Survival-enhancing learning in the Manhattan Hotel Industry, 1898-1980
Joel A C Baum, Paul Ingram. Management Science. Linthicum: Jul
1998.Vol.44, Iss. 7; pg. 996, 21 pgs
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Subjects:
Hotels & motels, Organizational learning, Operations research, Studies, Mathematical models
Classification Codes
2600 Management science/operations research, 9130 Experimental/theoretical treatment, 8380 Hotel
restaurant industries, 9190 US
Locations:
New York, US
Author(s):
Joel A C Baum
Publication title:
Management Science. Linthicum: Jul 1998. Vol. 44, Iss. 7; pg. 996, 21 pgs
Source type:
Periodical
ISSN/ISBN:
00251909
, Paul Ingram
ProQuest document ID: 32786300
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11960
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http://proquest.umi.com/pqdweb?did=32786300&sid=6&Fmt=4&clientId=42456&RQT=309&VName=PQ
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Abstract (Document Summary)
How experience at the level of the organization, the population, and the related group affects the
failure of Manhattan hotels is examined. It is found that organizational experience has a U-shaped
effect on failure; that organizations enjoy reduced failure as a function of population experience
before their founding, but not after; and that related organizations provide experience that lowers
failure, but it matters whether their experience is local or non-local, and if it was acquired before or
after the relationship was established. These results indicate both the difficulty of applying different
types of experience to reduce the risk of organizational failure, and the relevance of experience
for the evolution of organizational populations.
Full Text (11960
words)
Copyright Institute of Management Sciences Jul 1998
[Headnote]
In this study, we examine how experience at the level of the organization, the population, and the related group
affects the failure of Manhattan hotels. We find organizational experience has a U-shaped effect on failure; that
organizations enjoy reduced failure as a function of population experience before their founding, but not after;
and that related organizations provide experience that lowers failure, but it matters whether their experience is
local or non-local, and if it was acquired before or after the relationship was established. These results indicate
both the difficulty of applying different types of experience to reduce the risk of organizational failure, and the
relevance of experience for the evolution of organizational populations. (Organizational Learning;
Interorganizational Learning; Organizational Failure; Organizational Ecology; Hotel Industry; Manhattan)
Many organizational activities are intended to acquire information or knowledge. Typically, such learning
is viewed as an organization-level phenomenon (e.g. Argyris and Schon 1978, Cyert and March 1963,
March and Olsen 1976). However, work by learning theorists (e.g. Darr et al. 1995, Foster and
Rosenzweig 1995), organizational ecologists (e.g. Haveman 1993a), neoinstitutionalists (e.g. Meyer and
Rowan 1977, DiMaggio and Powell 1983), and network theorists (e.g. Burt 1983, Davis 1991) suggests
that learning may often be produced by interactions among organizations, rather than by isolated
individual organizations. Learning by organizations may thus commonly unfold in populations and
broader communities of organizations as well as individual organizations. Learning processes are
present at the population and community levels most prominently in the form of vicarious learning from
the experience of other organizations (Levitt and March 1988, Lant and Mezias 1990, Miner and
Haunschild 1995).
In this paper, we empirically examine the effect of organizations' experience and the experience of other
related and unrelated organizations in the population, on a key organizational outcome, failure, over an
eighty-five year period in the Manhattan hotel industry. Although learning theorists have recognized that
experience may ultimately influence organizational failure (e.g. Huber 1991), the existing empirical
literature on the influence of experience on organizations typically uses efficiency as the dependent
variable (Yelle 1979). Efficiency is linked to failure, with more efficient organizations being less likely to
fail (Mitchell et al. 1994) but care must be taken in relating past learning research to effects of experience
on organizational failure. Organizations may be unable to learn due to the ambiguity and paucity of their
experience, or they may learn the wrong thing. If an organization fails, it could be because it didn't have
sufficient experience to produce efficiently and to effectively gauge consumer demands, or it could be
because it had an abundance of experience, but was influenced by that experience to make erroneous
decisions. We present ideas and examples of how experience affects learning, but our formal analysis is
of the relationship between experience and failure. Therefore, we refer to the type of learning we study
as "survival-enhancing learning," which we define as occurring when experience leads to a decrease in
an organization's risk of failure. There are a number of intermediate processes that can account for
survivalenhancing learning, and our analysis is meant to point to their collective importance, not to
determine the relative importance of any particular intermediate process.
Our examination of survival-enhancing learning relates experience to an outcome that matters for the
welfare of employees, owners and customers of an organization while the design of our study allows for
the significant possibility that different types of experience may not positively affect, or may even
negatively affect that outcome. The popular management literature emphasizes the possibility and
promise of organizational learning, while organizational theory repeatedly asserts that learning from
experience is difficult. The experience available to an organization may be limited and difficult to
interpret, organizations are difficult to change, and the environment organizations compete in may
change even if organizations can adapt (Levitt and March 1988, Levinthal and March 1993). Therefore,
we give explicit attention to the possibility that organizations' experience may lead them into competency
traps, and to the possibility that other organizations' experience may differ in its value to the focal
organization depending on when it is generated, and which other organizations-related or
unrelated-generate it.
Our focus is on the implications of organizations' operating experience.l By operating experience we
mean the cumulative history of core operations (production or service provision) of the organization.
Operating experience can generate both internal and external benefits for an organization (Ingram and
Baum 1997a). The internal benefit of operating experience is in the form of efficiencies of production or
providing service. Numerous learning curve studies have found that the unit-cost of production
decreases with cumulative production experience (Yelle 1979). Although most learning curve research
has been in manufacturing contexts, learning curves have also been demonstrated for services (Darr et
al. 1995). We see the primary external benefit of operating experience to be the opportunity to learn
about the organization's external environment, particularly about consumers' preferences (Ingram and
Baum 1997a). Through producing products or services and offering them to the market, organizations
learn what consumers want. Each time the organization goes to market, it gets feedback about what
consumers want, and revises its model of consumers' preferences (Cyert et al. 1993). Both the internal
and external benefits of experience may contribute to survival-enhancing learning.
Our examination of survival-enhancing learning highlights four potentially elementary processes: (1 ) the
nature of organizations' utilization of their own experience when there are costs and risks to
organizational change; (2) the significance of organizations' capacity for survival-enhancing learning from
the experience of other organizations in the population; (3) the importance of being imprinted by the
experience of other organizations in the population at the time of founding; and (4) the role of formal
interorganizational relationships for survival-enhancing learning from the experience of others. So, we
anticipate the possibility of survival-enhancing learning from experience at the levels of the organization,
the population, and the related group. Below we develop these possibilities more thoroughly, then we
test our ideas by analyzing the effects of experience on the failure of hotels in Manhattan from 1898 to
1980.
Survival-Enhancing Learning and Population Dynamics
Survival-Enhancing Learning from Organizational Experience
All organizations face the problem of how to divide attention and other resources between exploring new
routines and exploiting existing routines (Levinthal and March 1993, March 1991, Nelson and Winter
1982, Starbuck 1983, Tyre and Orlikowski 1994). An organization that engages in too much exploration
will not be able to harvest the value of its current competencies; an organization that engages in too
much exploitation can stagnate. Typically, short run rewards of exploitation drive out exploration since
each increase in competence at an activity increases the likelihood of obtaining rewards for engaging in
that activity, while returns from exploration are systematically less certain. As a result, attention and effort
applied to exploratory search and learning by organizations is often discontinuous. After an initial, brief
episode of exploration, knowledge quickly congeals, potentially embedding unresolved or unnoticed
problems. Each time an organization engages a particular routine it increases its competency at that
routine. The more experienced an organization becomes with a particular routine, the more likely it will be
to use the routine again, because it knows how to, and the more limited its experience with other
(potentially superior) routines becomes. Over time, this self-reinforcing bias toward exploitation of current
routines purges variation in organizational routines and impairs the capacity for exploratory learning.
Subsequent exploratory learning is erratic, triggered by the occurrence of poor performance and
disruptive events, and often entails more "retrofitting" of existing routines than learning of new routines.
Consequently, the routines to which organizations become committed tend to be determined more by
initial conditions, experiences, and actions than by information gained from later learning situations.
In the face of ambiguity and uncertainty, an emphasis on exploitation can prevent organizations from
adjusting their routines too quickly and detrimentally to idiosyncratic events and from engaging in costly
explorations into highly uncertain domains. Moreover, in the face of production pressures and the need
for reliability and consistency of action, exploitive learning may significantly enhance performance by
reducing variability in the quality or efficiency of task performance (Hannan and Freeman 1984).
Exploitation can become harmful, however, if the criteria for organizational success and survival change
after the organization has learned. Inevitably, the environment will change, a fickle market will be
attracted to a new competitor's innovative product, or the core technology employed by the organization
will become obsolete and unprofitable to manage. Then the organization may perform poorly and even
fail by doing well what it learned in the past; it may suffer the so-called competency trap (Levitt and
March 1988). The competency trap notion suggests that organizations may reduce their exploratory
activity prematurely and, in the case of a changing environment, not renew exploratory search and
learning activities despite the fact that new opportunities and threats are present. In this way,
organizations' experience contributes to the inertia that binds them to routines of the past. Exploitative
learning can lead organizations to employ routines of the past well beyond their point of usefulness,
which may ultimately result in organizational failure (Baum et al. 1995, Henderson and Clark 1990,
Starbuck 1983).
There are many examples of operating experience leading to internal efficiencies in the Manhattan hotel
industry. Manhattan hoteliers required systems to control their organizations, and they often attributed
the development of control systems to experience operating their hotels (e.g. Clarenbach 1907). Another
central issue for Manhattan hoteliers was maintaining a supply of qualified employees. Some hotels
appear to have learned modern personnel procedures through experience, and these hotels could
expect more efficient employees and lower turnover expense (Van Orman 1930). The organization of
labor was also critical, as demonstrated by the clever system for apportioning dining room duties
developed at Manhattan's Hotel Astor (Hotel Monthly, January, 1907: 22-27). In addition to these big
issues, there are hundreds of operational details that make the difference between a successful hotel
and a failed one. There are also examples of operating experience leading to improvements in
knowledge of consumer preferences (the external benefit of operating experience). Over the last century,
Manhattan hoteliers have struggled with issues such as whether to use the American or European plan
for meals, whether rates should be reduced during recessions, whether rates should be in round figures,
whether to relax hotel etiquette in response to changing social and travel practices, and how best to
appeal to particular classes of consumers, such as conventioneers or air-travelers.
There are also historical events consistent with the idea that hotels' experience can lead them into
competency traps. For example, Belasco (1979) describes in detail the difficulty that grand hotels had in
adjusting to changing travel patterns brought about by the automobile. Hotels that had in the past been
successful as defenders of tradition and etiquette continued to apply out-dated standards of propriety to
new travelers. Road-dirty families were given no option but to walk through a formal hotel lobby, and
hotel staff subjected these lucrative customers to the same disapproving looks that had been effective
five years earlier for discouraging undesirables. Predictably, these hotels lost business and were
eventually placed at risk of failure.
So, even though operating experience may lead to survival-enhancing learning through internal
efficiencies and knowledge of consumer preferences, we expect the risk of failure will increase with high
levels of experience. There is no contradiction in this: exploitive learning simultaneously enhances
performance in the short run and raises the risk of failure in long run. Thus, even as an organization's
absolute costs of production decline, its effectiveness relative to new rivals that begin with more effective
routines may decrease. A recent study of the relationship between organizational experience and
organizational failure among U.S. hotel chains supports this view (Ingram and Baum 1997a).
Therefore, we hypothesize:
HYPOTHESIS 1 (H1). The relationship between organizational failure rates and organizational
experience is U-shaped.
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A scope condition for H1 is that there is enough environmental change to antiquate an organization's
routines. To test this hypothesis, we defined the Organizational Experience of organization i as where
T^sub ifound^ is the first year of organization is existence, T - 1 is the year before the current year,
OE^sub it^ is the amount of operating experience accumulated by organization i in year t, and Discount,
which we describe in detail below, is one of four weights that depreciate values of OE^sub it^ over time to
account for possible antiquation (due to environmental change) or decay (due to forgetting) of knowledge
gained from organizational operating experiences in the past (Argote et al. 1990). For Manhattan hotels,
we computed this variable based on hotels' accumulation of operating experience measured in terms of
available rooms. In most past research, operating experience has been operationalized as an
accumulation of units produced (Epple et al. 1991). Operationalization is not as clear-cut in a service
industry. Candidates for measuring operating experience in the Manhattan hotel industry are
accumulations of rooms made available or guests served. Serving guests is key to successful hotel
operation, but the product of a hotel could also be viewed as an available room. We do not know
occupancy at the hotel level, so we define OE^sub it^ as the number of rooms a hotel has available at
time t. We discuss the robustness of this measure below.
For each hotel, we set the organizational experience variable equal to the (discounted) number of
roomyears the hotel had accumulated between the time the hotel was founded and the current year (x
10^sup -4^ for rescaling). As in learning curve estimation, the fact that the marginal value of experience
decreases as knowledge grows (i.e., there is not as much potential learning in the 100th unit of
experience as there was in the first) is accounted for by the exponential form of the models we estimate.
To test for the inverted U-shaped effect predicted by hypothesis 1, we modeled organizational
experience as a quadratic by including both organizational experience and organizational experience
squared in the analysis.
Vicarious Survival-Enhancing Learning from Population Experience
While individual organizations may tend toward too much exploitation, populations of organizations may
still engage in substantial exploration and generate new knowledge that their members may acquire for
themselves. At the population level, lack of cohesion (i.e., diverse goals and incentives) and authority
structures may allow the proliferation of new ideas and routines (Miner and Haunschild 1995). Even
recklessly innovative organizations that quickly fail can generate new knowledge that adds to the
experience of the population. Although learning in established organizations tends to focus on exploiting
old routines rather than on developing new ones, these organizations may, at relatively low cost, be able
to exploit new routines produced by the explorations and advances of other organizations in their
population. So, "the best strategy for any individual organization is often to emphasize the exploitation of
successful explorations of others" (Levinthal and March 1993:104).
By observing their population, organizations can potentially learn the multiplicity of strategies,
administrative practices and technologies employed by other successful organizations. Supporting this
idea, neoinstitutional theory holds that organizations imitate each other and that such behavior is
particularly ubiquitous under uncertain conditions (DiMaggio and Powell 1983). Copious evidence that
organizations learn new routines from each other can be found in studies of interorganizational imitation.
In their review of this work, Miner and Haunschild (1995) identify two basic mechanisms through which
organizations learn routines from their population: mimetic learning, which refers to selective copying or
vicarious learning of routines from other organizations (e.g., reverse engineering of a competitor's
product, benchmarking) and contact learning, which involves transmission of routines through personal
and formal relationships between organizations and their members (e.g., personal ties, board of director
interlocks, interorganizational relations). These mechanisms may also operate in a more direct fashion
when an organization hires employees away from organizations in their population judged to possess
knowledge of superior routines or absorbs a whole organization.2
The history of the Manhattan hotel industry is littered with examples of vicarious learning. Copying other
hotels is common practice, and considered vital to success in the industry. One article describing
methods to improve hotel effectiveness warned, "any hotel man who does not visit and inspect at least
ten hotels a year is slipping. When I say inspect the hotels, I mean that you must start at the roof and go
to the basement and listen to the good points the manager has to offer" (Hotel Monthly, October, 1939:
38). Testimony that hoteliers followed the practice of touring competitors is provided by a hotelier who
showed an interviewer some new ceiling lights and asked, "How do you like them? I saw them in the
Waldorf-Astoria and figured that they are pretty smart operators and should know what's what, so when I
needed some new fixtures I copied theirs" (Hotel Monthly, December, 1938: 20).
The hotel press facilitated mimesis by providing detailed operating information about successful hotels.
In 1907 alone, the Hotel Monthly had detailed features describing operations at Manhattan's Hotel Astor
and Plaza Hotel and gave shorter descriptions of visits to twenty-eight other Manhattan hotels. The
WaldorfAstoria even published its operating manuals in four volumes (Hotel Waldorf-Astoria Corporation
1947). Other published sources facilitated learning about routines to avoid. For example, in an article by
an employee of the Safety Research Institute, a statistical analysis of places of origin and causes of hotel
fires is presented (Maar 1941). Thus, population experience stored in statistics, courses, books, articles
and managers' memories allows experiences of organizations to benefit the population even after those
organizations cease to operate.
Contact learning was no less common. Various groups in the Manhattan hotel industry had regular
speaker series targeted at education. These talks dealt with both internal and external components of
learning from operating experience as illustrated by the Hotel Executives Club which heard a lecture on
"Banquet Management" on January 27, 1930 and one on "Aviation and its relation to hotel and traveling
public" on July 22 of the same year. National and regional associations are important in the hotel
industry, and their regular meetings provided both an opportunity for presentation of papers that
described hoteliers' operating experience and a context for personal contact between hoteliers. At one
1941 meeting, Lucius Boomer of Manhattan's Waldorf-Astoria answered questions ranging from when it
is economical to operate a bakery within the hotel, to whether it was a good investment to put radios in
every room (Hotel Monthly, July, 1941: 11, 46). There were also meetings for specific types of hotel
employees such as stewards and housekeepers.
Finally, supporting more direct forms of transmission of routines, practices, and structures (e.g., gaining
access to other organizations' routines by hiring employees away from organizations in their population
judged to possess knowledge of superior routines), labor mobility is notoriously high in the hospitality
industry (Uhrbrock 1930, Wyckoff and Sasser 1981). For example, in a study of 43 Manhattan hotel
managers, Baum and Lant (1993) found that these managers' average tenure in the Manhattan hotel
industry was 10.8 years but their average tenure at their current hotel was only 3.4 years.
Results of some recent studies of the impact of the experience of other organizations on learning curves
are consistent with the idea that vicarious learning can benefit an organization. Foster and Rosenzweig
(1995) found that Indian farmers benefit from their neighbors' experience. Zimmerman (1982) found that
construction companies experienced vicarious learning benefits in the construction of nuclear power
plants. And, Ingram and Baum (1997a) and Irwin and Klenow (1994) found evidence of vicarious learning
among U.S. hotel chains and in the worldwide semiconductor industry, respectively. In contrast,
however, Darr et al. (1995) found no effect of vicarious learning effects from the experience of other
unrelated organizations (i.e., different owners) in the pizza industry. Past studies of vicarious learning by
organizations tend to find only learning from related organizations, but such studies are few, and
generally suffer two design flaws that likely bias their findings (but see Ingram and Baum 1997a, 1997b).
First, past studies typically only study surviving organizations, biasing empirical estimates for effects of
population-level learning on performance outcomes. Second, past findings are based on incomplete
industry histories, making accurate computation and estimation of industry experience on organizational
outcomes impossible. As we have explained, survival-enhancing learning is different from improved
efficiency, and is not automatic. So, to provide a more direct test of the idea that the vicarious experience
of an organization's population can result in survival-enhancing learning, we test the hypothesis that:
HYPOTHESIS 2 (H2). Organizational failure rates decline with the amount of population experience that
accrues since the time of the organization's founding.
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To test H2 we developed a vicarious experience variable that parallels the organizational experience
variable described above (see Equation 1). We continue to rely on the number of rooms offered in a year
to measure operating experience, but now we consider the number of rooms offered by the entire
Manhattan hotel industry. We defined Population Experience for organization i as where T^sub ifound^ is
the first year of organization i's existence, T - 1 is the year before the current year, M^sub t^ is the mass
(total number of rooms in all hotels) of the population during year t, and Discount is as described below.
For each hotel, we set this variable equal to the Manhattan hotel population's cumulative (discounted)
operating experience between the current year and the time the hotel was founded (x 10^sup -5^ for
rescaling).
Congenital Survival-Enhancing Learning from Population Experience
Some theorists are less optimistic about the possibility of vicarious learning, contending that, as a result
of inertia, learning from population experience occurs primarily (or entirely) at the time of founding or very
early in the life-histories of organizations (Hannan and Freeman 1984). Huber (1991) refers to learning
from population experience before the organization's founding as congenital learning. Founders of new
organizations can inherit their population s routines through mimetic and contact learning, or more
directly, for example, when one or more employees of an existing organization leave to found their own,
new organization. New organizations can also acquire their population's routines by hiring away the
employees of organizations in the population judged to possess knowledge of superior routines. Thus,
the expertise of existing organizations is passed on to new members of their population. These
processes offer one explanation of how heredity takes place in an organizational population.
Consequently, as newly founded organizations inherit the current stock of knowledge gained from the
previous experience of their population, successive cohorts of organizations in a population should start
with a lower risk of failure than previous ones.
The same mechanisms that enable vicarious learning in the Manhattan hotel industry also facilitate
congenital learning. Hotel entrepreneurs may tour other hotels, attend conferences and lectures, engage
industry experts, read books and journals, or take university courses before founding their hotels. It is
also likely that they and their employees worked in the industry prior to founding the new hotel (Uhrbrock
1930). Several examples also illustrate the possibility that population experience may be more useful to
new hotels than to incumbents. In descriptions of Manhattan hotels, the hotel press often described
innovations in the physical design of the hotel and provided floor plans. For example, in February 1907,
Hotel Monthly presented diagrams detailing the innovative air conditioning and ventilation system at the
St. Regis, and in 1912 they gave floor plans for the Waldorf-Astoria and Hotel
Vanderbilt. Clark (1902)
also presented a plan to design, construct and furnish a 100-room hotel based on his experience in the
hotel industry. Such information would be very useful to a gestating hotel, but could only be incorporated
at great expense by an existing hotel. The report of the Safety Research Institute on places of origin and
causes of hotel fires (Maar 1941) is also potentially far more valuable to someone planning a new hotel
than to someone operating an established hotel.
Thus, even though the continuing exploration of the population is accessible to them, bounded
rationality, exploitive learning, and the hazardousness of change may cause ongoing organizations to
remain firmly imprinted with routines predominant at the time of their founding, unable to take full
advantage of their population's experience when doing so requires major changes. Consequently, an
organization's learning from the population may be based partly or even mostly on experience acquired
during the organization's founding. Consistent with this conclusion, in a study of learning by shipyards,
Argote et al. (1990) found that interorganizational learning lowered production costs more at the time that
shipyards initiated production than after production was ongoing. Relatedly, Ingram and Baum (1997a)
found that each unit of industry operating experience at the time of founding lowered U.S. hotel chains'
failure rates by an amount equal to more than five units of industry operating experience accumulated
after chains were founded. Therefore, we hypothesize:
HYPOTHESIS 3 (H3). Organizational failure rates decline with the amount of population experience at
the time of organizational founding.
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To test H3, we defined Congenital Population Experience for organization i as: where T^sub jfound^ is
the first year of the population s existence, T^sub ifound-1^ is the year before organization i's founding,
MAt is the mass of organizations alive in the year before organization i's founding, and Discount is as
described below. For each hotel, we set this variable equal to the Manhattan hotel population's
cumulative (discounted) mass of organizations alive in the year before founding (x 10^sup -5^ for
rescaling). Congenital Population Experience only includes other organizations alive in the year prior to
founding since other organizations that died prior to this time cannot be observed directly by the new
organization. We also tested an operationalization of this variable, used in Ingram and Baum (1997a),
which included the experience of all organizations that operated before the focal organization's founding.
That operationalization yielded comparable but slightly weaker results.
Figure 1, which shows the failure rates for three successive cohorts of organizations established at
different times during the history of a hypothetical population, summarizes the overall implications of
H1-H3 graphically. In the figure, the population's founding cohort, C1, has a high initial hazard rate that
declines at first with increases in organizational experience (H1) and vicarious survival-enhancing
learning from other organizations in the population (H2). C1's hazard rate begins to increase, however,
as exploitive learning takes hold and creates an increasing gap between the organizations in C1 and
shifting environmental demands (H1). The U-shaped trajectories of the hazard rates for C2 and C3 result
from the same processes. Each cohort begins with a lower initial failure rate than the initial cohort,
reflecting the effect of imprinting based on experience of the population prior to the cohort's appearance
(H3). The differences in the C2 and C3 trajectories serve to illustrate the implications of differences in the
rate of vicarious interorganizational learning during organizational lifetimes. The shape of the C2
hazard-an immediate survival advantage over C1 that increases moderately over time-is consistent with
limited vicarious survival-enhancing learning after founding. In the C2 scenario, strong inertial forces
exist, and, as a result, organizations' capacity to change in response to population experience during
their lifetimes is limited. In contrast, the trajectory of C3's hazard, an initial competitive disadvantage to
preceding cohorts that diminishes rapidly over time, is consistent with a high rate of vicarious
survival-enhancing learning after founding. The C3 scenario has a more Lamarckian (than Darwinian)
character in the sense that organizations are capable of adaptation during their lifetimes.
Survival-Enhancing Learning from Related Organizations
H2 and H3 argue that organizations can learn from the experience of other organizations. The difficulty of
collecting and interpreting experience, however, indicates that potential for learning from another
organization may depend on the presence of a formal tie to the organization. Weak ties such as those
between two unrelated organizations in a population may lead to awareness of innovations, but it is
possible that stronger ties are necessary to influence adoption and insure successful transmission of
innovations (Levinson and Asahi 1995). In cases where such relations exist, the access to the
experience of related organizations may be greater than if the organizations were unrelated, and the
potential for interorganizational learning higher. If organizations share stakeholders, then incentives to
hide information may disappear. Further, related organizations may have routines and incentives that
facilitate experience sharing, such as monthly meetings, and performance reviews. Darr et al. (1995), for
example, found that pizza outlets related by joint ownership benefited from each other's experience but
did not benefit from the experience of unrelated outlets. The network literature also suggests that direct
ties facilitate the diffusion of innovations (e.g., Davis 1991; Greve 1995, 1996; Haunschild 1993).
However, there may also be a cost to exposure to the experience of related organizations (Ingram and
Baum 1997b). The value of related organizations' experience is likely to depend on how similar their
environments are to the focal organizations' environment. The experience of related organizations in
different environments may not transfer easily. Further, nontransferable experience could be worse than
useless, it could be harmful. Given the difficulty of identifying means-ends relationships in complex
environments, managers may be unable to filter experience that applies in their environment from
experience that doesn't. Also, related organizations often use standardization, both because it can make
administration easier, and because there can be strategic advantages to presenting uniformity and
consistency to customers. But standardization may mean that routines developed to reflect the
experience of some organizations are forced on related organizations operating in environments where
the routines would be harmful.
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Figure 1
Ingram and Baum (1997b) argue that Manhattan is idiosyncratic in the U.S. hospitality industry, and
therefore that experience of related hotels in other parts of the country will not be as useful in Manhattan
as experience gained there. Consistent with this, small market hoteliers had their own professional
organizations, and at meetings they discussed the vast differences between their experience and the
experience of large city hotels (Hotel Monthly, April, 1940: 31-32). In a study of the influence of chain
relationships on the failure of Manhattan hotels, Ingram and Baum (1997b) found evidence that local
experience of related organizations was helpful while nonlocal experience was not, but that analysis did
not include measures of hotels' own experience, or the experience of unrelated organizations. It is critical
to examine the influence of related experience in the context of these two other sources of experience,
so with the scope condition that our empirical setting is one where local conditions are substantially
different from nonlocal conditions, we hypothesize:
HYPOTHESIS 4 (H4). Organizational failure rates decline as the local experience of related
organizations increases.
HYPOTHESIS 5 (H5). Organizational failure rates do not decline (i.e., - 0) as the non-local experience of
related organizations increases.
Organizational inertia may, however, moderate these effects of related organizations' experience.
Specifically, related organizations' greatest influence on a focal organization's routines may occur at the
time it becomes related (or at the time of founding if the organization is established by a related group),
with subsequent influence being inhibited by the difficulty of organizational change. To examine this
possibility, we separate initial and incremental effects of related organizations' experience in our
analysis.
We operationalize the experience of related organizations in the Manhattan hotel industry based on
chain relationships. We used the number of component hotels operated by the chain because the
information on the number of hotel rooms was unavailable for many component hotels outside
Manhattan. For the chains for which we knew the number of rooms, the correlation between the number
of rooms and the number of components was 0.91, so very little information is lost by basing the measure
on components rather than rooms.
The first two variables, operationalizing local experience for H4, are based on a hotel chain's operating
experience within Manhattan as a function of the number of component hotels it operated in Manhattan
during its history. The second two variables, operationalizing nonlocal experience for H5, are based on a
hotel chain's operating experience outside Manhattan as a function of the number of component hotels it
operated outside Manhattan during its history. As indicated above, for both Manhattan and
non-Manhattan experience, we created two variables to separate initial and incremental effects of chain
experience on component survival.
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We set the first variable, Initial Chain's Manhattan Experience, equal to hotel chain i's cumulative
(discounted) experience in Manhattan between the year component j joined chain i and the time hotel
chain i was founded (Equation 4). And, we set the second variable, Incremental Chain's Manhattan
Experience, equal to hotel chain i's cumulative (discounted) experience in Manhattan between the
current year and the time component j joined the hotel chain (Equation 5). More formally, where T;foUnd
is the first year of chain i's existence, T^sub jaffil-1^ is the year before component j joined chain i, T^sub
jaffil^ is the first year of component j's membership in chain i, T - 1 is the year before the current year,
N^sub CManhattan,t^ is the number of components operated in Manhattan by chain i in year t, and
Discount is one of the four depreciation weights described below. For hotels that were not affiliated with
chains, we set these variables equal to zero. In a parallel manner, we set Initial Chain's NonManhattan
Experience equal to the cumulative operating experience of hotel chain i at the time component j joined
chain i, and we set Incremental Chain's NonManhattan Experience equal to the cumulative experience of
hotel chain i since the time component j joined chain i by substituting N^sub CManhattan,t^, which is the
number of components operated outside Manhattan by chain i in year t, for N^sub CManhattan,t^ in
Equations 4 and 5.
Data Description
The data used in this study include life history information on 558 transient hotels that operated in
Manhattan between 1898 and 1980 (Baum 1995, Baum and Mezias 1992). Transient hotels are those
catering to short-term visitors. Four archival sources were used to construct these life histories: the Hotel
Redbook, published annually since 1887; the Manhattan Classified Directory / Yellow Pages, published
since 1929; the Annual Directory of the Hotel Association of New York City, published since 1940; and
the Hotel and Travel Index, published since 1951. Because detailed organizational data are missing for
many hotels prior to 1898, the observation period for this study begins in 1898 even though the archival
sources begin in 1887.
During the study period, 425 transient hotels were founded in Manhattan. 315 (74.1%) of the hotels
founded in Manhattan since 1898 had ceased operations by the end of 1980. Failure was defined as the
cessation of hotel services for short-term visitors. Changes in name or ownership were not included as
failures because, like other features of a hotel, the routines that reflect its learning most typically persist
after it is sold or changes name. In 1898,133 of the hotels in the sample were already in operation; thus,
the life histories for these hotels were left-censored (i.e., founded before the study period). With available
archival information, we were able to confirm founding dates for 112 (84.2%) leftcensored hotels.
Because their ages were not known, the 21 hotels with unknown founding dates could not be included in
the analysis, although information on these hotels was included in computations for the variables
described below. Therefore, the final sample for the analysis included 537 hotels, of which 350 (65.2%)
failed.
We identified chains, defined as any organization operating three or more hotels or motels (the industry
standard definition), operating in Manhattan using the Directory of Hotel and Motel Systems, which
provides a comprehensive listing of chains from 1931 forward, and, for the 1898-1930 period, the Hotel
Redbook. During the observation period, 103 different hotel chains operated one or more components in
Manhattan. The maximum number of components operated by a single chain was 25 by Knott Hotels.
The numbers of independent and component hotels in Manhattan in each year are shown in Figure 2.
Although the period of rapid growth in the late 1920s coincides roughly with the addition of the Yellow
Pages data source, this growth, which accompanied the rapid expansion of Manhattan during this time,
is reflected in the Redbook as well.
Discount Factors for Experience Variables
Past research has indicated that the benefit of experience to organizations may decay over time due to
forgetting and antiquation of learning (Argote, et al. 1990). To reflect the possibility of decay and
forgetting, we computed and analyzed four sets of organization-, population- and chain-experience
variables (Equations 1-5) based on different specifications of the discount factor.3 First, we set the
discount equal to 1, which assumes no depreciation in the value of past experience. Second, we set the
discount equal to the square root of the age of the experience, which assumes that depreciation of
experience is initially slower than linear, and slows further with time. Third, we set the discount equal to
the age of the experience, which assumes a linear depreciation in the value of prior experience.
Finally, we set the discount equal to the age of the experience squared, which assumes that the value of
past experience depreciates more rapidly than linear at first, and then accelerates further with time.
Figure 3, which gives values, using the four discount factors, for Population Experience computed for the
Manhattan hotel industry at the start of each year of the study period, shows the implications of these
different discount factor specifications graphically. For parsimony, in the analysis, we only model the
effects of organization-, population- and chain-experience variables based on the same discount factor.
To examine an alternative to our operationalization of operating experience as based on the number of
rooms offered by a hotel, we recalculated the organizational and population experience variables
weighting hotel size by occupancy rates to estimate the number of guests actually served. The
occupancy rates are at the level of the Manhattan hotel industry, so Organizational Experience did not
reflect cross-sectional differences in occupancy across hotels. The correlations between variables based
on the two operationalizations were very high, averaging 0.95. A re-estimation of equations from Table 1
using guest-based estimates of experience yielded results that were for all substantive purposes
identical. Thus, our results are robust to the change in the basic unit of operating experience from rooms
offered to guests served.
We also investigated an alternative measure of population experience that was designed to reflect the
possibility that the value of other organizations' experience may depend on the similarity of their sizes to
the size of the focal organization. Haveman (1993a: 598) argued that "organizations attend carefully to
the actions of organizations of similar size and are therefore more likely to imitate . . . their size peers."
Interorganizational learning may be most prevalent among size peers because size is often closely tied
to the nature of an organization's operations (e.g., a small automobile producer is probably a job-shop,
while a large automobile producer most likely engages in mass production). Supporting this idea in the
Manhattan hotel industry, Lant and Baum (1995) found strong influences of relative size on Manhattan
hotel managers' attention patterns. The alternative measure was based on the size-localized competition
weight described by Ranger-Moore, Breckenridge, and Jones (1995), which weights each organization
j's contribution to population mass (calculated for each focal organization, i) as a decreasing function of
the difference between j's size and i's size, relative to the maximum size difference between any two
organizations in the population. Again, the correlations between the size-localized and original variable
was very high, averaging .97. Moreover, preliminary analysis indicated that the size-localized measure of
population experience was not a significant improvement over the original measure, so we report results
of models that use the generalized measure of population experience defined in Equation 2.
Control Variables
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Figure 2
Hotel Characteristics. Hotel Age was defined as the number of years since the date of a hotel's founding.
Hotel Size and Hotel Price were measured, respectively, as the number of rooms operated by a hotel
and the average daily room rate (in constant dollars) advertised by a hotel in each year of its existence.
The number of rooms a hotel operates is the standard measure of size used in the hotel industry
(Wyckoff and Sasser, 1981), and also captures the "total productive capacity installed," which Winter
(1990, 288) suggests reflects the differential competitive strength of larger organizations. Natural
logarithms of size and price were used to reduce these variables' skewness. Founded by Chain is a
dichotomous variable coded one for hotels that were affiliated with a chain at the time they were founded.
Lastly, a Left-Censored variable, coded 1 for hotels founded before 1898 was included to examine
whether such hotels had systematically different failure rates.
Hotel Chain Characteristics. We also controlled for several characteristics of chains that may affect the
fates of their component hotels. First, we controlled for Hotel Chain Age, measured as the age in years of
the chain to which a component hotel was affiliated at the start of each year. Second, since larger chains
are likely to possess greater market power and scale advantages (Bain 1956), as well as more slack
resources (Haveman 1993b), we controlled for (1) the natural logarithm of the Number of Manhattan
Rooms a chain operated at the start of each year, (2) the Number of Manhattan Hotels a chain operated
at the start of each year, and (3) the size of the chains to which component hotels were affiliated, which
we measured as the total number of hotels the chain operated at the beginning of each year. We
subtracted the number of Manhattan hotels from the total to compute the Number of Non-Manhattan
Hotels a chain operates to ensure mutually exclusive variables and simplify interpretations. We also
included a dummy variable, Component Hotel, to capture any additional effects of chain affiliation not
captured by our variables. This variable was coded one while the hotel was affiliated with a chain and
zero otherwise.
Environmental Characteristics. We also controlled for several factors influencing the environmental
carrying capacity for transient-hotel services in Manhattan and the intensity of competition. The potential
demand for hotel services was measured as the number of Visitors to New York City (x10-6 for rescaling)
in the prior year. This variable includes arrivals by sea, rail, and air. Because the hotel industry is
vulnerable to the state of the American economy (Wyckoff and Sasser 1981), annual Gross National
Product (GNP) Growth was included as a control. These variables were constructed using the Historical
Statistics of the United States, Colonial Times to 1970 (U.S. Department of Commerce, Bureau of the
Census 1975), Historical Abstracts of the United States (U.S. Department of Commerce, Bureau of the
Census 1970-1980), and Port of New York and New Jersey Authority annual reports (1930-1980).
We also operationalized density-dependent (Hannan and Carroll 1992), mass-dependent (Barnett and
Amburgey 1990) and size-localized (Baum and Mezias 1992) competition. Manhattan Hotel Density was
measured as the total number of hotels in Manhattan at the start of each year. Preliminary failed to find
the common non-monotonic effect of density (for a detailed analysis of density dependence in this
population see Baum 1995). Manhattan Hotel Mass was measured as the total number of hotel rooms
(i.e., the total productive capacities of the independent and chain segments of the industry) in Manhattan
at the start of each year. Following Barnett and Amburgey (1990), each hotel's number of rooms was
subtracted from the total mass, so the mass variables reflected the number of rooms operated by other
hotels, and a natural logarithm transformation used. Size-Localized Competition was measured following
Baum and Mezias (1992) by calculating a Euclidean distance between the size of hotel i and the size of
all other hotels within p units of hotel i's size at time t. This variable increases with the average size
difference between the focal hotel and other hotels within mu units, while the distances to hotels not
within H units of the focal hotel have no effect. Thus, large values imply less potential for size-localized
competition. The value for ,u was set to hotel i's size divided by two (i.e., p = Si, / 2) based on earlier
analysis of these data (Baum and Mezias 1992). Lastly, we included a time-trend variable, Calendar
Time, to ensure that our findings were not simply the result of the passage of time. Due to length
restrictions, we do not present a correlation matrix. The correlations are generally moderate, indicating
that multicollinearity does not pose a serious estimation problem, but that it may result in less precise
parameter estimates (i.e. larger standard errors) making hypotheses tests less efficient (Kennedy 1992).
In response to this possibility, we followed a strategy of estimating hierarchically-nested models and
testing for the overall significance of sets of added experience variables (Kmenta 1971, 371). We also
estimated models on randomly selected subsets of the data. Our results were robust to these
procedures.
Analysis
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The failure rate of Manhattan hotels is estimated using r(t), the instantaneous rate of failure. The hazard
rate of a hotel failing is defined as:
Results
Table 1 reports maximum-likelihood estimates for the analysis of Manhattan hotel failure rates. After
estimating a baseline model, we estimated four models, each identical except for the discount factor
used for experience. In preliminary analysis (not reported) we estimated nested models, adding the
organizational, population, and related experience variables sequentially, and each type of experience
variable significantly improved the models for the discounted specifications. Overall, the model with
experience discounted by the square-root of its age explains the data slightly better than the model with
experience discounted by its age as indicated by the log-likelihoods of models 2 and 3 (-1636.32 versus
-1637.72). Model 4, with experience discounted by the square of its age fits less well, but is an overall
improvement over the baseline. Model 1 with no discounting is clearly the worst performing.
The estimates support HI in two of three discounted specifications of the organizational experience
variable. For the square root discount specification in model 2, the minimum of the effect on the failure
rate occurs when Organizational Experience x 10-4 equals 8. At this level of organizational experience,
the multiplier of the failure rate is about 0.59, indicating a 41% lower failure rate compared to a new hotel
with no organizational experience. In the square root discounting, 8% of Manhattan hotels obtain
organizational experience of 8 or more, and the contemporaneous size of those hotels ranged from 400
to 2,500 rooms. The mean size of all hotels was 369 rooms. So, although only a small percentage of
Manhattan hotels ever accumulate enough organizational experience that they suffer an increased
failure rate, some of the hotels that did so were not atypically large. The quickest route to a competency
trap may be to accumulate high levels of experience each year, but moderate levels of annual
experience over a longer period can cause similar problems.
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Table 1
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H2, which predicted survival-enhancing learning from Population Experience is not supported. In fact,
although they are not significant, coefficients for all specifications of the variable have a positive sign.
However, H3, which predicted lower failure rates as a function of increasing Congenital Population
Experience at the time of organizational founding, is supported by estimates from the three discounted
specifications. Thus, Manhattan hotels do not benefit from population experience accumulated during
their lifetimes, but they do benefit from population experience at the time they are founded. Based on the
coefficient for the square root discount specification of the variable in model 2, from the turn of the
century, the starting failure rate declined nearly 75% by 1930 and more than 90% by 1960. Since the
mid-1960s, however, the trend has reversed as a result of a decline in the rate at which the population
gained experience (Figure 3).
Turning to the experience of related organizations, the significant negative coefficients for Initial Chain's
Manhattan Experience in all discounted specifications, and the significant negative coefficients for
Incremental Chain's Manhattan Experience in all specifications give strong support to H4. Formal
relationships facilitate interorganizational learning when the related organizations are in the same
environment. Further, consistent with our expectation that chain influence is greatest at the time of
affiliation, in every discounted specification, comparison-of-the-means tests indicate that the coefficient
for Initial Chain's Manhattan Experience is significantly larger than the coefficient of Incremental Chain's
Manhattan Experience (test statistics are given in the table). For nonlocal experience, Initial Chain's
NonManhattan Experience is negative and significant in two of three discounted specifications.
Incremental Chain's Non-Manhattan Experience is positive and significant in the square root of
experience age discount specification. Thus, H5, that nonlocal experience will not lead to a decline (i.e.,
=>0) in the mortality rate receives mixed support, indicating that close attention is required regarding how
nonlocal experience affects failure. The finding that initial non-local experience lowers mortality, while
incremental nonlocal experience raises mortality could be explained as a selection effect (Ingram and
Baum 1997b). Perhaps chains choose to enter the Manhattan market based on the similarity of
Manhattan to other markets they operate in. After a component joins the chain may drift in another
direction, however.
Of the control variables, most relevant to the analysis of survival-enhancing learning is the result for age.
Age dependence in a piecewise exponential model is indicated by statistical differences in the
coefficients representing the failure rates of adjacent age ranges. None of the age coefficients in the
baseline or any of the models with experience variables is significantly different from zero. This indicates
that there is no age dependence in hotel failure, regardless of the inclusion of Organizational Experience.
At least for Manhattan hotels, age and organizational experience operate separately.
Discussion and Conclusion
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Figure 3
Taken together, the results illustrate that the occurrence of survival-enhancing learning should not be
taken for granted. Much like cohort C2 in Figure 1, successive generations of new hotels exploited their
population's experience at the time they were founded, but then remained largely unable to take
advantage of subsequent advances in population experience. Organizations' own experience also
affects survival in a complex manner, with high levels of own experience actually leading to higher failure
rates. A positive result for the potential of survival-enhancing learning concerns related hotels in the
same environment, which appear to continue to gain from each other's experience after their initial
affiliation.
Our results confirm and extend previous findings in the empirical literature on organizational and
interorganizational learning. We found that the stock of population experience at the time of founding
played a much larger role in lowering failure rates of Manhattan hotels than gains in population
experience accumulated since their founding. This is consistent with Argote et al.'s (1990) study of
learning by shipyards, which showed that interorganizational learning played a much larger role in lower
production costs at the time that shipyards initiated production than after production was ongoing, and
with Ingram and Baum's (1997a) study of U.S. hotel chains, which showed that industry operating
experience at the time of founding lowered hotel chains' failure rate far more than industry operating
experience accumulated after chains were founded.
Further, consistent with Darr et al.'s (1995) findings that organizational experience and the experience of
related organizations, but not industry experience, lowers the cost of pizza production, we find that
Manhattan hotels' own experience and the experience of related hotels improves hotels' survival
chances. Notably, both these results are at odds with Ingram and Baum's (1997a) study showing
significant influences of organizational and industry experience on U.S. hotel chains' failure. This may be
explained by Darr et al.'s incomplete history of industry experience, on the one hand, and the varying
inertial properties of hotels and hotel chains on the other. In industries like the hotel industry, where the
organization is a physical asset that cannot be easily modified or relocated, organizational inertia may be
atypically high. Organizations with less inherent inertia, such as hotel chains, which can adapt by adding
and dropping components, might be able to learn vicariously from industry experience after their
founding. Our evidence of the importance of interorganizational relations for interorganizational learning
reinforces arguments that learning is a primary motivation for engaging in joint-ventures (Hamel 1991,
Inkpen and Crossan 1995, Kogut 1988, Powell and Brantley 1992, Khanna et al. 1994).
Our results are also consistent with widely held wisdom regarding the conditions in which experience
leads to improved performance. The importance of access to the right type of experience is evident in the
finding of survival-enhancing learning from the local experience of related others. The importance of a
capacity to change routines as implied by experience is shown by the survival-enhancing learning from
congenital experience in comparison to the lack of survival-enhancing learning from population
experience after founding. The importance of carrying out activities in a context where the lessons of the
past are relevant is shown by the eventual effect of own experience to increase failure.
Turning from learning to organizational ecology, it is notable that while our results can be viewed as
consistent with that theory's bedrock principle of inertiaManhattan hotels do not appear to be able to
conceive and implement correctly changes that exploit successful explorations of other Manhattan hotels
reliably in the face of competition-they also provide some challenges to the theory. Partially, the
motivation for our study came from the observation that research in organizational ecology typically
neglects the fact that organizational forms are not homogenous across time. Here, we find evidence that
successive cohorts of new organizations are improved as a function of the experience of the population.
This evidence that foundings are historydependent points to the neglected topics of organizational
gestation, and the origin of organizational variation (Stinchcombe 1965). That new organizations benefit
from the experience of their population suggests that entrepreneurs are not strangers to the populations
they enter; they recognize the unexploited potential of the organizations they challenge. The analysis of
organizational founding, which has up to now primarily addressed the question of "when" might
reasonably turn to the questions of "what," "who," and "why?" (Baum and Haveman 1997).
Situated at the intersection of learning and ecological theories of organization, the population-level
learning framework (Miner and Haunschild 1995) seems well positioned to tackle these and related
questions. In population-level learning, variation and selective retention of bundles of organizational
routines as well as organizations are important forces for population-level change. By emphasizing both
organizations' learning of routines and organizational selection (i.e., births and deaths of organizations)
as mechanisms for population level change, population-level learning admits both adaptation and
selection and is consistent with evolutionary perspectives on organizations that emphasize the selective
survival of units at various levels (e.g., Baum and Singh 1994). Thus population-level learning
complements and extends traditional ecological analyses of organizational founding and failure, which
emphasize that organizational change and variability reflect primarily inert organizations replacing each
other but do not typically explore the implications of entrepreneurs and ongoing organizations engaging
in vicarious selective learning of new routines.
Other avenues for research suggested by our analysis concern refinements of the measures of
experience. The local, non-local distinction is particularly promising. Here we made a coarse sorting of
hotels into local or non-local based on whether or not they were in Manhattan. More generally, "local"
could be considered in multidimensional space, and we would predict that organizations will be more
likely to observe and benefit from each other's experience the closer the organizations are in
multidimensional space. This could apply for both related and unrelated organizations. Our preliminary
analysis did not indicate that survival enhancing learning of Manhattan hotels was size-localized.
Nevertheless, size-localized learning might take place in other populations, and other product
dimensions such as price and quality might generate localized learning in this and other populations.
Moreover, studies of interorganizational imitation suggest that large and successful organizations may
serve as especially important role models. Organizations may also contribute differentially to population
experience depending on their developmental stage. For example, as a result of exploitive learning,
organizations may contribute more to population experience when they are young. So, we see a range of
possible extensions and refinements that may add value to the basic approach to modeling experience
we advanced here. Between-organization differences in interorganizational learning also deserve
examination. It is likely that organizations within a population vary in their capacities for
interorganizational learning (Cohen and Levinthal 1990). Consequently, the fact that, on average,
population experience since founding does not improve Manhattan hotels' survival chances does not rule
out the possibility that some hotels with superior capacities for interorganizational learning improved their
survival chances, while the fates of other more or less inferior learners were either harmed or unaffected
by their efforts to imitate or acquire new routines. Of course, the possibility that some organizations
benefit from interorganizational learning need not imply either rational adaptation or an absence of
structural inertia. The same result can be achieved on a probabilistic basis through random change
attempts (Hannan and Freeman 1984, March and Olsen 1976). Regardless, we think future efforts to
incorporate heterogeneity in interorganizational learning into the approach we advanced here would
extend it in an important way. These modeling opportunities are best explored in populations with more
measurable organizational variation than Manhattan hotels. For example, pharmaceutical companies
have substantial spatial variation in experience (research experience in different diagnostic classes) and
measurable differences that may represent absorptive capacity (e.g.,research budgets, research staffs).
The rate of environmental change experienced by a population is also relevant, particularly as a direct
contributor to competency traps, and the decay of experience.
Mechanisms for learning from population experience may also differ. Although we expected that there
would be ample channels for Manhattan hotels to access the experience of their population, we were
surprised by the frequency of public descriptions of their organizations' experience by Manhattan
hoteliers. A reasonable prediction would be that managers will try and hide their innovations from
competitors, but the history of the Manhattan hotel industry suggests that when hoteliers had a good
idea, they couldn't resist telling their competitors. The willingness of organizations to share their
experience may be a function of industry norms and the nature of competition. When competition is
localized and orchestrated, as it appears to be in the Manhattan hotel industry (Haveman and Baum
1997, Baum and Lant 1993, Baum and Mezias 1992, Lant and Baum 1995), organizations may be more
willing to share their experience because of a lower likelihood it will return, through competition, to haunt
them.
As an early investigation into survival-enhancing learning, there is much to take from this study. We
found effects of three types of experience on the failure rates of Manhattan hotels: 1) own experience
initially reduced the failure rate, but then increased it; 2) at the population level, organizations enjoy a
lower failure rate as a function of population experience at the time of, but not after, their founding; 3) the
experience of related organizations lowers the failure rate, but local experience helps more than nonlocal
experience, and experience before the relationship is formed is more useful than experience after.
Although experience can sometimes enhance survival, there are many barriers to this. Consistent with
past arguments in organizational learning, the source and applicability of the experience appears to
matter. Consistent with the assumptions of organizational ecology, constraints on organizations' capacity
for adaptation appears to restrict their ability to use their own and others' experience to reduce their risk
of failure. Most important, however, this study extends these theories. For organizational learning, we
find support for some classic ideas for which systematic support is scarce. Further, we do this while
studying a dependent variable that is new to empirical learning studies, but relevant to learning theories
and to organizations. For organizational ecology, we offer evidence that organizational failure depends
on the experiential history of both the organization and its population, and on the pattern of formal
relationships within the population. There are many opportunities to build on the identification here of the
interrelationship between the development of populations and organizational learning.4
[Sidebar]
Accepted by Gabriel Bitran; received October, 1997. This paper has been with the authors 4 months for 2
revisions.
[Footnote]
1 Most empirical learning studies examine operating experience, but competitive experience (Barnett et al.
1994, Miller and Chen 1994, Ingram and Baum 1997a), collaborative experience (Simonin and Helleloid 1993 )
and foreign entry experience (Li 1995, Barkema et al. 1996) have also been examined. While the implications
of different types of experience for organizational learning are different, we think the tendency to exploit one's
own experience and vicarious and congenital learning based on others' experience apply to other types of
experience besides operating experience.
[Footnote]
2 In addition to mimetic and contact learning, Miner and Haunschild (1995) identify broadcast transmission, in
which a single source (e.g., organization or government agency) is responsible for diffusing a new routine,
practice, or structure across a population of organizations. Although beyond the scope of this study, in which
we focus on interorganizational learning within a population, it is important to note that the New York City Hotel
Association (NYCHA), professional accounting firms, and university hotel schools engaged in broadcast
transmission that influenced the Manhattan hotel industry (Ingram 1996 ).
[Footnote]
3 The values we compute for Congenital Population Experience are incomplete because our detailed data on
the history of the Manhattan hotel industry goes back only to 1898. The variable is therefore understated for
the earliest observations, but quickly approaches its true value.
[Footnote]
4 For comments on an earlier version of this paper, we are grateful to Linda Argote, Martin Evans, Christine
Oliver and Jitendra Singh, to seminar participants at
Cornell University and University of Illinois,
Urbana-Champaign, and to Management Science's Associate Editor and anonymous reviewers. For their data
collection and coding efforts, thanks also go to Gretchen Dematera, Corrine Imbert, Bridget Ingram, Colin
Ingram, Bill Krause, Alan Mibab, Kiril Okun, and Sheila Peterson.
[Reference]
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[Author Affiliation]
Joel A. C. Baum Paul Ingram
University of Toronto, Joseph L. Rotman School of Management, 105 St. George Street, Toronto, Ontario,
Canada, M5S 3E6
Graduate School of Industrial Administration,
15213-3890
Carnegie Mellon University, Pittsburgh, Pennsylvania