Evaluation of hotel brand competitiveness based on hotel features ratings

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Evaluation of hotel brand competitiveness based on hotel features ratings Haiyang Xiaa,b, Huy Quan Vuc, Rob Lawd, Gang Lie,f,

⁎

a

School of Computer Science, Xi’an ShiYou University, China Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, China Department of Information Systems and Business Analytics, Deakin University, Australia d School of Hotel & Tourism Management, The Hong Kong Polytechnic University, Hong Kong SAR, China e School of Information Technology, Deakin University, Geelong, VIC 3216, Australia f Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, 830011, China b c

A R T I C LE I N FO

A B S T R A C T

Keywords: Hotel rating Brand competitiveness Data mining Online review Probability distribution Earth mover’s distance

Understanding the competitiveness of hotel brands is important for hotel managers to shape their brands and initiate effective marketing strategies and business developments. However, evaluating hotel brand competitiveness is challenging due to the complexity of information involved. A hotel brand often comprises many hotels with different performances. Hotel brands are also evaluated against multiple hotel features, thereby making the application of traditional evaluation techniques impractical. This paper introduces a novel technique for automatically evaluating the competitiveness of hotel brands based on probability distribution and earth mover’s distance. We demonstrate the effectiveness of our proposed method by conducting a case study that involves major hotel brands in Hong Kong. The proposed method can be applied in various contexts and can help researchers and managers evaluate the competitiveness of hotels as well as other branded products in the hospitality and tourism sectors.

1. Introduction The tourism industry has witnessed great advancements and gradually become one of the main driving forces of the world economy over the past decades (Law, 2011). The development of this industry has also induced an intense level of competition, particularly amongst hotel businesses, given that accommodation is central in shaping tourism experiences (Tsai et al., 2009). Choosing which hotel to stay in is amongst the key priorities of tourists when planning their trips (Li et al., 2013). Therefore, hotel managers must invest more in their marketing activities to attract and retain customers and maintain their place in the industry (Cai and Hobson, 2004). Branding has become an important part of hotel marketing strategies (O’Neill and Mattila, 2010) owing to the common belief that brand provides added value to guests and hotels and promotes brand loyalty (O’Neill and Xiao, 2006). Branded hotels generally outperform nonbranded ones (Forgacs, 2003) possibly due to the inclination of people to consume the brands they are familiar (Vu et al., 2018). One primary requirement for hotel managers in developing effective marketing strategies is to identify the competitive advantages of their brand, which they can use to distinguish themselves from their competitors (Xia et al., 2019). However, identifying the competitiveness of hotel

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brands is challenging. A hotel brand can comprise multiple hotels, with each hotel showing a unique performance level. The evaluation of hotel brands should also consider multiple hotel features, thereby making the application of traditional techniques impractical. The majority of the existing literature on hotel competitiveness has mainly focused on identifying the influencing factors or developing methods for measuring the competitiveness of individual hotels (Kim and Kim, 2005; Tung et al., 2009; Hsieh and Lin, 2010). Few studies have attempted to identify or evaluate the competitiveness of hotel brands explicitly (Khan and Rahman, 2017). The recent development of Internet technologies has allowed travellers to share their experiences on various online platforms in the form of hotel reviews and feature ratings. These feature ratings are deemed authentic and are therefore utilised by hotel managers as selling points in their marketing materials to shape the first impression of their customers about their hotels (Li et al., 2016). Hotel managers can use the competitive advantages or disadvantages of hotels, which are identified from online feature ratings, to promote their hotels or to address their shortcomings (Xia et al., 2019). Unfortunately, online feature ratings have not been utilised effectively in promoting and developing hotel brands because the features ratings available on review platforms are often for individual hotels rather than for hotel brands.

Corresponding author at: School of Information Technology, Deakin University, Geelong, VIC 3216, Australia. E-mail addresses: [email protected] (H. Xia), [email protected] (H.Q. Vu), [email protected] (R. Law), [email protected] (G. Li).

https://doi.org/10.1016/j.ijhm.2019.102366 Received 25 March 2019; Received in revised form 21 July 2019; Accepted 21 August 2019 0278-4319/ © 2019 Elsevier Ltd. All rights reserved.

Please cite this article as: Haiyang Xia, et al., International Journal of Hospitality Management, https://doi.org/10.1016/j.ijhm.2019.102366

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competitiveness of specific hotels (Xia et al., 2019). As the level of competition in the hotel industry intensifies, hotel managers are urged to find ways to enhance the uniqueness of their products and services (Choi and Chu, 2001). Branding strategies have emerged as a new trend in the global hotel industry (Kayaman and Arasli, 2007). A strong brand can enhance the market value (O’Neill and Xiao, 2006) of a hotel as well as its average price, occupancy, revenue, reinvestment rate and financial performance (Forgacs, 2003). Strong hotel brands reduce the perceived risks and search costs for travellers, thereby guaranteeing high-quality service (Kayaman and Arasli, 2007). During an economic recession, branded hotels tend to achieve a higher net operating income compared with non-branded ones (O’Neill and Carlbäck, 2011). Although branding has become popular in the hotel industry, research on hotel brand competitiveness remains in its infancy (Khan and Rahman, 2017). Moreover, a method that can effectively support hotel managers in evaluating the competitiveness of their hotel brands has not been reported in the literature.

An effective method that allows hotel managers to assess the competitiveness of their brands based on hotel feature ratings has yet to be developed. To address these shortcomings, we introduce a novel method for evaluating the competitiveness of hotel brands based on hotel feature ratings. We adopt and extend the definition of hotel competitiveness evaluation introduced by Xia et al. (2019) as the identification of unique aspects (a feature or a set of features) that largely distinguish a hotel brand from its competitors. A unique aspect can represent a distinct advantage or disadvantage. Hotel brand managers can deliver positive messages to their potential customers based on the identified distinct advantages (Rianthong et al., 2016) and dedicate their resources to improving the features identified as critical disadvantages (Hargreaves, 2015). To identify the distinct aspects of hotel brands, we introduce an automatic evaluation technique based on probability distribution and earth mover’s distance (EMD) (Rubner et al., 2000) that can account for multiple features of numerous hotels simultaneously. We demonstrate the effectiveness of this technique by conducting case studies that involve major hotels brands in Hong Kong, which is a major tourist destination in Southeast Asia. Identifying the competitiveness of hotel brands can help hotel managers, especially those based in Hong Kong, in designing effective marketing strategies and maintaining the competitiveness of their hotel brands in the industry. The proposed method can also be applied in various contexts to evaluate the brand competitiveness of different products and services in the hospitality and tourism industries. In addition, the method can help researchers and business managers in utilising online ratings to develop and promote their businesses. The remainder of this paper is organised as follows. Section 2 reviews the related works on hotel competitiveness and online hotel ratings and presents a critical analysis of the gaps in the literature. Section 3 describes in detail our proposed method for evaluating hotel brand competitiveness. Section 4 presents a case study of evaluating the competitiveness of hotel brands in Hong Kong. Section 5 concludes the paper and presents future research directions.

2.2. Online ratings and hotel competitiveness The availability of online social networks has greatly reduced the cost of communication amongst travellers (Boyd and Ellison, 2007). An increasing number of people are willing to share their opinions on a service or product that they have experienced by sharing ratings or text reviews on social networks (Wasserman and Faust, 1994). For tourists, these online reviews have become the main source of information that can greatly affect their subsequent purchase intentions (Liu, 2011). To reduce their purchase uncertainty, consumers tend to refer to users who have previously experienced a service or product, especially experiential goods such as services in the catering and hospitality industry (Hoyer and Brown, 1990). The positive reviews or high ratings for a hotel can positively affect the attitudes of consumers and increase their booking intentions (Vermeulen and Seegers, 2009). By contrast, negative reviews or low ratings can lead to a low purchasing rate and may damage the reputation of service providers (So et al., 2016). Online reviews and ratings affect the purchase decision of approximately 50% of global travellers (Ye et al., 2009). Moreover, travellers are willing to recommend hotels with high ratings to their friends (Mandabach et al., 2014). As such, hotel managers often use the online ratings of hotels, especially those with high scores, as selling points to attract potential customers (Li et al., 2016). Given the increasing popularity and importance of online reviews, researchers have used such data to study the expectations and preferences of travellers and help hotel managers develop strategies for improving the competitiveness of their hotels. For example, Yang and Fang (2004) examined how online reviews can provide insights into the satisfaction level of consumers. Liu et al. (2013) used online reviews to investigate the hotel selection preferences of travellers and to propose some suggestions for personalised marketing strategies. Li et al. (2015) studied the hotel feature preferences of different traveller groups based on hotel reviews to help managers accommodate the diverse needs of potential customers. Mariani and Visani (2019) incorporated online reviews into the measure of hotel efficiency to evaluate their competitive performance. Online feature ratings are also used in to assess the competitiveness of hotels (Xia et al., 2019) owing to their influence on the brand image (Jiang et al., 2014) or offline popularity of hotels (Xie et al., 2011). However, competitiveness evaluation in prior studies is often limited to individual hotels (Xia et al., 2019). The potential benefits of online ratings in generating insights into the competitiveness of hotel brands have yet to be fully explored.

2. Literature review 2.1. Hotel competitiveness studies According to Barney (1991), the competitive advantage of a firm is closely related to the strategy of how the firm competes. Low cost and differentiation are two basic strategic orientations that a firm can adopt (Porter, 1985). Low cost-oriented firms focus on improving operation efficiency that allows them to offer products and services with competitive prices, whereas differentiation-oriented firms hinge on valuechain activities that aim to increase perceived value, uniqueness or quality (Fainshmidt et al., 2019). Majority of early studies on competitiveness at the industry level have focused on manufacturing-related sectors (Pires, 1995; Lau, 2002; Molina-Morales, 2005; Koksal and Ozgul, 2010). Researchers have begun to examine competitiveness in the hospitality and tourism industries (Enright and Newton, 2004) owing to their continuous growth and important roles in the economic development of nations. For instance, efficient management was proven to improve hotel productivity and reduce cost (Barros, 2005), which was identified as a key factor in maintaining hotel competitiveness (Sigala, 2004; Yang and Lu, 2006; Reynolds and Thompson, 2007). Quality service was identified as a factor that improves perceived value and influences hotel competitiveness (Campos-Soria et al., 2005; Reynolds and Thompson, 2007) because travellers tend to be loyal to hotels that satisfy their needs (Akbaba, 2006). Some studies have evaluated hotel performance as indicator of hotel competitiveness (Yeung and Lau, 2005; Wang et al., 2006; Tung et al., 2009; Hsieh and Lin, 2010). Prior studies have focused mainly on factors that influence hotel competitiveness, but very few studies have explicitly compared and evaluated the

2.3. Problem definition Hotel managers are actively seeking new ways to differentiate their brands from those of their competitors due to the increasing level of competition in the hotel industry (Kim, 2008; Khan and Rahman, 2

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platforms; 2) feature rating representation, where the collected feature ratings of individual hotels are aggregated into a suitable format that represents the feature quality of a hotel brand; 3) brand distinctiveness evaluation, where the distinctiveness of a targeted brand is computed against that of its competitors based on different combinations of the aggregated hotel brand features; and 4) competitiveness analysis, where the computed distinctiveness is analysed to identify the hotel features that best distinguish a hotel brand from its competitors. These steps are discussed in detail in the subsequent sections.

2017). The traditional approaches for assessing the competitiveness of hotel brands often rely on financial reports or questionnaires, whose data usually require a relatively long time to collect and aggregate for analysis and reporting (Cai and Hobson, 2004; Kayaman and Arasli, 2007). Online hotel ratings have demonstrated their importance in influencing customer-purchasing behaviour (Vermeulen and Seegers, 2009; So et al., 2016) and reflecting hotel competitiveness (Xia et al., 2019). However, very few studies have utilised online hotel ratings to assess the competitiveness of hotel brands. One major barrier for these studies is that the ratings available on online review platforms are mostly for individual hotels and not for hotel brands. This problem may be considered trivial because a hotel brand rating may be estimated by computing the average rating of all individual hotels from the same brand. However, the performance of hotels under the same brand may greatly vary, thus explaining variances in their online ratings. The average rating of a hotel brand can also be affected by outlier ratings (Jacquier et al., 2003) that do not fairly reflect the competitiveness of this brand. For example, assume that we have two hotel brands, namely, A, which comprises six hotels, and B, which comprises seven hotels. The ratings of these two hotel brands for room comfort are {7, 7, 7, 8, 8, 8} and {2, 8, 8, 8, 8, 9, 9}, respectively. The hotels in brand B usually receive high ratings for room comfort (8 and 9), whereas those in brand A usually receive moderate ratings (7 and 8). However, one hotel in brand B may be a new entrant in the hotel market and may have not performed well at the beginning, thus receiving a low rating of 2. If we compute for the average rating of all hotels under this brand, then the overall rating for hotel brand A is 7.5, which is higher than that of hotel brand B (7.43). This approach is not fair for hotel brand B because customers intuitively tend to receive the best room comfort when staying at the hotels of brand B. In addition, the ratings of multiple hotel features must be considered when evaluating hotel brand competitiveness. Consider the case in which most hotels under brand A receive high ratings for service but low ratings for cleanliness, most hotels under brand B receive low ratings for service and high ratings for cleanliness and most hotels under brand C receive moderate ratings for cleanliness and service. Clearly, hotel brand A shows competitiveness for service, whereas hotel brand B shows competitiveness for cleanliness. Hotel brand C can also be competitive given its ability to attract customers who seek hotels with moderate ratings for service and cleanliness. Evaluating the competitiveness of hotel brands is a complex and challenging task that cannot be addressed by traditional methods. Moreover, a systematic method that can help hotel managers generate insights into the competitiveness of hotel brands is lacking, whereas research on hotel brand competitiveness remains in its infancy (Khan and Rahman, 2017). The present study aims to address these shortcomings by introducing a novel method that utilises online hotel features ratings to evaluate the competitiveness of hotel brands. The method accounts for ratings of multiple hotels by representing feature ratings as a probability distribution (Cinlar, 2011), by which hotel brands can be evaluated against each other based on EMD. We selected EMD because it is a metric specifically designed to measure distances between probability distributions (Levina and Bickel, 2001). EMD has been used widely in various applications in computer science (Xu et al., 2015; Wang et al., 2016) and medical science (Ye et al., 2010; Hassan and Hassan Bhuiyan, 2016; Yuan et al., 2018). EMD was recently applied in tourism management for studying movement patterns of tourists (Zheng et al., 2019). The next section examines this method in detail and presents demonstrative examples of its application.

3.1. Hotel rating extraction This study utilises the hotel ratings available on various online review platforms as its data sources. Online reviews are a type of big data, which have received increasing attention from hospitality and tourism researchers for various business applications (Mariani et al., 2018). Sample of popular review platforms include Booking.com, TripAdvisor, MakeMyTrip and Airbnb (O’Connor, 2010; Mellinas et al., 2015; Liu et al., 2013). These platforms allow users to share their experiences in the form of ratings on a range of features, such as cleanliness, comfort and location. These ratings can directly reflect the satisfaction of travellers toward a hotel. If a hotel receives low ratings on some or all features, then this hotel is deemed to have low quality and thereby appear unattractive to future customers. We utilise feature ratings as an indicator of the quality or attractiveness of hotels to evaluate their competitiveness. Although hotel ratings are available to the public, they are not directly downloadable because most review platforms do not provide such functionality. We use a web crawler with a third-party application programming interface to collect these ratings. BaZhuaYu (www. bazhuayu.com), Webmagic (webmagic.io), Shenjian (www.shenjian.io), Heritrix (github.com/internetarchive/heritrix3) and Beautiful Soup (https://github.com/wention/BeautifulSoup4) are examples of such applications. We used Beautiful Soup to implement a web crawler based on Python. The program can automatically navigate the webpages of individual hotels, parse the source codes of websites and identify data of interest, such as hotel information and ratings, for extraction. The ratings on these platforms are for the features of individual hotels rather than for hotel brands. These ratings must be transformed into suitable representations that can capture the quality of hotel brands, as discussed further in the following section. 3.2. Feature rating representation This stage aggregates the feature ratings of hotels into a new representation that best reflects the quality of their hotel brand. Let {a1, a2 , …an} denote a set of hotels belonging to brand A. Each of these hotels is rated by consumers on a set of d features F= {f1 , f2 , …fd } , with each feature taking a value x ∈ [Rmin , Rmax ], where Rmin denotes the minimum rating (usually 1) and Rmax denotes the maximum rating in each platform (i.e. 5 for ratings on TripAdvisor or 10 for ratings on Booking.com). The ratings of feature fi for the hotels in brand A can be denoted as {f ia1 , f ia2 …,f ian } . Afterwards, we represent brand feature rating as the probability distribution of all member hotels over the rating scale [Rmin , Rmax ]. For simplicity, we apply discrete probability distribution even though continuous probability can also be used (Cinlar, 2011). We count the number of hotels with respect to their corresponding rating values before performing normalization. Suppose, there are R discrete values between Rmin and Rmax The discrete probability distribution vector of f f f quality rating for feature fi is denoted as P fi = {p1 i , p2 i …,pRi } , which

3. Methodology

R

f

satisfies the constraints ∑ j = 1 pj i = 1. The hotel feature ratings from review platforms are usually aggregated from multiple customer reviews. However, the values of these ratings may be expressed in decimals and should be rounded off to the nearest integer before computing

This section presents our proposed method for evaluating the competitiveness of hotel brands on the basis of online ratings. Our method can be divided into four major stages: 1) hotel rating extraction, where the feature ratings of hotels are extracted from online review 3

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be evaluated based on the EMD between P and Q . This approach can be used to effectively measure the distance between probability distributions (Rubner et al., 2000).

Table 1 Sample ratings of hotel features. 　

Original Ratings

　

Cleanliness

Comfort

Location

Cleanliness

Comfort

Location

4.3 2 1.3 2.8

4.1 3.4 2.2 3

5 4.7 3.7 4.8

4 2 1 3

4 3 2 3

5 5 4 5

Hotel Hotel Hotel Hotel

Rounded Ratings

R

1 2 3 4

EMD (P , Q) =

R

∑u = 1 ∑v = 1 du, v cu, v R

R

∑u = 1 ∑v = 1 cu, v

(3.1)

D = [du, v] is the distance between elements Pu ∈ P and Q v ∈ Q that can be measured by Euclidian distance (Anton, 1994), whereas cu, v is the flow between pu and qv that can be measured by the following optimization problem:

for the brand feature representation.

R

R

min ∑

Example 1. Assume that hotel brand A has four member hotels that are rated by travellers on three features (cleanliness, comfort and location). Table 1 provides the original and rounded ratings of these hotels. The ratings are rounded because we adopt discrete probability distribution, which describes the occurrence of each value according to a list of nonnegative integers. The number of hotels is then counted for each corresponding rating value in the range of [1, 5] and stored in a vector, such as [1, 1, 1, 1, 0], for cleanliness. The values are then normalised as [0.25, 0.25, 0.25, 0.25, 0]. We visualise the probability distributions of these feature ratings in Fig. 1, which shows that the hotels in this brand tend to receive high and moderate ratings for the location and comfort features, respectively. Therefore, feature-rating representation can be used to evaluate brand distinctiveness.

∑ d u, v c u, v

u=1 v=1

s. t .

⎧ cu, v ≥ 0, ⎪ R ⎪ ∑ cu, v ≤ pu , ⎪V =1 ⎪ R

1 ≤ u, v ≤ R 1≤u≤R

⎨ ∑ cu, v ≤ qv , ⎪V =1 ⎪ R R R ⎧ ⎪ ⎪ ∑ ∑ cu, v = min ⎨ ∑ pu , ⎩u=1 ⎩u=1 v=1

1≤v≤R R

∑ qv ⎫⎬. v=1

⎭

(3.2)

Therefore, EMD can be referred to as the minimum cost of turning one distribution to another (Rubner et al., 2000), where the probability values are regarded as the weight of the distribution that needs to be moved. The optimal cu, v is used to compute EMD (P , Q) following Eq. (3.1). Assume that hotel brand A has z number of competing brands, denoted by RVS, whose probability distribution of ratings on feature fi is f f f {Q1 i , Q2 i , …Qz i} . The distinctiveness of brand A against its competitors with respect to feature fi is computed as

3.3. Brand distinctiveness evaluation Brand distinctiveness evaluation can be treated as a problem of outlying feature set characterization, which stems from a brand of data mining called outlier detection (Aggarwal, 2017). Different from traditional outlier detection, this field focuses on providing explicit explanations for the targeted object (Wang et al., 2018). This line of research is also known as outlying subspace detection (Zhang et al., 2004) or outlying aspect mining (Vinh et al., 2016). Amongst the methods proposed in this area, score and search-based approaches (Duan et al., 2015) are suitable candidates for addressing the problem of evaluating hotel brand distinctiveness, in which a scoring function is used to evaluate outlying degree or distinctiveness of query object for each candidate feature set. However, existing techniques are not applicable to measuring the distinctiveness at the group level for hotel brands with multiple hotels. Thus, we propose a new technique with a new scoring function for measuring the distinctiveness based on a probability distribution of hotel feature ratings. Details of the technique are outlined below. Let P = {p1 , p2 , …pR } and Q = {q1, q2 , …qR} denote the probability distribution of feature fi for hotel brands A and B, respectively. We omit the feature index fi from the above notations for easy reading. The distinctiveness between the hotel brands with respect to feature fi can

z

Distinct (A, RVS ) fi =

∑ EMD (P fi, Qbfi) b=1

(3.3)

Example 2. Suppose that we have three hotel brands whose probability distribution on a feature is shown in Table 2. The computed distance between brands A and B is EMD (PA, PB ) = 0.1 because the only difference between PA and PB is at positions p1 and p2 . We only need to move a weight of 0.1 from position p1 to position p2 to turn PA to PB . Consider the pair A and C, whose distance is EMD (PA, PC ) = 0.2 . We need to move the weight 0.1 from p1 to p3 (two steps) instead of p2 (one step), thereby increasing the final distance. The distinctiveness between brand A and its competitors can then be expressed as Distinct (A, RVS ) = EMD (PA, PB ) + EMD (PA, PC ) = 0.3. The above distinctiveness computation is only applicable for feature sets that contain a single feature. If the distinctiveness is evaluated with respect to two or more features at the same time, then the rating of the

Fig. 1. Probability distributions of hotel feature ratings. 4

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Table 2 Sample probability distribution of feature ratings.

Brand A Brand B Brand C

Table 4 Competitive scores of hotel brands.

p1

p2

p3

p4

p5

Feature Set

A vs. others

B vs. others

C vs. others

0.1 0 0

0.2 0.3 0.2

0.5 0.5 0.6

0.2 0.2 0.2

0 0 0

{ f1 }

5.22

5.10

3.48

{ f2 }

4.70

4.42

3.08

{ f3 }

0.00

0.00

0.00

{ f1 ,f2 }

3.54

3.67

4.56

feature sets is represented as the joint probability distribution of individual features. Suppose that S is a feature set with two or more features (S ∈ F , |S| ≤ d ) . Therefore, the joint probability distribution is

{ f1 ,f3 }

3.40

3.32

3.40

{ f2 ,f3 }

2.92

3.00

3.00

{ f1 ,f2, f3 }

0.11

0.70

4.64

R|S|

P S = {p1S , p2S , …, p S|S| } , ∑ j = 1 pjS = 1. For example, if the maximum rating R is Rmax = 5 and if the distinctiveness is evaluated with respect to two features |S| = 2 , then the length of the probability distribution vector is R|S| = 25. The distinctiveness values for two features can be computed the same way as computing the distinctiveness values for one feature by using Eqs. (3.1) and (3.3). Although the probability distributions can be computed for feature sets with various sizes |S| ≤ d , the feature sets with a small number of features are recommended in cases when only few hotel brands are being evaluated. Performing a comparison in high dimensions with only few samples may be insufficient because of sparsity in the data, whereas interpolating and analysing low-dimensional feature sets can easily determine the competitiveness of a hotel brand (Xia et al., 2019).

competitiveness value computed based on f3 is equal to 0 because all brands have the same distribution for feature f3 . Brands A and B are competitive on feature f1, whereas brand C is competitive when all three features are combined. We then examine the original rating to make sense of the competitiveness. Brand A has the competitive advantage of being the only brand with many top ratings on f1, brand B has the competitive disadvantage of being the only brand with many low ratings on f1, and brand C has the competitive advantage of being the only brand with many moderate ratings on all three features. In the next section, we further demonstrate the practical application of the proposed method by conducting a case study with real hotel ratings.

4. Case study 3.4. Competitiveness analysis This section presents a case study to demonstrate the effectiveness of the proposed method in identifying the competitiveness of a hotel brand as reflected in hotel ratings. Data collection is initially discussed, followed by group feature extraction, identification and interpretation of hotel brand competitiveness. The competitiveness advantages and disadvantages of the selected hotel brand are then presented along with practical implications.

This stage aims to identify aspects (feature sets) that best distinguish a hotel brand from its competitors. The distinctiveness values of all feature sets S (S ∈ F , |S| ≤ d ) are computed. These values tend to increase along with |S| by following the defined computations because considering additional hotel features will increase the distinctiveness of a hotel brand from its competitors. However, the feature sets (including a mixture of low and high features), whose individual features show extreme variances in their ratings, are not desired when analysing brand competitiveness. Hotel managers are mostly interested in the feature sets with similar rating distributions, such as mostly high (to identify competitive advantages) or low ratings (to identify competitive disadvantages). Therefore, we measure the differences between the individual feature ratings of target hotel brands as follows: |S|

Diff (A) S =

4.1. Data collection We selected one of the most popular online hotel booking and review platforms, Booking.com, as the data source for our case study. The review ratings on this platform are realisable indicators of customer satisfaction (Mellinas et al., 2016; Mariani and Borghi, 2018; Mariani et al., 2019a,b) and are therefore suitable for analysing the competitiveness of hotels. The hotels listed on Booking.com are rated by travellers on seven features, namely, cleanliness, comfort, facilities, location, staff, value and WiFi. The rating scale ranges from 1 to 10. A low rating reflects the unfavourable opinion of a traveller toward a specific hotel feature, whereas a high rating reflects the opposite. The website provides an overall rating for each individual hotel feature by aggregating the ratings from traveller’s reviews. These seven features are standard hotel features on many hotel review platforms, which are frequently used in prior studies on hotel feature analysis (Li et al., 2013; Xia et al., 2019). For our case study, we focused on hotel brands in Hong Kong, which is a major tourism destination in Southeast Asia. We used a web crawler to extract the ratings of the hotels that are members of major hotel brands in Hong Kong. We collected the overall ratings for the seven features of each hotel on Booking.com. The hotels without ratings on any of these features were excluded from the data collection. We collected ratings on 70 hotels from 10 major brands, and the relevant statistics are shown in Table 5. Table 6 lists the average and standard deviation of the feature ratings for each hotel brand. These values are presented mainly to describe the rating dataset. The average rating values alone cannot obtain the variations in the ratings of member hotels and are therefore not suitable for reflecting the competitiveness of hotel brands. Given that we attempted to analyse the competitiveness of hotels based on their features, we excluded the overall ratings of hotels from our study. Moreover, the ratings on

|S|

∑ ∑ EMD (P fi, P f j), (i ≠ j) (3.4)

i=1 j=2

The final competitiveness score of a feature set is then defined as

COMP (A) = Distinct (A, RV S ) S − Diff (A) S

(3.5)

Example 3. Suppose that we have three hotel brands A, B and C, with each brand comprising five hotels. These hotels are rated with respect to three features, as shown in Table 3. We compute the competitiveness of each brand versus that of its competitors with respect to different feature sets (Table 4). The underlined values indicate the feature sets that receive the highest competitiveness values for each brand. The Table 3 Example feature ratings of hotel brands. Brand A

Hotel Hotel Hotel Hotel Hotel

1 2 3 4 5

Brand B

Brand C

f1

f2

f3

f1

f2

f3

f1

f2

f3

5 5 4 5 5

1 2 1 1 2

2 4 3 3 3

1 1 2 2 1

4 4 5 4 5

4 2 3 3 3

2 3 3 4 5

4 3 3 3 2

3 3 4 2 3

5

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feature set best distinguishes a targeted hotel from its competitors. For instance, the Butterfly Boutique Hotels brand (B1) is most distinctive from the other brands when the features staff and value are both considered. This feature set corresponds to index 26 in Fig. 2 and has a competitiveness value of 43.15, which is the highest amongst all the other feature sets of B1. We also selected individual features from the seven one-feature sets that receive the highest scores given that hotel managers are also interested in the individual features that can enhance the distinctiveness of their brands from their competitors. For example, staff is the most distinctive feature of the Butterfly Boutique Hotels brand (B1), whereas WiFi is the most distinctive feature of the Harbour Plaza Hotel Resorts brand (B2). Notably, the most distinctive features of some hotel brands are not included in their best feature sets. For example, the most distinctive feature of Holiday Inn Express (B3) is WiFi, but its best feature set only comprises cleanliness and facilities. This observation indicates that either cleanliness or facilities is not as distinctive as WiFi, but the combination of these two factors is highly useful in distinguishing B3 from its competitors. Our proposed method supports the identification of the most distinctive feature sets. Hotel managers must examine the feature ratings further to determine if a particular competitive feature is an advantage or a disadvantage.

Table 5 Hotel brands. Brand ID

Brand Name

Number of Hotels

B1 B2 B3 B4 B5 B6 B7 B8 B9 B10

Butterfly Boutique Hotels Harbour Plaza Hotel Resorts Holiday Inn Express Hong Kong Hostel Group Langham Hospitality Group L’Hotel Group Marco Polo Hotels Regal Hotels International Limited Sino Hotels Tang’s Living

5 10 4 12 4 5 4 9 6 11

Booking.com are updated from time to time. Therefore, our findings only reflect the competitiveness of hotel brands at the time of the data collection. Such competitiveness may change in the future when many travellers post their reviews and ratings. Our case study aims to demonstrate the application of the proposed method in analysing the competitiveness of hotel brands.

4.2. Competitive feature set identification 4.3. Hotel brand competitiveness analysis The ratings of hotel features were initially rounded to the nearest integer and were then transformed into feature rating representations for hotel brands by using the method described in the methodology section. For each evaluation, we selected one targeted hotel brand and treated the other nine hotel brands as its competitors. We computed the competitive scores of the targeted hotel brand with respect to feature sets of various sizes. Given that our case study involves a small number of hotel brands, we only evaluated the competitiveness of hotel brands for feature sets with up to three features to avoid the data sparsity problem with higher dimensions. From the seven hotel features, we generated 63 feature sets that comprised 7 single-feature sets, 21 twofeature sets and 35 three-feature sets. We computed the competitive scores of these feature sets with respect to the targeted hotel brands and then plotted these scores against the indices, as shown in Fig. 2. All hotel brands obtain a high competitive score for the feature set that contains two features than for the feature set that only contains one feature. In other words, combining features may increase the distinctiveness of a hotel brand from its rivals. However, the combination of these features may not always lead to high competitiveness. For example, the feature set index 23, which corresponds to the feature set containing facilities and staff, has a low competitive score. The combination of facilities and staff does not improve the distinctiveness of hotel brands. On the contrary, the feature set with three features receives a lower score compared with the feature set with two features, suggesting that feature sets with up to two features can be used in our case study to analyse the competitiveness of hotel brands. We then selected a feature set with the highest digestive scores amongst all feature sets of a targeted hotel, as shown in Table 7. This

We then examined the competitiveness of hotel brands with respect to their most distinctive individual features. We visualised the probability distribution of these features by using a bubble plot, as shown in Fig. 3. The vertical axis represents the hotel brands, whereas the horizontal axis represents the feature rating. The size of the bubbles indicates the probability value, with high values corresponding to large circles. The probability values of the targeted hotel are highlighted by a light colour, whereas those of the other hotels are highlighted with a dark colour. For example, for the most distinctive feature of B1 (staff), we plotted the probability distribution of staff for all hotel brands and then highlighted the probability value of the targeted hotel in B1. In the bubble plot, the blue and red circles indicate the competitive advantages and disadvantages of hotel brands, respectively. Our findings are summarised below. Fig. 3a shows that all hotels under B1 received the top rating of 9 for staff. Although some other brands (such as B2 and B3) have also received a rating of 9 for this feature, these brands only account for a small proportion, as indicated by the small circles in the bubble plot. The majority of the hotels in these brands have received a rating of 8 or below for staff. In other words, staff is the competitive advantage of B1. Fig. 3b shows the probability distribution on the location ratings of the hotel brands. The majority of the member hotels of brands B1 to B9 received a rating 8 or 9 for location. The members of the targeted hotel brand Tang’s Living (B10) are located in different locations. Although some of its member hotels received a high rating for location, a large proportion of these hotels have received low ratings of 6 and 7. Therefore, location can be interpreted as a competitive disadvantage of

Table 6 Feature Rating Statistics. Brand ID

Cleanliness

Comfort

B1 B2 B3 B4 B5 B6 B7 B8 B9 B10

8.64 7.64 8.25 7.18 8.68 8.14 8.73 7.89 8.60 8.06

8.36 7.44 8.05 6.85 8.58 8.12 8.65 7.69 8.48 7.59

± ± ± ± ± ± ± ± ± ±

0.35 0.78 0.45 0.54 0.67 0.41 0.54 0.38 0.58 0.56

± ± ± ± ± ± ± ± ± ±

0.40 0.90 0.38 0.39 0.74 0.31 0.61 0.20 0.65 0.62

Facilities

Location

Staff

8.08 7.33 7.75 6.94 8.43 7.84 8.45 7.50 8.28 7.58

8.70 7.36 8.33 8.72 8.80 7.78 9.10 7.96 8.35 7.60

8.94 7.67 8.13 7.35 8.78 8.08 8.65 7.72 8.60 8.01

± ± ± ± ± ± ± ± ± ±

0.23 0.89 0.38 0.47 0.75 0.36 0.53 0.23 0.52 0.55

6

± ± ± ± ± ± ± ± ± ±

0.54 1.04 0.56 0.23 0.52 0.67 0.18 0.84 0.59 1.04

Value ± ± ± ± ± ± ± ± ± ±

0.17 0.74 0.46 0.41 0.51 0.33 0.45 0.25 0.49 0.48

7.90 7.13 7.60 7.48 7.85 7.62 7.65 7.23 7.80 7.60

± ± ± ± ± ± ± ± ± ±

WiFi 0.20 0.57 0.29 0.41 0.52 0.26 0.50 0.29 0.53 0.39

8.56 6.87 8.08 7.63 8.73 7.32 8.53 7.69 8.73 8.06

± ± ± ± ± ± ± ± ± ±

0.29 1.20 0.53 0.98 0.21 1.89 0.51 0.44 0.48 0.52

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Fig. 2. Competitiveness evaluation with different feature sets.

managers. Fig. 3 only displays the distribution of some features (staff, WiFi, comfort and location) because they are identified as the most distinctive features (Table 7). Other hotel features (cleanliness, facilities and value) are not shown because they are not identified as distinctive features by our algorithm. Afterwards, we examined the rating distribution for the feature sets that contain two features. Given the space constraints, we only analysed the ratings of the first four hotel brands for demonstration purposes. The joint probability distribution on these ratings is plotted with respect to two features (Fig. 4). The position of the circle reflects the ratings, with a position closer to the top right of the plot indicating a better rating. The size of the circle reflects the probability value. Given that different brands may have some hotels that share similar ratings, the plots for these hotels may lie on top of one another, and these overlapping plots appear as dark bubbles with multiple circles within them. The results are summarised below. All hotels in B1 received high ratings for staff and value as indicated by the largest circle at rating position [8, 9] (Fig. 4a). The other brands have some member hotels that receive similarly high ratings, but only a few of these ratings are for both features. Therefore, staff and value are the competitive advantages of B1 because this is the only brand that has received many top ratings on both of these features. Fig. 4b shows that B2 tends to receive low ratings on staff and WiFi. Some hotels under this brand have received the lowest ratings on both of these features. Therefore, staff and WiFi are the competitive disadvantages of B2. Cleanliness and facilities distinguish B3 the most from its competitors. Given that the ratings of the member hotels under this brand are generally at the middle range (Fig. 4c), we cannot fully determine whether cleanliness and facilities constitute a competitive advantage or

B10. Fig. 3c and d show the probability distribution on the WiFi ratings of the hotel brands. The hotels under Harbour Plaza Hotel Resorts brand (B2) are more likely to receive low ratings for WiFi compared with those under the other brands, thereby suggesting that WiFi is the competitive disadvantage of B2. On the contrary, all hotels under the Langham Hospitality Group brand (B5) received a high rating of 9 for WiFi. Although some hotels in brands B4 and B9 received a high rating of 10 for this feature, many other member hotels have received a rating of 8 or below. Therefore, B5 can be treated as the brand with the best WiFi rating, with WiFi being its competitive advantage. Fig. 3c and d are identical because they both show the probability distributions of ratings over the WiFi feature. However, when interpreting the competitiveness of a hotel brand against its competitors, a separate figure that highlights the distribution of the targeted hotel is recommended for the ease of interpretation. Similarly, two separate figures (3e and 3f) were used here to emphasise that comfort is a competitive disadvantage for Hong Kong Hostel Group (B4) and a competitive advantage for Marco Polo Hotels (B7). WiFi has also been identified as the most distinctive feature of brands B3, B6, B8 and B9. However, their rating distribution is not clearly high or low. For example, B6 has some member hotels that received very low ratings for WiFi, but the majority of these hotels have received high ratings of 8 or 9 (Fig. 3c). We cannot determine whether WiFi is a competitive advantage or disadvantage of this brand. Nevertheless, our proposed method continues to be useful in helping hotel managers identify the individual features whose rating distributions are the most distinctive amongst all seven hotel features. In the case of B6, the low ratings obtained by some member hotels for WiFi can be perceived as an outlier that warrants further attention from hotel brand Table 7 Competitive Feature Sets. Brand ID

Most Distinctive Feature

Competitiveness

Best Feature Set

Competitiveness

B1 B2 B3 B4 B5 B6 B7 B8 B9 B10

{Staff} {WiFi} {WiFi} {Comfort} {WiFi} {WiFi} {Comfort} {WiFi} {WiFi} {Location}

7.55 12.23 6.00 12.20 9.67 9.83 7.37 7.57 9.50 7.36

{Staff, Value} {Staff, WiFi} {Cleanliness, Facilities} {Location, Staff} {Facilities, Value} {Cleanliness, Location} {Comfort, Value} {Location, Staff} {Cleanliness, Facilities} {Cleanliness, Location}

43.15 54.01 39.52 66.35 50.14 37.36 52.62 30.44 37.54 34.40

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Fig. 3. Feature rating distribution of hotel brands. (For interpretation of the references to colour in the text, the reader is referred to the web version of this article.)

comfort (Fig. 3a, d and f) are considered the major advantages of Harbour Plaza Hotel Resorts, Langham Hospitality Group and Marco Polo Hotels, respectively. Managers of these brands can develop differentiation-oriented competitive strategies (Porter, 1985), such as focusing on advertising about these features to improve customer perceptions about their value, uniqueness and quality. We would also like to emphasise that improving competitiveness is not always about creating or highlighting differentiation. In some cases, making a hotel brand less distinct from its competitors may also improve its competitiveness, especially when such distinction is a disadvantage. For example, a WiFi system (Fig. 3c) is a distinct disadvantage of Harbour Plaza Hotel Resorts. Managers of this brand should improve the WiFi feature to make their brand less distinct from its competitors, which may improve their competitiveness. Managers of the Hong Kong Hostel Group can adopt a similar strategy and focus on improving the comfort

disadvantage for this brand. However, this result suggests that hotel managers should focus on cleanliness because some hotels under this brand have received high ratings for facilities but low ratings for cleanliness. Similarly, we cannot conclude whether staff and location present an advantage or disadvantage for B4, as shown in Fig. 4d. Most of the hotels under this brand have received a high rating for staff, but their ratings for location are lower compared with those for the other features. 4.4. Discussion and implications The case study demonstrated the application of the proposed method in evaluating the competitiveness of major hotel brands in Hong Kong. The findings can provide hotel brand managers with practical implications, which are summarised as follows. Staff, WiFi and 8

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Fig. 4. Rating distribution of feature sets.

the proposed method with real rating data. Therefore, only a small number of hotel brands were included, and the ratings were retrieved from only one hotel review platform. Future studies may consider using ratings from multiple review platforms for improved generalization of hotel competitiveness. When the ratings collected from different platforms are obtained by using different scales, users should standardise these scales before evaluating the competitiveness of hotels. Hotel ratings are also subject to change when customers post more reviews and ratings online. In this way, the competitiveness of hotels may also change. Hotel managers should collect and use the latest rating data for their practical applications. Owing to the nature of the ratings in the collected dataset, feature sets of two features achieved the highest competitive scores among all possible feature combinations (Fig. 2). Therefore, the competitiveness of hotels was only analysed for the feature sets with up to two features in the case study. The best feature set with the highest competitive score may have up to three or more features, such as that shown in Example 3, but this scenario depends on the situation of the rating data under consideration. However, a feature set with a small number of features (three or less) is recommended to facilitate visualization and interpretation. Apart from feature ratings, hotel managers should study textual review comments to generate insights into the opinions of travellers. As our focus is on the

feature of their rooms and services (Fig. 3e) to eliminate the clear disadvantage of their hotel brand. Location is considered the greatest disadvantage of Tang’s Living brand. Therefore, the manager of this brand should provide additional transportation services to their customers and allocate more resources in finding ideal locations when planning to open new hotels in the future. Identifying competitive feature sets can also help brand managers develop attractive marketing materials. Taglines, such as ‘the hotel chain with the best staff and value for money’, can be placed in the advertising materials of Butterfly Boutique Hotels to attract travellers who have high expectations for both of these features (Fig. 4a). Managers of Harbour Plaza Hotel Resorts should improve the quality of their staff and WiFi to enhance the competitiveness of their brand (Fig. 4b). In case no clear advantage or disadvantage can be identified for a hotel brand, hotel managers can conclude that their hotel shows the same level of performance compared with the majority of their competitors. They may adopt differentiation-focused strategies (Porter, 1985) to direct their limited resources on significantly improving one or few features, which have not been identified as a competitive advantage of any other brand to turn these features into one of their competitive advantages. The case study mainly attempts to demonstrate the application of 9

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competitiveness of hotel brands as reflected in their feature ratings, we do not use such textual reviews in our study. The major contribution of this paper lies in the methodological aspect, that is, we propose a new technique for evaluating the competitiveness of hotel brands. Although some tourism studies have used online reviews and ratings to study the purchasing behaviour of travellers (Vermeulen and Seegers, 2009; So et al., 2016) or the competitiveness of hotels (Xia et al., 2019), studies on hotel brands are limited, and no effective method has been introduced for evaluating the competitiveness of hotel brands based on online ratings. Khan and Rahman (2017) only focused on evaluating how consumers experience a hotel brand and the influence of the brand toward their behaviour, but they did not directly evaluate or compare the competitiveness between hotel brands. Very few studies have attempted to directly assess the competitiveness of a hotel, such as Xia et al. (2019). However, their method is only applicable to evaluate the competitiveness of individual hotels rather than for hotel brands with multiple hotels involved. Such a limitation is addressed by our proposed method, which can automatically evaluate multiple features ratings and brands to identify the most distinctive features that can reflect the competitiveness of a hotel brand. The introduced method is general and can be applied to other contexts to evaluate the competitiveness of other branded products and services in the hospitality and tourism industries.

improve our method for continuous probability distribution and evaluate its performance on various types of rating data with skewed distributions. In addition, the data on online review platforms keep being generated, thus the competitiveness of tourism products is subjected to change over time (Mariani et al., 2018). This proposed method works in batch mode, but it can be extended to the incremental mode. Accordingly, another interesting future study can investigate on incremental methods that can model and capture such changes of the competitiveness to provide businesses with up to date information for in time decision making. Acknowledgements The work described in this paper was supported by a research grant funded by the Hong Kong Polytechnic University and the Guangxi Key Laboratory of Trusted Software (No KX201528). The work was completed when G. Li is on ASP in Chinese Academy of Sciences, and we also would like to thank Deakin University’s ASL fund and Xinjiang research fund with Chinese Academy of Sciences. References Akbaba, A., 2006. Measuring service quality in the hotel industry: a study in a business hotel in Turkey. Int. J. Hosp. Manag. 25 (2), 170–192. Anton, H., 1994. Elementary Linear Algebra, 7th ed. John Wiley & Sons, pp. 170–171. Aggarwal, C.C., 2017. Outlier Analysis, 2nd ed. Springer International Publishing. Barney, J., 1991. Firm resources and sustained competitive advantage. J. Manag. 17, 99–120. Barros, C.P., 2005. Measuring efficiency in the hotel sector. Ann. Tour. Res. 32 (2), 456–477. Boyd, D.M., Ellison, N.B., 2007. Social network sites: definition, history, and scholarship. J. Comput. Commun. 13 (1), 210–230. Cai, L.A., Hobson, J.S.P., 2004. Making hotel brands work in a competitive environment. J. Vacat. Mark. 10 (3), 197–208. Campos-Soria, J.A., García, L.G., García, M.A.R., 2005. Service quality and competitiveness in the hospitality sector. Tour. Econ. 11 (1), 85–102. Choi, T.Y., Chu, R., 2001. Determinants of hotel guests’ satisfaction and repeat patronage in the Hong Kong hotel industry. Int. J. Hosp. Manag. 20 (3), 277–297. Cinlar, E., 2011. Probability and Stochastics. Springer, New York. Duan, L., Tang, G., Pei, J., Campbell, A., Campbell, A., Tang, C., 2015. Mining outlying aspects on numeric data. Data Min. Knowl. Discov. 29 (5), 1116–1151. Enright, M.J., Newton, J., 2004. Tourism destination competitiveness: a quantitative approach. Tour. Manag. 25 (6), 777–788. Fainshmidt, S., Wenger, L., Pezeshkan, A., Mallon, M.R., 2019. When do dynamic capabilities lead to competitive advantage? The importance of strategic fit. J. Manag. Stud. 56 (4), 758–787. Forgacs, G., 2003. Brand asset equilibrium in hotel management. Int. J. Contemp. Hosp. Manag. 15 (6), 340–342. Hargreaves, C.A., 2015. A comparative analysis of hotel ratings and reviews: an application in Singapore. Am. J. Mark. Res. 1 (3), 118–129. Hassan, A.R., Hassan Bhuiyan, M.I., 2016. Automatic sleep scoring using statistical features in the EMD domain and ensemble methods. Biocybern. Biomed. Eng. 36 (1), 248–255. Hoyer, W.D., Brown, S.P., 1990. Effects of brand awareness on choice for a common, repeat-purchase product. J. Consum. Res. 17 (2), 141–148. Hsieh, L.F., Lin, L.H., 2010. A performance evaluation model for international tourist hotels in Taiwan-an application of the relational network DEA. Int. J. Hosp. Manag. 29 (1), 14–24. Jacquier, E., Kane, A., Marcus, A.J., 2003. Geometric or arithmetic mean: a reconsideration. Financ. Anal. J. 59 (6), 46–53. Jiang, J., Gretzel, U., Law, R., 2014. Influence of star rating and ownership structure on brand image of Mainland China hotels. J. China Tour. Res. 10 (1), 69–94. Kayaman, R., Arasli, H., 2007. Customer based brand equity: evidence from the hotel industry. Manag. Serv. Qual.: Int. J. 17 (1), 92–109. Khan, I., Rahman, Z., 2017. Development of a scale to measure hotel brand experiences. Int. J. Contemp. Hosp. Manag. 29 (1), 268–287. Koksal, M.H., Ozgul, E., 2010. The export competitive advantages of Turkish manufacturing companies. Mark. Intell. Plan. 28 (2), 206–222. Kim, H.B., Kim, W.G., 2005. The relationship between brand equity and firms’ performance in luxury hotels and chain restaurants. Tour. Manag. 26 (4), 549–560. Kim, W.G., 2008. Branding, brand equity, and brand extensions. Handbook of Hospitality Marketing Management. Elsevier, London, pp. 87–118. Law, R., 2011. Identifying changes and trends in Hong Kong outbound tourism. Tour. Manag. 32 (5), 1106–1114. Lau, R.S.M., 2002. Competitive factors and their relative importance in the US electronics and computer industries. Int. J. Oper. Prod. Manag. 22 (1), 125–135. Levina, E., Bickel, P., 2001. The earth mover’s distance is the mallows distance: some insights from statistics. Proceedings of International Conference on Computer Vision.

5. Conclusion With the ever-growing popularity of online review platforms, hotel reviews and ratings have been increasingly influential in the decisionmaking process of travellers. Hotel managers have also utilised ratings as important indicators of the performance and competitiveness of their hotels in the market. However, very few researchers have attempted to evaluate the competitiveness of hotel brands, which can help hotel managers develop brand promotion strategies. One serious barrier is the complexity of multiple feature ratings, hotels and brands during evaluation, making the application of the traditional trivial method impractical. To address this barrier, this paper introduces a method that automatically evaluates the distinctiveness of hotel brands based on probability distribution and EMD. The competitiveness of hotel brands can be effectively identified by analysing feature sets with high competitive scores. We demonstrate the practical application of our proposed method by conducting a case study that involves major hotel brands in a popular tourism destination, namely, Hong Kong. Our result highlights the competitive advantages and disadvantages of some hotel brands, through which hotel managers can propose effective development and marking plans. The competitiveness can also be evaluated for other products in the tourism and hospitably industries, such as restaurant chains, theme parks or entertainment complexes. Hence one natural extension of the presented method is to evaluate its effectiveness in evaluating brand competitiveness of hotels at different tourism destinations. In addition, we are also interested in addressing the following limitations of the presented method: Firstly, the proposed method requires the feature ratings to be available, which is not always the case on other hotel review platforms or reviews on other hospitality and tourism products. Some review platforms only ask travellers to provide comments but no overall ratings on individual product features. Future work can investigate techniques that can determine or estimate ratings on various features from reviews comments, so that brand competitiveness can be evaluated using the proposed method. An example of such technique is recommendation system techniques, which could be adopted to infer or impute the ratings when some key ratings are not available (Ren et al., 2013, 2015). Secondly, our presented competitiveness evaluation method employed discrete probability distribution, which may not reflect the actual distribution of ratings when the ratings are skewed around certain values (Mariani and Predvoditeleva, 2019). Future work can further 10

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