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Product diversification and property performance in the urban lodging
market: The relationship and its moderators
Yang Yang, Ph.D.
School of Tourism and Hospitality Management
Temple University, Philadelphia, Pennsylvania, USA
Email: yangy@temple.edu
Yang Cao
School of Hotel, Restaurant, and Tourism Management
University of South Carolina, Columbia, South Carolina, USA
Email: cao22@email.sc.edu
Li-Ting (Grace) Yang
School of Hotel, Restaurant, and Tourism Management
University of South Carolina, Columbia, South Carolina, USA
Email: Grace.Yang@hrsm.sc.edu
Yang, Y., Cao, Y., and Yang, G. (2017). Product diversification and property
performance in the urban lodging market: The relationship and its
moderators. Tourism Management, 59: 363-375.
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Product diversification and property performance in the urban lodging
market: The relationship and its moderators
Abstract
In this study, we investigate the relationship between product diversification and hotel property
performance as well as the moderators of this relationship in the urban lodging market. Using
stochastic frontier analysis with panel data, we calibrate the efficiency scores of 377 urban hotels
in Beijing from 1994 to 2005. We then investigate the impact of product diversification on
performance as measured by efficiency score. Results from panel data models indicate that the
degree of product diversification exhibits a positive relationship with hotel performance. Hotel
location, diversification expansion rate, and foreign ownership/operation are found to be
significant moderating factors determining the effect of product diversification. Specifically,
hotels that (a) are located farther from the city center, (b) expand diversification more slowly,
and (c) are domestically owned are more likely to leverage the benefits stemming from product
diversification. We provide a series of practical evaluation modules to help hoteliers improve
performance.
Keywords: product diversification; hotel performance; stochastic frontier analysis;
diversification expansion rate; hotel location
1. Introduction
In response to today’s increasingly competitive business environment, diversification
the act of expanding into other products, markets, sectors, industries or segments (Gemba &
Kodama, 2001; Park & Jang, 2012; Wang, Ning, & Chen, 2013)has become a major strategic
initiative in the hospitality industry. This strategy has been widely applied in various business
3
fields such as marketing, management, retailing, and international business (Chang & Wang,
2007). More than a tool for reducing business risks and uncertainties, diversification enables
firms to gain, and more importantly, secure competitive advantages and market dominance that
would otherwise be unattainable (Li & Greenwood, 2004; Park & Jang, 2013b). In addition, from
a resource-based perspective, diversification enables firms to exploit intangible resources
(Andreu, Claver, & Quer, 2009) and generates economies of scale and scope that bolster
management skills, customer loyalty, and brand reputation (George & Kabir, 2012; Wang, et al.,
2013). In order to satisfy a broader spectrum of customer needs, hotels have diversified their
products and services beyond traditional accommodation to include meeting and event planning
services, food and beverage services, casinos, and retail businesses (Chen & Chang, 2012; Kang,
Lee, & Yang, 2011; Yeh, Chen, & Hu, 2012).
Understanding how diversification affects the performance of hotels has become an
important research topic in hospitality management (Jang & Tang, 2009; Lee & Jang, 2007; Park
& Jang, 2013b). However, most prior studies have been based on data from a sample of publicly-
traded hospitality companies covering multiple properties, and have yielded little insight into the
performance of individual hotel properties due to distinct diversification strategy options at the
property level. Also, sample sizes were very limited in prior studies, which focused on only a
handful of corporations. To fill this research gap, we analyze the relationship between product
diversification and hotel performance from a micro perspective using panel data for 377 star-
rated hotel properties in the urbanized area of Beijing from 1994 to 2005. The use of panel data
enables to evaluate the trajectory of performance over time because the impacts of diversification
strategies are considered to be “environment-dependent” and “time-dependent” (Benito-Osorio,
Guerras-Martín, & Zuñiga-Vicente, 2012). In addition, we measure hotel performance using
4
efficiency scores generated via stochastic frontier analysis (SFA); this measure is expected to
better reflect the multi-faceted nature of the hotel business than more conventional measures,
such as occupancy rate, revenue per available room (RevPAR) and labor productivity (Neves &
Lourenço, 2009; Tsai, 2009). Lastly, we further examine several plausible contingency factors as
moderators of the diversification-performance relationship: hotel location, diversification
expansion rate, hotel size and foreign ownership/operation.
Since diversification generates both benefits and costs (Benito-Osorio, et al., 2012), a
fuller understanding of the effectiveness of diversification could help hoteliers formulate
appropriate diversification strategies to improve hotel performance. In other words,
understanding the relationship between diversification and performance could help clarify
whether individual hotel properties should diversify product and service offerings, and if yes,
what specific strategies should be leveraged. In this study, we aim to shed light on the
relationship between product diversification and performance as well as contingency factors
moderating this relationship. Results of the study can be used as a benchmark for determining
whether product diversification is an effective strategy for securing a competitive advantage and
enhancing performance in a competitive hotel market such as Beijing. The results can also help
Beijing tourism policymakers better understand the hotel market and better cultivate future hotel
development strategies to effectively manage resources and maximize hotel performance.
2. Literature and hypotheses
Over the past decades, strategic management scholars have examined several types of
diversification strategies. Geographic diversification is a strategy based on operating in multiple
geographic markets (Barney & Hesterly, 2008). Firms are expected to benefit from this strategy
by organizing bundles of activities internally to develop and exploit firm-specific advantages in
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knowledge and products, an approach that has been substantiated by internalization theory
(Buckley & Casson, 1976). As a specific type of geographic diversification, international
diversification is focused on increasing the size of a firm’s foreign operations relative to its
overall business portfolio (Tihanyi, Griffith, & Russell, 2005). Another well-known
diversification strategy, product diversification, is focused on increasing the scope of a firm’s
product portfolio (Wang, et al., 2013). Product diversification can be further categorized into
related and unrelated diversification. The former refers to expansion within markets that are
related to a firm’s core product offering, whereas the latter refers to expansion into non-core
product markets (Chang & Wang, 2007).
Many strategic management scholars have studied the relationship between product
diversification strategies and firm performance (Alesón & Escuer, 2002; Benito-Osorio, et al.,
2012; Kim & Gu, 2003; Lee & Jang, 2007; Li & Greenwood, 2004; Siggelkow, 2003). From a
resource-based perspective, product diversification improves firm performance by enhancing
synergy, achieving economies of scale and scope, and improving the efficiency of resource
allocation (Li & Greenwood, 2004; Purkayastha, Manolova, & Edelman, 2012). As product
diversification level increases, more opportunities become available for deploying resources
(such as customer bases, sales and distribution facilities, and knowledge on existing products)
across different product categories, and create more complementary value to customers (Zahavi
& Lavie, 2013). This perspective is embodied in the premium diversification model, which posits
a positive relationship between diversification and performance (Benito-Osorio, et al., 2012).
Several theories have been used to explain the benefits associated with penetration into other
product categories. First, according to market power theory, diversified firms are able to
establish market power advantages that are largely unavailable to their more concentrated peers
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(Leslie, Laura, & C Chet, 2000) through aggressive operation efforts. The strengthened market
power is partly attributed to the reciprocal buying and selling within the diversified firm (Grant,
Jammine, & Thomas, 1988), and this vertical integration substantially reduces
operation/production costs. Second, market efficiency theory suggests that unlike concentrated
firms, the diversified firm gains remarkable financial benefits by accessing internally generated
resources in capital formation, which is generally less costly than external financial resources
(Taylor & Lowe, 1995).
On the other hand, from an agency-based perspective, the costs involved in
diversification may outweigh benefits, and a negative relationship between the two is plausible
(Braakmann & Wagner, 2011); this perspective is summarized in the discount diversification
model (Benito-Osorio, et al., 2012). Two types of costs arise when firms diversify their product
portfolio. Adjustment costs refer to the inefficiency in transferring resources to different product
categories (Hashai, 2015), and this cost can be explained by the negative transfer effect along
diversification expansion, which suggests that when firms penetrate to other product dimensions,
they may inappropriately deploy nonfungible resources that are useful for one product but might
not be proper for the other (Zahavi & Lavie, 2013). On the other hand, coordination costs refer to
the costs of sharing and creating linkage across different product categories (Hashai, 2015).
Results of a number of studies support this argument and show that diversification strategies can
result in increased costs through diversified operations, ultimately decreasing performance
(Denis, Denis, & Yost, 2002; Fauver, Houston, & Naranjo, 2004; Kang, Lee, Choi, & Lee, 2012).
In today’s hotel industry, diversification has become integral to gaining a competitive
advantage, especially for international businesses (Tang & Jang, 2010); yet diversification
strategies in the hospitality industry vary from country to country. For example, the U.S. lodging
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industry generally employs geographic and brand diversification (Kang, et al., 2012), whereas
the Chinese hotel industry is characterized by a high degree of product diversification, targeting
distinct market segments (Gu, Ryan, & Yu, 2012). Despite the prevalence of diversification
studies in strategic management (Benito-Osorio, et al., 2012), few researchers have considered
the hospitality industry specifically (Lee & Jang, 2007; Park & Jang, 2013a, 2013b). In the
hospitality literature, previous diversification studies were focused on market diversification
(Lee & Jang, 2007), geographic/international diversification (Jang & Tang, 2009; Kang & Lee,
2014; Kwun, 2010; Tang & Jang, 2010), product diversification (Chen & Chang, 2012; Kang, et
al., 2011; Wang & Xu, 2009), and brand diversification (Choi, Kang, Lee, & Lee, 2011). Table 1
provides a summary of previous studies of diversification strategies in the hospitality and
tourism industry. As the table shows, the literature is based mainly on hotel group data and
financial/accounting measures of performance in the U.S. context, and offers mixed results.
(Please insert Table 1 about here)
Product diversification is prevalent in the hotel industry as a strategy to assimilate
demand externalities, generate intra-firm knowledge diffusion, share resources, and reduce
operational costs (Lin & Liu, 2000; Yang, Wong, & Wang, 2012). As a substantial amount of
business in on-site restaurants and shopping centers come from hotel guests, the demand
externalities from accommodation services provide a great opportunity for hotels to expand to
other product/service categories. Also, hotels can leverage their familiarity with the
accommodation customers to improve the product quality when introducing related new product
categories, and sales team can use accumulated knowledge to sell these complementary products
to customers. Both related and unrelated diversification strategies could provide hotels with
competitive advantages by leveraging customer loyalty under the same brand. The “halo effect”
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of brand equity enables loyal hotel guests stay loyal with other related products/services that the
brand is offering (So, King, Sparks, & Wang, 2014), making the marketing efforts more
rewarding. Also, product diversification has been recognized as an effective strategy to increase
market power over suppliers, competitors, and customers (Hitt, Hoskisson, & Ireland, 1994), and
a bundle of diversified products provides hotels the ability to charge a price premium and even
conduct collusive pricing to elevate the room rate (Gan & Hernandez, 2012). Moreover, by
implementing product diversification, hotels are able to utilize excess resources (Wan, Hoskisson,
Short, & Yiu, 2011), and share assets (space and equipment) and labor across the different
product categories. Since most products that hotels offer are service products, with previous
skills trained for one service product, employees can relatively easily transfer their knowledge to
the new service product along diversification expansion. Lastly, diversification reduces
operational and transaction costs (Amit & Livnat, 1988), and by achieving the economies of
scope through product diversification, the cost of each product category can reduce significantly.
In the field of hospitality management, Chen and Chang (2012) studied the hotel industry
in Taiwan and found that hotels that diversify by offering food and beverage products and
services tend to have both a higher profit margin and a higher risk of instability, highlighting a
trade-off between profit and risk. Park and Jang (2013b) studied the association between product
diversification and restaurant performance in the United States, and suggested that while
implementing short-term diversification strategies does not benefit restaurant businesses, long-
term diversification strategies ultimately contribute to greater financial performance. Although
some hospitality studies using corporate level data revealed a non-linear relationship between
product diversification and performance (Kang, et al., 2011; Park & Jang, 2012), we argue that at
the property level, the relationship is linear and monotonic. This is because properties have a
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different set of diversification options compared to corporations. According to the data collected
by CNTA (Chinese National Tourism Administration), four main categories of products and
services are offered by hotels: accommodation, food and beverage, shopping and other (mostly
space rental). Since accommodation is generally the dominant service offering in the hotel
industry (CNTA, 2013), a hotel’s diversification strategy is largely shaped by the decision to
expand to one or more of the other categories. Most hotel properties still focus on
accommodation, and excessive diversification seldom occurs, making it difficult to observe an
over-diversification. Therefore, we argue that the negative effect of diversification on
performance due to over-diversification can hardly occur at a property level. We propose the
following research hypothesis:
Hypothesis 1. For each individual hotel property, product diversification exhibits a positive
relationship with hotel performance.
Several authors suggested that the inconclusive results on the diversification-performance
relationship may be partly attributable to unaccounted interaction effects among different types
of diversification (Gleason, Mathur, & Wiggins Iii, 2003; Kang & Lee, 2014; Kwun, 2010). The
complexity of the relationship between product diversification and firm performance suggests a
need to broaden the scope of investigation to consider contingent factors that possibly moderate
the focal relationship.
Hotel location
As suggested in previous literature, location plays a vital role in determining hotel
demand as well as performance (Yang, Luo, & Law, 2014). A well-established urban geography
model, the monocentric model, has been used in hospitality management to explain and predict
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hotel location patterns in a city with a single functional center (Egan, Chen, & Zhang, 2006;
Egan & Nield, 2000). In a monocentric city, the locations of different economic activities are
largely determined by the bid-rent curve reflecting the firm’s willingness to pay for locations
with regard to city center accessibility. Several researchers adopted and modified this model to
understand location zones for hotels (Yokeno, 1968), the spatial hierarchy of hotels across a city
(Egan & Nield, 2000), and the spatial dichotomy of hotel demand in a city (Shoval, 2006).
According to past literature, the accessibility of a hotel property to a city center is associated
with a price premium for the hotel room rate (Lee & Jang, 2011) and a higher level of
operational performance (Sainaghi, 2011).
Different types of products offered by an urban hotel are characterized by different bid-
rent curves, which reflect their sensitivities to central location. In Yokeno’s (1968) monocentric
model, hotel guests are supposed to be willing to pay more in return for easy access to the city
center. The author suggested that hotels generally value a central location more than most other
businesses; the model highlights a hotel district located between the city’s core business district
and commercial zones including shopping districts. According to Sloan, Caudill, and Mixon
(2016), restaurants, especially mid-scale and low-end establishments, typically cannot afford
preferable locations, and as a result, tend to end up in inferior locations. In sum, results of
previous studies indicate that across different product categories that a hotel can offer,
accommodation services are characterized by a steeper bid-rent curve, suggesting its higher level
of preference toward central locations than other products/services, such as shopping and dining.
Therefore, when hotels expand their product lines into services other than accommodation, the
location advantage that was first positioned for accommodation tends to be underutilized. In
other words, for hotels in “nice” locations, embarking on a less location-sensitive business
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endeavor does not fully take advantage of location factors, leading to a lower productivity level
compared to similar counterparts. Hence, we propose the following research hypothesis:
Hypothesis 2. A hotel’s distance to the city center positively moderates the relationship between
product diversification and hotel performance such that properties more distant from the city
center perform better with a high degree of product diversification.
Diversification expansion rate
In previous literature on the diversification-performance relationship, researchers largely
overlooked the dynamic nature of diversification expansion (Hashai, 2015). There are two major
reasons why the diversification expansion rate can attenuate the diversification-performance
relationship. First, at a higher diversification expansion rate, coordination and adjustment costs
outweigh synergy benefits, and as a result, the benefits reaped from diversification diminish
quickly. A higher expansion rate to other product categories escalates the coordination costs of
product management due to the complexity associated with creating effective linkages between
accommodation and other services within a short period of time. For example, along with the
quick diversification expansion, a surge of human resource expensed is expected due to the high
training costs to equip employees with a different skill set for new products/services. Also, when
quickly penetrating other product categories, hotels may encounter diseconomies of time
compression (Dierickx & Cool, 1989), and higher adjustment costs are likely, due to the
conflicting resource allocation between existing and new product categories.
Second, a higher diversification expansion rate generally represents a lower level of
accumulated experience with diversification. As suggested by Zahavi and Lavie (2013), learning
from prior experiences enriches practices and knowledge to reduce the costs and inefficiencies
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associated with diversification. For example, hotel managers who have successfully diversified
into food and beverage know how to reasonably allocate marketing resources between it and
other product categories. Also, prior experience facilitates the practice of “learn by doing,” and
improves hoteliers’ abilities to absorb feedback from past decisions and operations (Pennings,
Barkema, & Douma, 1994), leading to an environment that nurtures the assimilation of
diversification benefits. Therefore, we propose the following research hypothesis:
Hypothesis 3. A hotel’s diversification expansion rate negatively moderates the relationship
between product diversification and hotel performance such that properties with a higher
diversification expansion rate perform worse with a high degree of product diversification.
Hotel size
It is widely accepted that small businesses fundamentally differ from large businesses in
terms of ownership, organizational structures, management systems and resource availability
(Bausch & Krist, 2007). Firms seek to offer a spectrum of diversified products when they have
sufficient resources (Penrose, 1959). By achieving economies of scale and scope, large firms are
more likely to operate at lower costs and accumulate managerial experience, which in turn
enhance expansion and their ability to grow and gain even more experience (Glaum & Oesterle,
2007). From this perspective, firms do not directly reap various benefits directly from
implementing diversification strategies, but indirectly from the increased scale and scope
resulting from diversification.
By implementing diversification strategies, larger firms gain competitive advantages in
several ways over smaller firms. First, firms with large scale and scope enjoy sufficient resources
and can prevent other firms from entering the market by limiting access to suppliers, marketers
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and customers (Gaba, Pan, & Ungson, 2002; Kirca, Hult, Deligonul, Perryy, & Cavusgil, 2012;
Kobrin, 1991). For example, in the hospitality industry, the larger hotels possess stronger
negotiation power to reach a deal with local suppliers to reduce the inventory cost, and they may
also be able leverage predatory pricing to stifle small-size competitors (Peterson, 1998). Second,
larger firms have resource advantages that enable them to invest in innovation and expansion
(Kirca et al., 2012). Within a large hotel, more affluent financial and human resources are
available to expand to other product categories, and at the same time, they are able to cross-
subsidize among different product categories. Third, larger firms have increased access to
learning channels, and thus are able to reduce risk through diversification. Lastly, from an
external environment perspective, larger firms are more powerful and thus are better able to gain
concessions from local governments (Kirca et al., 2012), which is particularly important in China
as the Chinese government agencies plays a very active role in regulating the hospitality business
(Gu, et al., 2012).
In a recent study, Benito-Osorio and Colino (2015) explicitly analyzed firm size to
evaluate its moderating effect on the diversification-performance relationship and found that the
larger the firm, the higher the optimal level of diversification. Under the condition of over-supply
in the Chinese lodging market associated with the problem with low operating profits (Yu & Gu,
2005), only large firms with sufficient resources are able to successfully leverage the benefits
stemming from product diversification (Colpan, 2008). Therefore, we propose the following
hypothesis regarding the moderating effect of hotel size:
Hypothesis 4. Hotel size positively moderates the relationship between product diversification
and hotel performance such that properties of larger size perform better with a high degree of
product diversification.
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Foreign ownership/operation
Unlike in most developed countries, ownership structure, i.e., foreign vs. domestic (both
state-owned and private), has been identified as one of the most critical factors affecting hotel
performance in China (Mak, 2008). Having a better understanding of the effects of ownership
structure helps firms that are entering the Chinese market develop effective marketing strategies
(Delios et al., 2008). In studies of the diversification-performance relationship, firms with
different ownership structures have been found to exhibit different characteristics in certain
contexts (Glaum & Oesterle, 2007; Zhao & Luo, 2002). Ownership structure greatly influences a
firm’s governance, marketing strategies and firm performance (Chen & Yu, 2012). Results of
previous studies show that ownership structure influences the effect of a diversification strategy,
which leads to different outcomes of firm performance (Denis, et al., 2002). In addition to
ownership, operation strategy of hotel property also matters. In the Chinese hospitality industry,
most franchised hotels with international brands are Chinese-owned but operated under the
management contract agreement with international hotel management companies (Xiao, O’Neill,
& Wang, 2008). In general, the foreign managerial team is responsible for daily operation
including human resource management, marketing and pricing, and the domestic owners did not
get involved. In the Chinese firm registration database, this type of hotels is coded as the
category of “Sino-Foreign Cooperative Joint Ventures.”
Although ownership/operation structure clearly influences the effects of a diversification
strategy, it is worth noting that very limited studies have been performed to understand the
moderating effect of foreign ownership/operation on the diversification-performance relationship.
When foreign-owned and operated firms enter the local market, knowledge about the new market
is considered vital to success (Bausch & Krist, 2007). According to transaction cost theory, firms
15
incur risk and transaction costs when expanding to foreign markets due to a lack of “localized”
managerial and technological competences (Hitt, Dacin, Levitas, Arregle, & Borza, 2000), and as
a result, foreign-owned/operated firms are less capable of alleviating the liabilities of foreignness
and obtaining knowledge of the local market (Zhao & Luo, 2002). Compared to their foreign
counterparts, local firms are in a better competitive position, with a higher capability to control
resources and suppliers as well as sales of final products (Gaur & Kumar, 2009). This advantage
is emerging in the Chinese hotel industry. Service firms rely heavily on relational and social
capital in their operations (Hitt, Tihanyi, Miller, & Connelly, 2006). Foreign-owned/operated
hotels are less familiar with Chinese-based business practices and have a less developed social
network with other business partners, leading to higher transactional/operational costs associated
with implementing a diversification strategy. As a result, foreign-owned/operated hotels find it
difficult to fully benefit from product diversification.
Hypothesis 5. Foreign ownership/operation of a hotel negatively moderates the relationship
between product diversification and hotel performance, such that foreign-owned/operated hotel
properties perform worse with a high degree of product diversification.
3. Empirical model and data
Using a sample of individual hotel properties in Beijing, China, we investigated the
relationship between product diversification and hotel property performance. We used the SFA
model to obtain a reliable measure of hotel performance over time (Assaf & Magnini, 2012). In
contrast to other performance measures, such as labor productivity, RevPAR and occupancy rate,
the SFA incorporates multiple inputs simultaneously to reflect the multi-faceted nature of the
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hotel business, making it a more reliable measure of overall performance. Compared to data
envelopment analysis, the SFA enables statistical inferences to be made on efficiency scores and
separation of error terms from inefficiency terms. More importantly, the SFA allows for
longitudinal analysis of an unbalanced panel data set.
Drawing on the hospitality literature and based on data availability, we specified two
input variables to obtain efficiency scores as a performance measure. Input variables include the
total number of available guest rooms (rooms) and the total number of employees (employees).
The output variable is the total value_added (the difference between sales revenue and cost, in
10,000 CNY) for the hotel. For the panel data SFA, we chose the true fixed effects model with
exponential distribution for the inefficiency term (Greene, 2005). According to Kneller and
Andrew Stevens (2003), in specifying production function in the SFA, the translog production
function form, which incorporates the quadratic terms and interaction terms, is preferred to the
Cobb-Douglas form. The empirical SFA model is specified in a translog production function
form as:
22
1 2 3 4
5
ln _ ln ln ln ln
ln ln
it i it it it it
it it it it
value added rooms employees rooms employees
rooms employees
(1)
where i indexes hotel property, and t indexes year.
it
is the random errors, assumed to be i.i.d.
with N(0,
2
) distribution, whereas
it
is the non-negative term representing technical
inefficiency with an exponential distribution. Owing to the existence of inefficiency terms, each
hotel’s output must lie either on or below its productivity frontier. The model in Equation (1)
17
specifies the intercept term as
i
instead of
0
to separate cross-property heterogeneity from
inefficiency terms. Therefore, the efficiency score is predicted as:
*ˆ
ˆ
ˆ
exp
it it it it
it
Ee
(2)
where
*
it
is the efficiency score from the SFA model for property i in year t. After obtaining the
efficiency score from the panel data SFA model, we used fixed effects panel data models to
examine the impact of product diversification on hotel performance. The model is specified as
follows:
*__
it it it it it i it
prod div prod div z
X
(3)
The major variable of interest, prod_diver, measures the degree of product diversification. This
measure is the reciprocal of the Herfindahl-Hirschman index (HHI), which is a continuous
measure of concentration/diversification (Kang, et al., 2011). This diversification measure is
defined as 1 over the sum of the squares of the percentage of four major revenue sources from
different products (
2
1
i
s
): accommodation revenue, shopping revenue, food and beverage
revenue and other revenue. Product offerings are more concentrated as prod_diver gets closer to
1 and are perfectly diversified when prod_diver equals 4.
Based on the availability of data and research findings from past studies, we specified the
following variables as control variables (
X
in Equation 1) to account for the performance
(efficiency scores) of Beijing hotels:
18
lnagglomeration is the log number of other star-rated hotels within a 2 km radius. Hotel
clustering can generate positive economic externalities (Marco-Lajara, Claver-Cortés,
Úbeda-García, & Zaragoza-Sáez, 2014);
lnsubway_dist is the log of the hotel’s geographic distance (in km) to the nearest subway
station. As shown by Sainaghi (2011), accessibility is positively associated with hotel
performance;
star is the star rating of the hotel, and serves as a surrogate measure of quality. Star rating
is positively associated with performance (Pine & Phillips, 2005);
lnage is the log number of years that the hotel had been in operation. Older hotels enjoy
early-mover advantages in terms of location, managerial skills and network, which are in
turn associated with better performance (Lee & Jang, 2013).
ownership indicates the ownership of the hotel, where 0 = Chinese, and 1 = Foreign-
owned (either entirely or partially)/operated, consisting of three types: wholly foreign
owned, sino-foreign equity joint venture, and sino-foreign contractual joint venture
(Lardy, 1995).
prod_diver_n indicates the average of prod_diver values of other star-rated hotels within
a 2 km radius, measuring the average level of product diversification of neighboring hotel
properties.
To test Hypothesis 1, we estimated prod_diver. To test Hypotheses 2–5, we further considered
interaction terms between prod_diver and four moderating variables (z in Equation 3),
respectively: expansion_rate (the change in prod_diver from the previous year to the current
year), lnd_center (the log geographic distance in meter from the hotel to the city center, i.e.,
Tian’anmen Square), rooms, and ownership. Compared to random effects panel data model, the
19
fixed effects one places fewer restrictions and allows interdependence between
i
and other
explanatory variables. We also apply two statistical tests to select between fixed and random
effects models, and they are the Hausman test (Hausman, 1978) and the Sargan and Hansen test
(Sargan, 1958).
We obtained our data from the Beijing Tourism Statistical Yearbook 1995–2006, which
covers the financial indicators and physical attributes of 377 urban hotels from 1994 to 2005.
Urban hotels refer to hotel properties located in the urbanized area in Beijing consisting of eight
main districts: Dongcheng, Xicheng, Chongwen, Xuanwu, Chaoyang, Fengtai, Haidian, and
Shijingshan during the research period. Therefore, our sample excludes many resort and
interstate hotels in the rural area. Due to confidentiality concerns from reported hotels, the
Beijing Tourism Bureau started to follow the standard format of nationwide tourism statistical
system and did not release and publish the property-level hotel business data after 2005. To the
best of our knowledge, these were the most current property-level data that were accessible at the
time of this study. Beijing is a monocentric city that spreads in every direction from the city
center, the Tian’anmen Square. Major commercial, cultural and administrative hubs are located
around the city center. Most upscale hotels also are located close to the city center, giving them
the competitive advantage of easy market access to high-end business travelers (Yang, et al.,
2012). Figure 1 presents the hotel location map in Beijing, which shows a remarkable proportion
of hotels are clustered around the city center.
(Please insert Figure 1 about here)
Table 2 presents the descriptive statistics of input/output variables in the SFA as well as
the explanatory variables used in the panel data model of performance. The diversification
20
measure, prod_diver, ranges from 1 to 3.779, showing a large variation in the degree of product
diversification across Beijing hotels. The average value of prod_diver is 2.121, suggesting that
the average level of product diversification is moderate among urban hotels. Hotels in the data
set had an average star rating of 2.814. Moreover, 23% of observations are from foreign
owned/operated hotels, and the other 77% from entirely Chinese owned hotels. A correlation
coefficient table (Table 3) shows that, out of the 21 pairwise Pearson correlation coefficients, 19
are below 0.4 in magnitude, and only two are moderately larger than 0.5, suggesting the absence
of a multi-collinearity problem in the specified models (Leeflang, Wittink, Wedel, & Naert,
2000).
(Please insert Table 2 about here)
(Please insert Table 3 about here)
4. Results and discussion
4.1 SFA model
Table 4 presents the estimation results of panel data SFA model using the translog
function form. All variables are statistically significant at least at the 0.10 level. We also fit the
panel data SFA model using the Cobb-Douglas function form, and according to various
goodness-of-fit indexes, the translog function form outperforms. In Figure 2, we present the box
plot of efficiency scores over years obtained from the SFA. The average efficiency score was
0.761, suggesting that a typical Beijing hotel experienced a relative inefficiency of about 33.9%
during the research period. The results imply that hotels in Beijing did not use resources
efficiently to maximize revenue and increase productivity. Furthermore, the average efficiency
21
score fluctuated over time. Except for the low score in 2003 due to the SARS outbreak, the
efficiency score demonstrates an increasing trend over the later years in the study period. Lastly,
as shown in Figure 2, the distribution of efficiency scores is heavily left skewed toward zero,
suggesting that many urban hotels have been suffering from severe operating inefficiencies.
(Please insert Table 4 about here)
(Please insert Figure 2 about here)
4.2 Panel data models of performance
After obtaining the efficiency scores from the SFA model, we employed the fixed effects
panel data model proposed in Section 3 to investigate the relationship between product
diversification and hotel performance. We used the efficiency score as the dependent variable
and present the estimation results in Table 5. Model 1 displays results of the model without any
quadratic or interaction terms of prod_diver. The negative value of Hausman test indicates an
inconclusive result, and the significant value of Sargan and Hansen test reject the null hypothesis
that the random effects model meets the overidentifying restrictions. Therefore, the estimation of
fixed effects model is supported. In this model, prod_diver is estimated to be statistically
significant and positive. In Model 1, the marginal effect is estimated to be 0.0666 (with a 95% CI
from 0.0340 to 0.0992), suggesting that an increase of 1 in prod_diver is associated with a
0.0666 increase in efficiency score, ceteris paribus. This result indicates that a higher degree of
product diversification leads to a higher level of productivity, supporting Hypothesis 1. This
positive relationship suggests the synergies associated with implementing product diversification
outweigh diversification costs.
22
Regarding other control variables in Model 1, lnagglomeration is estimated to be
significantly negative, highlighting the spatial competition effects of hotel properties in the
lodging market. Another independent variable, lnsubway_dist is found to be statistically
insignificant. The estimated coefficient star is statistically significant and positive, suggesting
that hotels with a higher star rating operate at a higher level of performance. Furthermore, the
results show that the efficiency score of foreign-owned/operated hotels (ownership = 1) are
higher than entirely Chinese-owned hotels (ownership = 0) after controlling for other factors.
We incorporated an additional quadratic term of prod_diver, prod_diver_square, and re-
ran the model. According to the results, prod_diver_square is estimated to be statistically
insignificant, and a loglikelihood ratio test between Models 1 and 2 rejects the outperformance of
Model 2. Therefore, the relationship between product diversification and performance is linear
instead of quadratic.
(Please insert Table 5 about here)
To test the remaining hypotheses, we incorporated interaction terms of prod_diver and
other moderating variables. Model 3 tests hotel distance to the city center as a moderating factor,
and the interaction term prod_diver * lnd_center is estimated to be positive and statistically
significant at the 0.01 level. This result supports Hypothesis 2, indicating that hotels distant from
the city center perform better than hotels proximate to the city center when implementing
product diversification strategies, ceteris paribus. Model 4 further tests the moderating effect of
diversification expansion rate by introducing the interaction term prod_diver * expansion_rate.
This term is estimated to be negative and statistically significant at the 0.05 level, supporting
Hypothesis 3; rapid expansion to other product categories erodes the benefits derived from
23
diversification. Model 5 tests the moderating effect of hotel size by introducing the interaction
term prod_diver * lnrooms. This term is estimated to be statistically insignificant at the 0.10
level, albeit positive. Therefore, Hypothesis 4 is not supported. Lastly, Model 6 incorporates the
interaction term prod_diver * ownership to evaluate the moderating effect of ownership (either
partially or entirely)/operation, which is negative and statistically significant at the 0.10 level,
corroborating Hypothesis 5. According to Model 6, the positive effect of diversification is
significant for domestic hotels only. Across Models 2-6, the significant test values of Sargan and
Hansen test support the use of fixed effects estimation instead of random effects one.
To better interpret the results from interaction terms, we plotted the varying marginal
effect of product diversification across different levels of significant moderating factors
lnd_center and expansion_rate in Figures 3 and 4, respectively. Figure 3 shows that both positive
and negative effects of diversification exist, depending on the location of hotels. For hotels
extremely close to city center with a distance of 1.34 (exp(7.2)/1000) km or less to the
Tian’anmen Square, the expansion to other products/services rather than accommodation will
dampen performance. Their huge competitive advantage associated with central location renders
accommodation services more productive than others. For hotels with a distance of 4.45
(exp(8.4)/1000) km or further to the Tian’anmen Square, they can successfully leverage the
benefits associated with product diversification, and the positive effect of diversification
increases as hotels get further away from the city center. Regarding the moderating effect of
diversification expansion rate, Figure 4 shows that as the positive diversification expansion rate
increases, the positive effect of product diversification fades, but relatively slowly. Over the
range with an expansion rate between 0 and 0.90, the marginal effect of production
diversification on hotel performance remains statistically significant at the 0.05 level.
24
(Please insert Figure 3 about here)
(Please insert Figure 4 about here)
Since the effect of diversification can be lagged, we re-run the fixed effects panel data
model using the lagged value of prod_diver, prod_diver_lag. The results are presented in Table 6.
In Model 7, the estimated coefficient of prod_diver_lag is positive and significant, supporting
Hypothesis 1. However, the magnitude of coefficient is smaller than its un-lagged counterpart in
Model 1(Table 5). Similar to the result of Model 2, the quadratic term of prod_diver_lag,
prod_diver_lag_square, is insignificant, indicating that the quadratic relationship between
diversification and hotel performance is not evident with our sample. Similar to results presented
in Table 5, Hypotheses 2 and 3 are supported by the significant interaction terms in Models 9 and
10, respectively, whereas Hypothesis 4 is rejected. Lastly, even though prod_diver*ownership is
estimated to be moderately significant at the 0.10 level in Model 6, prod_diver_lag*ownership
becomes statistically insignificant albeit negative, leading to a rejection of Hypothesis 5 with the
lagged diversification specification.
(Please insert Table 6 about here)
5. Conclusion and implications
On the basis of SFA models, results of the study indicate that overall, the efficiency of
Beijing hotels has improved significantly over recent years. The analysis of fixed effects panel
data models of performance pointed out a positive liner relationship between product
25
diversification and performance of urban hotel properties instead of a non-linear quadratic
relationship, and an increase of 1 in the diversification index (the reciprocal of HHI index) is
associated with a 0.0666 increase in efficiency score, ceteris paribus. Furthermore, we identified
several contingency factors that moderate the positive diversification-performance relationship,
and they are hotel location, diversification expansion rate, and foreign ownership/operation.
More specifically, hotel’s distance to city center positively moderates the relationship. Centrally
located hotel properties encounter diversification deficiency, whereas hotels located further away
from the city center are more likely to internalize diversification benefits. We found an inflexion
point of product diversification in terms of hotel location: hotels with a distance of 4.45 km or
further to the city center, the Tian’anmen Square, can boost their economic performance by
implementing product diversification strategies and increasing the diversification level. The
results also suggested that hotels undergoing faster diversification expansion and/or foreign-
owned/operated hotels perform worse when embracing the same level of product diversification
compared to their counterparts.
The results of this study suggest several important managerial implications for hoteliers
and tourism policymakers wishing to better understand the complexities associated with
diversification strategies. First, when urban hotels in Beijing expand into other product areas
other than accommodation services, they are generally able to benefit from product
diversification. This result suggests that Beijing urban hotels can achieve the economies of scale
and scope and create synergy through different product categories. Because these product
categories are still highly related to accommodation, and they require some similar service skill
sets from employees, leading to a low expansion and coordination cost. Hence, the synergy
26
benefits associated with diversification outweigh the coordination and adjustment costs, resulting
in a positive effect of product diversification on hotel performance.
Second, our study found that diversification strategy is not a panacea to improve the
performance for all types of hotels, and the location of hotel property matters. Based on our
estimation results, implementing diversification strategy into other product categories rather than
accommodation is not recommended for hotels located within a 1.34 (exp(7.2)/10) km radius
from the Tian’anmen Square. Within this geographic range, hotels in the areas of Wangfujing,
Xidan, Xuanwanmen, and Chongwenmen are advised to concentrate their business in
accommodation services, and their ideal location shapes a competitive advantage to maintain a
high level of lodging demand. For hotels located in the suburbs, product diversification can
significantly improve their performance, and tactic planning of product diversification is
recommended.
Third, according to our results, a high diversification expansion rate erodes the benefits
stemming from product diversification. Therefore, expanding to other product dimensions
requires long-term planning, and a hasty expansion would make the hotels unprepared to
internalize diversification benefits. Through step-by-step strategical expansion, it is important for
hotels to handle the complexities associated with diversification strategy by assimilating new
managerial skills and knowledge through “learn by doing,” and learning how to improve the
organizational structure to reduce the adjustment and coordination costs along diversification.
For foreign-owned/operated hotels, product diversification is not effective to boost their business
performance. Therefore, to establish competitive advantages, foreign-owned/operated hotels may
consider leveraging other strategies rather than product diversification, such as lodging product
differentiation and niche market targeting.
27
Lastly, our efficiency score model highlighted certain hotels as having superior
performance, such as hotels geographically separated from others, hotels with higher star ratings,
and hotels entered market earlier. Tourism policymakers should consider improving the overall
performance of city hotels by leveraging productivity spillover from hotels demonstrating
superior performance via various channels, such as labor mobility, demonstration, and imitation
(Fu, Helmers, & Zhang, 2012). Moreover, further investment to achieve the standard of higher
star rating is worthwhile for hotels consistently struggling with a low level of efficiency.
Furthermore, since agglomeration was not observed in our analysis, further efforts should be
made to help hoteliers to differentiate from neighbors to fully internalize the positive
externalities from co-location and at the same time, alleviate the cross-neighbor competition
(Becerra, Santaló, & Silva, 2013).
This study provides practical evaluation modules to hoteliers and tourism authorities to
improve current efficiency and performance by leveraging proper product diversification
strategies. Several limitations of the study could, however, impede the generalization of our
results. Owing to the limited availability of data, the input and output variables are mainly from
the data published by the local tourism bureau, and therefore, ensuring the accuracy and
completeness of secondary data was beyond the researchers’ control. Furthermore, we are unable
to collect the performance data of non-star budget hotels in Beijing because the local tourism
bureau is not entitled to collect their data into the tourism statistics system. Therefore, our results
are restricted to government-certified star-rated hotels. Further research should include
international data sets covering a comprehensive set of hotels to better understand the
heterogeneity of diversification-performance relationship in the hotel sector.
28
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36
Table 1
Summary of previous studies of diversification strategy in the hospitality industry
Authors
Research
unit
Diversification
type
Performance
measures
Major findings
Singh and Gu (1994)
73 foodservice
firms in U.S.
(1988-1991)
Strategic
diversification
measure
Return on
assets (ROA),
return on equity
(ROE), net
profit margin
(NPM)
Insignificant relationship
between diversification and
financial performance
Lee and Jang (2007)
46 publicly
traded hotel
companies in
the U.S.
(1997-2001)
Market
diversification
dummy (85%
cutoff point)
ROA, ROE,
and NPM.
Diversification strategy does
not enhance firm profits, but
diversification partly improves
the stability of performance.
Jang and Tang (2009)
51 U.S. hotel
companies
(1990-2004)
International
diversification
(ratio of
international to
total revenue)
ROA
Inverted U-shaped
relationship
Wang and Xu (2009)
20 publicly
traded tourism
companies in
China (2001-
2007)
Product
diversification
(Herfindahl
Index (HI),
Entropy Index)
ROE
Positive relationship for
attraction-operating
companies, insignificant
relationship for the tourism
hotels
Tang and Jang (2010)
482 U.S. hotel
firms (1990-
2006)
International
diversification
(ratio of non-
U.S. revenue to
total revenue)
Excess market
value, excess
Q
U-shaped relationship
Choi, Kang, Lee, and Lee (2011)
46 U.S.
restaurants
(2003–2007)
Brand
diversification
(HI)
Tobin’s q
Negative relationship
Kang, Lee, and Yang (2011)
15 U.S. casino
firms (2001-
2008)
Product
diversification
(modified HI)
Tobin’s q, ROA
Inverted U-shaped
relationship
Lee, Qu, and Kang (2011)
7 U.S. hotel
companies
(1994-2009)
Market
diversification
Sharpe ratio,
stock return,
Inverted U-shaped
relationship
Chen and Chang (2012)
72 Taiwan
tourist hotels
(1996-2008)
Product
diversification
(Herfindahl–
Profit growth
and instability
Hotels with higher level of
diversification tend to have
higher growth in profit but
37
Hirschman
Index)
with higher instability.
Kang, Lee, Choi, and Lee(2012)
14 U.S.
casinos (2000-
2008)
Geographic
diversification
(Herfindahl
Index)
Tobin’s q
Negative relationship
Park and Jang (2012)
308 U.S.
restaurant
firms (1980-
2008)
Related and
unrelated
diversification
(Entropy
measure)
Tobin’s q, ROA
Non-linear relationship. A
combination of related and
unrelated diversification
maximizes profitability.
Park and Jang (2013b)
288 U.S.
restaurant
firms (1980 to
2008)
Within-industry
and related
diversification
(Entropy
measure)
ROA and net
sales
Negative association between
within-industry diversification
and performance in short
term, while positive between
within-industry diversification
and performance in long
term. Positive association
between related
diversification and profitability
in short term, while negative
in long term.
Kang and Lee (2013)
Publicly traded
U.S. lodging
firms (1993-
2010)
Geographic
diversification
(Berry-
Herfindahl
index)
Tobin’s q, ROA
Positive relationship
38
Table 2
Descriptive statistics of variables
Variable
Obs
Mean
Std. Dev.
Min
Max
lnvalue_added
2746
7.537
1.377
2.900
11.694
lnrooms
2746
5.173
0.814
2.639
7.536
lnemployees
2746
5.480
0.981
1.386
9.199
prod_diver
2746
2.121
0.532
1
3.779
lnagglomeration
2746
2.825
0.573
0.693
3.871
lnsubway_dist
2746
7.299
1.002
2.398
9.866
star
2746
2.814
1.091
1
5
lnage
2746
2.245
0.688
0
4.489
ownership
2746
0.230
0.421
0
1
prod_diver_n
2746
0.454
0.335
0.047
3.159
lnd_center
2746
8.602
0.595
6.648
10.419
expansion_rate
2367
-0.019
0.291
-1.484
1.512
39
Table 3
Correlation matrix of independent variables
prod_diver
lnagglomeration
lnsubway_dist
star
lnage
ownership
lnagglomeration
-0.049
lnsubway_dist
0.003
-0.549
star
0.373
0.258
-0.122
lnage
-0.019
0.093
-0.066
0.036
ownership
0.151
0.213
-0.150
0.579
0.074
prod_diver_n
0.158
-0.190
0.189
0.035
0.055
0.010
40
Table 4
Estimation results of true fixed-effect stochastic frontier model
Variable
Coefficient
Standard error
lnrooms
0.388**
0.155
lnemployees
0.507***
0.110
(lnrooms)2
0.0455*
0.024
(lnemployees)2
0.0418***
0.013
lnrooms*lnemployees
-0.0971***
0.035
σv
0.313
ση
0.165
number of groups
377
number of observations
2844
Log likelihood
-755.437
Notes: * indicates p < 0.10, ** indicates p < 0.05, *** indicates p < 0.01..
41
Table 5
Estimation results of panel data models with efficiency score
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
prod_diver
0.0666***
0.0726
-0.835***
0.0701***
0.0306
0.0782***
(0.017)
(0.077)
(0.196)
(0.020)
(0.050)
(0.018)
prod_diver_square
-0.00138
(0.017)
prod_diver*lnd_cent
er
0.104***
(0.022)
prod_diver*expansio
n_rate
-0.0141**
(0.007)
prod_diver*lnrooms
0.00711
(0.010)
prod_diver*ownershi
p
-0.0744*
(0.042)
lnagglomeration
-0.0952**
-0.0953**
-0.0932**
-0.0504
-0.0954**
-0.0959**
(0.038)
(0.038)
(0.038)
(0.045)
(0.038)
(0.039)
lnsubway_dist
0.00175
0.00170
0.000434
-0.00243
0.00212
0.000820
(0.014)
(0.014)
(0.014)
(0.015)
(0.014)
(0.014)
star
0.114***
0.114***
0.115***
0.111***
0.113***
0.115***
(0.018)
(0.018)
(0.017)
(0.018)
(0.017)
(0.018)
lnage
0.0608**
0.0609**
0.0616**
-0.0268
0.0587**
0.0659**
(0.026)
(0.026)
(0.025)
(0.034)
(0.026)
(0.026)
ownership
0.0195
0.0198
0.0123
0.0249
0.0186
0.209*
(0.031)
(0.031)
(0.029)
(0.032)
(0.031)
(0.111)
42
prod_diver_n
0.00573
0.00577
0.00924
-0.0132
0.00426
0.00742
(0.028)
(0.028)
(0.027)
(0.032)
(0.028)
(0.028)
constant
0.372**
0.366**
0.382**
0.548***
0.378**
0.151
(0.154)
(0.176)
(0.154)
(0.187)
(0.153)
(0.199)
number of
observations
2819
2819
2819
2429
2819
2819
number of groups
374
374
374
374
374
374
Hausman test
-133.61
-132.75
-180.17
-81.17
-138.93
-139.92
Sargan-Hansen test
79.188***
78.865***
62.157***
112.218***
82.572***
80.225***
Within R2
0.163
0.163
0.179
0.159
0.164
0.166
Between R2
0.0104
0.0104
0.000000
319
0.0596
0.0116
0.0104
Overall R2
0.0345
0.0345
0.0118
0.0494
0.0339
0.0335
Notes: * indicates p < 0.10, ** indicates p < 0.05, *** indicates p < 0.01. Robust standard errors are presented in parentheses.
Estimates of yearly dummies are not presented for brevity.
Table 6
Estimation results of panel data models with lagged diversification measures
Model 7
Model 8
Model 9
Model 10
Model 11
Model 12
prod_diver_lag
0.0504***
0.0356
-0.528***
0.0498**
0.0141
0.0597***
(0.016)
(0.083)
(0.181)
(0.022)
(0.054)
(0.018)
prod_diver_lag_squ
are
0.00333
(0.018)
prod_diver_lag
*lnd_center
0.0667***
(0.021)
prod_diver_lag
*expansion_rate_lag
-0.0123*
(0.007)
prod_diver_lag
0.00710
43
*lnrooms
(0.011)
prod_diver_lag
*ownership
-0.0528
(0.040)
lnagglomeration
-0.0487
-0.0488
-0.0482
-0.00492
-0.0495
-0.0510
(0.046)
(0.046)
(0.046)
(0.053)
(0.046)
(0.046)
lnsubway_dist
-0.000829
-0.000708
0.000137
0.00172
-0.000530
-0.00120
(0.015)
(0.015)
(0.015)
(0.015)
(0.015)
(0.015)
star
0.111***
0.111***
0.110***
0.109***
0.110***
0.111***
(0.018)
(0.018)
(0.018)
(0.018)
(0.018)
(0.018)
lnage
-0.0277
-0.0282
-0.0288
-0.0535
-0.0290
-0.0231
(0.034)
(0.034)
(0.033)
(0.047)
(0.033)
(0.033)
ownership
0.0318
0.0313
0.0305
0.00179
0.0312
0.165
(0.033)
(0.032)
(0.031)
(0.014)
(0.033)
(0.103)
prod_diver_n
-0.0127
-0.0127
-0.00955
-0.0161
-0.0140
-0.0119
(0.033)
(0.033)
(0.033)
(0.036)
(0.032)
(0.033)
constant
0.562***
0.579***
0.561***
0.520**
0.568***
0.407*
(0.187)
(0.209)
(0.188)
(0.232)
(0.186)
(0.220)
number of
observations
2429
2429
2429
2044
2429
2429
number of groups
374
374
374
346
374
374
Within R2
0.153
0.153
0.160
0.145
0.154
0.155
Between R2
0.0568
0.0572
0.0389
0.0936
0.0582
0.0529
Overall R2
0.0477
0.0479
0.0323
0.0564
0.0466
0.0456
Notes: * indicates p < 0.10, ** indicates p < 0.05, *** indicates p < 0.01. Robust standard errors are presented in parentheses.
Estimates of yearly dummies are not presented for brevity.
44
Figure 1. Location map of sampled urban hotels in Beijing
45
Figure 2. Box plot of SFA efficiency scores over years
46
Figure 3. Marginal effects of product diversification over distances to city center
47
Figure 4. Marginal effects of product diversification over diversification expansion rates
