Contents lists available at ScienceDirect

Tourism Management

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

Wisdom of crowds: Conducting importance-performance analysis (IPA)through online reviewsJian-Wu Bia, Yang Liua,∗, Zhi-Ping Fana,b, Jin Zhangaa Department of Information Management and Decision Sciences, School of Business Administration, Northeastern University, Shenyang, 110169, Chinab State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, 110819, China

A R T I C L E I N F O

Keywords:Importance-performance analysis (IPA)Online reviewsLatent dirichlet allocation (LDA)Support vector machine (SVM)Ensemble neural network (ENN)

A B S T R A C T

This paper proposes a methodology for conducting importance-performance analysis (IPA) through online re-views. The methodology is composed of three stages: (1) mining useful information from online reviews, (2)estimating each attribute's performance and importance, and (3) constructing IPA plot, where the latentdirichlet allocation (LDA), the improved one-vs-one strategy based support vector machine (IOVO-SVM) and theensemble neural network based model (ENNM) are respectively used. A case study on two five-star hotels isgiven, and the results obtained by the proposed methodology through online reviews are compared with thoseobtained by the existing methods through questionnaires (or online ratings). The results indicate that the pro-posed methodology can obtain effective analysis results with lower cost and shorter time since online reviews arepublicly available and easily collected. The proposed methodology can give managers or market analysts onemore choice for conducting IPA or serve as a preparing process of large-scale survey.

1. Introduction

Importance-performance analysis (IPA) is a commonly used busi-ness research technique for understanding customer satisfaction andformulating improvement strategies for products/services (Martilla &James, 1977). Although IPA was originally developed for marketingpurposes, it has been employed in various fields, such as tourism(Azzopardi & Nash, 2013; Boley, McGehee, & Hammett, 2017; Caber,Albayrak, & Matzler, 2012; Deng, 2007; Deng & Pei, 2009; Guizzardi &Stacchini, 2017; Lai & Hitchcock, 2015; Sever, 2015), healthcare(Abalo, Varela, & Manzano, 2007; Hawes & Rao, 1985) and education(Chen & Chen, 2012; O'Neill & Palmer, 2004), etc. Typically, the dataused for conducting IPA are obtained from customers through surveys(Azzopardi & Nash, 2013; Boley et al., 2017; Caber et al., 2012; Deng,2007; Deng & Pei, 2009; Martilla & James, 1977). However, surveys areexpensive in terms of time and money. Besides, the quality of the dataobtained from surveys depends on the complexity or length of thequestionnaire and the willingness of the respondents to participate(Groves, 2006). Moreover, the data obtained from surveys may quicklybecome outdated (Culotta & Cutler, 2016). Therefore, it is worthwhileto consider other alternative sources of data for conducting IPA.

With advances in information technology and Internet, customersincreasingly post online reviews concerning products/services on theInternet (Fang, Ye, Kucukusta, & Law, 2016; Zhang, Ye, Law, & Li,

2010; Zhang, Zhang, & Yang, 2016). These online reviews contain awealth of information, such as customers’ concerns, sentiments andopinions (Farhadloo, Patterson, & Rolland, 2016; Guo, Barnes, & Jia,2017; Pournarakis, Sotiropoulos, & Giaglis, 2017; Qi, Zhang, Jeon, &Zhou, 2016; Xiao, Wei, & Dong, 2016). Relative to surveys, online re-views are not only publicly available, easily collected, low cost, spon-taneous, passionate and insightful, but also simpler for firms to monitorand manage (Guo et al., 2017; Qi et al., 2016; Tirunillai & Tellis, 2014;Xiao et al., 2016; Ye, Law, & Gu, 2009). In addition, the number ofonline reviews is very large, and these reviews that are contributed byhundreds of thousands of customers can be viewed as “wisdom ofcrowds” (Guo et al., 2017; Surowiecki & Silverman, 2007; Tirunillai &Tellis, 2014). Now, online reviews have been successfully used as thedata source of several kinds of decision analysis, such as productsranking/recommending (Liu, Bi, & Fan, 2017a; Siering, Deokar, &Janze, 2018), customer satisfaction modelling (Farhadloo et al., 2016),products/services improvement (Gao, Tang, Wang, & Yin, 2018; Liu,Jiang, & Zhao, 2018), brand analysis (Culotta & Cutler, 2016; Tirunillai& Tellis, 2014), customer preferences analysis (Xiao et al., 2016),market structure analysis (Chen, Kou, Shang, & Chen, 2015; Netzer,Feldman, Goldenberg, & Fresko, 2012), guest experience and satisfac-tion analysis (Xiang, Schwartz, Gerdes Jr., & Uysal, 2015), and serviceperformance evaluation (Li, Tung, & Law, 2017), etc. Thus, online re-views can also serve as a promising data source for conducting IPA. If

https://doi.org/10.1016/j.tourman.2018.09.010Received 17 March 2018; Received in revised form 6 July 2018; Accepted 20 September 2018

∗ Corresponding author.E-mail addresses: jianwubi@126.com (J.-W. Bi), liuy@mail.neu.edu.cn (Y. Liu), zpfan@mail.neu.edu.cn (Z.-P. Fan), zjin0709@163.com (J. Zhang).

Tourism Management 70 (2019) 460–478

0261-5177/ © 2018 Elsevier Ltd. All rights reserved.

T

IPA can be conducted through online reviews, then it would be con-venience for decision-makers or managers to understanding customersatisfaction and formulating improvement strategies for products/ser-vices considering multiple competitors and different time periods sincethe online reviews concerning multiple competitors and different timeperiods can be easily collected from the Internet. However, studies onconducting IPA through online reviews have not been found.

The objective of this paper is to propose a methodology for con-ducting IPA through online reviews. In the proposed methodology, theimportant product/service attributes concerned by customers are firstextracted from online reviews using latent dirichlet allocation (LDA).The sentiment strengths (e.g. very negative, negative, neutral, positiveand very positive) of online reviews concerning the important attributesare identified using an improved one-vs-one strategy based supportvector machine (IOVO-SVM) algorithm. Then, according to the identi-fied sentiment strengths, the performance of each attribute of the pro-duct/service is measured. Furthermore, to estimate the importance ofeach attribute, an ensemble neural network based model (ENNM) isproposed. On this basis, the IPA can be conducted. Specifically, fourtypes of IPA (i.e., standard IPA (SIPA), importance performance com-petitor analysis (IPCA), dynamic IPA (DIPA) and dynamic IPCA(DIPCA)) are conducted, respectively. Based on the results of the IPA,improvement strategies for products/services can be formulated.

The remainder of this paper is organized as follows. Section 2 brieflyreviews the relevant literature of IPA. Section 3 presents a methodologyfor conducting IPA through online reviews. In Section 4, a case study ofIPA for two five-star hotels is presented to illustrate the use of theproposed methodology. Finally, discussions and conclusions are givenin Section 5.

2. Literature review

SIPA was first proposed by Martilla & James, 1977, which helpsmanagers to allocate limited resources or formulate improvementstrategies for products/services by investigating the attribute's perfor-mance and the attribute's importance of the product/service (Martilla &James, 1977). In SIPA, by dividing each one of the two dimensions (i.e.,the attribute's performance and the attribute's importance) into twolevels, attributes of the product/service are classified into four quad-rants or categories. An example of SIPA plot is given in Fig. 1. In Fig. 1,Quadrant 1 (Q1) is termed as ‘keep up the good work’. The attributepositioned in Q1 has a higher attribute's performance and a higher at-tribute's importance. Thus, the attributes positioned in Q1 can be re-garded as the major strengths and potential competitive advantages ofthe product/service. Quadrant 2 (Q2) is termed as ‘concentrate here’.The attribute positioned in Q2 has a lower performance and a higherimportance. Thus, the attributes positioned in Q2 can be regarded asthe major weakness of the product/service. Quadrant 3 (Q3) is termed

as ‘low priority’. The attribute positioned in Q3 has a lower perfor-mance and a lower importance. Thus, the attributes positioned in Q3can be regarded as the minor weakness of the product/service. Quad-rant 4 (Q4) is termed as ‘possible overkill’. The attribute positioned inQ4 has a higher performance but a lower importance. Thus, the attri-butes positioned in Q4 may waste the limited resources.

Although SIPA was originally developed for marketing purposes, ithas been employed in various fields, such as tourism (Azzopardi &Nash, 2013; Boley et al., 2017; Caber et al., 2012; Deng, 2007; Deng &Pei, 2009; Guizzardi & Stacchini, 2017; Lai & Hitchcock, 2015;McKercher, 2018; Sever, 2015), healthcare (Abalo et al., 2007; Hawes &Rao, 1985), education (Chen & Chen, 2012; O'Neill & Palmer, 2004),food (Tontini & Silveira, 2007), e-business (Levenburg & Magal, 2005),transportation (Chou, Kim, Kuo, & Ou, 2011; Chou, Tserng, Lin, & Yeh,2012), information technology (Skok, Kophamel, & Richardson, 2001)and banking (Yeo, 2003), etc. Besides, some studies on improving theSIPA have been carried out. Most of these studies can be classified intofour categories, i.e., (1) studies on the determination of importance ofeach attribute, (2) studies on the determination of crosshair placement,(3) studies on the asymmetric effects of product/service attributes onoverall satisfaction, and (4) studies on IPA considering the perfor-mances of competitors. The brief review of the studies in each categoryis given below.

2.1. Studies on the determination of the importance of each attribute

Determination of the importance of each attribute is a crucial aspectof IPA. Till now, some approaches for reasonably determining the im-portance of each attribute in IPA have been proposed, which can beclassified into two categories, i.e., the self-stated approach and theimplicit approach (Taplin, 2012a, 2012b; Van Ittersum, Pennings,Wansink, & Van Trijp, 2007). The self-stated approach is to determinethe importance of each attribute directly through questionnaire surveys(Batra, Homer, & Kahle, 2001; Bottomley, Doyle, & Green, 2000). Themajor strength of the self-stated approach is its simplicity. However, itis often criticized because of the attribute importance obtained from theself-stated approach would be influenced by the attribute performance(Lai & Hitchcock, 2016; Mikulić & Prebežac, 2012). For the reason,some researchers suggest to use implicit approaches to obtain the at-tribute importance (Deng, 2007; Deng, Chen, & Pei, 2008; Deng, Kuo, &Chen, 2008; Mikulić & Prebežac, 2012; Van Ryzin & Immerwahr,2007). The implicit approach is to calculate the importance of eachattribute by implicit approaches (e.g. statistical based approaches andartificial intelligence-based approaches) (Deng, 2007; Deng, Chen,et al., 2008; Deng, Kuo, et al., 2008; Mikulić & Prebežac, 2012). Forexample, Deng, Chen, and Pei (2008) argued that the importance ob-tained by the self-stated approach is not the attribute's actual im-portance, and proposed a back-propagation neural network (BPNN)based IPA model. In the model, the attribute's importance is determinedby training a BPNN, where natural logarithmic attribute's performanceand overall customer satisfaction are respectively used as the input andoutput variables of the BPNN. It is regarded that the attribute im-portance obtained from the implicit approach would be lesser influ-enced by the attribute performance than that obtained from the self-stated approach (Lai & Hitchcock, 2016; Mikulić & Prebežac, 2012).

2.2. Studies on the determination of crosshair placement

The crosshair divides all areas of IPA plot into four quadrants(Matzler et al., 2013). The studies on the determination of crosshairplacement are to respectively determine the value of attribute's per-formance and the value of attribute's importance that divide each one ofthe two dimensions into two levels. Up to now, two kinds of methodshave been developed to determine the crosshair placement, i.e., thescale-centred method (Martilla & James, 1977; Tonge & Moore, 2007)and the data-centred method (Azzopardi & Nash, 2013; Beldona &Fig. 1. An example of SIPA plot.

J.-W. Bi et al. Tourism Management 70 (2019) 460–478

461

Cobanoglu, 2007; Deng, 2007). In the scale-centred method, the mid-point of the scale is used as the value that divides each one of the twodimensions (i.e., the attribute's performance and the attribute's im-portance) into two levels (please see the red lines in Fig. 2). For ex-ample, if 5-point Likert scale is used, then 3 (the midpoint of the scale)is determined as the value that divides each one of the two dimensionsinto two levels. But, since the values of attribute's performance andattribute's importance obtained by surveys are usually higher than themidpoint, the majority of the attributes would be classified into Q1 bythe scale-centred method (Taplin, 2012a). In the data-centred method,the means, medians or midpoints of the values concerning importanceand performance are used for distinguishing the two levels of the at-tribute's performance and the attribute's importance (please see theblue lines in Fig. 2). The data-centred method has a high discriminativepower and is the most frequently applied method (Eskildsen &Kristensen, 2006). Besides, some researchers attempt to employ anupward sloping diagonal line (the green line in Fig. 2) to divide the IPAplot into two triangular areas (Bacon, 2003; Eskildsen & Kristensen,2006). Attributes fell into the upper triangular area (importance ishigher than performance) are the major weakness of the product/ser-vice. Conversely, attributes fell into the lower triangular area (im-portance is lower than performance) are the major strengths of theproduct/service. Compared with the scale-centred and data-centredmethod, diagonal method produces less information and offers limiteddiscriminative (Sever, 2015).

2.3. Studies on the asymmetric effects of product/service attributes onoverall satisfaction

The SIPA assumes that the attribute's positive performance and ne-gative performance have the symmetric influences on the overall sa-tisfaction of product/service. However, some existing studies havepointed out that the attribute's positive performance and negative per-formance may have asymmetric effect on overall satisfaction. On the onehand, the same amount changes of attribute's positive and negativeperformances would lead to different amount changes of overall sa-tisfaction (Albayrak & Caber, 2013; Caber, Albayrak, & Loiacono, 2013;Mikulić & Prebežac, 2008; Mittal, Ross, & Baldasare, 1998; Slevitch &Oh, 2010). On the other hand, the attributes of product/service may beclassified into different categories, such as excitement attributes, must-beattributes and performance attributes, etc (Albayrak & Caber, 2015;Kano, Seraku, Takahashi, & Tsuji, 1984; Lai & Hitchcock, 2016). Forexample, based on two empirical studies, Mittal et al. (1998) discoveredthat the relationship between product/service attributes and overall sa-tisfaction is asymmetric and nonlinear. Slevitch and Oh (2010) providedadditional evidence that can support the conclusions obtained by Mittalet al. (1998) and provided an explanation to the observed asymmetry.Considering the asymmetric effects of product/service attributes onoverall satisfaction, some extended versions of IPA are proposed (Caberet al., 2013; Geng & Chu, 2012; Lai & Hitchcock, 2016; Mikulić &

Prebežac, 2008; Ramakrishnan & Usha, 2016). For example, Mikulić andPrebežac (2008) proposed an impact-asymmetry analysis (IAA) analy-tical framework by considering attribute's impact asymmetry and attri-bute's impact range. In the framework, each attribute's impact asym-metry value is calculated according to the potential of each attribute forgenerating satisfaction and dissatisfaction; and each attribute's impactrange value is measured according to the high and low performance ofthe attribute's impacts on overall customer satisfaction. By locating im-pact asymmetry value on the vertical axis and attribute's impact rangevalue on the horizontal axis, an IAA plot can be constructed. Accordingto the obtained attribute's impact asymmetry values, the attributes can becategorized as “delighters”, “satisfiers”, “hybrids”, “dissatisfiers” and“frustrators”. Meanwhile, according to the attribute's impact range va-lues, the attributes can also be categorized as “high-impact”, “medium-impact”, and “low-impact”. On the basis of Mikulić and Prebežac (2008),Caber et al. (2013) proposed an extended version of IAA by locatingperformance values of attribute(s) on a horizontal axis.

2.4. Studies on IPA considering the performances of competitors

Traditional IPA ignores the performance of competitors, which mayresult in misleading managerial actions (Albayrak, 2015; Chen, 2014;Mikulić & Prebežac, 2012). To overcome this shortcoming, somescholars attempted to conduct IPA considering the performances ofcompetitors, and proposed some extended versions of IPA (Albayrak,2015; Guizzardi & Stacchini, 2017; Mikulić & Prebežac, 2012; Taplin,2012a). For example, Taplin (2012a) proposed a competitive IPAmodel. In the model, the differences between the focal product/serviceand competing product/service concerning performance and im-portance are respectively measured. Then, by locating the differencesconcerning performance and importance on the horizontal axis andvertical axis respectively, the competitive IPA plot can be constructed.On the basis of the study of Taplin (2012a), Albayrak (2015) proposedan importance performance competitor analysis (IPCA) model. In IPCA,a gap score is calculated by measuring the difference between an at-tribute's importance and the attribute's performance of the focal pro-duct/service. Then, the gap score is used to substitute the differencebetween the focal product/service and competing product/serviceconcerning importance in Taplin's model.

It can be seen from the above analysis that IPA is an effective andpopular technique which has attracted much attention during the pastfew years. In the existing studies, the data used for conducting IPA areobtained from customers through surveys. However, surveys are ex-pensive in terms of time and money, especially when competitive IPA orIPCA is conducted since the data concerning multiple companies areneeded. Online review is a kind of emerging information resource,which is not only publicly available, easily collected, low cost, spon-taneous, passionate and insightful, but also simpler for firms to monitorand manage. If the attribute's performance and the attribute's im-portance can be measured through the huge amount of online reviews,then IPA plot can be constructed and improvement strategies for pro-ducts/services can be obtained considering multiple competitors anddifferent time periods since the online reviews concerning multiplecompetitors and different time periods can be easily collected from theInternet. Thus, it is necessary to develop the new methodology forconducting IPA through online reviews.

3. Methodology

In this section, we present a new methodology for conducting IPAthrough online reviews. The framework of the methodology is shown inFig. 3. The framework is composed of three stages, i.e.,

• Stage 1. Mining useful information from online reviews• Stage 2. Estimating each attribute's performance and importance• Stage 3. Constructing IPA plot

Fig. 2. Graphical comparison of different methods for determining the IPAquadrants.

J.-W. Bi et al. Tourism Management 70 (2019) 460–478

462

In the first stage, online reviews are converted into structured datathat can be used for analysis. Specifically, important attributes of theproduct/service are extracted from online reviews using LDA, and thenthe customers’ sentiment strengths (e.g. very negative, negative, neu-tral, positive and very positive) towards the extracted important attri-butes are identified using IOVO-SVM. In the second stage, in ac-cordance with the structured data obtained from the first stage, eachattribute's performance of the product/service is measured by statisticalanalysis, and each attribute's importance of the product/service is es-timated by ENNM. In the final stage, based on the obtained perfor-mance and importance of each attribute, the IPA plot can be con-structed. Specifically, four types of IPA (i.e., SIPA, IPCA, DIPA andDIPCA) plots are constructed, respectively. The detailed descriptions ofthe three stages are respectively illustrated in Section 3.1, Section 3.2and Section 3.3.

3.1. Mining useful information from online reviews

Online reviews are written by customers in their own words, whichcannot be directly used for analysis. To conduct IPA through onlinereviews, it is necessary to mine useful information from online reviews,including the important attributes of the product/service concerned bycustomers and customers’ sentiment strengths towards the importantattributes. Thus, the processes of mining useful information from onlinereviews mainly include two parts, (1) extracting the important attri-butes of the product/service from online reviews using LDA, and (2)identifying the customers’ sentiment strengths towards the importantattributes using IOVO-SVM. The detailed descriptions of the two partsare respectively given in Section 3.1.1 and Section 3.1.2.

3.1.1. Extracting the important attributes of the product/service from onlinereviews using LDA

LDA is an unsupervised machine learning technique that can beused to identify hidden topic information in large-scale document col-lections or corpus (Blei, Ng, & Jordan, 2003). Studies have shown thatLDA is an effective and most commonly used technique to extract theimportant attributes of the product/service from online reviews (Guoet al., 2017; Tirunillai & Tellis, 2014). Therefore, LDA is used to extractthe important attributes of the product/service from online reviews inthis study. The processes of extracting the important attributes of theproduct/service from online reviews mainly include two steps, i.e., (1)preprocessing online reviews for LDA, and (2) extracting the importantattributes using LDA. The details of the two steps are given below.

3.1.1.1. Preprocessing online reviews for LDA. The textual contents inonline reviews contain not only the words concerning the product/service but also a large number of words that are not informative aboutthe product/service. To improve the effectiveness and efficiency ofextracting the important attributes of the product/service, onlinereviews are first preprocessed. Specifically, for each online review,we first eliminate non-English characters and words, punctuations andstop words (e.g. a, an, the). Then, all uppercase letters are convertedinto lowercase letters, and all the words in online reviews are stemmed(e.g. “using” and “useful” are converted into “use”) using the stemmingalgorithm (Porter, 1980). Furthermore, the reviews are processed bypart-of-speech tagging. Finally, according to the results of part-of-speech tagging, negation words, sentiment degree words and sentimentwords are filtered out. To conduct the preprocessing online reviewssimply, the modules of the Natural Language Toolkit (www.nltk.org)can be used as a substitute of the above process.

3.1.1.2. Extracting the important attributes using LDA. LDA is agenerative statistical model that can be utilized for extracting thetopics from a huge number of reviews (Blei et al., 2003). In LDA, eachreview can be viewed as a mixture of various topics, where each topicis a set of words that represent some “meaning”, such as one conceptor aspect (e.g. an attribute of the product/service) (Blei et al., 2003;Poria, Cambria, & Gelbukh, 2016). For a given set of online reviews,topic distributions in each review and the word distributions in eachtopic can be obtained by the generative process of LDA (Blei, Andrew,& Michael, 2003; Qian, Zhang, Xu, & Shao, 2016). Based on theobtained topic distributions and the word distributions, the majortopics of the online reviews can be inferred, where each topic is a setof related words representing some “meaning”. Since there may besome noisy words in the extracted topics, to obtain more accurateresults, decision-makers or managers can manually merge the topicswith similar meanings, filter the noisy words in each topic, select theimportant topics and assign a label to each important topic. Afterthat, following Guo et al. (2017) and Tirunillai and Tellis (2014), thelabel of each important topic can be regarded as an attribute of theproduct/service. Finally, a set of labeled topics (attributes) and a setof words concerning each labeled topic (attribute) can be determined.At present, some open source modules are available to implementLDA in Python programming environment, such as PyPI (https://pypi.org/) and Gensim (https://radimrehurek.com/gensim/index.html), etc.

Fig. 3. The framework for conducting IPA through online reviews.

J.-W. Bi et al. Tourism Management 70 (2019) 460–478

463

3.1.2. Identifying customers’ sentiment strengths towards the importantattributes using IOVO-SVM

Usually, an online review may contain several sentences concerningdifferent attributes of the product/service. Table 1 shows three ex-amples of online reviews concerning a hotel, where the words in boldtype represent the important attributes of the hotel. It can be seen fromTable 1 that an online review contains several sentences concerningdifferent attributes of the hotel. In addition, the attribute in differentreviews may be different. Thus, to identify the customers’ sentimentstrengths towards the important attributes, the sentences that con-cerning each attribute need to be extracted from the online reviews.Specifically, each online review is first divided into several sentencesaccording to the punctuations. For the case that there are multiplesentences concerning one attribute in an online review, we suppose thatthe multiple sentences have been merged into one sentence. Then, byextracting the sentences containing the words concerning each attributeor topic, a set of sentences concerning each attribute or topic can beobtained. To illustrate the above process more clearly, we take the three

online reviews shown in Table 1 as examples. By the above process, thesentences concerning the related attributes of the hotel can be extractedfrom the three online reviews, which are shown in Table 2.

To identify the sentiment strength of each sentence concerning eachattribute, several multi-class sentiment classification algorithms pro-posed by existing studies can be used, in which the IOVO-SVM algo-rithm (Liu, Bi, & Fan, 2017b) is regarded as a superior one since thehigher classification accuracy. Thus, in this study, the IOVO-SVM al-gorithm (Liu et al., 2017b) is employed to classify the sentimentstrengths of online reviews into five categories, i.e., Very Negative(VNeg), Negative (Neg), Neutral (Neu), Positive (Pos) and Very Positive(VPos). The framework of the IOVO-SVM algorithm is shown in Fig. 4.It can be seen from Fig. 4 that the algorithm includes two steps: (1)Feature construction and (2) SVM sentiment classifier training. In thefirst step, a set of labeled reviews concerning each of the five sentimentstrengths is constructed, where the reviews could be crawled from thesame website and the sentiment strength of each review could be la-beled manually beforehand. Then, both labeled and unlabeled reviewsare converted into feature vectors using bag-of-word (BOW) model, andthen the information gain (IG) algorithm is employed to select im-portant features. In the second step, 10 sets of training samples areconstructed, where each set is the union of the labeled reviews con-cerning two kinds of sentiment strengths, i.e., “VNeg-Neg”, “VNeg-Neu”, “VNeg-Pos”, … …, “Neu-VPos” and “Pos-VPos”. Then, accordingto the 10 sets of training samples, 10 binary SVM classifiers can betrained, i.e., classifier of “VNeg-Neg”, classifier of “VNeg-Neu”, classi-fier of “VNeg-Pos”, ……, classifier of “Neu-VPos” and classifier of “Pos-VPos”. For identifying the sentiment strength of an unlabeled review, aclassification confidence score can be obtained by each of the 10 binarySVM classifiers. Besides, the relative competence weight of each binarySVM classifier concerning a given unlabeled review can be obtainedaccording to the average distance between the unlabeled review andthe K nearest neighbors of each sentiment class. Thus, for each un-labeled review, 10 classification confidence scores and the 10 relativecompetence weights can be obtained. On this basis, the sentimentstrength of the unlabeled review can be identified by integrating the 10classification confidence scores and the 10 relative competenceweights. More detailed process of the IOVO-SVM algorithm can befound in the literature of Liu et al. (2017b). If the IOVO-SVM algorithmis difficult to be carried out by the practical analysts or managers, someopen source software or modules can also be used as substitutes, suchas, SentiStrength (http://sentistrength.wlv.ac.uk/) and Stanford Cor-eNLP (https://stanfordnlp.github.io/CoreNLP/), etc.

Using the IOVO-SVM algorithm, the sentiment strength of eachsentence concerning the attribute can be identified. On this basis, the

Table 1Three examples of online reviews concerning a hotel.

Review Content

1 "Great room, I was upgraded which was nice but unnecessary as I wasleaving early the next day. Staff is very nice. Their breakfast is nice,they have everything you may need. Great hotel"

2 "The services are extremely bad. I did not get any help, any comfortsetting, I will never come back again"

3 "Great location! Very comfortable rooms. Good breakfast, starting at5:30 a.m. The staff is very helpful"

Table 2The extracted sentences concerning each attribute of the hotel.

Review Attributes

Room Staff Breakfast Location Service

1 Great room Staff isvery nice

Theirbreakfast isnice

– –

2 – – – – The servicesare extremelybad

3 Verycomfortablerooms

The staffis veryhelpful

Goodbreakfast

Greatlocation!

–

Fig. 4. The framework of the IOVO-SVM algorithm.

J.-W. Bi et al. Tourism Management 70 (2019) 460–478

464

online reviews can be converted into the nominally coded data ofsentiment strengths concerning related attributes. For example, basedon the above process, the three online reviews in Table 2 can be con-verted into the nominally coded data shown in Table 3, where “Mv”denote “missing value” indicating that there is no sentence concerningthe attribute.

3.2. Estimating the performance and importance of each attribute

According to the obtained nominally coded data of online reviews,the performance and importance of each attribute can be respectivelyestimated. The processes of estimating the performance and importanceof each attribute are respectively given in Section 3.2.1 and Section3.2.2.

3.2.1. Estimating the performance of the product/service concerning eachattribute

The sentiment strengths of online reviews concerning the relatedattributes reflect the customers’ perceptions of the product/serviceconcerning the related attributes and can be regarded as the actualperformances of the product/service concerning the related attributes.According to the obtained customers’ sentiment strengths concerningeach attribute, the performance of the product/service concerning eachattribute can be estimated. The details are described as follows.

For the convenience of further analysis, the nominally coded data(i.e., the sentiment strengths of online reviews) are first transformedinto score values. Let = …A A A A{ , , , }I1 2 denote the set of attributes ortopics extracted from the online reviews, where Ai denote the ith at-tribute or topic, = …i I1,2, , . Let = …R r r r{ , , , }M1 2 be the set of M onlinereviews, where rm denotes the mth online review in R, = …m M1,2, , .Let Sim denote the score value corresponding to the sentiment strengthof online review rm concerning attribute Ai, = …i I1,2, , , = …m M1,2, , .Then, by the following Eq. (1), each kind of sentiment strength can betransformed into a corresponding score value. For example, the nom-inally coded data in Table 3 can be transformed into the score valuesshown in Table 4.

=

======

= … = …S i I m M

5, if sentiment strength "VPos"4, if sentiment strength "Pos"3, if sentiment strength "Neu"2, if sentiment strength "Neg"1, if sentiment strength "VNeg"0, if sentiment strength "Mv"

, 1,2, , , 1,2, ,im

(1)

Let Peri denote the performance of the product/service concerning

attribute Ai, Peri can be calculated by Eq. (2), i.e.,

= = …= SE

i IPer , 1,2, ,imM

i

1 im

(2)

where Ei denotes the number of online reviews including the sentenceconcerning attribute Ai. For example, if only the three reviews shown inTable 4 are considered, then the performance of the attribute (Room)can be calculated using Eq. (2), i.e., = =+Per 4.5room

5 42 .

3.2.2. Estimating the importance of each attributeIt can be seen from Section 2 that the attribute importance obtained

from the implicit approach would be lesser influenced by the attributeperformance than that obtained from the self-stated approach. There-fore, in this study, we try to estimate the importance of each attributeby an implicit approach. In the existing studies, several implicit ap-proaches have been proposed to estimate the importance of each at-tribute through online reviews and online ratings, where the onlinerating is regarded as the overall customer satisfaction (OCS) concerningthe product or service (Farhadloo et al., 2016; Qi et al., 2016; Xiaoet al., 2016). These approaches are mainly based on the following as-sumptions: (1) the OCS follows a Gaussian distribution, (2) the OCS is alinear combination of customer sentiments concerning all the attributesmentioned in the review, and (3) multicollinearity between differentattributes is low. However, in some practical problems, these assump-tions cannot be satisfied since the attributes are mined from the onlinereviews whereas obtained from the designed questionnaires. The onlinereviews and OCS in practical problems usually have the followingcharacteristics: (1) the OCS usually follows a positively skewed,asymmetric, bimodal (or J-shaped) distribution (Hu, Pavlou, & Zhang,2009, 2017); (2) the OCS may be a non-linear combination of customersentiments concerning all the attributes mentioned in the review; and(3) multicollinearity between different attributes may be relativelyhigh. Thus, considering the above characteristics, new model should bedeveloped to estimate the importance of each attribute through onlinereviews and OCS.

The neural network (NN) is a powerful approach for predictiontasks, which significantly outperforms model based approaches (e.g.,multiple regression models) in the situations that non-normal data, non-linear relationship or multicollinearity relationship exist (Deng, Chen,et al., 2008; Deng, Kuo, et al., 2008; Mikulić & Prebežac, 2012). Al-though the NN was developed for forecasting purposes, it could also beused for determining the weights of the input valuables (Deng, Chen,et al., 2008; Deng, Kuo, et al., 2008; Mikulić & Prebežac, 2012; Tsaur,Chiu, & Huang, 2002). Therefore, there is no doubt that the NN is apowerful alternative approach for estimating the importance of eachattribute through online reviews and OCS. However, since the nomin-ally coded data of online reviews are relatively sparse and the para-meters (weights and biases) of the NN are initialized randomly, theimportance obtained by a single NN would have great randomness,such as ANNs (Tsaur et al., 2002), BPNN (Deng, Chen, et al., 2008) andextended BPNN (Mikulić & Prebežac, 2012). To overcome this shortage,in this study, an ENNM is proposed to estimate the importance of eachattribute through online reviews and OCS. The framework of the ENNMis shown in Fig. 5. It can be seen from Fig. 5 that the ENNM includes Zneural networks (NNs). For the training of each NN, the transformedscore values of online reviews (e.g. data in the form of those shown inTable 4) are used as input variables, and online ratings (i.e., OCS)corresponding to the reviews are used as output variables. Then, basedon the zth trained NN, a vector of attribute's importance( = …W W W W{ , , , }z z z

Iz

1 2 ) and the weight of the zth NN (wz) can be ob-tained, where Wi

z denotes the importance of attribute Ai obtained bythe zth trained NN, = …i I1,2, , , = …z Z1,2, , . Furthermore, since thetransformed score values of online reviews are relatively sparse and theparameters (weights and biases) of the NNs are initialized randomly,the values of elements in set = …W W W W{ , , , }z z z

Iz

1 2 would have great

Table 3Nominally coded data of online reviews.

Review Sentiment strength of each review concerning each attribute

Room Staff Breakfast Location Service

1 VPos Pos Pos Mv Mv2 Mv Mv Mv Mv VNeg3 Pos Pos Pos VPos Mv

Table 4Transformed score values of the nominally coded data in Table 3.

Review Transformed score value of each review concerning each attribute

Room Staff Breakfast Location Service

1 5 4 4 0 02 0 0 0 0 13 4 4 4 5 0

J.-W. Bi et al. Tourism Management 70 (2019) 460–478

465

randomness, = …z Z1,2, , . Thus, to obtain more accurate results, theoutliers are eliminated, and Z vectors in …W W W, , , Z1 2 are remained,which are denoted as …W W W, , , Z(1) (2) ( ). Finally, by aggregating

…W W W, , , Z(1) (2) ( ) and the weights of the corresponding NNs, thevector of attribute importance of ENNM ( = …Imp {Imp , Imp , , Imp }I1 2 )can be obtained. The detailed process of the ENNM is described as thefollowing five steps.

3.2.2.1. Training NNs. It can be seen from Fig. 5 that there are Z NNs inthe ENNM, and there are V hidden units ( …H H H, , , V1 2 ) in each NN.According to the transformed score values of online reviews, thestructure of each NN is determined, and Z trained NNs can be obtained.

3.2.2.2. Calculating the importance of each attribute according to eachtrained NN. Let iv

z denote the weight between attribute Ai and the vthhidden unit in the zth NN, =i I1,2, , , =z Z1,2, , , =v V1,2, , .Let v

z denote the weight between output unit (OCS) and the v thhidden unit in the zth NN, =z Z1,2, , , =v V1,2, , . According tothe iv

z and vz, the importance of attribute Ai obtained by the zth

trained NN (i.e., Wiz) can be calculated by Eq. (3), i.e.,

=+

+= = ==

= =

W v V z Z i I( )

( ), 1,2, , , 1,2, , , 1,2, ,i

z vV

ivz

vz

iI

vV

ivz

vz

1

1 1

(3)

Thus, based on the zth trained NN, a vector of attribute's importancecan be obtained, i.e., = …W W W W{ , , , }z z z

Iz

1 2 , =z Z1,2, , .

3.2.2.3. Calculating the weight of each trained NN. The smaller theprediction error of NN is, the better fitting result of the NN will be.Thus, a larger weight should be assigned to the NN with smaller error;conversely, a smaller weight should be assigned to the NN with greatererror. Let z denote the absolute error of the zth NN. Let wz denote theweight of the zth NN, wz can be calculated by Eq. (4), i.e.,

= × =w z Z12

1exp( )

, 1,2, ,zz (4)

3.2.2.4. Eliminating outliers. Since the transformed score values ofonline reviews are relatively sparse and the parameters (weights andbiases) of the NNs are initialized randomly, the values of elements in set

= …W W W W{ , , , }z z zIz

1 2 would have great randomness, = …z Z1,2, , .Thus, to obtain more accurate results, the outliers need to beeliminated. To do this, the scree plot technique is employed (Chen

et al., 2015). Let … …W W W Wi i iz

iZ(1) (2) ( ) ( ) denote a

ranking of the absolute values of … …W W W W, , , , ,i i iz

iZ1 2 from the

smallest to the greatest. Let zVar( )i denote the variance of…W W W, , ,i i i

z(1) (2) ( ) , =i I1,2, , , = …z Z1,2, , . Obviously, zVar( )i isa increasing function of z, =i I1,2, , , = …z Z1,2, , . After calculating

zVar( )i for each z , a scree plot can be drawn with zVar( )i on the verticalaxis and z on the horizontal axis, =i I1,2, , . The line in scree plotbecomes steeper while z gets larger. The steeper part of the line couldbe regarded as outliers. If an attribute's importance obtained by the zthtrained NN (i.e.,Wi

z) is regarded as outlier, then the vector of attribute'simportance obtained by the zth trained NN ( = …W W W W{ , , , }z z z

Iz

1 2 ) iseliminated. After eliminating outliers, Z vectors in …W W W, , , Z1 2 areremained, which are denoted as …W W W, , , Z(1) (2) ( ).

3.2.2.5. Calculating the importance of each attribute obtained by theENNM. Let = …Imp {Imp , Imp , , Imp }I1 2 denote the vector of attributeimportance obtained by the ENNM, where Impi denote the importanceof attribute Ai, =i I1,2, , . Let w z( ) denote the weight of the NNcorresponding with W z( ), …w w w w{ , , , }z Z( ) 1 2 , =i I1,2, , ,

= …z Z1,2, , . To determine the = …Imp {Imp , Imp , , Imp }I1 2 , theweighted sum method is used. The Impi can be calculated by thefollowing Eq. (5), i.e.,

= × ==

w W i IImp , 1,2, ,iz

Z

z iz

1( )

( )

(5)

where w z( ) is the normalized value of w z( ), which can be represented by

= = …=

ww

wz Z

,1,2, ,z

z

zZ

z( )

( )

1 ( ) (6)

3.3. Constructing the IPA plot

According to the obtained Peri and Impi, the IPA can be conducted.In this study, four types of IPA are conducted, i.e., SIPA, IPCA, DIPA andDIPCA. Detailed description of each type of IPA is given below.

3.3.1. SIPAThe purpose of SIPA is to analyze the attribute's performance and

importance of the target company. In accordance with the obtained Periand Impi, the SIPA plot can be drawn with Impi on the vertical axis andPeri on the horizontal axis, as shown in Fig. 1 in Section 2.

Fig. 5. The framework of the ENNM.

J.-W. Bi et al. Tourism Management 70 (2019) 460–478

466

3.3.2. IPCAThe purpose of IPCA is to analyze the attribute's performance and

importance of the target (or focal) company by considering the attri-bute's performance of the competing company. Thus, in the process ofIPCA, it is necessary to determine the attributes for IPCA based on theonline reviews of the two competitive companies (thereafter, the focalcompany and the competing company) and the subjective preferencesof the managers. In this situation, two approaches can be used to de-termine the attributes for IPCA, i.e., (1) an attribute set is first con-structed by extracting attributes from the union of online reviews of thetwo companies, and then some important attributes are selected fromthe attribute set according to the subjective preferences of the man-agers; (2) two attribute sets are respectively constructed by extractingattributes from the online reviews of each company, and then someimportant attributes are selected from the union of the two attributesets according to the subjective preferences of the managers. On thisbasis, the processes shown in Section 3.1 and Section 3.2 can also beemployed to analyze the online reviews concerning each product/ser-vice, respectively. The obtained attribute's performances of the focalcompany and the competing company are denoted by Peri

f and Peric,

respectively; the obtained attribute's importance of the focal companyis denoted by Impi

f , =i I1,2, , . Let GAPif denote the gap score of the

product/service of the focal company concerning attribute Ai,=i I1,2, , . According to the study of Albayrak (2015), GAPi

f can becalculated by

= =i IGAP Per Imp , 1,2, ,i i if f f (7)

where Perif and Impi

f respectively denote the normalized values of Perif

and Impif , i.e.,

= ==

i IPer PerPer

, 1,2, ,ii

iI

i

ff

1f (8)

= ==

i IImpIm p

Im p, 1,2, ,i

i

iI

i

ff

1f (9)

Let PDi denote the difference between the focal company and thecompeting company on the performance of attribute Ai, PDi can becalculated by

= =i IPD Per Per , 1,2, ,i i if c (10)

In accordance with the obtainedGAPif and PDi, the IPCA plot can be

drawn. An example of the IPCA plot is shown in Fig. 6.According to the study of Albayrak (2015), the detailed meaning

and interpretation of the four quadrants in Fig. 6 are as follows.

(i) Q1 is termed as ‘solid competitive advantage’. The attribute posi-tioned in Q1 has both positive GAPi

f and PDi, indicating that theattribute has both a higher performance score than its importancescore and a higher performance score than that of the competitor.Thus, the attributes positioned in Q1 can be regarded as the majorstrengths, and the focal company should aim to keep these attri-butes’ performance level.

(ii) Q2 is termed as ‘head-to-head competition’. The attribute posi-tioned in Q2 has a positive GAPi

f and a negative PDi, indicatingthat although the attribute's performance is higher than the cus-tomer expectations, the attribute's performance is lower than thatof its competitor. For these attributes positioned in Q2, the focalcompany has to at least reach the performance level of its com-petitor.

(iii) Q3 is termed as ‘urgent action’. The attribute positioned in Q3 hasboth negative GAPi

f and PDi, indicating that the attribute has botha lower performance score than its importance score and a poorperformance against the competitor. Thus, the attributes posi-tioned in Q3 can be regarded as the major weakness, and the focalcompany should take urgent action to improve them.

(iv) Q4 is termed as ‘null advantage’. The attribute positioned in Q4 hasa negative GAPi

f and a positive PDi, indicating that although theattribute's performance is higher than that of its competitor, theattribute's performance is not meet the customer expectations.These attributes positioned in Q4 is not the real advantage of thefocal company since the customer expectations have not been ex-ceeded.

If the managers want to conduct IPCA considering multiple com-petitors, then the mean of the attribute's performance of all the com-peting companies (Peri

c_mean) (Chen, 2014; Taplin, 2012a; 2012b) or thebest attribute's performance among all the competing companies(Peri

c_max) (Mikulić, Krešić, Prebežac, Miličević, & Šerić, 2016; Mikulić &Prebežac, 2012) can be used to substitute the attribute's performance ofthe competing company (Peri

c) in Eq. (10), =i I1,2, , . On this basis,the IPCA plot can also be drawn and the related analysis can also beconducted.

3.3.3. DIPATo analyze the development trends of important attributes of the

product/service, it is necessary to conduct SIPA considering differenttime periods. For this, this paper proposes a DIPA, which enables themanager to track the attribute's performance and the attribute's im-portance of the product/service over time.

To conduct DIPA, T time periods are first defined, where a timeperiod may be a year, a season or a month, etc. Then, according to thedefined time periods, the collected online reviews are divided into Tsubsets. By analyzing the online reviews concerning the tth time periodusing the processes shown in Section 3.1 and Section 3.2, the perfor-mance and importance of attribute Ai at the tth time period can beestimated, which are respectively denoted by Peri

t and Impit ,

=i I1,2, , , =t T1,2, , . Thus, according to the obtained Perit and

Impit , =i I1,2, , , a SIPA plot can be drawn with respect to the tth time

period, =t T1,2, , , and a 3-Dimensional (3D) diagram of DIPA can beobtained by combining the SIPA plots concerning the T time periods.An example of the DIPA plot is shown in Fig. 7, where Fig. 7(a) is the 3Dimage of DIPA plot, Fig. 7(b), (c) and (d) are respectively the Timeperiod-Performance, Time period-Importance and Performance-Im-portance images of the 3D image.

Based on Fig. 7, three possible patterns or trends through time areselected as examples to illustrate the use of the DIPA plot.

(i) If an attribute is always in a certain quadrant (e.g. attribute A1 inFig. 7(d)), then this indicates that the importance and performanceof the attribute are not significantly changed through time, and themeaning and management strategy for this attribute are the sameas those given for the attribute positioned at the same quadrant inSIPA plot.

Fig. 6. The IPCA plot.

J.-W. Bi et al. Tourism Management 70 (2019) 460–478

467

(ii) If the position of an attribute is changed from Q1 to Q2 throughtime (e.g. attribute A2 in Fig. 7(d)), then this indicates that theattribute is important whereas the attribute performance is de-creasing through time. For this, more resources should be allocatedto improve the performance of this attribute.

(iii) If the position of an attribute is changed from Q3 to Q2 (e.g. at-tribute A3 in Fig. 7(d)), then this indicates that people pay moreand more attention to this attribute, but the performance of thisattribute has always been relatively poor. Thus, more attentionshould be paid to the attribute.

3.3.4. DIPCAConducting IPCA with a dynamic perspective would enable the

manger to capture the evolution process of the competitive trend be-tween the focal company and the competing companies. The use ofonline reviews makes it convenience to conduct IPCA with a dynamicperspective. For this, this paper further develops a dynamic IPCA(DIPCA) version.

To conduct DIPCA, T time periods are first defined. Then, accordingto the defined time periods, the collected online reviews concerning theproducts/services of the focal company and the competing company arerespectively divided into T subsets. By analyzing the online reviews ofthe tth time period using the processes shown in Section 3.1 and Section3.2, the performances of the focal company and the competing com-pany concerning attribute Ai can be estimated which are respectivelydenoted by Peri

tf and Peritc , =i I1,2, , , =t T1,2, , . Meanwhile, the

attribute's importance of focal company is obtained, which is denotedby Impi

tf , =i I1,2, , , =t T1,2, , .Let GAPi

tf denote the gap score of the product/service of the focalcompany concerning attribute Ai at the tth time period, which can becalculated by Eq. (11), i.e.,

= = =i I t TGAP Per Imp , 1,2, , , 1,2, ,it

it

itf f f (11)

where Peritf and Impi

tf are the normalized values of Peritf and Peri

tc , i.e.,

= = ==

i I t TPer PerPer ,

1,2, , , 1,2, ,it i

t

iI

it

ff

1f (12)

= = ==

i I t TImpIm p

Im p, 1,2, , , 1,2, ,i

t it

iI

it

ff

1f (13)

Let PDit denote the difference between the focal company and the

competing company concerning the performance of attribute Ai at thetth time period, PDi

t can be calculated by Eq. (14), i.e.,

= = =i I t TPD Per Per , 1,2, , , 1,2, ,it

it

itf c (14)

In accordance with the obtainedGAPitf and PDi

t , =i I1,2, , , a IPCAplot can be drawn with respect to the tth time period, =t T1,2, , , anda 3D diagram of DIPCA can be obtained by combining the IPCA plotsconcerning the T time periods. An example of the DIPCA plot is shownin Fig. 8, where Fig. 8(a) is the 3D image of DIPCA plot, Fig. 8(b), (c)and (d) are respectively the Time period-PD, Time period-GAP and PD-GAP images of the 3D image.

Based on Fig. 8, four possible patterns or trends through time areselected as examples to illustrate the use of the DIPCA plot.

(i) If an attribute is always in a certain quadrant (e.g. attribute A1 inFig. 8(d)), then this indicates not only the gap between its per-formance and importance but also the performance differencebetween the focal company and the competing company are notsignificantly changed through time, and the meaning and man-agement strategy for this attribute are the same as those given forthe attribute positioned at the same quadrant in IPCA plot.

(ii) If the position of an attribute is changed from Q1 to Q2 (e.g. at-tribute A2 in Fig. 8(d)), then this indicates that although the at-tribute's performance is always higher than the customer ex-pectations, the attribute's performance has been decreasing andhas been lower than that of the competing company. For this, thefocal company has to allocate more resources to improve the

Fig. 7. An example of the DIPA plot.

Fig. 8. An example of the DIPCA plot.

J.-W. Bi et al. Tourism Management 70 (2019) 460–478

468

attribute's performance so as to reach the attribute's performanceof the competing company.

(iii) If the position of an attribute is changed from Q1 to Q3 (e.g. at-tribute A3 in Fig. 8(d)), then this indicates that the attribute'sperformance has significantly decreased through time, and hasbeen lower than both the attribute's performance of the competingcompany and the customer expectations. Thus, the focal companyshould take urgent action to improve the attribute's performance.

(iv) If the position of an attribute is changed from Q1 to Q4 (e.g. at-tribute A4 in Fig. 8(d)), then this indicates that although the at-tribute's performance is higher than that of the competing com-pany, the gap score of the attribute has been decreasing. Now, theattribute's performance is lower than its importance. Thus, theattribute has changed into a ‘null advantage’ from a ‘real ad-vantage’.

Similar to those discussed in IPCA, if the managers want to conductDIPCA considering multiple competitors, the mean of the attribute'sperformance of all the competing companies in the tth time period(Peri

tc _mean) or the best attribute's performance among all the competingcompanies in the tth time period (Peri

tc _max) can be used to substitute theattribute's performance of the competing company (Peri

tc ) in Eq. (14),=i I1,2, , , =t T1,2, , .

4. Case study

In this section, a case study of IPA for two five-star hotels inSingapore is given to illustrate the validity of the proposed metho-dology. The related data are collected from Tripadvisor (https://www.tripadvisor.com), one of the most leading tourism websites. In the fol-lowing, the data used in the case study are first introduced. Then, theprocedures of the case study and some important experimental resultsare given.

4.1. Data

The proposed methodology can be used to conduct IPA with respectto different hierarchies, such as different brands, different products/services or different attributes of products/services. In this paper, thecase study is conducted with respect to two five-star hotels, i.e., MOSand MBS, where the MOS is selected as the focal company and MBS isselected as the competing company. The related data of the two hotelsare collected from Tripadvisor (https://www.tripadvisor.com). Fig. 9shows an example of the collected data. It can be seen from Fig. 9 thatthe collected data include the traveler's review, the traveler's rating(i.e., OCS) and the travel date. A total number of 30,315 online reviewsabout the two hotels were collected by December 2017. After removingthe invalid and non-English reviews, we obtained 24,276 valid onlinereviews, including 9624 reviews on MOS and 14,652 reviews on MBS.The related information of the collected reviews is given in Table 5.

4.2. Mining useful information from online reviews

To save the space of this paper, we will conduct the four types of IPA(i.e., SIPA, IPCA, DIPA and DIPCA) through a same data mining process.Thus, a union set of the online reviews of MOS and MBS is first con-structed, and then an attribute set is constructed by extracting attri-butes from the union set of online reviews. Further, according to thesubjective preferences of the managers, nine attributes are selectedfrom the attribute set, which are shown in Table 6. In Table 6, “Fre-quent words” indicates the frequent words concerning each attribute;“Number of attribute words” indicates the number of words concerningeach attribute; “Total frequency” indicates the times that the attributewords appear in all the reviews; and “Number of reviews” indicates thenumber of reviews that contain at least one word belonging to the at-tribute.

According to the process given in Section 3.1.2, the IOVO-SVM al-gorithm is employed to identify the sentiment strength of each onlinereview concerning each attribute. For the training of sentiment classi-fiers in the IOVO-SVM algorithm, 2500 labeled online reviews of hotelsare used, where 500 labeled online reviews concerning each of the fivesentiment strengths are included. The results are converted to nomin-ally coded data, as shown in Table 7, and the statistical results of thesentiment strengths concerning each attribute of the two hotels areshown in Fig. 10.

4.3. Estimating the performance and importance of each attribute

4.3.1. Estimating the performance of each attributeBy Eq. (1), the nominally coded data shown in Table 7 can be

converted into score values, as shown in Table 8.Based on the score values of online reviews concerning MOS and

MBS, each attribute's performance of the two hotels can be calculatedby Eq. (2). The results are shown in Table 9.

4.3.2. Estimating each attribute's importanceBased on the score values of online reviews and OCS, the ENNM is

trained (here =V 21 and =Z 500). With the increasing of the value of zfrom 1 to 500, the obtained values ofWi

z are recorded, which are shownin Fig. 11(a), =i 1,2, , 9, =z 1,2, ,500. It can be seen formFig. 11(a), the importance obtained by a single NN has greater ran-domness, which can not accurately capture the importance of eachattribute. Even if other types of single NN are used, such as ANNs (Tsauret al., 2002), BPNN (Deng, Chen, et al., 2008) and extended BPNN

Fig. 9. An example of the collected data.

Table 5The related information of the collected reviews.

Hotel Online reviews distribution over years Totalnumber ofreviews

Total numberof words

Before2013

2014 2015 2016 2017

MOS 1195 2400 2373 2469 1187 9624 1,019,212MBS 5733 2360 2364 2456 1739 14,652 2,252,172

J.-W. Bi et al. Tourism Management 70 (2019) 460–478

469

(Mikulić & Prebežac, 2012), the shortage in the aspect of randomness ofresults still cannot be overcome. To obtain more stable and accurateimportance, the outliers are eliminated using the scree plot technique(detailed process can be found in Section 3.2.2), and the results areshown in Fig. 11(b). After eliminating outliers, 426 valid NNs are re-mained, i.e., =Z 426. Meanwhile, the importance of each attributeImpi obtained by ENNM is calculated, and the obtained values of Impiwith the increasing value of z are recorded, as shown in Fig. 11(c). Itcan be seen from Fig. 11(c) that the importance of each attribute trendsto stable with the increasing value of z . The final importance of eachattribute Impi can be obtained, i.e., =Imp 0. 10491 , =Imp 0.04982 ,

=Imp 0. 13403 , =Imp 0. 10224 , =Imp 0.15175 , =Imp 0. 10606 ,=Imp 0. 12657 , =Imp 0. 12518 and =Imp 0.09989 .

4.4. Constructing the IPA plot

In accordance with the obtained Peri and Impi, the IPA plot can be

constructed. Here, the SIPA, IPCA, DIPA and DIPCA plots are respec-tively constructed, where MOS is regarded as the focal company andMBS is regarded as the competing company. The process and someresults of each IPA are given below.

4.4.1. SIPAIn accordance with the obtained Peri and Impi, the SIPA plot can be

drawn with Impi on the vertical axis and Peri on the horizontal axis, asshown in Fig. 12. In Fig. 12, Service (A5) is positioned in Q1. The at-tribute has a higher performance and a higher importance, which is themajor strength and potential competitive advantage of the hotel. Room(A3), Check in/out (A7) and Facility (A8) are positioned in Q2. Each ofthe three attributes has a lower performance and a higher importance,which is the major weakness of the hotel. Value (A1) and Wifi/internet(A9) fall into Q3, indicating that each of the two attributes has a lowerperformance and a lower importance, which is the minor weakness ofthe hotel. Location/transport (A2), Cleanliness (A4) and Food/drink

Table 6Nine important attributes concerning the two hotels extracted from the 24,276 online reviews.

Attribute Frequent words Number of words Total frequency Number of reviews

Value (A1) Value, price, money, cost, … 16 7254 4818Location/Transport (A2) Location, transport, subway, … 21 28,915 13,682Room (A3) Room, bedroom, bathroom, … 28 61,357 18,186Cleanliness (A4) Cleanliness, cleaner, tidy, … 12 9471 6524Service (A5) Service, stuff, attitude, … 19 27,886 13,194Food/drink (A6) Food, breakfast, meal, lunch, … 31 25,270 11,818Check in/out (A7) Check-in, check-out, arrival, … 15 17,466 8630Facility (A8) Facility, pool, gymnasium, … 24 25,598 12,596Wifi/internet (A9) Wifi, bandwidth, internet, … 11 6776 3809

Table 7Nominally coded data of online reviews concerning MOS and MBS.

Hotel Review Attribute OCS

A1 A2 A3 A4 A5 A6 A7 A8 A9

MOS 1 Mv Pos Mv Neg Mv Pos Mv Pos Mv 402 Neu Mv Neu Mv Mv Mv VNeg Mv Neg 10

9624 Mv Pos Mv VPos Mv Mv Pos Mv Mv 50MBS 1 Neg Mv Neg VNeg Mv Neg Mv Neg Mv 10

2 Mv VPos Mv Pos Mv VPos Pos Mv Mv 50

14,652 Mv Neu Mv Neg Mv Pos Mv Mv Neu 30

Fig. 10. The sentiment strengths concerning each attribute of the two hotels.

Table 8Score values of online reviews concerning MOS and MBS.

Hotel Review Attribute OCS

A1 A2 A3 A4 A5 A6 A7 A8 A9

MOS 1 0 4 0 2 0 4 0 4 0 402 3 0 2 0 0 0 1 0 2 10

9624 0 4 0 5 0 0 4 0 0 50MBS 1 2 0 2 1 0 2 0 2 0 10

2 0 5 0 4 0 5 4 0 0 50

14,652 0 3 0 2 0 4 0 0 3 30

J.-W. Bi et al. Tourism Management 70 (2019) 460–478

470

(A6) are positioned in Q4, indicating that each of the three attributeshas a higher performance but a lower importance, which is possiblewaste of limited resources of the hotel. Therefore, as far as resourceallocation is concerned, Service (A5) need to be well-maintained; Room(A3), Check in/out (A7) and Facility (A8) deserve further attention andinvestments; Value (A1) and Wifi/internet (A9) are likely to receivelower priority due to their relatively low importance; and Location/transport (A2), Cleanliness (A4) and Food/drink (A6) may deserve acloser examination for future exploration.

4.4.2. IPCAAccording to Eqs. (7)–(9), the gap scores of the focal company

concerning the nine attributes are calculated, i.e., GAPif are obtained,

=i 1,2, , 9. According to Eq. (10), the difference between attribute'sperformances of the focal company and the competing company iscalculated, i.e., PDi is obtained, =i 1,2, , 9. In accordance with theobtained GAPi

f and PDi, the IPCA plot can be drawn with GAPif on the

vertical axis and PDi on the horizontal axis, as shown in Fig. 13. InFig. 13, Value (A1) and Location/transport (A2) fall into Q1, indicatingthat the two attributes are the strengths of MOS. Cleanliness (A4),

Food/drink (A6) and Wifi/internet (A9) are positioned in Q2, indicatingthat the performances of MOS on the three attributes are beyond cus-tomer expectations, but have lower performance than those of MBS.Room (A3) and Facility (A8) are positioned in Q3, indicating that thetwo attributes are the major weaknesses of MOS. Value (A1), Service(A5) and Check in/out (A7) are positioned in Q4, indicating that al-though the three attributes of MOS have an advantage over MBS, theattributes are not a real advantage since customer's expectations are notmet. Therefore, compared with MBS, the performances of MOS con-cerning Room (A3) and Facility (A8) need to be improved urgently; theperformances concerning Value (A1) and Location/transport (A2) needto be well-maintained; the priorities of Cleanliness (A4), Food/drink(A6) and Wifi/internet (A9) follow Value (A1) and Location/transport(A2); Service (A5) and Check in/out (A7) are likely to receive relativelylower priority due to the two attributes of MOS have advantages overMBS.

4.4.3. DIPAThe online reviews are divided into 5 subsets according to the

posted years. With respect to the online reviews in each subset, theprocesses in Section 3.1 and Section 3.2 are used, and the performance

Table 9The performance of MOS and MBS concerning each attribute.

Hotel Per1 Per2 Per3 Per4 Per5 Per6 Per7 Per8 Per9

MOS 3.3250 3.5577 3.5714 3.7660 3.4541 3.5617 3.2851 3.6105 3.4312MBS 3.3978 3.8149 3.4581 3.6444 3.5077 3.5049 3.3659 3.2517 3.2203

Fig. 11. The values of each attribute's importance.

Fig. 12. The SIPA plot.Fig. 13. The IPCA plot.

J.-W. Bi et al. Tourism Management 70 (2019) 460–478

471

(Perit) and importance (Impi

t) of attribute Ai at the t th time period canbe calculated, =i 1,2, , 9, =t 1,2, , 5. According to the obtainedPeri

t and Impit , the DIPA plot can be drawn, which is shown in Fig. 14.

Fig. 14(a) is 3D image of DIPA plot, Fig. 14(b), (c) and (d) are re-spectively the Year-Performance, Year-Importance and Performance-Importance images of the 3D image. Fig. 14(b) and (c) can respectivelyreflect the changes of each attribute's performance and importance overtime. Fig. 14(d) reflects the changes of quadrants that each attributepositioned in over time, where arrows represent the time sequenceamong different points concerning an attribute. It can be seen fromFig. 14(b) that the attributes Location/transport (A2), Cleanliness (A4),Service (A5) and Food/drink (A6) have a relatively higher performanceand the attributes Check in/out (A7), Facility (A8) and Wifi/internet(A9) have a relatively lower performance at each time period. Besides,the performance of Location/transport (A2) is relatively stable at eachtime period; the performance of Facility (A8) shows a overall downwardtrend, and the performances of Value (A1), Room (A3), Cleanliness (A4),Service (A5), Food/drink (A6), Check in/out (A7) and Wifi/internet (A9)show an overall upward trend. It can be seen from Fig. 14(c) that theimportance of each attribute in different time periods is not the same.On the whole, the importance of Location/transport (A2), Cleanliness(A4) and Food/drink (A6) show an overall downward trend, and theimportance of Value (A1), Room (A3), Service (A5), Check in/out (A7),Facility (A8) and Wifi/internet (A9) show an overall upward trend. Itcan be seen from Fig. 14(d), the quadrants that an attribute falls intowould be changed with respect to different time periods. According tothe image shown in Fig. 14(d), the managers should pay more attention

to Check in/out (A7) and Facility (A8). For Check in/out (A7), on theone hand, its importance is higher than the average importance, whichmeans it is relatively important to the customers. On the other hand,although its overall performance shows an increasing trend, its per-formance is still lower than the average performance of all the attri-butes. Thus, Check in/out (A7) is still the major weakness of MOS. ForFacility (A8), the quadrant that it falls into is changed from Q3 to Q2,which indicates that it has changed into a major weakness from a minorweakness. For this, more resources should be allocated to improve theperformances of Check in/out (A7) and Facility (A8). Besides, Wifi/in-ternet (A9) is the minor weaknesses of MOS, which are likely to receivelower priority due to their relatively low importance. The resourcesallocated to Cleanliness (A4) and Food/drink (A6) have exceeded thepractical needs and may cause the waste of limited resources.

4.4.4. DIPCAIn a similar way of DIPA, the online reviews are divided into 5

subsets according to the posted years. Using the processes in Section 3.1and Section 3.2, the performance (Peri

t) and importance (Impit) of at-

tribute Ai at the t th time period can be calculated, =i 1,2, , 9,=t 1,2, , 5. According to Eqs. 11–13, the gap score concerning attri-bute Ai of MOS is calculated considering the t th time period, i.e.,GAPi

tf

is obtained, =i 1,2, , 9, =t 1,2, , 5. According to Eq. (14), the dif-ference between MOS and MBS concerning attribute's performance ineach time period is calculated, i.e., PDi

t is obtained, =i 1,2, , 9,=t 1,2, , 5. According to the obtained GAPi

tf and PDit , the DIPCA plot

can be drawn, which is shown in Fig. 15. Fig. 15(a) is the 3D image of

Fig. 14. The DIPA plot.

J.-W. Bi et al. Tourism Management 70 (2019) 460–478

472

the DIPCA plot, Fig. 15(b), (c) and (d) are respectively the Year-PD,Year-GAP and PD-GAP images of the 3D image. Fig. 15(b) and (c) re-spectively reflect the changes of difference between attribute's perfor-mances of the two hotels (PDi

t) and gap score (GAPitf ) over time,

=i 1,2, , 9, =t 1,2, , 7. Fig. 15(d) reflects the changes of quadrantsthat each attribute positioned in DIPCA plot over time, where arrowsrepresent the time sequence among different points concerning an at-tribute. It can be seen from Fig. 15(b) that Value (A1), Location/trans-port (A2), Service (A5) and Check in/out (A7) have relatively highervalues of PDi

t , and Room (A3), Cleanliness (A4), Food/drink (A6), Fa-cility (A8) and Wifi/internet (A9) have relatively lower values of PDi

t ateach time period. Besides, the values of PDi

t concerning Location/transport (A2), Service (A5), Facility (A8) and Wifi/internet (A9) showoverall downward trends, and the values of PDi

t concerning Value (A1)show an overall upward trend on the whole. It can be seen fromFig. 15(c) that the gap scores of different attributes in different timeperiods are not the same. On the whole, the gap scores of Room (A3),Check in/out (A7), Facility (A8) and Wifi/internet (A9) show overalldownward trends, and the gap scores of Value (A1), Location/transport(A2), Cleanliness (A4) and Food/drink (A6) show overall upward trends.It can be seen from Fig. 15(d) that the quadrants that an attribute fallsinto would be changed with respect to different time periods. Accordingto the image shown in Fig. 15(d), the managers should pay more

attention to Service (A5) and Facility (A8). The quadrants that Service(A5) falls into are changed from Q4 to Q3, and the quadrants that Fa-cility (A8) falls into are changed from Q2 to Q3. The performances ofService (A5) and Facility (A8) have been decreasing over years. Now,the two attributes’ performances are not only failing to meet customerexpectations but also lower than those of MBS. Besides, Room (A3) isalways in Q3, which can be regarded as the major weakness. Therefore,MOS should take urgent action to improve Room (A3), Service (A5) andFacility (A8).

It is necessary to point out that, if the managers want to obtain moresuggestions with respect to specific attributes of the two hotels, the fine-grained IPA can be conducted with respect to the specific attributes(e.g., food) by repeating the above processes based on the reviews orsentences concerning the specific attributes or sub-attributes (e.g.,breakfast, lunch, etc.).

4.5. Validation of results

To verify the validity of the proposed methodology, it is necessary tocompare the results obtained by the proposed methodology with thoseobtained by the existing methods or methodologies. Since DIPA andDIPCA are first proposed in this paper, there are no existing methods

Fig. 15. The DIPCA plot.

J.-W. Bi et al. Tourism Management 70 (2019) 460–478

473

can be used as the baselines of DIPA and DIPCA. Besides, since DIPAand DIPCA are the extensions of SIPA and IPCA, it can be regarded thatthe results of DIPA and DIPCA are validity if the results of SIPA andIPCA obtained by the proposed methodology are validity. Thus, in thissection, we only verify the validity of results of SIPA and IPCA obtainedby the proposed methodology. The details are given below.

In addition to online reviews and OCS, Tripadvisor also encouragescustomers to provide their online ratings concerning five specific at-tributes (Value, Location, Room, Cleanliness and Service) using a 5-point Likert scale, where 1 and 5 respectively indicate the lowest degreeand the highest degree of satisfaction. These online ratings can be re-garded as customer questionnaires and can be used as the data sourcefor conducting SIPA and IPCA using the existing methods. The methodsproposed by Van, Ryzin & Immerwahr (2007) and Albayrak (2015) areselected as the baselines for conducting SIPA and IPCA, respectively. Ifthe results of Van, Ryzin & Immerwahr (2007)'s method and Albayrak(2015)'s method through online ratings from Tripadvisor are respec-tively consistent with the results of SIPA and IPCA obtained by ourmethodology through online reviews, then it can be concluded that ourmethodology is valid. For the convenience of further analysis, Van,Ryzin & Immerwahr (2007)'s method is denoted as rating-based SIPA(Rating-SIPA), Albayrak (2015)'s method is denoted as rating-basedIPCA (Rating-IPCA) and the SIPA and IPCA by the methodology pro-posed in this paper are respectively denoted as review-based SIPA(Review-SIPA) and review-based IPCA (Review-IPCA). The processes ofRating-SIPA and Rating-IPCA through the online ratings from Tri-padvisor are given below.

Since the online ratings of the five attributes are non-mandatoryoptions when customers post their online reviews, there are somemissing values concerning the online ratings of the five attributes. Afterremoving the data with missing values, the final data sets of the onlineratings concerning the two five-star hotels are obtained, as shown inTable 10.

To conduct Rating-SIPA, the performances of MOS and MBS con-cerning the five attributes are measured by calculating the averagescores, the results are shown in Table 11. The calculation of perfor-mance (average score) of MOS concerning attribute A1 is given as anexample, i.e.,

= × + × + × + ×

+ × =

Performance (1 197 2 453 3 1825 4 2621

5 1822)/6918 3.783A( )1

According to Van, Ryzin & Immerwahr (2007)'s method, multiplelinear regression model is used to estimate the importance of each at-tribute, where OCS is the dependent variable and the five attributes areindependent variables. The results of the regression model based on theonline ratings of MOS are shown in Table 12. Table 12 shows that thefive attributes explain 76% of the variance in OCS, indicating that thereis a strong relationship between the combination of five attributes andOCS. All the coefficients of the five attributes in the model are

significant at the 0.1% level of significance. Besides, the variance in-flation factor (VIF) of each attribute is less than the conservativethreshold of 5, indicating that multicollinearity is not a major issue. Bynormalizing the B values in Table 12, the importance of the five attri-butes can be obtained, as shown in Table 13.

In accordance with the obtained performance (shown in Table 11)and importance (shown in Table 13) of each attribute, the Rating-SIPAplot concerning the five attributes can be drawn, as shown in Fig. 16(a). Meanwhile, the Review-SIPA plot concerning the five attributes isredrawn, as shown in Fig. 16 (b). It can be seen from Fig. 16 (a) andFig. 16 (b) that the results obtained by Rating-SIPA and those obtainedby Review-SIPA are consistent with each other. Therefore, the validityof Review-IPA can be verified. It is necessary to explain that the plotshown in Fig. 16 (b) is not the same as that shown in Fig. 12. This isbecause, in the study, the data-centred method (Azzopardi & Nash,2013; Beldona & Cobanoglu, 2007; Deng, 2007) is used to determinethe crosshair placement of SIPA. Thus, the crosshair placement and thequadrants that attributes fall in could be changed if different numbersof attributes are considered.

By the similar way, the Rating-IPCA plot and the Review-IPCA plotconcerning the five attributes are obtained, which are shown inFig. 17(a) and Fig. 17(b), respectively. It can be seen from Fig. 17(a)and (b) that the results obtained by Rating-IPCA and those obtained byReview-IPCA are consistent with each other. Therefore, the effective-ness of Review-IPCA can be verified.

It is necessary to further explain that although Rating-SIPA andRating-IPCA can be used to obtain analysis results through online rat-ings, the development of Review-SIPA and Review-IPCA are still ne-cessary and important. The reasons are discussed as follows: (1)Review-SIPA and Review-IPCA can conduct analysis with respect to awide range of attributes, whereas Rating-SIPA and Rating-IPCA canonly conduct analysis with respect to several pre-defined attributes. Forexample, in this paper, nine important attributes are mined from onlinereviews (more attributes can be mined if necessary) and the analysisconcerning the nine attributes are conducted by Review-SIPA andReview-IPCA, whereas only the analysis concerning the five pre-definedattributes can be conducted by Rating-SIPA and Rating-IPCA; (2) Now,online reviews concerning products/services can be collected from notonly e-business websites but also other websites, such as news websites,online communities etc., whereas only a few e-business websites re-quire customers to provide their online ratings concerning pre-definedattributes of products/services. Thus, comparing with Rating-SIPA andRating-IPCA, Review-SIPA and Review-IPCA have more channels for

Table 10The final data sets of the online ratings concerning the two five-star hotels.

Hotel Attribute Numbers of different scores concerning eachattribute

Totalnumber

1 2 3 4 5

MOS Value (A1) 197 453 1825 2621 1822 6918Location (A2) 6 13 153 1577 5169 6918Room (A3) 121 216 1023 2486 3072 6918Cleanliness (A4) 113 305 1256 2669 2575 6918Service (A5) 66 168 825 2583 3276 6918

MBS Value (A1) 327 351 777 930 726 3111Location (A2) 39 120 483 872 1597 3111Room (A3) 75 101 387 789 1759 3111Cleanliness (A4) 61 139 418 831 1662 3111Service (A5) 56 110 297 765 1883 3111

Table 11The performances of MOS and MBS concerning the five attributes.

Hotel Performance

Value (A1) Location (A2) Room (A3) Cleanliness (A4) Service (A5)

MOS 3.783 4.718 4.053 4.277 4.011MBS 3.442 4.243 4.252 4.385 3.781

Table 12The results of the regression model based on the online ratings of MOS.

Attribute B t Sig. Collinearity Statistics

Tolerance VIF

Value (A1) 0.192 21.330 0.000 0.413 2.421Location (A2) 0.099 7.951 0.000 0.801 1.248Room (A3) 0.251 25.612 0.000 0.395 2.530Cleanliness (A4) 0.083 8.009 0.000 0.438 2.283Service (A5) 0.420 52.884 0.000 0.458 2.181(Constant) −2.343 −4.315 0.000

R2=0.760 (p < 0.001).

J.-W. Bi et al. Tourism Management 70 (2019) 460–478

474

collecting related data and can be used for conducting SIPA and IPCAwith respect to more products/services. (3) Even if the websites en-courage customers to provide their online ratings, the number of onlineratings is still significant fewer than that of online reviews. For ex-ample, in this study, the number of collected online ratings is 10,029,which is significant fewer than the number of online reviews 24,276.Thus, the development of Review-SIPA and Review-IPCA are still ne-cessary and important.

5. Discussions and conclusions

In this paper, we proposed a methodology for conducting IPAthrough online reviews. In the methodology, the important product/service attributes are extracted from online reviews using LDA, andsentiment strengths of online reviews toward the important product/service attributes are identified using the IOVO-SVM algorithm. Theperformance of each attribute is measured based on the obtained sen-timent strengths, and the importance of each attribute is estimated bythe ENNM. Based on the obtained performance and importance of eachattribute, four types of IPA can be conducted, i.e., the SIPA, IPCA, DIPAand DIPCA, and practical suggestions for management can be obtainedbased on the results of IPA. Finally, a case study on two five-star hotelsis given, and the results obtained by the proposed methodology throughonline reviews are compared with those obtained by the existingmethodology through online ratings. In the following, some discussionsare given in Section 5.1, and the contributions of this paper are sum-marized in Section 5.2.

5.1. Discussions

To further analyze the advantages and disadvantages of the pro-posed methodology, some discussions are given below. The discussionsmainly focus on the four aspects, i.e., (1) the representativeness ofonline reviews, (2) the identification and treatment of fake reviews, (3)the determination of important attributes for conducting IPA, and (4)the estimation of importance of each attribute. Detailed discussionconcerning each aspect is given below.

5.1.1. The representativeness of online reviewsIn this paper, a novel methodology is proposed to conduct IPA,

where online reviews are used as the data source. But, it may be arguedthat how representative the online reviews are regarded as the custo-mers’ opinions concerning a certain hotel, restaurant or other types ofbusiness. For this, we will give the following discussions. On the onehand, with the rapid development of information technology andInternet, more and more people began to buy products/services andpost related reviews through online website. Until now, there are morethan 3.9 billion internet users in the world (http://www.internetlivestats.com/internet-users/). According to a survey con-ducted by Global Web Index, more than 60% internet users havewritten online reviews for a brand or product (https://www.globalwebindex.com/), which leads to a rapidly increasing number ofonline reviews. For this, online reviews have been successfully used asthe data source of several kinds of decision analysis, such as productsranking/recommending (Liu et al., 2017a; Siering et al., 2018), cus-tomer satisfaction modelling (Farhadloo et al., 2016), products/servicesimprovement (Gao et al., 2018; Liu et al., 2018), brand analysis(Culotta & Cutler, 2016; Tirunillai & Tellis, 2014), customer preferencesanalysis (Xiao et al., 2016), market structure analysis (Chen et al., 2015;Netzer et al., 2012), guest experience and satisfaction analysis (Xiang,Schwartz, Gerdes Jr., & Uysal, 2015), and service performance eva-luation (Li et al., 2017), etc. On the other hand, the representativenessof online reviews could be different if different products/services ordifferent brands are considered. For example, if most customers of theproduct/service are more familiar with Internet and like to post onlinereviews (such as customers of hotels in theme parks), then the greaterrepresentativeness of online reviews would be; conversely, if mostcustomers of the product/service are not familiar with Internet and donot like to post online reviews (such as customers of hotels in sana-toriums), then the smaller representativeness of online reviews wouldbe. Thus, in general, with the increasing number of online users, moreand more representativeness of online reviews would be. As for prac-tical applications, whether the online reviews could be used as the datasource for conducting IPA depends on the specific situations of thecertain hotel, restaurant or other types of business.

Table 13The importance of the five attributes.

Attribute Value (A1) Location (A2) Room (A3) Cleanliness (A4) Service (A5)

Importance 0.184 0.095 0.240 0.079 0.402

Fig. 16. The SIPA plot concerning the five attributes.

J.-W. Bi et al. Tourism Management 70 (2019) 460–478

475

5.1.2. The identification and treatment of fake reviewsIt is an undeniable fact that fake reviews exist in most of the current

websites. To some extent, the existence of fake reviews may affect theaccuracies of analysis results. If the methods for identifying and treatingthese fake reviews can be developed or used, then it would be helpful toimprove or increase the accuracies of analysis results. Till now, somemethods have been proposed to identify and treat fake reviews(Dewang & Singh, 2018; Lau et al., 2011; Munzel, 2016; Ott, Cardie, &Hancock, 2012; Wang, Xie, Liu, & Yu, 2012). Majority of these methodscan be grouped into two categories. The one is the methods based onfake user's characteristics (Munzel, 2016; Wang et al., 2012), and theother is those based on fake review's characteristics (Lau et al., 2011;Ott et al., 2012). In the methods based on fake user's characteristics, thedefinitions or characteristics of users who post fake reviews should bepredefined or trained; whereas in the methods based on fake review'scharacteristics, the definitions or characteristics of fake reviews shouldbe predefined or trained. Then, based on the predefined or traineddefinitions or characteristics, the fake reviews are identified. It is ne-cessary to point out that, with respect to different products/services ordifferent websites, the definitions or characteristics of fake users andfake reviews could be different. Thus, with respect to conduct IPAthrough online reviews, specific studies on identifying and treating fakereviews should be further conducted since the definitions or char-acteristics of fake users and fake reviews with respect to conduct IPAcould be different from those in the existing studies.

5.1.3. The determination of important attributes for conducting IPAUsually, consumers make purchase decisions on products/services

by comparing many different products/services from various aspects orattributes to meet their personal needs. Thus, it is necessary to conductdifferent types of IPA (i.e., SIPA, IPCA, DIPA and DIPCA) consideringmultiple important attributes. In the proposed methodology, to de-termine the important attributes for conducting IPA, some topics con-cerned by consumers are first extracted from online reviews using LDA.Then, decision-makers or managers can manually merge the topics withsimilar meanings, filter the noisy words in each topic, select the im-portant topics and assign a label to each important topic. Finally, a setof labeled topics (attributes) and a set of words concerning each labeledtopic (attribute) can be determined. It can be seen that both objectivefactors (online reviews) and subjective factors (manager's preference)are considered in the process of the determination of important attri-butes. On the one hand, if the attributes determined only depends onobjective factors (online reviews), then the analysis results could not beapproved or acceptable by the manager since the manager's subjectiveconcerns or preferences are not involved in the analysis process. On the

other hand, if the attributes determined only depends on subjectivefactors (manager's preference), which are similar to let customers to fillout the questionnaire online, then the limitations of Rating-SIPA andRating-IPCA would occur which have been discussed in Section 4.5.

5.1.4. The estimation of importance of each attributeIn the proposed methodology, the importance of each attribute is

estimated by the ENNM, where the transformed score values of onlinereviews (e.g. data in the form of those shown in Table 4) are used asinput variables and online ratings (i.e., OCSs) corresponding to thereviews are used as output variables. The uses of online reviews andonline ratings are based on a potential assumption that the OCS re-presents the sentiment strengths of the attributes mentioned in thecorresponding online review. It can be regarded that the assumption issatisfied for the websites, in which online reviews, online ratings con-cerning pre-specified attributes and OCSs are provided independently,such as in the websites TripAdvisor (https://www.tripadvisor.com/)and Booking (https://www.booking.com), Agoda (https://www.agoda.com), etc. But, the assumption cannot be satisfied for the websites inwhich OCSs are automatically generated according to the online ratingsconcerning pre-specified attributes, such as Ctrip (http://www.ctrip.com/). If the assumption is satisfied, then the ENNM can be used andthe validity of the obtained results has been verified in Section 4;whereas if the assumption is not satisfied, the validity of the results hasnot been verified. Besides, although descriptions concerning attributesvastly exist in online reviews, such as “not good enough”, “so, so” and“it is good, but … …”, it is unreasonable and difficult to determine theimportance of attribute directly based on these descriptions. That isbecause the descriptions in online reviews usually focus on the per-formance of products/services concerning attributes, which is differentfrom the importance of attribute. Thus, it is unreasonable and difficultto determine the importance of attribute according to the descriptionsconcerning attribute performance.

5.2. Conclusions

Based on the above analysis, the major contributions of this paperare discussed as follows.

First, this paper proposes a methodology for conducting IPAthrough online reviews by the combination of LDA, IOVO-SVM andENNM. As far as we know, it is the first attempt to conduct IPA throughonline reviews. The proposed methodology can obtained effectiveanalysis results with lower cost and shorter time since online reviewsare low cost and easily collected. Thus, the proposed methodology cangive managers or market analysts one more choice for conducting IPA

Fig. 17. The IPCA plot concerning the five attributes.

J.-W. Bi et al. Tourism Management 70 (2019) 460–478

476

or serve as a preparing process of large-scale survey.Second, an ENNM is proposed to estimate the importance of each

attribute through online reviews. The proposed ENNM is a data-drivenself-adaptive model in which no priori assumption about the distribu-tion of online ratings is required. Comparing with the importance ob-tained by single NN, such as ANNs (Tsaur et al., 2002), BPNN (Deng,Chen, et al., 2008) and extended BPNN (Mikulić & Prebežac, 2012),more stable and accurate importance of each attribute can be obtainedby the proposed ENNM.

Third, by the incorporation of online reviews into IPA, the im-portance and performance of each attribute of the products/servicesprovided by competitive companies can be evaluated considering dif-ferent time periods. This makes it possible for conducting DIPA andDIPCA more efficiently, and the DIPA and DIPCA are valuable attemptsfor developing and enriching theories and methods of IPA.

The study also has some limitations, which may serve as avenues forfuture research. First, the main motivation of this study is to extend thedata source of tradition IPA from surveys to online reviews, while thecomplex factors in IPA are not considered, such as asymmetric effects ofproduct/service attributes on overall satisfaction and interaction effectsamong quality attributes. Second, fake reviews are quite prevalentnowadays on major websites like TripAdvisor. To obtain more accuracyanalysis results of IPA, the identification and treatment of fake reviewsshould be paid more attention.

Author contributions

Jian-Wu Bi conceived and designed the study, and took the lead inwriting the manuscript. Jian-Wu Bi and Yang Liu wrote and revised themanuscript. Zhi-Ping Fan gave important suggestions for the writingand revision of the manuscript. Jin Zhang collected and processed thedata used in the study. All authors read and approved the manuscript.

Acknowledgement

This work was partly supported by the National Science Foundationof China (Project Nos. 71771043 and 71871049), Foundation of ChinaScholarship Council (No. 201706080090), the Fundamental ResearchFunds for the Central Universities, China (Project No. N170605001),and the 111 Project (B16009).

References

Abalo, J., Varela, J., & Manzano, V. (2007). Importance values for importance perfor-mance analysis: A formula for spreading out values derived from preference rankings.Journal of Business Research, 60, 115–121.

Albayrak, T. (2015). Importance performance competitor analysis (IPCA): A study ofhospitality companies. International Journal of Hospitality Management, 48, 135–142.

Albayrak, T., & Caber, M. (2013). The symmetric and asymmetric influences of destina-tion attributes on overall visitor satisfaction. Current Issues in Tourism, 16(2),149–166.

Albayrak, T., & Caber, M. (2015). Prioritisation of the hotel attributes according to theirinfluence on satisfaction: A comparison of two techniques. Tourism Management, 46,43–50.

Azzopardi, E., & Nash, R. (2013). A critical evaluation of importance-performance ana-lysis. Tourism Management, 35, 222–233.

Bacon, D. R. (2003). A comparison of approaches to importance-performance analysis.International Journal of Market Research, 45(1), 55–73.

Batra, R., Homer, P. M., & Kahle, L. R. (2001). Values, susceptibility to normative in-fluence, and attribute importance weights: A homological analysis. Journal ofConsumer Psychology, 11(2), 115–128.

Beldona, S., & Cobanoglu, C. (2007). Importance-performance analysis of guest tech-nologies in the lodging industry. Cornell Hotel and Restaurant Administration Quarterly,48(3), 299–312.

Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal ofMachine Learning Research, 3, 993–1022.

Boley, B. B., McGehee, N. G., & Hammett, A. T. (2017). Importance-performance analysis(IPA) of sustainable tourism initiatives: The resident perspective. TourismManagement, 58, 66–77.

Bottomley, P. A., Doyle, J. R., & Green, R. H. (2000). Testing the reliability of weightelicitation methods: Direct rating versus point allocation. Journal of MarketingResearch, 37(4), 508–513.

Caber, M., Albayrak, T., & Loiacono, E. T. (2013). The classification of extranet attributesin terms of their asymmetric influences on overall user satisfaction: An introductionto asymmetric impact-performance analysis. Journal of Travel Research, 52(1),106–116.

Caber, M., Albayrak, T., & Matzler, K. (2012). Classification of the destination attributesin the content of competitiveness (by revised importance-performance analysis).Journal of Vacation Marketing, 18(1), 43–56.

Chen, K. Y. (2014). Improving importance-performance analysis: The role of the zone oftolerance and competitor performance. The case of Taiwan's hot spring hotels.Tourism Management, 40, 260–272.

Chen, J. K., & Chen, I. S. (2012). An Inno-Qual performance system for higher education.Scientometrics, 93(3), 1119–1149.

Chen, K., Kou, G., Shang, J., & Chen, Y. (2015). Visualizing market structure throughonline product reviews: Integrate topic modeling, TOPSIS, and multi-dimensionalscaling approaches. Electronic Commerce Research and Applications, 14(1), 58–74.

Chou, J. S., Kim, C., Kuo, Y. C., & Ou, N. C. (2011). Deploying effective service strategy inthe operations stage of high-speed rail. Transportation Research Part E: Logistics andTransportation Review, 47(4), 507–519.

Chou, J. S., Tserng, H. P., Lin, C., & Yeh, C. P. (2012). Critical factors and risk allocationfor PPP policy: Comparison between HSR and general infrastructure projects.Transport Policy, 22, 36–48.

Culotta, A., & Cutler, J. (2016). Mining brand perceptions from twitter social networks.Marketing Science, 35(3), 343–362.

Deng, W. (2007). Using a revised importance–performance analysis approach: The case ofTaiwanese hot springs tourism. Tourism Management, 28(5), 1274–1284.

Deng, W. J., Chen, W. C., & Pei, W. (2008). Back-propagation neural network basedimportance–performance analysis for determining critical service attributes. ExpertSystems with Applications, 34(2), 1115–1125.

Deng, W. J., Kuo, Y., & Chen, C. (2008). Revised importance-performance analysis: Threefactor theory and benchmarking. Service Industries Journal, 28(1), 37–51.

Deng, W. J., & Pei, W. (2009). Fuzzy neural based importance-performance analysis fordetermining critical service attributes. Expert Systems with Applications, 36(2),3774–3784.

Dewang, R. K., & Singh, A. K. (2018). State-of-art approaches for review spammer de-tection: A survey. Journal of Intelligent Information Systems, 50(2), 231–264.

Eskildsen, J. K., & Kristensen, K. (2006). Enhancing importance-performance analysis.International Journal of Productivity and Performance Management, 55(1), 40–60.

Fang, B., Ye, Q., Kucukusta, D., & Law, R. (2016). Analysis of the perceived value ofonline tourism reviews: Influence of readability and reviewer characteristics. TourismManagement, 52, 498–506.

Farhadloo, M., Patterson, R. A., & Rolland, E. (2016). Modeling customer satisfactionfrom unstructured data using a Bayesian approach. Decision Support Systems, 90,1–11.

Gao, S., Tang, O., Wang, H., & Yin, P. (2018). Identifying competitors through com-parative relation mining of online reviews in the restaurant industry. InternationalJournal of Hospitality Management, 71, 19–32.

Geng, X., & Chu, X. (2012). A new importance–performance analysis approach for cus-tomer satisfaction evaluation supporting PSS design. Expert Systems with Applications,39(1), 1492–1502.

Groves, R. M. (2006). Nonresponse rates and nonresponse bias in household surveys.Public Opinion Quarterly, 646–675.

Guizzardi, A., & Stacchini, A. (2017). Destinations strategic groups via multivariatecompetition-based IPA. Tourism Management, 58, 40–50.

Guo, Y., Barnes, S. J., & Jia, Q. (2017). Mining meaning from online ratings and reviews:Tourist satisfaction analysis using latent dirichlet allocation. Tourism Management, 59,467–483.

Hawes, J. M., & Rao, C. P. (1985). Using importance-performance analysis to develophealth care marketing strategies. Journal of Health Care Marketing, 5(4), 19–25.

Hu, N., Pavlou, P. A., & Zhang, J. (2009). Why do product reviews have a J-shapeddistribution? Communications of the ACM, 52(10), 144–147.

Hu, N., Pavlou, P. A., & Zhang, J. (2017). On self-selection biases in online product re-views. MIS Quarterly, 41(2), 449–471.

Kano, N., Seraku, N., Takahashi, F., & Tsuji, S. (1984). Attractive quality and must-bequality. The Journal of the Japanese Society for Quality Control, 14(2), 39–48.

Lai, I. K. W., & Hitchcock, M. (2015). Importance–performance analysis in tourism: Aframework for researchers. Tourism Management, 48, 242–267.

Lai, I. K. W., & Hitchcock, M. (2016). A comparison of service quality attributes for stand-alone and resort-based luxury hotels in Macau: 3-Dimensional importance-perfor-mance analysis. Tourism Management, 55, 139–159.

Lau, R. Y., Liao, S. Y., Kwok, R. C. W., Xu, K., Xia, Y., & Li, Y. (2011). Text mining andprobabilistic language modeling for online review spam detecting. ACM Transactionson Management Information Systems, 2(4), 1–30.

Levenburg, N. M., & Magal, S. R. (2005). Applying importance-performance analysis toevaluate e-business strategies among small firms. e-service Journal, 3(3), 29–48.

Li, N., Tung, V., & Law, R. (2017). A fuzzy comprehensive evaluation algorithm foranalyzing electronic word-of-mouth. Asia Pacific Journal of Tourism Research, 22(6),592–603.

Liu, Y., Bi, J. W., & Fan, Z. P. (2017a). Ranking products through online reviews: Amethod based on sentiment analysis technique and intuitionistic fuzzy set theory.Information Fusion, 36, 149–161.

Liu, Y., Bi, J. W., & Fan, Z. P. (2017b). A method for multi-class sentiment classificationbased on an improved one-vs-one (OVO) strategy and the support vector machine(SVM) algorithm. Information Sciences, 394, 38–52.

Liu, Y., Jiang, C., & Zhao, H. (2018). Using contextual features and multi-view ensemblelearning in product defect identification from online discussion forums. DecisionSupport Systems, 105, 1–12.

J.-W. Bi et al. Tourism Management 70 (2019) 460–478

477

Martilla, J. A., & James, J. C. (1977). Importance-performance analysis. Journal ofMarketing, 77–79.

McKercher, B. (2018). What is the state of hospitality and tourism research–2018?International Journal of Contemporary Hospitality Management, 30(3), 1234–1244.

Mikulić, J., Krešić, D., Prebežac, D., Miličević, K., & Šerić, M. (2016). Identifying driversof destination attractiveness in a competitive environment: A comparison of ap-proaches. Journal of Destination Marketing & Management, 5(2), 154–163.

Mikulić, J., & Prebežac, D. (2008). Prioritizing improvement of service attributes usingimpact range-performance analysis and impact-asymmetry analysis. Managing ServiceQuality: International Journal, 18(6), 559–576.

Mikulić, J., & Prebežac, D. (2012). Accounting for dynamics in attribute-importance andfor competitor performance to enhance reliability of BPNN-based im-portance–performance analysis. Expert Systems with Applications, 39(5), 5144–5153.

Mittal, V., Ross, W. T., & Baldasare, P. M. (1998). The asymmetric impact of negative andpositive attribute-level performance on overall satisfaction and repurchase inten-tions. Journal of Marketing, 62, 33–47.

Munzel, A. (2016). Assisting consumers in detecting fake reviews: The role of identityinformation disclosure and consensus. Journal of Retailing and Consumer Services, 32,96–108.

Netzer, O., Feldman, R., Goldenberg, J., & Fresko, M. (2012). Mine your own business:Market-structure surveillance through text mining. Marketing Science, 31(3),521–543.

O'Neill, M. A., & Palmer, A. (2004). Importance-performance analysis: A useful tool fordirecting continuous quality improvement in higher education. Quality Assurance inEducation, 12(1), 39–52.

Ott, M., Cardie, C., & Hancock, J. (2012). Estimating the prevalence of deception in onlinereview communities. Proceedings of the 21st international conference on World wide Web(pp. 201–210). .

Poria, S., Cambria, E., & Gelbukh, A. (2016). Aspect extraction for opinion mining with adeep convolutional neural network. Knowledge-based Systems, 108, 42–49.

Porter, M. F. (1980). An algorithm for suffix stripping. Program, 14(3), 130–137.Pournarakis, D. E., Sotiropoulos, D. N., & Giaglis, G. M. (2017). A computational model

for mining consumer perceptions in social media. Decision Support Systems, 93,98–110.

Qian, S., Zhang, T., Xu, C., & Shao, J. (2016). Multi-modal event topic model for socialevent analysis. IEEE Transactions on Multimedia, 18(2), 233–246.

Qi, J., Zhang, Z., Jeon, S., & Zhou, Y. (2016). Mining customer requirements from onlinereviews: A product improvement perspective. Information & Management, 53(8),951–963.

Ramakrishnan, R., & Usha, R. (2016). A new rational IPA and application to cruisetourism. Annals of Tourism Research, 61, 264–267.

Sever, I. (2015). Importance-performance analysis: A valid management tool? TourismManagement, 48, 43–53.

Siering, M., Deokar, A. V., & Janze, C. (2018). Disentangling consumer recommendations:Explaining and predicting airline recommendations based on online reviews. DecisionSupport Systems, 107, 52–63.

Skok, W., Kophamel, A., & Richardson, I. (2001). Diagnosing information systems success:Importance-performance maps in the health club industry. Information Management,38, 409–419.

Slevitch, L., & Oh, H. (2010). Asymmetric relationship between attribute performanceand customer satisfaction: A new perspective. International Journal of HospitalityManagement, 29(4), 559–569.

Surowiecki, J., & Silverman, M. P. (2007). The wisdom of crowds. American Journal ofPhysics, 75(2), 190–192.

Taplin, R. H. (2012a). Competitive importance-performance analysis of an Australianwildlife park. Tourism Management, 33(1), 29–37.

Taplin, R. H. (2012b). The value of self-stated attribute importance to overall satisfaction.Tourism Management, 33(2), 295–304.

Tirunillai, S., & Tellis, G. J. (2014). Mining marketing meaning from online chatter:Strategic brand analysis of big data using latent dirichlet allocation. Journal ofMarketing Research, 51(4), 463–479.

Tonge, J., & Moore, S. A. (2007). Importance-satisfaction analysis for marine-park hin-terlands: A western Australian case study. Tourism Management, 28(3), 768–776.

Tontini, G., & Silveira, A. (2007). Identification of satisfaction attributes using competi-tive analysis of the improvement gap. International Journal of Operations & ProductionManagement, 27(5), 482–500.

Tsaur, S. H., Chiu, Y. C., & Huang, C. H. (2002). Determinants of guest loyalty to inter-national tourist hotels—a neural network approach. Tourism Management, 23(4),397–405.

Van Ittersum, K., Pennings, J. M. E., Wansink, B., & Van Trijp, H. C. M. (2007). Thevalidity of attribute-importance measurement: A review. Journal of Business Research,60(11), 1177–1190.

Van Ryzin, G. G., & Immerwahr, S. (2007). Importance-performance analysis of citizensatisfaction surveys. Public Administration, 85(1), 215–226.

Wang, G., Xie, S., Liu, B., & Yu, P. S. (2012). Identify online store review spammers viasocial review graph. ACM Transactions on Intelligent Systems and Technology, 3(4),1–21.

Xiang, Z., Schwartz, Z., Gerdes, J. H., Jr., & Uysal, M. (2015). What can big data and textanalytics tell us about hotel guest experience and satisfaction? International Journal ofHospitality Management, 44, 120–130.

Xiao, S., Wei, C. P., & Dong, M. (2016). Crowd intelligence: Analyzing online productreviews for preference measurement. Information & Management, 53(2), 169–182.

Ye, Q., Law, R., & Gu, B. (2009). The impact of online user reviews on hotel room sales.International Journal of Hospitality Management, 28(1), 180–182.

Yeo, A. Y. C. (2003). Examining a Singapore bank's competitive superiority using im-portance-performance analysis. Journal of American Academy of Business, 3(12),155–161.

Zhang, Z., Ye, Q., Law, R., & Li, Y. (2010). The impact of e-word-of-mouth on the onlinepopularity of restaurants: A comparison of consumer reviews and editor reviews.International Journal of Hospitality Management, 29(4), 694–700.

Zhang, Z., Zhang, Z., & Yang, Y. (2016). The power of expert identity: How website-recognized expert reviews influence travelers' online rating behavior. TourismManagement, 55, 15–24.

Jian-Wu Bi is a doctoral student in the School of BusinessAdministration at Northeastern University. He is currentlyalso a visiting student in the School of Computer Scienceand Engineering at Nanyang Technological Universityfunded by China Scholarship Council. His research interestsare information technology, customer satisfaction analysisand Internet applications. He has published papers injournals such as Information Sciences, Information Fusion,Expert Systems with Applications and International Journalof Information Technology & Decision Making.

Yang Liu received the B.E. degree and the M.S. degree inmanagement science and engineering from DalianUniversity of technology, Dalian, China, in 2001 and 2004,and the Ph.D. degree in management science and en-gineering from Northeastern University (NEU), Shenyang,China, in 2010. He is currently a professor in theDepartment of Management Science and Engineering,School of Business Administration, NEU. He is the author orcoauthor of over 20 refereed articles published in interna-tional journals including the European Journal OperationalResearch, the Computers & Operations Research, the IEEETransactions on Systems, Men and Cybernetics, theInformation Sciences, the Information Fusion, and theExpert Systems with Applications. He current research in-

terests include decision analysis and operations research.

Zhi-Ping Fan received the Ph.D. degree in control theoryand applications from Northeastern University (NEU),Shenyang, China, in 1996. He is currently a Professor in theDepartment of Management Science and Engineering,School of Business Administration, NEU. He was ResearchFellow at City University of Hong Kong, Kowloon, HongKong, in 2001, 2003, 2004, and 2005, respectively. He isthe author or coauthor of over 80 refereed articles pub-lished in international journals including the EuropeanJournal Operational Research, the Computers & OperationsResearch, the IEEE Transactions on Systems, Men andCybernetics, the IEEE Transactions on EngineeringManagement and the Information Sciences. He current re-

search interests include decision analysis, operations research, and knowledge manage-ment.

Jin Zhang is a doctoral student in the School of BusinessAdministration at Northeastern University. Her researchinterests are sentiment analysis, information technologyand Internet applications.

J.-W. Bi et al. Tourism Management 70 (2019) 460–478

478