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

October 2018
Tourism Management 68

DOI:10.1016/j.tourman.2018.03.016

Authors:

Yang Yang

Temple University

Xi Leung

University of North Texas

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.

. Descriptive statistics of continuous variables.

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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

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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

e-mail: yangy@temple.edu

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

e-mail: xi.leung@unt.edu

* = 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

decisions.

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

HotelTonight.

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, Hotwire.com, Orbitz.com, Expedia.com,

Booking.com, and Hotels.com. 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. Breakfast’s 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 Rate’ from 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

sampled.

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Table 1. Descriptive statistics of continuous variables.

Variable

Definition

Dependent Variable



Price discount on the HotelTonight app compared to the lowest rates from major OTAs for

hotel

Independent Variables of Major Interest

Dif_rating

Relative TripAdvisor review valence compared to that of HotelTonight, calculated as the

difference between a hotel’s TripAdvisor rating and its HotelTonight rating (in %) divided by

20.

Dif_reviews

Relative TripAdvisor review volume compared to that of HotelTonight, calculated as the

difference between a hotel’s TripAdvisor review number (in thousands) and its HotelTonight

review number (in thousands)

Breakfast

A dummy variable indicating whether complimentary breakfast is included in the HotelTonight

rate. Breakfast = 1 if complimentary breakfast is included, and Breakfast = 0 if not.

Rm_type

A categorical variable capturing the room type booked via HotelTonight. Three room types

are used on HotelTonight: rooms with 1 bed (Rm_type = 1), rooms with 2 beds (Rm_type =

2), and rooms selected by the hotel (Rm_type = 3).

Mkt_power

Percentage of a hotel property’s rooms relative to the total number of hotel rooms in the zip

code area of the hotel.

Control Variables

Booking_hour

A dummy variable indicating whether the price discount was calculated based on the rates

collected at 9 am (Booking_hour = 1) or 9 pm (Booking_hour = 2).

Rooms

Number of rooms in the hotel (in thousands)

Chain

A dummy variable indicating whether the hotel was affiliated with a hotel chain. Chain = 1 if

the hotel was affiliated with a hotel chain, and Chain = 0 if not.

Location

A categorical variable indicating hotel location type by STR: 1= airport, 2 = interstate, 3 =

resort, 4 = small metro/town, 5 = suburban, and 6 = urban

Class

A categorical variable indicating hotel class by STR: 1 = economy, 2 = midscale, 3 = upper

midscale, 4 = upscale, 5 = upper upscale, and 6 = luxury.

Day

A categorical variable indicating the day of week.

Table 2. Descriptive statistics of continuous variables.

Variable

Observations

Mean

Std.

Dev.

Min

Max



3,766

0.116

0.160

-1.338

0.787

Dif_rating

3,615

-0.375

0.357

-2

0.95

Dif_reviews

3,757

0.086

1.512

-9.437

8.757

Mkt_power

3,766

0.158

0.199

0.002069

Rooms

3,766

0.266

0.247

0.015

2.019

Table 3. Descriptive statistics of categorical variables.

Frequency

Percent

Cum. Percent

Breakfast = 0 (breakfast excluded) †

3,361

89.32

Breakfast = 1 (breakfast included)

402

10.68

100.00

Rm_type = 1 (room with 1 bed) †

566

15.18

Rm_type = 2 (room with 2 beds)

326

8.74

23.93

Rm_type = 3 (room selected by hotel)

2,836

76.07

100.00

Booking_hour = 0 (9am) †

1,845

48.99

Booking_hour = 1 (9pm)

1,921

51.01

100.00

Chain = 0

1,359

36.09

Chain = 1

2,407

63.91

100.00

Location = 1 (Airport) †

1.17

Location = 2 (Interstate)

0.21

1.38

Location = 3 (Resort)

1.22

2.60

Location = 4 (Small Metro/Town)

0.13

2.73

Location = 5 (Suburban)

1,100

29.21

31.94

Location = 6 (Urban)

2,563

68.06

100.00

Class = 1 (Economy)

159

4.22

Class = 2 (Midscale)

2.52

6.74

Class = 3 (Upper Midscale) †

261

6.93

13.67

Class = 4 (Upscale)

995

26.42

40.09

Class = 5 (Upper Upscale)

1,616

42.91

83.01

Class = 6 (Luxury)

640

16.99

100.00

Day = Sunday

650

17.26

Day = Monday †

492

13.06

30.32

Day = Tuesday

457

12.13

42.46

Day = Wednesday

468

12.43

54.89

Day = Thursday

421

11.18

66.06

Day = Friday

636

16.89

82.95

Day = Saturday

642

17.05

100.00

(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

Dif_rating

-0.0201***

-0.0176**

(0.007)

Dif_reviews

-0.0131***

-0.00489**

(0.002)

Breakfast = 1

-0.0374***

-0.0519***

(0.007)

(0.008)

Rm_type = 1

-0.00174

-0.00805

(0.008)

Rm_type = 3

0.106***

0.104***

(0.007)

(0.008)

Mkt_power

0.0117

-0.0189

(0.012)

Booking_hour

= 1

-0.00145

-0.00298

-0.000809

-0.00121

-0.000876

-0.00145

-0.00181

(0.005)

Rooms

-0.114***

-0.124***

-0.0861***

-0.119***

-0.0736***

-0.115***

-0.0775***

(0.000)

Chain = 1

-0.0111

-0.00513

-0.00949

-0.00876

0.0133**

-0.0103

0.0227***

(0.007)

(0.006)

(0.007)

Location = 2

-0.0350

-0.00624

-0.0126

-0.0391

-0.0128

-0.0346

0.00244

(0.039)

(0.038)

(0.040)

(0.042)

(0.046)

(0.038)

(0.063)

Location = 3

-0.0359

-0.0337

0.00412

-0.0403

-0.0131

-0.0339

-0.00602

(0.026)

(0.028)

(0.026)

(0.027)

(0.026)

(0.030)

Location = 4

-0.0555

-0.0313

-0.0290

-0.0580

0.0630

-0.0548

0.117

(0.083)

(0.103)

(0.083)

(0.103)

Location = 5

-0.0446**

-0.0473**

-0.0213

-0.0465**

-0.0236

-0.0448**

-0.0170

(0.023)

(0.024)

(0.023)

(0.025)

(0.023)

(0.027)

Location = 6

0.0109

0.0101

0.0325

0.00738

0.0177

0.0123

0.0194

(0.022)

(0.023)

(0.024)

(0.023)

(0.024)

(0.022)

(0.026)

Class = 1

0.0442***

0.0126

0.0426***

0.0283

0.0104

0.0449***

-0.0479***

(0.015)

(0.016)

(0.015)

(0.016)

Class = 2

-0.0596***

-0.0749***

-0.0623***

-0.0710***

-0.0625***

-0.0612***

-0.0931***

(0.013)

(0.017)

(0.014)

(0.013)

(0.017)

Class = 4

0.0201**

0.00784

0.0142

0.0135

0.0129

0.0206**

-0.0109

(0.010)

(0.011)

(0.010)

Class = 5

0.0503***

0.0471***

0.0433***

0.0379***

0.0208**

0.0509***

-0.00123

(0.010)

(0.011)

(0.010)

(0.011)

(0.010)

(0.011)

Class = 6

0.0448***

0.0436***

0.0403***

0.0325***

0.0273***

0.0456***

0.00581

(0.011)

(0.010)

(0.011)

(0.012)

Day = Sunday

0.0234***

0.0264***

0.0230***

0.0223**

0.0231***

0.0235***

0.0244***

(0.009)

Day =

Tuesday

0.00801

0.0118

0.00887

0.00829

0.00489

0.00814

0.00773

(0.010)

Day =

Wednesday

0.0112

0.0179*

0.0126

0.0103

0.00893

0.0112

0.0123

(0.009)

(0.010)

(0.009)

Day =

Thursday

0.0418***

0.0439***

0.0414***

0.0405***

0.0358***

0.0420***

0.0361***

(0.010)

(0.009)

(0.010)

(0.009)

Day = Friday

0.00551

0.00799

0.00606

0.00485

0.00216

0.00568

0.00481

(0.008)

(0.009)

(0.008)

Day =

Saturday

-0.0278***

-0.0254***

-0.0256***

-0.0279***

-0.0258***

-0.0278***

-0.0220**

(0.009)

constant

0.118***

0.115***

0.0933***

0.136***

0.0220

0.114***

0.0294

(0.024)

(0.025)

(0.026)

(0.025)

(0.027)

(0.025)

(0.030)

3766

3615

3757

3763

3728

3766

3586

R-sq

0.083

0.082

0.096

0.087

0.143

0.083

0.162

Adj. R-sq

0.078

0.077

0.091

0.082

0.138

0.078

0.156

(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

-0.0165**

-0.0179**

-0.0175**

-0.0142*

(0.007)

(0.008)

Dif_reviews

-0.0103***

-0.00488**

-0.00519***

-0.00506***

(0.003)

(0.002)

Dif_rating * Dif_reviews

-0.0121**

(0.005)

Breakfast = 1

-0.0535***

-0.0515***

-0.0524***

-0.0523***

(0.008)

Parking = 1

0.00222

(0.008)

WiFi = 1

-0.00767

(0.008)

Rm_type = 1

-0.00770

-0.00858

-0.00587

-0.00784

(0.008)

Rm_type = 3

0.105***

0.102***

0.115***

0.104***

(0.008)

(0.010)

(0.008)

Rm_type = 3 * rooms

-0.0316*

(0.016)

Mkt_power

-0.0204*

-0.0166

-0.0196

0.174***

(0.012)

(0.013)

(0.012)

(0.039)

Mkt_power * Class

-0.0405***

(0.008)

Booking_hour = 1

-0.00166

-0.00176

-0.00163

-0.00195

(0.005)

Rooms

-0.0799***

-0.0791***

-0.0609***

-0.0754***

(0.012)

Chain = 1

0.0237***

0.0238***

0.0243***

0.0215***

(0.007)

Location = 2

-0.00102

0.00430

0.00275

0.00489

(0.064)

(0.063)

(0.065)

(0.062)

Location = 3

-0.00706

-0.00682

-0.00676

-0.00798

(0.030)

Location = 4

0.114

0.111

0.126

0.114

(0.103)

(0.104)

(0.103)

Location = 5

-0.0186

-0.0167

-0.0162

-0.0168

(0.027)

Location = 6

0.0175

0.0180

0.0191

0.0181

(0.026)

(0.027)

(0.026)

(0.027)

Class = 1

-0.0474***

-0.0461***

-0.0506***

-0.0664***

(0.016)

(0.017)

(0.016)

Class = 2

0.00519

0.00415

0.00493

-0.00269

(0.012)

Class = 4

-0.0916***

-0.0913***

-0.0942***

-0.107***

(0.017)

(0.018)

Class = 5

-0.000888

-0.00226

-0.00191

0.00679

(0.011)

(0.012)

(0.011)

(0.012)

Class = 6

-0.0103

-0.0121

-0.0112

0.00328

(0.010)

(0.011)

(0.010)

(0.011)

Sunday

0.0244***

0.0240***

0.0242***

(0.009)

Tuesday

0.00804

0.00770

0.00794

0.00772

(0.010)

Wednesday

0.0121

0.0122

0.0116

(0.009)

Thursday

0.0362***

0.0358***

0.0365***

(0.009)

Friday

0.00478

0.00439

0.00469

0.00496

(0.008)

Saturday

-0.0222**

-0.0219**

(0.009)

constant

0.0321

0.0310

0.0233

0.0253

(0.030)

(0.032)

(0.030)

3586

R-sq

0.163

0.162

0.166

Adj. R-sq

0.157

0.155

0.156

0.160

(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

Disparity

Last-minute price

discounts

Retailer (Hotel)

Characteristics

Review

valence

Review

volume

Product

Characteristics

Added services

with high

variable costs

Uncertainty in

room type

Market

Characteristics

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.

Appendix

Table A1. Correlation matrix of independent variables.

Dif_rati

Dif_revi

ews

Breakfa

st = 1

Rm_typ

e = 2

Rm_typ

e = 3

Mkt_po

wer

Booking

hour = 1

Room

Chain =

Locatio

n = 2

Locatio

n = 3

Locatio

n = 4

Locatio

n = 5

Locatio

n = 6

Class =

Dif_reviews

0.178

Breakfast = 1

0.099

-0.034

Rm_type = 2

-0.074

0.183

-0.095

Rm_type = 3

0.104

-0.267

0.097

-0.560

Mkt_power

-0.202

0.054

0.003

-0.009

0.033

Booking_hou

r = 1

0.023

0.025

-0.001

-0.002

0.005

-0.002

Rooms

-0.213

0.315

0.172

0.218

-0.216

0.018

-0.014

Chain = 1

-0.173

0.136

-0.134

0.217

-0.368

-0.057

-0.025

0.295

Location = 2

-0.012

0.002

-0.031

0.036

-0.010

0.028

0.013

-0.028

0.031

Location = 3

0.042

0.060

0.038

-0.026

0.044

-0.044

-0.004

-0.060

-0.127

-0.005

Location = 4

-0.012

0.003

0.011

0.020

-0.061

0.004

0.016

-0.031

-0.044

-0.001

-0.004

Location = 5

-0.105

0.009

-0.134

0.134

-0.138

0.261

0.008

-0.139

0.073

-0.025

-0.070

-0.021

Location = 6

0.101

0.006

0.136

-0.130

0.128

-0.258

-0.006

0.151

-0.048

-0.062

-0.171

-0.051

-0.936

Class = 1

0.115

0.019

0.067

-0.040

0.047

0.007

-0.021

-0.120

0.086

-0.008

-0.023

0.168

0.138

-0.135

Class = 2

0.140

-0.001

0.109

-0.008

0.049

-0.056

0.021

-0.047

-0.149

-0.019

-0.052

-0.015

-0.016

0.043

-0.092

Class = 4

-0.063

-0.004

-0.024

-0.008

-0.030

0.100

0.030

-0.090

0.047

0.140

-0.016

-0.005

0.028

-0.040

-0.029

-0.066

Class = 5

-0.066

0.065

0.223

-0.043

0.129

0.010

-0.013

0.351

-0.116

-0.036

0.123

-0.029

-0.051

0.028

-0.175

-0.406

-0.126

Class = 6

-0.084

-0.089

-0.192

0.033

-0.137

-0.045

-0.002

-0.185

0.144

-0.025

-0.062

-0.020

-0.088

0.093

-0.120

-0.278

-0.086

-0.531

(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.)

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Aug 2024
Tourism Rev

Purpose- This paper aims to estimate the price premium for a sea view on room rent in a Nordic context, i.e. where proximity to the sea is not valued for the presence of swimmable beaches and suntanning activities. The analysis also explores regional and seasonal variations in price premiums. Design/methodology/approach- To do so, the study uses information from a Web search of room rents during winter and summer peak seasons. The investigation is based on hotels located along the St. Lawrence River in the Province of Quebec (Canada), where about 40 to 60 km separate both shores. A matching procedure and hedonic pricing models are used to identify the causal impact of a sea view on individual room rents. Findings- Results suggest that the view price premium varies between 0% and 20%. It is relatively stable on the North Shore, but varies highly on the South Shore, where touristic activities are mainly operating in summertime. The estimation suggests a median local economic benefit of about $30.1M/year. Practical implications- The analysis reveals that a hedonic pricing model might fail to identify causal effects, especially if it does not account for hotel characteristics. A multiple linear regression model does not ensure a causal interpretation if it neglects unobserved characteristics correlated with the view. Originality/value- The paper proposes a matching identification procedure accounting for spatial confounding to retrieve the causal impact of the view of the sea on hotel room rents. A heterogeneity analysis suggests that view price premium on room rent can vary within seasons but mainly across regions, even for the same amenities.

Improving customer satisfaction in the hotel industry by fusing multi-source user-generated content: An integration method based on the heuristic-systematic model and evidence theory

Article

Full-text available

Jul 2024
APPL INTELL

Improving customer satisfaction is the key factor in enhancing the core competitiveness of hotels, as higher customer satisfaction can lead to long-term benefits such as a positive reputation, customer loyalty, and sustained profitability. Multi-source user-generated content (UGC) can provide high-quality and sufficient information for improving customer satisfaction; however, related research is limited. Therefore, a method considering multi-source UGC to improve customer satisfaction is proposed in this paper. First, the service attributes of the hotels that customers care about are obtained from multi-source UGC. Then, evaluation information is obtained by processing multi-source UGC based on the heuristic-systematic model, and the credibility of the evaluation information is measured. Furthermore, evaluation information is combined based on evidence theory to estimate the importance and performance of hotel service attributes. Finally, impact asymmetry-gap analysis (IAGA) is proposed to generate customer satisfaction improvement strategies for different attributes. The application of the method is illustrated using data from actual hotels.

Focal Points in Recent Tourism Price Research

Article

Apr 2024

Helena Nemec Rudez

The article aims to provide a comprehensive overview of recent academic studies on pricing in tourism and to deepen the understanding of the recently prevailing research streams on this topic. This article reviews research articles on tourism pricing published in journals in the Scopus database between 2017 and early 2023. An analysis of the selected literature identifies three research streams on pricing: research on price determinants, price development and customers’ price decisions. In terms of methodology, empirical studies predominate, and in terms of period, the distribution over the years shows a stable and consistent level of research activity. The article is essential for further research in the field by presenting a synthesis of recent academic work.

Research on the integration of resource-based theory and transaction cost theory Taking the relationship between OTA and traditional tourism enterprises as an example

Conference Paper

Full-text available

Jul 2022

As two classical theories in the field of strategic management, resource-based theory andtransaction cost theory, the ultimate purpose is to analyze how enterprises obtainsustainable competitive advantage. Firstly, this paper combs the research progress andknowledge core of the two theories respectively, and points out that the starting point ofresource-based theory is to realize the value of resources, while transaction cost theory pursues the cost saving of resource flow. These two theories can be effectively integratedwhen analyzing the competition and cooperation between enterprises. Further, taking the relationship between online travel service providers (OTAs) and traditional tourism enterprises as an example, this paper discusses the essence of their competition andcooperation, and points out that the key to building a sustainable competition and cooperation relationship between them is to formulate a reasonable value distributionmodel. This paper helps to further deepen the understanding of the relationship betweenresource-based theory and transaction cost theory, helps to understand the essence of thecompetition and cooperation relationship between online and offline tourism enterprises, and has some enlightenment for the research of organizational cooperation.

Advance booking pricing strategies: A comparison of the pre- and post- Covid-19 situation in the cruise industry

Article

Jun 2024

Digital transformation in the hospitality industry: A bibliometric review from 2000 to 2023

Article

Apr 2024
Int J Hospit Manag

This study reviews the rich yet fragmented literature on digital transformation in the hospitality industry. Using 2527 peer-reviewed English articles published between 2000 and 2023, we employed bibliometric and content analyses to determine the overall performance and research themes in this field. Employing co-occurrence analysis, four pivotal research themes were identified: (1) digital technology adoption, (2) stakeholder outcomes of digital transformation, (3) online purchase determinants, and (4) online review analysis. Subsequently, a nuanced content analysis of these themes unveiled specific subthemes. Digital technology adoption mainly consists of three subthemes: determinants of digital technology adoption, strategies for adoption, and the impact of digital technology adoption on firm performance. Subthemes pertaining to stakeholder outcomes of digital transformation involve service and product quality, stakeholder relationship quality, and stakeholder behaviors/ well-being. The subthemes of online purchase (intention) determinants primarily encompass online reviews, price strategies, and website quality. Subthemes within online review analysis center on online reviews’ engagement, contents (and variations), impacts, and coping strategies. This work enhances our understanding of the progress and limitations of research on hospitality digital transformation.

Framing memorable hiking tourism experiences through embodiment: The case of Mount Huangshan, China

Article

Mar 2024

Are all tourism review information on the platforms equally useful?

Article

Dec 2023

Price determinants of sharing economy based accommodation rental: A study of listings from 33 cities on Airbnb.com

Article

Full-text available

Apr 2017
Int J Hospit Manag

The advent of the “sharing economy” challenges not only the business of hotel industry but also the theories and models based on the conventional hotel industry. A key dimension of the hospitality industry is pricing. The aim of this study is to identify the price determinants of sharing economy based accommodation offers in the digital marketplace. Specifically, a sample of 180,533 accommodation rental offers in 33 cities listed on Airbnb.com is investigated using ordinary least squares and quantile regression analysis. Twenty-five explanatory variables in five categories (host attributes, site and property attributes, amenities and services, rental rules, and online review ratings) are explored for the intricacies of the relationships between pricing and its determinants.

Systematic effects of crime on hotel operating performance

Article

Full-text available

Jun 2017
TOURISM MANAGE

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.

Exploring TripAdvisor

Chapter

Full-text available

Jan 2016

This case study explores TripAdvisor in order to understand its role in social media within the tourism landscape and specifically, in relation to the “Open Innovation in Tourism” phenomenon. The key features, business model and innovation approaches of TripAdvisor are analysed and its value added services for travellers as well as businesses, are described. This study also examines TripAdvisor’s co-creation ecosystem that facilitates various exchanges and integrates resources for value co-creation. The key challenges for the future and insights for the tourism industry are highlighted and discussed.

The evolution of hotel revenue management

Article

Full-text available

Jul 2016

Sheryl E Kimes

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.

Market accessibility and hotel prices in the Caribbean: The moderating effect of quality-signaling factors

Article

Full-text available

Oct 2016
TOURISM MANAGE

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.

Price parity, channel conflict, and hotel rooms in Macao

Article

Dec 2016

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.

Pricing strategies of tour operator and online travel agency based on cooperation to achieve O2O model

Article

Oct 2017
TOURISM MANAGE

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.

Do Customer Reviews Drive Purchase Decisions? The Moderating Roles of Review Exposure and Price

Article

Mar 2017
DECIS SUPPORT SYST

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.

HotelTonight usage and hotel profitability

Article

Aug 2016

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.

Are innovations relevant for consumers in the hospitality industry? A hedonic approach for Cuban hotels

Article

Aug 2016
TOURISM MANAGE

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.