Lomax (2008) demonstrated

J. Rong et al. / Tourism Management 33 (2012) 731e740 733

that positive reviews increased the purchasing intention ofcustomers regarding a familiar brand, but that negative reviewsdid not significantly decrease such an intention. Positive reviewsmay help in developing the trust and the online shoppingintention of consumers (Cheung, Lee, & Thadani, 2009). Positivereviews were found to affect search goods more strongly thanexperience goods, but negative reviews were found to have nosignificant influence on consumer behavior toward either type ofgoods (Hao, Ye, Li, & Cheng, 2010). Here, search goods are definedas those products or services with features and characteristics(e.g., quality or price) that are easily evaluated prior to purchase,in contrast to the features and characteristics of experiencegoods, which can only be evaluated upon consumption (Nelson,1974). Recently, Bambauer-Sachse and Mangold (2011) showedthat negative online product reviews did have a significantlynegative impact on brand impartiality, and that even amongexperienced consumers with brand knowledge, the negativeeffect was still significant. However, Bae and Lee (2011) foundthat negative online reviews had a greater effect on the purchaseintention of women than that of men. Because of the significantimpact and complexity of online reviews, there remains a needfor a better understanding of eWOM.

2.2. Factors influencing the effect of eWOM

The factors influencing the effect of eWOM on consumer behaviorcan be classified into different categories, including informationsources, receivers, the information itself, the intensity of the relation-ship, and the level of perceived risk (Cheng & Zhou, 2010b; Gao, 2010).The characteristics that lead to such groupings are elements in deter-mining the level of persuasiveness of eWOM. Hence, researchers haveinvestigated eWOM issues by exploring those characteristics andmeasuring the extent to which they affect consumer behavior.

In measuring the effect of online reviews, Sweeney, Soutar, andMazzarol (2008) determined that the richness and strength ofa message can affect the potential of eWOM to influence consumerperceptions and actions in professional service environments. Inanother study, the volume of online posting was found to be thebiggest factor directly influencing the opening day box office ticketsales of movies, which indicates the positive effect of eWOM onraising awareness (Duan, Gu, & Whinston, 2008). However, whena large number of online reviews are available, consumers havea harder time making purchasing decisions, which is known as theinformation overload effect (Park & Lee, 2008).

Regarding information sources, one of the most importantelements of eWOM is credibility, which comprises two components:expertise and trustworthiness. Cheng and Zhou (2010a) revealed thatthemore trustworthy a site is and themore expertise is demonstratedby the reviewers, themore likely consumers are to believe the reviewsposted on thatwebsite,whichpositively affects their buyingdecisions.The identification of an online reviewer has been shown to inducetrust in the information source (Chu&Li, 2010). Xie,Miao, Kuo, and Lee(2011) conducted a study of online hotel reviews and discovered thatthe offering of personal identifiable information by online reviewersincreased the level of credibility of the reviews.

Another element that influences the effect of eWOM isconsumer satisfaction, which is considered as the foundation ofeWOM. It increases the impact of not only eWOM but alsomarketing efforts (Yang, Zhang, & Zuo, 2009). Customer dis/satis-faction can generate both negative and positive effects. Forexample, word of a negative movie experience usually spreadsmore quickly and widely than that of a positive one (Ladhari, 2007).

Online information practices vary among different groups ofreaders. For example, the level of influence of eWOM differsbetween males and females (Bae & Lee, 2011), and eWOM has less

effect on consumers who have considerable knowledge aboutpurchasing products (Xue & Zhou, 2011). Accordingly, we can seethat while sharer perceptions are affected by the level of productsatisfaction, which governs the content that sharers post online,browser awareness is affected by the trustworthiness of the infor-mation source, presentation of the message, and prior experience.Therefore, it is especially important for business managers to havean overall picture of sharers and browsers in the context of theeffect of eWOM. In the next section, the research methods used toinvestigate this issue are reviewed.

2.3. Existing studies of sharers and browsers

As discussed in the previous section, sharers and browsers exerta crucial influence on the effect of eWOM. The identification ofthese groups is thus very important to the decision-makingprocesses of business managers. As such, a number of techniqueshave been proposed and tested to identify effectively thesepotentially influential users. For instance, Li, Lin, and Lai (2010)applied data mining techniques, an artificial neural networkalong with a pointwise mutual information (PMI) measure and anadaptive recency, frequency, and monetary (RFM) model, to eval-uate the extent of influence of online reviewers. Along similarlines, Zhang, Wang, and Xia (2010) represented social networks asa directed graph of potential customers and then designed anapproximation searching algorithm to identify those users withgreat influence. Both of the proposed methods were verified usingdata collected from Epinions.com. Kumar, Petersen, and Leone(2010) proposed another technique to compute customer referraland customer lifetime values and identify their applications to thefinancial services and retailing industries. However, the afore-mentioned approaches focus on identifying only a number of keyusers among all of those online. To address the need for moredetailed user information, Rong, Li, and Law (2009) performeda contrast analysis of online hotel web service purchasers andbrowsers. The significant differences among the responses sug-gested that managers should consider different designs for Englishvs. Chinese language versions of hotel websites. Bronner and DeHoog (2010, p. 15) attempted to answer the key questions of:“Which type of vacationers do post, with which motivations dothey post reviews, onwhich type of sites, and what are the messagecharacteristics?” They firstly considered the profile of people whoposted messages vs. those who did not, and then examined themotivations and characteristics of each group.

2.4. Problem definition and research objectives

Prior studies have attempted to build profiles of people whopost or read online reviews. However, online reviewers for websitescan also be browsers of other online posts, and some people mightfrequently read online reviews but rarely share their experiences. Insome cases, reviewers may have written many online reviews butnever read those of others. Existing studies have been unable tocapture the characteristics of these different groups of people. Toaddress this gap in the literature, we investigate online reviewposters and readers as well as the relationships between themusing the positive and negative association rule mining technique.This technique was initially tested in a study of outbound tourism(Li et al., 2010), and subsequently used in the identification ofchanges and trends in Hong Kong’s tourism industry (Law, Rong,Vu, Li, & Lee, 2011). The main objectives of the present researchcan be summarized as follows.

Identification of user groups. In the context of eWOM, four groupsof people are identified based on their activity: sharing and

J. Rong et al. / Tourism Management 33 (2012) 731e740734

browsing, sharing but not browsing, browsing but not sharing,and neither sharing nor browsing. The identification of thesegroups, which cover all possible cases of involvement, could leadto the better targeting of customers.Profile construction. Based on the identified groups, we constructa complete group profiles that covers travel motivation, demo-graphic information, and past travel experience. The profiles canbe represented in the form of association rules. For example,consider the rule “Young, high education➔ Share and browse”.This rulemeans that young peoplewith a high level of educationare likely to both share and browse travel experiences online.Such information is easy for tourism managers to understand.

3. Targeted association rule mining

In this section, we firstly introduce the concept of targeted asso-ciation rules and theirmetrics. Then,weprovide a detailed descriptionof the processes for extracting positive/negative association rules fromadata set of domestic tourism for outboundpleasure travel, using datafrom a survey that was recently conducted in Hong Kong in 2010.

3.1. Targeted association rules

Let T be a user-specified targeted item with a possible valueT1; T2;.. Association rules in the form A0Tj are the targetedpositive rules, and those in the form A/:Tj are the targetednegative rules, where A represents item sets appearing in the datatransactions/records, and Tj is a single target item. The identifica-tion of targeted positive and negative association rules is based onthree metrics: support, leverage, and conditional-probabilityincrement ratio [CPIR].

Suppose that we have a data set D with N transactions,D ¼ fd1; d2;.; dNg, and let I be the set of possible items appearingin the data transactions, I ¼ fx1; x2;.; xmg. Support refers to howfrequently an item set appears in the transaction data set, and canbe defined as:

supp ðAWTÞ ¼ number of transactions that contain AWT in Dtotal number of transactions in D

:

(3.1)

If an item set satisfies the user-specified minimum support(support threshold) ds, namely, supp(xi)� ds, then it is calleda positive candidate item set, which is represented in the formAWTj; if it does not, then it is a negative candidate item set, which isrepresented in the form AW:Tj.

Leverage refers to howmany times more often an item set A andTj occurs together than would be expected if A and Tj were statis-tically independent.

� For an interesting positive candidate item set AWTj, the cor-responding targeted positive rule A0Tj is interesting if

jlever�A; Tj�j ¼ ��supp�AWTj

�� suppðAÞsupp�Tj��� � dl; (3.2)

where dl is the user-specifiedminimum leverage (leverage threshold).

� For an interesting negative candidate item set AW:Tj, thecorresponding targeted negative rule A0:Tj is interesting if

jlever�A;:Tj���¼ ��supp�AW:Tj

��suppðAÞsupp�:Tj����dl: (3.3)

The CPIR is used to evaluate the extent of the dependency ofpositive/negative rules.

� The CPIR of a positive rule Aki 0Tj is evaluated as

� ðkÞ � p�Tj���AðkÞ

i

�� p

�Atj

�� �

CPIR Ai 0Tj ¼

1� p Tj

¼supp

�AðkÞi WTj

�� supp

�AðkÞi

�supp

�Tj�

supp�AðkÞi

��1� supp

�Tj�� :

(3.4)

� The CPIR of a negative rule Aki 0:Tj is evaluated as

� ðkÞ � p�:Tj

���AðkÞi

�� p

�:Atj�

� �

CPIR Ai 0:Tj ¼1� p :Tj

¼supp

�AðkÞi W:Tj

�� supp

�AðkÞi

�supp

�:Tj�

supp�AðkÞi

��1� supp

�:Tj��

(3.5)

3.2. Overall processes of positive/negative association rules

Based on the three metrics (support, leverage, and CPIR), thedevelopment procedure of targeted positive and negative associa-tion rules is expressed in Fig. 1.

(1) The data set is scanned and all frequent one-item setsFð1Þ ¼ fAð1Þ

1 ;Að1Þ2 ;.g are identified.

(2) For each item set Ai(k) in the frequent k-item sets F(k), we

consider it together with different target values T1; T2;. of thetarget attribute T: if suppðAðkÞ

i WTjÞ � ds, then we have AðkÞi WTj,

which we call an interesting positive candidate item set;otherwise, we have AðkÞ

i W:Tj, which is termed an interestingnegative candidate item set.

(3) The leverage of candidate item sets is then examined, and thosewith leverage greater than a user-specified threshold dl arekept. Here, we derive two item sets: the interesting positivecandidate item set F(k), and the interesting negative candidateitem set I(k).

(4) From the interesting positive candidate item sets F(k), weremove the involved target attribute item Tj, and form thefrequent (kþ 1) item sets F(kþ1). Then, steps (2)e(4) are iterateduntil no further interesting item set can be generated, whichresults in two lists of item sets: a list of interesting positivecandidate item sets: Fð1Þ; Fð2Þ;., and one of interesting nega-tive candidate item sets: Ið1Þ; Ið2Þ;..

(5) The CPIR value is then calculated for each positive item setAðkÞi WTj corresponding to a positive rule AðkÞ

i 0Tj and eachnegative item set AðkÞ

i WTj corresponding to a negative ruleAðkÞi 0:Tj. If the absolute value of a rule’s CPIR is greater than

a predefined threshold dCPIR, then the association between theconditioning items and the target item is considered to bestrong. Then, the target rule is used in the final result set.

4. Experiment and analysis

In this section, we firstly introduce the experimental data, whichwere collected from a survey carried out in Hong Kong. Next, weprovide a description of the experimental method. The experimentresults are then presented in a table of association rules alongwith a detailed analysis and explanation. The last subsectioncontains a summary of this study with managerial implications and

Fig. 1. Development procedure of targeted positive and negative association rule mining.

J. Rong et al. / Tourism Management 33 (2012) 731e740 735

suggestions for tourism managers on how to improve the level ofperformance of their businesses.

4.1. Data collection

The data set used in this study comes from a large-scaledomestic tourism survey of outbound pleasure travel in HongKong, which was conducted in 2010 and targeted at local residentsaged 16 or above. The questionnaire was originally developed inEnglish and then translated into Chinese by a professional trans-lator using the back-translation process to ensure the quality oftranslation. Employing a computer assisted random-digit dialingmethod, a sample list of Hong Kong residential telephone numberswas generated. A total of 14,983 telephone calls were made. Afterscreening the sample for Internet experience, 1708 (11.4% of thesuccessful dialed telephone lines) data samples were collected fromHong Kong outbound travelers.

The questionnaire consisted of the following sections.

Demographics. In this section, the demographic information ofrespondents was gathered, including gender, age, educationlevel, travel experience, and income level. In the experiment, wedivided annual income into three categories: low income (belowHK$30,000, HK$7.8¼USD1), middle income (HK$30,000 toHK$49,999), and high income (HK$50,000 and above). For travelexperience, we converted the original five-point scale rangingfrom 1 (inexperienced tourist) to 5 (very experienced tourist) toa binary scale: responses in the range from 1 (inexperiencedtourist) to 3 (about average experience) were considerednegative, and those from 4 (experienced tourist) to 5 (veryexperienced tourist) were considered positive. This informationis described and labeled in Table 1.Past travel destination. This part of the survey collected infor-mation on the travel experience of respondents over the past 12

Table 1Demographic attributes in the outbound tourism data set.

Attribute Description Possible values Label

Age Age 1 (25 Years old or less) a12 (26e35) a23 (36e45) a34 (46e55) a45 (56e65) a56 (66 Years old or more) a6

Gender Sex 1 (Male) gM2 (Female) gF

Education Education levelattained

1 (Did not complete secondary/high school)

e1

2 (Completed secondary/high school)

e2

3 (College or university) e34 (Completed college/university/diploma)

e4

5 (Completed postgraduatedegree)

e5

Income Annual householdincome

1 (Less than $30,000) i12 ($30,000e$49,999) i23 ($50,000 or more) i3

Travex Level of self-definedtravel experience

1 (Experienced tourist) t0 (Inexperienced tourist) :t

J. Rong et al. / Tourism Management 33 (2012) 731e740736

months in terms of their destination: Macau, Guangdong,mainland China and overseas. These items were used asconditional items, as shown in Table 2.Travel motivation. Questions in this part of the survey related to theimportance of specific aspects and attributes of travel as perceived

Table 2Travel information attributes in the outbound tourism data set.

Attribute Description Possiblevalues

Label

Past travel destinationMacau Have been to Macau in the past

12 months1 (Yes) mc0 (No) :mc

Guangdong Have been to Guangdong in the past12 months

1 (Yes) gd0 (No) :gd

Mainland Have been to mainland China in thepast 12 months

1 (Yes) ml0 (No) :ml

Overseas Have been to overseas in the past12 months

1 (Yes) os0 (No) :os

Travel motivationFuturetrip Have travel plans in the coming

12 months1 (Yes) futrip0 (No) :futrip

Importance Consider travel to be an importantactivity

1 (Yes) impo0 (No) :impo

Family Spend time with family or friends 1 (Yes) fami0 (No) :fami

Meetpeople Meet different people 1 (Yes) mtpe0 (No) :mtpe

Relax Rest and relaxation 1 (Yes) relax0 (No) :relax

Getaway Get away from one’s daily routine 1 (Yes) gtway0 (No) :gtway

Discover Discover new places/things 1 (Yes) disco0 (No) :disco

Knowledge Increase knowledge 1 (Yes) know0 (No) :know

Newplace Be somewhere totally different 1 (Yes) newpl0 (No) :newpl

Online experienceWebsites Have used a travel website in the

past 2 years1 (Yes) browse0 (No) :browse

Sharing Have shared travel experiencesonline in the past 2 years

1 (Yes) share0 (No) :share

by travelers, which could be used to uncover the motivation oftravelers. Respondents were asked about the likelihood of theirtravel and the importance of travel, as well as their reason(s) fortravel including rest and relaxation, to discover new places orthings, to increase knowledge, to meet different people, to spendtime with family or friends, and to get away from their dailyroutine. People were asked to rate the importance of each item ona seven-point scale ranging from 1 (very unimportant) to 7 (veryimportant). In our analysis, we converted this seven-point scaleinto a binary scale: all responses in the range from 5 (somewhatimportant) to 7 (very important) were considered positive andthose from 1 (very unimportant) to 4 (neither important norunimportant) were considered negative. After the conversion,these items were used as conditional items, as shown in Table 2.Online experience. The third part of the survey inquired into theonline experience of respondents. People were asked if they hadvisited travel websites or shared their own travel experiencesonline in the past two years. For the purpose of this study, theseitems were grouped into different categories (browse, not browse,share, not share) and used as target items, as shown in Table 2.

4.2. Experiment design

The study cases of web browsers (B.) and experience sharers (S.)are presented in Fig. 2.

From Fig. 2, we can see that there are four possible cases ofinvolvement.

Browse and share. This group is represented in the full cross-linearea (B.S.). These respondents had visited travel websites to seekinformation to plan their trips and had also shared their travelexperiences with others.Browse and NOT share. This group is represented in the dashedcross-line area (B.). People in this group had visited travelwebsites but had never shared their travel experiences online.Share and NOT browse. This group is represented in the dottedcross-line area (S.). These people had never used a travel websitebut had shared their travel experiences online.NEITHER browse NOR share. This group is represented in thewhite area (:B.:S.) These people neither used any travel web-site nor shared their travel experiences online.

In this study, we want to construct complete profiles of trav-elers, specifically, travel website visitors and online travel experi-ence sharers, and to compare their profiles. Therefore, we cover allof the possible cases for all groups by dividing the original data setinto four sub-data sets, as shown in Table 3.

We then apply the target association rule mining algorithm toeach individual data set. For the user-specified parameters, we setthe minimum support threshold as 0.05, the minimum leveragethreshold as 0.01, and the CPIR threshold as 0.5.

Fig. 2. Study cases of browsers and sharers.

Table 3Structure of the sub-data set for each set of targeted items.

Sub-dataset

Target attribute value Label Description

1 Browse AND share bs Visited travel websites and sharedexperiences

NOT browse OR share :b:s NEITHER visited travel websitesNOR shared experiences

2 Browse BUT NOT share b:s Visited travel websites but did NOTshare experiences

NOT browse OR share :b:s NEITHER visited travel websites NORshared experiences

3 Browse AND share bs Visited travel websites and sharedexperiences

Browse BUT NOT share b:s Visited travel websites but did NOTshare experiences

4 Browse AND share bs Visited travel websites and sharedexperiences

NOT browse BUT share :bs Did NOT visit travel websitesbut did share experiences

Table 5Selected targeted rules for online browsers.

Association rules CPIR Rule(ID)

a2, gF, e4/ b 0.7837 B1a2, gF, os/ b 0.8053 Bse2, ml, :t/ b 1.0000 B3e4, i3, gF/ b 0.7005 B4e4, i3, gM/ b 1.0000 B5i1, t/ b 1.0000 B6os, t, e4/ b 0.8502 B7os, t, i3/ b 0.7836 B8a4, e1, :os/ :b 0.9276 B9a5, e1, :mc/:b 0.9633 B10a6/:b 0.8961 B11e1, :gd/:b 0.9430 B12

Table 6

J. Rong et al. / Tourism Management 33 (2012) 731e740 737

4.3. Analysis of empirical results

All four sub-data sets are input into the targeted association rulealgorithm, which generates positive and negative association rulescorresponding to the different targeted groups, as depicted in Fig. 2.Among the generated rules, only rules with a CPIR value greaterthan 70% are kept. We then extract rules targeting online browsersand online sharers to construct their profiles based mainly ondemographic information and past travel experience, as shown inTables 4e6. The differences in travel motivation among thedifferent targeted groups are explored and presented in Table 7.

4.3.1. Profile of online experience sharersOn the basis of the remaining rules extracted from the previous

process, we extract rules that clearly show the profile of onlineexperience sharers. This leaves us with 12 strong rules, which arepresented in Table 4.

The profile of online experience sharers (s) can be summarizedas follows.

� Both male and female respondents (gM, gF) are more likely tobe sharers if they have a high education level (college oruniversity) (e4) and earn more than HK$50,000 a year (i3), asshown in rules S3 and S4, respectively. This possibility issignificantly high among men, with a CPIR value of 1.0, but lessso among women. Among women aged 26e35 (a2), there isa high likelihood that they will have shared their travel expe-riences online if they also have a high education level orexperience in overseas travel (rules S1 and S2, respectively).

Table 4Selected targeted rules for online experience sharers.

Association rules CPIR Rule(ID)

a2, gF, e4/ s 0.7837 S1a2, gF, os/ s 0.8053 Sse4, i3, gF/ s 0.7005 S3e4, i3, gM/ s 1.0000 S4i1, t/ s 1.0000 S5os, t, e4/ s 0.8502 S6os, t, i3/ s 0.7836 S7a5, :t/:s 0.7216 S8a6/:s 0.9006 S9e1, :gd/:s 0.9568 S10e1, :os/:s 0.9193 S11e1, i1, :mc/:s 0.9111 S12

� Travel experience is found to be a strong predictor of onlinesharing. In rule S5, even among people earning less thanHK$30,000 a year (low income; i1), if respondents have a highlevel of travel experience (t), then there is a large likelihoodthat theywill share their experiences with other people online.Among experienced travelers who have visited overseas, if theyhave a high income (i3) or a high education level (e4), they thenhave a high propensity to share their experiences, as demon-strated in rules S6 and S7, respectively.

Old age and low education level are the main factors associatedwith people who have not shared their travel experiences online(:s). More specifically:

� People who are older than 55 years (a5, a6) are found to behighly unlikely to share their travel experiences (rules S8 andS9, respectively). In particular, those aged 56e65 (a5) are lesslikely to share if they do not have travel experience (:t).

� Among the people with a very low education level (did notcomplete high school) (e1), if they have not been to Guangdong(:gd), overseas (:os), or to Macau (:mc), then there is littlechance that they will share travel experiences (rules S10eS12).

4.3.2. Profile of travel website browsersTo construct a profile of browsers, we extract rules from the

previous experiment targeting browsers. The strong rules areselected, and are presented in Table 4. Most rules for browsers (b)are similar to those for sharers (s), as shown in Table 5. This findingindicates that people who are sharers are also likely to be browsers.For the rules B1, B2, B4eB8, and B11 in Table 5, we do not re-explainthe meaning for each rule because the two groups have the same

Selected targeted rules for browsing and sharing vs. neither browsing nor sharing.

Association rules CPIR Rule(ID)

Browsers and sharersa2, gF, os/ bs 0.8053 R1a2, gF, e4/ bs 0.7836 Rse4, i3, gM/ bs 1.0000 R3e4, i3, gF/ bs 0.7005 R4os, t, i3/ bs 0.7837 R5os, t, e4/ bs 0.8502 R6

Neither browsers nor sharersa4, :t, i1/ :b:s 0.8863 R7a5, gF, e2/:b:s 0.9477 R8a5, :t, :mc/:b:s 1.0000 R9a6/:b:s 1.0000 R10e1, i1/ :b:s 1.0000 R11e1, gM/:b:s 0.9635 R12

Table 7Selected targeted rules for motivation identification.

Association rules CPIR Rule(ID)

:disco, ml/ b:s 0.7217 M1

:disco, :mc, :os/:b:s 0.8661 Ms

:impo, :know, :os/:b:s 0.8405 M3

:impo, know, os, :newpl/ bs 0.7709 M4

:impo, :newpl, ml/ b:s 0.7367 M5

:impo, :newpl, mtpe, :os, relax/ :bs 1.0000 M6

:fami, :relax, :impo, :disco/:b:s 1.0000 M7

:fami, :relax, :know, :gtaway/:b:s 0.8889 M8

futrip, :os, :mc/:b:s 0.8889 R9

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characteristics. The profile of browsers corresponding to these rulescan be obtained from that of sharers in the previous subsection.

There are a number of interesting discoveries about browsers,which are presented below.

� There is a high probability that inexperienced travelers (:t) willuse travel websites (b) if they have a high school education (e2)and experience traveling in mainland China (ml), as shown inrule B3.

� Low education level (e1) is identified as an important indicatorof people who do not browse travel websites. Rule B12 showsthat there is a high likelihood that people with a low level ofeducation who have no experience traveling to Guangdong(:gd) will not browse travel websites (:b). In particular, thoseaged 46e55 years old (a4) are highly unlikely to browse travelwebsites if they do not have experience traveling overseas (B9),while those aged 56e65 years old are likely not to browse suchsites if they do not have experience traveling to Macau (B10).

4.3.3. Browsers and sharers vs. neither browsers nor sharersIn this section, we investigate the differences between people

who are likely to browse travel websites and share their travelexperiences and those who are not. Among the rules generatedfrom the abovementioned experiment, we extract only strong ruleswith targeted attributes corresponding to bs and :b:s. We thenlook for those rules which can clearly show the difference betweenthese two targeted groups. A total of 12 rules are selected, whichare reported in Table 6.

In general, old people are more likely than young people are toneither browse travel websites nor share their travel experiencesonline. Specifically, people who are older than 46 years (a4, a5, a6)are unlikely to use travel websites or to share their travel experi-ences (see rules R7eR10). This is especially true for people who areolder than 65 years (:b:s). In contrast, females (gF) aged 26e35years old (a2) are highly likely to browse and share (bs), as shown inrules R1 and R2, respectively. Significant differences are foundamong females in different targeted groups in Rules R2 and R8.Young females (a2) who have a high education level (e4) are highlylikely to be both browsers and sharers (bs); whereas those who areolder (a5) and have a lower education level (e2) are highly unlikelyto use travel websites or to share online their travel experiences(:b:s).

Education and income level are found to be important indicatorsof sharers and browsers. There is a strong likelihood that peoplewho have a college or university degree (e4) and a high income (i3)will use the Internet to find travel information and share theirtravel experiences with others (bs), as shown in rules R3 and R4,respectively. However, rule R11 shows that people who have notcompleted high school (e1) and have a low income (i1) do not usethe Internet for their travel-related activities (:b:s). There isa strong chance that males (gM) in particular will be neitherbrowsers nor sharers if they have a low education level (e1), as

shown in rule R12. However, if they are highly educated (e4) andhave a high income, they may browse websites for travel infor-mation and share their travel experiences (bs), as shown in rule R3.In addition, rules R5 and R6 indicate that those who have a highincome (i3) or are highly educated (e4) are highly likely to browseand share (bs) if they have visited places overseas and have travelexperience.

4.3.4. Discovery of motivationsIn this section, we present interesting findings onmotivation for

the different targeted groups. Those rules showing clear differencesin motivation are extracted, and are presented in Table 7.

Table 7 reveals the following.

� People who are uninterested in discovering new things(:disco) are very unlikely to share their travel experiencesonline (:s), as demonstrated in rules M1 and M2. Among thisgroup, those with experience traveling to mainland China butnot to Macau or overseas are likely to use travel websites toplan for their trips (b:s).

� The desire to gain knowledge and overseas travel experienceare found to be important indicators dividing the targetedgroups “browse and share” (bs) and “neither browse nor share”(:b:s) among people who do not consider travel to beimportant (:impo). If those respondents have neither overseastravel experience (:os) nor the desire for more knowledge(:know), they are very likely to be neither browsers nor sharers(see rule M3). However, if they have such a desire (know), havetraveled overseas (os), and do not desire to discover new places(:newpl), then they are more likely to use travel websites andshare their travel experiences online (see rule M4).

� People who do not consider travel to be important (:impo) andhave no desire to travel to new places (:newpl) are highly likelyto browse but not to share (b:s) if they have been to mainlandChina, as shown in rule M5. If they want to meet new people(mtpe), enjoy rest and relaxation (relax), and lack overseastravel experience, there is a strong probability that they willshare but not browse (:bs), as shown in rule M6.

� Neither the desire to get way to relax (:relax) nor the desire tospend time with family or friends (:fami) is found to besignificantly correlated with user behavior, as rulesM7 andM8,respectively, show. Specifically, if respondents have no desireeither for more knowledge (:know) or to get away (:gtaway),then they are highly unlikely to browse travel websites or toshare their travel experiences online (:b:s), as indicated byrule M8. The unlikelihood is even greater if they do not seetravel as important (:impo) or wish to discover new things(:disco), as shown in rule M7.

� Another interesting finding is revealed in rule M9; namely,people who intend to travel in the future (futrip) and have noexperience traveling either toMacau or overseas are unlikely tolook for travel information or to share their travel experienceswith others (:b:s) on the Internet.

4.4. Discussion

The revolution of Internet technology has changed the waythrough which people collect and/or disseminate information suchas reading or posting beautiful pictures taken from their recenttrips, browsing, and sharing their travel experiences or storieshappened during their journeys. However, not all Internet userslike to share their own travel experiences online, and most of themchoose to be a browser or ‘reader’ of others’ stories. As such, thisstudy aims to understand the difference in behavioral expressionsbetween Web sharers and browsers in Hong Kong by analyzing

J. Rong et al. / Tourism Management 33 (2012) 731e740 739

their demographic characteristics and traveling motivations.Experimental results suggest that the differences among thosesharing, not sharing, browsing, and not browsingmainly depend onage, education level, income level, travel experience level, andcertain travel motivations.

The findings show that young people are more likely to searchfor travel information and share their travel experience online thanold people. This is likely because the former typically use infor-mation technology more often and have a faster learning ratecompared with the latter. Older people may continue to obtaintravel-related information through such traditional means asnewspapers, printed advertisements, and travel agents. Therefore,different marketing approaches should be designed to targetpeople in different age groups. For example, online marketingshould be targeted toward younger people, and newspaper adver-tisement and flyers toward older people. Furthermore, botheducation and income levels are found to be the key factors influ-encing the behavior of respondents in seeking travel-relatedinformation. This is consistent with the reality that highlyeducated people are more likely to use information technology.This group is also likely to have a high income, which enables themto afford to use Internet technology and to travel. As a consequence,they aremore likely to browse travel websites and share their travelexperiences online. The highly educated and the affluent are thegroups that managers can, and should, consider more whendesigning travel websites or creating new travel packages.

Another common sense to be tracked from the resulting asso-ciation rules is that the past travel experience plays an importantrole that promotes people to browse or share information on travelwebsites. Moreover, we found that the destination of the pasttravels affects a lot on a person’s browsing or sharing behavior. Thefurther the person went, the higher the chance that this personwould be interesting in online traveling information. Comparingwith those who only have been to some places that are close toHong Kong, the others who have overseas travel experience spendmore time on reading and posting messages or comments throughthe Internet.

In addition, a number of discovered association rules show thatfemales spend more time on browsing websites for travel infor-mation than males do. Females are also more active to share theirown experiences online. Such findings could be attributed to thatfact that females are easier to be attracted by beautiful pictures orwonderful stories, and the imagination brings them stronger desireto experience their dreamland in person. A further study focusingon the next actions of female browsers after browsing can beproposed, in which they will be followed up to track whethera future trip plan to the browsed destination will be tuned out.While for males, more work is still needed to better understandtheir behaviors. Therefore, tourism practitioners should pay moreattention to those attributes when planning and making business-related decisions.

5. Conclusion

As one of the fastest growing industries in the world, tourismoffers many new opportunities and challenges. Effective planningand decision making can confer multiple benefits to the tourismindustry. Managers can allocate limited resources to specific groupsof customers, and level of investment risk can be reduced. Also,policy makers can establish appropriate guidelines to bettermanage the relationship between practitioners and high-yieldtravelers. However, these tasks are difficult because consumerbehavior is influenced not only by tourism marketing activities butalso informal communication of other travelers, or eWOM. Inaddition, the use of the Internet is becomingmore popular, which is

triggering major changes in how people communicate. Thesesignificant changes demand a better understanding of how eWOMworks. The research outcomes presented in this study indeedcontribute to tourism decision making, including that concerningmarketing and promotion strategies.

In this study, we have demonstrated the importance of eWOMby showing how it can affect consumer behavior, and identified thefactors that can directly influence the effect of eWOM. Among thosefactors, we focus on sharers and browsers as well as the associationbetween them. Applying a new method of targeted positive andnegative association rule mining, we construct complete profiles ofboth sharers and browsers. The study findings reveal the charac-teristics of not only those who share their travel experiences onlineand who read online reviews but also those who neither sharenor read travel information online. In addition, the relationshipbetween sharers and browsers is explored. In summary, age,education level, income level, travel experience level, and travelmotivations are the key factors, together with gender and overseastravel experience, influencing the behavior of respondents inseeking travel-related information.

A natural extension of this study is to include more data,preferably from different geographical regions, for further testingthe present approach. Such an expansion of the data set couldvalidate the feasibility of the presented approach. Anotherdirection for future research is to compare and contrast empiricalfindings by applying the presented approach and some otherdata mining techniques. Lastly, it would be valuable to test thequality of the induced rules by seeking feedback from industrialpractitioners.

Acknowledgments

Wewould like to thank the three anonymous reviewers for theirconstructive comments on improving an earlier version of thispaper. The work described in this paper was supported by a grantfunded by the Research Grants Council of the Hong Kong SpecialAdministrative Region, China (Project Number: PolyU 5461/11H),and an internal grant funded by the Hong Kong PolytechnicUniversity (Project Number: G-YX5J).

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