Understanding the Impact of Online Reviews on Hotel Performance

Abstract

Understanding consumers’ needs and wants has been a major source of success for hotel organizations. Notwithstanding, investigating the valence of online reviews and modeling hotel attributes and performance is still a rather novel approach. Using partial least squares path modeling, Swiss country-level data for online reviews from 68 online platforms, together with data from 442 hotels, we test 11 hypotheses. Our research model includes three distinctive areas of the hotel: physical aspects, quality of food and drink, and human aspects of service provision. RevPAR and occupancy are employed as performance metrics. We also test for mediation effects. Results indicate that hotel attributes, including the quality of rooms, Internet provision and building show the highest impact on hotel performance, and that positive comments have the highest impact on customer demand. This study contributes to theories of valence on hotel performance and presents salient implications for practitioners to enhance performance.

Full text

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ANTECEDENTS AND EFFECTS OF ONLINE REVIEWS ON
HOTEL PERFORMANCE
Journal:
Journal of Travel Research
Manuscript ID
JTR-15-09-10.R1
Manuscript Type:
Empirical Research Articles
Keywords:
Hotel Attributes , Hotel Performance, Partial least squares modeling
(PLSPM) RBV, Valence of Online Reviews
Abstract:
Understanding consumers’ needs and wants has been a major source of
success for hotel organizations. Yet, investigating the valence of online
reviews and modelling them with hotel attributes and performance is still a
rather novel approach. Using partial least squares path modelling on Swiss
country-level data of online reviews from 68 online platforms together with
data from 442 hotels, we test eleven hypotheses. Our research model
includes three distinctive areas of the hotel: physical aspects; quality of
food and drink; human aspects of service provision. RevPar and occupancy
are employed as the performance metrics. We also test for mediation
effects. Results indicate that hotel attributes such as quality of rooms,
Internet and building show the highest impact on performance while
positive comments on hotels have the highest impact on customer
demand. This study contributes to theories of valence on hotel
performance and presents salient implications for practitioners to enhance
performance.
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ANTECEDENTS AND EFFECTS OF ONLINE REVIEWS ON HOTEL
PERFORMANCE
Introduction
During the first decade of the 21
st
century, tourism infrastructures have become more digital
with increased interconnections between suppliers, firms and customers. Within these
challenging competitive environments, tourism organizations need to identify real sources of
business value creation. Several authors advocate that tourism organizations need to be
continuingly fine-tuning their products and services based on information received from
customers (Levy, Duan and Boo 2013; Zeng and Gerritsen 2014).
With the increasing popularity of the Internet, electronic word-of-mouth (eWOM) on social
media has become an important tool for customers seeking and sharing information on
products and services (Filieri and McLeay 2013; Podnar and Javernik 2012; Zhou et al. 2014).
Online customer reviews as a particular form of eWOM have become the most important
information source in customers’ decision making (Ye et al. 2011) and are deemed more
successful in influencing consumer behavior than traditional marketing, information provided
by product providers, or promotion messages of third-party websites (Gretzel and Yoo 2008;
Yang and Mai 2010; Zhang et al. 2010). Consequently, social media marketing has emerged
as a dynamic and challenging field in a marketing manager’s toolkit (Dev, Buschman and
Bowen 2010). Also tourism organizations can no longer ignore the information exchange that
is happening among their consumers (Riegner 2007).
Due to the growing importance of online reviews for companies, empirical research has
heavily focused on their impact on consumer perceptions and decision-making processes (Liu
and Park 2015; Pantelidis 2010; Park and Nicolau 2015; Ryu and Han 2010; Zhang et al.
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2010) but less investigate the impact of consumer reviews on business performance (Duverger
2013; Ye et al. 2011). For analytical purposes, online reviews are often decomposed into
valence, variance and volume of reviews with valence being of particular importance for
business performance (Chevalier and Mayzlin 2006; Chintagunta, Gopinath and
Venkataraman 2010; Dellarocas, Zhang and Awad 2007). Hence, this study focuses its
investigations onto the valence of online reviews. More specifically, we aim to analyze in
greater detail the antecedent effects of hotel attributes and hotel business performance
considering customers’ voice as expressed in the valence of reviews. Within the hotel
industry, there is limited evidence of research into the impact of customers’ voice on
attributes such as physical hotel attributes, service and hotel location on business
performance. We propose a model that helps explain which aspects of visitor experience, as
voiced through social media, have the greatest impact on hotel Demand (Room Occupancy)
and subsequently Revenue (RevPAR). We further advance existing research (which used a
sample of a single market, see e.g., Blal and Sturman 2014) by expanding the geographical
location to country level and by using a ‘soft modeling - PLS’ approach rather than ‘hard
modeling – LISREL’ to validate antecedents of hotel performance as encouraged by Xie,
Zhang and Zhang (2014), Anderson (2012) and Phillips et al. (2015). In addition, instead of
using only one source of online reviews (e.g., Xie, Zhang and Zhang 2014; Blal and Sturman
2014) we use an aggregated score of 68 review platforms to analyze 22 hotel attributes. By
using actual RevPar and occupancy performance data matched to the online reviews we
further contribute to the tourism literature as we advance previous studies that used proxies
such as hotel room sales or booking data (Chevalier and Mayzlin 2006; Ghose and Ipeirotis
2011; Ye, Law and Gu 2009 and Ye et al. 2011).
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The remainder of the paper is organized as follows. We consider the emergence of online
reviews, valence and its relationship with business performance. The Swiss hotel industry is
briefly described to provide some context, before we explain our proposed model, variables
and hypotheses development. The research methodology is outlined. We then present the
results of our empirical analysis and conclude with a discussion and conclusion with
recommendations for researchers and practitioners, and limitations of the study.
Online reviews
Due to the emergence of Web2.0 and the increasing number of platforms, consumers
frequently communicate and interact online with other web users to share their experiences
about products and services (Buhalis and Law 2008; Dellarocas, Zhang and Awad 2007;
Filieri 2015; Leung et al. 2013). The information exchanged online is termed user-generated
content (UGC) or e-WOM which refers to ‘any positive or negative statement made by
potential, actual or former consumers about a product or company, which is made available to
a multitude of people and institutions via the Internet’ (Hennig-Thurau et al. 2004, 39). It not
only captures online reviews, recommendations and opinions exchanged by consumers (Serra
Cantallops and Salvi 2014) but also forms the bases on which consumers revise their purchase
decisions and ultimately change their buying behavior (Serra Cantallops and Salvi 2014;
Sparks and Browning 2011).
As a result, existing research has predominantly adopted a marketing perspective and
extensively analyzed the impact of online reviews on consumer behavior and decisions (see
e.g., Chen and Huang 2013; Purnawirawan, Pelsmacker and Dens 2012; Sparks and Browning
2011). It was found that consumers pay close attention to various dimensions when reviewing
consumer comments (Jang, Prasad and Ratchford 2012; Liu 2006; Öğüt and Taş 2012).
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Previous research has further decomposed online reviews into valence and volume, for
example, and analyzed the relevance of these elements for consumer decision-making and
business performance (for a comprehensive review see Floyd et al., 2014 and Kostyra et al.
2015). While the volume of online reviews has been extensively researched, Floh, Koller and
Zauner (2013) argue that the valence (sometimes referred to as quality) of online reviews has
received less attention.
The valence of online reviews refers to the average numerical rating, i.e., positive, negative or
neutral reviews, or the absence or presence of those on websites (Chevalier and Mayzlin
2006; Duan, Gu and Whinston 2008; Liu 2006; Tang, Fang and Wang 2014; Ye et al. 2011).
While it was found that positive opinions may enhance customers’ attitude and choice
probability for a product, negative reviews were found to discourage potential customers from
purchasing (Dellarocas, Zhang and Awad 2007; Floyd et al. 2014). By examining the valence
intensity, a considerable amount of research within the field of marketing suggests that due to
the negativity effect (Tsang and Prendergast 2009) negative reviews are stronger, more
influential and difficult to resist than positive reviews (Baumeister et al. 2001; Casalo et al.
2015; Chevalier and Mayzlin 2006; Cui, Lui and Guo 2012; Maheswaran and Meyers-Levy
1990; Papathanassis and Knolle 2011) and hence influence consumers’ decision-making more
than positive reviews (Xie, Zhang and Zhang 2014). Some researchers found positive reviews
to have minimum or no effect (e.g., Duan, Gu and Whinston 2008).
Upon investigating the impact of valence on business performance, previous research has
identified different effects of positive, negative and neutral reviews (see e.g., Chevalier and
Mayzlin 2006; Duan, Gu, and Whinston 2008; Godes and Mayzlin 2004; Sun 2012; Tang,
Fang and Wang 2014). Overall, the majority of previous studies have found a positive
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relationship (mainly in movie and e-book industries) (Chevalier and Mayzlin 2006;
Chintagunta, Gopinath and Venkataraman 2010; Clemons, Gao and Hitt 2006; Dellarocas,
Zhang and Awad 2007; Dhar and Chang 2009; Sun, 2012; Ye et al. 2011) and some no
significant relationship (Amblee and Bui 2011; Duan, Gu and Whinston 2008; Liu 2006).
Neutral reviews were found to have a positive effect on sales (Sonnier, McAlister, and Rutz
2011) or a mixed effect as shown by Tang, Fang and Wang (2014), who found that mixed-
neutral reviews had a positive impact on business performance, while indifferent-neutral
online reviews negatively affected it. Examining the effect within the hotel industry, Ye, Law
and Gu (2009) found a significantly positive relationship between online consumer ratings
and the number of hotel bookings, which they used as proxies for hotel performance. In a
follow up study, Ye et al. (2011) showed that the valence of traveler reviews had a significant
impact on the online sales of hotel rooms.
In order to explain the contradictory findings across different industries, some researchers
explored contextual variables such as market (Chintagunta, Gopinath, and Venkataraman
2010), product type in terms of degree of involvement (Mudambi and Schuff 2010), and
whether it is a search or experience product (Gu, Park, and Konana 2012). With regards to the
hotel industry, it was found that the type of hotel influences the effect of online review on
business performance (Blal and Sturman 2014). Looking into the effect of online reviews for
certain hotel attributes (i.e., services, location, price, room, and cleanliness) Xie, Zhang and
Zhang (2014) showed that these are significantly associated with hotel performance. More
specifically, they showed that ratings of location and cleanliness positively influence hotel
performance, and ratings of purchase value are negatively associated with performance.
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Overall, the majority of studies have investigated online reviews from a marketing
perspective and explore their impact on customer behavior and decision-making (see e.g.,
Serra Cantallops and Salvi 2014; Sparks and Browning 2011). The importance of analyzing
the components of online reviews in more depth has proven useful as differences were found
in relation to volume, variance and valence. A more in-depth analysis of valence has revealed
different effects of positive, negative and neutral comments. Yet, investigating the impact of
valence on business performance and hence adopting a strategic management perspective, has
received less attention, probably due to the difficulty of matching online reviews to actual
performance data. The results of the few studies that have been conducted in this area
revealed mixed results and thus, some researchers followed the call for research into the
impact of contextual factors (e.g., Kim, Lim and Brymer 2015; Xie, Zhang and Zhang 2014).
Similarly, we argue that a more differentiated view into customers’ preferences as shown in
the valence of online reviews can provide more detailed insights into the relationship between
online reviews and business performance. The vast majority of present research has largely
neglected the potential interaction effects among hotel attributes and their impact on business
performance (see e.g., Xie, Zhang and Zhang 2014) and considering customers’ voice would
clearly provide meaningful insights needed for the strategic management of online reviews.
Table 1 summarizes existing research on the impact of valence on business performance for
the hotel industry.
Table 1 about here
Swiss hotel industry characteristics
With its small size, safety and clean air, Switzerland was among the first tourism markets to
take off and in the 1950s Switzerland was among the top five destinations worldwide in
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volume (Howarth 2013). However, the Swiss tourism industry has been stagnating for the
past four decades (Sund 2006), and felt the pain of the recession between 1992 and 1996
(OECD 2000). The advance of globalization continues to lessen the appeal of the traditional
alpine leisure resort and the Swiss tourism industry continues to be challenged with small
firms scattered across the country with small marketing budgets and rather tired property
stock.
Switzerland is divided into 26 cantons with German-speaking and French-speaking cantons,
one Italian-speaking and some dual-speaking cantons where German and French are spoken.
Domestic and international tourism still remain integral to the Swiss economy. The hotel and
restaurant industry is the sixth biggest employer and involves 234,000 employees (Swiss
Tourism Federation 2010). The Swiss franc is particularly strong compared to other
currencies, which together with the relatively high cost of living makes it imperative for the
sector to demonstrate quality. At the time of this study, this observation is rather pertinent as
the worldwide international tourism market was rebounding after the 2008 financial crisis.
The accommodation sector accounts for more than 25% of the tourism value added (Swiss
Tourism Federation 2010). Yet, the Swiss hotel supply consists of approximately 10% of
branded hotels, which is one of the lowest in Europe (Schofield and Partners 2013).
In 2010 the Swiss hotel industry recorded a total of 36.2m overnight stays, which was an
increase of 1.7% from the previous year. Indigenous demand amounted to 15.8m overnight
stays and foreign demand generated 20.4m overnight stays. These being increased by 2.2%
and 1.4% respectively. Germans accounted for the strongest demand with 5.8m overnight
stays, (a fall of 3.6% compared with 2009), followed by United Kingdom 1.9m (-0.1%) and
the USA 1.5m (8.9%). In 2010 visitors stayed an average of 2.2 nights in hotels in
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Switzerland. Foreign visitors stayed an average of 2.4 nights while Swiss visitors stayed 2.1
nights. The Swiss hotel industry needs to remain attractive to its salient international markets,
and at the same time develop strategies to reposition itself in terms of regional and national
markets.
Proposed model, variables and hypotheses development
The research model
Hotel attributes and online reviews
Consumers’ preferences can be dynamic and expensive to monitor, but advances in
technology have reduced the cost of collecting and mining data in an efficient and
nonintrusive manner (Li et al. 2015). Previous studies have identified hotel characteristics
associated with online customer satisfaction (e.g., Radojevic, Stanisic, and Stanic 2015).
Consumers are influenced by customer ratings of hotel attributes, which affect bookings and
ultimately performance. Echoing these statements, the influential nature of online customer
reviews is considered as one of the most crucial content for understanding firm performance
in hospitality and tourism (Fillieri 2015; Mauri and Minazzi 2013; Serra Cantallops and Salvi
2014). Yet, efforts to address such issues have been limited (Li et al. 2015). Moreover, despite
some theoretical advances, one question previous research leaves open is what antecedent
factors influence both hotel occupancy and hotel Revpar. More generally, this study adopts a
strategic perspective to investigate the impact of the valence of reviews for hotel attributes on
business performance. Xiang et al. (2015) assert that it is important to understand the
antecedents of hotel guest satisfaction. Such antecedents will include the core hotel product
together with an array of facilities supporting and augmenting the guest experience.
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Predefined hotel attributes used in prior studies have been summarized by Li et al. (2015). As
the increasing interaction among consumers reinforces the importance of social media
through online reviews, selected hotel attributes need to incorporate and provide information
to aid the strategic planning process. In seeking to consider what salient hotel attributes to
include in the model, this study acknowledges that hotels may seek to deliver excellence at
every moment of truth. However, the guest experience can be rather complex, as they involve
a diverse array of services and amenities (Crotts, Mason and Davis 2009). Accordingly, the
high investment cost together with limited resources available to hoteliers during strategic
decision-making, make it imperative to know what is appreciated by consumers together with
the impact on performance.
To be within potential consumer’s consideration set, the hotel product and services must
provide a basic level of attributes. However, from a research perspective there appears to be a
wide and extreme heterogeneity in the selection of hotel attributes (Dolnicar and Otter 2003).
The authors reviewed past approaches published between 1984 and 2000 in hospitality,
tourism and business journals and extracted 173 hotel attributes. Dolnicar and Otter (2003)
categorize these into image, price value, hotel, and room. Previous research in the hospitality
industry (e.g., Callan and Kyndt 2001; Choi and Chu 2001; Lockyer 2003) has identified
attributes such as room cleanliness, convenience of location, value for money, and
friendliness of staff as important for service quality. Albayrak and Caber (2015) using
importance-performance analysis study noted hotel attributes such as food & beverage,
personnel, room and beach as items to concentrate upon. Ady and Quadri-Felitti (2015) in
their study of what attributes are most important to travelers when making a booking used the
following hotel attributes: room, breakfast, service, wellness, Wi-Fi, food, cleanliness,
amenities and comfort. Interestingly, in their conclusion they state that once a hotel becomes
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part of a traveler’s consideration set that the hotelier should focus on those attributes that
trigger a booking. These being: Wi-Fi, food, rooms, and amenities.
Figure 1 about here
The hotel variables selected for the study (see Figure 1) incorporate hotel attributes used in
prior studies (Albayrak and Caber 2015; Ady and Quadri-Felitti 2015; Dolnicar and Otter
2003). The attributes cover three distinctive, logically-related areas of the hotel. The first
relates to physical aspects of hotel provision (grounds, building, ambiance, rooms and
internet). The second relates to the quality of food and drink, influenced by the menu and
beverages. The final area relates to human aspects of service provision for the hotel, which is
an important enough element to be considered alone.
Data was provided by TrustYou, a private German company offering online reputation
management tools to the hospitality industry. The company has developed a semantic search
engine for online evaluations and offers four key products: TrustYou Analytics (online
reputation analysis and management), TrustYou Stars (search engine optimization from
reviews), TrustYou Meta-Review (review summaries posted on travel and search sites), and
TrustYou Radar (a mobile dashboard on hotel performance). TrustYou was founded in 2008
and is headquartered in Munich, Germany. The company has more than 100 employees and
operates in 22 countries. It is a prominent company in the hotel sector online reputation
management marketplace. It is particularly dominant in German-speaking Europe. It has more
than 50,000 hotels among its customers, including Mövenpick, Accor Hotels, Sofitel, Arcotel
Hotels, Best Western, Hard Rock, Linder Hotels and Resorts, Motel One, Petit Palace
Hoteles, Rydges, Hotel Santika and Trump Hotel Collection (TrustYou 2015).
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The user-generated online review scores were made available by TrustYou too. The scores for
positive and negative sentiment were created by TrustYou using their machine-learning
algorithm (https://github.com/trustyou). The final scores were divided by the number of
rooms in a hotel to ensure that the results are not influenced by hotel size. TrustYou
aggregates customer online generated reviews for all Swiss hotels, and in 2010 included 68
evaluation platforms (see Table 2), such as TripAdvisor, HolidayCheck and booking.com.
This created a constraint on the variables used in the model as TrustYou have to collect data
across the 68 evaluation platforms in a systematic manner, so that reports can be used by its
clients.
Table 2 about here
Performance
With regards to business performance previous studies have shown mixed results about the
importance of hotel attributes (e.g., Dolnicar and Otter 2003; Sainaghi 2011; Yavas 2003) and
a very limited number of studies have examined the role of online reviews when investigating
the impact of a multiple set of hotel attributes on hotel performance (e.g., Phillips et al. 2015).
Previous eWOM studies have used revenue per available room (Revpar) (Anderson 2012;
Blal and Sturman 2014; Scaglione Schegg and Murphy 2009) and occupancy rates (Levy,
Duan and Boo 2013), which are two leading hotel performance metrics. The selected
variables for this study were hotel attributes which guests would rate (based on sentiment
analysis of reviews) and two performance variables, namely Revpar and Occupancy Rate.
Hotel performance data were provided by the major stakeholders of the sector: Swiss Federal
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Statistical Office, Switzerland Tourism and hotelleriesuisse, the major trade organization for
the hotel industry.
Valence of reviews
The valence of reviews was measured using positive and negative comments on these hotel
attributes. Investigating the impact of valence on hotel attributes reveals insights into
consumer’s perceived service quality and potential purchase risks (Liu 2006; Sun 2012). Xie,
Zhang and Zhang (2014) argue that consumers of hotel services weigh positive reviews more
than negative reviews, which would imply a positive impact of review valence on hotel
performance.
This forms the basis for the following hypotheses:
H1: Positive sentiment about physical hotel attributes is positively related to hotel demand.
H2: Negative sentiment about physical hotel attributes is negatively related to hotel demand.
H3: Positive sentiment about food and drink is positively related to hotel demand.
H4: Negative sentiment about food and drink is negatively related to hotel demand.
H5: Positive sentiment about staff service is positively related to hotel demand.
H6: Negative sentiment about staff service is negatively related to hotel demand.
H7: Positive sentiment about hotel location is positively related to hotel demand.
H8: Negative sentiment about hotel location is negatively related to hotel demand.
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Mediation effects
Attention has also been directed in the literature toward more concrete insights into the
determinants of tourism performance (e.g., Assaf and Josiassen 2012). So, we sought to
improve and explain the predictive nature of performance within our research model. We
were also interested in examining the extent to which the key elements of sentiment and
Demand carried forwards the effects of their antecedents. The results of such an analysis
would enable us to examine the mediation effects of these variables.
H9: The sentiment about hotel attributes on revenues is mediated by hotel demand: (a)
positive sentiment about physical hotel attributes; (b) negative sentiment about physical hotel
attributes; (c) positive sentiment about food and drink; (d) negative sentiment about food and
drink; (e) positive sentiment about staff service; (f) negative sentiment about staff service; (g)
positive sentiment about hotel location; (h) negative sentiment about hotel location.
H10: The effect of positive sentiment about hotel (a), grounds (b), building (c), ambiance (d)
rooms; and (e) internet on demand is mediated by positive sentiment about physical hotel
attributes.
H11: The effect of negative sentiment about hotel (a), grounds (b), building (c), ambiance (d)
rooms; and (e) internet on demand is mediated by negative sentiment about physical hotel
attributes.
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Research Methodology
Hotel Sample
The sample consists of 442 hotels operating in Switzerland in 2010. Table 3 provides a
summary of the sample dataset.
Table 3 about here
According to the Swiss Tourism Federation (2010), there were 4,827 open establishments (in
terms of being open for trading) out of 5,477 surveyed hotels for 2010. The sampling frame
for our study comprises independent small and medium-sized hotels, who were members of
hotelleriesuisse being the main hotel association for the hotel sector in Switzerland which is
responsible for the Swiss hotel classification. In 2010, there were 2,196 member hotels of
hotelleriesuisse representing roughly 60% of hotel beds in Switzerland (157,634 beds
compared to an overall capacity of 275,193 hotel beds) and generating over three quarters of
overnight stays (hotelleriesuisse and SGH 2011).
Table 4 about here
In addition (see Table 4), only 40.1% (being 2,196/5,477) of the properties were given a
category (star rating) with 338 being of no stars (i.e., other categories). The sample of 442
hotels represents 22.2% of the Swiss hotel industry. Two further limiting factors relating to
performance data reduced the available sample size: obtaining RevPar and occupancy data for
2010 for the hotels. Overall, the sample consists of 78,171 reviews with 63,026 (80.6%)
positive (+) reviews and 11,406 (19.4%) negative (-) reviews.
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Data analysis
The research model was tested using PLS-PM in the XLSTAT software package (XLSTAT
2015). PLS-PM is a variance maximization structural equation modeling technique that makes
no distributional assumptions for data samples (and is sometimes referred to as ‘soft
modeling’). It has greater statistical power than covariance-based structural equation
modeling and excels at testing complex, predictive models with formative indicators (Mode
B) and single-item measures (Hair et al. 2014). Since our research focuses on a complex
model based solely on single-item formative indicators, the method is considered a suitable
choice for our study.
In our study it was necessary to employ single-item measures due to the nature of the
TrustYou data set. Single-item measures, while having some drawbacks, can provide useful
summative measures for unambiguous constructs (Bergkvist and Rossiter 2007; Wanous,
Reichers and Hudy 1997). Our single-item measures were created based on well-known hotel
business metrics and the application of a standard sentiment analysis algorithm to visitor
comments across the websites evaluated, and can therefore be considered unambiguous. Since
the constructs are formative, single-item measures, we were unable to conduct discriminant
validity tests via Fornell and Larcker’s (1981) method or cross-loadings (Chin 1998), or to
assess internal consistency using reliability statistics such as Cronbach’s Alpha. However, we
examined the condition index (Chin 1998; Duarte and Raposo 2010), which confirmed the
absence of multicollinearity in our model: the condition index for each of our variables does
not exceed the recommended ceiling of 30, the highest value being 20.079.
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Since the principal objective of PLS-PM is prediction, the goodness of a theoretical model is
not assessed using traditional metrics (e.g., Goodness-of-Fit in covariance-based structural
equation modeling) but via the evaluation of the strength of each structural path and the
combined predictiveness (R
2
) of exogenous constructs (Chin 1998; Duarte and Raposo 2010).
According to Falk and Miller (1992), the level of acceptable predictiveness for R
2
is 0.1.
Thus, based on this criterion, all endogenous constructs in our model displayed an acceptable
level of predictiveness, leading to a positive overall evaluation of the nomological validity of
our model.
Research Results
Descriptive statistics
Table 5 presents descriptive statistics for our sample of 442 hotels. The highest scoring
maximum positive (+) hotel attributes were Service (11.357), Rooms (11.111), Location
(9.980) and Food & Drink (9.000). In terms of mean (+) scores the three hotel attributes
scoring highest were Rooms (0.798), Service (0.699), and Hotel (0.583). The three highest
scoring maximum negative (-) hotel attributes were Rooms (5.893), Food & Drink (3.000),
and Hotel (2.214). In terms of highest mean (-) scores the three highest hotel attributes were
Rooms (0.244), Hotel (0.088), and Food & Drink (0.084).
Table 5 about here
The highest scoring (+) hotel attributes would support the observation that customers value
customer service, and if they are pleased will write a positive comment. In terms of the
business dynamics of the hotel business the room, food and drink and location are also crucial
areas that can lead to positive comments. However, the mean (+) scores did not follow the
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same pattern with Rooms being followed by Service and then the Hotel. Rooms, Food &
Drink and the Hotel also dominated the maximum and mean (-) sentiment analysis for the
sample of hotel reviews.
Testing the research model using partial least squares path modeling
We used a t-test to examine the difference between the mean star-rating of the sample
(M=2.532; SD=1.460; n=442) and the mean star-rating of the population (M=2.622;
SD=1.158; n=1995), but found no significant difference (t=1.411; df=2435; p=0.158).
In order to gauge the adequacy of our sample for partial least squares path modeling, we
conducted a post-hoc power analysis using G*Power 3.1 (Faul et al., 2007). The analysis
(α=0.05, 1-β=0.8) indicated that the sample (n=442) is adequate even for very small
population effects (e.g., the effect size is f²≥0.018 for demand, and smaller for other
endogenous variables).
Table 6 about here
Table 6 shows the statistical results of testing our research model using PLS-PM. The
research model explains 31.7% of variance in Revenue (measured by RevPar) and the
relationship between Demand (measured by percent occupancy) and Revenue is extremely
significant (β=0.543, t=14.286, p<.001). The research model explains a modest but acceptable
level of variance in Demand (R²=0.111). This variance in Demand is explained by one
significant construct, Hotel (+) (β=0.359, t=2.079, p=0.038), providing support for H1.
Positive comments about the hotel are the most significant determinant of customer demand.
Notwithstanding, H2 to H8 are not supported by the data.
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Turning to the Hotel (+) construct we note an R
2
of 0.907. All five sub-factors contribute
towards explaining the variance in Hotel (+), with the strongest being Rooms (+) (β=0.649,
t=21.809, p<.001), Internet (+) (β=0.217, t=10.894, p<.001) and Building (+) (β=0.133,
t=5.924, p<.001), followed by Grounds (+) (β=0.054, t=3.344, p=.001) and Ambiance (+)
(β=0.054, t=2.373, p=.018). Looking deeper, Table 7 provides an overview of the analysis of
the impact and contribution of the five variables to the variance of Hotel (+). Rooms
contribute the vast majority to variance: 66.9% of the R
2
of positive voice of the hotel. This is
followed by the Internet, contributing 16.2%, and Buildings, contributing 10.7% to R².
Together these three hotel attributes contribute 93.8% to the variance of Hotel (+). This is
illustrated in Figure 2.
Table 7 and Figure 2 about here
Overall, the results show that positive experiences regarding the hotel (Hotel +), as voiced
through social media, have the greatest impact on hotel Demand (measured through
occupancy rates) and subsequently Revenue (measured through Revpar). In other words,
positive voice about the hotel is the most important of the constructs examined, driven by five
sub-factors, with Rooms being the most important, followed by Internet and Building.
Interestingly, none of the paths for negative reviews to performance were significant. Only
positive reviews had a significant impact on performance through Hotel (+).
Tests for mediation effects
For the purpose of testing for mediation effect we ran Sobel (1982) tests. Scores for the Sobel
tests (see Table 8) show that Hotel (+) is significantly mediated by Demand (Z=2.054,
SE=0.098, p=0.040), i.e., the effects of positive word-of-mouth about a hotel are strong
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enough to be carried through to revenues. This provides support for H9a. However, since the
direct paths for the other aspects of sentiment were not significant, H9b to H9h are not
considered in the analysis. Similarly, Rooms (+) (Z=2.066, SE=0.113, p=0.039), Internet (+)
(Z=2.038, SE=0.038, p=0.042) and Building (+) (Z=1.963, SE=0.024, p=0.050) are all strong
enough that the effects are mediated by Hotel (+) and carried through to Demand. Apparently,
demand and revenues are driven by social chatter about good quality rooms, good Wi-Fi and a
nice building. This provides support for H10b, H10d and H10e, but not H10a or H10c. H11 is
not considered, since the direct paths in the model are not significant.
Table 8 about here
Discussion and Conclusion
Although information-based businesses have been around for more than a century in physical
format, digital business strategy creates new opportunities for value creation. The rise of
social media presents such opportunities for newer insights and tourism practitioners can fine-
tune their action and personalize their offerings. Online reviews allow customers to
democratize content for sharing, which have dramatically altered the relations between the
firm and customers. Such shifts have allowed new forms of intermediaries which are able to
create revenue streams. Social media online reviews can provide a cost effective way of
monitoring the customer voice, and can be a competitive edge for even the smallest hotel.
The purpose of this research was to analyze in more detail how customer voice may affect
hotel business performance by considering the antecedent effects of hotel attributes on hotel
occupancy and RevPar. We identify both theoretical and practical issues of the role hotel
attributes play in consumers’ minds. Knowing the attributes that lead to higher levels of
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performance will enable optimal decision-making and a better allocation of valuable
resources. We propose a model of hotel attributes that integrates aggregated TrustYou
customer online reviews arguing that by identifying performance driving attributes resources
can be allocated purposefully- which consequently leads to higher business performance if not
to the creation of competitive advantage.
This study uses data from TrustYou, which searches, analyzes and distils opinions from
reviews written across the Internet. It uses online reviews to produce online reputation
management tools to hotels, restaurants, and destinations. As well as reviews that can
positively influence travelers’ bookings, negative reviews can adversely affect the booking
intention. For the purposes of this study, we argue that it is strategically important for hotel
managers to understand how via customer voice, various hotel attributes interact and affect
business performance.
The analysis of data from 442 Swiss hotels suggests a complex relationship across a number
of salient variables. Specifically, we identify a number of hotel attributes that can be used to
predict hotel performance. While some studies have produced models linking some hotel
attributes with performance via the development and measurement of social constructs (e.g.,
Dolnicar and Otter 2003; Millar, Mayer, and Baloglu 2012), we advance these studies by
analyzing in more detail the antecedent effects of hotel attributes on hotel business
performance based on the empirical voice of actual customers, i.e., the valence of reviews.
We focus on the impact of the valence of reviews as it strongly influences the consumers’
decision-making process when selecting a hotel for consideration. Price is no longer the sole
consideration, when consumers’ select a hotel (Noone and McGuire 2013). Consumers will
turn to reviews and ratings to inform their hotel purchase decision.
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Both positive and negative reviews are a potentially important customer voice (Luo 2009), but
prior studies either treat them in isolation, or not fully in terms of hotel performance (Chi and
Gursoy 2009). By investigating the hotel landscape of an entire country, namely Switzerland,
and by matching actual performance data with online reviews we contribute to the tourism
literature showing which hotel attributes matter the most for tourists in Switzerland. The data
generated from 68 evaluation platforms, such as TripAdvisor, HolidayCheck and
booking.com allowed a more comprehensive view on the impact of online reviews and
thereby expand on studies that only used one source of online reviews (e.g., Xie, Zhang and
Zhang 2014; Blal and Sturman 2014).
As expected, and in accordance with prior theory of visitor needs in terms of attributes (see
e.g., Mohsin and Lockyer 2010; Ramanathan and Ramanathan 2013), positive voice about the
hotel room is a significant contributor to higher levels of performance. Interestingly, the
Internet was ranked second. In other words, positive voice about the hotel is the most
important of the constructs examined, driven by five sub-factors, with rooms being the most
important, followed by Internet and building (we acknowledge that this might be different
today). In 2010, Wi-Fi infrastructure was not at the same level as today. Social media started
to become popular during these years, so the need for connections became “urgent”).
This provides some evidence of consumers’ fear of being offline (FOBO). Hotel guests not
only want Wi-Fi, but as Bulchand-Gidumal, Melian-Gonzalez and Lopez-Valcarcel (2011)
note hotels can gain significant advantages by offering free Internet. This raises some
pertinent issues, and given the paucity of academic research assessing consumers’ perceptions
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of ICT use in hotels (Line and Runyan 2012) opens up some interesting lines of future
enquiry.
Overall, the results show that positive experiences regarding the hotel (Hotel +), as voiced
through social media, have the greatest impact on hotel Demand (measured through
occupancy rates) and subsequently Revenue (measured through RevPAR).
With these findings, this study contributes to the current debate on the effect of positive
reviews on sales and revenues. Previous research has shown mixed results for the impact on
consumer decision making (see e.g., Duan, Gu and Whinston 2008; Vermeulen and Seegers
2009) with extant literature arguing that negative reviews influence consumers’ decision
making more than positive reviews (see Chevalier and Mayzlin 2006; Papathanassis and
Knolle 2011). With regards to business performance, our research shows that only positive
reviews have an impact on RevPar while the effect of negative reviews is insignificant. This
might imply that the negativity effect was not strong enough for the single hotel attributes to
be carried through to performance. This finding contributes to the ongoing debate between
researchers such as Chevalier and Mayzlin (2006) and Duan, Gu and Whinston (2008)
showing that positive reviews increase product sales and revenues, whereas negative online
reviews decrease revenues and Chen, Wu and Yoon (2004) arguing that online reviews are
not correlated with sales. A negative relationship was, however, identified by Berger,
Sorensen and Rasmussen (2010) who found that negative online feedback leads to increasing
sales.
As Switzerland attempts to recover its position in the global tourism market, the findings of
the study present several opportunities at the regional, national and international levels for
hoteliers. The 26 cantons within Switzerland are of various sizes, ranging from Grigioni
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(7,105km
2
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2
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2
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too small to meet the challenges created by globalization. This presents a significant challenge
to the Swiss government in terms of a national tourism policy.
Swiss hoteliers need to enhance their hotel product and service levels to obtain uplift in
demand. This will provide an additional benefit such as an increase in the market value,
which could be attractive for local and overseas investors. With approximately only 10% of
branded hotels the real estate market will tend to be illiquid, which will be unattractive for
potential investors (Schofield and Partners 2013). This can be illustrated by the fact that in
2010, the level of construction in the Swiss hotel and restaurant sector was just in excess of
CHF 800 million, which continued the downward trend from the 2007 peak of more than
CHF 1100 million (Swiss Tourism Federation 2013). The results of this study provide some
specific areas to consider when renovating a tired hotel product or when making an
investment decision. Schofield and Partners (2013) state that from an investment perspective
the Swiss hotel market can be categorized as: resort/mountain hotels; Geneva/Zurich hotels;
and other city hotels. Competition for hotels operating within these marketplaces will be
different. In terms of hotel attributes discerning travelers will be looking at the attributes
included in this study, but will have differing expectations. Ideally, future research can delve
down into these three categories and incorporate traveler’s characteristics into the research
model and subsequent analysis.
The Swiss hotel market could be innovative and create products and services that are
attractive for new markets such as bleisure (business and leisure) and framily (friends and
family). The business cities of Geneva and Zurich benefit from a favorable mix of business
and leisure clientele and are ideally suited. Bridgestreet global hospitality (2014) “Bleisure
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Report” states that the majority of annual travel is for leisure, but 83% of their respondents
use business trips to explore the city that they are visiting. They also state that hotels need to
provide additional local services and really bring their brand to life. Switzerland with its
strong financial services sector could help provide finance for many indigenous hotel units to
develop brands that can design products and services for travelers’ blurred lifestyles. The
results of the study provide a platform to develop a bespoke offer that will translate into
higher levels of performance.
The Swiss hotel model will help researchers and practitioners explain which aspects of visitor
experience, as voiced through social media, have the greatest impact on hotel demand and
hotel performance. Based on these insights hotel managers can purposefully allocate scarce
resources. Since positive comments were high for quality of rooms, service and hotel both
tangible and intangible resources can be allocated to sufficient personnel, interior and exterior
design and facilities such as Internet. Consumers were found to express dissatisfaction with
rooms, hotel and food and drinks. Knowing that consumers complain about dissatisfactory
services, hotel managers need to direct resources into the establishment of effective policies
and processes in order to prepare for adequate responses to costumers both online and on site
(Xie, Zhang and Zhang 2014). Ramanathan and Ramanathan (2013) point out that high
performance in terms of various service attributes is vital in order to achieve customer loyalty.
Thus, managers should ensure they provide the required resources and capabilities to perform
various services. Yet, since we found that only positive reviews had an impact on
performance, we recommend hoteliers to focus resources on hotel attributes customers are
happy with and maintain their quality, but also address the negative ones even though a
significant impact on performance was not found in our study. Listening and responding to
negative reviews is important, e.g., for reputational purposes, and previous studies have
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highlighted the importance of responding to negative reviews for driving hotel performance
(Kim, Lim and Brymer 2014; Chen and Xie 2008). This finding considerably influences the
strategic thinking of hotel managers in that they should take advantage of positive reviews
and take respective action to further advance those. We argue that managers should act
proactively and purposefully manipulate those key hotel attributes through the creation of
dynamic capabilities and core competences. Due to the dynamic environment in which the
resource-intense tourism sector currently operates, strategic decisions have to be dynamic
(Phillips and Moutinho 2014) and the allocation of resources be informed by knowledge
about real drivers of performance.
The study has a number of limitations. Firstly, the research focuses on a sample of hotels in a
developed European destination, Switzerland. The overall balance of star ratings in the
sample is quite high. Further research is needed to ensure that the results are generalizable to
other contexts, for example in developing economies. Second, the research is reliant on data
collected using the TrustYou platform. This is a broad-reaching platform, but is not the only
available source of visitor comments regarding hotels and so provides a specific perspective
on the views of hotel customers. Third, the use of sentiment analysis for specific positive and
negative categories of customer comment led to single-item formative variables being used in
the study. This has limitations regarding the traditional measures for validity and reliability
that can be used in confirmatory factor analysis. The research would benefit from the analysis
of customer characteristics, more current and longitudinal data covering further time periods.
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References
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FIGURES
Figure 1. Research Model.
Note: + indicates positive comments; - indicates negative comments.
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Figure 2. The Hotel (+) Variable
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TABLES
Table 1. Previous research on the impact of the valence of online reviews on business
performance in the hotel industry
Author(s)
(Year)
Purpose of Research
Data Analysis
Technique
Findings
Sector
Data Source of
Online Reviews
Ye et al. (2009)
Impact of online
consumer-generated
reviews on hotel room
sales
Log-linear
regression model
Positive online
reviews significantly
increased the
number of hotel
bookings
Hotels
Ctrip.com
(Chinese travel
website)
Ye et al. (2011)
Impact of online user-
generated reviews on
hotel room sales
Log-linear
regression model
using number of
reviews as a proxy
for hotel room sales
Positive impact of
review valence on
online room sales
Hotels
Ctrip.com
(Chinese travel
website)
Anderson
(2012)
Impact of user reviews
on hotel pricing power,
consumer demand, and
revenue performance
Logistic Regression
Positive relationship
confirmed
Hotels
comScore and
TripAdvisor
Öğüt and Taş
(2012)
Impact of star rating
and customer rating on
hotel room sales and
prices
Regression analysis
using OLS
Improvement in
customer ratings
result in higher sales
and higher pricing of
hotel rooms
Hotels
Booking.com
(hotel booking
website)
Blal and
Sturman (2014)
Impact of contextual
factors such as product
type on relationship
between eWOM and
sales performance
Hierarchical linear
modeling
Valence has a
greater effect on
luxury hotels’
RevPAR while the
volume of reviews
has a greater effect
on lower-tier hotels.
Hotels
Tripadvisor.com
Nieto et al.
(2014)
Explore effects of
eWOM on business
performance
Regression analysis
More positively
valenced reviews
positively affect
performance; more
negatively valenced
reviews negatively
affect performance.
Rural
lodging
establish
ments
Toprural.com
Xie et al.
(2014)
Impact of consumer
reviews and
management responses
on performance
Linear regression
modeling
Overall ratings
influence hotel
performance most,
followed by review
variation and the
amount of reviews
posted
Hotels
Tripadvisor.com
This Study
Impact of online
customer reviews
related to 22 hotel
attributes on business
performance
Partial Least Square
Path Model
Positive comments
about hotel most
important, driven by
sub-attributes rooms,
internet and building
Hotels
TrustYou Score
(aggregated data
from 68 online
platforms)
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Table 2. Overview of online platforms aggregated by TrustYou
Ab-in-den-Urlaub
MYTravelGuide
Atrapalo.com
NeckermannReisen.de
ATraveo.de
Opodo
Ayda.ru
Orbitz
Booking.com
Priceline.com
Ciao.co.uk
Qype.com
CosmoTourist.de
Qype.co.uk
CustomerAlliance
RakutenTravel
EBookers
Reisen.de
Expedia
Roomex
Falk
Schneehoehen.de
FastBooking.com
ThomasCook.de
Fodors.com
Tiscover.com
Google Places
TravBuddy
HolidayCheck.com
Traveluation
HolidayranKing.de
Travelocity.com
Holidays Uncovered
TravelPost.com
HolidayInsider.de
TripAdvisor
Hostelworld.com
Tripwolf
Hotels.com
Trivago.co.uk
Hotel.de
Trivago.de
Hotel-ami.de
TOPHotels.ru
Hotelcheck.de
Urlaub.de
HotelClub
VakantieReisWijzer.nl
HotelKatalog24.de
Varta Guide.com
HRS.de
Varta Guide.de
HRS.com
Venere.com
IgoUgo
Vinivi
Kayak.com
VirtualTourist.com
Lastminute.de
Votello.de
Lastminute.com
Weg.de
LateRooms.com
YahooTravel
Merian
Zoover.de
Monvoyager
4travel.jp
Table 3. Summary of dataset
Swiss hotel data
Number of Hotels
Number of Rooms
Number of Beds
Number of Positive Reviews
Number of Negative Reviews
442
18,425
32,451
63,026
11,406
Total Number of Reviews
78,171
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Table 4. Number of hotels participating and Swiss average (2010)
No. of Hotels in sample
No. of Hotels in Switzerland
Category
1 star
0
42
1.9%
2 Star
82
19%
261
11.9%
3 Star
166
37%
960
43.7%
4 Star
83
19%
443
20.2%
5 Star
25
6%
91
4.1%
Other
categories
86
19%
399
18.2%
Total
442
100%
2196
100%
No information
3281
Swiss Total
5477
Table 5. Descriptive statistics
Variable
Minimum
Maximum
Mean
Std. deviation
Ambiance +
0
2.714
0.089
0.216
Beverages +
0
0.684
0.036
0.080
Building +
0
1.409
0.075
0.160
Food & Drink +
0
9.000
0.527
0.979
Grounds +
0
1.707
0.099
0.248
Hotel +
0
7.400
0.583
1.059
Internet +
0
2.286
0.052
0.164
Location +
0
9.980
0.474
0.961
Menu +
0
0.182
0.002
0.015
Room +
0
11.111
0.798
1.463
Service +
0
11.357
0.699
1.318
Ambiance -
0
0.267
0.010
0.026
Beverages -
0
0.214
0.006
0.022
Building -
0
0.786
0.022
0.067
Food & Drink -
0
3.000
0.084
0.222
Grounds -
0
0.324
0.013
0.037
Hotel -
0
2.214
0.088
0.180
Internet -
0
0.425
0.019
0.045
Location -
0
0.900
0.041
0.102
Menu -
0
0.167
0.001
0.010
Room -
0
5.893
0.244
0.535
Service -
0
0.745
0.055
0.097
Occupancy
4.805
100.000
49.539
19.672
RevPAR
3.091
464.745
84.007
66.435
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Table 6. Test of Research Model
Relationship
Path
Coeff.
St.
Error
t
Pr >
|t|
Grounds + ! Hotel +
0.054
0.016
3.344
0.001
Building + ! Hotel +
0.133
0.022
5.924
0.000
Ambiance + ! Hotel +
0.054
0.023
2.373
0.018
Internet + ! Hotel +
0.217
0.020
10.894
0.000
Rooms + ! Hotel +
0.649
0.030
21.809
0.000
Hotel +: R2 = 0.907 (F=845.910, Pr > F <.001)
Grounds - ! Hotel -
0.130
0.025
5.137
0.000
Building - ! Hotel -
0.106
0.037
2.839
0.005
Ambiance - ! Hotel -
-0.009
0.024
-0.362
0.718
Internet - ! Hotel -
0.036
0.030
1.184
0.237
Rooms - ! Hotel -
0.715
0.045
15.954
0.000
Hotel -: R2 = 0.770 (F=291.520, Pr > F <.001)
Menu + ! Food & Drink +
0.269
0.038
7.174
0.000
Beverages + ! Food & Drink +
0.528
0.038
14.061
0.000
Food & Drink +: R2 = 0.430 (F=165.632, Pr > F <.001)
Menu - ! Food & Drink -
0.141
0.044
3.189
0.002
Beverages - ! Food & Drink -
0.476
0.044
10.724
0.000
Food & Drink -: R2 = 0.306 (F=96.582, Pr > F <.001)
Hotel + ! Demand (Occupancy)
0.359
0.173
2.079
0.038
Hotel - ! Demand (Occupancy)
-0.057
0.090
-0.638
0.524
Food & Drink + ! Demand (Occupancy)
-0.037
0.097
-0.379
0.705
Food & Drink ! Demand (Occupancy)
-0.027
0.074
-0.368
0.713
Service + ! Demand (Occupancy)
-0.261
0.187
-1.393
0.164
Service - ! Demand (Occupancy)
0.104
0.062
1.669
0.096
Location + ! Demand (Occupancy)
0.188
0.123
1.530
0.127
Location - ! Demand (Occupancy)
0.074
0.071
1.041
0.299
Demand (Occupancy): R2 =0.111 (F=6.728, Pr > F < .001)
Demand (Occupancy) ! Revenue (RevPAR)
0.563
0.039
14.286
0.000
Revenue (RevPAR): R2 = 0.317 (F=204.086, Pr > F < .001)
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Table 7. Impact and contribution of the variables to Hotel +
Rooms +
Building +
Internet +
Ambiance +
Grounds +
Correlation
0.934
0.732
0.679
0.654
0.380
Path coefficient
0.649
0.133
0.217
0.054
0.054
Correlation x path coefficient
0.607
0.097
0.147
0.035
0.021
Contribution to R² (%)
66.922
10.717
16.224
3.870
2.267
Table 8. Sobel Tests for Mediation
Mediation Path (A ! B ! C)
a (A!B)
SEa
b (B!C)
SEb
Z (A!C)
SE
p
Grounds + ! Hotel + ! Demand
0.054
0.016
0.359
0.173
1.768
0.011
0.077
Building + ! Hotel + ! Demand
0.133
0.022
0.359
0.173
1.963
0.024
0.050
Ambiance + ! Hotel + ! Demand
0.054
0.023
0.359
0.173
1.555
0.012
0.120
Internet + ! Hotel + ! Demand
0.217
0.020
0.359
0.173
2.038
0.038
0.042
Rooms + ! Hotel + ! Demand
0.649
0.030
0.359
0.173
2.066
0.113
0.039
Hotel+ ! Demand ! Revenue
0.359
0.773
0.563
0.039
2.054
0.098
0.040
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