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Market accessibility and hotel prices in the Caribbean:
The moderating effect of quality-signaling factors
Yang Yang a
a School of Tourism and Hospitality Management, Temple University,
Philadelphia, Pennsylvania 19122, USA
(Email: yangy@temple.edu)
Noah Mueller b
b Department of Geography, University of Florida, Gainesville, Florida 32611, USA
(Email: nmueller@ufl.edu)
Robertico Croes c
c Rosen College of Hospitality Management, University of Central Florida, Orlando, Florida
32819, USA
(Email: Robertico.croes@ucf.edu)
* Yang Yang is the corresponding author
Tele: 1-(215)-204-5030
Fax: 1-(215)-204-8705
Please cite as:
Yang, Y., Mueller, N., and Croes, R. (2016). Market accessibility and hotel prices
in the Caribbean: The moderating effect of quality-signaling factors. Tourism
Management, 56, 40-151.
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Market accessibility and hotel prices in the Caribbean:
The moderating effect of quality-signaling factors
Abstract The purpose of the paper is to investigate the influence of market accessibility on hotel prices
and how this influence is moderated by various quality-signaling factors, such as online user ratings,
“thumbs up” (recommendation) percentage, hotel class, and chain affiliation. Using a randomized sample
of hotels in the Caribbean islands, we employ a three-level mixed-effect linear regression model to
investigate the plausible relationship between market accessibility and hotel prices. After controlling for
unobserved island-level and hotel-level characteristics, the model indicates that in most periods, low
market accessibility (high flight costs) leads to lower hotel prices, and this influence is mitigated by well-
established positive reputations as represented by the quality-signaling factors. Our findings imply that
hotels should work to increase their reputations to help buffer the impacts of inaccessibility. In an effort to
increase market accessibility, one course of action is to reduce airport landing taxes and fees.
Keywords: hedonic price model; Caribbean hotels; market accessibility; quality-signaling factor; mixed-
effect linear regression
1. Introduction
Market accessibility is defined as the ability of an individual to benefit from a set of opportunities or
activities at a destination. This accessibility is determined by the number of opportunities at a place and
the cost of realizing those opportunities, which is shaped by several spatial factors (Hanink & Stutts,
2002). Consumers’ decisions are shaped by costs of travel, which are related to geographical distance
(Nicolau & Mas, 2006). For example, a hotel’s location is a fixed attribute. Once established, hotels can
hardly move to relocate, therefore consumers must travel to hotels (Yang, Luo, & Law, 2014).
Mainstream tourism demand studies have defined market accessibility mainly in terms of distance as
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revealed in transportation costs, and distance is linked to travel costs in tourists’ budgets (Peng, Song, &
Crouch, 2014). Hotel demand is also linked to travel costs: lower travel costs increase destination access,
which leads to increased arrivals and hotel room demand, and consequently, an increase in price.
However, to our knowledge, since most hotel pricing studies used the sample of urban hotels without
clear information on the origin of hotel guests, the hotel pricing literature has largely overlooked this
natural tourism demand process.
Search costs are high when products are perishable and intangible (e.g., hotel products), making it
difficult for people to gauge product quality (Woodside & King, 2001). Consequently, consumers search
for information to reduce uncertainty when purchasing, for example, hotel rooms. Online consumer
reviews have become important sources of information about the quality and image of a product or
service (Gretzel & Yoo, 2008). Consumers consider observable quality signals, such as third-party
endorsements (e.g., word of mouth), reputation, guarantees and price, and numerous scholars have
investigated how signals affect perceptions of quality and purchasing risk when information is limited
(Gretzel & Yoo, 2008; O'Connor, 2010). In the past literature, it is assumed that the effects of quality
signaling factors are same across different properties. This one-fit-all assumption is highly problematic
due to the great heterogeneity of hotel properties and products they offer. Hence, the relationship between
hotel price and quality signals can be contingent upon moderating factors, such as market accessibility.
However, these moderators of this relationship has been largely overlooked in the past hotel pricing
literature.
To fill an important research gap, we investigate the relationship between market accessibility and hotel
price using a hedonic pricing framework based on a multi-level dataset from hotels located on Caribbean
islands. In particular, we scrutinize the moderating effect of various quality-signaling factors, such as
online reviews, chain affiliation, and star rating. This paper is expected make several contributions to the
current body of literature. First, this study represents one of the first efforts to investigate the determinants
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of room prices for Caribbean hotels, as past pricing studies are dominated by urban hotels (Yang, et al.,
2014). Second, we are particularly interested in the effect of market accessibility on Caribbean hotel
prices. Since most Caribbean hotels are categorized as resorts and distant from major markets, market
accessibility could play a more substantial role in determining hotel price. Third, although many scholars
have incorporated quality signals (especially online signals) as a determinant of room rate (Öğüt & Onur
Taş, 2012; Yacouel & Fleischer, 2012), none have investigated how quality signals may help hotels
overcome competitive disadvantages. As information technology becomes more important in shaping
customer decision-making processes, understanding the influence of online reputation indicators would
help inform marketing strategies and customer service practices. Finally, mainstream hedonic price
models do not consider the hierarchy of the dataset nor the unobserved effects stemming from specific
factors (Goodman & Thibodeau, 2003; Orford, 2000). In order to address these shortcomings, we employ
a multi-level mixed-effect linear regression technique to estimate the proposed hedonic price model. The
outputs of this study are particularly useful for practitioners. Understanding the dynamic relationship of
these factors has important implications for the role of online consumer opinion platforms in hotel pricing
(Zhu & Zhang, 2010). These opinions, to a certain extent, control the strategic considerations of hotel
managers.
The dynamics affecting tourism price structures in the hotel sector have important economic implications
because the sector is a significant driver of local economic benefits due to its vast purchasing power and
inputs required to support daily operations (Croes, 2006). The relevance of market accessibility is
particularly dominant for island destinations due to natural barriers based on geography, distance and
time. Competition on island destinations is condensed to the local level because at a large enough scale,
periphery locations do not exist. Almost every inbound visitor planning to stay overnight on an island
destination uses air transportation, thereby making air travel costs one of the most important
considerations in the decision to visit an island destination. Tourism demand expansion drives economic
growth in the short term, and ultimately in the long term as well (Vanegas & Croes, 2003). Market
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accessibility is thus an overriding and constant concern for Caribbean island destinations and for small
island destinations in general, where tourism is an economic driver and the lodging industry is a prevalent
catalyst for continued economic growth and security(Croes, 2011).
2. Literature Review
Hedonic pricing theories were advanced in the mid-1970s by pioneers such as Rosen (1974) and
Goodman (1978) in efforts to understand the value of embedded product attributes or characteristics
according to revealed market behavior. Based on Lancaster’s consumer theory, this modeling strategy
assumes that a product’s price can be specified as a function of its associated immanent utility-bearing
characteristics or attributes (Thrane, 2005). Therefore, a hedonic price strategy enables the total price of a
particular good to be disaggregated into separate implicit prices that are attributed to certain inherent
attributes or characteristics of the good based on the utility consumers can perceive (Dwyer, Forsyth, &
Dwyer, 2010); these implicit prices basically reflect consumer willingness-to-pay (Goodman &
Thibodeau, 2003). This method of determining price has been applied to a wide range of products and
services that can be decomposed into different attributes with their own implicit prices in equilibrium.
The observed price reveals information about potential customers’ underlying preferences for those
attributes and how businesses can increase the price by including particular characteristics. Empirically,
the hedonic price model regresses observed prices on a set of structural, perceptual, and transaction-
related factors, and the regression coefficients provide vital information about consumers’ willingness-to-
pay. Typically, a positive willingness to pay indicates a positive contribution to the level of utility
perceived by consumers. Papatheodorou, Lei, and Apostolakis (2012) conducted a thorough review of
previous hedonic price model applications in tourism and hospitality management, and they pointed out
several advantages of this pricing method, including the ability to price non-market characteristics and the
flexibility to accommodate a wide range of pricing factors,
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In the case of hotels and lodging, the price of a particular hotel’s offerings is a function of several
attributes of that establishment as well as the local environment. The nature and variety of the specific
attributes can be divided into two main categories: internal and external (Chen & Rothschild, 2010).
Internal drivers are attributes over which the hotel has control. Attributes that have been considered to
impact a hotel’s price include, but certainly are not limited to: star rating (Bull, 1994; Espinet, Saez,
Coenders, Fluvi, & M., 2003; Israeli, 2002; Thrane, 2007), hotel age (Bull, 1994; Hung, Shang, & Wang,
2010), affiliation with hotel chains/brands (Thrane, 2007; White & Mulligan, 2002), hotel infrastructure
(Espinet, et al., 2003; Thrane, 2005, 2007), the number of rooms available (White & Mulligan, 2002), and
the availability of parking (Bull, 1994; Chen & Rothschild, 2010; Espinet, et al., 2003; Hamilton, 2007;
Hung, et al., 2010). External drivers of price are attributes over which the hotel operator has no direct
control, such as competition (Balaguer & Pernías, 2013). External attributes are complex and often are
considered to have significant influence on pricing as well (Bull, 1994; Chen & Rothschild, 2010; Rigall-
I-Torrent, et al., 2011; Saleh & Ryan, 1992).
Additionally, there is one particular attribute that is unique: location. Although all other internal and
external attributes may change, a hotel’s location is fixed (Bull, 1994). Location determines proximity to
attractions for hotel guests, such as activities or a city center (Chen & Rothschild, 2010; Hung, et al.,
2010), beaches (Rigall-I-Torrent, et al., 2011), or public goods (Rigall-I-Torrent & Fluvià, 2011).
Moreover, as suggested by spatial interaction theory and the location-allocation model in business
geography, a hotel’s location relative to another can be captured by market accessibility, such as general
accessibility for hotel customers (Fleischer, 2012; Yang, et al., 2014). In the context of hotel demand,
spatial interaction theory states that hotel customers trade off the perceived attractiveness of hotel
properties against the deterrent effect of travel cost, which can be regarded as market accessibility
(Fotheringham & O'Kelly, 1989). In several previous studies, scholars have empirically highlighted the
importance of market accessibility in determining tourism and hospitality demand (Hanink & Stutts,
2002; Luo & Yang, 2013; McKercher, 1998). Therefore, it is expected that market accessibility would
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also play an important role in determining hotel prices, especially on small tourist islands in the
Caribbean where hotel supply, at least in the short run, is relatively inflexible.
Although a large number of tangible structural factors have been investigated in the previous literature,
only a few scholars have examined the effect of quality signaling factors (Abrate, Capriello, & Fraquelli,
2011; Andersson, 2010; Zhang, Ye, & Law, 2011) as a particular type of internal attribute influencing
hotel room rates. Quality signaling factors refer to various factors that reduce the information
asymmetries in the market by offering buyers information on the quality of products they intend to
purchase, and typical quality signaling factors include quality certificates, customer rating, and third-party
evaluation. Several theories in information economics explained the importance of these factors during
the communication between sellers and buyers. Due to the experience nature of hotel products, the sellers
inherently possess more information on product quality than prospective buyers, leading to information
asymmetries in the lodging market (Woodside & King, 2001). Akerlof (1970)'s classic "lemons" model
highlighted market inefficiency in the presence of severe information asymmetries: if quality cannot be
signaled, only lower-quality products will be offered for sale. Therefore, hotel guests increasingly search
for various quality-signaling factors online owing to the high search costs associated with the perishable
and intangible nature of hotel products (Woodside & King, 2011). Grossman (1981)’s "unfolding" model
states that when quality signaling is effective, firms offering high-quality service can get a price premium.
Hence, from the supply side, hotels now recognize the importance of establishing and maintaining
customer relationships through social media with a low transaction cost; these efforts are reflected in
quality-signaling factors and help secure a competitive advantage over peers. Abrate et al. (2011) pointed
out the importance of reputation-based quality signals including star rating, brand affiliation, presence in
the Michelin Guide, and status of quality assured hotels. Apart from these factors, various online
reputation factors have become available with the ongoing penetration of IT technology. For example,
some quality signaling factors such as online user rating and “thumbs up” percentage (reflecting the
percentage of guests who recommend a hotel) are easily accessible for potential customers via hotel-
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booking websites. As suggested by customer loyalty theory, electronic word of mouth information about
hotels helps potential guests to overcome disadvantages associated with information asymmetry when
making booking decisions, and this source of information is believed to be more convincing and reliable
than others for gauging the quality of hotels (Öğüt & Onur Taş, 2012). Sparks and Browning (2011)
showed that a high review rating facilitates hotel booking intentions, and Ye, Law, and Gu (2009)
suggested that positive reviews increase the number of bookings for a lodging establishment.
Alternatively, if hotel operators are aware of these third-party review websites, it is not hard to imagine
room prices being influenced by consumer opinions. According to Grossman (1981)’s "unfolding" model,
hoteliers tend to charge price premiums when their establishments have excellent reputations. The
effective and reliable quality signals reduce the search costs of buyers, and at the same time, degrade the
uncertainty of purchase, making buyers willing to pay a higher price for the products with positive quality
signals. Therefore, it is reasonable to expect a positive association between reputation signals and hotel
price. Andersson (2010) unveiled the significant impact of online ratings for facility and food-and-
beverage quality in explaining the transaction room prices of Singapore hotels. Zhang, Ye, and Law (2011)
evaluated the impact of online reviews on hotel price, and found that the rating scores on room quality
and location significantly influence hotel prices. Schamel (2012) included a comprehensive multi-score
online rating in the hotel hedonic model from various search engines, and the empirical results confirm
that guests are willing to pay a higher premium for a higher rating. Yacouel and Fleischer (2012)
proposed a theoretical model showing that a hotelier can charge a premium when a high level of service
quality is revealed. They also empirically validated that guests pay a premium when a hotel’s service
quality has a high rating on travel agent websites. Using a sample of hotel room prices in Paris and
London, Öğüt and Onur Taş (2012) found that online customer ratings are positively associated with hotel
price and highlighted that the influence of these ratings is different across different types of hotels: room
rates for upscale hotels are more sensitive to star ratings. As shown in previous hotel pricing literature,
although various quality-signaling factors have been incorporated directly into the hedonic price model,
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few (if any) researchers have considered the moderating effect of quality-signaling factors on other
pricing determinants.
The impact of quality signaling effect can be moderated by location-related factors. Gao, Gopal, and
Agarwal (2010) found that the return on third-party quality signals is heterogeneous for the Indian
software services industry, and this return is dependent on a vendor's location strategy. They showed that
the value from a quality signal is particular higher for firms distant from industrial clusters because this
signal is effective to reduce the pernicious effects of information asymmetry in a highly uncertain
environment related to locational disadvantages. Likewise, McDevitt (2011) indicated that quality signals
are more important for firms located in small markets than those in large markets because word of mouth
diffuses more rapidly in a small market.
From a statistical point of view, most researchers who investigated determinants of hotel prices in the past
applied a hedonic price model based on simple linear regression, which manifests a number of
shortcomings that compromise the understanding of hotel price dynamics, such as non-stochastic effects
and endogeneity. In addition, mainstream hedonic price models do not consider the hierarchy of the
dataset nor the unobserved effects stemming from specific factors (Goodman & Thibodeau, 2003; Orford,
2000). In order to address these shortcomings, in this study we employ an innovative multi-level mixed-
effect linear regression to estimate the proposed hedonic price model. Scholars have largely overlooked
that market accessibility may be a key driver of market prices in the hotel industry (Abrate, Capriello, &
Fraquelli, 2011; Zhang, Zhang, Lu, Cheng, & Zhang, 2011); market accessibility is ignored in the state
space model (Kaidou, Moore, & Charles-Soverall, 2012), the geographically weighted regression model
(Zhang, Zhang, Lu, Cheng, & Zhang, 2011), and the spatial econometric model (Santana-Jiménez,
Suárez-Vega, & Hernández, 2011).
3. Research Design and Model Specification
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We considered Caribbean islands readily accessible to the U.S. market. Cuba, the largest island in the
Caribbean, was excluded due to travel restrictions for U.S. citizens. Hispaniola and Saint Martin were
excluded because the existence of more than one country on these islands may have introduced unwanted
effects on hotel prices. Several remaining islands were excluded because of their large size or because
English is not a commonly spoken language, thus impeding visitation from American tourists.
Exceedingly small islands were also eliminated due to concerns over the number of hotels available. From
the pool of remaining destinations, we selected ten islands with a wide geographic distribution and
considerable heterogeneity in terms of accessibility and levels of tourism development: New Providence,
Grand Cayman, Providenciales, St. Thomas, Aruba, St. Vincent, St. Kitts, Grenada, St. Lucia, and St.
Barthelemy. The tourism industry for most of these islands depends heavily on the U.S. market.
According to the 2013 UNWTO Yearbook of Tourism Statistics, the U.S. market share for inbound
tourists exceeds 40% for seven out of the ten islands. The locations of these islands are shown in Figure 1.
(Please insert Figure 1 here)
Using the website www.TripAdvisor.com, we obtained a list of hotels that included hotel class, user
rating, and thumbs up percentage (O'Connor, 2010). For consistency, individual websites for each island
or nation were avoided as they utilize different internet travel search services. For each island, we used a
random number generating software to randomly select ten hotels among all hotels with user reviews on
TripAdvisor.com. This created a set of 100 hotels. To measure the dependent variable, we used the hotel
room price for a one-week stay, a common length of stay for our selected islands (Caribbean Tourism
Organization, 2010). Hotels often charge higher rates for immediate occupancy, so the one-week stay was
selected far enough into the future to avoid this phenomenon (Rigall-I-Torrent, et al., 2011). Further, to
account for price differences during peak and off-peak seasons (Israeli, 2002; Monty & Skidmore, 2003;
White & Mulligan, 2002), we selected three different one-week stays, each in a different month of the
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forthcoming year relative to the time of data collection. Two of the one-week stays were selected in the
low season outside of the shoulder season months of November and April. (Caribbean low season runs
from May through October and corresponds to summer in the Northern Hemisphere and tropical cyclone
season in the Caribbean.) The single high season rate was selected in the winter months when American
travel to the Caribbean is most pronounced at times that did not coincide with major United States
holidays such as Christmas and New Year’s Eve. These high and low season patterns are easily observed
in tourist arrival data provided by the Caribbean Tourism Organization 2010 individual country statistical
report (Caribbean Tourism Organization, 2010). Sunday to Sunday stays were selected to more accurately
represent the typical conditions for an average American vacationer who would take a week off from
work. The three one-week stays were: June 3–10, 2012 (low season); September 9–16, 2012 (low season);
and January 13–20, 2013 (high season).
For each of the 100 hotels, the least expensive available price for double occupancy was obtained for each
of the three weeks, resulting in 300 initial observations. (The most expensive prices were not collected
because there are no limits for luxury.) The total cost of a stay was collected either via automatic online
price checking and availability functions for each of the individual hotels, or calculated manually based
on published rates on the hotels’ websites along with all known taxes and fees. In a handful of cases, we
communicated with hotel booking personnel via telephone or email to inquire about missing or expired
rates. If the selected week’s stay was not available, the prior or following week was used. A price for one
hotel was discarded because all of the least expensive accommodations were booked, and those prices
were unavailable. All hotels on the island of St. Barthelemy were also eliminated, because half the hotels
from this island were not available for booking in September and only 15% of its tourists arrived from the
United States. One hotel in Grenada was excluded as well due to a similar data availability problem.
(Please insert Table 1 here)
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To reflect the hierarchical data structure embedded in our research design, we performed a three-level
mixed-effects linear regression to estimate the hedonic price model. There are several noticeable
advantages of applying the mixed-effect linear regression model in this study. First, the model takes full
advantage of the hierarchical structure of our dataset and accounts for the unobserved hotel- and island-
specific factors that influence hotel price that have not been incorporated in the model, such as location
relative to major tourist attractions, tourist density, and infrastructure to support tourism. As suggested by
several applications of mixed-effect linear models on real estate pricing, overlooking the hierarchical data
structure may lead to severe estimate biases (Cervero & Kang, 2011; Goodman & Thibodeau, 2003;
Orford, 2002). Second, the model provides an improved estimate to overcome the violation of observation
independence assumption embedded in traditional simple linear regression. Compared to other
sophisticated models utilized to rectify this problem, the specified random effects mimic the inter-
dependence of error terms in a more straightforward way (Orford, 2000, 2002).
We have multiple observations for each hotel over time, and the individual hotels are located on islands.
Hence, a three-level hierarchical structure is embedded in this data set. The first-level observation is the
hotel price at a particular time, the second-level observation is the individual hotel, and the third-level
observation is the island where the individual hotel is located. This mixed-effect regression model is
specified as follows:
1
pmm
ijt ijt i ij ijt
m
yx
where t indicates the period of observation (the first level observation), and t = 1, 2, 3; j indicates the hotel
(the second-level observation) to which the first-level observation belongs; i indicates the island (the
third-level observation) where the hotel is located; α is a constant; and x is a set of p explanatory
variables. Apart from the usual error term in a linear regression model,
ijt
, the model also incorporates
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two additional random effects:
i
denotes the island-level (third-level) random effect that captures
unobserved characteristics of the island, whereas
ij
denotes the random effect of the hotel-level (second-
level) random effect that captures unobserved attributes of the hotel. These random effects are not directly
estimated, but are summarized based on their estimated variances. Moreover,
i
,
ij
, and
ijt
are
assumed to follow an independent normal distribution with a mean of 0 and an unknown variance. To
estimate the proposed mixed-effect linear regression model, we utilize the full maximum likelihood
estimation with the expectation-maximization algorithm. This method generates robust estimates that are
asymptotically efficient and consistent (Hox, Moerbeek, & van de Schoot, 2010).
Based on past literature and data availability, we first specify four quality-signaling factors as
independent variables to explain room prices for Caribbean hotels:
rating: The hotel’s online user rating (on a scale from 1.0 to 5.0 with increments of 0.5) from a
common travel review website, TripAdvisor.com, which captures the electronic word of mouth
effect and reputation (O'Connor, 2010).
thumbs: The thumbs up percentage for the hotel from the TripAdvisor website, which reflects the
percentage of guests who recommend the hotel. Unlike the user rating based on a five-point scale,
users face two options: thumbs up or thumbs down (Pekar & Shiyan Ou, 2008).
class: The hotel’s class based on the TripAdvisor website. This variable ranges from one to five
stars, from low to high (Ghose, Ipeirotis, & Li, 2012).
chain: A dummy variable indicating if the particular hotel was part of a known chain. In our
model, chain = 1 indicates a chain affiliation, and chain = 0 otherwise. Chain-affiliated hotels are
expected to provide standardized products and services to guests, and their quality assurance
policies enable guests to reduce potential risks associated with a first-time stay (Abrate, et al.,
2011). We designated a hotel as being a part of a chain if the parent company held more than 30
individual hotels to ensure brand familiarity among most U.S. customers.
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As suggested in prior literature, we incorporated other independent variables into the hedonic price model:
lnaccess: Calculated as log(1/cost), where cost is the average cost of a round trip flight (in USD)
from New York, Chicago, and Los Angeles to the island where the hotel is located.
period: A nominal variable showing the week associated with the room price (Rigall-I-Torrent, et
al., 2011); period = 1 for the week June 3–10, 2012; period = 2 for the week September 9–16,
2012; and period = 3 for the week January 13–20, 2013.
beach: A dummy variable indicating access to a private or shared beach (Espinet, et al., 2003;
Rigall-I-Torrent, et al., 2011); beach = 1 indicates access, and beach = 0 otherwise.
business: A dummy variable indicating the presence of a business center on site (Chen &
Rothschild, 2010); business = 1 indicates the presence of a business center, and business = 0
otherwise.
fitness: A dummy variable indicating the presence of a fitness center on site (Andersson, 2010;
Chen & Rothschild, 2010); fitness = 1 indicates the presence of a fitness center, and fitness = 0
otherwise.
breakfast: A dummy variable indicating the availability of free breakfast on site (Chen &
Rothschild, 2010; Fleischer, 2012; Lee & Jang, 2011; White & Mulligan, 2002); breakfast = 1
indicates the availability of free breakfast, and breakfast = 0 otherwise.
internet: A dummy variable indicating the availability of free high-speed internet on site (Chen &
Rothschild, 2010; Lee & Jang, 2011); internet = 1 indicates the availability of free high-speed
internet, and internet = 0 otherwise.
pool: A dummy variable indicating the presence of a swimming pool on site (Andersson, 2010;
Chen & Rothschild, 2010; Rigall-I-Torrent, et al., 2011; White & Mulligan, 2002); pool = 1
indicates the presence of a swimming pool, and pool = 0 otherwise.
Data for these amenity variables were collected from the hotels’ websites.
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Market accessibility in this study is measured as a function of destination choice, which integrates
geographical distance and travel perceptions and is captured by the transportation price. In other words,
market accessibility to a Caribbean hotel is measured by the cost of a flight from major source markets to
the island. One could reason that people who can afford a higher travel cost could also afford a higher
cost for a week’s lodging. Therefore, high flight costs would correlate with higher hotel rates. But, it
could also be argued that the increased flight costs would drive the cost of lodging down, owing to the
fact that higher transportation costs would decrease demand for lodging and therefore, room costs. We
collected data for flight cost (cost) using an online search function, and used the cheapest published rates
for available flights coinciding with each of the week-long stays. We selected the three largest cities in the
United States (New York, Chicago, and Los Angeles) to offer a range of points of departure.
Descriptive statistics for the dependent and independent variables are presented in Table 2. We ran
several models in order to gain a better understanding of how the independent variables relate to the
dependent variable. After preliminary modeling efforts, we decided to use a log-linear model because it
outperformed other function forms (Hamilton, 2007; H. Zhang, et al., 2011). The dependent variable is
lnprice, which refers to the log of total cost (in USD) of a seven-night stay in a hotel. The average user
rating for the sampled Caribbean hotels is 4.022, corresponding to a relatively high level of customer
satisfaction. The average thumbs up percentage is around 80% and the average hotel class is around 3.5
stars. Only 16.0% of the hotels in the sample are affiliated with a hotel chain, and 53.4% have beach
access. In the sample, 50.7% of the hotels have a business center on site, and 50.7% have an on-site
fitness center. A free breakfast is available in 25.4% of the hotels, free high-speed internet is offered by
57.5% of the hotels, and a swimming pool can be found at 85.4% of the hotels.
(Please insert Table 2 here)
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Severe multi-collinearity problems will render questionable coefficient estimates in hedonic price models
(Andersson, 2010). To detect possible multi-collinearity problems, we created a correlation matrix for the
independent variables. By calculating their bivariate correlations, we were able to identify the variables
contributing to this problem. As expected, the three quality-signaling variables obtained from the
TripAdvisor website (rating, thumbs, and class) are strongly correlated, with Pearson correlation
coefficients ranging from 0.592 to 0.785. This result warns that they cannot be included in a single model
at the same time. For other pairwise correlation coefficients, we did not discover strong multi-collinearity.
Only two are above 0.4, and they are the correlation coefficients for class and fitness (0.494) and fitness
and business (0.416).
4. Results and Discussion
As noted in the previous section, we ran several models in order to gain a better understanding of how the
independent variables relate to the dependent variable. Table 3 presents the estimation results of these
models that fit the entire data set and data in different periods. Models 1 and 2 are estimated with the
traditional OLS linear regression without mixed effects and the mixed-effect linear regression,
respectively. Even though the estimated coefficients of rating are similar across these two models, the
coefficients of the non-hotel-specific variables, such as lnaccess and period, are very different. Therefore,
the results suggest that overlooking the hierarchical structure of the price data leads to unreliable
statistically inference in the empirical pricing model. Therefore, only mixed-effect linear regression
models are estimated and discussed to answer our research questions. Models 3–4 include all observations
and incorporate thumbs and class, respectively. The three online quality-signaling factors in Models 1 to 3
are estimated to be significant and positive, suggesting that guests are willing to pay more in return for a
high level of quality and a low level of uncertainty. We denote ex as an exponential function of x. Their
estimated coefficients show that one more point in online reviewer rating, 10% more thumbs up, and one
more point in hotel class are associated with hotel price premiums of 53.0% ((e0.425 – 1) × 100% ), 21.8%
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((e0.197 – 1) × 100%), and 75.4% ((e0.562 – 1) × 100%), respectively. Another quality-signaling factor,
chain, is positive but statistically insignificant in all three models, corroborating the results from Israeli
(2002). The insignificant estimated coefficient of lnaccess provides little support for the plausible positive
relationship between hotel room price and accessibility to major markets. For the nominal variable period,
period = 1 is set as the reference group. The results in Models 2–4 show that, in general, room rates for
Caribbean hotels are higher in January than in June and September.
With regard to hotel facilities and service, three factors are estimated to be significant, namely, fitness,
breakfast, and pool, which is consistent with the results from White and Mulligan (2002) and Andersson
(2010). To explain the marginal effects of these three variables on room rate, we use the estimated
coefficient obtained from Model 1. The results indicate that the presence of a fitness center, the
availability of free breakfast, and the presence of a swimming pool contribute to room rate premiums of
42.5% ((e0.351 – 1) × 100%), 18.3% ((e0.168 – 1) × 100%), and 55.4% ((e0.441 – 1) × 100%), respectively. To
our great surprise, even though beach access should be a great competitive advantage for Caribbean hotels
based on the recreational and aesthetic values associated with beaches in Caribbean destinations, access to
a beach does not significantly contribute to a high room price, as indicated by the insignificant coefficient
of beach in most models, which is inconsistent with the finding from Rigall-I-Torrent, et al. (2011). For
the estimated random-effect coefficients in the lower panel of Table 3, the standard deviation of the
island-level random effect is much smaller than its counterpart, the hotel-level random effect, suggesting
that hotel-level differences explain much more of the variation in hotel prices than island-level differences.
In Models 5–7, we estimate the hedonic price model for room rate in three different periods. To keep our
sample as large as possible, these three models include rating as the online quality-signaling measure.
Unlike its counterpart in Models 2–4, lnaccess is estimated to be positive and significant in Models 5 and
7, indicating that market accessibility plays an important role in determining hotel room rates in June and
January for Caribbean hotels. Some coefficients are estimated to be stable over periods, such as rating
17
and lnaccess, although they are moderately larger in the high season (Model 7). The variable beach is
estimated to be positive and statistically significant in Model 7, suggesting that hotels with beach access
charge higher prices during the high season. The beach access contributes to room rates in January with a
premium of 30.5% ((e0.266 – 1) × 100%). Moreover, Caribbean hotel customers do not value free high-
speed internet service, which corroborates the findings from Schamel (2012). For the random-effect
coefficient, the estimated standard deviation of island-level random effects is insignificant, showing that
there is no significant between-island difference in explaining hotel price after controlling for other
factors. Finally, to check the validity of model estimates, a further look at the leverage-versus-squared-
residual plot from the model (when additional random effects are removed) suggests the absence of
substantial outliers in the sample (Ruppert, 2004).
(Please insert Table 3 here)
To unveil the moderating factor of the accessibility-price relationship, in Models 8–11 presented in Table
4, we estimate four additional models with the interaction terms between lnaccess and the four quality-
signaling independent variables: rating, thumbs, class, and chain, respectively. It turns out that the
interactions between lnaccess and rating, thumbs, and class are statistically significant at the 0.01 level,
whereas that the interaction with chain is significant at 0.10 level. These results corroborate the
moderating effect of various quality-signaling factors. To further explain the marginal effect of lnaccess
after introducing the interaction terms, we plot the average marginal effect of lnaccess in Figure 2. The
graphs visualize the marginal effect of lnaccess on hotels with different TripAdvisor user ratings (upper-
left), different thumbs up percentages (upper-right), different hotel classes (bottom-left), and chain
affiliation statuses (bottom-right). The results suggest that the influence of accessibility decreases along
with increases in online user ratings, thumbs up percentage, and hotel class. Therefore, accessibility exerts
a smaller effect on room price for hotels with well-established reputations. We can apply the same
18
argument to explain the bottom-right graph, which shows that accessibility is not an important factor
affecting room prices at chain hotels. This is further evidence to support the statement that the room
prices at hotels with good reputations are less sensitive to the influence of accessibility.
(Please insert Table 4 here)
(Please insert Figure 2 here)
The models we presented include a wide range of variables that account for the price of a hotel room in
the Caribbean. The most intriguing variable is the role of accessibility in determining a hotel’s price,
especially in the high season. More accessible hotels, measured in this case using the proxy of lower cost
of airfare to the island destination, leads to a premium in the hotel price. These findings reflect the
distribution of costs a vacationer is willing to spend. Assuming that a traveler has a fixed budget, a lower
flight cost to a location would allow for greater expenditure on other items, such as lodging. Generally,
more accessible locations will have a wider appeal to vacationers with different attitudes and economic
backgrounds. According to Butler’s (1980) tourist area cycle of development, a lack of accessibility
restricts the number of visitors. As facilities and access improve, the number of visitors will increase. This
is supported by Table 1, which shows that generally, more accessible islands have more visitors. Islands,
however, can have a limited number of available rooms for guests. As carrying capacity is reached due to
land scarcity, crowding or overuse, the number of tourists to the area will begin to stagnate (Butler, 1980).
The economic principles of supply and demand explain the relationship between accessibility and hotel
price. As the number of visitors increases due to increased accessibility, demand for available rooms
increases. This demand, however, cannot be met immediately with an increase in the supply of rooms for
several reasons. The tourist area lifecycle states that during the stagnation phase, new development will
occur at the periphery. However, small island destinations that have reached the stagnation phase simply
cannot keep expanding outwards. They do not have a peripheral area, and off-island alternatives are
19
geographically too distant to be relevant. Hotel and resort construction is also hampered on small islands
that are economically less developed and in the tourist area development stage due to a lack of resources
and infrastructure.
Accessibility to island hotels is largely dictated by accessibility to the island. Once a visitor has decided to
visit a certain island, hotel operators must compete for that visitor. As an island becomes more accessible
and consequently has a larger supply of visitors, hotel operators can charge higher rates because demand
has increased while bed supply has not. This is particularly noticeable in tourist areas with high
seasonality where demand fluctuates throughout the year, but bed supply does not. It does not make sense
for hotel entrepreneurs to build accommodations if the primary demand for those rooms is only during
peak seasons. Our results provide evidence for seasonal effects of accessibility on hotel prices by showing
that accessibility has a greater influence on hotel prices during peak season. The number of beds is fixed,
yet demand increases during peak season, contributing to the increase in hotel prices. Less accessible
islands are subject to the same pressures, but since there is lower overall demand for rooms due to the
reduced number of visitors, higher hotel prices are not warranted. This is particularly important for any
hotel on a developing island. Efforts to increase accessibility to the island should translate into higher
demand for the hotel throughout the year.
5. Conclusions
Using a three-level mixed-effect linear regression model, we found that prices charged by Caribbean
hotels are influenced by the level of market accessibility, online quality-signaling factors, hotel class,
availability of free breakfast, and the presence of a fitness center and a swimming pool. This study
contributed to the current literature by introducing the concept of market accessibility into the hedonic
pricing model of hotel room rate. Past literature only considered accessibility to attractions (Chen &
Rothschild, 2010; Hung, et al., 2010; Rigall-I-Torrent, et al., 2011) and public goods (Rigall-I-Torrent &
Fluvià, 2011) without looking into the accessibility to origin market. Due to the spatial configuration of
20
source market of Caribbean hotels, we were able to clearly quantify the level of market accessibility
measured by travel costs. By simultaneously modelling variables at different levels as well as their
interactions, the study revealed that hotel prices in the small island destinations in the Caribbean are
associated with the reputation of the hotel, which is consistent with results from Andersson (2010), Öğüt
and Onur Taş (2012), and Z. Zhang, et al. (2011). Moreover, we looked into the interaction effect
between quality signals and market accessibility, and found that price pressure related to inaccessibility is
mitigated when a hotel has a well-established positive reputation in the form of positive user ratings on
popular travel websites, or is affiliated with a chain. In other words, these hotel room prices are less
sensitive to the influence of accessibility implying that hotel reputation is a stronger driver of hotel prices
than travel costs. All previous studies assume the identical effect of quality signals on hotel price, and
none of past studies look into the heterogeneity of this effect (Abrate, et al., 2011; Andersson, 2010; Öğüt
& Onur Taş, 2012; Yacouel & Fleischer, 2012; Z. Zhang, et al., 2011). In fact, to our knowledge, this
study represents a pioneering research effort to investigate the quality-signaling factor as a moderator of
the relationship between accessibility and hotel price.
This study also contributed to the current literature of hotel price modeling from a methodological point
of view. The empirical hedonic pricing models are derived from standard ‘pooled’ linear regression
models that assume independently and identically distributed (IID) residuals (H. Zhang, et al., 2011).
Substantively, these pricing models take any two higher-level units and pool them into one singular
population. Surely, this assumption seems unreasonable when the model is faced with variables
characterized by dependence over time, thereby biasing the standard errors. Additionally, traditional
hedonic pricing models have not been effective in capturing unobserved higher-level effects, such as
region-specific and destination-specific effects (like market accessibility, tourist density, and
infrastructure quality), that may influence hotel prices. This study alleviated the problem by embedding
the hierarchical structure in the research question, as well as by applying a hedonic pricing model based
on a multi-level mixed-effect linear regression technique. Our results found that this new technique
21
avoided plausible unreliable statistical inference and outperformed the ‘pooled’ regression in terms of
statistical goodness-of-fit. There are several reasons why the mixed-effect model is superior. First, by
considering the unobservable and island-specific attributes, the model considered two distinct sources of
variance, i.e., between hotels due to differences among destinations and within hotels due to attributes’
changes over time. Second, this technique resolved the issue of endogeneity that could cause and/or result
from correlated covariates and residuals in pricing modeling, which incorporate the spatial autocorrelation
of pricing data to some extent (Santana-Jiménez, et al., 2011).
These theoretical propositions have two meaningful managerial implications at both the micro (hotel
managers) and macro levels (destination managers). Because customers influence hotel pricing not only
through the actual use of the hotel (transaction), but also through social networking, hotel managers are in
a better position to influence their hotel prices by enhancing and controlling the reputations of their
establishments. Reputation is influenced to a large extent by the ways in which hotel managers stimulate
and engage with social media. The other main implication of this study is that hotel managers should
constantly and systematically track online reviews and participate in active “conversations” with their
customers. In prior studies, researchers assumed a positive relationship between online reviews and
hospitality firms’ performance and room rates. They focused on the valence and number of reviews
(Melián-González, Bulchand-Gidumal, & González López-Valcárcel, 2013; Wei, Miao, & Huang, 2013).
This study provides the empirical foundation supporting the link between social media (TripAdvisor
reviews in this case) and pricing, and demonstrates that social media may affect hotel reputation. Today’s
customers are able to cast a wider net of influence on other potential customers through the online
reviews. The act of posting online reviews influences how hotels set prices, therefore hotel practitioners
should actively engage in these online conversations. As suggested by the significant moderating effect of
online reviews on the accessibility-price relationship, these conversations are particularly important for
hotels located on islands with poor accessibility.
22
Consequently, there is a need for hotel managers to systematically monitor online customers’ engagement
behavior, and to create a system that may facilitate more control over consumers’ reviews of their hotel
experiences. Melián-González, Bulchand-Gidumal, and González López-Valcárcel (2013) found that the
higher the number of reviews (either positive or negative), the higher the ratings of the reviews. Hotel
managers may create direct channels through which customer conversations can be nurtured. One practice
could be shifting the review platform away from third-parties (e.g., Trip Advisor) to a hotel’s own
platform. In this platform, hotels could actively invite customers to share their opinions about their
experiences, both positive and negative. Wei, Miao, and Huang (2013) found that positive postings have a
greater impact on customers than negative ones, and sharing experiences online increases consumers’
perceptions of a hotel’s trustworthiness and credibility. Active engagement with customers provides the
opportunity for hotels to reveal their earnest efforts to practice service recovery where necessary, and to
amplify best practices that resonate with customers.
This active engagement not only impacts actual customers, but may also shape the image of the hotel for
potential customers who are searching the web for clues regarding the quality of products and services.
The credibility of this suggested practice relies, however, on a commitment to deliver a quality hotel
experience. The experiential properties embedded in a hotel stay induce a potential moral hazard problem
on the hotel’s side fueled by the transient nature of service consumption. A hotel manager has an
incentive to cheat on quality and thus may not place a high value on reputation. After all, cheating on
quality reduces costs immediately, while reputation is associated only with long term outcomes. In
addition, building and maintaining reputation is costly and only makes sense when a hotel room can be
sold at a premium above its operational costs (Keane, 1996). It appears, therefore, that although a hotel
manager must allocate significant resources (money, labor power and time) in order to engage in
systematic conversations with customers, the resulting increase in prices should outweigh the costs. In a
world of imperfect information and an environment of competition, this may be a tough sell. Achieving
and sustaining competitiveness within a high quality context may be a daunting task.
23
Hotel managers may have more incentive to enhance and protect their hotels’ reputations when demand
for travel to the destination is increased. Because islands operate under a limited demand scheme, more
demand means higher hotel prices. More demand can be generated by providing access to more
memorable experiences, marketing more aggressively and decreasing travel costs to the destination.
Airport taxes and fees create market distortions that potentially affect demand. In many cases, to help
recoup the cost of use, airport user fees and landing fees are charged to commercial airlines via
contractual agreements (Francis, Fidato, & Humphreys, 2003). The fees imposed upon the airlines are
passed along to the passengers (Sainz-González, Núnez-Sánchez, & Coto-Millán, 2011). These fees can
amount to 25% to 40% of the total cost of the airline ticket.
For example, a search on Expedia.com revealed that the total cost for a JetBlue round trip ticket from Fort
Lauderdale to Nassau on JetBlue for April 13–17, 2014 was $296.50—$165.00 in airfare and $131.50 in
taxes and fees. Taxes and fees represent 44% of the total price for this ticket. Since the taxes and fees are
fixed amounts, the shorter the flight or the lower the cost of the airline fare is, the greater the proportion
of airport fees and government taxes a passenger has to pay. If taxes and fees were eliminated, a round
trip ticket from Florida to the Bahamas would cost $199.00, resulting in higher demand for travel to the
island. Using JetBlue from New York, the taxes and fees on a flight to Aruba total $114 per person.
Without those taxes and fees, the lowest round trip airfare from New York to Aruba would be $341
instead of $455 for February 26–March 2, 2014, compared to $358 for a flight from New York to Las
Vegas for the same time period.
There are two possible ways to eliminate these potential distortions. First, small island destinations could
reduce fees unilaterally. During contract negotiations, airport managers and local governments may be
able to increase tourism revenue if they reduce airport fees, thus increasing island accessibility. This
should in turn increase tourism flows to the island, and result in higher lodging prices. The increased tax
24
flow from more successful hotels and other tourism based companies, may be greater than the revenue
lost due to reduced airport fees. Second, under the North American Free Trade Agreement (NAFTA) they
could negotiate, for example, with the United States government to eliminate all or a substantial portion
of those fees and taxes. This move could be interpreted as removing barriers to their mutual trade. In
addition, an agreement forged within the NAFTA framework would also “lock-in” any progress, thereby
preventing unilateral revocation if there were preferences, the hallmark of past practices. Endorsing free
trade is consistent with the international image of the United States, and supporting tourism in the
Caribbean is a good way to compensate for dwindling foreign aid to the region (Croes & Schmidt, 2007).
By removing or reducing the taxes and fees on tickets bilaterally with major source markets,
transportation costs to the Caribbean would be similar to prices for domestic travel within the United
States.
Before decreasing airport fees, the tax structure and revenue streams for a specific island should be
examined to determine if the reduction in airport fees could be adequately offset by increased tax revenue.
For instance, using our data and assuming taxes and fees total 25% of airfare, eliminating these costs
would result in an increase in hotel prices, ceteris paribus, by an estimated 9.74% in June (95% CI:
0.97%, 18.50%); 8.58% in the month of September (95% CI: -2.15%, 19.32%), and 11.05% in the month
of January (95% CI: 1.22%, 20.88%). The increase in hotel room rates and the commensurate hotel room
tax should be compared to the foregone revenues from airport fees and taxes. This process should include
stakeholders from the tourism sector who are equipped with necessary information to assess the potential
consequences of the tax-demand trade-off. Additionally, these stakeholders may be instrumental in
determining how best to use funds for promotional campaigns to increase demand.
Although this paper shows how accessibility to small island destinations can influence hotel prices,
further research should be conducted to examine if the main theoretical proposition of this study may be
generalized to other destinations. The proposition that prices for hotels located on small islands vary
25
directly with reputation and inversely with travel costs is compelling, with meaningful implications for
hotel and destination managers. By effectively managing hotel reputation and travel costs, destination
managers may be able to alter the competitive landscape in their favor. In the future, researchers should
investigate the nature of the relationship between relative island attractiveness and hotel reputation and
their interaction, and potential effects on the competitive landscape. In other words, should destination
managers pay more attention to hotel reputation or island attractiveness? Is hotel reputation or destination
attractiveness consumers’ primary consideration? Additionally, scholars should investigate the impact of
hotel prices on the sensitivity of different tourist segments to reputation and travel costs.
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31
Table 1.Area, visitor arrival, and accessibility for selected small Caribbean islands
Notes: The Air passenger and Cruise arrival data were taken from
http://www.onecaribbean.org/statistics/individual-country-statistics. Airfare search services (E.g. Orbitz)
was used to quote prices for a budget traveler by selecting the least expensive price quotes, regardless of
airline, number of stopovers, or departure times.
Island
Area (km2)
Air Passenger and
(Cruise) Arrivals
Accessibility
(Average Air Fare in US
Dollar)
New Providence
207
1,344,189
(4,161,269)
837.69
Grand Cayman
197
309,091 (1,401,495)
965.77
Providenciales
98
264,877(N/A)
1138.8
St. Thomas USVI
81
678,692 (2,008,991)
876.46
Aruba
180
871,316 (599,893)
1003.55
St. Vincent
340
73,866 (88,925)
1686.29
St. Kitts
168
106,408 (247,393)
1414.9
Grenada
344
118,295 (309,574)
1803.73
St. Lucia
620
312,404(630,304)
1415.22
32
Table 2. Descriptive statistics of variables
Variable
Obs
Mean
Std. Dev.
Min
Max
lnprice
268
7.338
0.715
6.223
9.575
rating
268
4.022
0.533
2.500
5.000
thumbs
223
79.395
13.347
42.000
100.000
class
214
3.474
0.772
1.500
5.000
chain
268
0.160
0.368
0.000
1.000
lnaccess
268
-7.081
0.278
-7.612
-6.666
beach
268
0.534
0.500
0.000
1.000
business
268
0.507
0.501
0.000
1.000
fitness
268
0.507
0.501
0.000
1.000
breakfast
268
0.254
0.436
0.000
1.000
internet
268
0.575
0.495
0.000
1.000
pool
268
0.854
0.353
0.000
1.000
33
Table 3.Estimation results of mixed-effect regression models
Variable
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
All (OLS)
All
All
All
Jun
Sep
Jan
rating
0.438***
0.425***
0.451***
0.381***
0.489***
(0.066)
(0.114)
(0.120)
(0.114)
(0.132)
thumbs
0.0197***
(0.005)
class
0.562***
(0.075)
chain
0.0143
0.0817
0.0782
0.0981
-0.0550
-0.0146
0.115
(0.091)
(0.129)
(0.122)
(0.104)
(0.204)
(0.164)
(0.202)
lnaccess
0.379***
0.0651
0.0198
0.0417
0.389**
0.343
0.442**
(0.140)
(0.129)
(0.147)
(0.136)
(0.179)
(0.219)
(0.201)
period=2
-0.100
-0.0739***
-0.0670***
-0.0701***
(0.079)
(0.027)
(0.024)
(0.026)
period=3
0.207**
0.204***
0.233***
0.245***
(0.086)
(0.039)
(0.039)
(0.040)
beach
0.170**
0.162
0.180*
0.152
0.0674
0.180*
0.266**
(0.079)
(0.106)
(0.093)
(0.108)
(0.101)
(0.104)
(0.128)
business
0.0332
0.0236
-0.0578
-0.111
0.0397
-0.00770
0.0689
(0.082)
(0.144)
(0.177)
(0.142)
(0.156)
(0.112)
(0.146)
fitness
0.328***
0.351***
0.442***
0.183
0.380***
0.297***
0.296**
(0.090)
(0.089)
(0.121)
(0.120)
(0.085)
(0.094)
(0.124)
breakfast
0.203**
0.168**
0.205**
0.298***
0.176**
0.256***
0.179**
(0.090)
(0.076)
(0.081)
(0.096)
(0.076)
(0.088)
(0.083)
internet
-0.00532
-0.00633
-0.0575
0.00425
0.0493
0.00513
-0.0720
(0.072)
(0.098)
(0.092)
(0.118)
(0.089)
(0.072)
(0.102)
pool
0.353***
0.441***
0.395***
0.166
0.348***
0.273***
0.428***
(0.099)
(0.098)
(0.149)
(0.206)
(0.098)
(0.080)
(0.078)
constant
7.594***
5.348***
5.218***
5.338***
7.635***
7.557***
7.959***
(1.003)
(1.267)
(1.308)
(1.108)
(1.384)
(1.672)
(1.518)
Random-effects coefficient
SD(μ)
5.54e-
12***
0.0672
0.0764**
5.09e-10
3.77e-13
2.08e-14
SD(ν)
0.505***
0.462***
0.385***
SD(ɛ)
0.199***
0.191***
0.191***
0.568***
0.454***
0.546***
N
268
268
223
214
90
88
90
AIC
444.4
183.8
129.5
102.2
171.6
128.7
164.4
BIC
487.5
216.1
156.7
129.2
194.1
151.0
186.9
(Notes: *** indicates significance at 0.01 level, ** indicates significance at 0.05 level, and * indicates significance at 0.10 level.
Robust standard errors are presented in parentheses. SD(μ) indicates the standard deviation of the island-level random effect,
SD(ν) indicates the standard deviation of the hotel-level random effect, and SD(ɛ) indicates the standard deviation of the normal
error term.)
34
Table 4.Estimation results of regression models with interaction terms
Variable
Model 8
Model 9
Model 10
Model 11
All
All
All
All
rating
-6.009***
0.425***
(1.308)
(0.112)
thumbs
-0.284***
(0.063)
class
-3.653***
(0.828)
chain
0.100
0.0835
0.109
-5.745*
(0.118)
(0.121)
(0.111)
(3.277)
lnaccess
3.839***
3.688***
2.238***
0.102
(0.787)
(0.762)
(0.419)
(0.147)
period=2
-0.0689***
-0.0559**
-0.0582**
-0.0844**
(0.026)
(0.022)
(0.024)
(0.034)
period=3
0.213***
0.255***
0.262***
0.192***
(0.040)
(0.041)
(0.041)
(0.043)
beach
0.161
0.212**
0.168*
0.147
(0.109)
(0.085)
(0.088)
(0.109)
business
0.0453
-0.00289
-0.0559
0.0368
(0.156)
(0.166)
(0.165)
(0.150)
fitness
0.302***
0.358***
0.214*
0.340***
(0.083)
(0.102)
(0.113)
(0.087)
breakfast
0.112*
0.192**
0.242***
0.156*
(0.061)
(0.075)
(0.094)
(0.080)
internet
-0.0345
-0.0993
0.00234
-0.00690
(0.091)
(0.084)
(0.112)
(0.097)
pool
0.487***
0.481***
0.129
0.438***
(0.090)
(0.148)
(0.267)
(0.096)
rating×
lnaccess
-0.922***
(0.195)
thumbs×
lnaccess
-0.0438***
(0.009)
class×
lnaccess
-0.594***
(0.118)
chain×
lnaccess
-0.852*
(0.472)
constant
31.66***
30.57***
20.86***
5.628***
(5.317)
(5.157)
(2.947)
(1.394)
Random-effects coefficient
SD(μ)
8.79e-12***
3.94e-11
0.0405
2.57e-12
35
SD(ν)
0.488***
0.433***
0.372***
0.505***
SD(ɛ)
0.196***
0.189***
0.187***
0.198***
N
268
223
214
268
AIC
172.9
116.5
89.48
182.1
BIC
205.2
143.8
116.4
214.5
(Notes: *** indicates significance at 0.01 level, ** indicates significance at 0.05 level, and * indicates significance at 0.10 level.
Robust standard errors are presented in parentheses. SD(μ) indicates the standard deviation of the island-level random effect,
SD(ν) indicates the standard deviation of the hotel-level random effect, and SD(ɛ) indicates the standard deviation of the normal
error term.)
36
Figure 1. Location map of the sampled Caribbean islands
37
Figure 2. Average marginal effect (AME) and confidence interval (CI) of lnaccess with interaction terms
-2 -1 0123
Marginal effect on lnprice
2.5 3.0 3.5 4.0 4.5 5.0
rating
-2 -1 0123
Marginal effect on lnprice
40.0 50.0 60.0 70.0 80.0 90.0 100.0
thumbs
-2 -1 0123
Marginal effect on lnprice
1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0
class
-2 -1 0123
Marginal effect on lnprice
0.0 1.0
chain
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