COMPETITIVE RIVALRY IN THE INTERNATIONAL
HOTEL INDUSTRY
by
VINITIA E. MATHEWS, B.S., M.B.A.
A DISSERTATION
IN
BUSINESS ADMINISTRATION
Submitted to the Graduate Faculty
of Texas Tech University in
Partial Fulfillment of
the Requirements for
the Degree of
DOCTOR OF PHILOSOPHY
Approved
December, 1997
\c^^\
^r ^
ACKNOWLEDGEMENTS
I thank my committee for their help with this dissertation. I would also like to
thank the following important people in my life: my parents for giving me the opportunity
to pursue my dreams; my sister, Marsha, for always being there to listen, offer support
and make me smile; my brother, Manoj, for his prayers and support; Catherine Duran for
being such an incredible friend throughout this process; Spike and Bonnie for their
unconditional love; and most importantly, I thank my husband, Siraj, for his patience,
support and encouragement. Thank you all!
In memory of Tom Mathews - your smile lives on forever.
11
TABLE OF CONTENTS
ACKNOWLEDGEMENTS
ii
ABSTRACT
v
UST OF TABLES
vi
LISTOFHGURES
viii
CHAPTER
L INTRODUCTION
1
The Research Question
4
Significance of the Study
5
n. LITERATURE REVIEW
7
Industry Characteristics
7
Interfirm Rivalry
15
Multiple Point Competition
18
Institutional Theory
23
Characteristic Similarity
30
m. DATA AND METHODOLOGY
36
Variables
36
The Methodology
38
IV. RESULTS
48
Regression Analysis Results
48
T-Test Results
60
Lagged Regression Results
63
111
Cluster Analysis Results
71
V. IMPLICATIONS AND CONCLUSIONS
205
Conclusions
207
Implications
215
Limitations and Strengths
218
Future Directions
220
REFERENCES
222
IV
ABSTRACT
Despite the importance of interfirm rivalry in competitor analysis, certain
fundamental questions have not been explored, such as how a firm can assess which rival
is most likely to retaliate, and how a firm can appropriately distinguish between its rivals.
In order to answer these questions and explain what drives the competitive relationship
between two firms, the extent to which pairs of firms share and compete within the same
sets of markets is examined in the intemational hotel industry. Choice of market location
is of great significance to competitive posture in this industry because of the distinct
industry characteristics of high fixed costs, sensitivity to capacity constraints, cycles of
overbuilding, high operating leverage and market segmentation.
The study draws upon mimetic isomorphism and multiple point competition
theories in an effort to explain why intemational hotel chains might share the same
markets with other chains. Data from 1984 to 1993 were obtained from the Directory of
Hotel and Motel management. The study makes use of a combination of quantitative and
qualitative research methods.
The study shows how market similarity provides a good starting point to identify
competitors, particularly in those industries where a local market presence is importcint.
Market similarity between two firms does not necessarily mean the pair of firms compete
directly; the intensity of rivalry also depends on other relevant factors. However,
similarity on such factors alone (without market similarity) also does not necessarily
mean a pair of firms compete directly.
LIST OF TABLES
1.
2.
3.
4.
5.
6.
78.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
1984 Regression Analyses Results
1984 Well-Predicted Chains
Characteristics of 1984 well-predicted chains
1985 Regression Analyses Results
1985 Well-Predicted Chains
Characteristics Of 1985 Well-Predicted Chains
1986 Regression Analyses Results
1986 Well-Predicted Chains
Characteristics Of 1986 Well-Predicted Chains
1987 Regression Analysis Results
1987 Well-Predicted Chains
Characteristics Of 1987 Well-Predicted Chains
1988 Regression Analysis Results
1988 Well-Predicted Chains
Characteristics Of 1988 Well-Predicted Chains
1989 Regression Analyses Results
1989 Well-Predicted Chains
Characteristics Of 1989 Well-Predicted Chains
1990 Regression Analyses Results
1990 Well-Predicted Chains
Characteristics Of 1990 Well-Predicted Chains
1991 Regression Analyses Results
1991 Well-Predicted Chains
Characteristics Of 1991 Well-Predicted Chains
1992 Regression Analyses Results
1992 Well-Predicted Chains
Characteristics Of 1992 Well-Predicted Chains
1993 Regression Analyses Results
1993 Well-Predicted Chains
Characteristics Of 1993 Well-Predicted Chains
T-Tests p-values
Mean of the Coefficients
1984 Lag Regression Results
1984 Lag Well-Predicted Chains
1985 Lag Regression Results
1985 Lag Well-Predicted Chains
1985 Characteristics Of Well-Predicted Lag Chains
1986 Lag Regression Results
1986 Lag Well-Predicted Chains
Characteristics Of 1986 Lag Well-Predicted Chains
1987 Lag Regression Results
vi
118
119
119
120
121
121
122
124
124
125
127
127
128
131
131
132
135
135
136
139
139
140
143
143
144
147
147
148
151
151
152
152
153
154
155
156
156
157
158
158
159
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
1987 Well-Predicted Lag Chains
1988 Lag Regression Results
1988 Well-Predicted Lag Chains
Characteristics Of 1988 Well-Predicted Lag Chains
1989 Lag Regression Results
1989 Well-Predicted Lag Chains
1990 Lag Regression Results
1990 Well-Predicted Lag Chains
1984 Clusters
1985 Clusters
1986 Clusters
1987 Clusters
1988 Clusters
1989 Clusters
1990 Clusters
1991 Clusters
1992 Clusters
1993 Clusters
Vll
160
161
163
163
164
166
167
168
169
171
173
175
177
180
183
186
189
192
LIST OF HGURES
1. 1984 Dendogram
2. 1985 Dendogram
3. 1986 Dendogram
4. 1987 Dendogram
5. 1988 Dendogram
6. 1989 Dendogram
7. 1990 Dendogram
8. 1991 Dendogram
9. 1992 Dendogram
10. 1993 Dendogram
195
196
197
198
199
200
201
202
203
204
Vlll
CHAPTER I
INTRODUCTION
A central objective of strategic management researchers is to predict the extent of
rivalry between firms. Rivalry occurs when firms strive for potentially incompatible
competitive positions (Caves, 1984; Scherer & Ross, 1990). Although interfirm rivalry
studies have typically focused on predicting the intensity of competition between firms,
limited effort has been made to distinguish between rivals (Chen, 1996). In fact, existing
research tends to treat competitors as undifferentiated entities (Chen, 1996). There is a
problem with treating all rivals the same. Unless a firm can distinguish which of its rivals
are greater threats to its competitive position, how can it appropriately channel its
resources and efforts to defend itself against those rivals? How should a firm determine
which of its rivals are more likely to imitate its actions? How should a firm decide which
of its rivals to imitate? Current research does not answer these questions adequately
(Chen, 1996). The aim of this study is to distinguish between rivals and make sense of
patterns of rivalry in a specific industry.
To fully understand rivalry, competition should be analyzed from the viewpoint of
a given firm, using a pair-wise analysis of every firm it competes against (Chen and
MacMillan, 1992; Smith, Grimm, Gannon & Chen, 1991; Chen, 1996). The relationship
between a pair of firms is determined by the extent to which the two firms share the same
sets of markets, and thus have what is known as market similarity (Kamani & Wemerfelt,
1985; Smith & Wilson, 1995). Market similarity is the extent to which firms operate in
the same locations. For example, if two hotel chains have hotels in many of the same
cities, they can be said to have market similarity. To distinguish between rivals, market
similarity is important because firms are likely to actively compete only if they share
many market locations (Chen, 1996). If two firms choose to operate in different market
locations, they are not likely to be direct competitors (Chen, 1996). This is because they
neither compete for the same resources or customers, nor do they face the same
constraints. In essence, choice of market location is a key determinant of one's rivals.
Choice of geographic location greatly influences the degree of overlap between firms, and
thus the extent of competition between them (Yuchtman & Seashore, 1967; Carroll &
Huo, 1986). The aim of the study is to distinguish between rivals. A good way to
separate competitors into greater versus lesser competitive threats is to use a pair-wise
approach by first establishing the extent of market similarity between firms. In this
dissertation, establishing market similarity between pairs of firms will be the primary way
to distinguish between rivals in a specific industry—the intemational hotel industry.
There are several reasons why the international hotel industry is appropriate for
this study. Choice of market location (the basis of market similarity) is of key
significance to competitive posture in this industry for two reasons (Bull, 1994). The first
reason is that location is the only attribute of the lodging "product" that is completely
fixed. Hotel buildings and grounds cannot be physically moved, even if a particular
location is found to be unsuitable. The second reason is that location is quite possibly the
most important variable in determining the feasibility of a new hotel (Bull, 1994; Hart &
Troy, 1986). A good location increases the value of a hotel and allows managers to
charge a relatively constant premium rate. Therefore, location is a major facet of a hotel's
competitive strategy. Choice of location can be critical to achieving and maintaining a
competitive advantage in this industry.
Location decisions are also important because they can be considered strategic
actions. Strategic actions require large resource commitments, are fairiy difficult to
reverse once the action has begun, and tend to be more risky than tactical moves (Chen &
MacMillan, 1992; Miller & Chen, 1994; Smith, Grimm, Gannon & Chen, 1991; Smith,
Grimm & Gannon, 1992). Market entries tend to be considered very important strategic
actions (Caves & Porter, 1977; Miller & Chen, 1994; Scherer & Ross, 1990; Tirole,
1988) because it takes large resource commitments to open a new hotel, and once
constmction has begun on a new hotel, the action is not simple to reverse. It will also be
shown that the intemational hotel industry has some distinct characteristics such as high
fixed costs, high operating leverage, capacity constraints, cyclicality and segmentation,
which further warrant the exaniination of the basis of market similarity ~ choice of
location. It is necessary to use an industry with clearly defined, regional markets for a
project involving market similarity (Evans & Kessides, 1994). The intemational hotel
industry is one such industry. It has been stated that competition should be analyzed from
the viewpoint of a given firm, using a pair-wise analysis of every firm it competes against
(Chen and MacMillan, 1992; Smith et al., 1992; Chen, 1996). Studies using a similar
perspective with clearly defined regional markets, have been conducted in the airline
industry (Chen and MacMillan, 1992; Smith et al., 1992; Chen, 1996). However, no
known studies using a pair-wise approach have been conducted in the intemational hotel
industry, which also has cleariy defined regional markets. At the present time, in fact,
only two known studies exist of the intemational hotel industry (Dunning & McQueen,
1981; Contractor & Kundu, 1995). It is, therefore, appropriate to select an unused pairwise approach to examine a relatively understudied industry for this study.
In the following sections, the research question and significance of the study are
presented.
The Research Question
The aim of the study is to address a yet unresolved issue in interfirm rivalry —
distinguishing between rivals. Specifically, the purpose is to distinguish between rivals in
the intemational hotel industry in order to help chains channel their resources and efforts
against their greatest competitive rivals. The results of this study may improve the ability
of firms in this and other similar industries to target key competitors, and appropriately
defend and compete against those competitors.
To further distinguish between rivals, the basis of market similarity is examined
over time. The objective is to examine whether one can distinguish between current and
future rivals using current conditions. In other words, if two chains have similar
characteristics currently, does this similarity help to explain the market similarity and thus
extent of rivalry of the two chains both now and in the future? The results may indicate
how firms can keep current track of both existing and potential rivals in order to channel
resources to maintain and improve their competitive position.
Significance of the Study
Interfirm rivalry studies typically focus on predicting the intensity of competition
between firms. However, limited effort has been made to distinguish between rivals
(Chen, 1996). In fact, the notion of considering rivalry from the view point of a given
firm has been ignored (Bamett, 1993). This study attempts to address this gap in the
literature, by using a given firm as the focal point to distinguish between rivals in the
international hotel industry. Since choice of geographic location greatly influences the
degree of overlap between firms, and thus the extent of competition between those firms
(Yuchtman & Seashore, 1967; Carroll & Huo, 1986), market similarity will be used to
distinguish between rivals of a focal firm. Market similarity is the extent to which firms
operate in the same locations.
The international hotel industry is of great significance in the world economy.
The industry is part of the service sector which in 1995, accounted for over 58% of
worldwide gross national product. The service sector comprises 72% of the U.S. GDP,
and over 60% of the economies of Japan, U.K., Canada, France and Italy ~ the five
largest economies (Contractor and Kundu, 1995). The intemational hotel industry is also
part of the tourism industry which is second to none in global influence (Teare & Olsen,
1992). At the present time, however, despite its importance, only two known studies
exist in the intemational hotel industry (Dunning & McQueen, 1981; Contractor &
Kundu, 1995) and neither of these two studies have the aim of distinguishing between
rivals. The current study provides further examination of this important industry with a
yet unexamined research question.
In the next chapter, relevant literature is reviewed. Chapter EI contains a review
of the data and methodology. Results are provided in Chapter FV, and finally,
conclusions and implications are discussed in Chapter V.
CHAPTER n
LITERATURE REVIEW
Chapter two provides a review of relevant literature and industry characteristics.
Industry characteristics will be reviewed first.
Industry Characteristics
The hotel industry has some distinct characteristics which make the issue of
choice of location particulariy interesting. Each of these characteristics is described
below.
High Fixed Costs
The hotel industry is distinct because of the high level of fixed cost tied up in real
estate. A hotel's physical property and building(s) cost millions of dollars to design,
equip, maintain and operate (Tas, LaBrecque & Clayton, 1996). To further illustrate this
point, normal property renovations typically mn from 3 to 5 percent of revenues, and
capital expenditures on major renovations typically mn from $10,000 to $50,000 per
room (Stipanuk & Lambert, 1994).
High fixed costs are particularly relevant in a study of location decision. Given
that there are tremendous fixed costs to be incurred every time a new hotel is built by a
chain, the decision to open a new hotel is no simple matter. In fact, the decision to enter a
market can be considered a strategic action (Caves & Porter, 1977; Miller & Chen, 1994;
Scherer & Ross, 1990; Tirole, 1988). Strategic actions require large resource commitments, are fairly difficult to reverse once the action has begun, and tend to be more risky
than tactical moves such as price cuts (Chen & MacMillan, 1992; Miller & Chen, 1994;
Smith et al., 1991, 1992). Once a decision has been made to build a hotel in a particular
location, that decision cannot easily be reversed because of the significant amount of
resources involved, and also because a hotel is a single-use property with constraints
unlike any other type of real estate (Butler & Rushmore, 1994). For these reasons, the
issue of location choice is cmcial for hotel chains.
Sensitivity to Capacity Constraints
The hotel industry is capacity sensitive. Any given location can only sustain a
finite number of hotel rooms. The Market Quality Ratings scheme developed by the
Landauer Associates is one way to determine the carrying capacity of a given location.
The scheme determines how many more rooms a market can absorb in the next five years
to be viable, given supply and demand trends, constmction in progress, and constmction
costs. The disadvantage of this scheme is that it assumes all rooms are the same price,
whereas, in actuality there could be more than one kind of room (Rowe & Wagner, 1995).
As such, the Ratings scheme may not be particularly useful. Also, such ratings are very
difficult to obtain when one is evaluating foreign cities. Foreign buyers, because of the
fact that they are not as familiar with a given foreign location, may incorrectly evaluate
the local environment (Corgel & deRoos, 1994). Before selecting an intemational
location, chains must try to predict the carrying capacity of the location accurately, but
carrying capacity is hard to determine, and being a foreigner makes it more so.
The issue of capacity is closely tied to critical mass and impact. Critical mass
means that a hotel typically has to have more than one or two token hotels in an area to
gain name recognition in that area (Koss, 1993). Many times there is sufficient demand
for more than one hotel from a chain to locate within the same city. Impact is when new
hotels entering one hotel's market not only share the same market, but also the same flag
and reservation system as the existing hotel (Jesitus, 1992). In the hotel industry, it is
therefore possible to compete with other hotels holding the same brand name. This
strategy of placing hotels of the same chain in close proximity to one another is also
known as twinning (WSJ, 07/08/96). Critical mass, and impact or twinning, complicate
the choice of locations. While the chain wants to have a critical mass in a given location,
too many hotels in one location will increase the impact issue.
Cycles of Overbuilding
The hotel industry always has been and probably always will be cyclical (Butler &
Rushmore, 1994). The hotel industry is particulariy vulnerable to the lodging needs of
travelers and various economic factors (Butler & Rushmore, 1994). The vulnerability of
the hotel industry to the welfare of the economy can be seen by examining cycles of
overbuilding in the past.
In the early 1970s, there was a building boom in the U.S. due to easily available
financing and the overly-aggressive pursuit of chain expansion (Butler & Rushmore,
1994). In the late 1970s, inflation caused interest rates and constmction costs to sky
rocket; travel decreased substantially due to the energy crisis; and the corresponding
recession caused fewer people to travel overall (Butler & Rushmore, 1994). As a
consequence, in the 1980s, there was an oversupply of hotel rooms (Rainsford, 1994).
Conditions got better in the mid 1980s, but in the eariy 1990s, the U.S. experienced
another economic downtum, and consequently, once again there was an oversupply of
rooms in the U.S. (Rainsford, 1994). In fact, between 1986 and 1991, the U.S. hotel
industry alone lost approximately $14 billion (Lodging Industry Overview, 1995) for
reasons such as overbuilding, excessive debt, declining real estate values, an economic
recession and the Gulf War (Umbreit, 1996).
In such a cyclical industry where business is influenced by forces outside the
control of a firm (Butler & Rushmore, 1994), it is cmcial that a chain pick hotel sites
carefully. Intemational expansion allows chains to reduce risks associated with
cyclicality, since different economic systems may be in different cycles at any given
period.
High Operating Leverage
In the 1970s, financing was readily available for hotel chains at relatively low
interest rates. In the early 1980s, financing was once again readily available, but at higher
interest rates (Butler & Rushmore, 1994). Over half the hotels in the U.S. were built or
refinanced with very high debt burdens (Butler & Rushmore, 1994). The loans which
were extended to hotel chains were based on cash-flow projections which caused chains
10
to be highly leveraged (Withiam, 1995). In the 1970s and 1980s, lenders could not
intervene in a hotel's operations even if the hotel was in financial trouble (Withiam,
1995). The less-stringent loan requirements (which caused a constmction boom) and the
laissez-faire attitude of the lenders in the 1970s and 1980s were partially to blame for the
serious downtum in the industry. The lenders of today, however, are much more cautious
(Withiam, 1995). In fact, lenders of today are so cautious, many are not willing to lend
unless a hotel is affiliated with a chain, and unless a new hotel shows clear market
strength (Withiam, 1995). However, the industry still has high operating leverage.
Given the fact that physical properties and buildings cost millions of dollars to
design, equip and maintain (Tas, LaBrecque & Clayton, 1996), few chains can afford to
start an intemational venture without extemal funding. In today's environment, given the
cautious nature of lenders, chains have to be very careful about a potential intemational
venture to ensure they have made a good investment. Choice of new hotel location is of
vital importance because the chain typically has to answer to lenders about that particular
investment.
Segmentation
The hotel industry is highly segmented. In the intemational hotel industry,
product segmentation has occurred predominantly according to price and level of
facilities (Go & Pine, 1995). It was the oversupply of rooms in many markets which led
to the need for more aggressive marketing (Roginsky, 1995; Rainsford, 1994). One of the
ways to better serve the needs of specific customers, was to use product segmentation.
11
For the most part, hotels of a certain price and facility level compete with other
hotels of similar price and facility level. However, when there is an oversupply of rooms,
many hotels lower room rates and consequently discover new competitors they have not
had to contend with in the past (Roginsky, 1995). Listed price level of a facility does not
necessarily define competitors because the listed price is highly variable. Given the many
promotions and alliances a given hotel may be touting, a customer may end up paying a
lower rate at a luxury hotel than the regular price at a lower-end hotel. One such instance
is found where the elite hotels have decided to target business travelers, by lowering rates
for business executives (Wall Street Joumal [WSJ], 2/21/97). Bjom Hanson, chairman of
Cooper & Lybrand's hospitality group has been quoted as saying "it's the industry trend"
(WSJ, 2/21/97). The high variability in listed room rates is causing chains who do not
normally compete, to now compete for the same customer. Therefore, virtually every
product segment can interact and compete with other segments (Go & Pine, 1995).
How is segmentation relevant to the location decision? Chains used traditional
segmentation to better target specific customers. Now, however, to better utilize hotels
with low occupancy rates, many chains lower listed rates, and consequently increase the
number of chains they compete against. The location decision for a chain is becoming
more and more complicated. Not only must the chain evaluate the foreign environment
and potential growth, but also the chain must evaluate potential competitors within the
same segment at the location, as well as potential competitors in different segments at
that location.
12
The objective of this study is to distinguish between rivals. It is necessary to take
hotel segments into account to distinguish between the threat of potential rivals. Even
though a segment can interact and compete with other segments (Go & Pine, 1995), it is
highly unlikely that segments on opposite ends of the spectmm would be strong
competitors since their customers are unlikely to be the same. For example, if a luxury
hotel chain and an economy hotel chain have hotels located within many of the same
cities, despite market similarity, they are unlikely to consider each other a major threat
due to different target markets.
There is a lot of controversy regarding the classification and grading of hotels
(Callan, 1989). Classification of hotels is done by grouping service accommodations
according to physical facilities (Callan, 1994). Hotels provide such a broad spectmm of
services that they can be classified into five, six, or even seven categories. Grading refers
to quality in this industry. Grading is a subjective assessment of the quality and service
of hotels (Callan, 1994). The hotel industry is noted for its division and lack of consensus
regarding classification and grading (Callan, 1994). In fact, surveys conducted in the
industry have revealed that not only is there a lack of consensus, but managers often
confuse the distinction between classification and grading (Callan, 1989). To illustrate
the scope of the problem, one need only look at a few of the classification systems in
existence. The World Tourism Organization has five classes of hotels. However, even
among its own members there are no accepted standards or classifications. Several
countries belonging to the World Tourism Organization set their own standards and use
different methods of classification (Lundberg, 1985). The classification system used by
13
the Official Hotel Guide has ten classes, none of which have clear definitions (Conner,
1994). Each category has vague delineations that make the classification process
difficult. Hotels are classified in a variety of ways, none of which are considered an
industry standard worldwide (Macintosh & Goeldner, 1986). There are many different
ways to classify hotels according to Go and Pine (1995) such as type of hotel, location,
size, standard and level of service, ownership and chain affiliation. Thus, although there
is no question that there are different types of hotels within the hotel industry (Dunning &
McQueen, 1982), there is no agreed upon method of classifying hotels.
In the intemational hotel industry, product segmentation has occurred
predominantly according to price and level of facilities (Go & Pine, 1995). It is
acknowledged that it would be almost impossible to obtain a classification scheme that
could be considered reliable across the many chains based in different countries, however,
this potential limitation is considered less detrimental than failing to distinguish between
different segments in the study. The failure to distinguish between product segments
would make an implicit assumption that all international hotels could be potential
competitors. This assumption would be partially incorrect, because for the most part,
hotels of a certain price and facility level compete with other hotels of similar price and
facility level. Where possible, therefore, type of facility is included in the current
research project.
In summary, choice of location in the intemational hotel industry is a complicated
decision. A chain must consider issues of critical mass and impact, carrying capacity of
the location, lender stipulations, economic cycles, and potential competitors in that
14
location, keeping in mind that due to exceedingly high fixed costs, the intemational
market entry decision cannot easily be reversed.
The following sections provide a review of relevant literature. Theories are
reviewed which help to explain the basis of market similarity. The theories relate to
interfirm rivalry, multimarket competition and institutional theory.
Interfirm Rivalry
Rivalry occurs when firms strive for potentially incompatible competitive
positions (Caves, 1984; Scherer & Ross, 1990). Interfirm rivalry studies have typically
focused on predicting competition between firms (Chen, 1996). One of the five forces
driving competition is intensity of rivalry (Porter, 1980). Interfirm research typically
focuses on the interplay between firms as they vie for a better competitive position. The
basic premise of this stream of research lies in the fact that firms are not independent, are
affected by the actions of other firms, and are, therefore, compelled to respond to such
actions (Smith et al., 1992; D'Aveni, 1994). The degree to which firms are aware of their
interdependence with other firms, and can use it to their advantage as they formulate
actions and anticipate reactions has important implications for improving their competitive position (Amit, Domowitz & Fershtman, 1988). It is necessary to distinguish
between an action and a reaction before proceeding further.
An action is a specific competitive move initiated by a firm to defend or improve
its competitive position (Smith et al., 1992). There are two types of competitive actions,
tactical and strategic. Tactical actions require relatively few resources and are considered
15
low in risk (Smith et al., 1992). Strategic actions, on the other hand, require much larger
resource commitments, are more difficult to reverse once the action has begun, and are
also relatively more risky than tactical actions (Chen & McMillan, 1992; Miller & Chen,
1994; Smith et al., 1991; Smith et al., 1992). The actions of firms compel a response
from other firms (Smith et al., 1992; D'Aveni, 1994). The response of a competing firm
designed to defend or improve its competitive position relative to the acting firm is
known as a reaction or counteraction (Smith et al., 1992).
There are other issues to be considered as firms jockey for incompatible
competitive positions. Knowing the response pattem of competitors is of importance if a
firm is to correctly anticipate reactions as it designs its own actions (Porter, 1980).
Failure to see a potential competitor because of competitive 'blind spots' can detrimentally affect one's competitive position (Zajac & Bazerman, 1991). In the case of the
intemational hotel industry, one potential 'blind spot' could arise due to segmentation.
For the most part, hotels of a certain price and facility level compete with other hotels of
similar price and facility level. However, when there is an oversupply of rooms, hotels
may lower room rates and consequently discover new competitors they have not had to
contend with in the past (Roginsky, 1995). Defining competitors by price level alone
could be dangerous in this industry. Chains should monitor not only chains of the same
price level and with market similarity, but also other chains in different segments with
market similarity.
Despite the consideration of many topics relevant to interfirm rivalry, there is a
gap in the literature in terms of predicting rivalry (Chen, 1996). Existing studies tend to
16
treat competitors as undifferentiated entities (Chen, 1996). Limited effort has been made
to distinguish between rivals (Chen, 1996). In fact, the notion of considering rivalry from
the viewpoint of a given firm has been ignored (Bamett, 1993). If a firm is to correctly
anticipate the actions and reactions of its rivals as it formulates its own strategies, it is
necessary for the firm to be able to determine which of its rivals are more of a
competitive threat. The firm should also be able to determine which rival is more likely
to retaliate to a given competitive move, so that it can properly defend itself against
possible retaliation. Unless a firm can determine which of its rivals are more of a threat
to its competitive position, how can it appropriately channel its resources and efforts?
Current research in interfirm rivalry does not answer these questions adequately (Chen,
1996). The aim of this study is to distinguish between rivals in the international hotel
industry. To distinguish between rivals, market similarity is important because firms are
likely to actively compete only if they share many market locations (Chen, 1996). If two
firms choose to operate in different market locations, they are not likely to be direct
competitors (Chen, 1996). This is because they neither compete for the same resources or
customers, nor do they face the same constraints. In essence, choice of market location is
a key determinant of one's rivals.
The following section outlines more literature relevant to the study. Multiple
point competition is reviewed first, followed by institutional theory. Both these theories
provide different explanations for the existence of market similarity. In other words, both
theories explain why a chain would choose to build hotels in the same or similar cities as
other chains.
17
Multiple Point Competition
The nature of the competitive relationship between a pair of firms can be assessed
by examining the extent to which the two firms share the same sets of markets and thus
have market similarity (Kamani & Wemerfelt, 1985; Smith & Wilson, 1995). Market
similarity is where firms operate in many of the same locations. The situation of firms
simultaneously competing in several different markets is also known as multiple point
competition (Kamani & Wemerfelt, 1985).
The extent to which firms have multiple market contact with other firms can
influence the intensity of rivalry among those firms (Kamani & Wemerfelt, 1985; Smith
& Wilson, 1995). This notion was first expoused by Edwards (1955) and is also known
as mutual forbearance or linked oligopoly. The potential for mutual forbearance exists in
any industry with a number of distinct geographic markets (Bernheim & Winston, 1990).
Edwards (1955) suggested that firms which share many markets simultaneously become
aware that their competitive actions in one market could result in competitors retaliating
in other markets. The outcome of sharing multiple markets, therefore, could be a
reduction in rivalry (Simmel, 1950; Edwards, 1955). Such firms may be more inclined to
cooperate with each other and not engage in intense rivalry for fear of retaliation
(Simmel, 1950; Edwards, 1955). Any firm which maintains a foothold in another firm's
market(s) has a "stick with which to discipline the other firm" (Kamani & Wemerfelt,
1985, p. 90). In other words, if a hotel chain does not retaliate by entering the same
markets as other chains, it may have no means by which to influence the other chains'
behavior. In fact, even if two markets are seemingly unrelated, a firm's actions in one
18
market can affect a competitor's strategies in another market (Bulow, Geanakoplos &
Klemperer, 1985). Market similarity may occur, therefore, as a means for a chain to
influence the behavior of other chains. There have been numerous studies investigating
the effects of multimarket contact. Some of these studies are reviewed next.
The first comprehensive study of multiple market contact was conducted in the
banking industry by Heggestad and Rhoades (1978). The results of their study provide
evidence that in the banking sector, interfirm rivalry is adversely affected (lowered) by
the degree of contact in multiple markets. There appeared to be less intense rivalry
among banks which shared multiple markets. Another study of multiple market contact
in the banking industry found similar results ~ interbank rivalry was adversely affected by
the extent to which banks shared the same markets (Martinez, 1990). Contact in more
than one market increased the amount of communication between banks, and thus
decreased the chances of intense rivalry and increased the chances of cooperation
between such firms (Martinez, 1990).
Evans and Kessides (1994) conducted a study of multiple market contact in the
airline industry. The airline industry was selected because it has geographically distinct
markets which can be accurately defined, and different airlines have airport dominance in
different markets (Evans & Kessides, 1994). The results of the study indicate the higher
the level of multiple market contact between rivals, the more likely the airlines are to set
higher fares (cooperate) on routes shared with those rivals.
An examination of the mobile telephone industry was conducted recently (Parker
& Roller, 1997). The authors established that duopolies were created because pairs of
19
firms were issued a license to compete in strictly defined product and geographic markets
in the mobile telephone industry. The results of the study show that multimarket contact
and cross-ownership yields noncompetitive prices.
In a study of the health care industry, researchers have found that multimarket
contact can influence market exit behavior (Boeker, Goodstein, Stephan & Murmann,
1997). The researchers examined hospitals and found that multimarket contact had a
negative influence on market exit. The more there was multimarket contact between
hospitals, the less likely the hospitals were to exit a given market, even if the hospital in
that market was not very successful.
Authors who conducted an examination of 25 regional markets of the U.S. cement
industry were able to make statements about the relationships between pricing and
multimarket contact (Jans & Rosenbaum, 1997). The researchers used a 16 year time
span for their project. The findings suggest that the extent of multimarket contact among
firms in the cement industry has a direct effect on the extent of divergence of price from
marginal cost in any one of those given markets.
Singal (1996) looked at the relationship between airline mergers and multimarket
contact. In the research project, airline mergers between 1985 and 1988 were examined.
The conclusion of the study is that degree of multimarket contact can significantly affect
airfares. An increase in multimarket contact alone was found to be sufficient cause to
raise airfares (Singal, 1996).
Another research project in the airline industry was conducted using over three
thousand pairs of city markets (Gimeno & Woo, 1996). These researchers concluded that
20
strategic similarity, a similarity in competitive orientation, moderately increased intensity
of rivalry, while multimarket contact strongly decreased intensity of rivalry.
In a study of mutual forbearance and conglomerates, some interesting conclusions
were reached (Phillips & Mason, 1992). The basic premise of multiple point theory is
that sharing multiple markets could result in a reduction in rivalry due to cooperation
(Simmel, 1950; Edwards, 1955). In their examination of conglomerates, however,
Phillips and Mason (1992) found that conglomerates do not act cooperatively even with
multimarket contact. The authors did find, however, that conglomerates encouraged
more cooperation in one market but not in others when there was multimarket contact.
Alexander (1985) examined 69 market areas using bank holding companies for
the 1975 time period. The results of this study are that multimarket contact has a positive
impact on service charges. However, no evidence was found that interest rates for the
short term business loan market were affected by multimarket contact.
A test using the U.S. Federal Trade Commission line of business data for the 1976
time period was conducted by examining the competition for sales in markets where
multimarket contact was found to exist (Feinberg, 1985). The results of the test indicate
collusion among firms can occur when there is mutual forbearance among rivals. Similar
results were found by Bemheim and Winston (1990). These authors found that firms
with multimarket contact were better able to engage in collusive behavior.
Multimarket contact may even influence firms in seemingly irrational ways. A
firm operating in multiple markets with its rivals may combine signaling efforts across all
the markets in order to convey tme cost information (Srinivasan, 1991). One outcome of
21
the signaling effort across all the markets is the possibility of the firm opening up an
unprofitable market to minimize overall signaling costs (Srinivasan, 1991).
Competing in multiple shared markets gives a firm more competitive options
(Smith & Wilson, 1995). When a competitor engages in a competitive action, rivals can
respond in one or more of four ways: do nothing, defend, counterattack or declare a total
war (Karnani & Wemerfelt, 1985). The decision to do nothing will result in the two
firms implicitly agreeing to share the market demand. The defend response will lead to a
situation where the firms compete intensively only in some of the markets where they
both compete. The counterattack response leads to a situation where each firm holds a
small share in the other firm's market. And a total war will result in one firm succeeding,
or both firms being destroyed (Karnani & Wemerfelt, 1985). Given the potential
outcomes, Kamani and Wemerfelt (1985) predicted that the response of counterattacking
and defending would be the most prevalent options, in order of prevalence. Smith and
Wilson (1995), however, found the most prevalent response was to do nothing, with the
counterattack option being the least prevalent of all four options.
Empirical studies of multiple market contact appear to vary in the extent to which
they validate the concept of multiple market contact (Baum & Korn, 1996). The above
review of multimarket research confirms this position. Some studies have found that
multiple market contact decreases rivalry as predicted; some have found no effects; and
some have found that multiple market contact did not decrease rivalry (Baum & Kom,
1996). Some studies have found that multiple market contact actually increases intensity
of rivalry (Alexander, 1985; Mester, 1987), while other studies have found no effects of
22
multiple market contact (Rhoades & Heggestad, 1985). One limitation of current
multimarket contact research may account for some of the ambiguity of results according
to Baum and Kom (1996). Most of the studies on multimarket contact use market-wide
indices to represent the contact. Such measures ignore the fact that rivalry occurs
between pairs of firms and, therefore, depends on the nature of the relationship between
firms (Baum & Kom, 1996; Chen, 1996). One objective of this study is to distinguish
between rivals using pair-wise analysis. In this manner, the study addresses a limitation
of current research.
The next section reviews additional relevant literature which helps to explain why
one chain might have a similar market profile to another chain.
Institutional Theory
Why might an intemational hotel chain want to choose locations close to those of
other chains? One reason to locate close to other chains is that it allows the chain to keep
track of competitor moves. Another reason is that the presence of the other chains
reinforces the area's attractiveness and downplays its risks or weaknesses (Markusen,
1990). There are other ways to explain the similarity of hotel location among
intemational hotel chains.
Institutional theory explains the emergence of similar forms and competencies
among firms (Selznick, 1957, 1996). These forms and competencies develop as a
response to both intemal and extemal environments (Selznick, 1957, 1996). Isomorphsim is the situation when firms in the same population have similar characteristics
23
(Oliver, 1988). Certain authors suggest isomorphsim arises as a result of competition,
because firms face the same environmental constraints which cause them to look similar
(Hannan & Freeman, 1977). Other authors suggest it is the environment which exerts
certain kinds of influence on a firm causing interacting firms in a similar environment to
look similar (DiMaggio & Powell, 1983). The environmental forces can have mimetic,
coercive and normative influences (DiMaggio & Powell, 1983). Mimetic isomorphism is
caused by firms imitating other firms due to uncertainty. A firm reduces uncertainty by
imitating the behavior of firms perceived to be successful (DiMaggio & Powell, 1983).
Coercive isomorphism arises due to pressure from regulatory agencies. The agencies
impose certain mles that all firms in that industry have to abide by. Normative
isomorphism arises due to pressures exerted primarily by professionalization (DiMaggio
& Powell, 1983) or professional inter-relationships (Scott, 1992). In the international
hotel industry, rather than chains being subject to the regulations of a single regulatory
agency, multiple agencies can and do influence the industry. This results in a lack of
industry-wide standards which would have had coercive pressures on chains in the
industry. For example, hotels are classified in a variety of ways none of which is
considered an industry standard worldwide (Macintosh & Goeldner, 1986). The industry
also does not have professionalization, and therefore normative pressures do not exist.
Mimetic pressures, however, are quite strong. Service firms (including hotels) seem to
rely heavily on imitation (Martin & Home, 1993). The hotel industry does not contain
specific knowledge which can be protected by a patent (Dunning & McQueen, 1982)
24
making imitation even more prevalent. The following section provides a review of
mimetic isomorphism.
Mimetic Isomorphism
Mimetic isomorphism is the achievement of conformity through imitation
(DiMaggio & Powell, 1983). Mimetic isomorphism can be thought of as a process that
enables features to be replicated from one firm to another (March, 1981; Tolbert &
Zucker, 1983; Fligstein, 1985). Mimetic isomorphism describes how firms change over
time and become more similar to other firms in their environment (DiMaggio & Powell,
1983). Mimetic isomorphism can be caused by uncertainty (DiMaggio & Powell, 1983)
or obligatory action (March, 1981). When firms are faced with uncertainty, imitating
other firms can economize on search costs (Cyert & March, 1963). 'Obligatory action'
happens when enough firms do things in a particular fashion, it becomes the norm and
from that point on, things are done that way without conscious thought (March, 1981). In
fact, once an action has become the norm, organizations may conform to the action
disregarding the impact of that conformance on organizational performance (Meyer &
Rowan, 1977). The adoption of proper norms may be necessary for the organization to
acquire necessary resources to survive, although the conformity to the accepted norms
may have more symbolic rather than task-related requirements (Tolbert & Zucker, 1983).
Mimetic change has been observed in many different industries (Haveman, 1993).
For example, mimetic isomorphism was observed in the spread of the multidivisional
form (Fligstein, 1985); in the changes in form by health maintenance organizations
25
(Wholey & Bums, 1993); and the adoption of matrix management programs by hospitals
(Bums & Wholey, 1993).
One research project explores the adoption of civil service procedures by
municipal govemments from 1880 to 1935 (Tolbert & Zucker, 1983). Data on the
adoption of civil service procedures were collected for 167 cities for the time period 1880
to 1935. The findings suggest that intemal organizational factors predict the extent to
which civil service procedures are adopted eariy in the process, however, once the process
is in progress, the intemal organizational factors do not predict the extent to which civil
service procedures are adopted (Tolbert & Zucker, 1983).
Another study examines the transformation of employment practices in U.S. firms
from the Depression to after World War E (Baron, Dobbin, & Jennings, 1986). Data
collected by the National Industrial Conference Board for 1927, 1935, 1939 and 1946
were used for the project. The Board collected information by surveying the entire
population of firms listed on the New York Stock Exchange, Dun and Bradstreet
publications and other firm listings. The surveys include anywhere between 2,452 firms
and 3,498 firms for the years used in the project. The employment practices examined
relate to specialization of roles, centralized hiring, promotion and firing, and salary
classification. The findings support those found by Tolbert and Zucker (1983) ~ namely
that efficiency objectives became less of a criteria in deciding which employment
practices to use as certain employment practices became standard operating procedure
(Baron, Dobbin & Jennings, 1986). The authors found that only when employment
26
practices were first beginning to be diffused throughout firms were intemal organizational
factors influential in determining which employment practices to adopt.
Bums and Wholey (1993) examined the adoption and abandonment of matrix
stmctures over a 17 year period in the hospital industry. The industry was picked due to a
prevalence of mimetic behavior. Mimetic behavior has been seen in the spread of
innovations across hospitals (Luft, Robinson, Gamick, Maerki & McPhee, 1986; Russell,
1979; Stevens, 1989). The authors examined the spread of matrix stmctures throughout
hospitals between the years of 1961 and 1978, at four intervals in time. A total of 1,375
hospitals were used in the project. The findings suggest the prestige of a hospital
influences both its own and its immediate neighbor's decisions to adopt a particular
practice. Also, organizational networks influence the diffusion of innovations (Bums &
Wholey, 1993).
The impact of interorganizational interlocks on the imitation of corporate
acquisition activity has been examined (Haunschild, 1993) to further examine the role of
networks and its ability to influence the diffusion of innovations. The interaction
between the employees of different firms through interlocking arrangements could result
in firms imitating their interlocked partners (Davis, 1991; Palmer, Jennings & Zhou,
1993). Haunschild (1993) used all medium- and large-sized firms listed in
COMPUSTAT databases for the time period 1981 to 1990. Four industries were used:
electrical equipment manufacturing, transportation equipment, wholesale trade and
business services. The author found that interaction between the employees of different
firms through interlocking arrangements resulted in firms imitating the corporate
27
acquisition activity of their interlocked partners (Haunschild, 1993). The reasons for
mimetic behavior and empirical examinations of the behavior have been discussed. It is
necessary to also discuss what existing research has revealed about which firms will be
imitated. The discussion follows.
Some authors suggest that firms imitate other firms in their population (Haveman,
1993). A population can be defined as firms within one industry (Carroll, 1985; Hannan
& Freeman, 1977; Delacroix & Swaminathan, 1991; Baum & Mezias, 1992; Haveman,
1993). Other authors imply firms imitate their closest competitors, defined as those
classified within the same industry and similar in size (Walsh & Kosnik, 1993).
Empirical measures of size which have been used include sales (Walsh & Kosnik, 1993;
Ghoshal, 1988), total average assets (Deephouse, 1996), typical assets (Sheppard, 1994)
asset base (Haveman, 1993) and total number of employees (Ghoshal, 1988). In the hotel
industry, the standard measure of size is number of rooms (Wyckoff & Sasser, 1981;
Baum & Mezias, 1992).
Ghoshal (1988) examined isomorphism in similar sized firms. Haveman (1993)
suggested that firms may imitate similarly sized firms in their population. This is because
firms of similar size are similar in stmcture and strategy, depend on the same
environmental resources, and have the same constraints (Hannan & Freeman, 1977).
Firms may monitor the actions of similarly sized rivals and, therefore, would be more
likely to imitate size peers (Scott, 1992). However, there is little theoretical analysis to
specify clearly which firms should be imitated (Haveman, 1993). For example, Haveman
28
(1993) found large firms imitate other large firms, however no support was found that
small- or medium-sized firms imitated their size peers.
In a study of the Manhattan hotel industry, results were found which suggest that
the more similar the type of hotel of a new entrant to incumbent chains, the more similar
their size; and the more similar the size of a new entrant to incumbent chains, the more
dissimilar their type of hotels (Baum & Haveman, 1997). The authors found that in the
Manhattan hotel industry, new entrants tend to locate close to other hotels of similar type
rather than similar price (Baum & Haveman, 1997). This is because hotels of similar
size, price and location tend to compete more intensely (Baum & Mezias, 1992; Baum &
Haveman, 1997). For this reason, hotels in the Manhattan area appear to enter markets
based on a combination of differentiation and agglomeration processes (Baum &
Haveman, 1997). Differentiation is where hotels located close to one another choose to
have different types of hotels; agglomeration refers to the economies which arise from
being located close to similar type of firms.
The theories reviewed thus far give possible explanations of market similarity. If
two firms operate within similar market locations, but do not have any other similar
characteristics, it is unlikely that they will consider each other a major competitive threat
(Chen, 1996). Therefore, market similarity alone cannot be used to achieve the objective
of distinguishing between rivals. Rather, it is also necessary to establish whether there is
similarity on other relevant dimensions (characteristics). The following section describes
the characteristics used in this project to distinguish between rivals in the intemational
hotel industry.
29
Characteristic Similarity
To distinguish between rivals, it is first necessary to identify whether firms
compete in enough of the same market locations to actively consider each other a great
threat. In addition to this, it is also necessary to assess the extent to which pairs of firms
have similar characteristics. If two firms operate within similar market locations, but do
not have any other similar characteristics, it is unlikely that they will consider each other
a major competitive threat (Chen, 1996).
What are the relevant characteristics for examining extent of market similarity
between intemational hotels chains? The nature of the business in the intemational hotel
industry is to engage in intemational expansion. Therefore, those factors affecting the
process of intemationalization such as size, international experience and degree of
intemationalization will be used to examine the extent of market similarity between
intemational hotels chains. A continuation of the literature review follows, which
describes each of the characteristics.
Firm Size
Foreign direct investment requires large amounts of capital to obtain information
and set up foreign operations (Buckley & Casson, 1976). For this reason, large firms are
typically among the first to establish an intemational perspective (Hymer, 1979). In fact,
it has been suggested that the larger the firm, the more likely it is to have operations in
many different countries (Buckley & Casson, 1976). Large firms seem more capable of
30
absorbing costs and risks and appear to possess more advantages to counter disadvantageous positions (Buckley & Casson, 1976; Kumar, 1984).
Size represents not only a major advantage, but also the means by which to go
abroad (Terpstra & Yu, 1988). Larger firms are better able to achieve global economies
of scale (Morrison, 1990). Larger firms are able to replicate their capabilities in different
nations at lower costs due to such economies of scale (Dunning & McQueen, 1981; Yip,
1989). Dunning (1988) found that large hotel chains were better able to achieve superior
economies of scale than their smaller counterparts.
Size is such an important factor, it has been suggested that gaining size is
important to the success of new hotels (Withiam, 1985). The implication is that hotels
need to have sufficient resources to initiate actions and match competitors actions if they
are to succeed. Size is important in the hotel industry because it results in localized
competition (Baum & Mezias, 1992). Similarly sized hotels tend to compete intensely
(Baum & Haveman, 1997).
In this study, the difference between the size of any two chains will be used to
assess extent of market similarity between those two chains. It is predicted, consistent
with the literature, that chains of similar size will have similar market profiles because
they have the means to imitate one another. If chains with a similar market profile do not
have similar size, there would be no evidence that chains imitate similarly sized chains in
choosing market locations. Therefore:
HI:
Difference in size has no effect on market similarity between pairs of chains.
31
International Experience
As firms increase their intemational experience, their confidence about
intemational markets also increases (Davidson, 1980). Prior investment in one foreign
country has a positive effect on investments in other foreign countries (Aharoni, 1966;
Johanson & Weidersheim-Paul, 1975; Davidson, 1980). The positive effect of prior
investment on future investments is caused by firms integrating knowledge of foreign
markets and operations over time. In other words, firms usually leam how to
continuously adapt to unfamiliar environments. As they do so, they increase their
confidence about operating in even more unfamiliar environments. There is definitely a
leaming curve or experience effect in the foreign direct investment process (Terpstra &
Yu, 1988). There is empirical support for the positive relationship between intemational
experience and extent of foreign direct investment (Ball & Tschoegl, 1982; Tschoegl,
1983).
In this study, the difference between the intemational experience of two chains
will be used to assess extent of market commonality between those two chains. It is
predicted that firms with similar amounts of international experience would have similar
knowledge and confidence about international expansion, and thus would have similar
market profiles (i.e., they would have hotels in similar sets of cities). If chains with a
similar market profile do not have similar intemational experience, there would be no
evidence that chains imitate chains with similar experience in choosing market locations.
Therefore:
32
H2:
Difference in intemational experience has no effect on market similarity between
pairs of chains.
Degree of Intemationalization
Intemationalization is the process of expanding outside of a firm's home base.
Degree of intemationalization is important because it is often viewed as a measure of
competitive success (Contractor & Kundu, 1995). It measures the relative importance of
intemational operations for the entire firm.
In this study, the difference between the degree of internationalization of pairs of
firms will be used to assess their extent of market similarity. It is predicted that firms
with a similar degree of intemationalization would place similar importance on
intemational operations, and thus would have similar market profiles (i.e., they would
have hotels in similar sets of cities). If chains with a similar market profile do not have a
similar degree of internationalization, there would be no evidence that chains imitate
chains based on importance of international operations, in choosing market locations.
Therefore:
H3:
Difference in degree of internationalization has no effect on market similarity
between pairs of chains.
There are certain other firm-specific factors, namely first mover behavior, leader
behavior and nationality, which might influence the market profile of a chain. These
factors are reviewed in the following section.
33
First Movers
Some firms strive to be industry leaders by being the first to respond to market
needs. Such firms can be called first movers or prospectors (Miles & Snow, 1978). The
advantage of being the leader in an industry is the monopolistic rent that can be eamed
due to imitators' lags in reacting to the action of the leading firm (Nelson & Winter,
1982).
While there are advantages to being a first mover, there are also risks. A firm that
chooses to act first in an industry does so because it perceives an unmet market need.
The risk lies in the possibility that the perceived market need does not actually exist, or
does not exist in the manner perceived by the firm. A firm has to incur great costs to be a
first mover as there is no-one to imitate and, therefore, the firm has to develop its own
strategies.
If there are chains which are first movers in this study, their market profiles may
look quite different from those of other chains because such chains are aggressive risktakers and would likely have distinct market profiles from non-first movers. In order to
account for first mover behavior which may influence market profile, the extent of
similarity in first mover behavior should be taken into account. Therefore:
H4:
Difference in first mover behavior has no effect on market similarity between
pairs of chains.
A somewhat related notion is that of a national leader. The first chain to go
abroad from a given nation is likely to have greater resources and be more aggressive than
34
other chains in its home country. It is hypothesized, therefore, that leaders would be
likely to have distinct market profiles from non-leaders. Therefore:
H5:
Difference in leader behavior has no effect on market similarity between pairs of
chains.
Nationality
The intemational management literature supports the notion that firms tend to
prefer those intemational markets most similar to their domestic market (Johanson &
Vahlne, 1977; Johanson & Weidersheim-Paul, 1975; Davidson, 1982) because of the ease
and compatibility of conducting business in those nations (Johanson & Vahlne, 1977).
For this reason, the market profiles of chains will likely be affected by the nationality of
the chain. There is empirical support for country effects in multinational firms
(Rosenzweig & Nohria, 1994). Strong differences were found between multinational
firms of different nationalities (Rosenzweig & Nohria, 1994).
It is predicted that chains in the same home nation are more likely to interact and
communicate with other chains from that region. For this reason, they will be able to
imitate the market profile of chains from the same nation. Therefore:
H6:
Difference in nationality has no effect on market similarity between pairs of
chains.
The next chapter describes the data, variables and methodology used in the study.
35
CHAPTER m
DATA AND METHODOLOGY
The source of data for this project is the Directory of Hotel and Motel
Management (also known as the Directory of Hotel and Motel Systems). The Directory is
published by the American Hotel Association Directory Association under the auspices of
the American Hotel and Motel Association. The Directory has been published since
1931. It includes information about worldwide hotel and motel chains (operating two or
more hotels, motels and/or resorts), referral groups and management companies. As this
is a study of the intemational hotel industry, only those chains with hotels in two or more
countries are included in the study. This definition of international hotels has been used
in prior empirical studies (Dunning & McQueen, 1981). The time frame of the study is
1984 to 1993.
Variables
Size (SIZE)
The standard measure of organization size in the hotel industry is the number of
rooms operated in each year (Wycoff & Sasser, 1981).
Intemational Experience (lEXP)
Amount of experience a chain has in foreign countries. This variable is measured
as the number of foreign countries within which a chain has operations.
36
Degree of Intemationalization (DOD
To represent importance of intemational operations, degree of intemationalization
is used. This variable is measured as the ratio of foreign rooms to domestic rooms
(Contractor & Kundu, 1995).
First movers (FSTMOV)
A first mover is the first chain in the intemational industry to enter any
intemational city (a city outside of its home nation) in a given year. The first time a new
intemational city is presented in the Directories, the chain entering that city is recorded.
Coding for first mover behavior begins with the second year of the study, 1985. This
variable is coded as a dummy variable ~ 1 if the chain is a first mover into at least one
new city in a given year, 0 otherwise.
Leader (LDR)
A leader is the first chain from a given nation to go intemational. That is, the first
domestic chain to establish a hotel outside of its home nation. Chains are therefore
compared to other chains from their home country. Coding for leader behavior begins
with the second year of the study, 1985. This variable is coded as a dummy variable ~ 1
if the chain is a leader in a given year, 0 otherwise.
37
Nationality (NAT)
The home country of each chain is recorded to take nationality into account in the
market choice decision.
The Methodology
The aim of the study is to distinguish between rivals. To do this, competition
should be analyzed from the viewpoint of a given firm, using a pair-wise analysis of every
firm it competes against. Network analysis allows one to conduct such a pair-wise
analysis. Network analysis examines the relationship among social entities (Wasserman
& Faust, 1994). Specifically, in a network, the major focus is the set of relations to and
from each actor in a system (Burt, 1976). In this case, the actors are intemational hotel
chains and the relations are the extent to which the chains have hotels in the same cities
as every other chain. The unit of analysis for network analysis rather than being the
individual (chain, in this case), is the collection of entities and the relationships among
them (Wasserman & Faust, 1994). When there are more than two actors in a network, the
nature and intensity of relationships between any two actors depends on the nature of
their relationships with all other actors in the network (Burt, 1976). Any two actors can
be said to be stmcturally equivalent when their relations with all other actors in the
network are identical (Burt, 1976). However, Burt (1976) points out that many times
there is little utility in such a strong measure of stmctural equivalence, since even a slight
difference between any two actors would deem them not stmcturally equivalent. Using a
more relaxed definition, stmcturally equivalent actors can be said to exhibit similarity in
38
their interactions with other actors in the network (Wasserman & Faust, 1994). In this
case, two chains would be said to be stmcturally equivalent if their pattems of market
location (i.e. choice of international cities) looked similar.
To further distinguish between rivals, the role of current characteristic similarity
between pairs of chains, on current and future rivalry between those pairs of chains is also
examined in the study. To carry out such an examination, a combination of regression
and lagged regression analysis was used in the study. To test the six hypotheses, the
combined results of the network analysis and regression analysis were used in t-tests.
The data were entered by year into an affiliation matrix. An affiliation matrix
represents the relationship of a set of actors with a set of events (Wasserman & Faust,
1994). The actors are chains and the event is presence in an intemational city. Each
chain is coded with a ' 1' if it has a hotel in a specific intemational city, and a '0' if it
does not, in a given year. This affiliation matrix therefore consists of columns of chains
and rows of cities. To give a simple example of what the affiliation matrix looks like, if
Marriott has hotels in London and Jakarta, and Sheraton has hotels in London and Paris
the affiliation matrix would be:
City
Marriott
Sheraton
Jakarta
1
0
London
1
1
Paris
0
1
The data (in the form of an affiliation matrix) were transformed into an
39
adjacency matrix using a program written by a leading network specialist at Camegie
Mellon University, Dr. David Krackhardt. An adjacency matrix is a symmetric chain by
chain matrix, reflecting the extent to which each chain is in the same cities as every other
chain. The adjacency matrix of the previous example would be:
Marriott
Sheraton
Marriott
2
1
Sheraton
1
2
The diagonal of the adjacency matrix shows the number of intemational cities
within which a given chain has hotels. In this example, Marriott and Sheraton have
hotels in two cities. Marriott and Sheraton have hotels in one shared city (London) which
is indicated by the off-diagonal entry of 1 above. If Marriott and Sheraton both had hotels
in eight of the same cities, the value in the off-diagonal would be eight. Note that the
adjacency matrix is symmetric. This is because whether we look from Marriott's
viewpoint or Sheraton's viewpoint, the number of cities within which they both have
hotels is still the same.
In the study, the number of columns (and rows) in the adjacency matrix
corresponds to the number of chains in the study in any given year. This number varies.
For example, in 1984, there were 71 chains; in 1985 there were 65 chains; in 1986 there
were 74 chains and so on up to 1993 where there were 104 chains.
The adjacency matrix for each year was used as the input data to compute
stmctural equivalence, using UCINETIV (Borgatti, Everett & Freeman, 1992), a network
analysis software package. Using this particular network software package, one has the
40
option of using different measures of profile similarity (measures of Euclidean distance
between the vectors or measures of Pearson correlation coefficients for every pair of
chains). If a researcher wants to measure similarity in overall pattem among actors,
rather than measure identity of ties between actors, it is recommended that the correlation
method be used (Wasserman & Faust, 1994). For this study, the adjacency matrix was
used as an input for the correlational measure of stmctural equivalence. The output was a
stmctural equivalence matrix for each year. The coefficients of the matrices are measures
of market similarity between pairs of chains. Since the correlational measure was used,
the stmctural equivalence matrix is essentially a correlation matrix. Therefore, for each
year of the study, each chain has a row of coefficients which reflect the extent to which it
has a similar market profile with every other chain. Coefficients can have values ranging
from -1 to 1. The stmctural equivalence matrices were analyzed using hierarchical cluster
analysis and regression analysis.
Cluster Analysis
The aim of the study is to distinguish between rivals. Rivals are determined
not only by market similarity, but also by similarity on other dimensions. Market
similarity is important because firms are likely to actively compete only if they share
many market locations (Chen, 1996). In addition to this, it is also necessary to assess the
extent to which pairs of firms have similar characteristics. If two firms operate within
similar market locations, but do not have any other similar characteristics, it is unlikely
that they will consider each other a major competitive threat (Chen, 1996).
41
Cluster analysis was used to identify those chains with similar market profiles (in
similar sets of cities). An assessment of the similarity of other characteristics (size,
intemational experience, degree of intemationalization, leader, first mover and
nationality) between pairs of chains was then conducted to distinguish between rivals.
Hierarchical cluster analysis groups firms into subsets such that firms within a
subset are relatively similar to each other (Wasserman & Faust, 1994). The aim of such
cluster analysis is to find firms which are approximately stmcturally equivalent.
Clustering is ideal for partitioning actors into stmcturally unique positions (Burt, 1976;
Wasserman & Faust, 1994). To reiterate, any two actors can be said to be stmcturally
equivalent when their relations with all other actors in the network are identical (Burt,
1976). However, many times there is little utility in such a strong measure of stmctural
equivalence, since even a slight difference between any two actors would deem them not
stmcturally equivalent (Burt, 1976). What is more useful, is a relaxed or approximate
measure of stmctural equivalence (Lorrain & White. 1971; Burt, 1976). Scott (1991) and
Wasserman and Faust (1994) suggest that a point be chosen which partitions the actors
into useful classes which make sense. There is, however, no established mle for
partitioning actors (Scott, 1991; Wasserman & Faust, 1994).
UCINET rV (Borgatti, Everett & Freeman, 1992) was used to cluster the data.
UCINET performed Johnson's hierarchical clustering on each of the ten (annual)
stmctural equivalence matrices. The algorithm finds a series of nested partitions of the
chains. The algorithm begins with an identity partition \s here each chain is treated as a
42
separate cluster. It then joins the most similar pairs of chains. The algorithm continues in
this way until all the chains have been joined into a single cluster (Borgatti, Everett &
Freeman, 1992).
The output of the hierarchical clustering is a dendogram which visually partitions
chains and also provides the level of similarity (a number between 0 and 1) between
chains. A dendogram has a series of 'skyscrapers' (peaks) and 'valleys'. Since there is no
established mle for partitioning actors, the researcher (using subject knowledge) must try
to assess which peaks are high enough to classify actors into one cluster and which
valleys are low enough to distinguish between clusters. To reiterate, a point should be
chosen which partitions the actors into useful classes which make sense (Scott, 1991;
Wasserman & Faust, 1994). To clarify how the clustering process works, an example is
given next.
An example of a dendogram can be seen in Figure 1. Figure 1 shows a series of
nested partitions which correspond to rows in the dendogram. Each row of the
dendogram indicates the degree of similarity among chains within clusters (Borgatti,
Everett & Freeman. 1992). Within a given row, an 'x' between two adjacent columns
indicates the level of similarity between the chains in the columns. For example, Kilbom
and Sara have a 0.649 level of similarity between them, indicated by the peak between
these chains. Hospitality Intemational (HOSINT), Rodeway (RODWY), Howard
Johnson (HOJO), Friendship (FRDSHP), and Travel Intemational (TRAVINT) can be
identified as one cluster. This is because the Valley' between the four chains in this
cluster and those adjacent to them is relatively low (i.e., 0.8157 on the left and 0.7343 on
43
the right). The four chains appear to be a 'skyscraper' compared to those chains around
them. The level of similarity among these four chains is 0.8894 at a minimum. However,
the level of similarity between Hospitality Intemational and Rodeway is 0.8992; between
Rodeway and Howard Johnson is .8894; between Howard Johnson and Friendship is
0.9287; and between Friendship and Travel Intemational is 0.9454. Now that one
possible cluster has been identified, one can see other possible 'skyscrapers' in Figure 1.
As there is no established mle for partitioning actors, an attempt was made to
separate chains into clear clusters and outliers. To check for reliability of the cluster
definitions, an independent rater was also asked to group chains into clear clusters and
outliers. Once the identities of the clusters and outliers were agreed upon, the groups
were then compared for similarities on the other dimensions of size, international
experience, degree of internationalization, leader, first mover and nationality.
Regression analysis was also used to examine the stmctural equivalence matrices.
The process is described below.
Regression Analysis
In an attempt to make sense of the rivalry in the international hotel industry, the
role of current characteristic similarity between pairs of chains, on current and future
rivalry between those pairs of chains was examined. Are there any similarities in
characteristics between pairs of chains which could explain current and future market
similarity (and thus rivalry) between those pairs of chains? What can account for market
similarity? What is it about these chains which can explain their similar choice of market
44
location? A combination of regression analysis and lagged regression analysis was used
to conduct this examination. One caveat is that the results of the regression anal\sis have
to be interpreted with caution, since the independence assumption of regression has been
violated. The violation occurs because the input (stmcmral equivalence) matrices reflect
the relationship of one chain with all other chains.
The stmctural equivalence matrix coefficients w ere used as the dependent variable
for each year. What is of relevance is the similarity between the dimensions of pairs of
chains in a given year. Therefore, for each of the \ ariables of size, intemational
experience, degree of intemationalization, first mo\ er, leader and nationality, the absolute
difference between pairs of chains was computed for ever) chain. For the size and
intemational experience variables, since there was such a large variance, a handful of
chains were found to be unduly influential. To correct for this problem, log transformations were used for both \ ariables, prior to computing the absolute difference. A log
transformation is the inverse of certain exponential functions. Log transformations are
typical for this kind of measurement (Christensen & Montgomery, 1981). The results are
not affected by log transformations, except to remove unduly influential chains from
skewing the results.
The first mover, leader and nationality variables were coded with a ' 1' if there
was a difference between a given pair of chains, and a '0' otherwise. The differences
between a given chain and every other chain for each of the \'ariables were then used as
the independent variables in the regression analysis. Therefore, each chain had its own
dataset for each year. Each of these datasets has the first column consisting of that chain's
45
stmctural equivalence coefficients with all other chains: a column for difference in size
consisting of the absolute difference in size between that chain and all other chains (after
the log transformations); and likewise columns for each of the other \ ariables of
intemational experience, degree of intemationalization, leader, first mover and
nationality. Multiple regression analysis was used to examine the extent to which market
profile similarity could be explained by the difference in the other variables. A separate
regression was mn for each chain in each year.
The role of current characteristic similarity between pairs of chains, on fumre
rivalry between those pairs of chains was examined using lagged regression analysis. In
other words, if two chains have similar characteristics currently, does this similarity help
to explain the market similarity and thus extent of rivalr) of the two chains in the future?
Since there is still a violation of the independence assumption of regression, the results of
the lagged regression have to be interpreted with caution. A three-year lag was used
because studies have shown that it takes three years to imitate intemational market entry
strategies (Flowers, 1976). A separate lagged regression equation was mn for each chain
occurring both in a given year and three years later. For example, the first set of lagged
regression equations used stmctural equivalence matrix coefficients from 1987 as the
dependent variable, and the 1984 difference of the other variables (size, intemational
experience, degree of intemationalization, leader, first mover and nationality) as the
independent variables.
The hypotheses examine the extent to u hich the variables used in the study
explain market similarity. T-test analysis \\ as used to test hypotheses 1 through 6. For
46
each chain in each year, the regression coefficients were used as the input for the t-tests.
For example, the input dataset for 1984 consists of the regression coefficients of each
chain for each of the variables ~ a chain by variable matrix. The PROC UNIVARIATE'
option of SAS statistical package was used to conduct the t-test. Descriptive statistics
were computed by the univariate procedure.
The following chapter includes the results and discussion of results.
47
CHAPTER IV
RESULTS
The results of the regression analysis will be presented first, followed by the
lagged regression analysis results, t-test results and cluster analysis results. All results are
presented annually.
Regression Analysis Results
1984 Results
The results of the regression analyses for 1984 are presented in Table 1. Separate
regression analyses were performed for each chain, for each year.
Market similarity is well explained for Hilton (not Hilton Intemational), Hyatt
Intemational, Intercontinental, Le Meridien, Ramada, Sheraton and SRS. The regression
coefficients of these chains are listed in Table 2. The sign and magnitude of the
coefficients of the seven chains does not appear to radically differ from all other chains.
As can be seen, the R-square values within this group range from 0.5731 - 0.669. The
magnitude of each of the four independent variable coefficients is about the same for the
seven chains. What is interesting is that the sign of the coefficients is the same for all
seven chains for only one variable ~ the intemational experience variable. A one unit
increase in the difference of logs for intemational experience will decrease market
similarity by approximately 0.15 among this group, holding all other variables fixed. To
allow for further examination of the similarity of these chains, the characteristics of each
of these chains is outlined in Table 3.
48
The size of the seven chains varies from 35,306 rooms to 129,623 rooms with the
exception of Hyatt Intemational and Le Meridien. The seven chains each have a lot of
intemational experience, ranging from 28 countries to 50 countries, with the exception of
Ramada. Degree of intemationalization in the group varies from 0.88 - 0.98, with the
exception of Sheraton and Ramada. Home countries include the U.S., France and
Germany. Ramada is a mid-priced chain. All the other chains are luxury chains.
These seven chains are multinational chains (i.e. intemational experience in many
countries) with a high degree of intemationalization and approximately similar types of
hotels. In general, this group consists of large, American and European multinational
luxury chains for whom intemational operations are fairly important.
It is interesting that not all chains with intemational experience in many countries
have well-explained market similarity. For example, Best Westem has intemational
experience in 22 countries and Holiday Inn has intemational experience in 49 countries,
but these chains have relatively lower R-squared values. The difference between the
chains with well-explained market similarity and Best Westem can be explained by a
combination of factors. Specifically, Best Westem is a luxury-budget chain, while the
others are luxury chains; Best Westem also has a relatively low degree of
intemationalization (0.2947). The difference between the chains with well-explained
market similarity and Holiday Inn can also be explained by a combination of factors.
Specifically, Holiday Inn is a mid-priced chain while the others are luxury chains, and
Holiday Inn also has a relatively low degree of intemationalization (0.1366). The
implication of the findings is that the variables used in this particular study may not
explain market similarity well, unless a chain is a large, multinational chain.
49
1985Resohs
The results of tbe 1985 regression analyses are sbovkn in Table ^. Market
siniilarity is well predicted only for Hiltoa biteniationaL H> art (DOt H>'an iaiCTnational).
ImefCoatiDeataL, Le Meridien. Sbeiatoo, SRS ar^d Westin. Theregressioncoefficients of
these ciiains are piesented in Table 5. The sign of the coefficients is the same among the
chains for the inteniatioDal expenenoe and leader vanables. A one unit increase in the
difTefCOoe of logs for international expenence will decrease market similarity by
qjpiDximatety 0.1. h Iding all otfjer vanables fixed, ff there is a difference in leadership
behavior, it woold deoease market similarit> by approximately 0.25. holding all other
variables fixedThe cfaaractehstics of these chains are shown in Table 6. The size in the group
varies from 32.447 rooms to 139,605 rooms v. iin the exc^Jtioo of Hyatt and Le Meridien.
Each of the ciiains has cxpeneace in at least 26 countries and in as many as 61 countries,
with the exception of Westin. Intonatioiial opraations are imp(xtant to all chains in the
group as indicated by the degree of intemationalization which ranges from 0.83 - 0.97.
with the exception of WestiiL There are no leaders among the chains, and the only first
niover is Sheraton. AD the chains are luxury chains. Home countries include the U.S.,
France and Germany. This groiq) consists of large, American and European multinational
hixmy chains withrelativelyhigh importance placed on intCToational o>peraiioos.
1986 Results
The results of the 1986 regression analyses are shown in Table 7. Market
similarity is well predicted for Hilton International, Hyatt International, Intercontinental
and SRS. The regression coefficients of these chains are presented in Table 8.
The sign of the coefficients is the same for the four chains for the intemational
experieiice, leader andfirstmover variables. A one unit increase in the difference of logs
50
for intemational experience will decrease market similarity by approximately 0.18,
holding all other variables fixed. If there is a difference in leadership behavior, it would
decrease market similarity by approximately 0.1, holding all other variables fixed. If
there is a difference in first mover behavior, it would decrease market similarity by
approximately 0.03, holding all other variables fixed.
The characteristics of these chains are shown in Table 9. The chains range in size
from 17,604 rooms to 46,008 rooms. They all have experience in at least 26 countries,
and in as many as 47 countries. They all have relatively high importance placed on
intemational operations ~ degree of intemationalization varies from 0.87 - 0.97. The
chains are all first movers, meaning they were the first intemational chain to enter at least
one intemational city in 1986. Home countries include the U.S. and Germany. All four
chains are luxury chains.
This group consists of multinational U.S. and German luxury chains, all
aggressive enough to be firstmovers.
It is not surprising that such chains with
experience in so many different countries would be comfortable with the risks of being
the first intemational chain to enter a given intemational city. It is precisely because they
have accumulated significant intemational experience that they are comfortable entering a
new foreign environment.
1987 Results
The results of the 1987 regression analyses are shown in Table 10. Market
similarity is well explained for Hyatt Intemational, Intercontinental, Le Meridien,
Ramada, Sheraton and SRS. The regression coefficients of these chains are presented in
Table 11. The sign of the coefficients is the same for the chains for the intemational
experience, leader and first mover variables. A one unit increase in the difference of logs
for intemational experience will decrease market similarity by approximately 0.17,
51
holding all other variables fixed. If there is a difference in leadership behavior, it would
decrease market similarity by approximately 0.05, holding all other variables fixed. If
there is a difference in first mover behavior, it would decrease market similarity by
approximately 0.02, holding all other variables fixed. With the exception of Le Meridien,
all the chains have the same sign for the size coefficient. A one unit increase in the
difference of logs for size will decrease market similarity by approximately 0.02, holding
all other variables fixed.
The characteristics of these chains are presented in Table 12. The chains vary in
size from 22,077 rooms to 143,557 rooms. International experience in the group varies
from 20 countries to 62 countries. The relative importance of intemational operations
varies widely in this group from 0.1384 - 0.9286. All six chains are first movers. They
are from the U.S., France or Germany. They are all luxury chains with the exception of
Ramada.
This group consists mostly of large, American and European multinational luxury
chains. Ramada has the lowest degree of intemationalization of the six chains (0.1384)
and is also the only mid-priced chain. Possible reasons for Ramada's well-explained
market similarity is its large size (97,903 rooms), high international experience (20
countries) and first mover behavior.
All chains in this group are firstmovers. The chains have experience in many
countries and, therefore, would likely be comfortable being the first foreign chain to enter
a given intemational city.
1988 Results
The results of the 1988 regression analyses are shown in Table 13. Market
similarity is well explained for Frantel, Golden Tulip, Hilton Intemational, Holiday Inn,
52
Hyatt Intemational, Intercontinental, Le Meridien, Novotel, Ramada, Sheraton, SRS,
Swiss Intemational and Tmsthouse. The regression coefficients of these chains are
presented in Table 14. With the exception of Swiss Intemational, the sign of the size
coefficients is the same for the chains. A one unit increase in the difference of logs for
size will decrease market similarity by approximately 0.04, holding all other variables
fixed. The sign of the coefficients is the same for the chains for the international
experience and leader variables. A one unit increase in the difference of logs for
intemational experience will decrease market similarity by approximately 0.17, holding
all other variables fixed. If there is a difference in leadership behavior, it would decrease
market similarity by approximately 0.1, holding all other variables fixed.
Golden Tulip is the only Dutch chain in the industry in 1988. The regression
equation for Golden Tulip could not include nationality since the variable is computed
using the difference between this chain's nationality and that of all other chains in the
year. Since Golden Tulip is different from all other chains in this year, nationality could
not be regressed against market similarity for this chain. In the same manner, SRS is the
only German chain in 1988, and therefore nationality could not be regressed against
market similarity for the chain.
The characteristics of these chains are shown in Table 15. Size in the group varies
from 19,211 rooms to 318,574 rooms with the exception of Swiss Intemational. The
chains have experience in at least 18 countries and in as many as 63 countries. Degree of
intemationalization is over 0.6 except for Golden Tulip, Hilton International and Ramada.
For most of the chains, degree of internationalization is over 0.8. Golden Tulip, Holiday
53
Inn, Ramada, Sheraton, SRS, Swiss Intemational and Tmsthouse are all first movers.
Home countries include France, the Netherlands, the U.S., Germany, Switzerland and the
U.K.. Holiday Inn and Ramada are mid-priced hotel chains. All the other chains of
known type are luxury hotels. This group consists of mostly large, American and
European multinational luxury chains.
1989 Results
The results of the 1989 regression analyses are shown in Table 16. Market
similarity is well explained for Frantel, Golden Tulip, Hilton Intemational, Holiday Inn,
Intercontinental, Le Meridien, Novotel, Ramada, SAS, Sheraton, Sofitel, SRS, Swiss
Intemational and Tmsthouse PLC.
The regression coefficients for these chains are presented in Table 17. The sign of
the coefficients is the same for all chains for the intemational experience variable. A one
unit increase in the difference of logs for intemational experience will decrease market
similarity by approximately 0.25, holding all other variables fixed.
The characteristics of these chains are shown in Table 18. Size of the group
varies from 19,977 rooms to 318,574 rooms with the exception of SAS, Sofitel and Swiss
Intemational (with fewer than 10,000 rooms). Intemational experience varies between 18
and 61 countries. Degree of intemationalization is over 0.6 except for Holiday Inn,
Ramada, SAS, Sheraton and Tmsthouse PLC. Frantel, Golden Tulip, Le Meridien,
Ramada, Sheraton, Sofitel, SRS and Tmsthouse PLC are first movers. Home countries
represented include France, the Netherlands, the U.S., Norway, Germany, Switzerland
54
and the U.K.. Holiday Inn and Ramada are mid-priced hotel chains. All the other chains
of known type are luxury hotels. This group consists mostly of American and European
multinational luxury chains.
1990 Results
The results of the 1990 regression analyses are shown in Table 19. Market
similarity is well explained for Frantel, Golden Tulip, Hilton Intemational, Holiday Inn,
Hyatt Intemational, Intercontinental, Le Meridien, Ramada, Sheraton, SRS and Swiss
Intemational. The regression coefficients of these chains are presented in Table 20. The
sign of the coefficients is the same for this group for the intemational experience and
leader variables. A one unit increase in the difference of logs for intemational experience
will decrease market similarity by approximately 0.2, holding all other variables fixed. If
there is a difference in leadership behavior, it would decrease market similarity by
approximately 0.26, holding all other variables fixed. The sign of the coefficients of the
degree of intemationalization variable is the same for all chains except Frantel and
Ramada. With the exception of these two chains, a one unit increase in difference of
degree of intemationalization would decrease market similarity by approximately 0.02,
holding all other variables fixed. The sign of the coefficients of the nationality variable is
the same for all chains except Golden Tulip, Hyatt Intemational and Intercontinental.
With the exception of these three chains, if there is a difference in nationality, it would
decrease market similarity by approximately 0.004, holding all other variables fixed.
Golden Tulip is the only Dutch chain in the industry in 1990. The regression equation for
55
Golden Tulip could not include nationality since the variable is computed using the
difference between this chain's nationality and that of all other chains in the year. Since
Golden Tulip is different from all other chains in this year, nationality could not be
regressed against market similarity for this chain.
The characteristics of these chains are shown in Table 21. The size in the group
varies from 7,102 rooms to 314,261 rooms. Intemational experience varies from 18 to 67
countries. The importance of intemational operations is 0.6 at a minimum except for
Holiday Inn and Ramada. First movers in the group are Golden Tulip, Holiday Inn,
Ramada, Sheraton, and SRS. Home nations represented are France, the Netheriands, the
U.S., Germany and Switzerland. The type of hotel is unknown for Frantel and Swiss
Intemational. The rest of the chains are luxury chains with exception of Holiday Inn and
Ramada, which are mid-priced hotels. This group consists mostly of American and
European multinational luxury chains.
1991 Results
The results of the 1991 regression analyses are shown in Table 22. Market
similarity is well explained for Frantel, Hilton International, Holiday Inn, Hyatt
Intemational, Le Meridien, Marriott, Nikko, Preferred, Sheraton, Sofitel, SRS, Swiss
Intemational and Tmsthouse PLC. The regression coefficients of these chains are
presented in Table 23. The sign of the coefficients is the same for all chains for the size,
intemational experience and leader variables. A one unit increase in the difference of
logs for size will decrease market similarity by approximately 0.2, holding all other
56
variables fixed. A one unit increase in the difference of logs for intemational experience
will decrease market similarity by approximately 0.27, holding all other variables fixed.
If there is a difference in leadership behavior, it would increase market similarity by
approximately 0.15, holding all other variables fixed. The characteristics of these chains
are shown in Table 24. The size of the group varies between 10,286 rooms and 319,135
rooms. International experience varies from 10 to 55 countries. Degree of
intemationalization varies upwards of 0.6, except for Holiday Inn, Marriott, Preferred,
Sheraton and Tmsthouse PLC. The only first mover in the group is Hilton Intemational.
Home nations include France, the U.S., Japan, Germany, Switzerland and the U.K.. Type
of hotel is unknown for Frantel, Nikko, Swiss International and Tmsthouse PLC. All the
other chains, with the exception of Holiday Inn (mid-priced) are luxury chains. This
group consists of American, European and Japanese multinational chains with mostly
luxury hotels.
1992 Results
The results of the 1992 regression analyses are shown in Table 25. Market
similarity is well explained for Choice, Frantel, Golden Tulip, Hilton Intemational,
Holiday Inn, Hyatt Intemational, Intercontinental, Le Meridien, Marriott, Ramada
Intemational (not Ramada), Sheraton and SRS. The regression coefficients of these
chains are presented in Table 26. The sign of the coefficients is the same for all chains
for the size and intemational experience variables. A one unit increase in the difference
of logs for size will decrease market similarity by approximately 0.06, holding all other
57
variables fixed. A one unit increase in the difference of logs for intemational experience
will decrease market similarity by approximately 0.2, holding all other variables fixed.
The sign of the coefficients of the nationality variable is the same for all chains except
Golden Tulip, Ramada Intemational and SRS. For the rest of the chains, if there is a
difference in nationality, it will increase market similarity by approximately 0.01, holding
all other variables fixed. Golden Tulip is the only Dutch chain in the industry in 1992.
The regression equation for Golden Tulip could not include nationality since the variable
is computed using the difference between this chain's nationality and that of all other
chains in the year. Since Golden Tulip is different from all other chains in this year,
nationality could not be regressed against market similarity for this chain.
The characteristics of these chains are shown in Table 27. Size varies from
21,882 rooms to 322,527 rooms with the exception of Frantel and Le Meridien. The
chains have experience in at least 16 countries and in as many as 59 countries. Degree of
intemationalization varies from 0.09 to 1.0. Choice, Golden Tulip, Intercontinental,
Sheraton and SRS are first movers. Home countries include the U.S., France,
Netheriands, the U.K., Hong Kong and Germany. Type of hotel is unknown for Frantel.
The rest of the chains are luxury chains, with the exception of Holiday Inn and Ramada
Intemational (mid-priced chains) and Choice which has many different types of hotels.
This group consists of large American, European and Hong Kong multinational chains
with mostly luxury hotels.
58
1993 Results
The results of the 1993 regression analyses are shown in Table 28. Market
similarity is well explained for Golden Tulip, Hilton Intemational, Holiday Inn, Hyatt
Intemational, Intercontinental, Le Meridien, Marriott, Preferred, Ramada Intemational
(not Ramada), Sheraton, Southem Pacific, SRS, Swiss Intemational and Tmsthouse PLC.
The regression coefficients of these chains are presented in Table 29. The sign of the
coefficients is the same for all chains for the size and international experience variables.
A one unit increase in the difference of logs for size will decrease market similarity by
approximately 0.05, holding all other variables fixed. A one unit increase in the
difference of logs for intemational experience will decrease market similarity by
approximately 0.2, holding all other variables fixed. With the exception of Marriott and
Southem Pacific, the sign of the coefficients for degree of intemationalization is the same
for all chains. A one unit increase in the difference of degree of intemationalization will
decrease market similarity by approximately 0.09, holding all other variables fixed. With
the exception of Hilton International, Marriott and Preferred, the sign of the coefficients
for the first mover variable is the same for the chains. If there is a difference in first
mover behavior, it would increase market similarity by approximately 0.06, holding all
other variables fixed.
The characteristics of these chains are shown in Table 30. The size in the group
varies from 11,613 rooms to 349,226 rooms. Intemational experience varies from 16
countries to 53 countries. Degree of intemationalization varies from 0.09 to 1.0, but most
of the chains have degree of intemationalization over 0.7. Hilton Intemational is the only
59
first mover in the group. The home countries include the Netherlands, U.K., the U.S.,
France, Hong Kong, Australia, Germany and Switzerland. Type of hotel is unknown for
Southem Pacific, Swiss Intemational and Tmsthouse PLC. All the other chains, with the
exception of Holiday Inn and Ramada Intemational (which are both mid-priced) are
luxury chains. This group consists of American, European, Australian and Hong Kong
multinational chains with mostly luxury hotels.
Summary of Regression Analysis Results
The results of the ten years of analyses indicate that the variables used in this
study explain what drives market similarity only for certain kinds of chains, namely large,
multinational luxury chains from industrialized nations.
T-Test Results
The results of the hypotheses test are presented in Table 31. The results are
presented annually. To reiterate, there are no values for the leader and first mover
variables in 1984 since that is the first year of the study and therefore there was no
previous year to serve as a comparison. In the last two years of the study (1992 and 1993)
no chains were leaders and therefore, no hypothesis testing was conducted for this
variable for these years. The hypotheses, results of the tests of the hypotheses and
implications follow.
HI:
Difference in size has no effect on market similarity between pairs of
chains.
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The results are mixed for this hypothesis. In all years except 1987, we fail to reject this
hypothesis. However, in 1987 the hypothesis is rejected (p=0.0003). It is not clear why
1987 yields different results from all other years in the study.
H2:
Difference in intemational experience has no effect on market similarity
between pairs of chains.
The results are also mixed for this hypothesis. In all years except 1987 and 1989, we fail
to reject this hypothesis. In 1987 the hypothesis is rejected (p<0.0001) and in 1989, the
hypothesis is rejected (p=0.003). It is not clear why 1987 or 1989 yield different results
from all other years in the study.
H3:
Difference in degree of intemationalization has no effect on market
similarity between pairs of chains.
The results are, once again, mixed for this hypothesis. In all years except 1984 and 1993,
we fail to reject this hypothesis. In 1984 the hypothesis is rejected (p=0.008), and in 1993
the hypothesis is rejected (p=0.0017). Once again, the reason for the mixed results is
unclear.
H4:
Difference in first mover behavior has no effect on market similarity
between pairs of chains.
In all except three of the years we reject the hypothesis at a significance level ranging
from 0.04 to 0.0001. We fail to reject the hypothesis in 1985, 1986 and 1988, without
any clear reason seen for the difference in results. The implication is that first mover
behavior may have some effect on market similarity.
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H5:
Difference in leader behavior has no effect on market similarity between
pairs of chains.
In all years, we reject the hypothesis at a significance level ranging from 0.0243 to
0.0001. The implication is that leader behavior may have some effect on market
similarity.
H6:
Difference in nationality has no effect on market similarity between pairs
of chains.
This hypothesis is rejected in all years, with significance levels ranging from 0.003 to
0.0001. The implication is that nationality may have some effect on market similarity.
To summarize, we fail to reject the hypotheses relating to size, intemational
experience and degree of intemationalization. The implication is that these variable may
not have any significant effect on market similarity. We reject the hypotheses relating to
first mover behavior, leader behavior and nationality. The implication is that that these
variables may have some effect on market similarity and thus rivalry. When trying to
distinguish between rivals, the t-test results show some support for including first mover
behavior, leader behavior and nationality constmcts.
Descriptive Statistics
The descriptive statistics for each of the variables, by year, is provided in Table
32. There is no clear pattem to the mean coefficients for the size variable. If there is a
difference in size between two chains, the results do not indicate a pattem in the extent of
market similarity between the two chains. The intemational experience variable has a
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negative mean coefficient in nine of the ten years. If there is a difference in intemational
experience between two chains, the market similarity between the two will be less. The
degree of intemationalization variable has a negative mean coefficient in eight of the ten
years. If there is a difference in intemationalization between two chains, the market
similarity between the two will be less. The first mover variable has a positive mean
coefficient in all of the years. If there is a difference in first mover behavior between two
chains, the market similarity between the two will be more. The leader variable has a
negative mean coefficient in six of the eight years possible. If there is a difference in
leader behavior between two chains, the market similarity between the two will be less.
The nationality variable has a negative mean coefficient in all ten years. If there is a
difference in the nationality of two chains, the market similarity between the two will be
less.
Lagged Regression Results
1984 Results
The results of the 1984 regression analyses are shown in Table 33. Separate
regression analyses were performed for each chain appearing both in 1984 and 1987. The
dependent variables were the 1987 market similarity coefficients obtained from the 1987
stmctural equivalence matrix. The independent variables were the 1984 differences
between the focal chain and all other chains for size, intemational experience, degree of
intemationalization, and nationality.
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Only Hilton, Hyatt Intemational, Intercontinental, Sheraton and SRS have market
similarity which is well-predicted with the independent variables. The regression
coefficients of these chains are presented in Table 34. The sign of the coefficients for
intemational experience is negative for all chains. A one unit increase in difference of
logs for intemational experience will decrease market similarity by approximately 0.18,
holding all other variables fixed.
The characteristics of these chains were shown previously in Table 2. The size of
the chains varies from 35,306 rooms to 129,623 rooms with the exception of Hyatt
Intemational. The chains each have a lot of intemational experience ranging from 28
countries to 50 countries. Degree of intemationalization in the group varies from 0.91 to
0.98, with the exception of Sheraton. Home countries include the U.S. and Germany. All
the chains are luxury chains.
These are multinational chains (i.e. international experience in many countries)
with a high degree of intemationalization and similar types of hotels. In general, this
group consists of large, American and European multinational luxury chains for whom
intemational operations are fairly important. The implication of the findings is that the
variables used in this particular study may not explain future market similarity well,
unless a chain is a large, multinational chain.
1985 Results
The results of the 1985 regression analyses are shown in Table 35. Separate
regression analyses were performed for each chain appearing both in 1985 and 1988. The
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dependent variables were the 1988 market similarity coefficients obtained from the 1988
stmctural equivalence matrix. The independent variables were the 1985 differences
between the focal chain and all other chains for size, intemational experience, degree of
intemationalization, leader, first mover and nationality.
Market similarity is well-predicted only for Hilton Intemational, Intercontinental,
SRS and Swissotel. The regression coefficients of these chains are presented in Table 36.
Swissotel is the only leader in 1985. Since Swissotel is different from all other chains in
this dataset, leader behavior could not be regressed against market similarity for this
chain. Swissotel and SRS are the only Swiss and German chains (respectively) in the
1985 lagged dataset. Since Swissotel and SRS are different from all other chains in this
dataset, nationality could not be regressed against market similarity for these two chains.
The sign of the coefficients for degree of intemationalization is the same for all
chains - negative. A one unit increase in difference of degree of intemationalization will
decrease market similarity by approximately 0.02, holding all other variables fixed. The
sign of the coefficients for the first mover variable is the same for the chains - also
negative. If there is a difference in first mover behavior, it would decrease market
similarity by approximately 0.03, holding all other variables fixed.
The characteristics of the chains are presented in Table 37. The size of the chains
varies from 1,809 rooms to 46,008 rooms. With the exception of Swissotel, the chains
each have a lot of intemational experience - the range is from 42 countries to 47
countries. Degree of intemationalization in the group varies from 0.63 - 0.94. Home
countries include the U.S., Germany and Switzerland. There are no first movers in the
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group. The type of hotel for Swissotel is unknown. All the other chains are luxury
chains. This group consists mostly of multinational, luxury chains from industrialized
Westem countries, with high relative importance placed on intemational operations.
1986 Results
The results of the 1986 regression analyses are shown in Table 38. Separate
regression analyses were performed for each chain appearing both in 1986 and 1989. The
dependent variables were the 1989 market similarity coefficients obtained from the 1989
stmctural equivalence matrix. The independent variables were the 1986 differences
between the focal chain and all other chains for size, intemational experience, degree of
intemationalization, leader, first mover and nationality.
Market similarity is well predicted for Days Inn, Econolodge, Hilton Intemational,
Intercontinental, Meridien, Sheraton and SRS. The regression coefficients of these
chains are presented in Table 39. The sign of the coefficients for intemational experience
is the same for all chains - negative. A one unit increase in difference of logs for
intemational experience will decrease market similarity by approximately 0.15, holding
all other variables fixed. The sign of the coefficients for the first mover variable is the
same for the chains. If there is a difference in first mover behavior, it would increase
market similarity by approximately 0.04, holding all other variables fixed.
The characteristics of these chains are presented in Table 40. The size of the
chains varies from 19,650 rooms to 145,183 rooms. With the exception of Days Inn and
Econolodge, the chains have intemational experience in the range of 31 countries to 61
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countries. Degree of intemationalization in the group varies from 0.002 - 0.94. Days Inn
and Econolodge are the only non-first movers in the group. Home countries include the
U.S., France and Germany. All the chains have luxury hotels except for Days Inn and
Econolodge, which have economy hotels. This group is not easy to classify. Days Inn
and Econolodge appear to look different from other chains in the group on all dimensions
except size and nationality. This group appears to be one of mostly large, multinational
luxury chains.
1987 Results
The results of the 1987 regression analyses are shown in Table 41. Separate
regression analyses were performed for each chain appearing both in 1987 and 1990. The
dependent variables were the 1990 market similarity coefficients obtained from the 1990
stmctural equivalence matrix. The independent variables were the 1987 differences
between the focal chain and all other chains for size, intemational experience, degree of
intemationalization, leader, first mover and nationality.
Market similarity is well predicted for Hilton Intemational, Intercontinental,
Meridien, Sheraton and SRS. The regression coefficients of these chains are presented in
Table 42. The characteristics of these chains were presented in Table 12. The sign of the
coefficients for intemational experience is the same for all chains. A one unit increase in
difference of logs for intemational experience will decrease market similarity by
approximately 0.2, holding all other variables fixed. The sign of the coefficients for the
first mover variable is the same for the chains. If there is a difference in first mover
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behavior, it would increase market similarity by approximately 0.04, holding all other
variables fixed.
The chains vary in size from 22,077 rooms to 143,557 rooms. Intemational
experience in the group varies from 28 countries to 62 countries. The relative importance
of intemational operations varies widely in this group from 0.3 - 0.9286. All five chains
are first movers. They are from the U.S., France or Germany. They are all luxury chains.
This group consists of large, American and European multinational, luxury chains.
1988 Results
The results of the 1988 regression analyses are shown in Table 43. Separate
regression analyses were performed for each chain appearing both in 1988 and 1991. The
dependent variables were the 1991 market similarity coefficients obtained from the 1991
stmctural equivalence matrix. The independent variables were the 1988 differences
between the focal chain and all other chains for size, intemational experience, degree of
intemationalization, leader, first mover and nationality.
Market similarity is well predicted for Hilton Intemational, Sheraton, SRS and
Westin. The regression coefficients of these chains are presented in Table 44. The sign
of the coefficients for size is the same for all chains. A one unit increase in difference of
logs for size will decrease market similarity by approximately 0.08, holding all other
variables fixed. The sign of the coefficients for intemational experience is the same for
all chains. A one unit increase in difference of logs for intemational experience will
decrease market similarity by approximately 0.15, holding all other variables fixed.
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The characteristics of these chains are presented in Table 45. The chains vary in
size from 40,048 rooms to 352,123 rooms. Intemational experience in the group varies
from 10 countries to 61 countries. The relative importance of intemational operations
varies widely in this group from 0.11 - 0.93. Sheraton and SRS are first movers. The
chains are from the U.S. or Germany. They are all luxury chains. This group consists of
American and European multinational, luxury chains.
1989 Results
The results of the 1989 regression analyses are shown in Table 46. Separate
regression analyses were performed for each chain appearing both in 1989 and 1992. The
dependent variables were the 1992 market similarity coefficients obtained from the 1992
stmctural equivalence matrix. The independent variables were the 1989 differences
between the focal chain and all other chains for size, international experience, degree of
intemationalization, leader, first mover and nationality.
Market similarity is well predicted for Golden Tulip, Hilton Intemational, Holiday
Inn, Intercontinental, Sheraton, and SRS. The regression coefficients of these chains are
presented in Table 47. The characteristics of these chains were presented in Table 18.
The sign of the coefficients for size is the same for all chains. A one unit increase in
difference of logs for size will decrease market similarity by approximately 0.11, holding
all other variables fixed. The sign of the coefficients for intemational experience is the
same for all chains. A one unit increase in difference of logs for intemational experience
will decrease market similarity by approximately 0.15, holding all other variables fixed.
69
Size of the group varies from 41,356 rooms to 318,574 rooms. Intemational
experience varies between 42 and 61 countries. Degree of internationalization is over 0.6
except for Holiday Inn and Sheraton. Golden Tulip, Sheraton and SRS are first movers.
Home countries represented include the Netheriands, the U.S. and Germany. Holiday Inn
is a mid-priced hotel chains. All the other chains are luxury hotels. This group consists
of large, American and European multinational luxury chains.
1990 Results
The results of the 1990 regression analyses are shown in Table 48. Separate
regression analyses were performed for each chain appearing both in 1990 and 1993. The
dependent variables were the 1993 market similarity coefficients obtained from the 1993
stmctural equivalence matrix. The independent variables were the 1990 differences
between the focal chain and all other chains for size, intemational experience, degree of
intemationalization, leader, first mover and nationality.
Market similarity is well predicted for Golden Tulip, Hilton Intemational,
Intercontinental, Meridien and SRS. The regression coefficients of these chains are
presented in Table 49. The characteristics of these chains were presented in Table 21.
The sign of the coefficients for size is the same for all chains. A one unit increase in
difference of logs for size will decrease market similarity by approximately 0.05, holding
all other variables fixed. The sign of the coefficients for intemational experience is the
same for all chains. A one unit increase in difference of logs for intemational experience
will decrease market similarity by approximately 0.18, holding all other variables fixed.
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The sign of the coefficients for degree of internationalization is the same for all chains. A
one unit increase in difference of degree of intemationalization will decrease market
similarity by approximately 0.03, holding all other variables fixed.
The size in the group varies from 18,693 rooms to 66,850 rooms. Intemational
experience varies from 34 to 67 countries. The importance of intemational operations is
0.8 at a minimum. First movers in the group are Golden Tulip and SRS. Home nations
represented are France, the Netheriands, the U.S. and Germany. The chains are all luxury
chains. This group consists of American and European multinational luxury chains.
Summary of Lagged Regression Analysis Results
The results of the seven years of lagged analyses indicate that the variables used in
this study explain what drives market similarity three years into the future only for certain
kinds of chains, namely chains which are multinational, American and European luxury
chains. The results of the lagged regression analyses thus further confirm the results of
the regression analyses.
Cluster Analysis Results
The results of the cluster analysis are presented in figures in the form of
dendograms. Characteristics of the chains in each cluster for each year are then presented
in the form of tables. For these tables, up to the first seven columns show the previously
obtained regression coefficients for each given chain. Some chains had missing data for
size and degree of intemationalization. For these chains, regression analyses could not be
71
performed due to the missing data. For these chains, therefore, there are no regression
coefficients.
The characteristics of each given chain are also included in each table. To
reiterate, chain size (SIZE) is number of total rooms; intemational experience (lEXP) is
number of countries within which a firm has operations; degree of intemationalization
(DOI) is ratio of foreign rooms to total number of rooms; nationality (NAT) is home
country affiliation; and type of hotel (TYPE) is price and facility level of most of the
chain's hotels. Type of hotel can vary from economy, to luxury budget to mid-priced to
luxury. Where information about type of hotel was unavailable, a question mark is used
in the tables.
Each cluster is made up of chains with a relatively high degree of market
similarity. Each cluster is examined to see what could be driving this market similarity.
Outliers in each year are also examined to see if the source of their unique market profile
can be uncovered.
1984 Results
The results of the 1984 cluster analysis are presented in Figure 1. The
characteristics of each cluster are presented in Table 50.
Cluster A : Hospitality Intemational. Rodeway, Howard Johnson, Friendship, and
Travel Intemational. The regression coefficients for each variable appear to be similar in
sign and magnitude for all chains. The extent to which market similarity is explained by
the regression model also appears similar within the group. The size in the group varies
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from 17,034 rooms to 56,692 rooms. This cluster has members with limited intemational
experience in 1 or 2 countries only, and little importance is placed on intemational
operations (0.02 - 0.08). All the chains are from the U.S., and those with known types are
luxury-budget hotels. The basis of characteristic similarity lies in the fact that this
appears to be a cluster of American luxury-budget chains with very little intemational
experience.
Mimetic isomorphism does not explain what drives the market similarity in this
cluster since the chains in this cluster are highly varied in size. However, the chains in
this cluster are the same type of chain from the same home country and, therefore, are
more likely to be direct competitors. Presumably they try to keep up with one another in
intemational expansion so that they can influence one another and perhaps take advantage
of agglomeration economies (Weber, 1929).
Cluster B: Regent Intemational, Novotel, Ramada, Hyatt Intemational, Le
Meridien, Sheraton, Hilton, Intercontinental. Golden Tulip, and SRS. The previously
obtained regression coefficients within the group for each variable appear to be similar in
sign and magnitude. The extent to which market similarity is explained by the regression
model also appears similar within the group with the exception of Regent Intemational.
Size in the group varies from 5,424 rooms to 129,623 rooms. Intemational experience
varies from 8 to 66 countries. Degree of intemationalization varies from 0.0983 to 1.0.
Home countries include Canada, France, the U.S., Netheriands, and Germany. All the
chains are luxury chains except for Ramada. The basis of characteristic similarity lies in
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the fact that this appears to be a cluster of North American and European multinational
luxury chains.
Mimetic isomorphism does not explain what drives the market similarity in this
cluster since the chains in this cluster are highly varied in size. However, the chains in
this cluster are the same type of chain, each with a significant amount of intemational
experience. They are likely to be direct competitors, and therefore, they try to keep up
with one another in international expansion so that they can influence one another and
perhaps take advantage of agglomeration economies (Weber, 1929).
Cluster C: Quality, Dorint, IBIS, Southem Pacific, Holiday Inn, Tmsthouse,
Sofitel, Best Westem, Accommodation, Super 8 and Westours. The previously obtained
regression coefficients within the group for each variable appear to be similar in sign and
magnitude. The source of similarity in this cluster is not immediately obvious. There
appears to be variation on each of the characteristics. Size in the group varies greatly
from 761 rooms to 307,980 rooms. Intemational experience also varies greatly from 1 to
49 countries. Degree of internationalization varies greatly from 0.002 to 0.8699. Home
countries include the U.S., Germany, Australia, the U.K. and France. Quality and
Holiday Inn are mid-priced chains; Best Westem and Super 8 are economy chains; Sofitel
is a luxury chain; and the type of hotel for the other chains in the group is unknown. A
basis of characteristic similarity does not appear to exist in this cluster. There appears to
be variation on each of the characteristics
Mimetic isomorphism does not explain the source of market similarity in this
cluster. Size in the group varies from small (761 rooms) to very large (307,980 rooms).
74
The chains are not from the same part of the world, are not the same type of hotels and do
not even place the same importance on intemational operations. It would be hard to make
a case for all chains in this cluster to be direct competitors.
Outliers: Kilbourne. Sara. Radisson, Jack Tar, Days Inn and York. What is it
about these chains that makes them outliers? Sara is the only Swedish chain in 1984.
Almost all of these outliers can be considered fairiy small chains (under 9,000 rooms) in
the industry. Also, they all have very limited intemational experience (in 1 or 2 countries
only). One reason for their unique market profiles could be that such firms have no clear
intemational strategy, but rather expand as opportunities come their way. This could
account for the unique profiles.
To summarize, besides market similarity, chains in two of the three clusters in
1984 seem to have at least one characteristic in common, namely intemational
experience, nationality or type of hotel. However, there does not appear to be one
characteristic which is found to be common in all clusters, although international
experience does appear relatively more dominant. Size is not similar among chains in
any of the clusters in 1984.
1985 Results
The dendogram for 1985 is shown in Figure 2. The characteristics of each cluster
are outlined in Table 51.
Cluster A: Commonwealth, Comfort and Thistle. The previously obtained
regression coefficients within the group for each variable appear to be similar in sign and
75
magnitude. The extent to which market similarity is explained by the regression model
also appears similar within the group. Size in the group varies from 4,637 rooms to
11,937 rooms. Intemational experience varies from 1 to 6 countries. Degree of
intemationalization varies from 0.14 to 0.37. None of the chains are first movers. Home
countries include Canada and the U.K.. Type of hotel is unknown except for Comfort
which is a mid-priced chain. The basis of characteristic similarity lies in the fact that this
appears to be a cluster of fairiy small, Canadian and British chains with limited
intemational experience and low relative importance given to intemational operations.
Mimetic isomorphism does not clearly explain the market similarity. While all
three chains are fairly small. Commonwealth is still almost twice the size of Comfort and
almost three times the size of Thistle. It would be hard to argue that the chains imitate
each other due to similarity in size. The chains in this cluster are dabbling in
intemational operations as evidenced by low degree of internationalization, but other than
that it is hard to see why these three chains might be considered direct competitors.
Cluster B: Rank, Jack Tar, Quality, BTH, Dorint, IBIS, Holiday, Tmsthouse.
Connor Jacobson, Frantel, Sofitel, Best Westem, Westours, Welcom, Radisson and
Grand Champions. The previously obtained regression coefficients within the group for
each variable are not that similar in sign and magnitude. There is also variation in the
extent to which market similarity is explained by the regression model. The source of
similarity in this cluster is not immediately obvious. There appears to be variation on
each of the characteristics. Size varies greatly from 381 rooms to 315,505 rooms.
Intemational experience varies greatly from 1 country to 52 countries. Degree of
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intemationalization varies from 0.007 to 1.0. Jack Tar, BTH and Best Westem are first
movers. Home countries include the U.K., the U.S., Germany, Guatemala, France and
India. Type of hotel is only known for the following: Best Westem is an economy chain;
Quality, Holiday and Radisson are mid-priced chains; and Jack Tar, Connor Jacobson and
Sofitel are luxury chains.
Mimetic isomorphism does not explain the source of market similarity in this
cluster. Size in the group varies from small (381 rooms) to very large (315,505 rooms).
The chains are not from the same part of the world, are not the same type of hotels and do
not even place the same importance on intemational operations. It would be hard to make
a case for all chains in this cluster to be direct competitors.
Cluster C: Westin, Le Meridien, Ramada, Hyatt, Hilton Intemational
Intercontinental, Sheraton and SRS. The regression coefficients within the group for
each variable appear to be similar in sign and magnitude. The extent to which market
similarity is explained by the regression model also appears similar within the group.
Size varies from 14,183 rooms to 139,605 rooms. Intemational experience varies from
12 to 61 countries. Degree of intemationalization varies from 0.83 to 0.97 except for
Westin and Ramada. Sheraton is the only first mover. Home countries include the U.S.,
France and Germany. All the chains are luxury chains except for Ramada which is midpriced. The basis of characteristic similarity lies in the fact that this appears to be a
cluster of American and European multinational luxury chains, which for the most part
consider intemational operations a significant part of the firm.
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Mimetic isomorphism does not explain the source of market similarity in this
cluster. Size in the group varies from moderate (14,183 rooms) to large (139,605 rooms).
However, the chains are similar in type, are from industrialized Westem nations, and all
have experience in many countries. They can be considered competitors, and as such one
explanation for the market similarity is that the chains want to keep track of their
competitors and so try to locate in similar locations, as multiple point competition would
suggest.
Outliers: Econolodge and Noahs. What is it about these chains that makes them
unique? Noahs is the only Australian chain in 1985. Nothing about Econolodge stands
out as being unique. It is interesting, however, that once again both outliers have very
limited international experience (in 1 country only).
To summarize, besides market similarity, chains in two of the three clusters in
1985 seem to have at least one characteristic in common, namely international
experience, nationality, type of hotel, or degree of intemationalization. However, there
does not appear to be one characteristic which is found to be common in all clusters,
although international experience does appear relatively more dominant. Chain size is
not similar within any of the clusters in 1985.
1986 Results
The dendogram for 1986 is shown in Figure 3. The characteristics of each cluster
are outlined in Table 52.
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Cluster A: Hospitality and Sunbelt. The previously obtained regression
coefficients for each variable appear to be similar in magnitude. The extent to which
market similarity is explained by the regression model also appears similar within the
group. Size varies from 1,585 to 15,991 rooms. Intemational experience is the same for
both ~ 1 country. Degree of intemationalization varies from 0.01 to 0.15. Neither of the
chains are first movers. They are both American chains. Type of hotel is unknown. This
appears to be a cluster of American chains with very limited intemational experience and
little importance for intemational operations.
Mimetic isomorphism does not explain the source of market similarity in this
cluster. The two chains have completely different sizes. Without knowing the type of
hotels each chain has, it is hard to propose that these two chains are direct competitors,
even though they are from the same nation and have the same intemational experience.
Cluster B: Four Seasons, Nikko, Regent Intemational, and Tai. The previously
obtained regression coefficients within the group for each variable appear to be similar in
magnitude. Size varies from 4,884 to 6,913 rooms. Intemational experience varies from
2 to 10 countries. Degree of intemationalization varies from 0.44 to 1.0. Four Seasons
and Regent Intemational are first movers. Home countries include Canada, Japan, and
India. Type of hotel is unknown for Nikko. The rest of the chains are luxury chains.
This appears to be a cluster of fairly small, luxury chains.
Mimetic isomorphism somewhat explains the similarity in this cluster. The chains
are all of approximately similar sizes. The chains are of the same type, but are from very
different home nations. It can be argued that although they are from different home
79
nations, they are competing for similar target markets, and thus are direct competitors,
which explains why they might want to keep track of their competitors and take
advantage of economies of agglomeration (Weber, 1929).
Cluster C: Peninsula, Merlin, Ming and Prince. The previously obtained
regression coefficients within the group for each variable appear to be similar in
magnitude. The extent to which market similarity is explained by the regression model
also appears similar within the group. Size varies from 4,535 to 10,928 rooms.
Intemational experience is identical in the group in 3 countries. Degree of
intemationalization varies from 0.09 to 0.59. Peninsula is the only non-first mover.
Home countries include Hong Kong, Malaysia and Japan. Type of hotel is unknown.
This appears to be a cluster of fairly small. Pacific Rim chains with identical limited
intemational experience.
Mimetic isomorphism explains the similarity in this cluster. The chains are all
fairly small. Since information about type of hotel is unavailable, it is hard to assess
whether these chains are really key competitors.
Cluster D: Comfort, Caledonian, Commonwealth and Thistle. The regression
coefficients within the group for each variable appear to be similar in magnitude. Size
varies from 1,957 to 11,776 rooms. Intemational experience varies from 1 to 6 countries.
Degree of intemationalization varies from 0.14 to 0.62. Comfort is the only first mover.
Home countries include the U.K. and Canada. Type of hotel is unknown for all except
Comfort, which is mid-priced. This appears to be a cluster of British and Canadian
chains with relatively limited intemational experience.
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Mimetic isomorphism explains the similarity in this cluster. The chains are all
fairiy small. Since information about type of hotel is unavailable, it is hard to assess
whether the chains should be considered key rivals.
Cluster E: Accor. Rodeway. Howard Johnson, Friendship and Travelodge. The
regression coefficients within the group for each variable appear to be similar in
magnitude. The extent to which market similarity is explained by the regression model
also appears similar within the group. Size varies from 3,340 to 57,522 rooms.
Intemational experience is in 2 countries or less. Degree of intemationalization varies
from 0.01 to 0.09. Rodeway and Howard Johnson are the only first movers. Home
country is the U.S.. Type of hotel is luxury-budget for all the chains. This appears to be a
cluster of American, luxury-budget chains with limited intemational experience and
which place little importance on intemational operations.
Mimetic isomorphism does not explain the similarity in this cluster. Size in the
group varies from small (3,340 rooms) to moderate ( 57,522 rooms). However, these
chains are of the same type, from the same home nation and even have the same amount
of intemational experience. They can be considered direct competitors, locating in
similar cities to keep track of each other, as multiple point competition would suggest.
Cluster F: Ramada. Hyatt Intemational. Le Meridien, Hilton Intemational.
Intercontinental, Sheraton. Golden Tulip, SRS, BTH, Quality, Frantel, Novotel. Sofitel.
Holiday Inn, and Tmsthouse. The regression coefficients within the group for each
variable appear to be similar in magnitude. The extent to which market similarity is
explained by the regression model appears to separate the cluster into two subgroups,
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which interestingly enough shows up in the dendogram too. Size varies from 7,613 to
314,909 rooms. Intemational experience varies from 9 to 63 countries. Degree of
intemationalization varies from 0.15 to 0.94. Holiday Inn is the only non-first mover.
Home countries include the U.S., France, Netheriands, Germany, and the U.K.. Type of
hotel is unknown for BTH, Frantel and Tmsthouse. Ramada, Quality and Holiday are
mid-priced hotels. The rest of the chains are luxury chains. The basis of characteristic
similarity lies in the fact that this appears to be a cluster of multinational, Westem, luxury
chains. It is not surprising to see most of the chains are first movers given their high
intemational experience.
Mimetic isomorphism does not explain the similarity in this cluster. Size in the
group varies widely. However, these chains are mostly of the same type, are from
Westem countries, and have the same amount of intemational experience. They can be
considered direct competitors, locating in similar cities to keep track of each other as
multiple point competition would suggest.
Cluster G: Jack Tar, Dorint, IBIS, Best Western, Westours, Clarion, Horizon.
Grand Champions, Radisson, Rank, Super 8, and Welcom. The regression coefficients
within the group for each variable are similar in magnitude. There is similarity in the
extent to which market similarity is explained by the regression model. The source of
similarity in this cluster is not immediately obvious. There appears to be variation on
each of the characteristics. Size varies from 974 to 242,895 rooms. Intemational
experience varies from 1 to 29 countries. Degree of intemationalization varies greatly
from 0.001 to 1.0. Jack Tar, Best Westem, Clarion, Horizon and Grand Champions are
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the only first movers. Home countries include the U.S., Germany, the U.K. and India.
Type of hotel is known only for the following: Super 8 is an economy chain; Best
Westem is a luxury-budget chain; Radisson is mid-priced; and Jack Tar and Clarion are
luxury chains.
Mimetic isomorphism does not explain the source of market similarity in this
cluster. Size in the group varies widely. The chains are not the same type of hotel and do
not even place the same importance on intemational operations. It would be hard to make
a case for all chains in this cluster to be direct competitors.
Outliers: Swissotel, Econolodge. Days Inn and York. What is it about these
chains that makes them unique? Swissotel in the only Swiss chain in 1986. Nothing else
stands out as being unique. It is interesting, however, that once again the outliers have
very limited intemational experience (in 1 country only).
To summarize, besides market similarity, chains in 1986 seem to have at least one
characteristic in common, namely international experience, nationality, type of hotel, size
or degree of intemationalization. However, there does not appear to be one characteristic
which is be common in all clusters, although intemational experience does appear
relatively more dominant. Size is only similar among chains in three of the clusters in
1986.
1987 Results
The dendogram for 1987 is shown in Figure 4. The characteristics of each cluster
are outlined in Table 53.
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Cluster A: Grand Champions and Canadian National. The two chains come from
similar home nations and both have intemational experience in only one foreign country.
Due to missing information, it is hard to further assess why these chains are in one
cluster.
Cluster B: Ramada. Hyatt Intemational, Le Meridien, Hilton, Intercontinental,
Sheraton, Golden Tulip, and SRS. The previously obtained regression coefficients within
the group for each variable are similar in magnitude. There is similarity in the extent to
which market similarity is explained by the regression model. Size varies from 22,077 to
143,557 rooms. Intemational experience varies from 3 to 63 countries, though most of
the chains have experience in over 20 countries. Degree of intemationalization varies
from O.lto 0.92. Hilton is the only non-first mover. Home countries include the U.S.,
France, Netherlands and Germany. Ramada is a mid-priced chain. The rest of the chains
are luxury chains. The basis of characteristic similarity lies in the fact that this appears to
mostly be a group of large, Westem, multinational, luxury chains. It is not surprising to
see most of the chains are first movers given their high international experience.
Mimetic isomorphism somewhat explains the similarity in this cluster. All the
chains are large in size, though there is variation within the group. These chains are
mostly of the same type, are from Westem countries, and almost all have a large amount
of intemational experience. They can be considered direct competitors, locating in
similar cities to keep track of each other, as multiple point competition would suggest,
and perhaps also to take advantage of economies of agglomeration (Weber, 1929).
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Cluster C: Econolodge and Park. The regression coefficients within the group for
each variable are similar in magnitude. There is similarity in the extent to which market
similarity is explained by the regression model. Size varies from 4,447 to 31,203 rooms.
Intemational experience is identical in 1 country. Degree of intemationalization varies
from 0.04 to 0.2. Neither of the chains are first movers. Home country is the U.S.. Type
of hotel for Econolodge is economy; for Park is unknown. The basis of characteristic
similarity lies in the fact that this appears to be a pair of American chains with limited
intemational experience.
Mimetic isomorphism does not explain the similarity in this cluster. Size in the
group varies widely. Since information about type of hotel is unavailable, it is hard to
assess whether the chains are key rivals.
Cluster D: Accor, Churchman and VMS. The regression coefficients within the
group for each variable are similar in magnitude. There is similarity in the extent to
which market similarity is explained by the regression model. Size varies from 4,785 to
19,381 rooms. Intemational experience varies from 1 to 3 countries. Degree of
intemationalization varies from 0.07 - 0.29. None of the chains are first movers. Home
country is the U.S.. Accor is a luxury-budget hotel. VMS has a wide range of hotel types
and Churchman is of unknown type. The basis of characteristic similarity lies in the fact
that this appears to be a group of American chains with limited intemational experience.
Mimetic isomorphism does not explain the similarity in this cluster. Size and type of
hotel in the group vary widely.
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Cluster E: Hospitality and Sunbelt. The regression coefficients within the group
for each variable are similar in magnitude. There is similarity in the extent to which
market similarity is explained by the regression model. Size varies from 1,250 to 16,192
rooms. Intemational experience is identical in 1 country. Degree of intemationalization
varies from 0.01 to 0.2. None of the chains are first movers. Home country is the U.S..
Type of hotel is unknown. The basis of characteristic similarity lies in the fact that this
appears to be a pair of American chains with limited intemational experience.
Mimetic isomorphism does not explain the similarity in this cluster. Size in the
group varies widely. Since information about type of hotel is unavailable, it is hard to
assess whether the chains are key rivals.
Cluster F: Americana and Radisson. The regression coefficients within the group
for each variable are similar in magnitude. There is similarity in the extent to which
market similarity is explained by the regression model. International experience varies
from 2 to 3 countries. None of the chains are first movers. Home country is the U.S..
Type of hotel for Radisson is mid-priced and for Americana is unknown. This appears to
be a pair of American chains with limited international experience.
Mimetic isomorphism cannot be assessed in this cluster since size information is
missing for Americana. Since information about type of hotel is unavailable, it is hard to
assess whether the chains are key rivals.
Cluster G: Pratt. Howard Johnson. Quality and Travelodge. The regression
coefficients within the group for each variable are similar in magnitude. There is
similarity in the extent to which market similarity is explained by the regression model.
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Size varies from 11,838 to 89,784 rooms. Intemational experience varies from 2 to 8
countries. Degree of intemationalization varies from 0.02 to 0.7. Pratt and Quality are
first movers. Home country is the U.S.. Type of hotel is unknown for Pratt; is luxurybudget for Howard Johnson and Travelodge and is mid-priced for Quality. The basis of
characteristic similarity lies in the fact that this appears to be a group of American chains
with little intemational experience. Mimetic isomorphism does not explain the similarity
in this cluster. Size and type of hotel in the group vary widely.
Cluster H: BTH. Hilton International, Dorint, IBIS, Southern Pacific. Scandic,
Sofitel, Frantel, Novotel, Holiday Inn, Tmsthouse, Connor Jacobson, Best Westem,
Rank, Clarion and Westours. The regression coefficients within the group for each
variable are similar in magnitude. The source of similarity in this cluster is not
immediately obvious. There appears to be variation on each of the characteristics. Size
varies from 381 to 315,648 rooms. International experience varies from 1 to 51
countries. Degree of internationalization varies from 0.004 to 1.0. Non-first movers are
Dorint, Southem Pacific, Connor Jacobson, Rank, Clarion and Westours. Home
countries include the U.S., Germany, Australia, Sweden, France, the U.K. and Guatemala.
Type of hotel is unknown except for the following: Best Westem is a luxury-budget
chain; Holiday is mid-priced; and Hilton International, Sofitel, Novotel and Clarion are
luxury chains. Mimetic isomorphism does not explain the source of market similarity in
this cluster. Size and type of hotel in the group vary widely.
Outliers: Canadian Pacific. Hungar. Jolly of Italy, Prince and Noahs. What is it
about these chains that makes them unique? Hungar and Noahs are respectively the only
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chain from their home country for the year. Nothing else stands out as being unique. It is
interesting, however, that once again the outliers have very limited intemational
experience (in 3 or less countries).
To summarize, besides market similarity, chains in 1987 seem to have at least one
characteristic in common, namely intemational experience, nationality, or type of hotel.
However, there does not appear to be one characteristic which is found to be common in
all clusters, although intemational experience does appear relatively more dominant. Size
is only similar among chains in one of the clusters in 1987.
1988 Results
The dendogram for 1988 is shown in Figure 5. The characteristics of each cluster
are outlined in Table 54.
Cluster A: Hyatt and Servico. The previously obtained regression coefficients
within the group for each variable are similar in magnitude. There is similarity in the
extent to which market similarity is explained by the regression model. Size varies from
15,267 to 51,961 rooms. International experience varies between 1 and 2 countries.
Degree of intemationalization varies from 0.01 to 0.05. Neither of the chains are first
movers. Home country is the U.S.. Type of hotel is unknown for Servico. Hyatt is a
luxury chain. The basis of characteristic similarity lies in the fact that this appears to be a
pair of American chains with limited intemational experience. Mimetic isomorphism
does not explain the similarity in this cluster. Size in the group varies widely. Since
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information about type of hotel is incomplete, it is hard to assess whether the chains are
key rivals.
Cluster B: Compri, Clarion, Days Inn. Friendship. Accor. Churchman, and VMS.
The regression coefficients within the group for each variable are similar in magnitude.
There is similarity in the extent to which market similarity is explained by the regression
model. Size varies from 5,251 to 88,904 rooms. Intemational experience varies between
1 and 3 countries. Degree of intemationalization varies from 0.005 to 0.26. Churchman
is the only first mover. Home country is the U.S.. Type of hotel is unknown for Compri,
Friendship and Churchman. Clarion is a luxury chain. Days and Accor are luxury-budget
chains, and VMS has a wide range of hotels. The basis of characteristic similarity lies in
the fact that this appears to be a group of American chains with limited intemational
experience. Mimetic isomorphism does not explain the source of market similarity in this
cluster. Size and type of hotel in the group vary widely.
Cluster C: Mercure, Marriott, Crest, Penta, Preferred and Sonesta. The
regression coefficients within the group for each variable are similar in magnitude. Size
varies from 3,500 to 71,720 rooms. Intemational experience varies between 3 and 9
countries. Degree of intemationalization varies from 0.05 to 0.86. There are no first
movers in the cluster. Home countries include the U.S., France and the U.K.. Type of
hotel is unknown for Mercure and Penta. The rest of the chains are luxury chains. The
basis of characteristic similarity lies in the fact that this appears to be a group of Westem,
luxury chains with moderate intemational experience.
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Mimetic isomorphism does not explain the source of market similarity in this
cluster. Size in the group varies widely. These chains are mostly of the same type, are
from Westem countries, and almost all have a moderate amount of intemational
experience. They can be considered direct competitors, locating in similar cities to keep
track of each other, as multiple point competition would suggest.
Cluster D: Commonwealth. Caledonian, Embassy and Thistle. The regression
coefficients within the group for each variable are similar in magnitude. There is
similarity in the extent to which market similarity is explained by the regression model.
Size varies from 1,927 to 11,437 rooms. International experience varies between 1 and 7
countries. Degree of internationalization varies from 0.12 to 0.61. Embassy is the only
first mover in the cluster. Home countries include Canada and the U.K.. Type of hotel is
unknown for all chains except Embassy, which is a luxury chain. The basis of
characteristic similarity lies in the fact that this appears to be a group of British and
Canadian chains, most with limited international experience.
Mimetic isomorphism somewhat explains the source of market similarity in this
cluster. Size in the group varies, but not too widely. Since information about type of
hotel is incomplete, it is hard to assess whether the chains are key rivals.
Cluster E: Budget. Forte. BTH. IBIS, Scandic. The regression coefficients within
the group for each variable are similar in magnitude. There is similarity in the extent to
which market similarity is explained by the regression model. Size varies from 7,805 to
32,522 rooms. Intemational experience varies between 1 and 10 countries. Degree of
intemationalization varies from 0.01 to 0.94. BTH and Scandic are first movers. Home
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countries include the U.S. and Sweden. Type of hotel is unknown for all chains except
Budget, which is a luxury-budget chain. The basis of characteristic similarity lies in the
fact that this appears to be a group of Western chains with moderate intemational
experience.
Mimetic isomorphism does not explain the source of market similarity in this
cluster. Size in the group varies widely. Since information about type of hotel is
incomplete, it is hard to assess whether the chains are key rivals.
Cluster F: Ramada, Hyatt International, Hilton International, Intercontinental,
Sheraton, Golden Tulip, SRS, Le Meridien, Swiss Intemational, Sofitel, Holiday Inn,
Frantel, Novotel and Tmsthouse. The regression coefficients within the group for each
variable are similar in magnitude. There is similarity in the extent to which market
similarity is explained by the regression model. Size varies from 22,077 to 318,574
rooms, with the exception of Hyatt International, Swiss International and Sofitel.
Intemational experience varies between 18 and 63 countries. Degree of intemationalization varies from 0.04 to 1.0. First movers are Ramada, Sheraton, Golden Tulip, SRS,
Swiss Intemational, Holiday Inn and Tmsthouse. Home countries include the U.S.,
Netheriands, Germany, France, Switzeriand, and the UK. Type of hotel is unknown for
Swiss Intemational, Frantel and Tmsthouse. All the rest are luxury chains except
Ramada and Holiday, which are mid-priced. The basis of characteristic similarity lies in
the fact that this appears to be mostly a group of large, Westem, multinational, luxury
chains. It is not surprising to see some of the chains are firstmovers given their high
intemational experience.
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Mimetic isomorphism somewhat explains the similarity in this cluster. All the
chains are large in size, though there is variation within the group. These chains are
mostly of the same type, are from Westem countries, and almost all have a large amount
of intemational experience. They can be considered direct competitors, locating in
similar cities to keep track of each other, as multiple point competition would suggest.
Cluster G: Jack Tar. Rank. Best Western. Continental. Aircoa. Steriing.
Westours. Connor Jacobson and York. The regression coefficients within the group for
each variable are similar in magnitude. There is similarity in the extent to which market
similarity is explained by the regression model. The source of similarity in this cluster is
not immediately obvious. There appears to be variation on each of the characteristics.
Size varies from 381 to 264,338 rooms. Intemational experience varies between 1 and 32
countries, although all are below 5 except for Best Western. Degree of intemationalization varies from 0.001 to 1.0. First movers are Jack Tar, Rank, Best Westem and
Sterling. Home countries include the U.S., the U.K., Guatemala and Canada. Type of
hotel is unknown for all chains except Best western (luxury-budget) and Jack Tar
(luxury). This appears to be a group of chains with similar intemational experience for
the most part. Mimetic isomorphism does not explain the source of market similarity in
this cluster. Size in the group varies widely.
Outliers: Caesar Park, Sol. Atina. Hudson, HUSA, Hungar, Riande and Viscount.
What is it about these chains that makes them unique? Caesar Park is the only Brazilian
chain in 1988, Hungar is the only Hungarian chain in 1988 and Riande is the only chain
from Panama in 1988. Nothing else stands out as being unique. It is interesting,
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however, that once again the outliers have very limited intemational experience (in 1
country only).
To summarize, besides market similarity, chains in 1988 seem to have at least one
characteristic in common, namely intemational experience, nationality, type of hotel, or
size. However, there does not appear to be one characteristic which is common in all
clusters, although intemational experience does appear relatively more dominant. Size is
only similar among chains in two of the clusters in 1988.
1989 Results
The dendogram for 1989 is shown in Figure 6. The characteristics of each cluster
are outlined in Table 55.
Cluster A: Delta and Riande. The regression coefficients within the group for
each variable are similar in magnitude. Size varies from 1,145 to 5,778 rooms.
Intemational experience is identical in 1 country. Degree of intemationalization varies
from 0.13 to 0.46. There are no first movers. Home countries include the U.S., and
Panama. Type of hotel is unknown. The basis of characteristic similarity lies in the fact
that this appears to be a cluster of small chains with very limited intemational experience.
Mimetic isomorphism gives one explanation for the source of market similarity in
this cluster. Both chains are small in size. Since information about type of hotel is
incomplete, it is hard to assess whether the chains are key rivals.
Cluster B: Compri. Clarion. Days Inn, Howard Johnson. Churchman,
Continental, Prince and VMS. The regression coefficients within the group for each
variable are similar in magnitude. There is similarity in the extent to which market
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similarity is explained by the regression model. Size varies from 4,261 to 116,543 rooms.
Intemational experience varies between 1 and 3 countries. Degree of intemationalization
varies from 0.009 to 0.16. Continental is the only first mover. Home countries include
the U.S. and Japan. Type of hotel is unknown except for Days Inn and Howard Johnson
(both luxury-budget). Clarion (luxury) and VMS (wide range). The basis of characteristic
similarity lies in the fact that this appears to be a group of American chains with limited
intemational experience.
Mimetic isomorphism does not explain the source of market similarity in this
cluster. Size and type of hotel in the group vary widely.
Cluster C: Movenpick, HUSA Intemational and Oberoi. The regression
coefficients within the group for each variable are similar in magnitude. The source of
similarity in this cluster is not immediately obvious. There appears to be variation on
each of the characteristics. Size varies from 4,343 to 17,581 rooms. Intemational
experience varies between 1 and 8 countries. Degree of intemationalization varies from
0.01 to 0.65. Oberoi is the only first mover. Home countries include Switzeriand, the
U.S., and India. Type of hotel is unknown except for Movenpick which is a mid-priced
chain.
Mimetic isomorphism somewhat explains the source of market similarity in this
cluster. Size in the group varies, but not too widely. Since information about type of
hotel is incomplete, it is hard to assess whether the chains are key rivals.
Cluster D: Jack Tar, Rank, Best Westem. Steriing. Savoy and Hawthome. The
regression coefficients within the group for each variable are similar in magnitude. Size
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varies from 908 to 264,338 rooms, but they all below 4,000 rooms except for Best
Westem. Intemational experience varies between 1 and 30 countries, but is below 5
countries for all chains except for Best Western . Degree of internationalization varies
from 0.06 to 1.0. First movers are Best Westem, Savoy and Hawthome. Home countries
include the U.S. and the U.K.. Type of hotel is unknown for all chains except Best
Westem (luxury-budget) and Jack Tar (luxury). This appears to be a group of mostly
small, American chains with limited intemational experience.
Mimetic isomorphism does not fully explain the source of market similarity in this
cluster. Size in the group varies, however, with the exception of Best Westem, they all
appear small. Since information about type of hotel is incomplete, it is hard to assess
whether the chains are key rivals.
Cluster E: Hyatt International, Le Meridien, Hilton International, InterContinental. Ramada, Sheraton, Golden Tulip, SRS, Swiss International, Sofitel, Consort,
BTH. IBIS, Tmsthouse, Scandic, Holiday Inn, Frantel, Novotel and Tmsthouse PLC. The
regression coefficients within the group for each variable are similar in magnitude. There
is similarity in the extent to which market similarity is explained by the regression model.
Size varies from 8,120 to 318,574 rooms. International experience varies between 4 and
61 countries, although it is above 25 countries for almost all chains. Degree of
intemationalization varies from 0.07 to 1.0. First movers are Le Meridien, Ramada,
Sheraton, Golden Tulip, SRS, Sofitel, Consort, BTH, Frantel and Tmsthouse PLC. Home
countries include the U.S., France, Netherlands, Germany, Switzerland, Sweden and the
U.K.. Type of hotel is unknown except for Hyatt International, Le Meridien, Hilton
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Intemational, Intercontinental, Sheraton, Golden Tulip, SRS, Sofitel and Novotel ( all
luxury chains) and Ramada and Holiday (both mid-priced chains). The basis of
characteristic similarity lies in the fact that this appears to be a group of Westem
multinational, luxury chains.
Mimetic isomorphism somewhat explains the similarity in this cluster. All the
chains are large in size, though there is variation within the group. These chains are
mostly of the same type, are from Westem countries, and almost all have a large amount
of intemational experience. They can be considered direct competitors, locating in
similar cities to keep track of each other, as multiple point competition would suggest.
Outliers: Caesar Park, Portman, Clement Chen, Brook and HUSA. What is it
about these chains that makes them unique? Caesar Park is the only Brazilian chain in
this year. Nothing else stands out as being unique. It is interesting, however, that once
again the outliers have very limited intemational experience (in 1 country only).
To summarize, besides market similarity, chains in 1989 seem to have at least one
characteristic in common, namely international experience, nationality, type of hotel, or
size. However, there does not appear to be one characteristic which is common in all
clusters, although intemational experience does appear relatively more dominant. All
clusters have at least one characteristic in common except for cluster C. Size is similar in
three of the clusters in 1989.
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1990 Results
The dendogram for 1990 is shown in Figure 7. The characteristics of each cluster
are outlined in Table 56.
Cluster A: Dorint and Sonesta. The regression coefficients within the group for
each variable are similar in magnitude. There is similarity in the extent to which market
similarity is explained by the regression model. Size varies from 3,990 to 4,994 rooms.
International experience varies between 4 and 5 countries. Degree of intemationalization
varies from 0.2 to 0.4. There are no first movers. Home countries include the U.S. and
Germany. Type of hotel is unknown except for Sonesta which is a luxury chain. The
basis of characteristic similarity lies in the fact that this appears to be a pair of fairly small
chains with limited intemational experience.
Mimetic isomorphism gives one explanation for the source of market similarity in
this cluster. Both chains are small in size. Since information about type of hotel is
incomplete, it is hard to assess whether the chains are key rivals.
Cluster B: Omni and Regal. The regression coefficients within the group for each
variable are similar in magnitude. There is similarity in the extent to which market
similarity is explained by the regression model. Size varies from 17,387 to 39,246
rooms. Intemational experience varies between 2 and 3 countries. Degree of
intemationalization varies from 0.07 to 0.84. There are no first movers. Home country is
the U.S.. Type of hotel is unknown. The basis of characteristic similarity lies in the fact
that this appears to be a pair of American chains with limited intemational experience.
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Mimetic isomorphism does not explain the source of market similarity in this
cluster. Both chains are different in size. Since information about type of hotel is
incomplete, it is hard to assess whether the chains are key rivals.
Cluster C: Mandarin, Otani. and Shangri-la. The regression coefficients within
the group for each variable are similar in magnitude. There is similarity in the extent to
which market similarity is explained by the regression model. Size varies from 3,894 to
13,500 rooms. Intemational experience varies between 3 and 6 countries. Degree of
intemationalization varies from 0.21 to 0.64 There are no first movers. Home countries
are Hong Kong and Japan. Type of hotel is unknown for Otani. The other two chains are
luxury chains. The basis of characteristic similarity lies in the fact that this appears to be
a trio of Pacific Rim chains.
Mimetic isomorphism gives one explanation for the source of market similarity in
this cluster. The chains are moderate in size. Since complete information about type of
hotel is unavailable, it is hard to assess whether the chains are key rivals. However, it
does appear that the chains are of a similar type, and thus, might be considered
competitors, in which case it would make sense that they would want to keep an eye on
their competitors by locating in similar cities.
Cluster D: Great Inns, Cunard. Rock and Taj. The regression coefficients within
the group for each variable are similar in magnitude. There is similarity in the extent to
which market similarity is explained by the regression model. Size varies from 1,044 to
3,378 rooms. Intemational experience varies between 2 and 5 countries. Degree of
intemationalization varies from 0.03 to 0.45 There are no first movers. Home countries
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are the U.S. and India. Type of hotel is unknown, except for Taj, which is a luxury chain.
The basis of characteristic similarity lies in the fact that this appears to be a group of
mostly small, American chains with limited intemational experience.
Mimetic isomorphism gives one explanation for the source of market similarity in
this cluster. The chains are small in size. Since complete information about type of hotel
is unavailable, it is hard to assess whether the chains are key rivals.
Cluster E: Horizon and Rank. The regression coefficients within the group for
each variable are similar in magnitude. There is similarity in the extent to which market
similarity is explained by the regression model. Size varies from 3,989 to 4,047 rooms.
Intemational experience is in 1 country. Degree of intemationalization varies from 0.03
to 0.44 There are no first movers. Home countries are the U.S. and the U.K.. Type of
hotel is unknown. The basis of characteristic similarity lies in the fact that this appears to
be a group of small, Westem chains with limited international experience. Mimetic
isomorphism gives one explanation for the source of market similarity in this cluster.
The chains are small in size. Since complete information about type of hotel is
unavailable, it is hard to assess whether the chains are key rivals.
Cluster F: Frantel, IBIS, Scandic, BTH, Consort, Quality, Tmsthouse, Best
Westem and VMS. The regression coefficients within the group for each variable are
similar in magnitude. There is similarity in the extent to which market similarity is
explained by the regression model. Size varies from 908 to 258,586 rooms. Intemational
experience varies between 4 and 31 countries. Degree of intemationalization varies from
0.08 to 1.0. First movers are IBIS, Scandic, Consort and Best Westem. Home countries
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are France, Sweden, and the U.S.. Type of hotel is unknown except for Quality (midpriced). Best Westem (luxury-budget) and VMS (wide range). The basis of characteristic
similarity lies in the fact that this appears to be a group of Western chains. Mimetic
isomorphism does not explain for the source of market similarity in this cluster. The
chains vary in size and type of hotel.
Cluster G: Jack Tar. Sofitel. Kuwait, and Westcoast. The regression coefficients
within the group for each variable are similar in magnitude. There is similarity in the
extent to which market similarity is explained by the regression model. Size varies from
1,436 to 10,286 rooms. Intemational experience varies between 1 and 18 countries.
Degree of intemationalization varies from 0.2 to 1.0. Kuwait is the only first mover.
Home countries include France, the U.S., and Kuwait. Type of hotel is unknown except
for Jack Tar and Sofitel ( both luxury chains). The basis of characteristic similarity lies in
the fact that this appears to be a group of small chains. Mimetic isomorphism does
explain the source of market similarity in this cluster. The chains are small in size. Since
complete information about type of hotel is unavailable, it is hard to assess whether the
chains are key rivals.
Cluster H: Hyatt Intemational, Le Meridien, Ramada, Hilton Intemational.
Intercontinental, Sheraton, Holiday Inn, Golden Tulip, SRS and Swiss Intemational. The
regression coefficients within the group for each variable appear to be similar in sign and
magnitude. The extent to which market similarity is explained by the regression model
also appears similar within the group. This appears to be a cluster of multinational
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American and European luxury chains, which for the most part consider intemational
operations a significant part of the firm.
Mimetic isomorphism does not explain the source of market similarity in this
cluster. Size in the group varies from moderate (7,102 rooms) to large (314,261 rooms).
The chains are similar in type (luxury), are from industrialized Westem nations (the US,
France, Netherlands, Germany and Switzerland) and all have experience in many
countries (18 to 67 countries). They can be considered competitors, and as such one
explanation for the market profile similarity is that the chains want to keep track of their
competitors and so try to locate in similar locations, as multiple point competition would
suggest.
Cluster I: Rydges, Interwest and Southem Pacific. The regression coefficients
within the group for each variable appear to be similar in sign and magnitude. The extent
to which market similarity is explained by the regression model also appears similar
within the group. This appears to be a cluster of small (under 7,000 rooms), Australian
chains. Type of hotel is unknown.
Mimetic isomorphism gives one explanation for the source of market similarity in
this cluster. The chains are small in size. The common home nation may make these
chains more likely to be direct competitors, assuming they have similar types of hotels.
Outliers: Portman. Tmsthouse PLC and Prince. What is it about these chains that
makes them unique? Nothing stands out as being unique about these chains. It is
interesting, however, that once again some outliers have very limited intemational
101
experience (in 1 or 2 countries only). However, this time, one outlier (Tmsthouse PLC)
has significant intemational experience (in 26 countries).
To summarize, besides market similarity, chains in 1990 seem to have at least one
characteristic in common, namely international experience, nationality, type of hotel, or
size. However, there does not appear to be one characteristic which is found to be
common in all clusters, although intemational experience does appear relatively more
dominant.
1991 Results
The dendogram for 1991 is shown in Figure 8. The characteristics of each cluster
are outlined in Table 57.
Cluster A: Budget and Hospitality Intemational. The regression coefficients
within the group for each variable appear to be similar in sign and magnitude. The extent
to which market similarity is explained by the regression model also appears similar
within the group. Size varies from 7,198 to 19,149 rooms. Intemational experience is in
1 country. Degree of intemationalization is 0.01. There are no first movers. Home
country is the U.S.. Type of hotel is unknown for Hospitality Intemational. Budget is a
luxury-budget chain. The basis of characteristic similarity lies in the fact that this appears
to be a pair of American chains with little intemational experience and little emphasis
placed on intemational operations. Mimetic isomorphism does not explain for the source
of market similarity in this cluster. The chains are varied in size. Since complete
102
information about type of hotel is unavailable, it is hard to assess whether the chains are
key rivals.
Cluster B: Hotel Management. Ming and Sunbelt. The regression coefficients
within the group for each variable appear to be similar in sign and magnitude. The extent
to which market similarity is explained by the regression model also appears similar
within the group. Size varies from 635 to 1,411 rooms. International experience is in 1
country. Degree of internationalization varies from 0.2 to 0.5. There are no first movers.
Home countries are the U.S. and Malaysia. Type of hotel is unknown for Ming and
Sunbelt while Hotel Management has a wide range of hotel types. The basis of
characteristic similarity lies in the fact that this appears to be a group of small chains with
little intemational experience.
Mimetic isomorphism gives one explanation for the source of market similarity in
this cluster. The chains are small in size. Since complete information about type of hotel
is unavailable, it is hard to assess whether the chains are key rivals.
Cluster C: Delta and Riande. The regression coefficients within the group for
each variable are similar in magnitude. This appears to be a cluster of small chains
(under 6,000 rooms) with very limited international experience (in 1 country). Home
nations include the U.S. and Panama. Type of hotel is unknown.
Mimetic isomorphism gives one explanation for the source of market similarity in
this cluster. Both chains are small in size. Since complete information about type of
hotel is unavailable, it is hard to assess whether the chains are key rivals.
103
Cluster D: Embassy. Thistle and Swallow. Due to lack of information, all that
can be said about this group of chains is that they are British firms with limited
intemational experience (1 or 2 countries). There are no firstmovers. Embassy is a
luxury chain. Type of hotel is unknown for the other chains. Since complete information
about type of hotel and size is unavailable, it is hard to assess whether the chains are key
rivals or whether mimetic isomorphism can explain the cluster membership.
Cluster E: Super 8. Tmsthouse and Westcoast. The regression coefficients within
the group for each variable appear to be similar in sign and magnitude. The extent to
which market similarity is explained by the regression model also appears similar within
the group. This appears to be a group of American chains with little intemational
experience (1 to 3 countries). There are no firstmovers. Super 8 is an economy chain.
Type of hotel is unknown for the other chains.
Mimetic isomorphism does not explain the source of market similarity in this
cluster. Size in the group varies greatly (3,625 to 48,360 rooms). Since complete
information about type of hotel is unavailable, it is hard to assess whether the chains are
key rivals.
Cluster F: Stouffer, Days Inn, Howard Johnson, Radisson, and Westin. The
regression coefficients within the group for each variable appear to be similar in sign and
magnitude. The extent to which market similarity is explained by the regression model
also appears similar within the group. This appears to be a group of American chains
with moderate intemational experience (2 to 10 countries). Stouffer and Westin are
104
luxury chains. Days Inn and Howard Johnson are luxury-budget chains. Radisson is a
mid-priced chain.
Mimetic isomorphism does not explain the source of market similarity in this
cluster. Size in the group varies greatly (16, 836 to 146,787 rooms). The chains vary
enough in type of hotel to make it hard to establish why these chains might be direct
competitors.
Cluster G: Grand Collection. Marriott, Penta, and Preferred. The regression
coefficients within the group for each variable appear to be similar in magnitude. This
group is made up of American and British chains with moderate intemational experience
(8 to 20 countries). Marriott and Preferred are luxury chains. Type of hotel is unknown
for the other chains.
Mimetic isomorphism does not explain the source of market similarity in this
cluster. Size in the group varies from 7,353 to 89,126 rooms. Since complete
information about type of hotel is unavailable, it is hard to assess whether the chains are
key rivals.
Cluster H: Choice, BTH, Consort. The regression coefficients within the group
for each variable appear to be similar in magnitude. Besides all being American chains, it
is hard to establish the basis of similarity in this group. Type of hotel is unknown except
for Choice which has a wide range of hotel types.
Mimetic isomorphism does not explain the source of market similarity in this
cluster. Size in the group varies from 2,778 to 235,340 rooms. Since complete
105
information about type of hotel is unavailable, it is hard to assess whether the chains are
key rivals.
Cluster I: Hyatt International, Le Meridien, Sofitel, IBIS, Holiday Inn. Hilton
Intemational. Sheraton, SRS. Scandic, Frantel and Tmsthouse PLC. The regression
coefficients within the group for each variable appear to be similar in sign and magnitude.
The extent to which market similarity is explained by the regression model also appears
similar within the group. International experience varies between 9 and 55 countries,
although almost have hotels in over 24 countries. Type of hotel is unknown for IBIS,
Scandic, Frantel and Tmsthouse PLC. The rest are luxury chains, except for Holiday,
which is mid-priced. This appears to be a cluster of mostly multinational American and
European luxury chains.
Mimetic isomorphism does not explain the source of market similarity in this
cluster. Size in the group varies from moderate (10,286 rooms) to large (319,135 rooms).
The chains are mostly similar in type, are from industrialized Western nations, and almost
all have experience in many countries. They can be considered competitors, and as such
one explanation for the market profile similarity is that the chains want to keep track of
their competitors and so try to locate in similar locations, as multiple point competition
would suggest.
Cluster J: Connor Jacobson. Best Western. Raintree. J & B and York. The
regression coefficients within the group for each variable appear to be similar in sign and
magnitude. The extent to which market similarity is explained by the regression model
also appears similar within the group. This appears to be a cluster of small chains(under
106
3,300 rooms) with limited intemational experience (1 to 3 countries), with the exception
of Best Westem (265,285 rooms and 31 countries). Type of hotel is unknown except for
Best Westem (luxury-budget).
With the exception of Best Westem (265,285 rooms), mimetic isomorphism gives
one explanation for the source of market similarity in this cluster. The rest of the chains
are small in size (under 3,300 rooms). Since complete information about type of hotel is
unavailable, it is hard to assess whether the chains are key rivals.
Outliers - Treadwav, HUSA, HIDC and Clement Chen. What is it about these
chains that makes them unique? Nothing stands out as being unique. It is interesting,
however, that once again the outliers have very limited intemational experience (in 1
country only).
To summarize, besides market similarity, chains in 1991 seem to have at least one
characteristic in common, namely international experience, nationality, type of hotel, or
size. However, there does not appear to be one characteristic which is found to be
common in all clusters, although intemational experience does appear relatively more
dominant. Size is similar in three of the ten clusters in 1991.
1992 Results
The dendogram for 1992 is shown in Figure 9. The characteristics of each cluster
are outlined in Table 58.
Cluster A: Rihga and Joumevs. The regression coefficients within the group for
each variable appear to be similar in magnitude. The extent to which market similarity is
107
explained by the regression model also appears similar within the group. This appears to
be a group of chains with little intemational experience (1 or 2 countries). Type of hotel
is unknown.
Mimetic isomorphism gives one explanation for the source of market similarity in
this cluster. Both chains are moderate in size (4,291 to 12,376 rooms). Since complete
information about type of hotel is unavailable, it is hard to assess whether the chains are
key rivals.
Cluster B: Flag and HIDC. The regression coefficients within the group for each
variable appear to be similar in magnitude. The extent to which market similarity is
explained by the regression model also appears similar within the group. This appears to
be a group of small (under 4,000 rooms), American chains with little intemational
experience (3 or less countries). Type of hotel is unknown.
Mimetic isomorphism gives one explanation for the source of market similarity in
this cluster. Both chains are small in size (under 4,000 rooms). Since complete
information about type of hotel is unavailable, it is hard to assess whether the chains are
key rivals.
Cluster C: Interwest and Rydges. The regression coefficients within the group for
each variable appear to be similar in sign and magnitude. The extent to which market
similarity is explained by the regression model also appears similar within the group.
This appears to be a group of srriall (under 5,000 rooms), Australian chains with little
intemational experience (in 1 country). Type of hotel is unknown.
108
Mimetic isomorphism gives one explanation for the source of market similarity in
this cluster. Both chains are small in size (under 5,000 rooms). Since complete
information about type of hotel is unavailable, it is hard to assess whether the chains are
key rivals.
Cluster D: Accor. Churchman and Durbin. The regression coefficients within the
group for each variable appear to be similar in sign and magnitude. The extent to which
market similarity is explained by the regression model also appears similar within the
group. This appears to be a group of small (under 5,000 rooms), American chains with
little intemational experience (in 1 country). Type of hotel is unknown except for Accor
(luxury-budget).
Mimetic isomorphism gives one explanation for the source of market similarity in
this cluster. The chains are small in size (under 5,000 rooms). Since complete
information about type of hotel is unavailable, it is hard to assess whether the chains are
key rivals.
Cluster E: Preferred, Hyatt International, Le Meridien, Frantel, Choice, Hilton
Intemational, Intercontinental, Ramada International, Holiday Inn, Sheraton, Golden
Tulip and SRS. The regression coefficients within the group for each variable appear to
be similar in magnitude. The extent to which market similarity is explained by the
regression model also appears similar within the group. Intemational experience varies
between 20 and 59 countries. Type of hotel is unknown for Frantel. Choice has a wide
range of hotel types. Ramada and Holiday are mid-priced chains. All the other chains are
luxury chains. This appears to be a cluster of multinational, luxury chains.
109
Mimetic isomorphism does not explain the source of market similarity in this
cluster. Size in the group varies from moderate (12,893 rooms) to large (322,527 rooms).
The chains are similar in type (luxury), are from industrialized nations (U.S., France,
U.K., Hong Kong, Netherlands and Germany), and have experience in many countries
(over 20). They can be considered competitors. One explanation for the market profile
similarity is that the chains want to keep track of their competitors and so try to locate in
similar locations.
Cluster F: Oberoi, Movenpick, and Swiss International. The regression
coefficients within the group for each variable appear to be similar in magnitude. It is
hard to establish the basis of similarity in this group. Size varies from 6,024 to 11,054
rooms. Intemational experience varies from 7 to 12 countries. Degree of
intemationalization is 0.5 to 0.7. There are no first movers. Home countries are India
and Switzerland. Type of hotel is unknown except for Movenpick which is mid-priced.
Mimetic isomorphism partially explains the source of market similarity in this
cluster. Size in the group varies, but not greatly (6,024 to 11,054 rooms). Since
complete information about type of hotel is unavailable, it is hard to assess whether the
chains are key rivals.
Cluster G: Prince. Raintree. Tai International, Dorint, Best Westem and VMS.
The regression coefficients within the group for each variable are similar in magnitude.
There is similarity in the extent to which market similarity is explained by the regression
model. This appears to be a group of chains with limited intemational experience(under
8 countries) except for Best Westem (32 countries). There does not seem to be much else
110
which explains the market similarity. Taj International is a luxury chain. Best Western is
a luxury-budget chain and VMS has a wide range of types of hotels. Type of hotel is
unknown for the rest of the chains.
Mimetic isomorphism does not explain the source of market similarity in this
cluster. Size in the group varies greatly (2,973 to 265,385 rooms). Since complete
information about type of hotel is unavailable, it is hard to assess whether the chains are
key rivals.
Cluster H: Southem Pacific. Consort, Inter, Scandic and Westin. The regression
coefficients within the group for each variable are similar in magnitude. There is
similarity in the extent to which market similarity is explained by the regression model.
This appears to be a group of chains with moderate international experience (5 to 12
countries) and moderate size (10,804 to 34,596 rooms). There does not seem to be much
else which explains the market similarity. Westin is a luxury chain. Type of hotel is
unknown for the rest of the chains.
Mimetic isomorphism does not explain the source of market similarity in this
cluster. Size in the group varies (10,804 to 34,596 rooms). Since complete information
about type of hotel is unavailable, it is hard to assess whether the chains are key rivals.
Outliers: Pacific Island. Cunard. Doubletree, Delta and Okura. What is it about
these chains that makes them unique? Nothing stands out as being unique. It is
interesting, however, that once again the outliers have very limited intemational
experience (in 4 or less countries).
Ill
To summarize, besides market similarity, chains in 1992 seem to have at least one
characteristic in common, namely intemational experience, nationality, type of hotel, or
size. However, there does not appear to be one characteristic which is found to be
common in all clusters, although intemational experience does appear relatively more
dominant. Size is similar in four of the eight clusters in 1992.
1993 Results
The dendogram for 1993 is shown in Figure 10. The characteristics of each
cluster are outlined in Table 59.
Cluster A: Friendly, Mount Charlotte and Swallow. The regression coefficients
within the group for each variable are similar in magnitude. There is similarity in the
extent to which market similarity is explained by the regression model. This appears to
be a group of mostly small (4,284 to 14,509 rooms), American and British chains with
little intemational experience (under 3). Type of hotel is unknown.
Mimetic isomorphism partially explains the source of market similarity in this
cluster. Size in the group varies, but not greatly. Since complete information about type
of hotel is unavailable, it is hard to assess whether the chains are key rivals.
Cluster B: Best Western. Devon and TCC. The regression coefficients within the
group for each variable are similar in magnitude. There is similarity in the extent to
which market similarity is explained by the regression model. This appears to be a group
of American and Canadian chains with varied international experience (1 to 41
112
countries). Best Westem is a luxury-budget chain. Type of hotel is unknown for the
other chains.
Mimetic isomorphism does not explain the source of market similarity in this
cluster. Size in the group varies greatly (970 to 265,385 rooms). Since complete
information about type of hotel is unavailable, it is hard to assess whether the chains are
key rivals.
Cluster C: BTH, Connor Jacobson, Consort, Inter, and Stakis. The regression
coefficients within the group for each variable are similar in magnitude. There is
similarity in the extent to which market similarity is explained by the regression model.
This appears to be a group of mostly small chains (381 to 14,305 rooms) with limited
intemational experience (2 to 12 countries). Type of hotel is unknown.
With the exception of Consort, mimetic isomorphism gives one explanation for
the source of market similarity in this cluster. The rest of the chains are small in size
(under 5,000 rooms). Since complete information about type of hotel is unavailable, it is
hard to assess whether the chains are key ri\'als.
Cluster D: Mandarin, Regent Intemational. Pan Pacific and Shangri-la. The
regression coefficients within the group for each variable are similar in magnitude. There
is similarity in the extent to which market similarity is explained by the regression model.
This appears to be a group of mostly small (3,899 to 10,608 rooms), luxury chains from
Hong Kong, with moderate intemational experience (6 to 10 countries). The chains are
all luxury chains.
113
Mimetic isomorphism somewhat explains the similarity in this cluster. All the
chains are moderate in size (under 11,(X)0 rooms), though there is variation within the
group. These chains are of the same type (luxury), are from industrialized countries, and
all have a moderate amount of intemational experience (6 to 10 countries). They can be
considered direct competitors, locating in similar cities to keep track of each other, as
multiple point competition would suggest.
Cluster E: Nikko, Marriott, and Preferred. The regression coefficients within the
group for each variable are similar in magnitude. There is similarity in the extent to
which market similarity is explained by the regression model. This appears to be a group
of luxury chains with moderate intemational experience (11 to 19 countries).
Mimetic isomorphism does not explain the source of market similarity in this
cluster. Size in the group varies greatly (13,550 to 93,484 rooms). These chains are of
the same type (luxury), are from industrialized countries (US and Japan), and all have a
moderate amount of intemational experience (11 to 19 countries). They can be
considered direct competitors, locating in similar cities to keep track of each other, as
multiple point competition would suggest.
Cluster F: SAS, Scandic, HUSA intemational, Southem Pacific, Hyatt
Intemational, Intercontinental, Hilton Intemational. Le Meridien, Choice. Ramada
Intemational, Holiday Inn, Sheraton, Golden Tulip, SRS and Tmsthouse PLC. The
regression coefficients within the group for each variable appear to be similar in
magnitude. Size varies from 9,807 to 349,226 rooms. Intemational experience varies
from 21 to 53 with the exception of SAS, Scandic, HUSA Intemational and Southem
114
Pacific. Type of hotel is unknown for Scandic, HUSA Intemational, Southem Pacific and
Tmsthouse PLC. SAS, Ramada Intemational and Holiday are mid-priced hotels. The
rest are luxury chains. This appears to be a cluster of multinational, luxury chains.
Mimetic isomorphism does not explain the source of market similarity in this
cluster. The size varies too greatly (9,807 to 349,226 rooms). The chains are mostly
similar in type (luxury), and have experience in many countries (over 21). They can be
considered competitors. One explanation for the market profile similarity is that the
chains want to keep track of their competitors and so try to locate in similar locations, as
multiple point competition would suggest.
Cluster G: Days Inn, Radisson and Westin. The regression coefficients within the
group for each variable appear to be similar in magnitude. Size varies from 35,233 to
138,721 rooms. Intemational experience varies from 4 to 12 countries. Days Inn is a
luxury-budget chain, Radisson is mid-priced and Westin is a luxury chain. This appears
to be a highly varied group of American chains. Mimetic isomorphism does not explain
the market similarity, because of the large variation between chains on size (35,233 to
138,721 rooms). Given the wide range of types of hotels, it is difficult to establish how
these chains could be key rivals.
Cluster H: Super 8, Tmsthouse and Westcoast. The regression coefficients
within the group for each variable are similar in magnitude. There is similarity in the
extent to which market similarity is explained by the regression model. This appears to
be a group of American chains with little international experience (1 or 2 countries).
Super 8 is an economy chain. Type of hotel is unknown for the other chains.
115
Mimetic isomorphism does not explain the source of market similarity in this
cluster. Size in the group varies greatly (4,673 to 56,643 rooms). Since complete
information about type of hotel is unavailable, it is hard to assess whether the chains are
key rivals.
Cluster I: Budget. Hospitality Intemational and York. The regression coefficients
within the group for each variable are similar in magnitude. There is similarity in the
extent to which market similarity is explained by the regression model. This appears to
be a group of American and Canadian chains with little international experience (1 or 2
countries). Budget is a luxury-budget chain. Type of hotel is unknown for the other
chains.
Mimetic isomorphism does not explain the source of market similarity in this
cluster. Size in the group varies (1,336 to 19,165 rooms). Since complete information
about type of hotel is unavailable, it is hard to assess whether the chains are key rivals.
Outliers: Caesar Park, Interbenelux, Pacific Island, Ritz, Doubletree and HUSA.
What is it about these chains that makes them unique? Besides the fact that Caesar Park
is the only Brazilian chain in 1993, nothing else stands out as being unique about these
chains. It is interesting, however, that once again the outliers have very limited
intemational experience (in 1 or 2 countries).
Besides market similarity, chains in 1993 seem to have at least one characteristic
in common, namely intemational experience, nationality, type of hotel, or size. However,
there does not appear to be one characteristic which is common in all clusters, although
116
intemational experience does appear relatively more dominant. Size is similar in three of
the nine clusters in 1993.
To reiterate, the aim of the study is to distinguish between rivals. Rivals are
determined not only by market similarity, but also by similarity on other dimensions.
Cluster analysis was used to identify those chains with market similarity. An assessment
of the similarity of other characteristics (size, intemational experience, degree of
intemationalization, leader, first mover and nationality) between pairs of chains was then
conducted because firms with similar market locations, but no other similar
characteristics are unlikely to be considered a major competitive threat.
The results of the cluster analysis and subsequent analysis of characteristics for
the entire ten year period yields some interesting findings. Some clusters appear to have a
very clear source of characteristic similarity, besides market profile. However, there are
also some clusters which appear to have no basis of similarity other than market
similarity. Those clusters with characteristic similarity among group members appear to
have at lest one variable in common, however, no one variable was common in all of
these clusters. Specifically, the size variable is not common in all these clusters.
In the final chapter of the dissertation, the conclusions, implications, strengths and
weaknesses of the study are discussed.
117
Table 1. 1984 Regression Analyses Results
Chain
Intercept
Size
lEXP
DOI
Nationality
Model p
Rsq
Accommodation
0.6083
0.0193
0.0455
0.0945
-0.0089
0.5952
0.0510
American
0.6007
-0.0093
0.1177
0.0557
-0.0668
0.0035
0.2559
Best Westem
0.7116
0.0035
-0.0285
0.1194
-0.0165
0.7160
0.0389
BTH
0.7236
0.1092
0.0116
-0.0631
0.0129
0.0043
0.2490
Budget
0.5184
0.1596
-0.0592
0.0491
-0.0395
0.0076
0.2305
Ciga
0.7943
-0.0010
0.0957
0.1380
-0.2823
0.0310
0.1810
Commonwealth
0.6386
-0.0049
0.0792
0.0717
0.0221
0.0078
0.2299
Connor Jacobson
0.6902
-0.0634
0.2317
-0.0313
0.0000
0.4113
Canadian Pacific
0.5493
-0.0999
0.1886
0.0167
0.0577
0.0001
0.3526
Crest
0.7021
0.0185
0.0020
0.0344
-0.0833
0.6619
0.0444
Cunard
0.6721
0.0716
0.0076
-0.1090
0.0310
0.0024
0.2675
Days Inn
0.6015
-0.0611
-0.0621
0.0630
-0.1083
0.0123
0.2147
Dorint
0.5731
0.0725
0.0680
0.0913
0.0237
0.0480
0.1656
Omni
0.5433
-0.0673
0.0264
0.1053
0.0462
0.1970
0.1075
First Hospitality
0.4498
0.0579
0.0031
0.0851
0.0153
0.3685
0.0777
Four Seasons
0.6526
0.0158
0.1554
-0.1015
-0.0089
0.0002
0.3432
Frantel
0.8366
0.1251
-0.0524
0.0814
-0.2000
0.0048
0.2458
Friendship
0.7073
0.0450
0.0376
0.0559
-0.0758
0.1165
0.1303
Hilton
0.9781
-0.0278
-0.1524
-0.0519
0.0281
0.0000
0.6690
Howard Johnson
0.8092
-0.0843
-0.0291
0.0766
-0.0986
0.0029
0.2623
Holiday Inn
0.8948
-0.0251
-0.0863
0.0886
-0.0044
0.0018
0.2763
Hospitality Consultants
0.5733
0.0470
0.0202
0.0814
-0.0481
0.0053
0.2428
Hospitality International
0.6381
-0.0545
0.0640
-0.0223
-0.1223
0.0070
0.2334
HUSA
0.6619
0.0256
-0.0210
0.1738
0.0810
0.1181
Hyatt Intemational
0.9653
-0.0128
-0.1704
-0.0499
0.0062
0.0000
0.5731
Intercontinental
0.9794
-0.0059
-0.1603
-0.0468
0.0300
0.0000
0.6356
Jack Tar
0.6179
-0.0107
0.0882
-0.0047
-0.0604
0.1833
0.1108
Jolly of Italy
0.8245
-0.1181
0.1756
0.0212
-0.3189
0.0050
0.2445
Loews
0.6152
0.0525
0.0846
0.0335
-0.0082
0.0162
0.2051
Marriott
0.7851
-0.0735
-0.2293
0.1228
0.0758
0.0000
0.4247
Le Meridien
0.9485
0.0116
-0.1644
-0.0857
0.0169
0.0000
0.5795
Merlin
0.5191
0.0003
0.1701
0.0644
.
0.0010
0.2565
Penta
0.8070
0.0388
-0.2604
0.0820
-0.0291
0.0007
0.3034
Pratt
0.6421
0.0447
0.0326
-0.0255
-0.0868
0.0156
0.2060
Preferred
0.8627
-0.0649
-0.2604
0.1109
0.0207
0.0000
0.4461
Princess
0.6016
-0.0011
0.1394
-0.0018
-0.0772
0.0102
0.2210
Quality
0.7501
-0.0497
0.0450
0.1176
-0.0134
0.0630
0.1522
Radisson
0.5229
-0.0254
0.1346
0.0811
0.0146
0.0000
0.4337
Ramada
0.9231
-0.0760
-0.1250
0.0721
-0.0038
0.0000
0.5746
Rank
0.5541
0.0306
0.1201
0.1337
0.0169
0.0000
0.4044
Regent International
0.8678
0.0889
-0.1341
-0.1588
-0.0026
0.0000
0.3968
Rodeway
0.6794
-0.0538
0.0782
-0.0060
-0.1012
0.0041
0.2513
Sara
0.3925
-0.0059
0.1372
0.0936
0.0000
0.4285
Sas
0.7034
0.0983
-0.1036
-0.1670
.
0.1955
0.0840
Sheraton
0.9368
-0.0144
-0.1383
0.0691
0.0095
O.OOOC
0.6261
118
Table 1. Continued
Intercept
Chain
Size
lEXP
DOI
Nationality
Model p
Rsq
Sofitel
0.8713
0.1561
-0.0235
0.0113
-0.1992
0.0048
0.2458
Sonesta
0.5825
0.1240
-0.0222
-0.0434
0.1117
0.0134
0.2103
SRS
1.0706 -0.0082
-0.1563 -0.0478
-0.0896
0.0000
0.5734
Super 8
0.5355
0.1662
0.0432
0.0923
-0.0032
0.0160
0.2054
Tai
0.8395
-0.0487
-0.0499
-0.2173
0.0017
0.2453
Thistle
0.6605
0.1130
-0.0055
0.0959
-0.0741
0.0140
0.2100
Tollman
0.4531
0.0771
-0.0316
0.1156
0.0870
0.1770
0.1123
Travel Intemational
0.7478
-0.0517
0.0107
0.0465
-0.0955
0.0475
0.1659
Trusthouse
0.9519
0.0290
-0.1182
-0.0775
-0.0424
0.0015
0.2831
Westin
0.8367
-0.0850
-0.0882
0.0498
-0.0453
0.0003
0.3226
Westours
0.5944
0.0387
0.0241
0.1267
-0.0054
0.4575
0.0666
Wyndham
0.5263
0.0234
0.1175
0.0039
0.0295
0.0019
0.2746
York
0.3607
0.1180
-0.1108
0.1408
0.0710
0.0248
0.1820
Table 2. 1984 Well-Predicted Chains
Chain
Intercept
Size
DOI
lEXP
Nationality
Model p
Rsq
Hilton
0.9781
-0.0278
-0.1524 -0.0519
0.0281
0.0000
0.6690
Hyatt Intemational
0.9653
-0.0128
-0.1704 -0.0499
0.0062
0.0000
0.5731
Intercontinental
0.9794
-0.0059
-0.1603 -0.0468
0.0300
0.0000
0.6356
Le Meridien
0.9485
0.0116
-0.1644 -0.0857
0.0169
0.0000
0.5795
Ramada
0.9231
-0.0760
-0.1250
0.0721
-0.0038
0.0000
0.5746
Sheraton
0.9368
-0.0144 -0.1383
0.0691
0.0095
0.0000
0.6261
SRS
1.0706 -0.0082
-0.0478
-0.0896
0.0000
0.5734
-0.1563
Table 3. Characteristics of 1984 well-predicted chains
Chain
SIZE
Hilton
35306
Hyatt Intemational
16893
Intercontinental
36788
Le Meridien
14183
Ramada
88150
Sheraton
129623
SRS
40314
0.9111
NAT
US
US
US
0.8882
France
Luxury
0.0983
US
US
Mid Priced
DOI
lEXP
43
28
48
29
15
50
40
0.9170
0.9830
0.2914
0.9485 Germany
119
TYPE
Luxury
Luxury
Luxury
Luxury
Luxury
Table 4. 1985 Regression Analyses Results
Chain
Intercept
SIZE
lEXP
DOI
0.1343
0.0352 -0.4059
0.0811 -0.1954
American
0.6469 -0.0293
Best Western
0.8583
0.0362 -0.1107
BTH
0.8054
0.0790 -0.0121
Budget
0.5681
0.1303
Caledonian
0.8732
0.0380 -0.0104
Ciga
0.7780 -0.0556
Comfort
0.8259
Commonwealth
0.6646 -0.0069
0.0655
Connor Jacobson
0.7575 -0.0723
0.2433 -0.1151
Canadian Pacific
0.5076 -0.0443
Crest
0.6957 -0.0079
Cunard
0.6747
Days Inn
LDR
FSTMOV
NAT
Model p
0.0001
0.3688
0.0019
0.1456
0.1494
-0.3728
-0.0114 -0.0060
0.0301
0.2108
0.0082 -0.0547 -0.2649
0.0405 -0.0331
0.0050
0.2685
-0.0628
0.0000
0.4560
-0.1059
-0.2365
-0.3339
0.0267 -0.0242
Rsq
-0.1037
-0.0079
0.0439
0.2370 -0.1964
-0.0466 -0.2559
0.0054
0.2671
0.0740 -0.1017
0.0459 -0.2834
0.0197 -0.0890
0.0025
0.2908
0.0938 -0.3386
0.0735 -0.0180
0.0080
0.2540
-0.1624
0.0935
0.0005
0.3086
0.1213
0.0704 -0.3734
-0.0462
0.0718
0.0001
0.3917
0.0534
0.0629 -0.2966
0.0692 -0.0981
0.0120
0.2422
0.0671
0.0000
0.4191
0.5906 -0.0643 -0.0087 -0.0029 -0.2104
0.0975 -0.0480
0.0069
0.2601
Dorint
0.6532
0.1226
0.0431 -0.2288
0.0951 -0.0190
0.0198
0.2254
Econolodge
0.5689 -0.1284 -0.0111
-0.0549 -0.2271
-0.0867
0.0000
0.4152
Four Seasons
0.7593
0.0684
0.0535
-0.0003 -0.0303
0.0002
0.3652
Frantel
0.8768
0.1517 -0.0471
-0.2123
0.0931 -0.2208
0.0053
0.2680
Friendship
0.7348
0.0331
0.0845 -0.0187 -0.3212
0.0537 -0.0438
0.0035
0.2813
Grand Champions
0.6064
0.0121
0.0479
0.1074 -0.2080
0.1496
0.0348
0.0862
0.1712
Hilton International
0.9658 -0.0203
-0.1153
-0.0897 -0.2613
-0.0217
0.0182
0.0000
0.5998
Holiday
0.9518 -0.0124 -0.0821
-0.0772 -0.2783
0.0429
0.0012
0.0005
0.3331
Hospitality
0.6841 -0.0672
0.0580 -0.0839
-0.3033
-0.0141
-0.1044
0.0001
0.3764
IBIS
0.7635
0.0572
0.0645 -0.1637
-0.3830
0.1025
0.0115
0.0306
0.2103
Okura
0.5705 -0.0017
0.1046 -0.1797 -0.3176
0.0320
0.1160
0.0189
0.2271
Howard Johnson
0.8294 -0.0907
0.0042
0.0031 -0.3454
0.0525 -0.0507
0.0000
0.4609
HUSA
0.6436
0.0390 -0.1433
0.2279 -0.1656
-0.0558
,
0.0004
0.3157
Hyatt
0.9469 -0.0036
-0.1402
-0.0806 -0.2586
-0.0328
0.0113
0.0000
0.5966
Intercontinental
0.9683
-0.0766
-0.2402
-0.0271
0.0058
0.0000
0.5961
Jack Tar
0.7459 -0.0211
0.2132 -0.0885
-0.3359
-0.0984
0.0195
0.0098
0.2489
Jolly of Italy
0.7778 -0.0792
0.1132
0.1475 -0.2528
-0.1077 -0.3064
0.0010
0.3176
Kilbourne
0.4901 -0.0583
0.1615
0.0546
0.0692
0.0001
0.3708
Loews
0.6361 -0.0095
0.0727
0.0484 -0.3512
0.0130 -0.0291
0.0018
0.3002
Magic Key
0.6241
0.0511
Meridien
0.8908
Noahs
0.5396
0.0673
Peninsula
0.7254 -0.0063
Penta
0.8885
Preferred
0.8154 -0.0504
Prince
0.0530 -0.0341
0.0336
-0.1882
0.0755 -0.3934
0.0060 -0.1271
-0.0225
-0.1146
0.1582
-0.0226
-0.0138
0.0636
-0.3046
-0.0131
-0.0561
0.0040
0.2767
0.0486 -0.1388
-0.0817 -0.2506
-0.0350
0.0243
0.0000
0.5193
0.0101
0.0582 -0.2639
0.0548
.
0.0231
0.1967
0.0826 -0.4338
-0.3603
-0.0964
.
0.0008
0.2969
-0.1722
-0.2548
-0.0656
-0.0382
0.0000
0.4545
-0.2069
0.1265 -0.1899
-0.0665
0.0244
0.0000
0.4894
0.5034 -0.0879
0.0800
0.0690 -0.3641
-0.0724
-0.0074
0.0571
0.1873
Quality
0.8979 -0.0562
0.0059 -0.1463
-0.3681
0.0667
0.0115
0.0016
0.3035
Radisson
0.5459
0.1422
0.0841
0.0473 -0.2358
0.0872
0.0225
0.0088
0.2522
Ramada
0.7213 -0.0551
0.1185
0.1125
-0.4140
0.0171
0.0082
O.OOOC)
0.4768
Rank
0.6009
0.0387
0.0542
0.1025
-0.2375
0.0286
-0.0054
0.014^ \
0.2363
Regent International
0.9142
0.0291 -0.1363
-0.1609
-0.0627
-0.0735
-0.0187'
O.OOOC)
0.4797
0.0680 -0.0551
0.0193 -0.1611
120
Table 4. Continued
Intercept
Chain
SIZE
lEXP
DOI
Rodeway
0.7078 -0.0779
0.1111 -0.0432
Sara
0.5327
0.0218
0.0919
SAS
0.7367
0.0694 -0.0590
Sheraton
0.9699 -0.0115
Sofitel
LDR
NAT
FSTMOV
-0.3805
-0.0010
0.1676 -0.1297
Model p
Rsq
0.0000
0.4978
-0.0361
0.0001
0.3585
-0.0809
0.0201
0.2014
-0.0562
-0.2883
-0.2760
-0.1073
-0.0861
-0.2502
0.0165 -0.0054
0.0000
0.5270
0.9391
0.1606 -0.0826
-0.0532
-0.2584
0.0656 -0.2096
0.0012
0.3129
Sonesta
0.5988
0.0900
0.0103 -0.1223
-0.3882
0.0337
0.0998
0.0024
0.2919
SRS
1.1136 -0.0162
-0.0672
-0.3105
0.0152 -0.1447
0.0000
0.5051
Super 8
0.7391 -0.1062
0.0183 -0.0501
-0.3052
0.0268 -0.0924
0.0000
0.4634
Swissotel
0.5650 -0.0375
0.0502 -0.3971
0.0001
0.3121
Taj
0.6553
0.0726 -0.1112
0.0009
0.3835
0.1023
Thistle
0.7131
0.0698
0.0516
0.0086 -0.2576
0.0562 -0.0882
0.0019
0.2989
Tollman
0.4758 -0.0199
-0.0262
0.2000 -0.2309
0.0396
0.0062
0.2631
Travelers
0.4614
0.0619
0.0544 -0.0851
-0.2852
-0.0353 -0.0273
0.0010
0.3096
Travelodge
0.7309 -0.0230
0.1211 -0.0366
-0.3291
0.0439 -0.0476
0.0007
0.3265
Trusthouse
0.8903
0.0726 -0.2034
0.0764 -0.0537
0.0046
0.2727
VMS
0.5071 -0.0942
0.0775 -0.1020 -0.2133
-0.0827 -0.0999
0.0030
0.0060
Welcom
0.4884
0.0387
0.0214
0.1383 -0.2057
0.1157
0.1210
0.0540
0.1895
Westin
0.8311 -0.0765
-0.1254
0.1611 -0.2891
0.0078 -0.0005
0.0000
0.5532
Westours
0.6650 -0.0404
0.1101
0.0384 -0.1661
0.1381 -0.0126
0.1067
0.1625
Wyndham
0.5193
0.0540
0.0815 -0.0010 -0.1629
0.0756
0.0314
0.0223
0.2214
York
0.5495
0.0567
0.0254
0.1011 -0.0086
0.0098
0.2490
-0.1139
0.0550 -0.1369
-0.0669
-0.0141
0.1646
0.0261 -0.2376
0.0005
-0.0548
Tables. 1985 Well-Predicted Chains
Intercept
Chain
SIZE
lEXP
DOI
LDR
NAT
FSTMOV
Model p
Rsq
Hilton International
0.9658 -0.0203
-0.1153
-0.0897 -0.2613
-0.0217
0.0182
0.0000
0.5998
Hyatt
0.9469 -0.0036
-0.1402
-0.0806 -0.2586
-0.0328
0.0113
0.0000
0.5966
Intercontinental
0.9683
0.0060 -0.1271
-0.0766
-0.2402
-0.0271
0.0058
0.0000
0.5961
Meridien
0.8908
0.0486 -0.1388
-0.0817 -0.2506
-0.0350
0.0243
0.0000
0.5193
Sheraton
0.9699 -0.0115
-0.1073
-0.0861
-0.2502
0.0165 -0.0054
0.0000
0.5270
SRS
1.1136 -0.0162
-0.1139
-0.0672
-0.3105
0.0152 -0.1447
0.0000
0.5051
Westin
0.8311 -0.0765
-0.1254
0.1611 -0.2891
0.0078 -0.0005
0.0000
0.5532
Table 6. Characteristics Of 1985 Well-Predicted Chains.
Chain
SIZE
Hilton International
36378
Hyatt
17581
Intercontinental
37121
Le Meridien
14183
Sheraton
139605
SRS
46008
Westin
32447
lEXP
45
26
47
31
61
42
12
DOI
0.9181
0.9747
0.8832
0.8882
0.8355
0.9406
0.4480
121
LDR FSTMOV
NL
S
NL
S
S
NL
NL
S
F
NL
NL
S
NL
S
NAT
US
US
US
TYPE
Luxury
Luxury
Luxury
France
Luxury
US
Luxury
Germany
Luxury
US
Luxury
Table 7. 1986 Regression Analyses Results
Chain
Accor
Intercept
0.5549
DOI
LDR
0.0359
-0.0521
-0.0508
0.0833
-0.0520
0.0165
0.2043
SIZE
lEXP
0.0496
FSTMOV
NAT
Model p
Rsq
Best Western
0.8019
0.0037
-0.0140
0.0723
0.0464
-0.1370
-0.0494
0.0649
0.1603
BTH
0.8204
0.0374
-0.0607
0.0329
-0.0957
-0.1041
-0.0360
0.0832
0.1516
Budget
0.5137
0.1330
-0.0613
0.0205
-0.0346
0.1072
-0.0622
0.0006
0.2938
Caledonian
0.7770
0.1027
-0.1066
-0.0292
-0.0499
0.0739
-0.1322
0.0000
0.4223
Ciga
0.8071
-0.0364
0.0728
0.1905
-0.2168
0.0514
-0.3248
0.0075
0.2275
Clarion
0.6976
0.1421
-0.0269
0.0290
0.0406
-0.1397
-0.0156
0.0272
0.1889
Comfort
0.9046
0.0651
-0.1297
0.0442
-0.1353
-0.0878
-0.1144
0.0004
0.3034
Commonwealth
0.5962
-0.0161
0.0345
0.0997
-0.1566
0.0716
0.0243
0.0452
0.1725
Connor Jacobson
0.6155
-0.0279
0.1884
-0.0260
0.0097
0.1136
0.0002
0.3020
Canadian Pacific
0.5175
-0.1031
0.1858
0.0240
-0.0555
0.0333
0.0462
0.0000
0.4169
Crest
0.6820
-0.0324
0.0478
0.0456
-0.1141
0.0875
-0.1114
0.0152
0.2067
Cunard
0.5684
0.0818
0.0060
-0.1490
-0.0730
0.0191
0.0799
0.0000
0.3737
Days Inn
0.5265
-0.0470
-0.0235
0.0124
0.0024
0.0805
-0.0837
0.0048
0.2401
Dorint
0.5279
0.0808
0.0417
0.0079
0.0673
0.1192
0.0487
0.0192
0.1998
Econo
0.4990
-0.1075
-0.0025
-0.0480
-0.0494
0.0312
-0.0943
0.0002
0.3224
Four Seasons
0.7084
-0.0098
0.1207
-0.2185
0.1288
-0.0436
0.0036
0.0001
0.3383
Frantel
0.9547
0.0491
-0.1483
0.0765
-0.0082
-0.0758
-0.0640
0.0000
0.3812
Friendship
0.7084
0.0064
0.0535
-0.0466
-0.0425
0.1123
-0.0892
0.0024
0.2584
Grand Champions
0.7740
0.0106
-0.0082
0.0442
0.0843
-0.1512
-0.0590
0.0856
0.1506
Hilton International
0.9705
-0.0162
-0.1812
-0.0100
-0.1024
-0.0265
0.0370
0.0000
0.7130
Howard Johnson
0.8157
-0.0707
0.0304
-0.0074
-0.0637
-0.0555
-0.0812
0.0002
0.3162
Holiday Inn
0.8588
-0.0023
-0.0921
-0.0018
-0.0366
0.1027
-0.0291
0.0000
0.3620
Horizon
0.7050
0.0918
-0.0527
0.0752
0.1245
-0.1316
-0.0219
0.0761
0.1548
Hospitality
0.6114
-0.0717
0.0565
-0.0574
-0.0947
0.0333
-0.1031
0.0026
0.2564
Hungar
0.3682
-0.1540
0.2745
-0.0916
-0.0770
-0.0281
,
0.0000
0.4356
HUSA
0.7445
0.0408
-0.1448
0.1440
-0.0502
-0.1091
,
0.0001
0.3189
Hyatt International
0.9558
0.0013
-0.1943
-0.0033
-0.1159
-0.0481
0.0182
0.0000
0.6555
Intercontinental
0.9737
0.0025
-0.1838
0.0023
-0.0877
-0.0330
0.0305
0.0000
0.6581
Jack tar
0.6585
-0.0151
0.1724
0.0069
0.0817
-0.1111
-0.0454
0.0222
0.1952
Jolly of Italy
0.7967
-0.1325
0.2329
0.0907
-0.1556
-0.0041
-0.3335
0.0001
0.3365
Kilbourne
0.3827
-0.0410
0.2119
-0.0225
-0.0497
0.0018
0.1027
0.0000
0.5686
Loews
0.6251
-0.0114
0.0255
0.0122
-0.2121
0.0780
-0.0341
0.0211
0.1969
Marriott
0.7809
-0.0523
-0.2660
0.0895
-0.1276
0.0205
0.0873
0.0000
0.5474
Mercure
0.6041
-0.0870
0.2257
0.0090
-0.0114
-0.0255
0.0250
0.0000
0.3890
Le Meridien
0.9437
0.0260
-0.1584
-0.0216
-0.0931
-0.0646
0.0083
0.0000
0.5586
Merlin
0.9018
0.0360
0.0510
0.0269
-0.0715
-0.0804
-0.2878
0.0017
0.2684
Ming
0.8916
0.0756
0.0010
-0.0528
0.0310
-0.0646
-0.3332
0.0000
0.3549
Nikko
0.9023
0.0317
-0.2828
-0.1736
-0.1688
0.0402
-0.0538
0.0000
0.4318
Novotel
0.9649
0.0231
-0.1511
0.1165
-0.0919
-0.0741
-0.0567
0.0000
0.4082
Peninsula
0.5930
0.0354
-0.0120
-0.3026
-0.1266
0.0726
,
0.0047
0.2186
Penta
0.8535
0.0170
-0.2488
-0.1285
-0.1278
0.0567
-0.0235
0.0000
0.3626
Pratt
0.7096
-0.0311
0.0742
0.0217
-0.0884
-0.0690
-0.0829
0.0099
0.2196
Preferred
0.8975
-0.0635
-0.3157
0.0111
-0.2218
0.0196
0.0322
O.OOOC
0.5757
122
Table 7. Continued
Chain
Intercept
SIZE
lEXP
DOI
LDR
FSTMOV
Prince
0.7436 -0.0399
0.0552
0.0755 -0.1027
Princess
0.5493
0.0442
0.0415
0.0200 -0.0336
Quality
0.9130 -0.0539
-0.0639
0.0340 -0.0383
Radisson
0.5568
Ramada
0.9851 -0.0526
Rank
0.5910
0.1221 -0.0848
0.0939
Regent International
0.8990
0.0214 -0.2533 -0.1002
Rodeway
0.7202 -0.0900
0.0768 -0.0500 -0.0110
Sara
0.7781 -0.0333
0.0944
0.0962 -0.0249
SAS
0.7358
0.0595 -0.1092
-0.2617 -0.1743
Scandic
0.8524
0.0081
0.1281
0.0912 -0.0288
Scan
0.6341 -0.0213
0.0895
0.0043
Sheraton
0.9899 -0.0084
Sofitel
Model p
Rsq
-0.0905
0.0059
0.2344
0.0873 -0.0534
0.0126
0.2124
-0.0256
0.0027
0.2557
0.1638 -0.0486
0.0182
0.2013
-0.0167
0.0000
0.5842
0.1217
0.1568 -0.0551
0.0256
0.1909
-0.1513
0.0526 -0.0190
0.0000
0.5336
-0.0794
0.0001
0.3283
0.0205 -0.3293
0.0002
0.3254
.
0.0014
0.2499
-0.0574 -0.2272
0.0000
0.3647
.
-0.1239
0.0010
0.2346
-0.1407
0.0204 -0.0727
-0.0588
0.0027
0.0000
0.5630
0.9326
0.0664 -0.1171
0.0379 -0.0093
-0.0994 -0.0835
0.0001
0.3464
Sonesta
0.5084
0.1097 -0.0392
-0.0253 -0.1546
0.0869
0.1238
0.0003
0.3142
SRS
1.0794 -0.0004
0.0052 -0.0753
-0.0531
-0.0985
0.0000
0.6300
Sunbelt
0.3733
0.0910 -0.0188
Super 8
0.5567
0.1064
0.0223 -0.0144
-0.1721
-0.1736
0.0228
-0.0060 -0.0949
-0.0701
NAT
-0.0905
-0.0396
-0.0498
0.0720
-0.0001
-0.0976
0.0121 -0.0526
0.0116
0.2148
0.0955
0.0350 -0.0243
0.0256
0.1636 -0.0553
0.0222
0.1953
Swissotel
0.3954 -0.0652
0.1473 -0.1259
0.1524
0.0236
,
0.0000
0.3559
Tai
0.7423
0.0455 -0.1264 -0.0605
-0.1224
-0.0878
0.0063
0.1402
0.1324
Thistle
0.6999
0.0633
0.0430 -0.0253
-0.0960
0.0643 -0.1368
0.0049
0.2396
Tollman
0.3867 -0.0022
0.0783
0.0970
0.0062
0.2330
Travelodge
0.6975 -0.0273
0.0705 -0.0543
0.1001 -0.0891
0.0008
0.2860
Trusthouse
0.9681
VMS
0.4758 -0.1311
Welcom
0.3698
Westin
0.8468 -0.0571
Westours
0.5724
0.0442 -0.0265
Wyndham
0.5145
0.0207
York
0.3318
0.0990 -0.0649
0.1698 -0.1866
-0.0223
-0.0014
0.1245 -0.0836
-0.0995
-0.0841
0.0001
0.3361
0.1152 -0.0761
-0.1708
-0.0221
-0.0547
0.0007
0.2906
0.0860
0.1022
0.1608
0.1764
0.0217
0.1960
0.0260 -0.1465
0.0571 -0.0280
0.0000
0.5405
0.0992
0.1562 -0.0366
0.0416
0.1752
0.0153 -0.1165
0.1107 -0.0746
-0.1704
0.0554
0.0833
0.0429 -0.0643
0.0810
0.0024
0.0225
0.1949
0.0222
0.0711
0.0313
0.0407
0.1759
0.1034
123
Tables. 1986 Well-Predicted Chains
Chain
Hilton International
Hyatt International
Intercontinental
SRS
SIZE
lEXP
DOI
LDR FSTMOV NAT Model p
Intercept
0.0000
0.9705 -0.0162 -0.1812 -0.0100 -0.1024
-0.0265 0.0370
0.0000
0.9558 0.0013 -0.1943 -0.0033 -0.1159
-0.0481 0.0182
0.0000
0.9737 0.0025 -0.1838 0.0023 -0.0877
-0.0330 0.0305
1.0794 -0.0004 -0.1736 0.0052 -0.0753
0.0000
-0.0531 -0.0985
Table 9. Characteristics Of 1986 Well-Predicted Chains
SRS
SIZE lEXP
LDR
DOI
36734
45 0.873441 NL
17604
26 0.974722 NL
47 0.90096 NL
36157
42 0.940576 NL
46008
124
FSTMOV
F
Ll-
Chain
Hilton International
Hyatt International
Intercontinental
F
F
NAT
US
US
US
TYPE
Luxury
Luxury
Luxury
Germany Luxury
Rsq
07130
0.6555
0.6581
0.6300
Table 10. 1987 Regression Analysis Results
Chain
Accor
Best Westem
BTH
Budget
Caledonian
Ciga
Clarion
Canadian National
Comfort
Connor Jacobson
Canadian Pacific
Crest
Cunard
Days Inn
Distinction
Dorint
Econolodge
Embassy
Four Seasons
Frantel
Friendship
Hilton Intemational
Howard Johnson
Holiday Inn
Hospitality Consultants
Hospitality Intemational
Hungar
HUSA
Hyatt Intemational
IBIS
Intercontinental
Jack Tar
Jolly of Italy
Mandarin
Mercure
Le Meridien
Merlin
Ming
Nikko
Noahs
Novotel
Oberoi
Okura
Park
Peninsula
Intercept
0.4143
0.8187
0.8566
0.5834
0.7966
07107
0.5119
0.1233
0.7585
0.6327
0.3716
0.9271
0.5571
0.5911
0.5623
0.4426
0.6737
0.7841
0.5636
1.0043
07163
0.8481
0.7864
0.9889
0.5487
0.6468
0.3446
0.6115
0.9728
0.8548
0.9963
0.5113
0.4757
0.8105
0.7019
1.0023
0.7257
07185
0.9601
0.3987
1.0038
0.8961
0.8627
0.4259
0.6677
SIZE
-0.0133
0.0102
0.0776
0.1517
0.1162
0.0784
0.1292
0.0619
0.0823
0.0687
-0.0479
0.0245
0.1136
-0.0519
0.0954
0.2056
-0.0615
0.1279
-0.0379
0.0206
0.0934
0.0120
-0.0616
-0.0236
0.1222
-0.0124
-0.1755
0.0411
-0.0135
0.0489
-0.0359
0.0211
-0.1355
0.1058
0.0036
0.0004
0.0421
0.0619
0.0637
0.0825
0.0053
0.0959
0.0091
0.0960
0.0870
lEXP
0.0842
-0.1122
-0.0993
-0.0612
-0.1170
-0.0846
0.0084
-0.1137
-0.0843
0.0265
0.0966
-0.1074
-0.0512
0.0152
0.0169
-0.0976
-0.0143
-0.1073
0.1328
-0.1974
-0.0295
-0.0572
-0.0198
-0.1212
-0.0729
-0.0086
0.2893
-0.2035
-0.2346
-0.0021
-0.1891
0.0737
0.1726
-0.2086
0.0975
-0.2165
-0.0076
-0.0472
-0.2208
-0.0608
-0.1772
-0.2223
0.0054
-0.0653
-0.0707
DOI
-0.1054
0.2072
0.0555
0.0445
-0.1316
0.0827
0.0915
0.0377
0.0200
-0.1761
0.0171
-0.0353
-0.2613
0.0858
-0.0849
0.1325
-0.0457
0.0734
-0.2548
0.0163
-0.0165
0.0420
0.0089
0.0310
0.0636
-0.0697
-0.0794
0.1217
-0.0288
-0.0295
-0.0443
0.0114
0.0526
-0.3393
0.0208
-0.0543
-0.0315
-0.0395
-0.2896
0.0692
0.0242
-0.2155
-0.3856
-0.0713
-0.1417
125
LDR
-0.0317
-0.2796
-0.2711
-0.4200
-0.1089
-0.1878
-0.4619
-0.2009
-0.1933
-0.2814
-0.0704
-0.0584
0.2811
-0.1600
-0.1880
-0.4349
-0.2025
-0.2642
-0.1628
-0.1478
-0.3516
-0.3172
-0.2391
-0.2207
-0.1986
-0.1723
0.4339
-0.0147
-0.0681
-0.3350
-0.0407
-0.3430
0.1857
-0.0517
-0.0768
-0.0880
-0.0535
-0.0878
0.1393
-0.2206
-0.1350
-0.1115
-0.0310
-0.260C
-0.0736
FSTMOV
-0.0225
-0.1142
-0.1513
0.1454
0.0778
-0.1453
0.1511
0.0606
0.1178
0.1143
0.0581
-0.1628
0.0493
0.0583
0.0479
0.1497
0.0602
-0.0964
0.0767
-0.0643
-0.0932
-0.1419
0.0900
-0.0756
0.0742
0.0888
-0.0208
0.1670
-0.0145
-0.1721
-0.0143
0.1112
0.0234
-0.0299
-0.0837
-0.0199
0.1084
0.0448
0.0872
0.1046
-0.0611
-0.0712
0.1125
0.0554[
0.0544\
NAT
-0.0559
-0.0419
-0.0219
-0.0668
-0.1272
0.0943
-0.0209
0.0967
-0.1263
,
0.1799
-0.0553
0.0904
-0.0981
-0.0310
0.0929
-0.1410
-0.2038
0.0864
-0.0502
-0.0495
-0.0685
-0.1148
-0.0178
-0.0641
-0.1514
.
0.0311
-0.0423
0.0482
-0.0269
-0.0645
-0.0340
-0.0019
-0.0129
-0.1744
-0.2513
-0.1827
-0.0385
-0.032S
-0.1831
-0.0855)
-0.0677'
Model p
0.1502
0.0001
0.0000
0.0000
0.0000
0.0000
0.0000
0.3064
0.0000
0.0000
0.0000
0.0000
0.0000
0.0003
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0001
0.0006
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0002
0.0000
0.0044
0.0171
0.0000
0.0001
0.0000
0.0240
0.0118
0.0000
0.0002
O.OOOC
Rsq
0.1263
0.3282
0.3496
0.3846
0.5253
0.4435
0.3561
0.0973
0.5177
0.4167
0.3864
0.3513
0.5077
0.3022
0.4129
0.4364
0.4186
0.3950
0.4611
0.5042
0.3435
0.2875
0.4291
0.5009
0.3900
0.3753
0.4255
0.3617
0.6004
0.3169
0.5936
0.2361
0.1981
0.4417
0.3272
0.5544
0.1878
0.2090
0.4677
0.2949
0.5142
O.OOOC1 0.4832
0.0001
0.3332
O.OOOC)
0.3826
O.OOOCJ 0.3031
Table 10. Continued
Chain
Intercept
SIZE
lEXP
Penta
0.7173
0.0198 -0.1915
Pratt
0.7408
0.0310 -0.0005
Preferred
0.9275 -0.0553
Prince
0.6535 -0.0045
Princess
0.5722
Quality
0.8376 -0.0790
Radisson
DOI
-0.1124
LDR
FSTMOV
NAT
Model p
Rsq
0.0938
0.1346
0.0158
0.0001
0.3253
0.0705 -0.2170
-0.0923
-0.1269
0.0001
0.3416
-0.0022
0.0566
0.0000
0.5282
0.0775 -0.1176
0.0146
0.2027
-0.3173
0.0923 -0.0850
0.0000
0.4133
-0.0366
0.0321 -0.1574
0.0908 -0.0637
0.0000
0.4486
0.5906 -0.0094
-0.0156
0.0135 -0.2121
0.1035 -0.1376
0.0000
0.3447
Ramada
0.9894 -0.0582
-0.1723
-0.0193
0.0000
0.5461
Rank
0.5347
0.1865 -0.0798
0.1296 -0.0773
0.0000
0.3969
Regent International
0.7454
0.0754 -0.2310 -0.1318
-0.0315
0.0924
0.0602
0.0000
0.5165
Rodeway
0.6379 -0.0313
0.0006 -0.0795
-0.1847
0.0953 -0.1181
0.0000
0.3484
Safat
0.4832 -0.0343
0.1483 -0.3338
0.0000
0.3182
Sara
0.6808 -0.0099
0.0198
0.0983 -0.2254
0.0001
0.3198
SAS
0.6939
0.0563 -0.0737 -0.2958
Scandic
0.9007
0.0699
Sheraton
0.9996 -0.0266
Sofitel
-0.3347 -0.1577
0.0268
0.1411 -0.0808
0.1897
0.0887 -0.1550
-0.0123
-0.0059
-0.0404
0.2061 -0.4408
-0.0341
-0.0481
0.1486 -0.1461
-0.1220
0.1094
.
0.0000
0.3269
0.0565
0.1655 -0.3700
-0.1359
-0.1915
0.0000
0.4004
-0.1672
0.0336 -0.1388
-0.0376
0.0174
0.0000
0.5596
0.9870
0.0380 -0.2160
-0.0189 -0.1707
-0.0772
-0.0611
0.0000
0.4745
Sonesta
0.5016
0.1132 -0.0862
-0.2213
-0.1137
0.1078
0.1699
0.0000
0.4015
SRS
1.1438 -0.0256
-0.0439
-0.0528
-0.0462
-0.1380
0.0000
0.5943
Sunbelt
0.4262
0.0413 -0.1141
0.0000
0.4125
Super 8
0.6927 -0.0510
0.0601 -0.1218
0.0000
0.3886
Swissotel
-0.1748
0.1177 -0.1113
0.0321 -0.1311
0.0213 -0.0429
-0.2026
0.8365
0.0633 -0.1494 -0.4426
0.0082
-0.0770
,
0.0000
0.3910
Taj
0.8897
0.0751 -0.2309
0.1180
-0.1461
-0.0194
0.0000
0.3450
Thistle
0.6604
0.1374 -0.1217
0.0717 -0.2802
0.1146 -0.1589
0.0000
0.3937
Tollman
0.3925 -0.0038
0.0112
0.1237
0.2697
0.0739
0.1180
0.0001
0.3212
Travelodge
0.8368 -0.0412
0.0347 -0.0136 -0.2675
-0.0955
-0.0999
0.0000
0.3505
Trusthouse
1.0095
0.1494 -0.1395
-0.0930 -0.0886
0.0000
0.4831
VMS
0.5931 -0.0615
0.0264 -0.2043
0.0634 -0.0688
0.0002
0.3141
Westin
0.8776 -0.0765
-0.1650 -0.0453 -0.0142
0.0501 -0.0681
0.0000
0.4286
Westours
0.4690
0.0793
0.0172
0.2161 -0.3258
0.1237 -0.0090
0.0000
0.3547
Wyndham
0.4885
0.0830 -0.0074
0.1062 -0.3530
0.1075 -0.0167
0.0004
0.3000
0.0078 -0.1822
0.0208
-0.1728
126
Table 11. 1987 Well -Predicted Chains
Intercept
Chain
SIZE
lEXP
DOI
LDR
FSTMOV
NAT
Model p
Rsq
Hyatt International
0.9728
-0.0135
-0.2346
-0.0288
-0.0681
-0.0145
0.0311
0.0000
0.6004
Intercontinental
0.9963
-0.0359
-0.1891
-0.0443
-0.0407
-0.0143
0.0482
0.0000
0.5936
Le Meridien
1.0023
0.0004
-0.2165
-0.0543
-0.0880
-0.0199
-0.0129
0.0000
0.5544
Ramada
0.9894
-0.0582
-0.1723
-0.0059
-0.0404
-0.0341
-0.0193
0.0000
0.5461
Sheraton
0.9996
-0.0266
-0.1672
0.0336
-0.1388
-0.0376
0.0174
0.0000
0.5596
SRS
1.1438 -0.0256
-0.1748
-0.0439
-0.0528
-0.0462
-0.1380
0.0000
0.5943
Table 12. Characteristics Of 1987 Well-Predicted Chains
Chain
SIZE
Intercontinental
41547
Le Meridien
22077
Ramada
97903
Sheraton
143557
SRS
48838
28
52
34
20
62
43
0.3073
0.8899
0.9281
0.1384
0.3239
0.9286
127
LDR FSTMOV
NAT
TYPE
NL
Luxury
US
Luxury
NL
US
F
France
Luxury
NL
NL
F
US
Mid-priced
Luxury
NL
F
US
Germany
Luxury
NL
F
Ll_
60705
DOI
Li_
Hyatt International
lEXP
Table 13. 1988 Regression Analys is Results
Chain
Intercept
Accor
0.4643
Aircoa
DOI
LDR
SIZE
lEXP
0.0094
0.0393 -0.0274 -0.2483
0.0018 -0.1325 0.000938 0.2201
0.5172 -0.0399
0.0722
0.1563 -0.0725
0.1567 -0.0440 0.000002 0.3314
Atina
0.3485
0.0199
0.0840
0.0280 -0.1504
0.0155 -0.0082 0.000000 0.3759
Atlas
0.3681
0.0959 -0.0228
0.0786 -0.1946
0.0637 -0.0578 0.000002
0.3337
Best Westem
0.8906 -0.0114 -0.1368
0.1338 -0.0640
-0.1498 -0.0198 0.000002
0.3306
BTH
0.9385
0.0351 -0.1387 -0.1259 -0.2366
Budget
0.5526
0.0358
Caesar Park
0.3434 -0.0862
0.2084 -0.1477 -0.0291
Churchman
0.5681
0.0771 -0.0025 -0.2406
Clarion
0.6003 -0.0562
0.0659
Clement Chen
0.4434 -0.0001
0.0899 -0.0478
Canadian National
0.3368 -0.0740
0.1761 -0.0380 -0.0312
Commonwealth
0.5592 -0.0242 -0.0014
Compri
0.5303 -0.0572
0.0516 -0.0507 -0.2110
0.0105 -0.1572 0.000055 0.2747
Connor Jacobson
0.5968
0.0560 -0.2168 -0.0188
0.1184
Continental
0.4988 -0.0127
0.0892
0.1543 -0.0458 0.000002
Caledonian
0.8381
0.1088 -0.1379 -0.1929 -0.1967
0.0780 -0.1578 0.000000 0.4795
Country Hospitality
0.3856
0.0888 -0.0185
0.0395 -0.2630
0.0252 -0.0761 0.000001 0.3444
Crest
0.6935 -0.0035 -0.1609
0.1967 -0.1242
0.0596 -0.0728 0.008042 0.1738
Cunard
0.5312
Days Inn
0.6476 -0.0954 -0.0300
0.0206 -0.2097
0.0259 -0.1269 0.000005
Econolodge
0.5962 -0.1075 -0.0209
0.0275 -0.1634
0.0655 -0.1429 0.000001 0.3424
Embassy
0.7321
0.0679
0.0504
0.0657 -0.2170
-0.0944 -0.1795 0.000001 0.3402
Forte
0.6808 -0.1056
0.0537
0.1309 -0.1948
0.1069 -0.0909 0.000000 0.4368
Four Seasons
0.9149 -0.0562
0.1711 -0.2858
0.0482
-0.1013 -0.1709 0.000000 0.5133
Frantel
1.0408 -0.0487 -0.2010 -0.1143 -0.0672
0.1029 -0.0807 0.000000 0.6288
Friendship
0.4579
0.0306 -0.0730 -0.2307
0.0100 -0.1372 0.000554 0.2308
Golden Tulip
0.9973 -0.0480 -0.1515
0.0988 -0.1959
-0.0733
Grand Collection
0.4356
0.1072 -0.1177
0.1942 -0.2313
0.0067
0.1753 0.000003 0.3270
HIDC
0.7040
0.0966 -0.1978
0.1064 -0.2535
-0.0541
0.0937 0.000000 0.3607
Hilton
0.5858 -0.0720
0.0620
0.1271 -0.2189
0.0938 -0.0108 0.000000 0.4426
Hilton Intemational
0.9317 -0.0351 -0.2008
0.0496 -0.1219
0.0323
Howard Johnson
0.7331 -0.1022
0.0072
0.0955 -0.2170
0.0766 -0.1205 0.000000 0.4345
Holiday Inn
1.1037 -0.0561 -0.1247 -0.1160 -0.1424
Hospitality Consultants
0.4740
0.0811 -0.3177
0.0374 -0.0411 0.000000 0.4058
Hospitality Intemational
0.5028 -0.0721 -0.0134 -0.0195 -0.1425
0.0561 -0.1724 0.000010 0.3060
Hudson
0.2980
0.0413 -0.0357 0.000012
0.0207
0.0659
0.0831
0.0707 -0.2170
0.0341 -0.2640
0.0024
0.2015 -0.2245
0.1486 -0.0843
0.0969 -0.0124 -0.2773 -0.2302
0.0163
0.0699
0.0367
0.0375
0.0185
0.1219 -0.1577
128
FSTMOV
NAT
Model p
Rsq
-0.1287 -0.0177 0.000001 0.3437
0.1163 -0.0699 0.000002
0.0345
0.3365
0.000000 0.4355
-0.0250 -0.0971 0.000035 0.2830
0.0439 -0.1525 0.000003 0.3257
0.0961 -0.0301 0.000013
0.3005
-0.0285
0.0166 0.000041 0.2803
0.1051
0.0254 0.000012 0.3016
0.0380
-0.0880
0.000000 0.4093
0.3286
0.1170 0.000000 0.4277
. 0.000000
0.3161
0.6663
0.0563 0.000000 0.7509
0.0029 0.000000 0.6013
0.3020
Table 13. Continued
Chain
Intercept
SIZE
lEXP
DOI
LDR
FSTMOV
NAT
Model p
Rsq
Hungar
0.3044 -0.0960
0.2395 -0.0073 -0.0269
-0.0287
0.000000
0.4430
HUSA
0.4259 -0.0944
0.2088
0.0005 -0.0392
-0.0471 -0.0668 0.000000
0.4533
Hyatt
0.5657 -0.0700
0.0120
0.0938 -0.1511
0.0243 -0.0803 0.000014
0.2992
Hyatt International
0.9801 -0.0396 -0.2332 -0.0583 -0.1226
0.0421
0.0347 0.000000
0.7529
IBIS
0.8384 -0.0573 -0.0617 -0.1689 -0.1733
0.1482 -0.0334 0.000001
0.3464
Intercontinental
1.0040 -0.0536 -0.1802 -0.0867 -0.1077
0.0357
0.0487 0.000000
0.7527
Jack Tar
0.7327
-0.1280 -0.0175 0.000015
0.2984
Jolly of Italy
0.4594 -0.0520
Mandarin
0.9537
Marriott
0.7723 -0.0704 -0.2763
0.0844 -0.1356
Mercure
0.6767 -0.0365
0.0680
0.0714 -0.1645 -0.1682
0.0271
0.1561 -0.2148
0.0726 -0.1873 -0.2971 -0.1196
0.0009
0.073994
0.1040
0.0297 -0.2395 0.000000
0.3740
0.1282 0.000000
0.5411
0.2141 -0.2287
0.0825 -0.1158 0.000006
0.3141
Meridien
1.0164 -0.0373 -0.1868 -0.0997 -0.0839
0.0727 -0.0388 0.000000
0.6843
Ming
0.4414
0.0695
0.0794 -0.0696 -0.1935
-0.0229
0.000262
0.2273
Movenpick
0.7792
0.0827
0.0717 -0.2208 -0.3526
-0.0950
0.0059 0.000000
0.4402
Nendels
0.3761 -0.0096
0.0608 -0.0135 0.000002
0.3322
Nikko
0.8649
0.0236
0.000000
0.4293
Novotel
1.0425 -0.0564 -0.1750 -0.1173 -0.1419
0.0962 -0.0640 0.000000
0.6399
Oberoi
0.8499
Omni
0.3690 -0.0339
0.1133
Peninsula
0.8549
0.0336
Penta
0.8870
Portman
0.3810 -0.0158
Pratt
0.5482
0.0661
0.0912
0.0870 -0.0959
0.0216 -0.3183 -0.2062 -0.0859
0.0824 -0.1785 -0.2502 -0.1811
-0.0119
-0.1087
0.0457 0.000000
0.3995
-0.0299
0.1265 0.001264
0.2140
0.0551 -0.3296 -0.1139
0.0511 -0.2451 0.000000
0.3932
0.0164 -0.2669 -0.1607 -0.1180
0.0214 -0.0282 0.000000
0.3839
0.1381 -0.1320 -0.0864
0.0416
0.0001 0.000000
0.4112
0.0618
0.0723
0.0382 -0.2405
0.0576 -0.0732 0.000000
0.4388
Preferred
0.6094 -0.0900
0.0643
0.0873 -0.2443
0.0409
0.1168 0.000023
0.2909
Prince
0.6094 -0.0869
0.0488
0.1279 -0.2563
0.0278 -0.0435 0.000042
0.2797
Princess
0.5678
0.0235 -0.0899 -0.2398
0.0951 -0.0571 0.000004
0.3208
Quality
1.0702 -0.1068 -0.0661 -0.1541 -0.1719
-0.0987 -0.0427 0.000000
0.4963
Radisson
0.6736 -0.1138 -0.0096
0.0945 -0.2315
0.0431 -0.1011 0.000000
0.3932
Ramada
1.0052 -0.0480 -0.2027
0.0317 -0.1683
-0.0431
0.0131 0.000000
0.7224
Rank
0.6715
0.0360
0.0944
0.1256 -0.0607
-0.1455 -0.0711 0.000034
0.2835
Regent International
0.6937
0.0419 -0.2194
0.1981 -0.0107
0.0524 -0.0350 0.000000
0.5214
Riande
0.3146 -0.0006
0.1284 -0.1446
0.0633
0.0757
, 0.000000
0.4626
Safat
0.4176 -0.0353
0.1801 -0.1979 -0.1333
-0.0534
, 0.000006
0.2945
Sara
0.9051
0.0037
0.0261
-0.1440 -0.2502 0.000064
0.2722
SAS
0.6711
0.0516 -0.0321 -0.2726 -0.1890
. 0.000472
0.2158
Scandic
0.9067
0.0025
0.0073
0.2738 -0.1440
-0.1469 -0.1936 0.000062
0.2726
Sen/ico
0.4324 -0.0190
0.0600
0.0156 -0.1136
-0.0074 -0.0862 0.006895
0.1773
0.0740
0.0443 -0.2086
0.2294 -0.0547
129
0.0891
Table 13. Continued
Chain
Intercept
SIZE
lEXP
DOI
LDR
0.0585 -0.0869 -0.2191 -0.2213
FSTMOV
NAT
Model p
Rsq
Shangrila
0.9751
Sheraton
1.0856 -0.0470 -0.1565 -0.1130 -0.1285
Sofitel
0.9932
Sol
0.4183 -0.0934
0.2025
someplace Different
0.4978
0.0800
Sonesta
0.4997
0.1339 -0.0747 -0.0307 -0.3609
0.0519
0.1779 0.000000
0.3912
SRS
1.0745 -0.0594 -0.1775 -0.1050 -0.0764
-0.0553
0.000000
0.7181
Sterling
0.5987
0.0442
0.0694
0.1679 -0.1167
-0.1294 -0.0227 0.000010
0.3050
Sunbelt
0.3197
0.1061 -0.0685
0.0581 -0.1936
0.0263 -0.0910 0.000000
0.3569
Super 8
0.7231 -0.0804
0.1111 -0.1807
-0.0905 -0.0963 0.000001
0.3474
Swiss International
1.0683
0.0103 -0.2809 -0.0607 -0.1150
-0.0615 -0.0270 0.000000
0.6878
Swissotel
0.8247
0.0190 -0.0578 -0.3627 -0.1158
-0.0439 -0.0369 0.000058
0.2738
Taj
0.8479
0.0375 -0.1612 -0.2286 -0.0263
-0.0975 -0.0337 0.003980
0.1895
Thistle
0.6194
0.0327
0.1108
0.0507 -0.1723
0.0803 -0.1491 0.000000
0.4668
Tollman
0.4295 -0.0087
0.0140
0.1054 -0.2931
Travelers
0.4281
0.0150
-0.0493 -0.1382 0.000000
0.4347
-0.0536
0.0249 0.000000
0.7031
0.1362 -0.1367 0.000000
0.5102
0.1782
0.0247 -0.1282 0.000000
0.5646
0.0122 -0.0177 -0.1908
-0.0461 -0.0920 0.000000
0.3619
0.0057 -0.2115 -0.0864 -0.0282
0.0372
0.0676
0.1274 0.002823
0.1970
0.0580 -0.2799
0.0045 -0.0663 0.000000
0.4137
Trusthouse
1.1580 -0.0688 -0.1628 -0.1238 -0.0689
-0.1014 -0.0814 0.000000
0.6031
Viscount
0.3535
0.0865 -0.1312
0.1068 -0.0341 0.000263
0.2455
VMS
0.5996 -0.0899
0.0228
0.0564 -0.2207
0.0512 -0.1182 0.000004
0.3224
Westin
0.8882 -0.0684 -0.1658
0.0680 -0.1851
0.0455 -0.0290 0.000000
0.5341
Westours
0.4343
0.0447
0.0705
0.1949 -0.0579
0.1635 -0.0123 0.000002
0.3292
Wyndham
0.4615
0.0142
0.0879
0.0503 -0.1423
0.1353 -0.0121 0.000042
0.2798
York
0.4882
0.0190
0.1213
0.1797 -0.1110
0.1421 -0.0456 0.000000
0.3940
0.0830
0.0734 -0.0006
130
-0.0048
Table 14. 1988 Well-Predicted Chains
Intercept
Chain
SIZE
lEXP
DOI
LDR
FSTMOV
NAT
Model p
Rsq
-0.0807
0.000000
0.6288
0.000000
0.6663
Frantel
1.0408 -0.0487
-0.2010 -0.1143
-0.0672
0.1029
Golden Tulip
0.9973
-0.0480
-0.1515
0.0988
-0.1959
-0.0733
Hilton Intemational
0.9317
-0.0351
-0.2008
0.0496
-0.1219
0.0323
0.0563
0.000000
0.7509
Holiday Inn
1.1037 -0.0561
-0.1247
-0.1160
-0.1424
-0.0880
0.0029
0.000000
0.6013
Hyatt Intemational
0.9801
-0.0396
-0.2332
-0.0583
-0.1226
0.0421
0.0347
0.000000
0.7529
Intercontinental
1.0040 -0.0536
-0.1802
-0.0867 -0.1077
0.0357
0.0487
0.000000
0.7527
Meridien
1.0164 -0.0373
-0.1868 -0.0997
-0.0839
0.0727
-0.0388
0.000000
0.6843
Novotel
1.0425 -0.0564
-0.1750
-0.1173
-0.1419
0.0962
-0.0640
0.000000
0.6399
Ramada
1.0052 -0.0480
-0.2027
0.0317
-0.1683
-0.0431
0.0131
0.000000
0.7224
Sheraton
1.0856 -0.0470
-0.1565
-0.1130
-0.1285
-0.0536
0.0249
0.000000
0.7031
SRS
1.0745 -0.0594 -0.1775
-0.1050 -0.0764
-0.0553
0.000000
07181
Swiss International
1.0683
0.0103
-0.2809
-0.0607 -0.1150
-0.0615
-0.0270
0.000000
0.6878
Trusthouse
1.1580 -0.0688
-0.1628
-0.1238
-0.1014 -0.0814
0.000000
0.6031
-0.0689
Table 15. Characteristics Of 1988 Well-Predicted Chains
Chain
SIZE
Frantel
20639
Golden Tulip
56835
Hilton International
40048
Holiday Inn
318574
Hyatt International
19211
Intercontinental
41365
Meridien
22077
Novotel
27032
Ramada
103683
Sheraton
140327
SRS
Swiss Intemational
Trusthouse
47961
5689
39796
lEXP
29
63
44
51
27
51
34
38
27
61
43
18
28
DOI
0.6631
0.0464
0.1110
0.9398
0.9769
0.8930
0.9281
0.6482
0.1710
0.8726
0.9362
1.0000
0.8412
131
LDR FSTMOV
NL
S
F
NL
NL
S
F
NL
NL
S
S
NL
NL
S
S
NL
F
NL
F
NL
F
NL
F
NL
F
NL
TYPE
NAT
?
FRA
Luxury
NET
Luxury
US
US Mid-priced
Luxury
US
Luxury
US
Luxury
FRA
FRA
Luxury
US Mid-priced
Luxury
US
GER
Luxury
SWI
UK
?
?
Table 16. 1989 Regression .A.nalyses Results
Chain
Intercept
SIZE
lEXP
DOI
LDR
FSTMOV
NAT
Model p
Rsq
Aston
0.4648 -0.0290
0.1241 -0.0936 -0.0377
0.0170 -0.0932 0.000080 0.2451
Atlas
0.4334
0.0408
0.0343
0.0420 -0.2478
0.0951 -0.0802 0.000006 0.2874
Best Westem
0.8916
0.0030 -0.1976
0.1808 -0.1567
-0.1175 -0.0230 0.000000 0.4055
Brook
0.3376 -0.0105
0.0894 -0.1572
0.1183 -0.0400 0.000001 0.3178
BTH
0.9832
0.0439 -0.2740 -0.0135 -0.1256
-0.1693 -0.0004 0.000000 0.4295
Budget
0.4827
0.0071
0.1044 -0.0079 -0.1033
Caesar Park
0.3172 -0.1127
0.2050 -0.0523 -0.2397
-0.0494
Churchman
0.4964
0.0438
0.0701 -0.0593 -0.0439
0.0286 -0.0951 0.000961 0.2002
Ciga
0.8242
0.0728 -0.2112
Clarion
0.0862
0.0846 -0.0558 0.000032 0.2606
0.000000 0.3192
0.0320
0.0082
0.1549 -0.1173 0.000394 0.2168
0.6275 -0.0891
0.0660 -0.0516
0.0418
0.0405 -0.1399 0.000223 0.2270
Clement Chen
0.3304 -0.0512
0.1840 -0.0318 -0.1684
-0.0236
Compri
0.5775 -0.0680
0.1211 -0.1167
-0.0206 -0.1293 0.000041 0.2564
Connor Jacobson
0.6381
Consort
0.9232 -0.0652 -0.0268 -0.1207 -0.0995
-0.1884 -0.0291 0.000412 0.2159
Continental
0.6421 -0.1036
Caledonian
0.1761
0.0963 -0.0044 -0.3144 -0.1191
0.0465
0.0385 0.000000 0.5711
0.0792
0.000000 0.4750
0.0178
0.0505
-0.0498 -0.0958 0.006323 0.1630
0.7723
0.1318 -0.1943 -0.2760
0.0781
0.0837 -0.0963 0.000000 0.4060
Country Hospitality
0.3761
0.0653
0.0218 -0.0081 -0.0179
-0.0294 -0.0566 0.001661 0.1897
Crest
0.5942 -0.0031 -0.0601
Cunard
0.4925
Days Inn
0.7200 -0.1035
0.0260 -0.0232
Delta
0.2235
0.0583
0.0298
Distinction
0.3564
0.0520
0.0588 -0.0071
Divi
0.6835
0.1274 -0.1062 -0.2181 -0.1794
0.1128
0.0216 0.000000 0.4656
Dorint
0.4988
0.0727 -0.1070
0.1758
0.0573 0.000004 0.2956
Econolodge
0.6423 -0.1695 -0.0291 -0.0438
Embassy
0.6215
Fimotel
0.3668 -0.0770
Four Seasons
0.8133
0.0069
Frantel
0.2147
0.0992
0.0855 -0.0071 -0.3367 -0.0789
0.0707
0.0557
0.1134
0.0926 -0.2046
0.1649
0.2105 -0.1220
0.0997 -0.0842 0.007123 0.1605
0.0427
0.1180 0.000002 0.3020
0.0044 -0.1009 0.000001 0.3108
0.0367
0.0833 0.000005 0.2907
-0.0530 -0.0744 0.001257 0.1951
0.0574
0.0496 -0.0913 0.000000 0.3593
0.0962 -0.2056
0.1137 -0.1495 0.000007 0.2863
0.2957
-0.0795 -0.0384 0.000000 0.5094
0.1366 -0.4097 -0.2746
0.0282 -0.1183 0.000000 0.5669
1.0511
0.0193 -0.2951 -0.0205 -0.0111
-0.0484 -0.0241 0.000000 0.6648
Friendship
0.4630
0.0091
0.0662 -0.1792
0.0030 -0.1069 0.000918 0.2011
Golden Tulip
1.0941 -0.0333 -0.2100 -0.0906 -0.0370
-0.0362
Grand Collection
0.4011
0.1166 -0.0916
0.1576 -0.0484
0.0045
Great Inns
0.3986
0.0151
0.0825 -0.0324
Hawthome
0.5890
0.0199
HIDC
0.7909
Hilton
0.6275 -0.1067
Hilton International
0.2436 -0.0177
0.0452
,
0.000000 0.6672
0.1561 0.000082 0.2446
0.0876
-0.0054 -0.0975 0.000003 0.3013
0.1200
0.1332 -0.2379
-0.1132 -0.0343 0.000000 0.3498
0.1018 -0.2415
0.0176 -0.1560
-0.1313
0.0609 0.000000 0.3827
0.1574
0.0359
0.0939
0.0069 O.OOOOOC 0.3847
1.0216 -0.0064 -0.2631 -0.0488
0.0652
-0.0268
0.0318 O.OOOOOC1 0.7607
0.0259
132
Table 16. Continued
Chain
Intercept
SIZE
lEXP
DOI
LDR
FSTMOV
NAT
Model p
Rsq
Howard Johnson
0.7971
-0.1586 -0.0310
0.0477
0.0900
0.0442
-0.1040
0.000000
0.3901
Holiday Inn
0.9652
-0.0283 -0.1944
0.0677
-0.0544
0.0615
-0.0030
0.000000
0.6020
Hospitality Consultants
0.4533
0.0255
0.0797
0.0171
-0.1083
0.0200
-0.0750 0.003535
0.1749
Hospitality International
0.5727 -0.1371
0.0082
-0.1106
0.0086
0.0404
-0.1262
0.000001
0.3157
HIS
0.4076
0.0258
0.0844
0.1037
0.0060
-0.0250
0.1136
0.009641
0.1542
Hungar
0.3316 -0.0281
0.2194
0.0808
0.4998
-0.0428
0.000001
0.3041
HUSA
0.3774 -0.0169
0.1422
-0.0546 -0.0815
-0.0727
0.0822
0.000003
0.3002
HUSA International
0.8205
-0.0400
0.0979 -0.0197
0.000000
0.5335
Hyatt
0.5714 -0.0763
0.0288
-0.0128 -0.0555
0.000609
0.2087
IBIS
0.8018
Intercontinental
1.0270 -0.0104 -0.2492
Jack Tar
0.6069
0.1000
Jolly of Italy
0.7388
0.0074
Kempinski
0.6668
Loews
0.0871
-0.2044 -0.2265
0.0371
0.0305
-0.0183 -0.1737 -0.0963 -0.1313
-0.0520
0.0709
-0.0998 -0.1511 -0.1976
0.2174
-0.0168
0.000000
0.3446
-0.0138
0.0323
0.000000
0.7611
0.1612 -0.0379
0.000000
0.3401
0.0340
0.0576
-0.3287
0.007345
0.1599
0.1140
-0.0628 -0.1950 -0.1446
0.0166
-0.0191
0.000000
0.3412
0.5258
0.0470
-0.0683
0.1502 -0.0385
0.000329
0.2201
Mandarin
0.9772
0.0840
-0.0794 -0.5422 -0.1311
0.0079
-0.2644 0.000000
0.4560
Marriott
0.7425
-0.0435 -0.3044
0.1096
0.2143
0.0096
0.1039
0.000000
0.5562
Mercure
0.6162
-0.0348
0.1817
0.1788
0.0774 -0.0576
0.000742
0.2051
Le Meridien
1.0354
0.0168
-0.2617 -0.0922
0.0119
0.0074
-0.0248
0.000000
0.7439
Ming
0.3239
0.1017 -0.0073 -0.0092
0.0248
-0.0496
,
0.000427
0.1986
Movenpick
0.8288
0.1036
-0.1365
-0.2597 -0.0492
0.1003
-0.1313
0.000000
0.3681
Nikko
0.9749
0.0440
-0.2897 -0.2421 -0.0090
-0.0079 -0.0816 0.000000
0.5799
Novotel
0.9902
0.0046
-0.2648
0.0019
0.000000
0.6455
Oberoi
0.9421
0.0811
-0.2687 -0.1419 -0.0647
-0.1314 -0.0109
0.000000
0.4804
Omni
0.4819
-0.0795
0.1198
-0.0642
0.0841
-0.0227 -0.0942
0.000021
0.2678
Otani
0.5550 -0.0066
0.0747
0.1883
-0.0520
-0.0487
0.000059
0.2503
Peninsula
0.8743
0.0696
-0.0340 -0.4688 -0.1576
-0.0130 -0.1525
0.000000
0.4911
Penta
0.7822
0.0548
-0.1736 -0.3603
Portman
0.3433
-0.0281
0.1308
Pratt
0.6067 -0.0764
0.0517
Preferred
0.9051
-0.0177
-0.3402
Prince
0.5422
-0.0881
Princess
0.6235
0.0671
Quality
0.8817 -0.0893 -0.1760
Radisson
0.0366
0.0898
0.1437
0.1300
0.0045
-0.1220
-0.0003
0.0450
0.0436
0.1043
0.0152
-0.0347
0.000000
0.3511
-0.1373 -0.0816
-0.0157
0.0167
0.000000
0.3590
0.1299
-0.0379
0.0901
-0.0726
0.001591
0.1906
-0.0366
0.2059
-0.0356
0.0414
0.000000
0.5828
0.0738
-0.0381 -0.0090
0.0577
0.0199
0.030000
0.1292
-0.0235
-0.0913 -0.2556
0.1050
-0.0629
0.000250
0.2250
0.0762
0.0085
0.0999
-0.0311
O.OOOOOC
0.5016
0.8177 -0.1492 -0.1254
0.0683
0.1588
0.041 £
-0.0441
O.OOOOOC1 0.4006
Ramada
1.0037 -0.0361 -0.2693
0.0076
0.0870
0.0062!
0.007€) O.OOOOOC)
07197
Rank
0.4148
0.1884
-0.231 S
0.1391
0.027^\ O.OOOOOC)
0.3349
0.0198
0.1037
133
Table 16 .
Chain
Intercept
Continued
DOI
LDR
SIZE
lEXP
-0.2206 -0.2304 -0.0498
Regent International
0.9470
0.0553
Riande
0.2823
-0.0187
0.0916
0.0480
Ritz
0.3865 -0.0555
0.2443
Rodeway
0.5010
-0.1175
Rosewood
0.3896
FSTMOV
NAT
Model p
Rsq
0.0197
-0.1317 0.000000
0.5122
-0.1029
0.0374
0.002724
0.1641
-0.0191
0.1399
-0.0964
0.0365
0.000000
0.5507
0.0066
-0.1022
0.0233
0.0502
-0.0881
0.000096
0.2419
0.0151
0.1541
0.0657
0.0260
-0.0386
0.0510
0.000021
0.2677
Royal
0.3796 -0.0035
0.2011
-0.1155
-0.0815
0.000000
0.3898
Safat
0.4068 -0.0444
0.2070
-0.2913
0.2056
-0.1154
0.000000
0.4132
Sara
1.0193
-0.0009 -0.3664 -0.2907
0.0958
-0.2657 0.000000
0.4644
SAS
0.9830 -0.0686 -0.2723 -0.0876
-0.0399
0.000000
0.6496
Savoy
0.7685
0.1122
-0.0179 -0.2217 -0.2356
-0.1438 -0.0097 0.000000
0.3326
Scandic
0.8146
0.0430
-0.2095
-0.0999
0.1945
-0.1106 0.000000
0.3566
Shangrila
0.8426
0.0491
-0.1054 -0.2414 -0.1082
0.0769
-0.0694 0.000000
0.4026
Sheraton
1.0192 -0.0084 -0.2474
0.0166
0.000000
07188
Sofitel
1.0521
0.0245
-0.2954 -0.1099 -0.1323
-0.0667 -0.0481
0.000000
0.6092
Sol
0.3219
-0.0763
0.2255
0.1334
0.0499
-0.0520 -0.0159
0.000000
0.5740
Someplace Different
0.5141
0.1003
0.0300
-0.2199
0.0702
-0.0095
0.0122
0.000000
0.4029
Sonesta
0.5934
0.1316
-0.1930 -0.0323 -0.0825
0.1172
0.1171
0.000000
0.3384
Southern Pacific
0.5642
0.0412 -0.0497
0.2168
-0.1831
0.000005
0.2733
SRS
1.0602 -0.0178 -0.2421 -0.0477
0.0823
0.000000
07183
Sterling
0.4779
0.0102
-0.0317 0.000000
0.3748
Sunbelt
0.4035
0.1112
Super 8
0.6706
0.0186
-0.0214
Swiss International
1.0529 -0.0032 -0.3219 -0.0672
Swissotel
0.7613
0.0558
-0.0531
Taj
0.6888
0.0884
Thistle
0.4960
0.0129
0.1366
Trusthouse
0.0179
0.1218
0.1783
0.0475
0.1293
0.1453
-0.0083
-0.2293
-0.0030 -0.0028 -0.0472
-0.0081
0.1866
-0.0240 -0.0190
0.1479
0.000018
0.2702
0.0662
-0.0922 0.000004
0.2941
0.0393
0.0239
-0.0429
0.000000
0.7264
-0.5213 -0.0773
0.0148
-0.0314
0.000000
0.3774
-0.2088 -0.0959 -0.0850
0.1003
-0.0386 0.010988
0.1514
0.0230
-0.0600
0.0619
-0.0720
0.000000
0.4230
0.8079 -0.0889 -0.1481
0.1177
-0.0536
0.1334 -0.0165
0.000000
0.4504
Trusthouse PLC
1.0637
0.0070
-0.2765
0.1281
-0.0623
-0.0648 -0.0849
0.000000
0.6198
VMS
0.6318
-0.1343
0.0098
0.0210
0.1577
0.0445
-0.1008
0.000378
0.2175
Westcoast
0.5627
0.1284
-0.0070
0.0318
-0.1782
0.0960
0.0227
0.000030
0.2617
Westin
0.8861
-0.0963 -0.2147
0.0374
0.1184
0.0332
-0.0346
0.000000
0.4899
Wyndham
0.4549
0.0053
0.0849
0.1684
-0.1568
0.1142
-0.0410
0.000048
0.2538
York
0.6670
0.1452
-0.1592
0.1515
-0.0551
0.1460
-0.0892
0.000000
0.4327
-0.1330 -0.0146
134
-0.0272 -0.0522
Table 17. 1989 Well-Predicted Chains
Intercept
Chain
SIZE
LDR
DOI
lEXP
0.0193 -0.2951 -0.0205 -0.0111
FSTMOV
NAT
Model p
Rsq
-0.0484 -0.0241 0.000000 0.6648
Frantel
1.0511
Golden Tulip
1.0941 -0.0333 -0.2100 -0.0906 -0.0370
-0.0362
0.000000 0.6672
Hilton International
1.0216 -0.0064 -0.2631 -0.0488
-0.0268
0.0318 0.000000 0.7607
Holiday Inn
0.9652 -0.0283 -0.1944
Intercontinental
1.0270 -0.0104 -0.2492 -0.0520
0.0709
Le Meridien
1.0354
0.0168 -0.2617 -0.0922
0.0119
Novotel
0.9902
0.0046 -0.2648
Ramada
1.0037 -0.0361 -0.2693
0.0652
0.0677 -0.0544
0.0615 -0.0030 0.000000 0.6020
-0.0138
0.0323 0.000000 0.7611
0.0074 -0.0248 0.000000 0.7439
0.0045 -0.0003
0.0450
0.0019 0.000000 0.6455
0.0076
0.0870
0.0062
0.0076 0.000000 0.7197
SAS
0.9830 -0.0686 -0.2723 -0.0876
0.1453
-0.0399
0.000000 0.6496
Sheraton
1.0192 -0.0084 -0.2474
0.0475 -0.0083
-0.0081
0.0166 0.000000 0.7188
Sofitel
1.0521
SRS
1.0602 -0.0178 -0.2421 -0.0477
0.0823
-0.0240 -0.0190 0.000000 07183
Swiss Intemational
1.0529 -0.0032 -0.3219 -0.0672
0.0393
0.0239 -0.0429 0.000000 0.7264
Trusthouse PLC
1.0637
0.1281 -0.0623
-0.0648 -0.0849 0.000000 0.6198
Table 18
Chain
Frantel
Golden Tulip
Hilton International
Holiday Inn
Intercontinental
Le Meridien
Novotel
Ramada
SAS
Sheraton
Sofitel
SRS
Swiss International
Trusthouse PLC
0.0245 -0.2954 -0.1099 -0.1323
0.0070 -0.2765
-0.0667 -0.0481 0.000000 0.6092
Characteristics O f 1989 W e l l - P r e d i c t e d Chains
SIZE lEXP
29
20639
56707
61
42
42557
318574
49
49
41365
19977
35
39
27032
26
107599
47
4954
55
139008
18
9790
43
54202
20
8120
25
38360
DOI
0.6631
0.9418
0.9065
0.1548
0.8676
0.8791
0.6448
0.1861
0.5858
0.3436
0.6330
0.8880
1.0000
0.4763
TYPE
NAT
LDR FSTMOV
?
F
France
NL
Luxury
Netherlands
F
NL
Luxury
US
S
NL
Mid-priced
US
S
NL
Luxury
US
S
NL
Luxury
France
F
NL
Luxury
France
S
NL
Mid-priced
US
F
NL
Luxury
Nonway
S
NL
Luxury
US
F
NL
Luxury
France
F
NL
Luxury
Germany
F
NL
?
Switzerland
S
NL
?
UK
F
NL
135
Table 19. 1990 Regression Analyses Results
Chain
Intercept
SIZE
lEXP
DOI
LDR
FSTMOV
NAT
Model p
Rsq
Best Westem
0.8401
0.0432 -0.1532
0.0650 -0.3964
-0.1268 -0.0218 0.000196
0.2690
BTH
0.6727
0.0504 -0.0569 -0.0656 -0.3393
0.1335 -0.0049 0.000459
0.2514
Budget
0.5277 -0.0145
0.0509
0.1014 -0.0675 0.005677
0.1952
Caesar Park
0.5742
0.0012
0.1266 -0.0422 -0.3172
Ciga
0.7719
0.0408 -0.0646
Clarion
0.4965 -0.0054
0.0825
Clement Chen
0.3478
0.1488 -0.1138 -0.1814
Colony
0.3027 -0.0373
0.0643
Connor Jacobson
0.5482
0.0295
0.0765 -0.1355 -0.2410
Consort
0.8062 -0.0327
0.0838 -0.0570 -0.3147
Continental
0.4661
0.0143
0.0162
0.1317 -0.3271
0.1466
0.0156 0.002391
0.2154
Caledonian
0.5982
0.1070 -0.0992
0.0481 -0.1565
0.0282 -0.0371 0.049404
0.1396
Country Hospitality
0.5426
0.0203
0.1024
0.0437 -0.2672
0.0830 -0.0246 0.000052
0.2951
Crest
0.5325 -0.0367
0.0020
0.1295
0.1169 -0.0090 0.059223
0.1345
Cunard
0.4293
0.0599 -0.0776 -0.1419
Days Inn
0.5606 -0.1192 -0.0241
Divi
0.4335
Dorint
0.0225
0.0500
0.0234 -0.2760
0.001937
0.2013
0.0002 -0.0831
0.1058 -0.1343 0.111902
0.1156
0.0433 -0.2813
0.0237 -0.0573 0.016675
0.1686
0.0101 -0.1406
0.1607
-0.0666
-0.0704
0.0191 0.000008
0.3297
0.0925 -0.0172 0.057157
0.1355
0.0803
0.000068
0.2703
-0.1441 -0.0080 0.003026
0.2100
0.0398 0.389439
0.0724
0.0834 -0.1720
-0.0111 -0.0240 0.006949
0.1904
0.0216 -0.0437 -0.3076
0.0766 -0.0225 0.048211
0.1403
0.5289 -0.0015
0.0087
0.0977
0.2681
0.1320 -0.0049 0.009937
0.1816
Embassy
0.4956
0.0112
0.1217
0.0240 -0.2304
0.0745 -0.0117 0.000201
0.2684
Four Seasons
0.1173
0.0415
0.1400
0.0194 -0.2585
Frantel
0.9522
0.0149 -0.1924
0.0184
-0.0131
-0.0494
0.3449 0.001766
0.2222
0.0341 -0.2303
0.0789 -0.1014 0.000000
0.4571
Friendship
0.3410 -0.0100 -0.0088 -0.0110 -0.2288
0.0767 -0.0605 0.150377
0.1063
Golden Tulip
1.0089
0.0001 -0.1894 -0.0162 -0.2561
-0.0512
0.000000
0.5137
Grand Collection
0.5330
0.1141 -0.0831
0.1081 -0.2747
-0.0106
0.0338 0.224660
0.0929
Great Inns
0.2700
0.0539
0.0599
0.0795 -0.0391
-0.0664
0.0219 0.493810
0.0623
HIDC
0.5748
0.0148
0.1090
0.0831 -0.2781
0.0477
0.0063 0.000118
0.2792
Hilton
0.7331 -0.0601 -0.0592
0.1564 -0.2854
-0.0865 -0.0195 0.000001
0.3639
Hilton International
0.9490
0.0022 -0.2082 -0.0156 -0.2890
0.0075 -0.0048 0.000000
0.5454
Howard Johnson
0.6673 -0.0946
0.1147 -0.1821
0.0259 -0.0327 0.000621
0.2450
Holiday Inn
0.9762
0.0218 -0.1988 -0.0016 -0.3656
-0.0783 -0.0075 0.000000
0.5017
Horizon
0.3831
0.0103
0.1306
0.0327 -0.3566
0.0633
0.0446 0.000002
0.3524
HUSA
0.6035
0.0239 -0.0713
0.1205 -0.3206
0.1094
, 0.000295
0.2412
Hyatt International
0.9287 -0.0267 -0.2145 -0.0365 -0.2716
0.0209
0.0000 0.000000
0.5090
IBIS
1.0741
-0.1593 -0.2127 0.000011
0.3240
Intercontinental
0.9719 -0.0042 -0.2090 -0.0289 -0.2424
0.0110
0.0030 0.000000
0.5735
Inten/vest
0.8509
0.0047
0.0964 -0.0002 -0.3244
0.0498 -0.4584 0.000000
0.3931
J&B
0.5382 -0.0058
0.1035 -0.0643 -0.3033
0.1020
-0.0533 0.000005
0.3383
0.0292
0.0205 -0.1538
0.0359 -0.1000
136
Table 19. Continued
Chain
Intercept
SIZE
lEXP
DOI
LDR
FSTMOV
NAT
Model p
Rsq
Jack Tar
0.5924
0.0682
0.0227 -0.0862 -0.2880
0.1032
0.0023 0.002402
0.2152
Jolly of Italy
07126
0.0127
0.0316
0.1108
0.0317 -0.3849 0.014187
0.1727
Journeys
0.1253 -0.0243
0.0771
0.0535 -0.1816
0.0250
0.1256 0.002965
0.2104
Kempinski
0.6535
-0.0268 -0.0861 0.022307
0.1611
Kuwait
0.6312 -0.0009
0.1064
0.0245 -0.3755
-0.1090
. 0.001374
0.2088
Loews
0.4523 -0.0034
0.0686
0.0843 -0.4072
0.1336
0.0211 0.001700
0.2230
Mandarin
0.8397
-0.0248 -0.2342 0.000266
0.2628
Marriott
0.8025 -0.0243 -0.2891
0.0383
-0.0081
0.0384 0.000000
0.4188
Mercure
0.6878 -0.0390
0.0211 -0.1236
0.1185 -0.1359 0.001388
0.2276
Le Meridien
0.9358
0.0056 -0.2210 -0.0235 -0.2955
0.0107 -0.0081 0.000000
0.5600
Movenpick
0.8774
0.0657 -0.0038 -0.2056 -0.3243
0.0727 -0.2621 0.000011
0.3240
Nikko
0.7008
0.0708 -0.2179 -0.1743 -0.2318
Oberoi
0.5170
0.0218 -0.0656
0.1202 -0.3690
Ocean
0.3963
0.0393
0.1422
0.0230 -0.2124
Okura
0.4686
0.0454
0.0092 -0.2689
Omni
0.5386 -0.0577
Otani
0.4176
Parklane
0.4510
0.1330 -0.0303 -0.2337 -0.2510
0.1383 -0.0627 -0.2794 -0.2391
0.0853
0.0446
0.4010
-0.0127
0.0472 0.000570
0.2468
0.1488
0.1029 0.000423
0.2531
-0.0427 -0.0202 0.000009
0.3287
0.0387
0.0025 0.019580
0.1645
0.0829 -0.1842 -0.1070
-0.0427 -0.0004 0.036792
0.1478
0.0168
0.1140
-0.0285 -0.0029 0.002090
0.2184
0.3718
0.0549
0.1263 -0.0506 -0.0870
-0.0896
0.0087 0.311286
0.0811
Peninsula
0.7433
0.0761
0.0605 -0.2638 -0.2288
-0.0162 -0.1984 0.000003
0.3490
Penta
0.7230
0.0360 -0.2271 -0.0937 -0.1852
Portman
0.3075
0.0332
0.1878 -0.2389
0.0072 0.000266
0.2627
0.0986 -0.1822 -0.1434
-0.0420 -0.0056 0.000398
0.2544
Preferred
0.8714 -0.0446 -0.2535 -0.0315 -0.1585
0.0018 -0.0023 0.000000
0.3780
Prince
0.1935 -0.0338
0.1046
0.0808
0.2020 0.000874
0.2377
Princess
0.5536
0.0773 -0.0445 -0.3012
0.0666 -0.0124 0.000486
0.2502
Quality
0.7961
-0.0379 -0.1191
0.0815 -0.3005
0.1077 -0.0130 0.000001
0.3640
Radisson
0.6731 -0.1091 -0.0425
0.1333 -0.1930
0.0476 -0.0493 0.000058
0.2930
Ramada
0.9662 -0.0182 -0.1997
0.0089 -0.2803
-0.0408 -0.0067 0.000000
0.5330
Rank
0.4471 -0.0183
0.1449
0.0629 -0.3491
Regal
0.4736 -0.0740
0.0908
Regent International
0.8757
Rodeway
0.0605
0.0333 -0.2896
0.0363
0.0302 0.000005
0.3371
0.1497 -0.1324
-0.0611 -0.0295 0.000733
0.2415
0.0625 -0.1226 -0.1460 -0.3253
0.0338 -0.1765 0.000000
0.3799
0.4936 -0.0703
0.0742 -0.0013 -0.2065
0.0181 -0.0649 0.000497
0.2497
Ryan
0.2684 -0.0295
0.1269
• 0.213235
0.0662
Rydges
0.8059
0.0262
0.0345 -0.2102
0.0858 -0.5018 0.000020
0.3136
Sara
0.7028 -0.0252
0.0977 -0.0476 -0.3068
0.0831 -0.1001 0.009406
0.1830
Savoy
0.3993
0.0207
0.0550 -0.1037 -0.2322
0.1104 -0.0249 0.001953
0.2199
Scandic
0.9207
0.0627 -0.1014 -0.0897 -0.1285
-0.1244 -0.0716 0.000207
0.2679
Shangrila
0.8320 -0.0218
0.0412 -0.1871 0.001922
0.2203
0.0427
-0.0462
•
0.0553 -0.2697 -0.2506
137
0.1082
0.0161
Table 19. Continued
Chain
Intercept
SIZE
lEXP
DOI
LDR
FSTMOV
NAT
Model p
Rsq
-0.0304 -0.0012 0.000000
0.5480
0.1310 -0.1385 0.000060
0.2925
0.0872 0.003934
0.2039
0.1439 -0.4197
0.1401 -0.3220 0.000576
0.2466
SRS
0.9911 -0.0108 -0.1879 -0.0268 -0.2699
-0.0562 -0.0072 0.000000
0.5344
Stouffer
0.6111 -0.0277
0.1442 -0.0100 -0.3191
-0.0436 -0.0360 0.000132
0.2769
Super 8
0.5206 -0.0200
0.0299
0.0335 -0.2458
0.1343 -0.0463 0.008760
0.1847
Swiss International
0.9850 -0.0033 -0.2044 -0.0879 -0.3010
0.0576 -0.1410 0.000000
0.4625
Taj
0.5123
Thistle
0.3697 -0.0258
Treadway
Sheraton
0.9946 -0.0020 -0.1981 -0.0240 -0.2805
Sofitel
0.8596
0.0226 -0.1337
Sonesta
0.4832
0.0773 -0.0548 -0.0049
Southern Pacific
0.8445 -0.0037
0.0040
0.0142 -0.3819
0.2302
0.0629
0.1039 -0.0785 -0.0936 -0.2420
0.0156
0.0296 0.157493
0.1048
0.0811
0.0110 -0.2796
0.0958
0.0807 0.002323
0.2160
0.6151 -0.0325
0.1018
0.0430 -0.1618
-0.1173 -0.0146 0.009063
0.1839
Trusthouse
0.6074
0.0981 -0.0760 -0.2766
0.1424 -0.0014 0.000010
0.3254
Trusthouse PLC
0.6994 -0.0020 -0.1234 -0.0593 -0.1013
-0.0949 -0.0103 0.000000
0.4074
VMS
0.5931 -0.0258
0.1212
0.0664 -0.3344
0.1427 -0.0141 0.000263
0.2630
Westcoast
0.4304 -0.0246
0.1264
0.1592 -0.4015
0.1290
0.0272 0.005572
0.1957
Wyndham
0.5073 -0.0147
0.1368
0.0943 -0.3875
0.0718
0.0440 0.000053
0.2949
0.0493
138
Table 20. 1990 Well-Predicted Chains
Intercept
Chain
SIZE
DOI
lEXP
LDR
FSTMOV
Model p
Rsq
0.0789 -0.1014 0.000000 0.4571
Frantel
0.9522
0.0149 -0.1924
Golden Tulip
1.0089
0.0001 -0.1894 -0.0162 -0.2561
Hilton Intemational
0.9490
0.0022 -0.2082 -0.0156 -0.2890
0.0075 -0.0048 0.000000 0.5454
Holiday Inn
0.9762
0.0218 -0.1988 -0.0016 -0.3656
-0.0783 -0.0075 0.000000 0.5017
Hyatt Intemational
0.9287 -0.0267 -0.2145 -0.0365 -0.2716
0.0209
0.0000 0.000000 0.5090
Intercontinental
0.9719 -0.0042 -0.2090 -0.0289 -0.2424
0.0110
0.0030 0.000000 0.5735
Le Meridien
0.9358
Ramada
0.9662 -0.0182 -0.1997
0.0089 -0.2803
-0.0408 -0.0067 0.000000 0.5330
Sheraton
0.9946 -0.0020 -0.1981 -0.0240 -0.2805
-0.0304 -0.0012 0.000000 0.5480
SRS
0.9911 -0.0108 -0.1879 -0.0268 -0.2699
-0.0562 -0.0072 0.000000 0.5344
Swiss International
0.9850 -0.0033 -0.2044 -0.0879 -0.3010
0.0576 -0.1410 0.000000 0.4625
Table 21
SRS
Swiss International
0.0056 -0.2210 -0.0235 -0.2955
0.000000 0.5137
-0.0512
0.0107 -0.0081 0.000000 0.5600
Characteristics O f 1990 W e l l - P r e d i c t e d Chains
SIZE lEXP
29
15988
67
66850
43
41823
50
314261
27
21893
52
41365
34
18693
33
109182
60
7102
42
54813
18
10393
DOI
0.6165
0.9566
0.8740
0.1570
0.9797
0.8540
0.8774
0.2545
0.9211
0.9443
0.7778
TYPE
NAT
LDR FSTMOV
?
France
S
NL
Luxury
Netherlands
F
NL
Luxury
US
NL
S
Mid-priced
F
US
NL
Luxury
US
S
NL
Luxury
US
S
NL
Luxury
France
NL
s
Mid-priced
US
F
NL
Luxury
US
NL
Luxury
Germany
F
NL
?
Switzerland
S
NL
139
LL.
Chain
Frantel
Golden Tulip
Hilton International
Holiday Inn
Hyatt International
Intercontinental
Le Meridien
Ramada
Sheraton
0.0341 -0.2303
NAT
Table 22. 19 91 Regression.Analyses Results
Chain
Intercept
SIZE
lEXP
DOI
LDR
FSTMOV
NAT
Model p
Rsq
A.P.
0.5144 -0.0457
0.2135
0.0843
0.0678
-0.0417
Accor
0.5388
0.0033
0.1244 -0.0086
0.1017
-0.0098 -0.1172 0.000064 0.2601
Best Westem
0.8506
0.0081 -0.2375
0.1278
0.2040
-0.0205
0.0132 0.000000 0.3759
BTH
0.6720
0.1381 -0.1195 -0.1034
0.2790
0.0926
0.0363 0.000000 0.3591
Budget
0.5046
0.0233
0.0611 -0.0618
0.0605
0.0353 -0.0982 0.028203 0.1369
Caesar Park
0.5096
0.0658
0.0109 -0.1428
0.0329
0.1030
Choice
0.9252 -0.1192 -0.1068
0.1336
0.1883
0.0259 -0.0275 0.000000 0.4826
Churchman
0.4478
0.1108
0.0559 -0.0261
0.0723
0.0171 -0.0626 0.000000 0.3587
Ciga
0.8722
0.0383 -0.1332
0.0686
0.1397 -0.1668 0.005282 0.1746
Clement Chen
0.3425 -0.0859
0.2198
Connor Jacobson
0.6088
0.1115
0.0419 -0.2956
Consort
0.7236 -0.0655
0.0613 -0.1247
Caledonian
0.7269
0.0846 -0.0566
Country Lodging
0.5252
0.0502
0.1425
Cunard
0.4221
0.0207
Days Inn
0.8485 -0.1469 -0.0302
Delta
0.2217
0.0326
Dorint
0.6141
Double Tree
0.0011
0.0156 -0.0106
0.000000 0.3591
0.097397 0.0920
-0.0313
0.0461 0.000000 0.4059
0.0903
0.0524
0.000000 0.3685
0.1789
0.1153 -0.0059 0.019520 0.1456
0.1317
0.2240
-0.0618 -0.1601 0.000765 0.2139
0.0841
0.1866
0.0163 -0.0369 0.000000 0.4211
0.0555 -0.1376
0.2163
0.0456
0.0849
0.0935
0.0063 -0.0804 0.000000 0.3799
0.0682
0.1005
0.0446
0.0190
0.0468 -0.0114
0.2138
0.1200
0.1298 -0.0359 0.000774 0.2137
0.5282 -0.0830
0.1173 -0.0443
0.1519
-0.0062 -0.0669 0.000005 0.3033
Fimotel
0.3896 -0.0927
0.1944 -0.0701
0.0304
0.0196 -0.0242 0.000000 0.4000
Four Seasons
0.7863 -0.0321
0.0914 -0.2861
0.0287
0.0717 -0.0966 0.000012 0.2892
Frantel
1.0104 -0.0070 -0.2874
0.0010
0.1641
0.0434 -0.0403 0.000000 0.6001
Grand Collection
0.8098
0.0479 -0.2626 -0.0150
0.0937
0.0281 -0.0377 0.000000 0.3697
HIDC
0.3148 -0.0002
0.1421
0.0839
0.1267
0.0083 -0.0012 0.000000 0.3606
Hilton International
1.0587 -0.0312 -0.2282 -0.0741
0.1243
-0.0146
Howard Johnson
0.8059 -0.1639
0.0070
0.1101
0.1207
0.0240 -0.0801 0.000000 0.4163
Holiday Inn
0.9646 -0.0576 -0.1656
0.1211
0.1260
0.0239
Hospitality Intemational
0.5108 -0.0629
0.0206 -0.0606
0.0196
0.0216 -0.1185 0.003413 0.1838
Hotel Management
0.4203
0.1206 -0.0562 -0.0803
0.0620
0.0212 -0.0853 0.000573 0.2195
HIS
0.4288 -0.0667
0.1769 -0.0400
0.0201
0.0133 -0.0197 0.000000 0.3651
HUSA
0.5517
0.0085
0.1338 -0.0232
0.1535
0.0632 -0.1548 0.000091 0.2538
HUSA International
0.8459
0.1072 -0.1129 -0.2150
0.0846
0.1171 -0.1069 0.000283 0.2329
Hyatt
0.6572 -0.1082
0.0042
0.1225
0.1725
0.0298 -0.0875 0.000003 0.3130
Hyatt International
1.0132 -0.0424 -0.2409 -0.0440
0.0962
-0.0028
IBIS
0.9292
0.0944
0.1672
0.0582 -0.1116 0.000000 0.4367
Inter Benelux
0.4145 -0.0321
0.2080
0.0445
0.0005
0.0338
o.oooooo
0.4743
InterWest
0.8017
0.0702
0.0606
0.1086
0.0409 -0.3858 0.000035
0.2704
0.0012 -0.2706
0.0718
140
0.0026 0.014877 0.1518
0.0556 0.001049 0.2078
0.0072 0.000000 0.6859
0.0136 0.000000 0.5393
0.0048 0.000000 0.6592
Table 22 .
Chain
Intercept
Continued
DOI
SIZE
lEXP
0.2072 -0.0210
LDR
J&B
0.4625
0.0156
Jack Tar
0.5814
0.1530 -0.0296
Jolly of Italy
0.7427 -0.0523
Journeys
0.4210 -0.1183
Kempinski
0.8203
Loews
0.5146 -0.0085
Mandarin
0.9497
Marriott
0.9028 -0.0286 -0.2984
0.0363
0.1128
Mercure
0.7776 -0.0379 -0.0595
0.1511
Le Meridien
FSTMOV
NAT
Model p
Rsq
0.1693
0.0466
0.0358 0.000000
0.3568
-0.2501
0.1571
0.0378
0.0246 0.000000
0.3684
0.1859
0.0113
0.1029
0.0679 -0.2589
0.000005
0.3022
0.1120
0.1157 -0.0047
0.0283 -0.0565
0.000440
0.2246
0.0791 -0.2545
0.1013
0.0816 -0.1785
-0.1615
0.0009
-0.0164 -0.0652
0.000000
0.3566
0.1375
0.0904
0.0668 -0.0017
0.004766
0.1768
0.0208 -0.2707 0.000001
0.3337
-0.2978 -0.0330
0.0087 0.000000
0.6374
0.0855
0.1023 -0.1346 0.000390
0.2269
1.0241 -0.0214 -0.2393 -0.0800
0.1210
0.0055 -0.0156 0.000000
0.6272
Ming
0.2456
0.1373 -0.0997
0.1156
0.0118
Movenpick
0.7826
0.0640 -0.1216 -0.2030
0.0015
Nikko
0.9653 -0.0187 -0.3067 -0.0901
Oberoi
0.8648
Ocean
0.4514 -0.0084
Okura
0.7995
Otani
0.6332 -0.0502
Pacific Island
0.3602 -0.0482
Pan Pacific
0.7534
PARKLN
0.4809 -0.0904
0.1721
0.0886
0.0692
Parklane
0.3733
0.0263
0.1042 -0.0432
0.2141
Peninsula
0.9099
0.0735 -0.0513 -0.3537 -0.0835
Penta
0.8765
0.0027 -0.3047 -0.0019
Preferred
Premiere
0.8291
Prince
0.6520 -0.1279
Princess
0.5961
Radisson
0.8559 -0.1557 -0.1145
Raintree
0.4196
0.0411
Rank
-0.0021
0.012072
0.1407
0.0469 -0.1143
0.000078
0.2565
0.0358
-0.0294 -0.0986
0.000000
0.5132
0.0508
0.0481 -0.0948
0.000040
0.2682
0.1772
0.0055 -0.0308
0.000000
0.3843
-0.2085 -0.0832
0.0796 -0.1734
0.035530
0.1314
0.0226
0.2472 -0.0344
0.0504 -0.1456
0.020440
0.1445
0.2071
0.0499
0.0559
0.0248 0.000000
0.4169
0.0403 -0.2153 -0.1121
0.0432
0.0664 -0.2184 -0.1753
0.1617
0.0070 -0.0461
0.0342
-0.0039
-0.0076
0.0312 -0.0039
0.000001
0.3339
-0.0218
0.0048 0.000001
0.3324
0.0257
0.0098 0.163363
0.0913
0.0337 -0.3184
0.000050
0.2642
0.1150
-0.0046 -0.0245
0.000000
0.4590
0.9467 -0.0483 -0.2744 -0.0416
0.0929
-0.0094
0.0011 0.000000
0.5953
-0.1152
0.1299
-0.0852
0.0352 0.000001
0.3348
0.1258 -0.1435
0.1027 -0.0058
0.0288 -0.0070
0.001234
0.2045
0.1621
0.0366 -0.0546
0.000625
0.2179
0.0931
0.1280
-0.0041 -0.0716
0.000000
0.4582
0.1697
0.1415
0.1636
0.0381
0.0349 0.000000
0.3846
0.4780 -0.0805
0.2726
0.0748
0.1382
0.0701
0.0102 0.000004
0.3076
Regal
0.5078 -0.1248
0.0315
0.0639
0.0032
Riande
0.2717 -0.0187
0.1164
0.0474
0.0886
0.0222
Rihga
0.5622 -0.0512
0.1445
0.0849
RJS
0.4981
Rock
0.4494
0.0796
0.0032
0.0687 -0.0870
-0.0205 -0.0600
0.000724
0.2150
,
0.009159
0.1466
0.0077
0.0013 -0.1563
0.000265
0.2341
0.1310 -0.0355 -0.1495
0.1319
0.0727 -0.0021
0.000000
0.3588
0.0299
0.0375 -0.1666
0.1968
0.0660
0.0014 0.042546
0.1270
Rosewood
0.3709 -0.1024
0.2066 -0.0723
0.2995
0.0038 -0.0423
0.000572
0.2196
Royal
0.4129
0.1055 -0.0707
0.2246
0.0237
0.022649
0.1268
0.0228
141
Table 22 . Continued
Chain
Intercept
SIZE
lEXP
DOI
LDR
0.1359
FSTMOV
NAT
Model p
Rsq
Rydges
0.7200
-0.0281
0.1148
0.1180
0.0537
-0.4490
0.000000
0.3824
Sara
0.6774 -0.0494 -0.0038
-0.0207
0.0243
0.1326 -0.0310
0.147892
0.0941
SAS
0.8523
-0.2068 -0.0781
0.0826
-0.0801
0.000002
0.2976
Scandic
0.9450
-0.0257 -0.2433
0.0288
0.1793
-0.0642 -0.0441
0.000005
0.3037
Shangrila
0.9244
-0.0024 -0.0607 -0.2187
0.0242
0.0245
-0.2013
0.000059
0.2615
Sheraton
1.0278 -0.0251 -0.2384
0.0585
0.1672
0.0126
0.0068
0.000000
0.6714
Small Luxury
0.9457
-0.2873 -0.1017
0.0672
-0.0217 -0.0659
0.000000
0.4697
Sofitel
0.9987 -0.0056 -0.3003 -0.1051
0.0975
0.0260 -0.0486
0.000000
0.5406
Sonesta
0.5783
0.1104 -0.1507
0.0390
0.0428
0.1098
0.0905
0.000021
0.2794
Southem Pacific
0.9336
0.0081
-0.1259 -0.0863
0.1228
0.0744 -0.1943
0.014338
0.1526
SRS
1.0980 -0.0586 -0.2074 -0.0907
0.1429
0.0149
0.000000
0.6468
Stakis
0.5540
0.0238
0.1840
-0.0428
0.0736
0.000000
0.3194
Stouffer
0.6342
-0.0764
0.1839
0.0774
0.1989
0.0160 -0.0615
0.000000
0.3701
Sunbelt
0.4068
0.1664 -0.0923 -0.1276
0.0218
0.0223
-0.0875
0.000074
0.2573
Super 8
0.6676
-0.1129
0.0074
0.0394
0.1222
0.0345
-0.0853 0.002141
0.1934
Swallow
0.4480
0.0124
0.1193
0.0361
0.3091
0.0516
-0.0865
0.000000
0.3771
Swiss International
0.9699
-0.0095 -0.2937 -0.0637
0.0906
0.0082 -0.0319
0.000000
0.6273
Swissotel
07155
0.0181
-0.2277 -0.1611 -0.0614
0.0208
0.0348
0.000029
0.2736
Taj
0.9551
0.1046
-0.1934 -0.2104
0.1907
-0.0428 -0.1410
0.000000
0.3864
TCC
0.7860
Treadway
0.3790
Trusthouse
0.0413
0.0328
-0.0468
-0.0602 -0.1503
0.0793
0.1050
-0.0552 -0.0662
0.000917
0.2104
0.0023
0.0284
-0.0469
0.0783
0.0277 -0.0833
0.203579
0.0848
0.6772
-0.1045
0.1073
0.0384
0.1723
0.0358
-0.0729
0.000085
0.2550
Trusthouse PLC
0.9973
-0.0013 -0.2679
0.0697
0.2306
0.0289
-0.0502
0.000000
0.5771
VMS
0.6163 -0.0784 -0.0751
0.1095
0.0645
0.0406
-0.0671
0.002625
0.1893
Westcoast
0.4959
0.0977
0.1275
0.1953
0.0760 -0.0218
0.002574
0.1897
Westin
0.9277 -0.1064 -0.1952 -0.0122
0.1490
-0.0110 -0.0717
0.000000
0.4671
Wyndham
0.4563
-0.0203
0.0592
0.1621
0.0835
0.0931
0.0167
0.000866
0.2115
York
0.5481
0.0348
0.1987
-0.1590
0.2129
0.0654
0.0258
0.000008
0.2949
0.0772
142
Table 23. 1991 Well-Predicted Chains
Chain
Intercept
SIZE
lEXP
DOI
LDR
FSTMOV
NAT
Model p
Rsq
Frantel
1.0104 -0.0070
-0.2874
0.0010
0.1641
0.0434
-0.0403
0.000000
0.6001
Hilton International
1.0587 -0.0312 -0.2282
-0.0741
0.1243
-0.0146
0.0072
0.000000
0.6859
Holiday Inn
0.9646
-0.0576
-0.1656
0.1211
0.1260
0.0239
0.0136
0.000000
0.5393
Hyatt International
1.0132 -0.0424
-0.2409
-0.0440
0.0962
-0.0028
0.0048
0.000000
0.6592
Le Meridien
1.0241 -0.0214
-0.2393
-0.0800
0.1210
0.0055
-0.0156
0.000000
0.6272
Marriott
0.9028
-0.0286
-0.2984
0.0363
0.1128
-0.0021
0.0087
0.000000
0.6374
Nikko
0.9653
-0.0187
-0.3067 -0.0901
0.0358
-0.0294
-0.0986
0.000000
0.5132
Preferred
0.9467
-0.0483
-0.2744 -0.0416
0.0929
-0.0094
0.0011
0.000000
0.5953
Sheraton
1.0278 -0.0251
-0.2384
0.0585
0.1672
0.0126
0.0068
0.000000
0.6714
Sofitel
0.9987
-0.0056 -0.3003 -0.1051
0.0975
0.0260
-0.0486
0.000000
0.5406
SRS
1.0980 -0.0586
-0.2074 -0.0907
0.1429
0.0149
-0.0468
0.000000
0.6468
Swiss International
0.9699
-0.0095
-0.2937 -0.0637
0.0906
0.0082
-0.0319
0.000000
0.6273
Trusthouse PLC
0.9973
-0.0013 -0.2679
0.2306
0.0289
-0.0502
0.000000
0.5771
0.0697
Table 24. Characteristics Of 1991 Well-Predicted Chains
Chain
SIZE
Frantel
13863
Hilton Intemational
41823
Holiday Inn
319135
Hyatt International
22706
Le Meridien
18693
Marriott
89126
Nikko
11415
Preferred
24443
Sheraton
133119
Sofitel
10286
SRS
51101
Swiss International
11360
Trusthouse PLC
38290
lEXP
24
43
48
32
35
15
10
20
55
18
43
18
27
DOI
0.5933
0.9239
0.1572
1.0000
0.8774
0.0855
0.7512
0.4041
0.3599
0.6250
0.8749
0.7575
0.4536
143
LDR FSTMOV
NL
S
NL
F
NL
S
NL
S
NL
S
NL
S
NL
S
NL
S
NL
S
NL
S
NL
S
NL
S
NL
S
NAT
TYPE
France
?
US
US
US
Luxury
Mid-priced
France
Luxury
US
Luxury
Japan
?
US
US
Luxury
France
Luxury
Germany
Luxury
Switzerland
?
?
UK
Luxury
Luxury
r
Table 25.
Chain
Intercept
1992 Regression Analyses Results
SIZE
lEXP
DOI
0.0849
FSTMOV
NAT
Model p
Rsq
A.P.
0.4546 -0.0281
0.2072
Accor
0.4583
0.0187
0.1385 -0.0309
-0.0028 -0.0217 0.000465 0.2043
BelAIr
0.3198 -0.0193
0.1687 -0.0533
-0.0218
0.0550 0.000345 0.2098
Best Westem
0.8172 -0.0315 -0.2001
0.1000
0.0905
0.0000 0.000000 0.4341
Brook
0.2125 -0.0036
0.0819
0.0750 -0.0247 0.000125 0.2282
BTH
0.5361
Budget
0.4339 -0.0306
Choice
1.0047 -0.1097 -0.1667
Churchman
0.3488
0.0544
Ciga
0.7698
0.0322 -0.1085
Clement Chen
0.2751 -0.0346
Colony
0.0781
0.1747 -0.0970 -0.0879
0.1607 -0.0378
0.0909
0.0825 -0.0184
0.1461
0.0615
0.0835
0.000000 0.4418
0.0493 0.000001 0.3019
0.0739 -0.0446 0.000033 0.2514
-0.0581
0.0112 0.000000 0.6468
-0.0400 -0.0154 0.012638 0.1384
0.0909 -0.1626 0.000245 0.2161
0.1615
0.0466
-0.0085
0.0402 0.000001 0.3043
0.8148
0.0122 -0.2000
0.1449
-0.1270
0.0164 0.000000 0.3366
Connor Jacobson
0.5308
0.1230 -0.0129 -0.2386
0.0838
0.000000 0.4474
Consort
0.7267 -0.1255
0.0017 -0.1696
0.1523 -0.0094 0.000975 0.1903
Caledonian
0.5476
0.0483
0.0227
0.0437 -0.1380 0.000162 0.2236
Cunard
0.3154
0.1137 -0.0948 -0.1792
Delta
0.3280 -0.0287
Divi
0.1277
0.0160
0.0185 0.000004 0.2865
0.0425
-0.0331
0.0034 0.000000 0.4923
0.5869
0.1549 -0.1192 -0.1789
0.0603
0.0042 0.000000 0.4202
Dorint
0.5737
0.0618 -0.0593
-0.1410
0.0884 0.000442 0.2052
Doubletree
0.3915 -0.0820
0.1096 -0.0199
0.0566 -0.0356 0.000000 0.3666
Durbin
0.3498
0.0507
0.0886 -0.0351
-0.0406 -0.0160 0.011233 0.1409
Embassy
0.5327
0.0508
0.0679
Flag
0.3238
0.0636 -0.0069 -0.1227
Four Seasons
0.9205 -0.0405
Frantel
0.9585 -0.0434 -0.2983
Golden Tulip
1.0624 -0.0840 -0.2081 -0.0367
-0.0309
0.000000 0.7267
Grand Collection
0.6431
0.0062 -0.1015
0.1555
0.0917
0.0423 0.000561 0.2008
HIDC
0.2613
0.0272
0.0623
0.0938
0.0054 -0.0001 0.002893 0.1690
Hilton
0.6746 -0.1339 -0.0912
0.0793
0.0185 -0.0154 0.000001 0.3148
Hilton Intemational
0.9989 -0.0533 -0.2331 -0.0322
Howard Johnson
0.6623 -0.1289
Holiday
1.0302 -0.0680 -0.1912 -0.0585
Hospitality Intemational
0.4526 -0.0929
0.1064
0.0185
0.0635 -0.0445 0.000221 0.2179
Hotel Group
0.3984
0.1105
0.0129
0.0334
0.0183 0.006614 0.1521
HIS
0.3889 -0.0470
0.2061 -0.0978
0.0119
0.0360 0.000000 0.3435
Hungar
0.3966 -0.0067
0.2045 -0.0167
0.0029
0.000030 0.2339
HUSA Intemational
07145
0.0635 -0.1664 -0.1599
0.1184
0.000000
0.0246
0.1623
0.1760
0.0762
0.0597 -0.2910
0.1104
144
0.0705
0.0989
0.0375 -0.1727 0.000043 0.2467
0.0124
0.0170 0.002424 0.1725
0.0906 -0.2354 0.000004 0.2858
0.0803 -0.0328 0.000000 0.6955
0.0214
0.0045 0.000000 0.6993
0.0223 -0.0171 0.000000 0.3812
0.0631
0.0178 0.000000 0.6924
0.3875
Table 25. Continued
Chain
Intercept
SIZE
lEXP
DOI
FSTMOV
NAT
Model p
Rsq
Hyatt
0.4423
-0.0202
0.0875
0.1087
0.0414
-0.0135
0.007012
0.1509
Hyatt International
0.9431
-0.0687
-0.2408
-0.0013
0.0468
0.0202
0.000000
0.6291
Inter
0.9660
-0.1412
-0.1037
-0.1490
-0.1384 -0.0128
0.000008
0.2752
Intercontinental
1.0456 -0.0632
-0.2221
-0.0299
-0.0353
0.0042
0.000000
0.7488
InterWest
0.8015
0.0439
0.0994
0.0356
0.0845
-0.4590
0.000001
0.3156
Jack Tar
0.4838
0.1131
-0.0572
-0.1483
0.0732
-0.0082
0.000010
0.2707
Jolly of Italy
07176
-0.0611
0.1043
0.1686
0.0304
-0.3260
0.004390
0.1606
Journeys
0.6620
-0.0293
0.1624
0.0118
-0.0295
-0.4089
0.000005
0.2838
Kempinski
0.6190
0.0946
-0.1161
-0.1667
0.0270
0.0123
0.031439
0.1182
Loews
0.4725
-0.0577
0.1682
0.0342
0.0810
-0.0160
0.000000
0.3564
Mandarin
0.9277
0.0670
-0.0537 -0.2493
0.0724
-0.3218
0.000362
0.2089
Marriott
0.8698
-0.0985
-0.1947
0.0674
0.0305
0.0293
0.000000
0.5911
Mercure
0.5783
-0.0806
0.1167
0.1598
0.0811
-0.0310
0.000096
0.2329
Le Meridien
0.9585
-0.0597 -0.2373
-0.0208
0.0313
0.0134
0.000000
0.6787
Ming
0.3428
0.0148
-0.0193
-0.0015
0.011843
0.1232
Movenpick
0.8553
0.0473
-0.1724 -0.1474
0.1089
-0.1137
0.000000
0.3453
Mount Charlotte
0.5850
-0.0800
0.0846
0.0685
-0.1546
0.000040
0.2482
Nikko
0.8776
-0.0558
-0.1783 -0.1589
0.0793
-0.0768
0.000002
0.2946
Oberoi
0.7353
0.0470
-0.1960
-0.1730
0.1219
-0.0405
0.000006
0.2786
Ocean
0.4415
-0.0434
0.1316
-0.0452
0.0173
-0.0475
0.001751
0.1790
Okura
0.5709
-0.0220
0.0677
-0.1940
0.1307
-0.0903
0.002938
0.1687
Omni
0.4924
-0.1016
0.1261
0.1033
0.0428
0.0042
0.000306
0.2120
Otani
0.5592
-0.0830
0.1733
0.1387
0.0335
-0.1618
0.000034
0.2509
Pacific Island
0.3443
-0.0598
0.1700
-0.0100
0.0216
0.0066
0.000003
0.2899
Pan Pacific
0.7786
0.0445
-0.2094
-0.0551
-0.0905
0.0490
0.000000
0.3251
Park
0.6298
-0.0979
0.2273
0.0165
-0.0435
-0.0239
0.000000
04162
ParkLane
0.3505
-0.0130
0.1761
0.0290
0.0231
0.0427
0.004634
0.1594
Peninsula
0.8578
0.0521
0.0262
-0.2592
0.0353
-0.2694
0.000509
0.2026
Penta
0.7938
0.0103
-0.2550
-0.0083
0.0525
-0.0349
0.000001
0.3182
Preferred
0.8745
-0.0799
-0.2351
0.0090
0.0493
0.0412
0.000000
0.4897
Premiere
0.6524
0.1362
-0.0827
-0.0845
-0.0676
0.1161
0.000000
0.3646
Prince
0.5566
-0.1016
0.1121
0.1512
0.0227
-0.0217
0.000218
0.2182
Princess
0.5296
0.0670
0.0360
-0.1802
0.1658
0.0335
0.000087
0.2346
Raintree
0.4907
0.0671
0.0842
0.1563
-0.1014
0.0236
0.000012
0.2683
Ramada International
1.0130 -0.0822
-0.2221
-0.0156
0.0376
-0.0234
O.OOOOOC
0.6857
Rank
0.4169
-0.0124
0.1648
0.2034
0.0913
0.0286
0.000045
0.2460
Regal
0.5046
-0.0489
0.1722
0.0242
-0.0186
-0.0083
0.000026i
0.2554
0.1025
0.0945
145
Table 25. Continued
Chain
Intercept
SIZE
lEXP
0.0187 -0.2275
DOI
FSTMOV
NAT
Model p
Rsq
Regent Intemational
0.9002
Rihga
0.3685 -0.0950
0.2125
Ritz
0.3660 -0.0823
0.1849 -0.0633
-0.0100
RJS
0.3646
0.0377 -0.0902
0.0287 -0.0429 0.067159 0.1003
Rock
0.3309 -0.0153
0.1208
0.0757
0.0505 -0.0020 0.002561 0.1714
Royal
0.3611
0.0361
0.1361
0.0213
-0.0174
Rydges
0.7554
0.0892
0.0412
0.0379
0.0668 -0.4975 0.000000 0.3919
Sara
0.6587 -0.0492
0.1167
0.2286
0.0592 -0.2091 0.000179 0.2218
SAS
0.7477
Scandic
0.8317 -0.0852 -0.1939
0.0431
0.1374 -0.0947 0.000012 0.2690
Shangrila
0.9543 -0.0491 -0.0709 -0.1444
0.0951 -0.2320 0.000015 0.2645
Sheraton
1.0506 -0.0528 -0.2153
Small Luxury
0.8219
Sofitel
0.8719 -0.0508 -0.2890
0.0135
0.0924 -0.0254 0.000000 0.5429
Sonesta
0.4311
0.1182
0.0111
0.0838
0.0518
Southem Pacific
0.8001 -0.0509 -0.1107
0.1934
0.1550 -0.2167 0.000807 0.1939
SRS
1.0755 -0.0779 -0.2018 -0.0558
-0.0441 -0.0146 0.000000 0.7292
Stakis
0.4199
0.0390
0.1212
0.0523
0.0938
Stouffer
0.6014 -0.1061
0.1600
0.0700
0.0609 -0.0346 0.000000 0.3663
Sunbelt
0.3780
0.0707
0.0859
0.0422
0.0049
Super 8
0.6267 -0.1041
0.0930
0.0446
0.0561 -0.0309 0.000000 0.3238
Swallow
0.4705
0.0997 -0.0026
0.0140 -0.1833 0.000018 0.2613
Swiss lntematk)nal
0.8842 -0.0449 -0.2232 -0.0988
0.0918 -0.0907 0.000000 0.5183
Swissotel
0.7536
0.0224 -0.1668 -0.1498
0.0433 -0.0164 0.000113 0.2299
Taj lntematk)nal
0.7314
0.0675 -0.1664 -0.1637
0.1614 -0.0697 0.000000 0.3276
TCC
0.4692
0.0036 -0.0485
0.1966
0.0840 -0.0183 0.009977 0.1434
Trusthouse
0.4125 -0.1004
0.0742
0.1121
-0.0278
Westin
0.7758 -0.1242 -0.1700
0.1076
0.0956 -0.0179 0.000000
0.3537
Wyndham
0.4324 -0.0878
0.0273
0.2252
0.1104
0.0096 0.000817
0.1937
0.0351
0.0687
0.0709 -0.2330 0.000000 0.3259
0.0542
-0.0061 -0.0925 0.000097 02327
0.0370 -0.2106 -0.0855
0.0111
0.0788 -0.2734 -0.0328
0.0134
146
0.0004 0.000000 0.3519
0.000203 0.2009
0.000000 0.3331
0.0912
-0.0457
0.0157 0.000000 0.7180
0.0705 -0.0436 0.000000 0.4471
0.0897 0.000164 0.2233
,
0.000100 0.2133
0.0015 0.008246 0.1475
0.0216 0.000000 0.3303
Table 26. 1992 Well-Predicted Chains
Intercept
Chain
SIZE
DOI
lEXP
FSTMOV
NAT
Model p
Rsq
Choice
1.0047 -0.1097 -0.1667
0.0909
Frantel
0.9585 -0.0434 -0.2983
0.0705
Golden Tulip
1.0624 -0.0840 -0.2081 -0.0367
-0.0309
0.000000 0.7267
Hilton Intemational
0.9989 -0.0533 -0.2331 -0.0322
0.0214
0.0045 0.000000 0.6993
Holiday
1.0302 -0.0680 -0.1912 -0.0585
0.0631
0.0178 0.000000 0.6924
Hyatt Intemational
0.9431 -0.0687 -0.2408 -0.0013
0.0468
0.0202 0.000000 0.6291
Intercontinental
1.0456 -0.0632 -0.2221 -0.0299
-0.0353
0.0042 0.000000 0.7488
Le Meridien
0.9585 -0.0597 -0.2373 -0.0208
0.0313
0.0134 0.000000 0.6787
Marriott
0.8698 -0.0985 -0.1947
0.0305
0.0293 0.000000 0.5911
Ramada Intemational
1.0130 -0.0822 -0.2221 -0.0156
Sheraton
1.0506 -0.0528 -0.2153
SRS
1.0755 -0.0779 -0.2018 -0.0558
Table 27.
Chain
Choice
Frantel
Golden Tulip
Hilton Intemational
Holiday
Hyatt International
Intercontinental
Le Meridien
Marriott
Ramada International
Sheraton
0.0111
0.0112 0.000000 0.6468
0.0803 -0.0328 0.000000 0.6955
0.0376 -0.0234 0.000000 0.6857
-0.0457
0.0157 0.000000 0.7180
-0.0441 -0.0146 0.000000 0.7292
Characteristics O f 1992 W e l l - P r e d i c t e d Chains
SIZE lEXP
206571
21
22
12893
59
47929
43
41823
47
322527
31
21882
41671
46
36
18693
16
93712
34379
35
55
129483
46
58246
DOI
0.1086
0.5860
0.9383
0.9239
0.7736
1.0000
0.9457
0.8774
0.0909
0.9795
0.3328
0.8617
147
TYPE
NAT
LDR FSTMOV
Wide
F
US
NL
?
France
S
NL
Luxury
Netherlands
F
NL
Luxury
UK
S
NL
Mid-priced
US
S
NL
Luxury
US
NL
S
Luxury
UK
F
NL
Luxury
France
NL
S
Luxury
US
NL
S
Hong Kong Mid-priced
NL
S
Luxury
US
F
NL
Luxury
Germany
NL
Li-
SRS
0.0674
-0.0581
1
Table 28.
Chain
Intercept
1993 R e g r e s s i o n A n a l y s e s R e s u l t s
SIZE
lEXP
DOI
FSTMOV
NAT
Model p
Rsq
0.000000
0.5371
Accor
0.9437 -0.0342
-0.1889
-0.1296
Atlific
0.6070 -0.0387
0.1324
0.1642
-0.1721
0.0037 0.000000
0.3938
Austrotel
0.6285
0.0759
0.1193
0.0170
-0.0572
0.000000
0.3326
BelAIr
0.5722
0.0083
0.0539
0.2589
-0.1768
-0.0125
0.000009
0.2714
Best Western
0.7917
0.0325 -0.2022
0.1088
0.1572 -0.0357
0.000000
0.4228
BTH
0.6660
0.1040 -0.0205
-0.1343
0.0913 -0.0017
0.000072
0.2358
Budget
0.5050
0.0152
0.0866 -0.0049
0.0156 -0.0787
0.006951
0.1497
Caesar Park
0.3148 -0.0141
0.1559 -0.0463
-0.0025
0.000000
0.3321
Choice
1.1410 -0.0861
-0.1420 -0.1188
-0.0975
-0.0245
0.000000
0.5807
Churchman
0.5104
0.0634
0.0745 -0.0829
-0.0243
-0.0750
0.000000
0.3293
Ciga
0.9336
0.0610 -0.2189
-0.1152
0.0022 -0.1299
0.000200
0.2178
Colony
0.6155 -0.0171
0.0360
0.2306
0.1074 -0.0620
0.000148
0.2232
Connor Jacobson
0.6427
0.0875 -0.0493
-0.1797
0.1557
0.000000
0.3033
Consort
0.7604 -0.0572
0.0404 -0.2232
0.1794 -0.0408
0.000646
0.1963
Caledonian
0.6415
0.0362
0.0166
0.1000
0.0502 -0.1124
0.006355
0.1515
Country Lodging
0.5509
0.0266
0.1306
0.0116
0.0384 -0.0747
0.000000
0.3640
Days Inn
0.8056 -0.1234
0.0156
0.1304
0.0371 -0.0566
0.000000
0.3991
Delta
0.4106 -0.0166
0.1419
0.1236
0.1528 -0.0063
0.000000
0.5330
Devon
0.6187 -0.0146
0.1579
0.1257
0.0059 0.000000
0.3514
Divi
0.6269
0.0754 -0.0789 -0.2196
Dorint
0.6994
0.0259
0.0605
0.1947
Doubletree
0.4255 -0.0610
0.1078
Durbin
0.3463
Embassy
0.5791 -0.0843
Flag
0.5860
Four Seasons
0.7180 -0.0325
0.0614 -0.3319
Friendly
0.4781
0.0673
Golden Tulip
1.0458 -0.0706 -0.1845
Grand Collection
0.8153
HIDC
0.8507 -0.0297
Hilton
0.7788 -0.1123
Hilton International
0.0489
0.1124 -0.0445
-0.1506
0.1173 -0.0206
0.000000
0.4873
-0.0142
0.000091
0.2317
0.0301
0.0349 -0.0172
0.000000
0.3240
0.0668 -0.1033
-0.0794 -0.0536
0.000175
0.2202
-0.1348
0.0509
0.0843
0.0459 -0.0022
0.001281
0.1833
0.0596 -0.0739
-0.2169
0.1164 -0.0221
0.000000
0.3633
0.1126
0.0437 0.000000
0.3918
0.0771
0.0733
0.0458 0.008955
0.1444
-0.0886
0.0551
0.000000
0.7385
0.0071 -0.0464
0.000001
0.3031
0.020380
0.1267
0.0542
0.0096 -0.2263
0.0769
0.0863 -0.1912
-0.1363
-0.1648
0.1615
-0.0092
0.0171 0.000000
0.4776
1.0851 -0.0550
-0.1913
-0.0966
-0.0587
0.0097 0.000000
0.7476
Holiday Inn
1.0430 -0.0421
-0.1610 -0.0965
Hospitality Intemational
0.5213 -0.0552
0.0734
0.0124
HUSA
0.5289
0.0047
0.1005
HUSA International
1.0068 -0.0014
Hyatt
0.6093 -0.1038
-0.0051
0.0847 -0.0122
0.000000
0.6439
-0.0953
0.002131
0.1735
0.0256
0.0995 -0.1471
0.000060
0.2388
-0.2301
-0.1162
0.0830 -0.1123
0.000000
0.5072
0.0052
0.1607
0.0382 -0.0693
0.000000
0.4103
148
-0.0029
Table 28. Continued
Chain
Intercept
SIZE
lEXP
DOI
FSTMOV
NAT
Model p
Rsq
Hyatt International
1.0108 -0.0621
-0.2360 -0.0522
0.0389
0.0011
0.000000
0.7221
Inter
0.7731
0.0765
-0.1770 -0.0854
0.0968
-0.0079
0.000027
0.2524
InterBenelux
0.3750
-0.0413
0.2368
-0.0359
0.0132
-0.0433
0.000000
0.5774
Intercontinental
1.0304 -0.0595
-0.1921
-0.0906
0.0519
0.0097
0.000000
0.7520
Jack Tar
0.6097
0.0655
-0.0356
-0.2054
0.1667
-0.0426
0.000000
0.3675
Jolly of Italy
0.7656
-0.0555
0.0925
0.1480
0.0243
-0.2703
0.002015
0.1746
Journeys
0.3207
-0.0681
0.1055
0.0707
-0.0388
-0.0808
0.000074
0.2353
Jurys
0.2049
0.0145
0.0288
0.0035
0.0308
0.0379
0.688925
0.0307
Kempinski
0.8657
0.0628
-0.2355
-0.1351
-0.0117 -0.0520
0.000000
0.4288
Loews
0.5140
-0.0501
0.1372
0.0787
-0.0390
0.000000
0.3641
Mandarin
0.8898
0.0980
-0.0649
-0.4212
-0.0037 -0.2030
0.000002
0.2953
Marriott
0.9091
-0.0652
-0.2655
0.0431
-0.0059
0.000000
0.6765
Le Meridien
1.0097 -0.0559 -0.2228 -0.0932
0.0650
0.000000
0.7393
Movenpick
0.8873
0.0608
-0.1573
-0.2373
0.0479
-0.1341
0.000000
0.4404
Mount Charlotte
0.5778
-0.0256
0.0818
0.0721
0.1158
-0.1089
0.001925
0.1755
Nikko
0.9744
-0.0591
-0.2433 -0.1566
0.0188
-0.0781
0.000000
0.5086
Oberoi
0.8172
0.0693
-0.2253 -0.1384
0.0581
0.000000
0.3013
Ocean
0.4727
-0.0255
0.1426
-0.0259
0.0236
-0.0676
0.000000
0.3383
Okura
0.7597
-0.0125
0.1148
-0.2886
0.0453
-0.1163
0.001270
0.1835
Omni
0.5189
-0.0847
0.1554
0.0481
0.0096
-0.0558
0.000000
0.3885
Pacific Island
0.5418
0.0424
0.1045
-0.1368
0.0461
-0.0649
0.000000
0.4843
Pan Pacific
0.7957
0.0234
-0.1525
-0.2115
0.0371
-0.0091
0.000000
0.3371
Park
0.6365
-0.0848
0.1865
0.1046
0.0914
-0.0586
0.000000
0.5395
Parklane
0.4357
0.0121
0.1309
-0.0771
-0.0037 -0.0040
0.146448
0.0797
Penta
0.9037
0.0603
-0.2497 -0.1437
-0.0011 -0.0802
0.000000
0.4551
Preferred
0.9531
-0.0749
-0.2806 -0.0240
-0.0279
0.0237
0.000000
0.6499
Premiere
0.7825
0.1046
-0.1509
-0.1316
0.0650
0.0074
0.000000
0.4244
Prince
0.5553
-0.1028
0.0584
0.1606
0.1008
0.0546
0.000001
0.3152
Princess
0.6055
-0.0010
0.0964
-0.1851
0.1252
-0.0928
0.000000
0.3647
Principal
0.6313
0.0635
-0.0184
0.0876
0.0670
-0.1178
0.005820
0.1533
Radisson
0.9671
-0.1020 -0.1713
0.0740
-0.0329
-0.0402
0.000000
0.5656
Ramada International
0.9408
-0.0811
-0.1812
-0.0717
0.0608
0.0669
0.000000
0.6754
Rank
0.6887
0.0183
0.0781
-0.1411
0.1036
-0.1097
0.000000
0.3935
Regal
0.5196
-0.0702
0.1203
0.0610
-0.0167
-0.0428
0.000001
0.3050
Regent International
0.9084
0.0160
-0.2459
-0.1035
-0.0140 -0.1348
0.000000
0.4048
Rihga
0.5383
-0.0740
0.1745
0.0657
-0.0036
-0.0926
0.000000
0.3204
Ritz
0.3528
-0.0874
0.1451
-0.0203
-0.0099
-0.0122
0.000001
0.3100
149
0.0422
0.0360
Table 28. Continued
Chain
Intercept
SIZE
lEXP
DOI
FSTMOV
NAT
Model p
Rsq
-0.0289
0.000010
0.2689
0.000006
0.2577
Rock
0.5114
0.0145
0.1387
-0.1804
0.0436
Royal
0.4579
0.0486
0.1277
-0.2049
-0.0205
Ryan
0.1739
0.0553
0.0965
-0.0137
-0.0255
0.2546
0.000029
0.2514
Rydges
0.5139
0.0458
0.0792
-0.0055
0.0508
-0.1030
0.000189
0.2189
Sandal
0.6309
0.0607
-0.0041
-0.1997
0.1403
-0.0446
0.000000
0.3968
Sara
0.5625
-0.0248
0.1165
0.0948
0.0635
-0.1988
0.000001
0.3098
Sarova
0.5083
-0.0523
0.2134
-0.1132
0.0583
-0.0334
0.000000
0.3260
SAS
0.7992
0.0351
-0.2389
-0.0833
0.0433
0.0601
0.000000
0.4421
Scandic
0.8415
-0.0441
-0.1899
-0.0671
0.0947
0.0210
0.000029
0.2516
Shangrila
0.9408
-0.0914 -0.0607 -0.2367
0.0697
-0.1240
0.000000
0.3517
Sheraton
1.0653 -0.0662 -0.1583 -0.1416
0.0557
-0.0113
0.000000
07180
Small Luxury
0.9118
0.0736
0.0194
-0.0746
0.000000
0.5729
Sonesta
0.5520
0.0894
0.0741
-0.1234
0.0368
0.0572
0.001905
0.1757
Southem Pacific
1.0058 -0.0264 -0.2635
0.0264
0.0699
-0.0748
0.000000
0.6277
SRS
1.0467 -0.0607 -0.1666
-0.1101
0.0707
-0.0187
0.000000
0.7099
Stakis
0.5981
0.0455
0.1319
-0.1709
0.1040
0.000000
0.3006
Stouffer
0.6513
-0.0832
0.1638
0.0729
0.0672
-0.0814
0.000000
0.4792
Sunbelt
0.4149
0.0852
0.0598
-0.0529
-0.0568
-0.0686
0.000034
0.2488
Super 8
0.6586
-0.0972
0.0175
0.1120
0.0787
-0.0720
0.000018
0.2592
Swallow
0.5305
-0.0087
0.1049
-0.0301
0.0545
-0.1190
0.001113
0.1860
Swiss International
0.9666
-0.0194 -0.2772
-0.0719
0.0530
-0.0255
0.000000
0.6865
Swissotel
0.8378
0.0268
-0.2338 -0.1413
-0.0260
-0.0460
0.000000
0.3547
Taj International
0.8202
0.0850
-0.1585
-0.3214
0.1151
-0.0027
0.000000
0.4751
TCC
0.8293
-0.0150 -0.1625
0.2191
-0.1529
-0.0269
0.000005
0.2811
Trusthouse
0.7528
-0.0874
0.1009
-0.0784
0.0927
-0.0753
0.000000
0.3224
Trusthouse PLC
1.0104 -0.0313 -0.2373
-0.0190
0.0709
-0.0351
0.000000
0.6257
Westcoast
0.5463
0.0175
0.1319
-0.0061
0.0319
-0.0804
0.000020
0.2575
Westin
0.9380
-0.1165
-0.2088
0.0367
0.0143
-0.0411
0.000000
0.5011
Wyndham
0.5664
-0.0506
0.0201
0.1618
0.0690
-0.0690
0.000544
0.1995
York
0.3285
0.0760
0.0244
-0.0033
0.0388
0.0909
0.005784
0.1534
-0.2350 -0.1705
150
Table 29. 1993 Well-Predicted Chains
intercept
Chain
SIZE
lEXP
DOI
FSTMOV
NAT
Model p
Rsq
0.000000
0.7385
Golden Tulip
1.0458 -0.0706 -0.1845
-0.0886
0.0551
Hilton International
1.0851 -0.0550
-0.1913
-0.0966
-0.0587
0.0097
0.000000
0.7476
Holiday Inn
1.0430 -0.0421
-0.1610
-0.0965
0.0847
-0.0122
0.000000
0.6439
Hyatt International
1.0108 -0.0621
-0.2360 -0.0522
0.0389
0.0011
0.000000
0.7221
Intercontinental
1.0304 -0.0595
-0.1921
-0.0906
0.0519
0.0097
0.000000
0.7520
Le Meridien
1.0097 -0.0559
-0.2228
-0.0932
0.0650
0.000000
0.7393
Marriott
0.9091
-0.0652
-0.2655
0.0431
-0.0059
0.0360
0.000000
0.6765
Preferred
0.9531
-0.0749
-0.2806 -0.0240
-0.0279
0.0237
0.000000
0.6499
Ramada International
0.9408
-0.0811
-0.1812 -0.0717
0.0608
0.0669
0.000000
0.6754
Sheraton
1.0653 -0.0662
-0.1583
-0.1416
0.0557
-0.0113
0.000000
0.7180
Southern Pacific
1.0058 -0.0264 -0.2635
0.0264
0.0699
-0.0748
0.000000
0.6277
SRS
1.0467 -0.0607 -0.1666
-0.1101
0.0707
-0.0187
0.000000
0.7099
Swiss International
0.9666
-0.0194 -0.2772
-0.0719
0.0530
-0.0255
0.000000
0.6865
Trusthouse PLC
1.0104 -0.0313
-0.0190
0.0709
-0.0351
0.000000
0.6257
-0.2373
Table 30. Characteristics Of 1993 Well-Predicted Chains
Chain
SIZE
Golden Tulip
43403
Hilton International
50248
Holiday Inn
349226
Hyatt International
22809
Intercontinental
41671
Le Meridien
17774
Marriott
93484
Preferred
25858
Ramada International
30970
Sheraton
129483
Southern Pacific
12240
SRS
64255
Swiss International
11613
Trusthouse PLC
36188
lEXP
53
46
51
31
45
33
16
19
31
53
20
45
19
27
DOI
0.9292
0.8754
0.9391
1.0000
0.9457
0.8711
0.0951
0.3773
0.9781
0.7940
0.4377
0.8499
0.7851
0.4663
151
TYPE
LDR FSTMOV
NAT
Luxury
Netherlands
NL
S
UK
Luxury
NL
F
NL
Mid-priced
S
us
Luxury
S
NL
us
UK
Luxury
NL
S
Luxury
France
NL
S
Luxury
NL
S
us
Luxury
US
NL
s
Hong Kong Mid-priced
NL
s
Luxury
US
NL
s
?
Australia
NL
s
Germany
Luxury
NL
s
?
Switzerland
NL
s
?
UK
NL
s
Table 31. T-Tests p-values
lEXP
DOI
LDR
FST
NAT
YEAR
SIZE
1984
0.1151 0.7065 0.0081
1985
0.4196 0.3402 0.0965 0.0001 0.1195 0.0084
1986
0.3371 0.2096 0.8411 0.0001 0.5666 0.0001
1987
0.0003 0.0001 0.0654 0.0001 0.0492 0.0001
1988
0.4764 0.1755 0.5074 0.0001 0.0831 0.0001
1989
0.562 0.0039 0.0144 0.0243 0.0029 0.0001
0.0077
1990
0.0928
0.624 0.4798 0.0001 0.0013 0.0036
1991
0.8811 0.0673 0.1291 0.0001 0.0001 0.0001
1992
0.0548 0.4191 0.9753
0.0001 0.0001
1993
0.1381 0.0931 0.0017
0.0001 0.0001
Table 32. Mean of the Coefficients
YEAR
FIRST MOVER
LEADER
NATIONALITY
-0.03302
1984
1985
0.012441
-0.25076
-0.02908
1986
0.0015695
-0.05556
-0.05156
1987
0.02125
-0.14247
-0.05325
1988
0.014
-0.15171
-0.05069
1989
0.024182
-0.02979
-0.04243
1990
0.027624
-0.22001
-0.03954
1991
0.0127957
-0.10335
-0.06314
1992
0.037546
-0.05138
1993
0.034295
-0.04313
152
Table 33.
Chain
Intercept
1984 L a g Regression Results
SIZE
DOI
NAT
Model p
-0.1512
0.007000
0.2667
0.0768
0.0036
0.664000
0.0517
0.0791 -0.0356
0.0621
0.004500
0.2848
0.0298 -0.0239
0.028200
0.2145
0.0657
0.000000
0.5451
0.000080
0.3757
0.0755
0.000040
0.4329
0.0377 -0.0342
0.795000
0.0365
0.0908
0.000010
0.4615
0.068500
0.1763
0.0198 0.193000
0.1265
lEXP
American
0.6541 -0.0186
Best Westem
0.7334
0.0145 -0.0658
BTH
0.6751
0.1133
Budget
0.6350
0.1529 -0.0485
Ciga
0.5241 -0.0067
0.1615
Connor Jacobson
0.7218 -0.0761
0.2369 -0.0275
Canadian Pacific
0.4968 -0.0466
0.1551 -0.0288
Crest
0.7439
0.0414
0.0224
Cunard
0.5839
0.0421
0.0773 -0.1083
Days Inn
0.6355 -0.0283 -0.0001
0.0600 -0.0957
Dorint
0.6231
0.0486
Four Seasons
0.7101 -0.0045
Frantel
0.9168
0.1181 -0.1320
Friendship
0.6361
0.0525
0.0420 -0.0249
Hilton International
0.9609 -0.0183 -0.1840 -0.0460
Howard Johnson
0.8061 -0.0612
0.0051
Holiday Inn
0.9242 -0.0029
-0.1268
Hospitality Consultants
0.6012
Hospitality International
0.6599 -0.0516
HUSA
0.5869
0.0414 -0.0170
0.1996
Hyatt International
0.9426
0.0212 -0.1956
Intercontinental
0.9789 -0.0075
Jack Tar
0.5879
Jolly of Italy
0.5665 -0.1414
Le Meridien
0.9504
Merlin
0.5144 -0.0250
Penta
0.7465
Pratt
0.6022
0.0685
0.0491
0.0527 -0.0158
0.0436
0.2026 -0.2198
0.1511
R-squared
-0.0760
0.000034
0.4373
0.0907 -0.1405
0.000200
0.3844
-0.0550
0.256800
0.1114
0.0645
0.000000
0.7230
0.0074 -0.0966
0.023000
0.2220
0.0277
0.0155
0.000200
0.3808
0.0185 -0.0971
0.004000
0.2894
-0.1327
0.005600
0.2771
,
0.111600
0.1236
-0.0343
0.0420 0.000000
0.6922
-0.1806 -0.0464
0.0597 0.000000
0.7368
0.0252
0.0518 -0.0643
0.0292 -0.0021
0.0476 -0.0290
0.856000
0.0290
0.1836 -0.0351
-0.1098
0.024000
0.2205
0.0264 -0.1139 -0.0628
-0.0565
0.004300
0.2867
,
0.000100
0.3709
0.0447 -0.2530
0.1344 -0.0083
0.003900
0.2905
0.0487
0.0394
0.2014 -0.1075
0.002000
0.3110
Preferred
0.8276 -0.0508
-0.2896
0.0557 0.000010
0.4620
Princess
0.6145
0.0392
0.0471
0.0242 -0.0982
0.023300
0.2223
Quality
0.8298 -0.0802
0.0300
0.0465 -0.0505
0.017000
0.2344
Radisson
0.6135
0.0021 -0.0084 -0.1217
0.032200
0.2089
Ramada
0.9312 -0.0860 -0.1110
0.0684 -0.0076
0.000000
0.5605
Rank
0.5991
0.0664
0.1024 -0.0128
0.147700
0.1399
Regent International
0.9328
0.0840 -0.1858
Rodeway
0.0507
0.2318
0.0405
0.0397
0.1142
-0.1933
-0.0581
0.000022
0.4490
0.6432 -0.0290
0.0592 -0.0902
-0.0901
0.025000
0.2192
Sara
0.4604 -0.0663
0.1608
0.0811
,
0.000000
0.5457
SAS
0.7203
0.0551 -0.0143
-0.2238
,
0.281000
0.0806
Sheraton
0.9486 -0.0134
0.0493
0.0298
0.000000
0.6146
Sofitel
0.9352
0.1331 -0.1305
0.0308 -0.1688
0.000300
0.3700
Sonesta
0.4472
0.0994
0.2089
0.000300
0.3700
SRS
1.0786 -0.0057
-0.0500
-0.0872
0.000000
0.6816
Super 8
0.6866 -0.0354
0.0413 -0.0397
-0.1108
0.017500
0.2337
-0.1518
0.0872 -0.0854
-0.1787
153
Table 33. Continued
Chain
Tai
Thistle
Tollman
Trusthouse
Westin
Westours
Wyndham
NAT
Model p R-squared
Intercept
SIZE
lEXP
DOI
, 0.000390
0.3302
0.8424 -0.0026 0.0023 -0.3020
0.1286
0.6087 0.0921 -0.0358 0.0416 -0.0399 0.185400
0.2706
0.4075 0.0107 0.0885 0.1190 0.1114 0.006700
0.3584
0.9530 0.0595 -0.1472 -0.0556 -0.0522 0.000500
0.3683
0.8585 -0.0891 -0.1075 0.0686 -0.0682 0.000300
0.0807
0.5830 0.0401 0.0418 0.0874 0.0245 0.435600
0.1751
0.5284 0.0684 0.0470 0.0556 0.0193 0.070200
Table 34. 1984 L a g W e l l - P r e d i c t e d Chains
Chain
Intercept
SIZE
lEXP
DOI
NAT
-0.184
-0.046
0.0645
0.0000
0.723
Model p
R-squared
Hilton International
0.9609 -0.0183
Hyatt International
0.9426
0.0212 -0.1956 -0.0343
0.042
0.0000
0.6922
Intercontinental
0.9789 -0.0075 -0.1806 -0.0464
0.0597
0.0000
0.7368
Sheraton
0.9486 -0.0134 -0.1518
0.0493
0.0298
0.0000
0.6146
SRS
1.0786 -0.0057 -0.1787
-0.05 -0.0872
0.0000
0.6816
154
Table 35.
Chain
Intercept
1985 L a g R e g r e s s i o n R e s u l t s
SIZE
lEXP
DOI
0.0660
Best Westem
0.8910
0.0242
-0.0894
BTH
0.7529
0.1373
0.0230
Budget
0.5736
0.1390
0.0075
0.0312
Commonwealth
0.5674
0.0407
0.0595
0.1187
Connor Jacobson
0.7051
-0.0673
0.2096
Caledonian
0.8654
0.0359
Crest
0.7431
-0.0140
Cunard
0.5696
0.0534
Days Inn
0.7748
Econolodge
0.6486
Four Seasons
0.9384
0.0394
Frantel
0.9680
0.0522
Friendship
0.5972
Hilton Intemational
0.9270
Howard Johnson
0.8264
-0.1006 -0.0297
Holiday Inn
0.9106
-0.0068
Okura
0.7542
HUSA
LDR
FSTMOV
NAT
Model p
Rsq
-0.0885
-0.1920
0.0295
0.2689
0.1555
-0.1236 -0.2072
-0.0441
0.0411
0.0311
0.2657
-0.1916
0.1221
0.0218
0.015
0.2963
-0.0828
0.0423
0.0479
0.1185
0.2022
-0.1235 -0.0842
0.1311
0.0056
0.3040
-0.0283 -0.1766
0.0088
0.0410
-0.1247
0.0082
0.3198
-0.0665
0.1123
-0.0173
-0.0614
0.9258
0.0420
-0.0152 -0.1198
0.0571
0.0089
0.1063
0.0149
0.2964
-0.1711 -0.0969 -0.0138 -0.0697
-0.0909 -0.0174 -0.0453 -0.1155
0.0011
-0.1161
0.00001
0.5150
0.0356
-0.1451
0.0003
0.4252
0.0173
0.0754
-0.5075
0.0266
0.0418
-0.1727
0.0001
0.4595
-0.1310 -0.0462
0.0870
0.0604
-0.0997
0.003174
0.3547
-0.0676 -0.1667
0.00269
0.3605
-0.0962 -0.0116 -0.0976 -0.0202
0.0097
-0.1755 -0.0281
0.1256
-0.0289
0.0529
3.78E-08
0.6375
0.0070
-0.1179
0.0673
-0.0927
0.000634
0.4082
-0.1101
-0.0438
-0.0134
0.0879
0.0318
0.002776
0.3594
-0.0060
0.0765
-0.2369
0.1364
0.0147
-0.0479
0.195551
0.1747
0.5218
-0.0259
-0.1924
0.1119
0.3098
-0.1002
0.01816
0.2591
Hyatt
0.6495
-0.0602 -0.0669
0.0063
-0.0116
0.006786
0.3271
IBIS
0.6808
0.1012
Intercontinental
0.9335
0.0162
Jack Tar
0.7975
-0.0574
0.2692
Jolly of Italy
0.4979
-0.1170
0.1402
Le Meridien
0.9704
0.0754
Peninsula
0.7092
-0.0040
Penta
0.8837
0.0074
Preferred
0.8044
-0.0342 -0.2042
Prince
0.6594
-0.0168
0.1556
Quality
0.8564
-0.0666
0.0548
-0.1307 -0.1480
Radisson
0.6903
-0.0594
0.0728
-0.0885
0.0361
Ramada
0.8789
-0.0658
-0.1096
0.0918
0.0513
0.0077
Rank
0.5779
0.0476
-0.0067
0.1401
-0.1140
0.1961
Regent Intemational
0.9322
0.0103
-0.1675
-0.1541
0.2215
Sara
0.5677
-0.0035
0.1097
0.1342
-0.0097
SAS
0.7729
0.0619
0.0025
-0.4142
Sheraton
0.9705
-0.0138
-0.1211
Sofitel
0.9865
0.1226
Sonesta
0.6012
0.0951
SRS
0.9445
Super 8
0.0614
-0.0159 -0.0858
-0.1027 -0.2101
0.1595
0.0439
0.025379
0.2744
0.1353
-0.0307
0.0417
9.48E-08
0.6208
-0.0790 -0.2637
-0.1895
0.0417
0.032201
0.2642
-0.1707 -0.0257
0.0722
0.2312
-0.0930
.
0.070939
0.2002
-0.1281 -0.0931
0.0653
-0.0112 -0.0882
1.35E-05
0.5147
,
6.25E-05
0.4457
0.0948
-0.4872
-0.0107
-0.1482 -0.1750
0.1261
-0.0907 L-0.0373
0.012223
0.3044
0.0831
0.1981
-0.0751
0.0341
0.003397
0.3523
0.0164
-0.0300
-0.0656 -0.0916
0.029073
0.2686
-0.0044 0.053823
0.2410
-0.0345
0.1141
-0.0384 -0.1178
0.001053
0.3920
0.0293
2.48E-05
0.4996
-0.0280
0.120867
0.2011
-0.0625 -0.0840
0.000243
0.4372
0.0528
,
0.008846
0.2872
-0.0252
-0.0781
,
0.011439
0.2773
-0.0731
0.0476
-0.0075
0.0264
2.09E-05
0.5038
-0.1172 -0.1526
0.0743
0.0898
-0.1894
0.008299
0.3195
0.0024
-0.1717
0.0939
-0.0055
0.1162
0.053613
0.2412
0.0157
-0.1765
-0.0330
0.0800
-0.0152
,
6.13E-07
0.5570
0.5668
0.0321
0.0704
0.0004
-0.1403
0.0995
,
0.093432
0.1872
Swissotel
07106
-0.1020
0.1809
-0.2726
,
-0.1004
,
3.12E-08
0.5877
Tai
0.7413
0.0676
-0.0895
-0.2423
0.1759
-0.0125
,
0.268994
0.1314
Thistle
0.6534
0.0359
0.0665
0.0420
-0.0673
0.0743
-0.1114
0.014696
0.2971
155
Table 35. Continued
Chain
Intercept
SIZE
lEXP
DOI
LDR
FSTMOV
NAT
Model p
Rsq
Tollman
0.4742
-0.0173
0.0156
0.1227
0.1041
-0.0691
0.0703
0.208021
0.1711
Travelers
0.5104
0.0753
0.0406
-0.0715
0.0157
-0.0642
-0.0859
9.22E-05
0.4648
Trusthouse
0.9238
0.0625
-0.1632
-0.0254
0.0362
0.0782
-0.0529
0.003407
0.3522
VMS
0.6379
0.0053
0.0388
-0.1095
-0.0100
-0.0410 -0.1089
0.020024
0.2844
Westin
0.8068
-0.0941
-0.1002
0.1380
0.0462
0.0156
-0.0225
0.000791
0.4012
Westours
0.4844
0.0405
0.0266
0.1540
-0.1237
0.2624
0.0827
0.075091
0.2252
Wyndham
0.4770
0.0752
0.0565
0.0171
-0.1274
0.1299
0.0712
0.087296
0.2178
York
0.6382
-0.0183
0.0712
0.0805
0.0382
0.1605
-0.0659
0.090489
0.2160
Table 36. 1985 Lag Well-Predicted Chains
Intercept
Chain
SIZE
lEXP
DOI
LDR
FSTMOV
NAT
Model p
Rsq
Hilton Intemational
0.9270
0.0097
-0.1755
-0.0281
0.1256
-0.0289
0.0529
3.78E-08
0.6375
Intercontinental
0.9335
0.0162
-0.1707 -0.0257
0.1353
-0.0307
0.0417
9.48E-08
0.6208
SRS
0.9445
0.0157
-0.1765
0.0800
-0.0152 ,
6.13E-07
0.5570
Swissotel
07106
-0.1020
0.1809
-0.1004
3.12E-08
0.5877
-0.0330
-0.2726 .
Table 37. 1985 Characteristics Of Well-Predicted Lag Chains
Chain
SIZE
Hilton International
36378
Intercontinental
37121
SRS
46008
Swissotel
1809
lEXP
45
47
42
1
DOI
0.918137
0.883166
0.940576
0.632394
156
LDR FSTMOV
S
NL
NL
s
NL
s
L
s
NAT
US
US
Luxury
Germany
Luxury
Switzerland
?
TYPE
Luxury
Table 38. 1986 L a g Regresj)ion Results
Chain
Best Western
BTH
Budget
Ciga
Clarion
Connor Jacobson
Caledonian
Crest
Cunard
Days Inn
Dorint
Econolodge
Four Seasons
Frantel
Friendship
Hilton International
Howard Johnson
Holiday Inn
Hungar
HUSA
Hyatt Intemational
Intercontinental
Jack Tar
Joly of Italy
Loews
Marriott
Mercure
Le Meridien
Ming
Nikko
Novotel
Peninsula
Penta
Pratt
Preferred
Prince
Princess
Quality
Radisson
Ramada
Rank
Regent International
Rodeway
Intercept
0.7516
0.7427
0.5298
0.7389
0.7460
0.6060
0.7697
0.6125
0.4584
0.7737
0.5654
0.6661
0.7859
1.0036
0.6126
0.9353
0.8665
0.9502
0.4406
0.5231
0.9032
0.9402
0.5291
0.7569
0.5123
0.7345
0.5740
1.0088
0.3526
0.8093
1.0049
0.6973
0.7587
0.6154
0.7790
0.5499
0.5610
0.8547
0.6722
0.9088
0.4571
0.9674
0.5986
SIZE
0.0280
0.1143
0.0726
-0.0430
0.0075
0.0143
0.0841
0.0208
0.1292
-0.1132
-0.0707
-0.1059
0.0020
0.0615
-0.1095
-0.0101
-0.0907
-0.0031
-0.1215
0.0415
0.0110
-0.0102
0.0721
-0.0780
0.0257
-0.1250
-0.0757
0.0443
0.0298
0.0598
0.0415
-0.0287
0.0217
0.0115
-0.0397
-0.0369
0.0205
-0.0706
-0.0520
-0.0928
0.0525
0.0363
-0.0679
lEXP
-0.0222
-0.0184
0.0060
0.2103
-0.0030
0.1524
-0.0315
0.0797
0.0222
-0.0534
0.2197
-0.0661
0.1388
-0.2024
-0.0603
-0.2060
-0.0338
-0.1704
0.2631
0.0272
-0.2243
-0.2021
0.0889
0.1887
0.1057
-0.1543
0.2637
-0.2092
0.0743
-0.1092
-0.1976
0.1564
-0.1207
0.1131
-0.1977
0.1518
0.0884
-0.0234
0.1142
-0.1361
0.0595
-0.1891
0.0122
157
DOI
FSTMOV
-0.0987
-0.1302
-0.0788
-0.0890
-0.0994
0.0941
0.1431
0.0032
-0.1569
-0.0743
-0.1688
0.0359
-0.2985
-0.0176
-0.1534
0.0169
-0.2145
-0.0521
-0.0710
0.0611
-0.0233
0.0315
-0.1498
0.1037
-0.3969
0.0271
-0.0139
0.0105
-0.1549
0.0862
-0.0241
0.0472
-0.0479
-0.0689
-0.0434
0.0245
-0.0839
-0.0813
-0.0783
-0.0814
-0.0193
0.0329
-0.0159
0.0417
-0.0666
-0.0545
0.0405
-0.0359
0.0087
0.0527
0.2029
-0.0772
0.0220
0.0434
-0.0491
0.0422
0.0159
0.0055
-0.3830
-0.0171
0.0190
0.0092
-0.5217
-0.0299
-0.2521
-0.0326
0.0590
-0.0456
0.1236
-0.0128
-0.0773
-0.0199
0.0032
0.0612
0.0193
-0.0667
0.0028
-0.0370
0.0714
0.0169
0.0387
0.0805
-0.0166
-0.1849
-0.0583
-0.2185
NAT
0.0012
0.0386
-0.0338
-0.1307
-0.1503
-0.0468
-0.0074
0.1537
-0.1181
0.0387
-0.1250
-0.1016
-0.1134
-0.1638
0.0556
-0.1207
0.0170
0.0518
0.0537
0.0117
-0.2771
-0.0303
0.1492
-0.0032
-0.0985
0.0367
-0.0915
0.0260
-0.0588
0.0472
0.0071
-0.0546
-0.0064
-0.0536
0.0459
0.0244
-0.1253
-0.1133
Model p
0.174055
0.015466
0.000328
0.004185
0.000007
0.000086
0.029887
0.734893
0.000010
0.000000
0.002632
0.000000
0.000017
0.000001
0.000003
0.000000
0.000001
0.000002
0.000037
0.042343
0.000000
0.000000
0.018422
0.008282
0.016435
0.000056
0.000120
0.000000
0.133231
0.002043
0.000001
0.000250
0.030199
0.002747
0.012501
0.014075
0.004920
0.007707
0.000113
0.000024
0.004604
0.000363
0.000010
Rsq
0.1317
0.2270
0.3455
0.2705
0.4400
0.3519
0.2034
0.0496
0.4336
0.5251
0.2850
0.5955
0.4207
0.4794
0.4594
0.5452
0.4832
0.4644
0.3732
0.1646
0.5010
0.5464
0.2209
0.2483
0.2249
0.3915
0.3722
0.5235
0.1204
0.2928
0.4889
0.3240
0.2030
0.2837
0.2344
0.2303
0.2653
0.2507
0.3737
0.4120
0.2675
0.3427
0.4324
Table 38. Continued
Chain
Sara
Intercept
0.8645
0.7274
0.8763
0.9607
0.9889
0.5277
1.0297
0.4948
0.6547
0.5788
0.6544
0.5295
0.8655
0.6356
0.8152
0.4872
0.5236
SAS
Scandic
Sheraton
Sofitel
Sonesta
SRS
Sunbelt
Super 8
Swissotel
Taj
Thistle
Trusthouse
VMS
Westin
Wyndham
York
SIZE
-0.0442
0.0372
-0.0006
-0.0306
0.0695
0.0859
0.0161
0.0275
-0.0368
-0.0972
0.0348
0.0027
-0.0034
-0.0491
-0.0740
0.0216
0.0294
lEXP
0.1901
0.0463
0.1909
-0.1706
-0.1742
0.1002
-0.2082
-0.0179
-0.0209
0.2572
-0.0149
0.1136
-0.1117
0.0175
-0.0914
0.1002
0.1152
DOI
0.1559
-0.3559
0.0618
0.0455
-0.1593
-0.0470
0.0151
-0.0589
-0.1196
-0.1550
-0.1203
-0.0347
0.0117
-0.0934
0.0519
-0.0126
-0.0245
FSTMOV
-0.0077
0.0113
-0.0191
0.0198
-0.0041
0.0062
0.0063
0.0705
0.1151
-0.0869
-0.0183
0.0278
-0.0593
0.0637
0.0290
0.0528
0.0512
NAT
-0.3381
-0.2363
0.0447
-0.1323
0.1282
-0.0864
-0.0718
-0.1282
,
,
-0.0531
0.0009
-0.1274
-0.0385
-0.0173
0.0704
Model p
0.000005
0.038559
0.000023
0.000000
0.000265
0.019259
0.000000
0.006511
0.000006
0.000000
0.904523
0.000318
0.001267
0.000342
0.004414
0.032997
0.000002
Rsq
0.4479
0.1680
0.4135
0.5187
0.3512
0.2193
0.5330
0.2563
0.4459
0.4911
0.0186
0.3463
0.3070
0.3443
0.2688
0.1997
0.4646
Table 39. 1986 Lag Well-Predicted Chains
Chain
Days Inn
Econolodge
Hilton International
Intercontinental
Le Meridien
Sheraton
SRS
Intercept
0.7737
0.6661
0.9353
0.9402
1.0088
0.9607
1.0297
SIZE
-0.1132
-0.1059
-0.0101
-0.0102
0.0443
-0.0306
0.0161
lEXP
-0.0534
-0.0661
-0.2060
-0.2021
-0.2092
-0.1706
-0.2082
DOI
-0.0710
-0.1498
-0.0241
-0.0159
-0.0491
0.0455
0.0151
FSTMOV
0.0611
0.1037
0.0472
0.0417
0.0422
0.0198
0.0063
NAT
-0.1181
-0.1250
0.0556
0.0537
-0.0985
0.0447
-0.0864
Model p
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
T a b l e 4 0 . Characteristics O f 1986 L a g W e l l - P r e d i c t e d Chains
DOI
0.0026
0.0046
0.8734
0.9010
0.9192
0.3157
0.9406
158
LDR FSTMOV
NL
S
S
NL
NL
F
NL
NL
F
NL
NL
LL.
SRS
SIZE lEXP
1
48176
27904
1
36734
45
36157
47
31
19650
145183
61
46008
42
LL. LL-
Chain
Days Inn
Econolodge
Hilton International
Intercontinental
Le Meridien
Sheraton
NAT
US
US
US
US
TYPE
Economy
Economy
Luxury
Luxury
Luxury
France
Luxury
US
Germany Luxury
Rsq
0.5251
0.5955
0.5452
0.5464
0.5235
0.5187
0.5330
Table 41. 1987 Lag Regression Results
Chain
Intercept
SIZE
lEXP
DOI
FSTMOV
NAT
Model p
Rsq
Best Western
0.8541
-0.0405
-0.1391
0.1641
-0.0447 -0.0219
0.0061493
0.2755
BTH
0.7552
0.0723
-0.1197
0.1216
-0.1093 -0.0361
0.0083377
0.2649
Budget
0.5883
0.0353
0.0326
-0.1027
-0.0954
0.0022685
0.3086
Ciga
0.8362
0.0622
-0.0075
0.0095
-0.0773 -0.1249
0.1183665
0.1596
Clarion
0.5464
0.0151
0.0922
-0.0195
-0.0375 -0.0960
0.0261742
0.2228
Connor Jacobson
0.5138
0.0024
0.1633
-0.0626
0.0525 .
0.0007427
0.3129
Caledonian
0.7012
0.1209
-0.0548
-0.2561
0.0010
-0.0329
0.0107304
0.2560
Crest
0.7301
0.0745
-0.1051 -0.1917
-0.0868
0.0117
0.0280918
0.2201
Cunard
0.5179
0.1153
-0.0100 -0.3371
-0.0392
0.0483
0.0022104
0.3094
Days Inn
0.5886
-0.1464
0.0115
0.0651
-0.0648 -0.0575
0.0063801
0.2742
Dorint
0.5602
-0.0510
0.1349
-0.0110
0.0558
0.0105
0.0175485
0.2380
Embassy
0.5691
0.0919
0.0757
0.0114
-0.0211
-0.0659
0.0007143
0.3444
Four Seasons
0.4498
-0.0026
0.1548
-0.0438
-0.0565
0.0813
0.0849155
0.1745
Frantel
0.9422
0.0254
-0.2327
0.1846
-0.0074 -0.0671
0.0000144
0.4491
Friendship
0.4515
-0.0829
0.0997
-0.1599
-0.0094 -0.1161
0.0082703
0.2652
Hilton International
0.9710
-0.0314 -0.2167
0.0109
-0.0105
0.0000001
0.5599
Howard Johnson
0.7031
-0.0982
0.0991
-0.0315 -0.0382
0.0022378
0.3090
Holiday Inn
1.0192 -0.0205
0.0000040
0.4793
HUSA
0.5629
0.0807
0.0058
0.1071
0.1171 ,
0.0037845
0.2621
Hyatt International
0.9440
-0.0542
-0.2199
0.0078
0.0484
-0.0053
0.0000052
0.4732
IBIS
0.8244
0.0235
-0.1155 -0.0179
0.0288442
0.2190
Intercontinental
0.9823
-0.0248
-0.2165
-0.0096
0.0521
-0.0008
0.0000002
0.5428
Jack Tar
0.4733
0.1080
0.0669
-0.0033
0.0575
0.0169
0.0044149
0.2868
Jolly of Italy
0.7161
-0.0566
0.1071
0.1290
-0.0160 -0.3437
0.0555836
0.1926
Mandarin
0.7608
0.1063
-0.1431
0.0132673
0.2483
Mercure
0.6823
0.0112
0.1120
-0.0752
-0.0279 -0.0595
0.0043731
0.2871
Le Meridien
0.9430
0.0006
-0.2488
0.0173
0.0494
-0.0194
0.0000003
0.5326
Nikko
0.5138
0.1094
-0.0294 -0.3320
-0.0414
0.2136
0.0022174
0.3093
Oberoi
0.5093
0.1057
-0.0872
0.0606
-0.0980
0.2008
0.0081587
0.2657
Okura
0.4689
-0.0651
0.1126
-0.3624
-0.0205
0.0979
0.0278056
0.2205
Park
0.4414
-0.0250
0.2049
-0.0281
-0.0383
0.0371
0.0000016
0.4995
Peninsula
0.8151
0.0134
0.1531
-0.2717
-0.0791 -0.2189
0.0000879
0.4033
Penta
0.6694
0.0657
-0.0875
-0.1712
0.0506
0.0101907
0.2578
Preferred
0.8646
-0.0289
-0.2377
0.0101
-0.0039 -0.0214
0.0148775
0.2441
Prince
0.1818
0.0021
0.0879
-0.0077
0.0553
0.2221
0.0324415
0.2144
Princess
0.5041
0.0849
0.1049
0.0638
0.0151
-0.0109
0.0003563
0.3647
Quality
0.8113
-0.1090
0.0001
0.0844
0.0624
-0.0255
0.0008431
0.3394
Radisson
0.6152
-0.0443
0.1636
0.0149
-0.0570 -0.0620
0.0010185
0.3337
Ramada
0.9724
-0.0935
-0.1641
0.0397
0.0046
-0.0218
0.0000032
0.4843
Rank
0.4517
0.0741
0.1405
0.0224
0.0218
-0.0118
0.0000514
0.4174
Regent International
0.5873
0.0810
-0.0631
-0.1305
0.0218
0.1050
0.0131618
0.2486
Rodeway
0.6187
-0.1371
0.0385
-0.0325
0.0180
-0.1190
0.0000081
0.4628
Sara
0.7142
0.0454
0.0971
0.1251
0.0380
-0.1822
0.0008682
0.3385
0.0689
-0.2050 -0.0525
-0.0497 -0.0200
-0.0304 -0.2754
159
0.0924
0.0414
0.0119
0.0308
0.0377
-0.0195
Table 41. Continued
Chain
Intercept
SIZE
lEXP
DOI
FSTMOV
NAT
Model p
Rsq
0.0156901
0.2134
SAS
0.6660
0.0309
0.0193
-0.1738
Scandic
0.8278
0.0382
0.1293
-0.0020
-0.0638
-0.1174
0.0020047
0.3126
Sheraton
0.9976
-0.0271
-0.1903
0.0000
0.0255
-0.0109
0.0000004
0.5284
Sofitel
0.8726
0.0606
-0.2573
0.3052
-0.0407
-0.1202
0.0006994
0.3450
Sonesta
0.5720
0.0262
0.0362
-0.2026
0.0421
0.0746
0.0354389
0.2109
SRS
1.0005 -0.0364 -0.2086
0.0360
0.0183
-0.0414
0.0000004
0.5262
Super 8
0.6252
-0.0512
0.0489
-0.1205
0.0969
-0.0896
0.0011672
0.3295
Swissotel
0.5999
0.0009
-0.0839
-0.2424
-0.0253
0.0347092
0.1840
Taj
0.5456
0.1264
-0.0921
-0.1141
-0.0250
0.0503
0.1675566
0.1432
Thistle
0.4370
0.0124
0.0931
-0.0531
0.0363
0.0547
0.0529652
0.1946
Trusthouse
0.8217
-0.0314 -0.1568
0.3018
-0.0703 -0.0374
0.0013081
0.3260
VMS
0.6402
-0.0028
0.1541
-0.0519
0.0796
-0.0418
0.0033343
0.2961
Wyndham
0.4628
0.0529
0.1696
0.0989
0.0204
0.0469
0.0001360
0.3916
Model p
Rsq
0.0673 .
Table 42. 1987 Well-Predicted Lag Chains
Chain
Intercept
SIZE
lEXP
DOI
FSTMOV
NAT
Hilton Intemational
0.9710
-0.0314
-0.2167
0.0109
0.0414
-0.0105
0.0000001
0.5599
Intercontinental
0.9823
-0.0248
-0.2165
-0.0096
0.0521
-0.0008
0.0000002
0.5428
Le Meridien
0.9430
0.0006
-0.2488
0.0173
0.0494
-0.0194
0.0000003
0.5326
Sheraton
0.9976
-0.0271
-0.1903
0.0000
0.0255
-0.0109
0.0000004
0.5284
SRS
1.0005 -0.0364 -0.2086
0.0360
0.0183
-0.0414
0.0000004
0.5262
160
T a b l e 4 3 . 1988 L a g Regression Results
Chain
Intercept
SIZE
lEXP
-0.0390 -0.1531
DOI
LDR
0.0511
0.1154
NAT
Model p
Rsq
-0.0767
0.0351
0.000140
0.3894
FSTMOV
Best Western
0.8986
BTH
0.7345
0.0465
-0.1219
0.0459
-0.1689
-0.0515
0.0361
0.114579
0.1702
Budget
0.5188
0.0902
0.0267
-0.1311
-0.2063
0.0342
-0.0651
0.009797
0.2644
Caesar Park
0.5368
-0.0427
0.1987
-0.0959
-0.0425
0.0607
0.000167
0.3583
Churchman
0.5499
0.0510
0.0692
-0.0938
-0.1820
0.0125
-0.0879
0.000074
0.4056
Clement Chen
0.3549
-0.0320
0.1647
-0.0361
0.0427
-0.0316
0.0674
0.001303
0.3280
Connor Jacobson
0.5871
0.0717
0.0329
-0.1797
0.0712
0.0743
0.000447
0.3319
Caledonian
0.7427
0.0998
-0.1278
0.0191
-0.1486
-0.0061
-0.1674
0.000867
0.3398
Cunard
0.4089
0.0857
-0.0314
-0.2239 -0.1489
0.0045
0.0276
0.014009
0.2521
Days Inn
0.8493
-0.1515
-0.0004
-0.0276 -0.0535
0.000001
0.5078
Embassy
0.6679
0.0371
0.1013
-0.0317 -0.1340
-0.0113
-0.1720
0.001468
0.3244
Four Seasons
0.8822
-0.0589
0.1789
-0.2413
0.0695
-0.0264 -0.2189
0.000265
0.3727
Frantel
1.0025 -0.0295
-0.2192
0.0636
0.0224
0.0547
-0.0835
0.000001
0.4996
Grand Collection
0.5927
0.1204
-0.0423
0.0914
-0.3160
0.0304
0.0295
0.000553
0.3525
HIDC
0.4724
0.0574
-0.1015
0.1353
0.0446
-0.0858
0.0107
0.115082
0.1700
Hilton International
0.9592
-0.0636 -0.1677
0.0451
-0.0558
0.0037
0.0237
0.000000
0.5574
Howard Johnson
0.8119
-0.1364
0.0018
-0.1062
0.0017
-0.0635
0.000000
0.5227
Holiday inn
1.0320 -0.0754
-0.1366 -0.0026
0.0010
-0.0353
0.0333
0.000000
0.5315
Hospitality International
0.5434
-0.0386
0.0138
-0.1257 -0.1485
0.0086
-0.1122
0.006566
0.2777
HUSA
0.4295
0.0139
0.1329
-0.0920 -0.0474
0.0367
,
0.002244
0.2855
Hyatt
0.6425
-0.0816
0.0479
-0.1332
-0.0001
-0.0510
0.000149
0.3878
Hyatt International
0.9611
-0.0461
-0.2085
-0.0094 -0.0800
-0.0015
0.0110
0.000001
0.5065
IBIS
0.8647
-0.0544 -0.1476 -0.0723 -0.0404
0.0588
-0.0030
0.015771
0.2479
Jack Tar
0.5385
0.1420
-0.0124 -0.1136
0.0262
-0.0407
0.0712
0.000315
0.3680
Jolly of Italy
0.5270
-0.0039
0.0667
-0.2877
-0.0378
,
0.030074
0.1999
Mandarin
0.9605
0.0906
-0.0066 -0.2921
0.005503
0.2835
Marriott
0.8996
-0.1354
-0.2282
0.1184
-0.0735
-0.0175
0.0356
0.000001
0.5159
Mercure
0.6631
-0.0119
0.1100
0.1033
-0.2404
0.0252
-0.0585
0.010456
0.2622
Le Meridien
0.9983
-0.0360
-0.1864
-0.0376
-0.0088
0.0276
-0.0505
0.000000
0.5218
Ming
0.2278
0.1199
-0.0742
0.2433
-0.1340
-0.0391
0.000609
0.3234
Movenpick
0.7472
0.0494
0.0764
-0.1925
-0.2104
0.0012
0.009454
0.2656
Nikko
0.7961
0.0673
-0.2493
-0.1012
-0.1013
0.0103
0.004628I
0.2632
Oberoi
0.9104
0.0937
-0.1494
-0.043C
-0.0351
-0.0794[ -0.143£1 0.053291 0.2023
Peninsula
0.9283
0.0141
0.0631
-0.3369
-0.101C
-0.028CI
Penta
0.7634
0.0494
-0.1473
-0.101 e -0.2736>
-0.005J5
0.035J)
0.01461/r
0.2506
Preferred
0.6676
-0.0714
0.0969
0.1104[ -0.2417'
-0.022S)
0.049/7 0.01836"f
0.2425
0.0355
0.0891
0.0100
0.0228
-0.1644
-0.1433 -0.2493 -0.1670
161
-0.1364
) 0.2724
-0.322€> 0.00771 S
Table 43. Continued
Chain
Intercept
SIZE
lEXP
DOI
LDR
FSTMOV
NAT
Model p
Rsq
Prince
0.6539
-0.0863
0.0947
0.0138
-0.0954
0.0371
-0.0598
0.006345
0.2789
Princess
0.5214
0.1377
0.0391
0.0192
-0.2792
0.0072
0.0060
0.000000
0.5204
Radisson
0.7529
-0.1197
0.0729
0.0537
-0.1947
-0.0286
-0.0516
0.000049
0.4158
Rank
0.4662
-0.0474
0.2148
-0.0342
0.1307
-0.0271
0.0703
0.000012
0.4488
Regent International
0.6907
0.0578
-0.2225
0.1873
-0.0373
-0.0130 -0.0883
0.001272
0.3287
Riande
0.5002
0.0094
0.0800
0.000346
0.3389
Sara
0.7879
-0.0148
0.0266
0.476837
0.0959
SAS
0.6511
0.0355
0.0434
0.298212
0.1039
Scandic
0.7845
-0.0088
0.0533
0.1981
-0.1270
0.148497
0.1584
Shangrila
0.9290
0.0601
-0.0743
-0.2123
-0.1185
0.0022
-0.2000
0.002801
0.3049
Sheraton
1.0376 -0.0892 -0.1431
-0.0468
-0.0437
0.0011
0.0443
0.000000
0.5783
Sofitel
0.9971
0.0293
0.1115
0.0777
-0.1236
0.000006
0.4649
Sonesta
0.5102
0.1290
-0.2383
0.0245
0.1186
0.011712
0.2583
SRS
1.0327 -0.0820
-0.1667 -0.0282
0.0113
-0.0276
0.000000
0.5536
Sunbelt
0.3574
0.1682
-0.1243
-0.2318
-0.0348
-0.0334
0.000400
0.3615
Super 8
0.6861
-0.1077
0.0396
-0.0667 -0.0829
-0.0119
-0.0619
0.007174
0.2748
Swiss International
1.0182
0.0178
-0.2641
-0.0226 -0.0436
-0.0185
-0.0984
0.000001
0.5030
Swissotel
07169
0.0407
-0.0455 -0.2664 -0.1499
0.0253
-0.0184
0.260944
0.1307
Taj
0.8803
0.0716
-0.0334
-0.0763
-0.0757
-0.1953
0.159005
0.1552
Thistle
0.5909
0.0268
0.1062
-0.0667 -0.0549
0.0455
-0.1705
0.000383
0.3627
Trusthouse
0.7499
-0.1484
-0.0248
0.0977
-0.0963
-0.0157 -0.0030
0.000977
0.3364
VMS
0.5988
-0.0759
0.0554
0.0322
-0.0398
0.0580
-0.0773
0.004160
0.2925
Westin
0.9277
-0.1189
-0.0989
0.1566
-0.1125
-0.0283
-0.0046
0.000000
0.5602
Wyndham
0.4831
0.0069
0.1169
-0.0263 -0.0454
0.0558
0.0252
0.010313
0.2626
York
0.4901
0.0543
0.1261
0.0884
0.1128
-0.0235
0.000073
0.4060
-0.0029 -0.2671
0.1257
-0.0623
-0.0137 -0.2342
0.1961
0.0666
0.0116
-0.0325
-0.0462 -0.1790
0.0471
0.0736
0.0813
0.1020
162
-0.1729
-0.0447 -0.0981
Table 44.
Intercept
Chain
SIZE
1988 W e l l - P r e d i c t e d L a g Chains
lEXP
DOI
LDR
FSTMOV
NAT
Model p
Rsq
Hilton Intemational
0.9592 -0.0636 -0.1677
0.0451 -0.0558
0.0037
0.0237 0.000000 0.5574
Sheraton
1.0376 -0.0892 -0.1431 -0.0468 -0.0437
0.0011
0.0443 0.000000 0.5783
SRS
1.0327 -0.0820 -0.1667 -0.0282
-0.0276
0.000000 0.5536
Westin
0.9277 -0.1189 -0.0989
0.0113
0.1566 -0.1125
-0.0283 -0.0046 0.000000 0.5602
Table 45. Characteristics Of 1988 Well-Predicted Lag Chains
Chain
Hilton International
Sheraton
SRS
Westin
SIZE lEXP
44
40048
140327
61
47961
43
352123
10
DOI
0.1110
0.8726
0.9362
0.3947
163
NAT
TYPE
LDR FSTMOV
Luxury
NL
S
US
Luxury
F
US
NL
Germany Luxury
F
NL
Luxury
US
NL
S
Table 4 6 . 1989 L a g Regression 1Results
Chain
Intercept
SIZE
lEXP
DOI
LDR
FSTMOV
NAT
Model p
Rsq
Best Western
0.8822 -0.0301 -0.1670
0.0822 -0.1356
-0.0549 -0.0272 0.000369 0.3260
Brook
0.2277
0.0373
0.0176
0.1491 -0.1331
0.0312 -0.0444 0.017139 0.2175
BTH
0.7709
0.0394 -0.1370
0.0612 -0.1070
-0.1225 -0.0169 0.079957 0.1641
Budget
0.4866
0.0101
0.0935 -0.0521 -0.0167
0.0355 -0.0471 0.100111 0.1555
Churchman
0.3427
0.0387
0.1096 -0.0504
0.1310
-0.0713
Ciga
0.7056
0.0468
0.0092
0.1355
0.0314
0.0560 -0.1163 0.511512 0.0801
Clement Chen
0.2230
0.0344
0.1196
0.0819 -0.0624
-0.0898
0.1314 0.000032 0.3838
Connor Jacobson
0.5568
0.1123
0.0457 -0.2374 -0.1168
-0.0422
0.000000 0.4603
Consort
0.7891 -0.0535
0.0611 -0.0473 -0.1691
-0.1258 -0.0484 0.080843 0.1637
Caledonian
0.5882
0.0929 -0.0495 -0.1339 -0.0266
0.0192 -0.0929 0.096029 0.1571
Cunard
0.3036
0.0464 -0.0242 -0.0232 -0.1573
0.0920
0.0133 0.044243 0.1856
Delta
0.2820
0.0601
0.0890 -0.1276
-0.0401
0.0464 0.000011 0.4080
Divi
0.5447
0.1237 -0.0385 -0.0953 -0.1231
0.0456
0.0099 0.000352 0.3271
Dorint
0.3933
0.1253 -0.0072
0.1577 -0.2300
0.0899
0.1068 0.001435 0.2905
Embassy
0.3906
0.0422
0.0626 -0.0960
0.0850 -0.0017 0.003380 0.2666
Four Seasons
0.5600 -0.0060
Frantel
0.9711 -0.0630 -0.2292 -0.0042
0.0856
0.0379 -0.0359 0.000000 0.4941
Golden Tulip
1.0031 -0.1095 -0.1517 -0.1113
0.0775
0.0563
0.000000 0.5938
Grand Collection
0.5135
0.1377
0.0508
0.1243 -0.0979
-0.0167
0.1113 0.000118 0.3539
HIDC
0.4616
0.0472 -0.0907
0.0026 -0.0648
-0.1161
0.0180 0.047755 0.1829
Hilton
0.5630 -0.0938
0.0269
0.0306
-0.0827
0.0372 0.009518 0.2360
Hilton International
0.9839 -0.1069 -0.1496 -0.0988
0.0638
-0.0590
0.0712 0.000000 0.5766
Howard Johnson
0.6912 -0.1237
0.1010
0.0239
0.1585
-0.0468
0.0384 0.000151 0.3480
Holiday
0.9585 -0.1285 -0.0859
0.1382
0.0393
-0.0237
0.0573 0.000000 0.5715
Hospitality International
0.4978 -0.0763
0.0865 -0.0792
0.0653
0.0131 -0.0514 0.060139 0.1747
HIS
0.3372
0.0372
0.1656
0.0436
0.0019
-0.0814
0.1281 0.001922 0.2825
Hungar
0.4165
0.0061
0.1749
0.0085
0.4036
-0.0962
0.006322 0.2252
HUSA International
0.6650
0.1251 -0.0209 -0.2137 -0.1771
0.0158
0.000013 0.3795
Hyatt
0.4369 -0.0275
0.0553
0.0558
-0.0747
0.0412 0.038762 0.1902
Intercontinental
1.0097 -0.1042 -0.1652 -0.0763
0.0720
-0.0636
0.0606 0.000000 0.6375
Jack Tar
0.5247
Jolly Of Italy
0.6750 -0.0126
0.1074
Kempinski
0.6613
Loews
0.0972
0.0555
0.1634 -0.2165 -0.1776
0.0887
0.1212
-0.0254
0.0472 0.056449 0.1770
0.0602 0.000001 0.4589
0.0733 -0.0365 -0.1201 -0.1714
0.0939 -0.0362 0.005652 0.2517
0.1735 -0.0502
-0.0335 -0.2997 0.012088 0.2285
0.1189
0.0869 -0.3575 -0.0171
-0.1528 -0.0271 0.020435 0.2118
0.4846
0.0583
0.0001
Mandarin
0.9781
0.1032
0.1135 -0.4119 -0.1097
Marriott
0.7787 -0.1643 -0.0375
0.0542 -0.1517
0.1353
164
0.1775
0.1240
0.0155 0.025376 0.2046
-0.1040 -0.3700 0.001282 0.2935
-0.0293
0.0922 0.000003 0.4361
Table 46. Continued
Chain
Intercept
SIZE
lEXP
DOI
LDR
FSTMOV
NAT
Model p
Rsq
Mercure
0.5783
-0.0146
0.1443
0.0346
0.1448
0.0142
-0.0083
0.054504
0.1782
Le Meridien
0.9397
-0.0637
-0.1812
-0.1013
0.0630
0.0735
-0.0261
0.000000
0.4969
Ming
0.2822
0.0844
0.0321
0.1296
0.1418
-0.1012
0.067395
0.1497
Movenpick
0.8553
0.0954
0.0140
-0.2202
-0.1116
0.0611
-0.1766
0.001276
0.2937
Nikko
1.0056
0.0546
-0.0823
-0.3039 -0.0289
-0.0240
-0.2244
0.006692
0.2466
Oberoi
0.7749
0.0681
-0.1390 -0.1469
0.005028
0.2318
Omni
0.4487
-0.0016
0.1383
Otani
0.5804
-0.0231
0.1464
Peninsula
0.8753
0.0517
Penta
0.6870
Preferred
-0.1488
-0.0824
-0.0245
-0.0264
-0.0785
0.0465
0.033979
0.1948
0.2369
-0.0135
-0.0912
-0.1927
0.001499
0.2893
0.1230
-0.3567 -0.0489
-0.1151
-0.2424
0.000210
0.3400
0.0682
0.0206
-0.2550
0.0423
-0.0085
0.0028
0.218785
0.1231
0.7687
-0.1172
-0.1320
0.1268
0.1295
-0.0213
0.1073
0.000526
0.3169
Prince
0.6384
-0.0659
0.1601
0.0703
0.0586
-0.0711
-0.1068
0.004363
0.2592
Princess
0.5296
0.1135
0.0215
-0.1085
-0.3388
0.0915
0.0084
0.007542
0.2430
Rank
0.3668
-0.0136
0.1956
0.1063
-0.1169
-0.0043
0.0794
0.000000
0.5227
Regent International
0.9202
0.0909
-0.0077 -0.2368
-0.1079
-0.0321
-0.2411
0.001530
0.2887
Royal
0.3564
0.0494
0.1018
0.0263
0.025338
0.1594
Sara
0.8179
0.0053
0.1702
-0.2468
0.000374
0.3257
SAS
0.8347
0.0176
-0.1709
0.000039
0.3553
Scandic
0.6884
0.0052
0.0364
-0.1052
0.045633
0.1845
Shangrila
0.9043
0.0323
-0.0330 -0.2379
0.000880
0.3036
Sheraton
0.9171
Sofitel
-0.0967
-0.1811
-0.0465
-0.0166 -0.0119
-0.0486
0.1019
-0.1794
0.1491
0.1183
-0.2032
-0.1217
-0.0975
-0.1514
0.0710
0.0435
0.0552
0.0737
0.000000
0.6087
0.8939
0.0088
-0.2647
0.0249
-0.1401
-0.0588
-0.0123
0.000010
0.4100
Sonesta
0.4206
0.1136
0.0254
0.1084
-0.0848
0.0024
0.1414
0.001895
0.2829
Southern Pacific
0.5345
0.0612
0.0198
0.0887
-0.2970
0.1542 .
0.005111
0.2314
SRS
1.0455 -0.1196
-0.1439
-0.1041
0.1618
0.0546
-0.0624
0.000000
0.5575
Sunbelt
0.2882
0.1598
-0.0136
0.1346
0.0532
-0.1028
0.0901
0.004086
0.2612
Super 8
0.6441
-0.0923
0.0983
-0.0299
0.1123
0.0361
-0.0321
0.001555
0.2883
Swiss International
0.9875
-0.0050
-0.2160 -0.0316
0.0539
0.0263
-0.1725
0.000000
0.4816
Swissotel
0.7604
0.0749
0.0845
-0.4471
-0.0503
-0.0878
-0.0450
0.005354
0.2533
Trusthouse
0.5225
-0.0714
-0.1703
0.0542
-0.0023
0.0960
0.0015
0.000414
0.3230
VMS
0.5919
-0.0512
0.1231
0.0968
-0.1484
0.0801
-0.0245
0.005403
0.2530
Westin
0.7595
-0.1154
-0.1074
0.0640
-0.0087
0.1373
-0.0352
0.001137
0.2968
Wyndham
0.4341
0.0017
0.1162
0.0576
-0.2875
0.1384
-0.0240
0.003455
0.2660
165
-0.2150
Table 47.
Chain
Intercept
SIZE
1989 W e l l - P r e d i c t e d L a g Chains
lEXP
DOI
LDR
FSTMOV
NAT
Model p
Rsq
Golden Tulip
1.0031 -0.1095 -0.1517 -0.1113 0.0775
0.0563
0.000000 0.5938
Hilton International
0.9839 -0.1069 -0.1496 -0.0988 0.0638
-0.0590
0.0712 0.000000 0.5766
Holiday
0.9585 -0.1285 -0.0859
0.1382 0.0393
-0.0237
0.0573 0.000000 0.5715
Intercontinental
1.0097 -0.1042 -0.1652 -0.0763 0.0720
-0.0636
0.0606 0.000000 0.6375
Sheraton
0.9171 -0.0975 -0.1514
0.0552
0.0737 0.000000 0.6087
SRS
1.0455 -0.1196 -0.1439 -0.1041 0.1618
0.0710 0.0435
166
0.0546 -0.0624 0.000000 0.5575
Table 48. 1990 Lag Regression Results
Chain
Intercept
SIZE
lEXP
DOI
0.1285
LDR
FSTMOV
NAT
Model p
Rsq
Best Westem
0.7737
0.0213
-0.1378
-0.2081
-0.0314
-0.0304
0.003864
0.2904
BTH
0.6947
0.0841
-0.0291 -0.0735 -0.1449
0.0523
-0.0216
0.048124
0.2031
Budget
0.5300
0.0655
0.0305
-0.0057 -0.0484
0.0007
-0.0769
0.153992
0.1541
Caesar Park
0.3444
0.0369
0.1093
-0.1058
0.0079
0.0278
0.034722
0.1915
Ciga
0.8220
0.0081
0.0679
0.0068
0.0173
0.0541
-0.1346
0.457735
0.0968
Colony
0.6169
0.0193
0.1194
0.0422
-0.0885
-0.0056
-0.0620
0.000102
0.3920
Connor Jacobson
0.6338
0.0460
0.0482
-0.1206
-0.1720
0.0547
0.012529
0.2270
Consort
0.6334
0.1251
0.0898
-0.0896 -0.2810
-0.0170
-0.0260
0.049301
0.2021
Caledonian
0.6605
0.0605
-0.0155
0.0823
0.1212
0.0287
-0.1192
0.056143
0.1970
Days Inn
07147
-0.0189
0.0930
0.0093
-0.0003
0.0192
-0.0767
0.020969
0.2340
Divi
0.5996
0.1002
-0.0578 -0.1512
-0.0939
-0.0059
-0.0180
8.32E-05
0.3971
Dorint
0.5607
0.0758
0.0431
0.1849 -0.2117
0.0303
0.0073
0.003845
0.2906
Embassy
0.6078
0.0604
0.0441
0.0369
-0.0500
0.0005
-0.0877
0.048119
0.2031
Four Seasons
0.6200
-0.0247
0.2019
-0.0540 -0.0346
-0.0157
-0.0107
2.19E-06
0.4798
Golden Tulip
1.0333 -0.0595
-0.1726
-0.0416
0.0077
0.0210
4.75E-08
0.5290
Grand Collection
0.6815
0.0381
0.0343
0.1116
-0.1189
0.0207
-0.0238
0.216119
0.1381
HIDC
0.5870
0.0369
0.0937
0.1394
-0.1214
0.0453
0.0126
0.007737
0.2682
Hilton
0.8120
-0.1419 -0.1093
0.1177
0.0957
-0.0460
0.0094
0.001132
0.3273
Hilton Intemational
1.0104 -0.0536 -0.1834 -0.0345 -0.0173
0.0011
0.0001
7.74E-08
0.5444
Holiday Inn
0.9573
-0.0115
4.59E-06
0.4642
HUSA
0.4011
0.121133
0.1430
5.2E-07
0.5088
-0.0307 -0.1424
0.0743
-0.0809
-0.0108
-0.0062
0.0874
-0.0513
0.0353
Hyatt International
1.0073 -0.0864 -0.2252 -0.0206
0.0130
0.0018
0.0025
Intercontinental
1.0368 -0.0635
-0.1846
-0.0322
-0.0304
-0.0242
0.0068
Jack Tar
0.5930
0.0557
0.0062
-0.1415
-0.1531
0.0218
-0.0486
0.011065
0.2562
Jolly of Italy
0.7936
-0.0374
0.1904
0.0177
0.3357
-0.0065
-0.2775
0.000197
0.3751
Journeys
0.6282
-0.0678
0.1744
-0.0424
0.1354
-0.0686
-0.3871
0.000231
0.3710
Kempinski
0.7274
0.1099
0.0600
-0.2691
-0.0192
-0.0160
-0.0245
0.087723
0.1788
Loews
0.5448
-0.0160
0.1142
0.0472
0.0172
0.0020
-0.0272
0.128138
0.1625
Mandarin
0.9335
0.1061
0.0387
-0.4488 -0.0846
0.0160
-0.2541
0.000266
0.3673
Marriott
0.9475
-0.1236
-0.2288
0.1527
0.0007
0.0320
6.05E-07
0.5058
Le Meridien
1.0019 -0.0552
-0.2158
2.83E-08
0.5383
Movenpick
0.9208
Nikko
0.9107
Oberoi
0.6826
Ocean
Okura
0.0806
0.0779
0.0653
-0.0343 -0.0442
-0.0065 .
2.91 E-08 0.5615
-0.0137 -0.2380 -0.0416
0.0518
-0.2239
0.004539
0.2853
0.0720
-0.0721
0.039723
0.2104
0.171598
0.1280
0.03451
0.2158
-0.0273 -0.1332
-0.1852
0.0554
0.0403
-0.0316
0.1233
-0.1541
0.5004
0.0281
0.0773
-0.0216 -0.0101
-0.0051
-0.0593
0.7501
0.0053
0.1712
-0.3169
0.0559
0.0107
-0.0974
167
0.0495 .
0.005266» 0.2806
Table 48. Continued
Chain
Intercept
SIZE
lEXP
DOI
LDR
FSTMOV
NAT
Model p
Rsq
Omni
0.6387 -0.1036
0.0545 -0.0612 -0.0131
0.0591 -0.0498 0.010696 0.2574
Parklane
0.4314
0.0104
0.1301 -0.0183 -0.0450
-0.0082
0.0024 0.153055 0.1544
Penta
0.7687
0.0428 -0.1428 -0.1401 -0.0456
0.0696
0.0294 0.010524 0.2579
Preferred
0.9797 -0.1436 -0.2407 -0.0293
0.1292
0.0268
0.0231
Prince
0.5843
0.0079
0.0902
0.0234 -0.1309
0.0111
0.0160 0.077316 0.1841
Princess
0.5944
0.0336
0.0446 -0.1096 -0.2352
Radisson
0.8022 -0.0966 -0.0124
0.1163
0.0185
0.0580 -0.0355 0.004376 0.2865
Rank
0.5156
0.0142
0.1340
0.0445 -0.0920
-0.0337 -0.0445 0.001248 0.3244
Regal
0.6135 -0.1210
0.0274
0.0463
-0.0100 -0.0083 0.046555 0.2043
Regent International
0.9507
0.0353 -0.0788 -0.1277 -0.0575
0.0322 -0.2422
Ryan
0.4983
0.0026
0.1573 -0.0996
-0.0283
Rydges
0.6006
0.0393
0.0128
Sara
0.6131 -0.0096
Scandic
0.6807
Shangrila
0.9571 -0.0628
Sheraton
0.9927
0.0042 -0.1973 -0.0007 -0.0487
0.0152 -0.0205
Sonesta
0.5514
0.0824
0.0297
0.0384 -0.2121
0.0222
0.0630 0.066117 0.1905
Southern Pacific
0.5947
0.0533
0.0054
0.1033 -0.1665
0.0619
0.1102 0.055766 0.1973
SRS
1.0865 -0.0481 -0.1696 -0.0411 -0.0429
Stouffer
0.6723 -0.0209
0.1086
0.0632 -0.0328
0.0007 -0.0861 0.000963 0.3319
Super 8
0.5821
0.0636
0.0104 -0.0689
0.0327 -0.0915 0.166919 0.1504
Swiss International
1.0409 -0.0044 -0.2429 -0.0506 -0.0257
Trusthouse
0.6542
Trusthouse PLC
1.0204 -0.0342 -0.1805 -0.0337 -0.0526
Westcoast
0.5257
0.0432
0.0529
0.2469 -0.1106
0.0052 -0.0505 0.017029 0.2414
Wyndham
0.5525
0.0526
0.0663
0.0374 -0.1247
-0.0268 -0.0451 0.063912 0.1918
Intercept
0.0314 -0.0762 0.001168 0.3264
0.02806 0.2234
0.004091 0.2357
0.0891
0.0308
0.0576 -0.1700 0.144704 0.1570
0.0622 -0.0153
0.0904
0.0527 -0.1795 0.194125 0.1433
0.1173 -0.0857 -0.1310
0.0600
-0.0186
0.0233
0.0691
Table 49.
Chain
0.0275
1.87E-06 0.4830
SIZE
0.1348 -0.3024 -0.0220
0.0655 -0.0180 -0.1134
0.0737
0.00134 0.3224
0.0607 -0.2121 0.000533 0.3485
-0.0125 -0.0731
0.0501 -0.1276
1.08E-06 0.4943
1.71E-07 0.5299
2.91 E-06 0.4739
0.0166 -0.0652 0.043696 0.2068
-0.0320 -0.0382
5.46E-05 0.4074
1990 W e l l - P r e d i c t e d L a g Chains
lEXP
DOI
LDR
0.0077
FSTMOV
0.0210 ,
NAT
Model p
Rsq
4.75E-08 0.5290
Golden Tulip
1.0333 -0.0595 -0.1726 -0.0416
Hilton International
1.0104 -0.0536 -0.1834 -0.0345 -0.0173
0.0011
0.0001 7.74E-08 0.5444
Intercontinental
1.0368 -0.0635 -0.1846 -0.0322 -0.0304
-0.0242
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CHAPTER V
IMPLICATIONS AND CONCLUSIONS
The aim of the study is to address a yet unresolved issue in interfirm rivalry distinguishing between rivals. Specifically, the purpose is to distinguish between rivals in
the intemational hotel industry in order to help chains channel their resources and efforts
against their greatest competitive rivals. The competitive relationship between a pair of
firms is determined by the extent to which the two firms share the same sets of markets,
and thus have what is known as market similarity (Kamani & Wemerfelt, 1985; Smith &
Wilson, 1995). Market similarity is the extent to which firms operate in the same
locations.
There are several reasons why the intemational hotel industry is appropriate for
such a study. Choice of market location (the basis of market similarity) is of key
significance to competitive posture in this industry for two reasons (Bull, 1994).
Location is the only attribute of the lodging "product" that is completely fixed. Hotel
buildings and grounds cannot be physically moved, even if a particular location is found
to be unsuitable. Location is also quite possibly the most important variable in
determining the feasibility of a new hotel (Bull, 1994; Ellerbrock & Wells, 1983; Hart &
Troy, 1986). A good location increases the value of a hotel and allows managers to
charge a relatively constant premium rate. Location decisions are also important because
they are strategic actions requiring large resource commitments and tend to be fairly
difficult to reverse (Chen & Macmillan, 1992; Miller & Chen, 1994; Smith, Grimm,
205
Gannon & Chen, 1991; Smith et al., 1992). The intemational hotel industry also has
some distinct characteristics such as high fixed costs, high operating leverage, capacity
constraints, cyclicality and segmentation, which further make the choice of location a
particularly interesting strategic question to examine.
The aim of the study is to distinguish between rivals in the intemational hotel
industry. This objective was achieved by using a pair-wise analysis of market similarity.
To further distinguish between rivals, the basis of market similarity was also examined
over time to establish whether one could distinguish between current and fumre rivals
using current conditions. In other words, if two chains have similar characteristics
currently, to what extent does this help to explain the market similarity and thus extent of
rivalry of the two chains both now and in the fumre?
To achieve the objective of distinguishing between rivals a pair-wise analysis was
necessary. Network analysis allows one to conduct such a pair-wise analysis. In a
network, the major focus is the set of relations to and from each actor in a system (Burt,
1976). In this case, the actors are intemational hotel chains, and the relations are the
extent to which the chains have hotels in the same cities as every other chain. The output
of the network analysis was a stmctural equivalence matrix of market similarity for each
of the ten years of the study (1984 - 1993). The stmctural equivalence matrices were then
analyzed using hierarchical cluster analysis and regression analysis.
Cluster analysis was used to find approximately stmcturally equivalent chains
based on market similarity, that is, find those chains with the highest market similarity.
However, a high degree of market similarity alone does not make a pair of firms a
206
competitive threat to each other, unless they also have similarity on other relevant
dimensions. Are there any similarities in characteristics between pairs of chains which
could explain current and future market similarity (and thus rivalry) between those pairs
of chains? A combination of regression analysis and lagged regression analysis was used
to conduct this examination. To further examine the basis of market similarity (and thus
probable rivalry), hypothesis testing was conducted to examine the extent to which the
variables used in the study explain market similarity.
The results of the cluster analysis, regression analysis, lagged regression analysis
and hypotheses testing were presented in chapter four. The conclusions based on the
overall results are presented next. The implications of the findings for both research and
industry and then presented. Finally, the strengths and limitations and future directions of
the study are discussed.
Conclusions
Interpretation of Regression Results:
The results of both the regression analysis and lagged regression analysis indicate
that market similarity is explained well only for certain kinds of chains. The only chains
for which market similarity is well explained are the multinational chains (i.e., those with
intemational experience in many countries) with a high degree of intemationalization and
approximately similar types of hotels. In general, this group consists of large, American
and European, luxury multinational chains for which intemational operations are fairly
important.
207
It would appear that the variables in the study (size, intemational experience,
degree of intemationalization, first mover, leader and nationality) explain market
similarity well, and thus are useful for distinguishing between rivals only for certain types
of chains, namely large, American and European, luxury multinational chains. This
finding holds over time. In other words, the particular variables used in the study explain
market similarity currently and in the future only for those specific types of chains in the
industry.
Several additional factors could account for market similarity but are beyond the
scope of consideration of this study. It has been stated that the intemational hotel
industry is sensitive to capacity constraints. Certain cities may have excess capacity and
tremendous expected growth. Similarly, it may be cheaper to build and staff hotels in
certain cities or certain cities may be close to certain tourist attractions. Such factors
could explain why certain chains have similar market profiles.
The intemational hotel industry has been shown have high operating leverage.
Given the fact that physical properties and buildings cost millions of dollars to design,
equip and maintain, few chains can afford to start an intemational venture without
extemal funding. In today's environment, given the cautious nature of lenders, chains
have to be very careful about a potential intemational venmre to ensure they have made a
good investment. Investors may be more willing to give loans to chains if they go into
specific types of cities which may be perceived as less of a risky investment than other
cities. Such investor influence could account for some market similarity.
208
Travel pattems for business and leisure may promote similar market profiles
among different chains. It has been shown that the intemational hotel industry is highly
influenced by economic conditions and thus is cyclical. Travel pattems tend to reflect the
economic conditions of a region. As travel pattems change, chains may choose locations
to take advantage of new demand in untapped intemational cities. Travel pattems
therefore could account for some of the market similarity.
A chain may have strategic reasons for choosing particular city locations. For
example, if a chain has strategic alliance partners, those partners may have strategic
advantages in certain cities, and thus the chain may be more likely to go into those cities.
A chain could even acquire competitor's hotels and end up with a market profile very
similar to that of other chains. And, finally, coincidence could have some effect on
market profile. Perhaps certain chains act very independently of other chains as they
expand intemationally and by sheer chance end up with a similar market profile to other
chains.
Interpretation of Hvpotheses Tests
HI:
Difference in size has no effect on market similarity between pairs of
chains.
We fail to reject this hypothesis.
H2:
Difference in intemational experience has no effect on market similarity
between pairs of chains.
We fail to reject this hypothesis.
209
H3:
Difference in degree of intemationalization has no effect on market
similarity between pairs of chains.
We fail to reject this hypothesis.
H4:
Difference in first mover behavior has no effect on market similarity
between pairs of chains.
We reject this hypothesis. The implication is that first mover behavior may have some
effect on market similarity.
H5:
Difference in leader behavior has no effect on market similarity between
pairs of chains.
We reject this hypothesis. The implication is that leader behavior may have some effect
on market similarity.
H6:
Difference in nationality has no effect on market similarity between pairs
of chains.
We reject this hypothesis. The implication is that nationality may have some effect on
market similarity.
To summarize, we fail to reject the hypotheses relating to size, intemational
experience and degree of intemationalization. The implication is that these variables may
not have any significant ability to explain market similarity. We reject the hypotheses
relating to first mover behavior, leader behavior and nationality. The implication is that
these variables may have some effect on market similarity and thus rivalry. When trying
to distinguish between rivals, the results show some support for the evaluating first mover
behavior, leader behavior and nationality in addition to market similarity.
210
The descriptive statistics help interpret each of the hypotheses which were
rejected. The hypothesis relating to first mover behavior is rejected, and the variable has
positive mean coefficients. This suggests if there is a difference in first mover behavior
between two chains, the market similarity between the two will be more. The non-'first
moving' chains will likely imitate the first mover and enter those markets which the first
mover has entered. Therefore, except for the particular city (or cities) where the first
mover is the first to enter, the market profiles of the first movers and non-first movers will
likely be similar.
The hypothesis relating to leader behavior is rejected, and the variable has
negative mean coefficients. This suggests if there is a difference in leader behavior
between two chains, the market similarity between the two will be less. The first chain to
leave its home nation is likely to have a very different profile from other chains from its
home nation, since it is much more aggressive and likely has more resources. Since
research shows firms tend to go abroad into culturally similar markets when they have
little intemational experience, the market profile of leaders is also unlikely to look similar
to that of chains from other home nations.
The hypothesis relating to nationality is also rejected, and the variable has
negative mean coefficients. This suggests if there is a difference in nationality between
two chains, the market similarity between the two will be less. Chains in different home
nations are unlikely to know of, or be able to keep close track of chains from other home
nations. It is unlikely, therefore, that they would be able to imitate each other's market
location strategies. Research also shows that firms tend to initially go into culturally
211
similar nations. Chains from culturally dissimilar home nations are therefore unlikely to
have similar market profiles.
Interpretation of Cluster Analysis:
Almost all chains in a cluster appear to have at least one variable in common
besides market similarity, such as intemational experience, type of hotel, degree of
intemationalization, size, first mover behavior or nationality. However, there is no
variable, including type of hotel, which is found to be similar in all of the clusters,
although intemational experience does appear to be a driving force. The implication is
that there is no single variable, or set of variables which can be used to distinguish
between rivals. Different rivals may be similar to a given firm on different dimensions.
Chains should distinguish between rivals first based on market similarity, then other
relevant characteristics. This is because if two firms operate within similar market
locations, but do not have any other similar characteristics, it is unlikely that they will
consider each other a major competitive threat (Chen, 1996).
One pattem does appear to emerge from the cluster analysis and subsequent
evaluation. If a chain has intemational experience in many countries (over 25 or so), the
chains with highest market similarity to it have high intemational experience and similar
types of hotels. In other words, the most intense rivals of a chain with high intemational
experience have similar types of hotels and high intemational experience themselves.
The chains most likely to imitate or be imitated by a chain with high intemational
experience are those with market similarity, high intemational experience and similar
212
type of hotels. It is logical that such chains are rivals. They are likely to interact in many
of the same large, intemational city markets. Since they have a similar market profile to
competitors, it will be easier for them to keep up with competitor actions since they have
a similar market base on which to build. As they interact more, they w ill want to keep
track of each other and since they all have high intemational experience, they will be
more comfortable following rivals into new foreign locations, or even being the first in
the group to enter a new city. Their rivalry is intense because regardless of city location,
they are competing for the same type of customer. The competition for the same type of
customer also explains why they will want to follow a competitor into other locations, as
the competitor's presence there means there is the potential to take advantage of another
untapped market.
Another pattem appears to emerge from the cluster analysis and subsequent
evaluation. If a chain has intemational experience in few countries (under 5), the chains
with highest market similarity to it have low intemational experience and similar
nationality. In other words, the most intense rivals of a chain with low intemational
experience have low intemational experience themselves and come from the same home
nation or culturally similar home nations. The chains most likely to imitate or be imitated
by a chain with low intemational experience are those with market similarity, low
intemational experience and similar nationality. Chains are most likely to imitate other
chains they know and are familiar with. The chains most likely to be visible and known
to a given chain are those from the same home nation, or culturally similar home nations.
It is because such chains have little intemational experience, that they are uncomfortable
213
with foreign expansion, and therefore, to reduce uncertainty and potential risks, they
imitate the foreign expansion strategies of other known chains.
Another interesting finding is that size does not appear to be a factor in
distinguishing between rivals, contrary to what mimetic isomorphism theories would
suggest. In very few of the clusters is size similar among chains in a cluster. One
possible explanation for this finding is that chains try to take advantage of economies of
agglomeration (Weber, 1929; Baum & Haveman, 1997). Agglomeration refers to the
economies which arise from being located close to similar type of firms, such as a shared
infrastmcture, informational extemalities and reduction of consumer search costs. Thus,
chains would imitate the market profile of similar types of chains, regardless of size. This
explanation holds tme for multinational chains.
Another explanation for the finding on size, is that chains imitate other known
chains regardless of type or size, because they are familiar with the known chains, can
more easily keep track of their movements, and take advantage of high demand in that
location. The low costs and/or high demand of a particular location are known as
economies of location (Weber, 1929; Baum & Haveman, 1997). Thus, chains would
imitate the market profile of other chains, regardless of size or type. This explanation
holds tme for chains with little intemational experience. Overall, the results regarding
size suggest that size may not affect choice of location ( and thus rivalry) as much as
previously thought in mimetic isomorphism studies.
214
Implications
Research Implications
The results and conclusions of this study further validate the importance of
multiple point competition in industries where a local presence is required. The study
reveals the importance of market similarity in distinguishing between rivals. Although a
high degree of market similarity alone does not distinguish rivals, without market
similarity, it would be hard to make a case for firms to be rivals. To distinguish between
rivals, market similarity in addition to similarity on other relevant dimensions should be
assessed. The results suggest that the relevant dimensions will vary. In this particular
industry, the relevant dimension based on the cluster analysis appears to be intemational
experience. It is interesting that in the hypotheses testing, intemational experience does
not appear to have any significant effect on market similarity. The regression analysis
results, however, indicate intemational experience may have an effect on market
similarity for large, multinational chains and the cluster analysis results indicate
intemational experience may play a role in explaining market similarity for both
multinational chains and chains with little knowledge of intemational business. One
explanation for the interesting findings is that there are so few chains which are either
large, multinational chains or with little knowledge of intemational business, that the
results of the hypotheses tests are being driven by the remaining chains in each year.
More research is needed to establish whether results would differ if the remaining chains
were removed for each year. More research is also needed to examine whether the group
of remaining chains have a common base of rivalry other than that found in this study.
215
Further research is also needed to examine whether the bases of rivalry from one industry
can be applied to other industries.
The results suggest that size may not affect rivalry as much as previously thought
in mimetic isomorphism studies. When the basis of market similarity was empirically
examined in an attempt to distinguish between rivals, size did not appear to have any
significant effect on market similarity. In subsequent evaluation of cluster membership,
size once again did not appear to be an important factor. It would appear that size may
not play a significant role in distinguishing between rivals.
Industrv Implications:
A current trend of consolidation exists in the intemational hotel industry (WSJ,
2/26/97). For example, Hilton has been trying to acquire ITT Sheraton; Patriot American
Hospitality has begun to negotiate the purchase of the Wyndham chain; and Hyatt
Intemational is trying to acquire between 20 to 30 hotels over the next three years.
Experts suggest that by the next decade there may only be between ten and twenty chains
worldwide. This study provides an excellent way to deal with the consolidation trend.
Depending on the reason for the consolidation, using a pair-wise analysis of market
similarity such as in this research project, one may be able to identify suitable merger
and/or acquisition partners; identify strategic alliance partners; and also identify current
and potential rivals to keep track of. No other known studies using a pair-wise approach
have been conducted in the intemational hotel industry. Using the pair-wise approach
with market similarity, a chain can decide which other chains will allow it to strengthen
216
presence in a particular region of the world. If two chains have almost identical market
similarity, and are very similar in other dimensions, a case can be made for those two
chains to easily merge. However, in terms of accessing new markets and new customer
segments, a chain may be more interested in finding a chain with dissimilar market
profiles, and dissimilar characteristics on other dimensions. This study provides one way
for chains to identify both kinds of potential merger partners.
The affiliation matrix (which show which chains are located in which
intemational cities) is very valuable to the intemational hotel industry. Going through the
process of developing such an affiliation matrix is cmcial in developing strategic plans
for a chain. Developing an affiliation matrix provides a good way to identify outliers
(chains with very unique market profiles) and thus discover untapped markets which may
prove to be a lucrative investment. Developing an affiliation matrix also may provide a
way for a chain to differentiate itself in market profile from its rivals.
The results of the cluster analysis show that chains in almost every cluster have at
least one characteristic in common (besides market similarity). However, no characteristic was found to be common in all clusters. The implication is that chains should
search for the source of commonality with all chains with which it has market similarity,
and distinguish between rivals in this manner. The cluster analysis findings suggest that
if a chain has high intemational experience, type of hotel is likely to be the common
variable; if a chain has little intemational experience, nationality is likely to be the
common variable.
217
Limitations and Strengths
The study only considers intemational hotel chains. Information about domestic
hotels in every given nation is not included. One reason for this omission, is that
intemational hotel chains create intemational divisions when their intemational
operations increase substantially. The intemational divisions compete with and deal with
intemational issues, and the domestic divisions deal with domestic competitors and
issues. Another reason for the omission, is that information about domestic chains in
every country in the study was not readily available.
The study uses secondary data. If there are any errors in the directories, those
errors will be duplicated in this study. Also, if information about any chain is omitted
from a given directory for any reason, the chain would not be included in analyses for that
year.
Information about type of hotel and performance was unavailable for many nonAmerican chains, making it difficult to compare competitive intensity between some
chains. An attempt was made to collect as much information as possible about each chain
in the study, however, since some chains are privately held, and others are no longer in
existence, it was not possible to collect all information.
The study examines hotel presence at the city level. There is no distinction
between different locations or neighborhoods within a given intemational city. The study
addresses the question of whether a chain has at least one hotel in a given city, not
whether it has a hotel in a given neighborhood of a given city. The number of rooms per
218
city is not taken into account in this smdy. However, the total number of rooms outside
of the home country is considered.
Using indirect measures of rivalry may contribute to some of the ambiguity in
multiple market studies (Baum & Kom, 1996). This study makes use of one proxy
measure for intensity of rivalry, namely, market similarity. If this proxy is not a good
measure of rivalry in this industry, it may result in more ambiguous results. However, the
distinct characteristics of this industry (namely capacity sensitivity and high fixed costs)
make market similarity a good proxy for intensity. The high fixed costs associated with
building a new hotel combined with the sensitivity to capacity constraints means that
geographic proximity and thus market similarity will increase rivalry among intemational
hotel chains.
This research project is conducted in a single industry. As such, the
generalizability of the findings to other industries must be done with care. Market
similarity can only be assessed in an industry with clearly defined, regional markets. The
intemational hotel industry is one such industry. The results may apply best to this and
other industries with clearly defined, regional markets.
The results of the regression analysis must be interpreted with caution since the
regression assumption of independence is violated by using network data as input.
Another potential limitation is that the time period used for the study may not be
sufficiently long to capture first mover and leader behavior.
One strength of the study is that it addresses the relatively unexplored question of
how a firm should differentiate between competitors so as to channel its resources and
219
efforts. The study makes use of pair-wise analysis, which is recommended, but has
hardly been used in previous rivalry studies. The smdy also provides one more
examination of mimetic isomorphism, which at the present time has contradictory
research results.
Another strength is that the study combines qualitative and quantitative analysis
techniques in a unique fashion. To get at the namre of relationships between pairs of
chains, it was necessary to use appropriate methodology - hence the use of network
analysis.
A relatively understudied industry is used for this study. At the present time, only
two known smdies exist of the intemational hotel industry. No known studies using a
pair-wise approach have been conducted in the intemational hotel industry.
Future Directions
It would be very interesting to collect more information about the large,
multinational chains for which market similarity was well explained. It would be
interesting to examine over time the extent to which these chains have imitated each other
(if at all). The objective would be to try to establish whether the chains have similar
market profiles currently because they have imitated each other, or because of
coincidence or some other factor. It would especially be informative to try to get a sense
of the success of these chains, to see whether market profile has had any relationship with
success. Of these chains, which chain is most successful, and can that be explained by
their market profile or characteristics?
220
Another extension of the study would be to compare the market profiles of chains
from similar cultures. Nationality may have some effect on market similarity as indicated
by hypothesis 6. The cultural similarity and proximity of home nations appeared to play a
role in cluster definition. It would be interesting to conduct a research project which
compares the extent to which chains with cultural similarity imitate each other.
Additionally, a study which allows one to examine the pattems of intemational expansion
would be useful. Can cultural similarity or dissimilarity between home and host nation
explain the choice of intemational locations?
221
REFERENCES
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