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A better last-minute hotel deal via app? Cross-channel price disparities between HotelTonight and OTAs


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To better understand hotels' cross-channel/platform pricing mechanisms, this study aims to investigate various factors behind price discounts (as a type of price disparity) from a popular last-minute hotel deal app, Hotel-Tonight, compared to major online travel agency (OTA) websites. Using pricing data collected from the Hotel-Tonight app and OTA websites in six U.S. cities, we estimate several regression models to examine price discounts. The results reveal that after controlling for other variables, price discounts are largely shaped by online reputation metrics (i.e., relative review valence and volume on TripAdvisor compared to on HotelTonight), complimentary access to services with high marginal variable costs, and uncertainty in the room type offered. However, hotels’ market power does not explain price disparities. Lastly, implications are discussed.
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A better last-minute hotel deal via app?
Cross-channel price disparities between HotelTonight and OTAs
Yang Yang, Ph.D. *
Assistant Professor
Temple University
1810 N.13th Street, Speakman Hall 304,
Philadelphia, PA 19122 USA
Tel: +1-215-204-5030
Xi Y. Leung, Ph.D.
Assistant Professor
University of North Texas
1155 Union Circle, #311100
Denton, TX 76203-5017 USA
Tel: +1-940-565-2436
* = corresponding author
Please cite as:
Yang, Y. and Leung, X. Y. (2018). A better last-minute hotel deal via app? Cross-channel price
disparities between HotelTonight and OTAs. Tourism Management. 68, 198-209.
A better last-minute hotel deal via app?
Cross-channel price disparities between HotelTonight and OTAs
Abstract: To better understand hotels’ cross-channel/platform pricing mechanisms, this study aims to
investigate various factors behind price discounts (as a type of price disparity) from a popular last-minute
hotel deal app, HotelTonight, compared to major online travel agency (OTA) websites. Using pricing data
collected from the HotelTonight app and OTA websites in six U.S. cities, we estimate several regression
models to examine price discounts. The results reveal that after controlling for other variables, price
discounts are largely shaped by online reputation metrics (i.e., relative review valence and volume on
TripAdvisor compared to on HotelTonight), complimentary access to services with high marginal
variable costs, and uncertainty in the room type offered. However, hotels’ market power does not explain
price disparities. Lastly, implications are discussed.
Keywords: last-minute deal; price disparity; online reputation; HotelTonight
1. Introduction
Because of the perishable nature of hotel products and the high fixed costs of the hotel industry,
successful hotels always manipulate room prices in response to demand using sophisticated yield
management systems in order to maximize short-term revenue (Kimes, 1989). In addition to price
manipulation, hotels use a variety of distribution channels to sell their products to different market
segments (O’Connor, 2002). The advent of the Internet and the development of global distribution
systems has facilitated a paradigm shift in the hotel industry toward online distribution channels (Buhalis
& Law, 2008). In this new era, the proliferation of online distribution channels has also rendered hotel
rate structures transparent to travelers. Now, travelers are inclined to search online for the best deals or
lowest potential prices (Kimes, 2016; Schwartz, 2012). As a result, some degree of price disparity exists
across different distribution channels (Law, Chan, & Goh, 2007). It is therefore imperative for hotel
managers to carefully determine online pricing strategies given a better understanding of travelers’ online
deal-seeking behaviors (Marom & Seidmann, 2011).
One of the most important hotel pricing determinants is time, or advanced booking (Philips,
2005). Time-related pricing involves two contradictory assumptions of sellout risk and future discount
perception, leading to pricing practices like early-bird discounts and last-minute deals (Chen & Schwartz,
2013). Some studies on last-minute pricing have identified increases in hotel sales during the week before
a check-in date due to travelers’ heightened deal-seeking behaviors (Chen & Schwartz, 2013; Schwartz,
2012). Therefore, researchers suggest that last-minute hotel sales represent a substantial market segment
that should no longer be overlooked (Chen & Schwartz, 2013; Dacko, 2004). In general, this last-minute
booking market segment consists of tech-savvy consumers who are willing to compromise on factors like
hotel choice or room type to receive a better deal. Millennials constitute a large portion of this segment,
and mobile bookings dominate last-minute hotel bookings (Criteo, 2016). A study from Phocuswright
indicated that approximately 70% of mobile hotel bookings were for same-day or next-day check-ins
(Quinby, 2014). Another report by Criteo (2016) found that 60% of hotel bookings with same-day check-
ins were booked via smartphone, with another 7% completed via tablet.
Launched in January 2011, HotelTonight is a mobile hotel booking app that provides travelers
with last-minute discounted hotel rooms up to seven days in advance (HotelTonight, 2017; Leo, 2011).
As the first and leading app in hotel last-minute mobile booking, HotelTonight has spawned a number of
competitors, including the Asian-oriented HotelQuickly (launched in 2013), Roomlia (launched in 2014),
and One Night (launched in 2016) (Bishop, 2017; Huang, 2017). HotelTonight has received venture
funding totaling $117 million to date, leading to a $463-million valuation (O'Neill, 2017). As the
dominant last-minute hotel booking app in the Americas and Europe, HotelTonight serves more than
25,000 hotels in approximately 1,700 cities worldwide (Crook, 2017). HotelTonight differs from
traditional online travel agencies (OTAs) like Expedia or Priceline in two main respects: being mobile-
centric and dedicated to last-minute bookings (C.S.-W., 2013). First of all, HotelTonight is an app-only
hotel-booking tool that streamlines the booking process. Thus, consumers cannot book via the website as
they would through regular OTAs. Also, HotelTonight only sells last-minute rooms (up to seven days in
advance) with deep discounts off OTA-listed rates, whereas regular OTAs sell hotel rooms for any check-
in date (C.S.-W., 2013). What’s more, rather than listing every available hotel as regular OTAs do,
HotelTonight only allows a small number of listings in each city every day, so hotels compete against one
another to offer a good deal (Gaggioli, 2015). In sum, HotelTonight’s dedication to last-minute bookings
has generated new opportunities for hotel managers to fill rooms and maximize revenue at the last minute
(Makki, et al., 2016).
As a new and innovative online distribution channel, HotelTonight offers a unique last-minute
pricing mechanism that distinguishes itself from traditional OTAs. Well-developed OTA pricing
strategies may not work on HotelTonight; therefore, to take full advantage of last-minute sales, hotels
need to understand how such deals work in order to develop effective pricing strategies via HotelTonight.
In academia, HotelTonight, as an alternative distribution channel for hotels, has yet to garner significant
attention. The only previous study on HotelTonight looked at whether hotels’ performance was influenced
by using HotelTonight without a close examination of its unique last-minute pricing mechanism (Makki,
et al., 2016). To bridge this research gap, the present empirical study attempts to reveal the nature of last-
minute price discounts and their determining factors based on a multi-channel price comparison between
HotelTonight and traditional OTAs. Specifically, this study intends to address the following research
questions: 1) Is there a price disparity between HotelTonight and OTAs?; and 2) What are the factors
influencing this cross-channel price disparity (i.e., last-minute price discounts)? By answering these
research questions, this study intends to contribute to the hotel literature by shedding light on last-minute
pricing and, more importantly, understanding cross-channel price differentiation, which have been largely
untapped by previous literature. From a practitioner’s perspective, the findings of this study will help
facilitate hotel operators’ pricing decisions, revenue management, and online channel management.
2. Literature review
2.1. Hotel online distribution channels and pricing behavior
With the evolution of the Internet and various types of online distribution channels, Middleton
and Clarke (2001) predicted that the future hospitality market would never be dominated by a single
distribution channel. Therefore, hotels use an array of online and offline distribution channels to
maximize sales and market share while minimizing costs (Kang, Brewer, & Baloglu, 2007; Toh, Raven,
& DeKay, 2011). OTAs and third-party websites play an important role in hotels’ multi-channel
distribution strategy because they offer a vast selection of choices in a wide price range (Thakran &
Verma, 2013). Although hotels prefer to sell rooms through their own websites to reduce costs, OTAs
remain essential for hotel sales (Beritelli, Beritelli, Schegg, & Schegg, 2016) because of their accessibility
to potential consumers (Morosan & Jeong, 2008) and economies of scale associated with the large
number of hotels listed (Kim, Bojanic, & Warnick, 2009).
Various third-party websites (OTAs) use different business models, such as global distribution
system (GDS)-based travel agent models (Law, et al., 2007), merchant models (Leung, Guillet, & Law,
2014), opaque models (Anderson & Xie, 2014), and last-minute booking models (Jerath, Netessine, &
Veeraraghavan, 2010) to provide enticing deals to attract customers. In a study of hotel price fluctuations
in OTAs, Sun, Law, and Tse (2016) concluded that the last-minute booking mode was less frequently
adopted by OTAs, with most OTAs failing to keep their promise of a “best-rate guarantee.” In a case
study of Mediterranean hotels, Melis and Piga (2017) found that OTAs prefer uniform pricing to dynamic
pricing. OTAs’ and hotels’ optimal pricing strategies to maximize revenue have received considerable
academic attention. For example, Guo, Ling, Dong, and Liang (2013) and Ling, Guo, and Yang (2014)
explored an optimal pricing strategy through hotel–OTA cooperation based on a game model. Their
findings indicated that optimal pricing depends on hotel room capacity, occupancy rate, average room
rate, and the expected number of e-tourists. In addition, Guo, Ling, and Gao (2016) further analyzed
OTAs’ and hotels’ optimal pricing decisions when OTAs offered cash back for hotel bookings. Likewise,
Long and Shi (2017) studied optimal pricing strategies in the O2O model through cooperation between an
offline tour operator and an OTA based on a game model. The findings suggested that service level, unit
sale commission, service cost, and unit service compensation coefficient each influenced pricing
2.2. Hotel revenue management and dynamic pricing
Revenue management or yield management is the process of matching supply and demand by
allocating capacity to different customer segments at the right price in order to maximize revenue (Ivanov
& Zhechev, 2012; Upchurch, Ellis, & Seo, 2002). Revenue management practices have often been
embraced by industries with one or more of the following attributes: perishable inventory, limited
capacity, demand volatility, micro-segmentation of the market, advance booking availability, and high
fixed costs (Ivanov & Zhechev, 2012; Kimes, 1989). Because the hotel industry possesses all these
characteristics, revenue management techniques have been widely adopted to improve room revenue
(Kimes & Wirtz, 2003). More specifically, revenue management in the hotel industry focuses on using
information systems and pricing strategies to manipulate room rates in response to forecasted demand
(Kimes & Wirtz, 2003). Thanks to new technologies that make it easier to predict demand and adjust
prices accordingly (Elmaghraby & Keskinocak, 2003), dynamic pricing has enjoyed increasing popularity
in hotel revenue management (Ropero, 2011). Basically, dynamic pricing refers to the tactical practice of
determining optimal room rates contingent upon the day and time when a reservation is received (Abrate,
Fraquelli, & Viglia, 2012; Kannan & Kopalle, 2001).
Due to the perishability nature of hotel rooms, dynamic pricing on last-minute bookings plays a
vital role in hotel revenue management (Chen & Schwartz, 2013). Some hotels are intent on maximizing
occupancy by adopting last-minute deal pricing strategies or reducing prices closer to the date of stay
(Abrate, et al., 2012; Dacko, 2004). However, researchers have also warned that last-minute deals could
train customers to delay their purchases, which may ultimately compromise hotels’ long-term profitability
(Ovchinnikov & Milner, 2012). In fact, many hotels, especially those that are high-end, try to keep their
last-minute prices the same or even slightly higher than normal to protect long-term profitability and
brand equity (Abrate, et al., 2012; Dacko, 2004; Oses, Gerrikagoitia, & Alzua, 2016). Some studies have
also revealed that last-minute pricing strategies vary depending on seasonality (Dacko, 2004), guest
segmentation (Abrate, et al., 2012), stay day of the week (Abrate, et al., 2012; Oses, et al., 2016), and
hotel star rating (Abrate, et al., 2012; Oses, et al., 2016).
2.3. Multi-channel price disparity
For consumers who purchase travel products online, price is one of the most important motivators
behind their purchase decisions (Law & Wong, 2010; O’Connor, 2002). Because today’s travelers can
easily compare room rates across various online channels, cross-channel price parity has become a critical
issue for hoteliers and guests alike (Choi & Mattila, 2005; Gazzoli, Gon Kim, & Palakurthi, 2008). Kim,
Cho, Kim, and Shin (2014) demonstrated that online price parity exerts a positive impact on hotel
performance. However, most studies on rate parity have found room rates to be inconsistent across
distribution channels (Toh, et al., 2011). Researchers discovered that local travel agencies offer the lowest
rates for upscale and luxury hotels, whereas national OTAs tend to provide the best rates for mid-priced
hotels (Hui, Law, & Ye, 2009; Tso & Law, 2005). Surprisingly, hotels’ websites have been found to post
the most expensive prices (Hui, et al., 2009). Some scholars have also investigated room rate parity across
media. For example, Demirçiftçi, Cobanoglu, Beldona, and Cummings (2010) found no significant
difference between room rates from direct or indirect channels at an aggregate level. In the case of UK
hotels, Lim and Hall (2008) confirmed that hotel prices remained consistent across online and offline
distribution channels, and in the case of Macao hotels, Lee, Tang, and Fong (2016) observed room price
parity for three-star and four-star hotels only.
In the marketing field, market characteristics, retailer characteristics, and product characteristics
have been identified as key factors influencing multi-channel price differentiation (Chiu, Chu, & Wu,
2016; Eckert, 2011; Wolk & Ebling, 2010). Wolk and Ebling (2010) found that higher levels of online
competition and online reach foster greater price parity, whereas large companies with high revenue and
strong market power are more likely to engage in cross-channel price differentiation. Eckert (2011) also
identified that channel-based price differentiation is highest for service products, in terms of product type.
Chiu, et al. (2016) stated that high service quality with added benefits allows companies to increase price
dispersion. In the hospitality industry, researchers have also identified several factors that impact price
parity, such as the website business model (Tso & Law, 2005), hotel class (Hui, et al., 2009; O’Connor,
2003), hotel star rating (Law & Wong, 2010; Lim & Hall, 2008), and transaction booking time (Leung, et
al., 2014). For example, Lee, et al. (2016) found that high-class hotels tend to have greater price disparity
than low-class ones, and Toh, et al. (2011) indicated that small hotels are more likely to embrace price
parity across channels. Because hotels are generally likely to sell empty rooms at the last minute at low
prices to improve occupancy, studies have shown that channels offering last-minute booking also provide
the lowest rates (Law, et al., 2007; Leung, et al., 2014).
3. Model conceptualization and hypotheses development
Hotel pricing studies have been a popular research topic in revenue management since the early
1990s. Wang and Nicolau (2017) summarized five categories of consumer- and supplier-side factors that
influence hotel pricing: site-specific characteristics (e.g., hotel location); quality-signaling factors (e.g.,
hotel star rating, online rating, and chain affiliation); hotel services and amenities (e.g., availability of
complimentary breakfast and WiFi); property characteristics (e.g., an onsite restaurant and fitness center);
and external market factors (e.g., number of competitors and market accessibility). By comparing last-
minute prices on HotelTonight to the best available rates from major OTAs, this study evaluates cross-
channel price disparities by investigating last-minute price discounts. Three categories of multi-channel
price differentiation determinants have been identified through marketing research: retailer (hotel)
characteristics, product characteristics, and market characteristics (Chiu, et al., 2016; Eckert, 2011; Wolk
& Ebling, 2010). This study develops the following research hypotheses and proposed model based on
factors from these three categories and their influence on last-minute hotel room price discounts (see
Figure 1).
(Please insert Figure 1 about here)
First of all, quality or reputation has always been an important retailer characteristic in
differentiating price (Abrate, et al., 2012). According to Zhao, Wang, Guo, and Law (2015), two major
online reputation metrics exist: (1) review valence, which is measured by the review rating reflecting
either positive or negative feedback about product attributes; and (2) review volume, measured by the
total amount of reviews reflecting a product’s online popularity. In an online distribution channel, both
review valence and review volume are considered important indicators of service quality and reputation
(Abrate, Capriello, & Fraquelli, 2011; de la Peña, Núñez-Serrano, Turrión, & Velázquez, 2016; Sparks &
Browning, 2011). Schamel (2012) empirically confirmed the significantly positive relationship between
hotel room rates and online ratings based on customer reviews from an online meta-booking engine.
Regarding review volume, de la Peña, et al. (2016) found that hotels receiving a higher-than-average
number of reviews are associated with higher room rates after controlling for other price determinants.
Based on past literature, better online reputation metrics lead to a price premium in room rates published
on OTAs. Once a hotel earns a decent reputation on TripAdvisor, the most popular online reputation
system used by many OTAs (Yoo, Sigala, & Gretzel, 2016), it is likely to leverage this advantage by
using OTA channels more often. Therefore, a hotel with a better relative TripAdvisor reputation becomes
reluctant to offer deeper discounts via HotelTonight, which has its own independent online reputation
system. Two hypotheses are thus proposed as follows:
H1a: A higher relative review valence on TripAdvisor leads to a lower last-minute price discount
on HotelTonight.
H1b: A higher relative review volume on TripAdvisor leads to a lower last-minute price discount
on HotelTonight.
Hotel amenities and services represent important product characteristics in multi-channel price
differentiation (Abrate, et al., 2012). For instance, hotel rates were found to be higher if hotels offered in-
room amenities such as mini-bars, televisions, and safes (Lee & Jang, 2012; Schamel, 2012). Moreover,
some hotel services were associated with higher room rates, such as valet parking or free parking, express
checkout, complimentary breakfast, and an onsite pool (Schamel, 2012; Wang & Nicolau, 2017; Yang,
Mueller, & Croes, 2016). Hotel services can be further categorized based on their variable costs. Some
services like complimentary parking and WiFi access are characterized by low variable costs, whereas
others such as complimentary breakfast are characterized by higher variable costs (Frey Jr & Clyman,
2017). We argue that services with higher variable costs are likely to influence price discounts on last-
minute distribution channels. On one hand, the perceived fairness principle of revenue management states
that customers believe a higher hotel price associated with increasing costs is fair (Kimes, 1994);
therefore, they are willing to pay a premium price for products with a higher cost. On the other hand,
hotels may consider cost factors when making pricing decisions (Collins & Parsa, 2006); to maintain a
reasonable level of profit margin, hotels tend to offer limited room rate discounts for reservations with
complimentary services that have high variable costs. The second hypothesis posits:
H2: Offering complimentary access to services with high marginal variable costs leads to a lower
last-minute price discount on HotelTonight.
Another product characteristic influencing multi-channel price differentiation in the hotel industry
is room type uncertainty while booking. Unlike bookings via traditional OTAs, last-minute bookings via
HotelTonight may not provide transparent room selection. Many hotels did not specify room type on the
app; rooms may be either one-bed or two-bed layouts. As most customers are risk-averse by nature
(Bailey, Olson, & Wonnacott, 1980), they are willing to pay less when confronted with this type of
uncertainty. From the hotel’s perspective, choosing not to reveal the room type provides greater flexibility
in inventory management (Vinod, 2004), especially at the last minute, so the hotel is willing to lower the
price on last-minute deals. A typical example of room type uncertainty comes from opaque selling
channels for hotel products, where certain characteristics of the product or service, including room type,
are hidden during the purchase process (Anderson & Xie, 2012). By offering a lower rate, opaque pricing
allows hotels to effectively differentiate pricing on those channels while maintaining price parity on
transparent price channels (Anderson, 2009). The third hypothesis therefore states:
H3: Uncertainty in room type when booking leads to a higher last-minute price discount on
The last determinant category, marketing characteristics, such as market structure and the
competition landscape also influence hotel multi-channel pricing (Becerra, Santaló, & Silva, 2013). For
example, hotel pricing has been shown to be negatively impacted by the number and proximity of
competitors (Balaguer & Pernías, 2013; Becerra, et al., 2013). Another important external factor
influencing hotel pricing is market power. Market power refers to hotels’ ability to influence prices above
a competitive level to earn greater profits, a phenomenon referred to as monopoly power (Nappu, Bansal,
& Saha, 2013). The concept of market power originated from the structure-conduct-performance
industrial organization paradigm, which reveals a positive relationship between the market concentration
(market power) and profitability of a firm (Bain, 1951). Generally, strong market power and brand power
decrease consumers’ price sensitivity, thereby empowering a company to charge higher prices (Wolk &
Ebling, 2010). In the context of the airline industry, Borenstein (1989) indicated that an airline with high
market power was able to increase airfares. Stavins (2001) further found that an airline’s price
discrimination decreased with higher market power, meaning that the airline offered fewer price
discounts. In the hotel industry, a positive relationship between market power and hotel profitability has
been well established (Pan, 2005; Tung, Lin, & Wang, 2010). We therefore argue that hotels possessing
strong market power tend to focus more on conventional OTAs than on last-minute channels to sell
inventory, and they are less likely to offer impressive last-minute deals given a lack of competition. It is
thus hypothesized that:
H4: A hotel’s stronger market power leads to lower last-minute price discounts on HotelTonight.
4. Research method
4.1. Data collection
We trained the coders to collect data manually from the HotelTonight mobile app in six major
U.S. lodging markets: New York, NY; Atlanta, GA; Chicago, IL; Houston, TX; Denver, CO; and Los
Angeles, CA. These cities were selected for two reasons. First, being located in different regions, these
sampled cities are geographically representative of the U.S. Second, according to Smith Travel Research,
they are among the top 25 U.S. lodging markets (Canina & Carvell, 2005). Although HotelTonight offers
reservations up to three days after the booking date, we examined only same-day reservations to evaluate
the last-minute nature of hotel booking. On each day, two independent coders collected hotel prices and
other relevant information from the app twice, once at 9am and again at 9pm local time. Because
HotelTonight often offers a ‘Geo Rate’ that can be lower than the already-discounted rate based on the
geo-location of mobile devices, coders were required to turn off the geo-locating function on their mobile
devices. The data collection period spanned 24 days, beginning on May 19 and ending June 11, 2017. For
each city, 15 to 16 hotels were usually listed in the app, and the hotel list could change at different times
over different days. We searched on Google and each city’s DMO website to identify upcoming events
during the research period and did not find any events that might influence the city’s hotel demand any
more than usual. As HotelTonight claims, the listed hotels are carefully selected using the app’s algorithm
to offer consumers the best deal of the day (HotelTonight, 2018). Occasionally, the app indicated that
rooms were sold out at certain hotels. For each hotel, the coder recorded its name, reputation (i.e.,
recommendation percentage and number of ratings), whether complimentary breakfast was provided, the
room type, booking price, and taxes and fees. The app overview is presented in Figure 2. Based on the
city and check-in/check-out dates specified, the app presents a list of a limited number of hotels. After
clicking the link to each hotel, the user can review many hotel-specific characteristics including its
address, amenities, and customer ratings (recommendation percentages) and reviews posted on
HotelTonight by previous users. If the user decides to book, more detailed information is presented about
the reservation, including the room type and total booking cost.
(Please insert Figure 2 about here)
We also incorporated two additional data sources for empirical analysis. First, our coders
searched the lowest available room rate on TripAdvisor for each hotel listed on HotelTonight. Serving as
a mega search engine, TripAdvisor provides a search function that lists hotel room rates from various
OTAs as well as from the hotel’s official website (Yoo, et al., 2016). Therefore, users can compare room
rates from various major OTAs such as Travelocity,,,,, and Note that the room rates from hotel’s official website were excluded
because we consider OTA-listed rates only in this study. Also, the coder recorded the hotel’s online
reputation on TripAdvisor, including its overall customer rating and number of reviews. Second, we
collected operation data from the STR hotel census database for each hotel listed on HotelTonight. The
database covers around 98% of all U.S. hotel properties. To our knowledge, it is the largest of its kind and
is well-received by industry and academia (Kalnins, 2016). Some key hotel characteristics we obtained
included each hotel’s number of rooms, operation type, location type, and class. Also, with this database,
we were able to calculate the total number of hotel rooms in each zip code area that HotelTonight used.
After merging the HotelTonight price data with these two data sources, our final dataset consisted of
3,766 records.
4.2. Model specification
To test the proposed research hypotheses outlined in the previous section, we specified a
regression model for last-minute price discounts from HotelTonight. The model was specified as follows:
 =+ ++ (1)
where  represents the price discount on the HotelTonight app compared to the lowest rates from major
OTAs for hotel i at day t, such that  =
  
. Basically, the price discount is calculated
as the difference between the lowest OTA-listed room rate available (
) and the HotelTonight rate
) divided by the lowest OTA-listed room rate. This discount can also be depicted as a percentage,
showing the percentage higher or lower a HotelTonight rate is compared to the lowest available OTA-
listed rate.  can be either positive or negative: a positive value indicates a lower hotel room rate on
HotelTonight versus OTAs, whereas a negative value suggests a higher rate on HotelTonight versus
OTAs.  captures independent variables of interest that are used to test research hypotheses, and 
captures additional independent variables used as control variables. Lastly, represents a set of dummies
indicating the day of the week, and  is the normal error term in the regression that is independently and
normally distributed with a mean of zero and a given variance.
We defined several variables of interest, including Dif_rating, Dif_reviews, Dif_reviews,
Breakfast, Rm_type, and Mkt_power. Definitions of these variables are presented in Table 1. As suggested
by Hypotheses 1a and 1b, the estimated coefficients of Dif_rating and Dif_reviews were expected to be
negative. Furthermore, because a complimentary breakfast is characterized by a relatively high marginal
variable cost compared to complimentary parking and WiFi, the coefficient of Breakfast was expected to
be negative according to Hypothesis 2. Moreover, Hypothesis 3 predicts that the coefficient of Rm_type =
3 would be positive and larger than that of the other two levels, whereas Hypothesis 4 predicts a negative
coefficient for Mkt_power.
(Please insert Table 1 about here)
In addition to these independent variables of interest, we also included a set of control variables
in  whose definitions are also presented in Table 1. Because hotels face greater pressure to fill unsold
rooms at later hours, we expected to see higher discounts at 9 pm and thus predicted a positive coefficient
of Booking_hour = 2. Hotel size and chain affiliation have been found to inform hotels’ pricing strategies
and price dispersion across different distribution channels (Toh, et al., 2011); therefore, we included
Rooms and Chain as control variables in the model. We used two additional categorical variables,
Location and Class, to capture a hotel’s location and class, respectively. Bull (1994) and Lee and Jang
(2011) confirmed the effects of geographic location relative to the city center and airport on hotel pricing.
Also, different hotel classes serve different customer segments and face different types of demand in
revenue management (Boyd & Kallesen, 2004). Note that we did not employ the hotel class classification
used in the HotelTonight app (including 'Luxe', 'Hip', 'Solid', 'Basic', 'Charming', 'Crashpad', and 'High
Roller') because the STR classification is considered reputable in both industry and academia (Hua &
Yang, 2017; Kalnins, 2016). Lastly, we incorporated a set of dummy variables to capture the day-of-the-
week effect.
4.3. Data description
Table 2 presents the descriptive statistics of the continuous variables. First and foremost, the
dependent variable, , which represents the price discount offered via HotelTonight, had a mean value
of 0.116, suggesting that HotelTonight offers an average 11.6% discount compared to the lowest available
OTA-listed room rates. Its standard deviation was remarkably large, and both negative and positive
extreme values existed as indicated by the maximum and minimum values. We also investigated the
distribution of the dependent variable over sampled cities; Figure 3 shows the box plot of this variable in
different cities. We found that in general, the price discounts via HotelTonight were highest in New York
and Chicago and lowest in Atlanta. Moreover, we found a substantial level of variability in the discounts
in most cities as shown by the large distance between two adjacent lines.
(Please insert Table 2 about here)
(Please insert Figure 3 about here)
For the continuous independent variables, the mean of Dif_rating was -0.375, indicating that after
converting the HotelTonight rating into a five-point scale measure, the hotel’s TripAdvisor rating was
about 0.375 point lower than its HotelTonight rating. Moreover, we found that about 90% of our records
had a negative value for Dif_rating. These results suggest that hotels listed on HotelTonight usually
maintain a higher reputation on HotelTonight than on TripAdvisor. Another online reputation variable,
Dif_rating, had a mean value of 0.086, suggesting hotels have an average of 86 more reviews on
TripAdvisor compared to HotelTonight. This variable was also characterized by a large range as indicated
by its standard deviation and minimum and maximum numbers. Lastly, the mean values of Mkt_power
and Rooms suggested that guests tended to book a hotel via HotelTonight that accounts for an average of
15.8% of total rooms in the surveyed zip codes and an average of 266 guest rooms.
Table 3 presents the descriptive statistics of the categorical variables. We found that only a small
proportion, 10.78%, of price records included complimentary breakfast. Moreover, among the three room
types offered on HotelTonight, 76.07% were rooms selected by the hotel. Because of our balanced
sampling process, we had an approximate 50/50 division between morning and evening price records in
the sample. Furthermore, 63.91% of records in our sample were from chain-affiliated hotels. Regarding
hotel location, hotels in suburban and urban locations dominated. In particular, 68.06% of records came
from urban hotels. As for hotel class, high-end hotels dominated our sample, with 42.91% of records from
upper upscale hotels, 26.42% from upscale hotels, and 16.99% from luxury hotels. Lastly, during our 24-
day data collection period, we slightly over-sampled Friday, Saturday, and Sunday records. A matrix of
Pearson correlations was estimated to detect the multi-collinearity problem (see Appendix). Except for the
coefficient between dummies of the same categorical variable, pairwise coefficients were below 0.40,
showing an absence of multi-collinearity in our sample (Gujarati & Porter, 2010). We also calculated
variance inflation factor (VIF) values for all independent variables; the average VIF was 3.27.
(Please insert Table 3 about here)
5. Empirical results
We first estimated a model with control variables only; Model 1 in Table 4 presents these results.
The estimated coefficient of Booking_hour was neither significant nor consistent with our prediction,
suggesting that evening booking on HotelTonight for same-day check-in did not necessarily offer a better
deal. One possible explanation is that some popular hotels with better deals have already filled their
inventory before the evening, so different hotels are listed in the evening whose discounts may be pre-
determined by hotel managers when posting on HotelTonight. Moreover, we found the estimate of Rooms
to be negative and significant, suggesting that large hotels usually offered a lower discount via
HotelTonight. The magnitude of this estimate implies that a hotel with 10 more rooms is associated with a
price discount 0.114% lower on last-minute channels. The coefficient of another variable, Chain, was
estimated to be insignificant, and out of five dummies of Location, only the coefficient of Location = 5
was estimated to be significant and negative, suggesting that suburban hotels usually offered a lower
discount on HotelTonight compared to airport hotels (the reference group).
(Please insert Table 4 about here)
Regarding hotel class, the results of Model 1 show that compared to upper midscale hotels (the
reference group; Class = 3), midscale hotels (Class = 2) provided lower HotelTonight discounts whereas
hotels in other classes provided higher discounts. As indicated by the estimated coefficients, upper
upscale hotels (Class = 5) and luxury hotels (Class = 6) provided the highest last-minute discounts with
average rates that were 5.03% and 4.48% higher than those of midscale hotels. Figure 4 demonstrates the
predicted mean price discounts for hotels in different classes after controlling for other independent
variables. Interestingly, it shows that both very low-end (Class = 1, economy class) and very high-end
hotels (Class = 5, upper upscale class; Class = 6, luxury class) offered better discounts via HotelTonight.
In general, we found that after controlling for other factors, upper upscale hotels offered the highest
HotelTonight discount, which was about 13.1% lower than the lowest room rate available online. This
percentage was followed by the discount offered by luxury hotels (12.6%) and that offered by economy
hotels (12.5%). The comparatively impressive last-minute discounts offered by high-end hotels likely
represent efforts to protect brand equity (Xu & Chan, 2010) and maintain a reasonable level of perceived
price fairness for guests (Chung & Petrick, 2013); hence, these hotels appear more likely to lower their
room rates on less transparent booking channels, such as HotelTonight, instead of on transparent channels
like OTAs. For economy-class hotels, pricing mechanisms are among the few strategic tools they can
leverage in an absence of affluent resources and enticing features (Kalnins & Chung, 2004); thus, a heavy
price discount on less transparent booking channels might prove useful.
(Please insert Figure 4 about here)
Model 1 also presents the estimated coefficients of dummies indicating the day of the week.
Compared to the reference group (Monday), HotelTonight discounts were significantly higher on
Thursdays and Sundays and lower on Saturdays. More specifically, according to the estimated
coefficients, the last-minute discount rate was 2.34% higher on Sunday and 4.18% higher on Thursday
than on Monday. Figure 5 visualizes the predicted mean price discounts for hotels on different days of the
week after controlling for other independent variables. In general, we found that the hotel discounts via
HotelTonight were highest on nights adjacent to weekend nights. More specifically, the predicted mean
price discount was 15.0% for Thursday night and 13.2% for Sunday night. These discounts were lowest
on a typical weekend night, namely Saturday, when the predicted mean discount was only 8.1%. One
possible reason for this is that hotels experience higher demand on weekends and are therefore more
likely to achieve higher occupancy (Schamel, 2012), providing less incentive to offer significant
discounts via HotelTonight.
(Please insert Figure 5 about here)
Table 4 also presents the estimation results of models testing our proposed research hypotheses.
To minimize the impact of any multi-collinearity issues, we introduced each variable of interest
separately in addition to the specification of Model 1. In Model 2, we added Dif_rating, and its coefficient
was estimated to be negative and significant. This result corroborates Hypothesis 1a, and the coefficient
shows that a one-point increase in the rating gap between TripAdvisor and HotelTonight (on a five-point
scale) led to a 2.01% decrease in the price discount offered on HotelTonight. In Model 3, Dif_reviews was
added to test Hypothesis 1b. Its estimated coefficient was significant and negative, lending empirical
support to Hypothesis 1b. This result shows that a 100-count increase in the gap between TripAdvisor and
HotelTonight review totals led to a 0.13% decrease in price discounts via HotelTonight. In general, our
empirical results support Hypothesis 1, suggesting that price discounts on HotelTonight rely heavily on
the relative online reputation of TripAdvisor compared to HotelTonight.
In Model 4, the coefficient of Breakfast was estimated to be negative and significant, thus
supporting Hypothesis 2. Breakfasts estimated coefficient indicates that the discount would be 3.74%
lower if hotels offered complimentary breakfast with booking. In Model 5, we added two dummies of
Rm_type by setting Rm_type = 2 as the reference group; the coefficient of Rm_type = 3 was estimated to
be statistically significant and positive. Hence, Hypothesis 3 is supported, and the result indicates a 10.6%
higher discount for an uncertain room type compared to a guaranteed double room. Another variable of
interest, Mkt_power, was introduced in Model 6, and its coefficient is not statistically significant.
Therefore, Hypothesis 4 is rejected, as we did not find any evidence supporting the role of market power
in shaping HotelTonight price discounts. One explanation for this result is that market power does not
explain the extent of price discrimination in the presence of strong consumer heterogeneity in brand
preferences (Holmes, 1989). Lastly, we included all independent variables in Model 7. The estimates of
the variables of interest changed very little; most notably, their signs and statistical significance did not
change, suggesting a reasonable level of robustness of our results with regard to model specification.
We ran an additional regression analysis based on Model 7 to further explain the results of
hypothesis testing; Table 5 presents these results. First, in Model 8, we considered the interaction effect of
relative review valence and review volume (Dif_rating * Dif_reviews). As suggested by Maslowska,
Malthouse, and Viswanathan (2017), review volume reflects review valence credibility; therefore, a large
review volume can strengthen the beneficial effect of positive valence and the detrimental effect of
negative valence. In this model, the coefficient of the interaction term was estimated to be negative and
significant, highlighting the significant interaction effect between relative TripAdvisor valence and
volume in shaping last-minute discounts. Second, as suggested by Hypothesis 2, offering complimentary
services with lower marginal variable costs should have less effect on last-minute price discounts.
Therefore, we incorporated dummy variables, indicating the availability of free parking (Parking = 1) and
complimentary WiFi access (WiFi = 1), into Model 9. The coefficients of these two additional variables
were estimated to be insignificant, providing further support for Hypothesis 2. Third, when developing
Hypothesis 3, we argued that hotels offer better last-minute deals for unspecified room types in order to
allow for more last-minute flexibility in inventory management. We thus introduced an interaction term
of uncertain room type (Rm_type = 3) and number of hotel rooms (rooms) in Model 10, and the term’s
coefficient was estimated to be negative albeit not significant at the 0.05 level. Lastly, we wanted to
further investigate Hypothesis 4, which was rejected by Model 6. Initially, we treated each single hotel
room as being equal irrespective of hotel class when constructing the market power measure. Therefore,
we introduced an interaction term of market power (Mkt_power) and hotel class (Class) in Model 11. The
coefficient of this interaction term was estimated to be negative and statistically significant, while the
main effect of market power was positive and statistically significant. This result suggests that only high-
end hotels, such as luxury hotels (Class = 6), are expected to consider market power when offering last-
minute discounts. In sum, although high-end hotels in general are likely to offer a better last-minute deal
(see Figure 4) than others, those with weaker market power tend to make the deal even better.
(Please insert Table 5 about here)
6. Discussion
The issue of price disparity across multiple distribution channels has been an important topic in
hotel channel management since the rapid proliferation of online distribution options. With the advent of
mobile technology, price disparity has extended to online channels and mobile channels. Based on data
collected from a mobile last-minute booking channel (HotelTonight) and online channel (traditional
OTAs), this study presented an empirical analysis exploring cross-channel price disparities and their
underlying factors. The study investigated five factors influencing last-minute price discounts offered on
HotelTonight compared to OTAs using three channel-based price differentiation determinant categories in
marketing research. Review valence and volume, reflecting hotel reputation, were selected as retailer
(hotel) characteristics factors. Complimentary services with high marginal costs and room type
uncertainty in booking were selected as product characteristics factors. Market power was considered a
market characteristics factor.
Several findings were confirmed after regression analysis. First, the results showed that a higher
relative review valence and volume on TripAdvisor compared to HotelTonight led to lower last-minute
price discounts on HotelTonight, confirming previous marketing research showing that a positive seller
reputation enhances multi-channel price differentiation (Chiu, et al., 2016). Moreover, offering
complimentary access to services with high marginal variable costs, such as complimentary breakfast, led
to lower price discounts, whereas providing complimentary access to other services, like free parking and
free WiFi, did not influence discounts. Previous marketing research related to product characteristics
mainly focused on product types, such as durables, non-durables, resalable products, or service products
(Chiu, et al., 2016). Our study represents the first research effort to innovatively investigate the
relationship between products’ marginal costs and channel-based price differentiation. Furthermore, our
results indicated that uncertainty in room type when booking led to higher last-minute price discounts on
HotelTonight, particularly for smaller hotels where it is more feasible to offer greater discounts. These
findings may be explained by previous results suggesting that high product differentiation leads to
channel-based price premiums (Chiu, et al., 2016; Eckert, 2011). When the room type is uncertain, hotel
products become more homogeneous, ultimately resulting in less product differentiation. Therefore, the
hotel also loses the power to charge a price premium on last-minute booking channels. Last but not least,
market power was generally unable to explain price discounts on HotelTonight, contradicting previous
market research findings (Wolk & Ebling, 2010). However, we did find that market power shaped
discounts for luxury hotels. Regarding other factors used as control variables in the model, we found that
smaller hotels, higher class hotels, and hotels located in urban areas usually offered better HotelTonight
discounts, especially for last-minute check-ins on Thursdays and Sundays, confirming findings from Lee,
et al. (2016).
From a theoretical perspective, this study makes a pioneering research effort to collectively
examine, in a single empirical setting, two different dimensions of revenue management: cross-channel
price differentiation and last-minute pricing. The study contributed to the current literature on hotel
pricing in two major ways. First, unlike empirical studies focusing on room rate data from a single
distribution channel or platform (Yang, et al., 2016), we investigated price disparities across different
channels and platforms. Although some pioneering studies discovered cross-channel price disparities
among different online channels (Demirçiftçi, et al., 2010; Gazzoli, et al., 2008; Lim & Hall, 2008), this
study was the first to apply the marketing theory to unveil systematic underlying factors shaping price
disparities that have not been empirically evaluated in the literature. A better understanding of price
disparity helps clarify the nature of different hotel distribution channels and the advantages and
disadvantages associated with these channels for special hotels. Second, our study represents the first
research effort aimed at understanding price discounts from an emerging last-minute hotel booking app,
HotelTonight, which represents a new business model for hotel distribution channels in the information
technology era. We investigated the effects of some new factors on dynamic pricing of this channel, such
as room type uncertainty and booking hour on the day immediately prior to check-in. Last but not least,
this study substantiated the close relationship between revenue management and reputation management;
that is, tactical revenue management strategies depend heavily on reputation management.
7. Conclusion and Implications
These results provide several important practical implications for hotels. First, this study serves as
an eye-opener for hotel owners and managers who have not yet adopted HotelTonight as a distribution
channel. Compared to traditional OTAs, HotelTonight is still a relatively new product that not all U.S.
hotels use. This study presents a comprehensive picture of how HotelTonight works and its last-minute
pricing mechanism. The study results can thus be used by hotel owners and managers who want to learn
more about HotelTonight and decide whether to participate in this last-minute mobile distribution
channel. Second, for hotels who plan to or currently use HotelTonight, the empirical results from this
study can serve as benchmark parameters or best practices for hotels to propose pricing strategies on the
app. For example, hotels with better ratings on HotelTonight may be able to offer lower discounts to
maximize revenue. High-end hotels with less market power should offer better last-minute deals to
increase sales. The estimates of price discounts/premiums associated with room type uncertainty and the
provision of complimentary services can also be used for cost-benefit analysis once cost items are
available to formulate best practices for cross-channel/platform pricing. Last, this study highlights the
importance of reputation management on revenue management, especially considering that HotelTonight
has its own reputation system independent from TripAdvisor. Our results show that a better relative
HotelTonight reputation leads to lower HotelTonight discounts (i.e., a higher relative room rate on the
app). Therefore, HotelTonight is particularly promising as a distribution channel for hotels who suffer
from an unsatisfactory TripAdvisor reputation: having a better reputation on HotelTonight creates
opportunities to remain competitive.
The study findings also present practical implications for hotel distribution channels. On one
hand, for HotelTonight, the results revealed attributes of hotels that take greater advantage of last-minute
pricing strategies and that may be potential participants in the future. For example, this study found that
smaller, higher-class hotels located in urban areas usually offer better price discounts. In the future,
HotelTonight could target this type of hotel to increase the app’s ability to price discriminate from
traditional OTAs. On the other hand, for traditional OTAs like Expedia and Priceline, price disparities
between last-minute booking channels as revealed in this study could be interpreted as a niche market
suggestion. Especially due to the mobile nature of HotelTonight, it would be unusual for price disparity to
be evident to the general online public to the point that it would impair hotel brand equity. Traditional
OTAs could learn from this and create a mobile-only last-minute booking platform to supplement their
mainstream online booking channels.
Due to the high cost of manual data collection, our empirical sample only covered a relatively
short period of time and a limited number of hotels in a handful of cities. However, the focus of our study
was the cross-channel price disparity between last-minute booking apps and OTAs. Because the sample
hotels in this study are representative of the hotel population on last-minute booking apps, which is much
smaller than the population on OTAs, this study’s findings are meaningful and offer a comprehensive
picture of multi-channel price differentiation. Second, because of the scale difference in the rating
systems between TripAdvisor and HotelTonight, some compatibility issues may exist when calculating
rating differences. Third, due to difficulties with data collection, we did not analyze another innovative
pricing discrimination strategy called Geo Ratefrom HotelTonight, which offers higher price discounts
for some hotels based on app users’ geographic location. Lastly, we analyzed how cross-channel/platform
pricing strategies are integrated with the general dynamic pricing strategy, and we suspect that a tactical
combination of the two pricing strategies can be more effective in hotel revenue management (e.g., using
last-minute deals to complement hotels’ general dynamic pricing strategy). Therefore, we recommend that
future research efforts investigate the interaction effect between cross- channel/platform pricing and
dynamic pricing using a more comprehensive dataset with a longer research period and more cities
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Table 1. Descriptive statistics of continuous variables.
Dependent Variable
Independent Variables of Major Interest
difference between a hotel’s TripAdvisor rating and its HotelTonight rating (in %) divided by
difference between a hotel’s TripAdvisor review number (in thousands) and its HotelTonight
are used on HotelTonight: rooms with 1 bed (Rm_type = 1), rooms with 2 beds (Rm_type =
Control Variables
Table 2. Descriptive statistics of continuous variables.
Table 3. Descriptive statistics of categorical variables.
Cum. Percent
Breakfast = 0 (breakfast excluded)
Breakfast = 1 (breakfast included)
Rm_type = 1 (room with 1 bed)
Rm_type = 2 (room with 2 beds)
Rm_type = 3 (room selected by hotel)
Booking_hour = 0 (9am)
Booking_hour = 1 (9pm)
Chain = 0
Chain = 1
Location = 1 (Airport)
Location = 2 (Interstate)
Location = 3 (Resort)
Location = 4 (Small Metro/Town)
Location = 5 (Suburban)
Location = 6 (Urban)
Class = 1 (Economy)
Class = 2 (Midscale)
Class = 3 (Upper Midscale)
Class = 4 (Upscale)
Class = 5 (Upper Upscale)
Class = 6 (Luxury)
Day = Sunday
Day = Monday
Day = Tuesday
Day = Wednesday
Day = Thursday
Day = Friday
Day = Saturday
(Note: † indicates the reference category of categorical variables used for further regression analysis.)
Table 4. Estimation results of regression models for hypothesis testing.
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Breakfast = 1
Rm_type = 1
Rm_type = 3
= 1
Chain = 1
Location = 2
Location = 3
Location = 4
Location = 5
Location = 6
Class = 1
Class = 2
Class = 4
Class = 5
Class = 6
Day = Sunday
Day =
Day =
Day =
Day = Friday
Day =
Adj. R-sq
(Notes: *** indicates significance at the 0.01 level, ** indicates significance at the 0.05 level, and * indicates
significance at the 0.10 level. Robust standard errors are presented in parentheses.)
Table 5. Estimation results of additional regression models.
Model 8
Model 9
Model 10
Model 11
Dif_rating * Dif_reviews
Breakfast = 1
Parking = 1
WiFi = 1
Rm_type = 1
Rm_type = 3
Rm_type = 3 * rooms
Mkt_power * Class
Booking_hour = 1
Chain = 1
Location = 2
Location = 3
Location = 4
Location = 5
Location = 6
Class = 1
Class = 2
Class = 4
Class = 5
Class = 6
Adj. R-sq
(Notes: *** indicates significance at the 0.01 level, ** indicates significance at the 0.05 level, and * indicates
significance at the 0.10 level. Robust standard errors are presented in parentheses.)
Cross-Channel Price
Last-minute price
Retailer (Hotel)
Added services
with high
variable costs
Uncertainty in
room type
Market power
Figure 2. Screenshots of HotelTonight app.
Figure 3. Box plot of price discounts over sampled cities.
Predicted price discount from HotelTonight
Figure 4. Predicted price discounts on HotelTonight for different hotel classes.
Predicted price discount from HotelTonight
Figure 5. Predicted price discounts on HotelTonight for days of the week.
Table A1. Correlation matrix of independent variables.
st = 1
e = 2
e = 3
hour = 1
Chain =
n = 2
n = 3
n = 4
n = 5
n = 6
Class =
Class =
Class =
Class =
Breakfast = 1
Rm_type = 2
Rm_type = 3
r = 1
Chain = 1
Location = 2
Location = 3
Location = 4
Location = 5
Location = 6
Class = 1
Class = 2
Class = 4
Class = 5
Class = 6
(Note: The correlation coefficients with day of the week variables are not presented for brevity; their correlation coefficients with other
variables are below 0.10.)
... Of these, user-generated content is gathered mainly from social media platforms such as Tri-pAdvisor, Yelp, Facebook, Twitter, and Sina Weibo and from transactional platforms such as Expedia,, Ctrip, and Hotel Tonight (Gal-Oz, Grinshpoun, & Gudes, 2010;Li, Xu, Tang, Wang, & Li, 2018;Xiang, Du, Ma, & Fan, 2017;Yang & Leung, 2018); equipment data are gathered mainly from GPS, mobile roaming, Bluetooth, and other channels; operational data are collected mainly from Google searches, Baidu searches, webpage visits, online booking, purchasing, and other channels . ...
... Three situations have been identified for such data. In the first situation, the comments posted on transactional platforms are more trustworthy than those posted on social media platforms, e.g., hotel reputation information posted on Hotel Tonight is significantly more credible than that posted on TripAdvisor (Yang & Leung, 2018), and hotel ratings on Expedia are significantly more credible than those posted on Yelp (Xiang et al., 2017). Expedia and are more credible online review platforms than TripAdvisor because their security policy only allows actual/verified customers to post reviews, whereas on TripAdvisor, anyone can evaluate a company (Stebbins, 2015). ...
... That is, for the same number of reviews, there were more reviewers on Sina Weibo. Compared with the reviews on Ctrip, the data on Sina Weibo appeared much earlier and had a higher data volume, higher character count, and more reviewers and thus higher sufficiency, characteristics that were also observed in the English reviews (Xiang et al., 2017;Yang & Leung, 2018). In addition, the number of reviews on both types of platforms declined around 2016. ...
This thematic analysis examines whether reviews on transactional and social media websites can reflect the air quality of a tourist destination. We used linguistic and sentiment analysis methods to establish an analytical framework for assessing the credibility of the reviews with sufficiency and consistency analyses. We collected Ctrip and Sina Weibo reviews to analyze the sentiment values using deep learning and Baidu sentiment dictionary methods. We found that although the sentiment value of the Ctrip transactional comments on air quality was high, they hardly reflected reality. Conversely, the Sino Weibo social media comments were highly credible, despite their low sentiment values. Tourists' perception of air quality is not only mainly affected by intangible factors such as pollutants but also by tangible factors such as blue sky. The study uses online reviews to analyze air quality and provides a reference for the environmental management of destinations and decision making among tourists.
... By contrast, missing breakfast-included rates are obtained by adding the cost of breakfast or by subtracting the cost of lunch, as we note that these surcharge/discount rates are not subject to dynamic pricing (for a given hotel, they are the same for almost every t and k ). This is similar to the common approach of J o u r n a l P r e -p r o o f adding dummy variables in the estimated models (see among others Yang and Leung (2018)), but it allows us to keep the model more simple, and use all the prices an hotelier publishes (any t and k ) when the non-refundable, double room for single use with breakfast price is missing. ...
We develop an innovative framework to study how hoteliers apply inventory control and price discrimination taking into account seasonality. We end up with a time-varying model that, using publicly available information, connects the early booking and last-minute pricing decisions. In doing so, we account for the expected demand size and price elasticity, the inventory put on sales, and the last-minute demand shocks. An analysis focused on 100 hotels in Milan (Italy) shows that during the Covid-19 last-minute discounts/surcharges remain stable over long periods while the role of advance booking as a lever for revenue management is reduced. Moreover, the pandemic has increased the last-minute adjustment at the short advance booking, especially for midweek days.
... Yet in another study by Tidström et al. (2018), a company collaborates with a business partner to utilize their sales channel to sell their products in a new market, but the collaborative relationship also becomes competitive when the business partner starts producing and selling similar products. In our case, hotels cooperate with platforms to gain access to a broad customer base and sell their room inventory (Ling et al., 2014;Yang & Leung, 2018). However, hotels also compete with platforms because they charge high fees, which considerably reduces hotel profits (Bahar, Nenonen, & Starr Jr, 2021;Verhoef & Bijmolt, 2019). ...
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Coopetition—consisting of concurrent cooperation and competition—mainly focuses on business activities far from the customer, such as research and development. However, coopetition close to the customer, comprised of marketing and sales, remains an under-researched area. Applying grounded theory, we investigate how hotels and platforms (e.g., and pursue coopetition in close customer proximity. Our findings suggest that, contrary to insights from the literature, coopetition is not only competition dominant close to the customer. Rather, coopetition patterns range between cooperation- and competition-dominant approaches, depending on tension levels. Further, in contrast to current views, separating cooperation and competition over time is impossible when episodes of cooperation and competition are short. Instead, hotels utilize multiple spaces (platforms, physical hotels, and direct channels) to isolate the two forces.
... AI and ML can assist companies in estimating what consumers want and how much they are willing to pay (Erevelles et al., 2016;Ke, 2018;Stavins, 2001). Being able to change prices dynamically, based on the market conditions and the consumer's price sensitivity, allows firms to gain a great competitive advantage (Yang & Leung, 2018;Ye et al., 2018). One of the most known pricing strategies is Uber's "surge price" (e.g., in Guda & Subramanian, 2019). ...
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The emergence of consumer-generated data and the growing availability of Machine Learning (ML) techniques are revolutionizing marketing practices. Marketers and researchers are far from having a thorough understanding of the broad range of opportunities ML applications offer in creating and maintaining a competitive business advantage. In this paper, we propose a taxonomy of ML use cases in marketing based on a systematic review of academic and business literature. We have identified 11 recurring use cases, organized in 4 homogeneous families which correspond to the fundamentals leverage areas of ML in marketing, namely: shopper fundamentals, consumption experience, decision making, and financial impact. We discuss the recurring patterns identified in the taxonomy and provide a conceptual framework for its interpretation and extension, highlighting practical implications for marketers and researchers.
... The relationship between the hoteliers and OTAs often resulted in channel conflicts due to the heavy reliance on OTAs to distribute hospitality products and services (Law et al., 2015). As a result, this conflict can cause anxiety and stress for the hospitality employees and does not benefit the consumers (Yang and Leung, 2018). ...
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This study aims to discover if social relationship as measured by shared value, duration and non-economic satisfaction and cognitive relationship as measured by task performance and economic satisfaction affect OTAs-hoteliers’ relationship, which is measured through trust and commitment. Data was collected from hotels’ operation managers, senior managers, financial executives, business owners, and partners through online surveys. A total of 208 usable questionnaires were returned from 577, resulting in a response rate of 36.04 per cent. The hypotheses were tested using SEM and mediation effects were tested and translated using Hair et al. (2014) nested structural model concept. The results indicate partial mediation for cognitive domain - relationship commitment and full mediation for social domain - relationship commitment affects. Trust forms as the mediating variable.
... This seems to be the case in the tourism industry. Travelers who want to save money and discover attractions recognize the growing importance of price deals, last-minute discounts, and price comparisons [3,4]. Further, significant dispersion exists in the price of tourism products and services in a remarkably heterogeneous tourism market. ...
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In a remarkably heterogeneous tourism market, marketers apply a wide range of strategies which help them ward off competitors and attract customers. The openness of travel information such as product and service quality and price is essential but still a challenge for marketers since traveler characteristics are often multi-dimensional. This study devotes special attention to travelers’ price sensitivity, and aims to investigate whether price sensitivity can segment travelers and the effects on information search behavior. For this purpose, the research study conducted Analysis of Variance (ANOVA) and regression analysis using survey data of 310 respondents. The results confirm the existence of heterogeneity in price sensitivity and there is a clear difference in the use of information by travelers resulting from this variable. Marketers should therefore utilize different communication strategies for travelers with different price sensitivities. For example, to obtain price-sensitive travelers it is more efficient to provide travel information with a clear difference in price between products and services that will reduce their search efforts. On the other hand, to target price-insensitive travelers, marketers should provide sufficient information about product attributes through online personal information sources including organizations such as Trip Advisor, Twitter, Facebook, and Instagram.
... The diversity of types and characteristics of food is also the main reason for tourists to stay longer in a place (Y. Yang & Leung, 2018), (Soler & Gemar, 2018), (Annunziata et al., 2019). ...
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This study aims to determine the effect of price, service, product and location (PSPL) quality factors on tourist purchase interest and to find the factor most influencing tourists' purchase intention in local culinary in the Lake Toba area. The method used in this research was verification analysis using multiple linear regression. From the results of the research by conducting the F test, the variable price (X1), service quality (X2), product quality (X3), and location (X4) have a significant effect on purchase intention in local culinary in the Lake Toba area. Testing with the T test showed that the variables affecting the purchase intention of Arsik Fish Culinary are the variables of service quality (X2), product quality (X3), and location (X4). The variables influencing Naniura purchase intention are product quality (X3) and location (X4). The variables that affect the purchase intention of Manuk Napinadar are service quality (X2), product quality (X3), and location (X4). The variables that affect Natinombur purchase intention are price (X1), product quality (X2), and location (X4). The variables that influence Dali Ni Horbo's purchase intention are product quality (X3) and location (X4). The variables that affect Tanggo-Tanggo purchase intention are the variable product quality (X3) and location (X4). Overall from the type of culinary service, it is found that hospitality is the lowest, so it needs to be improved. The results of this study can be used by local culinary managers or owners in developing strategies and increasing sales performance.
... The best strategy is not suitable for each hotel. According to the strategic position analysis, overall cost leadership, variation and concentration are different categories that encounter any hotel to select their best (Yang and Leung, 2018). Once the breakdown of competitors has been Implementing dynamic revenue management performed, hotel managers should revisit their budgets to control costs effectively (Kimes and Renaghan, 2011). ...
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Purpose Practicing flexible revenue management (RM) at hotels during Covid-19 is essential. The well-performed hotels ponder how to transform the target from revenue to net profits. This paper aims, first, to develop a value stream mapping (VSM) model for a productive RM based on six key drivers: organizational culture, demand forecasting, dynamic distribution channels, competition breakdown, dynamic and customized pricing and daily reviewing, and, second, to examine the nexus between RM and hotel’s efficiency during Covid-19 using the wavelet analysis (WA) to visualize this relationship’s time and frequency-based lead–lag dynamics. Design/methodology/approach Using time-series data, a multiple case study of 31 luxury hotels in Egypt was applied based on semi-structured interviews and self-administered questionnaires. Findings The first phase results showed that consensus toward the RM framework was achieved, regardless of current challenges, indicating that RM managers and scholars could use it. In Phase 2, the WA confirmed a positive correlation and significant influence between Covid-19 and RM practices at most business cycle frequencies. Furthermore, overall high causal relationships between RM practices and hotel efficiency were discovered in the short and medium terms and through different occurrence cycles. Though, the dynamic pricing in the long term was apart from this relationship. The causal effects between Covid-19 and hotel efficiency are not observable in the long-run spectra, indicating that resilience efforts with Covid-19 perhaps mitigated the impact. Research limitations/implications Hotel managers could use the RM model developed from this study during the downturn to improve efficiency. The outcome may lead to the recovery of the hotel market and the whole economy. WA maps display possible directions for hotel managers to be more efficient based on the time and frequency domains. Originality/value This study shows opportunities for RM implementation during Covid-19 based on the VSM and the WA approaches in hotels.
The presented article focuses on the issue of customer segmentation in the hospitality industry and its use for price optimisation. To identify the market segments article uses the Two-Step cluster analysis. The analysis was based on the seven variables (length of stay, average room rate, distribution channel, reservation day, day of arrival, lead time and payment conditions). Six clusters were identified as following segments: Corporates, Early Bird Bookers, Last Minute Bookers, Product Seekers, Values Seekers and Last Minute Bookers. To optimise the price for these segments, article works with the coefficient of price elasticity of demand for the presented market segments. The price elasticity of demand is measured by the log-log regression analysis. Data were colected from the four-star hotel in Prague, Czech Republic and analysis is based on more than 9000 transactions. Last minute bookers segment was the only one with the positive coefficient of price elasticity and has the lowest value of lead time (9,27 in average). Product seekers have the highest coefficient of price elasticity (−3,413) and highest average room rate (3573 CZK in average). Early bird bookers, Long time stayers, Corporates and Value seekers was identified as pricely inelastic.
This article aims to present the evolution of online travel agencies, the main themes, authors, and methodologies, through a systematized review. The analysis has focused on 61 papers published from 2009 until 2020. The research was limited by the journal ranking in the subject category tourism, leisure and hospitality management in the Scimago Journal and Country Rank. Field research is the most frequent in studies in the area. However, the interest in experiments and content analysis grows, using the content generated by customers in the online travel agencies. This study helps to collaborate in the authors’ decision-making regarding the methodology to be used and which authors are being negotiated in future research. The results showed how the theme has evolved, changes in approaches, the way online travel agencies report to their partners (often in a conflictual way) and customers, pointing out new trends to be studied. There was no literature review about online travel agencies published in the journals used for this research, to the best of our knowledge. Cover many years and expand the search to other academic journals is our suggestion for future research.
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We examine the systematic effects of crime on hotel operating performance based on data from a sample of 404 Houston hotels from January 2009 to December 2014. Econometric results show that Part I crime (i.e., violent and property crime) incidents have a significantly negative impact on hotel operating performance (measured by revenue per available room), ceteris paribus. Also, the marginal effect of crime declines as crime density level increases. Separate examinations of violent and property crimes show that they exert significant and negative impacts on hotel operating performance, with the impact of violent crimes being more substantial. In addition, the results reveal that both nighttime and daytime crime incidents significantly and negatively impact hotel operating performance. Finally, as evidenced by the insignificant impact of crime incidents occurring on hotel premises, the results suggest that hotels are generally effective at maintaining systematic security measures and preventing crime incidents from occurring.
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In this article, I discuss the history of hotel revenue management (RM) and trace its evolution over the past 25 years. The most important change in hotel RM has been its evolution from a tactical inventory management approach to a more strategic marketing approach.
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PDF available from: The price is the single, most efficient tool that hoteliers have to adjust the demand and the offer in the short term. Dynamic pricing is the practice of changing the price charged for a product based on time. Using hotel room price data collected from an Internet distribution channel, this paper presents the research carried out to investigate the dynamic pricing practices of the hotels in the Basque Country in 2013 and 2014. The analysis shows that these hotels favour two price-changing patterns. The first pattern refers to the practice of changing a number of prices for contiguous, future target dates on the same date. The second pattern refers to the practice of changing the price a set number of days in advance of the target date. This paper and the research presented in it are an extension of the research published in the ENTER 2015 conference proceedings. The findings reinforce the previous conclusions by arriving to the same patterns after analysing data for a much wider geographical area, the Basque Country as opposed to Bilbao, and two years instead of one, 2014 as well as 2013 as previously.
The purpose of this research was to determine whether there was price parity for hotel rooms in Macao. The study collected room prices based on hotel star categories at various sales channels. The room prices included three types of booking: reservations made on the day of stay, those made 14 days ahead, and those made 30 days ahead. The results were the same for the three types: price parity was observed for rooms at three-star and four-star hotels but not at five-star hotels. The authors offer explanations for these results and suggestions for hotel guests who intend to book rooms during their visit to Macao.
This paper studies the optimal pricing strategies of a tour operator (TO) and an online travel agency (OTA) when they achieve the O2O model through online sale and offline service cooperation. By constructing a competition model, cooperation conditions, pricing strategies and revenues are analyzed and compared in the Stackelberg and Bertrand game. Results indicate that service level, unit sale commission, service cost coefficient and unit service compensation coefficient have different influences on the TO's and OTA's pricing decisions. When the unit sale commission is greater than the threshold, the TO's and OTA's pricing in the Bertrand game are higher than in the Stackelberg game. Being a leader is the dominant strategy for the TO. In addition, the revenues of TO and OTA in sale and service cooperation are analyzed by numerical examples and some suggestions for establishing cooperation contract are provided.
Customers read reviews to reduce the risk associated with a purchase decision. While prior studies have focused on the valence and volume of reviews, this study provides a more comprehensive understanding of how reviews influence customers by considering two additional factors—exposure to reviews and price relative to other products in the category. Data provided by two online retailers are used for the analysis. The results reveal a four-way interaction with the effect of valence on purchase probability strongest when (1) there are many reviews, (2) the customer reads reviews, and (3) the product is higher priced. The effects of valence are smaller, but still positive, in the other conditions. We develop theoretical explanations for the effects based on dual processing models and prospect theory, and provide a sensitivity analysis. We discuss implications for academics, manufacturers and online retailers.
Purpose The purpose of this study is to empirically evaluate the influence of a hotel’s listing on the last-minute hotel booking app, HotelTonight, and average daily rate (ADR) on the hotel’s net operating income (NOI). The study examines the mediating effect of hotel occupancy rate on the relationships between ADR and hotel app usage in terms of NOI. Design/methodology/approach The data for the study was graciously provided by Smith Travel Research, Inc. for 80 hotels located in the top Florida destinations listed on the HotelTonight app. Hierarchical multiple regression with a mediation effect was used in the study to test the mediating effect of occupancy between hotel app usage and ADR with NOI. Findings The research results show a positive association between a hotel’s HotelTonight listing and ADR in terms of its NOI. Occupancy is found to have a full mediation effect between a hotel’s usage of the mobile app and NOI. Originality/value Mobile apps that specialize in last-minute hotel bookings have proliferated in recent years by providing hotels a mobile platform to increase hotel occupancy. However, there is a dearth of studies examining the effect these apps have on a hotel’s bottom line profitability or NOI.
This paper evaluates the impact of innovative activity in the hotel industry on the willingness to pay by consumers. To this end, we estimate a hedonic price function where innovation is identified indirectly through certain attributes that previous literature has linked with hotel innovativeness. The test is performed on a representative sample of Cuban hotels, considering a large number of attributes of hotels and rooms. To solve the usual problems of collinearity, a million alternative models are estimated by choosing the variables included in a random manner and obtaining the final coefficients by means of an internal meta-analysis. The results are completed with a variance decomposition analysis. The results highlight the importance of the attributes linked to innovation and internationalization on the determination of room prices in Cuban hotels: membership of international hotel chains, high quality offers, diversified rooms, and adaptability to specific needs of each client.
Within many of the multioutlet branded chains that dominate the retail and services landscape, the organizational form (e.g., company management, franchising) used to manage an outlet varies from site to site, as do the prices charged at those sites. I propose that organizational form and prices may be systematically related as a result of brand externalities. In particular, I develop logic that the relevant form of externality should differ for upper quality tier brands and lower tier brands. Using panel data on price and organizational form from more than 6,700 branded U.S. hotels affiliated with 40 “dual-distribution” brands—those brands that simultaneously company manage and franchise individual outlets—I find that, consistent with the brand externality arguments, company-managed locations have higher prices within high-quality chains, whereas franchisees price higher in the lower tiers. This paper was accepted by Bruno Cassiman, business strategy.
Purpose Hotel managers are being challenged by the increasing multitude of distribution and sales channels. Online travel agencies (OTAs) in particular generate a great deal of uncertainty: Which are the best ones? Which ones offer the best conditions? How many channels are optimal for my hotel? How can I evaluate costs versus benefits? These and other questions concerning the optimal online distribution channel strategy have produced different reactions in practice. The aim of this paper is to challenge the need for an over-optimization of channel strategy by proposing that the consumer, at the end, deals with a network of information presented on one networked environment, including the Web. Hence, the network effect of the numerous online platforms is what drives consumer choice and, finally, bookings. Design/methodology/approach A series of multiple regressions with representative samples of hotels in Switzerland from the years 2009, 2010, 2011 and 2012 was performed to estimate the importance of the number of platforms against other independent variables. Additionally, further multiple regressions with samples from the years 2011 and 2012 using the most important platforms (first-tier channels) shows again that the number of platforms is more important. Findings The analyses show that the estimated number of online bookings by the respondents in the hotels is a result of the number of channels, not the type of channel. This is particularly true for non-categorized establishments and one- and two-star hotels. The analyses do not confirm the billboard effect, according to which particular platforms (first-tier channels) increase the probability of bookings. Thus, the survival strategy is to maximize share of shelf and to build on interdependencies and network effects. Research limitations/implications The study looks only at online bookings. Additional research into the connection between online and offline channels, particularly from the viewpoint of the consumer, will provide further insights. The study looks at the booking volume per channel, not the monetary sales volume or the profit. A study that quantifies not only the volume of bookings but also the total profit or the contribution to profit per channel could quantify the benefits of the multi-channel strategy. Originality/value The multiple online channel strategy seems to be the more effective approach to maximizing bookings online, regardless of the platforms chosen. Results of the study challenge the current opinion among practitioners that the multitude of distribution channels forces them to choose among single online channels and, therefore, drives the search for criteria to assess these channels or even to disregard them. The consistent results across 2009-2012 show that even in the turbulent phase of the advent of OTAs in the travel industry, hotels can adopt a winning strategy. Finally, the results suggest that the intermediation of online distribution of hotel beds has approached the condition of perfect competition, causing the OTA business model to be cannibalized.