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Survival of the Fittest? Entrepreneurial Human Capital and the Persistence of Underperforming Firms Author(s)

December 1997
Administrative Science Quarterly 42(4)

December 1997
42(4)

DOI:10.2307/2393656

Authors:

Javier Gimeno

INSEAD

Timothy Folta

University of Connecticut

Carolyn Y. Woo

The model developed here explains why some firms survive while other firms with equal economic performance do not. We argue that organizational survival is not strictly a function of economic performance but also depends on a firm's own threshold of performance. We apply this threshold model to the study of new venture survival, in which the threshold is determined by the entrepreneur's human capital characteristics, such as alternative employment opportunities, psychic income from entrepreneurship, and cost of switching to other occupations. Using a sample of 1,547 entrepreneurs of new businesses in the U.S., we find strong support for the model. The findings suggest that firms with low thresholds may choose to continue or survive despite comparatively low performance.

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Parameter Estimates of Economic Performance, Threshold of Performance, and Exit

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http://www.jstor.org

Survival of the Fittest? Entrepreneurial Human Capital and the Persistence of

Underperforming Firms

Author(s): Javier Gimeno, Timothy B. Folta, Arnold C. Cooper, Carolyn Y. Woo

Source:

Administrative Science Quarterly,

Vol. 42, No. 4, (Dec., 1997), pp. 750-783

Published by: Johnson Graduate School of Management, Cornell University

Stable URL: http://www.jstor.org/stable/2393656

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Survival

of the Fittest?

Entrepreneurial

Human

Capital

and the

Persistence of

Underperforming

Firms

Javier Gimeno

Texas

A&M

University

Timothy B. Folta

University

of Kentucky

Arnold C. Cooper

Purdue

University

Carolyn Y. Woo

Purdue

University

? 1997 by Cornell University.

0001 -8392/97/4204-0750/$1 .00.

We thank Terry Amburgey, Joel Baum,

Parthiban David, William J. Dennis, John

Garen, Michael Hitt, Robert Hoskisson,

Nancy Johnson, David Loree, Christine

Oliver, Jing Zhou, and three ASQ anony-

mous referees for their valuable com-

ments on earlier drafts, and Tim Bates,

Josef Bruderl, Linda Leighton, and Alfred

Nucci for sharing their knowledge of

other data sources for entrepreneurship

research. We also thank Javier Fernandez

Navas and Diane Roden for their research

help. We acknowledge the help and sup-

port of the National Federation of Inde-

pendent Business. An earlier version of

this paper received the 1992 Best Empiri-

cal Paper Award from the Entrepreneur-

ship Division of the Academy of Manage-

ment.

The model developed here explains why some firms sur-

vive while other firms with equal economic performance

do not. We argue that organizational survival is not

strictly a function of economic performance but also de-

pends on a firm's own threshold of performance. We ap-

ply this threshold model to the study of new venture sur-

vival, in which the threshold is determined by the

entrepreneur's human capital characteristics, such as al-

ternative employment opportunities, psychic income

from entrepreneurship, and cost of switching to other

occupations. Using a sample of 1,547 entrepreneurs of

new businesses in the U.S., we find strong support for

the model. The findings suggest that firms with low

thresholds may choose to continue or survive despite

comparatively low performance.'

It has been frequently

argued

that, at least in the long run,

well-performing

organizations

survive

while poorly

perform-

ing ones disappear

(Alchian,

1950; Friedman,

1953; Winter,

1964; Williamson,

1991). Penrose (1952: 810) summarized

this theoretical

view as stating that "positive profits

can be

treated as the criterion

of natural

selection-the firms that

make profits

are selected or 'adopted'

by the environment,

others are rejected and disappear."

This view implies a unidi-

mensional relationship

between economic performance

(de-

fined as the economic returns

to residual

claimants)

and sur-

vival, since the firms most likely

to discontinue

are those

that perform

the worst. From

this unidimensional

model, it

follows that economic performance

and survival

should have

the same determinants

or predictors.

Interestingly,

mounting

empirical

evidence suggests that the determinants

of perfor-

mance and survival may substantially

differ

(Blau,

1984; Car-

roll

and Huo, 1986; Meyer and Zucker,

1989; Kalleberg

and

Leicht,

1991; Levinthal,

1991) and that factors other than

performance

may play a systematic role in the survival

organizations.

This paper proposes a theoretical

reconciliation

of apparently

conflicting

empirical

findings

about the determi-

nants of performance

and survival.

Our

framework

explains

the persistence of underperforming

firms and identifies pre-

dictors of such conditions.

We depart

from the unidimensional

model of performance

and survival

by arguing

that organizational

survival

is deter-

mined by two main dimensions: (1) the organization's

eco-

nomic performance

and (2) the organization's

threshold

performance.

The threshold

of performance

is the level of

performance

below which the dominant organizational

con-

stituents will act to dissolve the organization.

This implies

that survival

is not strictly

a function

of economic perfor-

mance, but performance

relative to a firm-specific

threshold.

This simple elaboration,

we believe, has profound

conse-

quences for theoretical

and empirical

research on organiza-

tional performance

and survival.

For

example, by identifying

how thresholds differ

systematically

across firms, we can

explain

why, given the same level of performance,

some

firms exit (discontinue

operations)

while others do not. We

emphasize the internal attributes

of the organization

and, in

particular,

the human

capital

attributes

of owners of new

ventures, as determinants

of thresholds. By considering

or-

ganizational

exit as a choice, our focus on organizational

mor-

750/Administrative

Science Quarterly,

42 (1997): 750-783

Survival of the Fittest?

tality

complements existing literature

in population

ecology,

where exit is seen as being forced by environmental

condi-

tions hostile to the firm.

EXIT,

PERFORMANCE,

AND THE

THRESHOLD

PERFORMANCE

Our main

thesis is that organizations differ in their

thresholds

of performance,

and exit or survival is determined

whether economic performance

falls below or stays above

that firm-specific

threshold.

While thresholds may be shaped

by the multiple

voluntary

participants

in the organization

(Bar-

nard,

1938; Simon, 1945; Aoki, 1984), the inducements and

contributions of most participants are regulated

through a

nexus of contracts with owners. Accordingly,

it is the own-

ers' interests, as residual

claimants,

that are most closely

tied to the economic performance of the organization

(Al-

chian and Demsetz, 1972; Meyer and Zucker,

1989). The

willingness or ability

to withstand poor performance

is partly

determined by the mobility

of the assets and resources con-

trolled by the organization's

owners. When owners with a

residual

claim over these resources have alternative

uses for

these resources, they can liquidate

the firm for a reasonable

value. Consequently,

they may prefer

to dissolve the firm

when those alternatives

become more appealing

(Barnard,

1938; Caves and Porter,

1976; Porter,

1976). Exit,

however,

would not be rational at the first sign of low performance

(Bruderl

and Schussler, 1990). If

there is uncertainty

about

future payoffs, owners may be willing

to accept low levels

of performance with the hope that conditions

will improve

(Dixit and Pindyck, 1994). A firm's ability to withstand short

periods of low performance

should also be partly

determined

by buffers of accumulated

resources, such as organizational

slack (Cyert

and March,

1963) or initial

capital

endowments

and established relationships

(Bruderl

and Schussler, 1990;

Fichman and Levinthal,

1991; Levinthal,

1991). Organizations

would be able to survive

at least until their original

resources

were depleted.

Thresholds

of performance

may also be influenced when

owners have objectives other than, or in addition

to, the

maximization of economic returns to their equity. Owners

may seek "amenity potential"

from their businesses-gain-

ing utility

from being able to influence

the type of goods pro-

duced by the firm (Demsetz and Lehn, 1985: 203). For

own-

ers of professional

sports teams or media companies

(newspapers, TV) , winning

the World

Series or believing

that one is systematically

influencing public

opinion

plausibly

provides utility

even if profit

is reduced from levels other-

wise achievable. For owners of family-owned

businesses,

the firm may not only be a source of income but also a con-

text for family

activity

and embodiment of its pride

and iden-

tity (Meyer

and Zucker,

1989: 78).

Low organizational

performance also puts the interests of

owners and other organizational

constituencies (non-owners)

in direct conflict (Meyer

and Zucker,

1989). While owners

may want to terminate the business to redeploy

assets in a

more profitable

arena, non-owner

participants

(managers,

employees) who have developed firm-specific

skills may

stand to lose if the firm closes. With significant

costs of exit,

751/ASQ, December 1997

non-owners may thus exercise their voice (Hirschman,

1970)

through efforts to influence the decision-making structures

of the organization.

Thus, the threshold level of performance

would also be determined by the relative organizational

influ-

ence of non-owner

members. Firms in which non-owner

members exercise substantial organizational

influence may

remain in business at low levels of economic performance

despite a preference by owners to terminate the business

(Meyer and Zucker,

1989). In addition

to internal constituents

such as employees, external constituents (debt-holders,

cus-

tomers, suppliers,

government and community organizations)

may persuade low-performing but legitimate organizations

survive (as in the bailout of Lockheed

and Chrysler

by the

U.S. government)

or well-performing

but illegitimate

ones to

dissolve (such as cartels, trusts, or local businesses posing

environmental or social threats) by applying direct co-opta-

tion (Pfeffer

and Salancik, 1978) or institutional

(coercive and

normative) pressures (Meyer and Rowan, 1977; DiMaggio

and Powell, 1983). Thus, institutional embeddedness (Baum

and Oliver,

1991), legitimacy,

and co-optation by external or-

ganizations

may also keep organizations

alive despite low

performance.

Contrast with Existing Views on Organizational

Performance and Mortality

Our model of thresholds of performance

provides

a causal

link between the concepts of performance

and organizational

survival without assuming that they are unidimensional

con-

structs. Clearly,

higher

economic performance

increases the

likelihood of survival, everything

else remaining equal. Our

point is that other things are not equal, since differences in

firms' thresholds of performance

should also influence mor-

tality. Organizational

survival

is therefore influenced

by both

the determinants

of performance

and thresholds. Certain

variables

will be purely

related to economic performance,

while others may influence survival

only through

the firm's

threshold. For variables that simultaneously

influence perfor-

mance and threshold,

their survival

effect is determined by

their combined effects on both.

Our

paper

complements theories of decision making,

both at

the individual

(Kahneman

and Tversky, 1979) and the organi-

zational level (Cyert

and March,

1963; March, 1988; March

and Shapira,

1992), which posit that decision-making

choices

are determined by comparing possible outcomes relative to

some reference or aspiration

level. Viewing organizational

discontinuance as an individual

(in

the case of small ventures

run by an entrepreneur)

or organizational

choice, one may

equate reference or aspiration

levels to our threshold

con-

struct. From

that perspective, this is the first paper

we know

of that links

organizational

survival and exit to some refer-

ence level.

Our theoretical

perspective is also complementary

to a large

body of management research, mainly

in population

ecology,

that has examined the same issue we address, firm mortal-

ity (see reviews by Baum, 1996; Amburgey

and Rao, 1996).

Population

ecology studies have not been explicit,

however,

in recognizing whether firm mortality

is mediated by low per-

752/ASQ, December 1997

There are some important exceptions to

this generalization. Researchers studying

the liability of adolescence and honey-

moon effects (Bruderl

and SchOssler,

1990; Fichman and Levinthal, 1991;

Levinthal, 1991) have explicitly recog-

nized that the initial stock of assets

serves as a buffer between low perfor-

mance and mortality. Institutional and in-

terorganizational linkages can also serve

as a buffer and reduce mortality for high-

risk organizations (Baum and Oliver,

1991; Miner, Amburgey, and Stearns,

1990).

Survival of the Fittest?

formance or other mechanisms.' The popular

use of the

term "organizational

failure"

(Baum, 1996) seems to imply

that discontinuance

is primarily

attributable to low perfor-

mance, even though prominent

scholars in population

ecol-

ogy (Hannan

and Freeman,

1977: 940) and institutional

theory (Meyer and Rowan, 1977: 353) have explicitly re-

jected a unidimensional

interpretation of environmental

se-

lection based solely on organizational

efficiency. Meyer and

Zucker

(1989: 55) suggested that these literatures

view orga-

nizational

performance

and discontinuance as multiple

indica-

tors of an organization's

isomorphism

with its environment.

Such an approach,

unfortunately, avoids the causal link

be-

tween these constructs and has hindered

progress, since

"researchers'

understanding

of dissolution,

be it through

merger, absorption, or outright

failure,

is limited

by the

dearth

of studies that treat financial

performance

as a predic-

tor of mortality"

(Amburgey and Rao, 1996: 1274). Thus, our

theoretical

perspective complements and extends current

organizational

theories in that it explicitly

recognizes the cau-

sal effect of firm

performance

on selection processes while

also considering the selection effects of thresholds.

Exit, Performance, and Thresholds in New Ventures

While

the concept of threshold has broad

applicability,

a nar-

rower context of study can facilitate

theoretical

develop-

ment, empirical

specification,

and testing the determinants

of threshold in that specific context. This paper

investigates

performance,

thresholds, and exit in the specific context of

small entrepreneurial new ventures. Entrepreneurial

exit de-

cisions occur frequently,

with more than 800,000 businesses

discontinued in the United

States in 1992 alone (U.S. Small

Business Administration,

1994: 265). Understanding

the pro-

cesses that influence new venture survival has tremendous

implications

for the welfare of customers, suppliers,

employ-

ees, and especially for entrepreneurs.

Applying

the concept

of the threshold of performance

enables us to broaden the

current

understanding

of entrepreneurial

exit by considering

both economic performance

and nonperformance

reasons

for exit. Ronstadt

(1986) discovered that only 31 percent of

entrepreneurs

who exited did so solely because of financial

difficulties,

while 26 percent indicated that financial

reasons

played no part

in their exit decisions. Mayer

and Goldstein

(1961) found that 20 percent of all new business closures

were attributed

to nonfinancial

reasons, such as external

job

opportunities,

disappointment

with business ownership, or

unwillingness

to put up with "limited

success." At least in

some cases, dissolution is not forced upon the entrepreneur

but involves a proactive

decision to exit.

Recent studies have also found empirical

evidence suggest-

ing that factors influencing

the survival of new ventures may

be significantly

different

from those influencing

performance

(Carroll

and Huo, 1986; Kalleberg

and Leicht,

1991; Cooper,

Gimeno, and Woo, 1994). Up to now, there have been few

attempts to reconcile

theoretically

the lack of convergence

on the determinants of performance

and survival. We be-

lieve that considering

economic performance and thresholds

jointly

will shed light

on this important

topic.

The context of entrepreneurship

requires

a special consider-

ation of the determinants

of performance

and thresholds. In

753/ASQ, December 1997

small entrepreneurial firms, the entrepreneur

is likely to ex-

ert control over organizational decisions, and non-owners

therefore are less influential than in larger

or older firms,

where there is a separation of ownership and control (Meyer

and Zucker,

1989). Moreover, the organizational

contributions

of the owner to the venture are not limited

to founding capi-

tal but also include managerial and technical work and skills.

In that sense, when determining whether to continue sup-

port

for the venture, the entrepreneur will evaluate the joint

returns to both the financial and human resources contrib-

uted to the venture. Since the entrepreneur's

skills and ob-

jectives play

a dominant role in dictating

the direction of

newly founded businesses (Bruderl,

Preisenddrfer, and Zie-

gler, 1992), we focus here on how the entrepreneur's traits

and characteristics,

or human

capital,

influence the perfor-

mance threshold.

A Threshold Model of Entrepreneurial

Exit: Human

Capital Considerations

Human

capital

theory (Becker, 1975) uses economic logic to

study individual

decisions dealing

with investments in pro-

ductivity-enhancing

skills and knowledge (schooling, training,

firm-specific

knowledge investment), career choices (deci-

sion to work, switching employment, labor

mobility),

and

other work characteristics

(wages, reservation

wages, hours

of work). It is believed that individuals choose an occupation

or employment that maximizes the present value of eco-

nomic and psychic benefits over their lifetimes. Human

capi-

tal theorists have likened

the entrepreneurial

exit decision to

the more general case of an individual's decision to leave

current

employment (Evans

and Leighton,

1989; Evans and

Jovanovic, 1989; Campbell, 1995; Bates, 1995). Entrepre-

neurs can be viewed as choosing between remaining

in the

current venture or obtaining

alternative

employment. While

several studies have examined entrepreneurial

exit with hu-

man capital

theory (Bates, 1985, 1990; Preisendbrfer

and

Voss, 1990; BrOderl,

Preisend6rfer,

and Ziegler, 1992), by

assuming that the factors related to poor performance

will

be the same as those influencing exit, they have ignored

the

potential

for returns to the entrepreneur's

human

capital

alternative

settings. Others have theoretically

acknowledged

the importance

of alternative uses of human

capital

in entre-

preneurial

decision making (Evans

and Jovanovic, 1989;

Evans and Leighton, 1989; Bates, 1995; Campbell, 1995) but

have focused on how human

capital

influences entry, not

exit.

Expectations

for ventures are generally buoyant

in the early

start-up stage, being generally

formed under substantial un-

certainty

about market

acceptance, competitive responses,

or even the entrepreneur's

actual entrepreneurial

abilities

and satisfaction to be obtained

from the venture. As informa-

tion becomes available,

the entrepreneur

is likely

to examine

the efficacy of these expectations and reconsider

other op-

tions (Jovanovic,

1982). We would expect the entrepreneur

to terminate the business if the expected utility

of alternative

employment (UA) minus the cost inherent in switching (SC)

exceeds the revised expected utility

of remaining

in the en-

trepreneurial

venture (UE):

754/ASQ, December 1997

Survival of the Fittest?

discontinue

venture if: UE

< (UA

- SC). (1)

UE and UA differ because of unequal economic performance

and personal enjoyment, or psychic income (Becker, 1975;

Evans and Leighton, 1989), with the two options. The eco-

nomic performance depends in part on the entrepreneur's

previous investments in education and training, which may

provide general skills or skills specific to a particular

job con-

text (Becker, 1975). The entrepreneur's economic perfor-

mance (EPE)

is a function of his or her stock of general hu-

man capital, represented by the vector x1, and of the human

capital specific to the current business, represented by the

vector x2. Meanwhile, since specific skills cannot be trans-

ferred to alternative employment, the economic returns avail-

able in alternative employment opportunities (EPA)

are a

function of the stock of general human capital (x1) and of

human capital specific to the alternative occupation (X3),

but

not of the human capital specific to the current business

(x2). The individual's psychic income associated with either

the entrepreneurial venture (PIE) or alternative employment

(PIA) is influenced by a number of factors (respectively, X4

and X5), including the individual's preference for the occupa-

tion, or personal satisfaction (Evans and Leighton, 1989).

Thus, the utilities of entrepreneurship and alternative em-

ployment can be expressed as:

UE = EPE

(X1, X2)

+ PlE (X4). (2a)

UA = EPA (X1,

X3)

+ PIA

(X5). (2b)

The cost inherent in switching (SC) includes those costs,

usually transitory, that are a function of the expected eco-

nomic cost of searching for a new alternative and the psy-

chological cost of experiencing the uncertainty of job loss.

Factors influencing the cost of switching are captured by the

vector x6. These costs should not be confounded with the

potential loss of utility experienced by switching because of

lower personal enjoyment in the new alternative or an inabil-

ity to redeploy skills specific to the venture, which are al-

ready captured in the relative magnitudes of psychic income

and specific human capital. Substituting equations (2a) and

(2b) into equation (1) and isolating EPE

on the left-hand side,

leads to:

discontinue venture if:

EPE (X1, X2)

< EPA (X1,

X3)

+ P'A (X5)

- PIE

(X4)

- SC (X6). (3)

The right-hand side of equation (3) is the threshold of eco-

nomic performance required to sustain the entrepreneur's

involvement in the current venture (TE). Thus, the threshold

(TE) is determined by the expected economic returns avail-

able in other employment alternatives (EPA),

the difference

in psychic income between alternative employment and self-

employment (PIA

- PIE),

and the cost of switching to an al-

ternative occupation (SC). The model predicts that the entre-

preneur will discontinue or stay in business according to the

following rule:

{

discontinue venture if EPE

(X1, X2)

< TE (X1, X3,

X4, X5, X6)

stay in venture

(survive)

if EPE

(X1, X2) 2 TE (X1, X3,

X4, X5, X6).

(4)

A critical insight

that follows from the above model is that

there may be situations in which entrepreneurs

do not con-

755/ASQ, December 1997

tinue their business even though, in terms of economic per-

formance, they are better off than other entrepreneurs. They

may take this action because of the opportunity costs asso-

ciated with staying in business-their level of education and

training may warrant more attractive economic returns

in al-

ternative employment opportunities. Similarly,

a poorly per-

forming venture may continue because of the entrepreneur's

lack of other attractive options, strong psychic attachment to

the venture, or high costs associated with switching into

new employment. As these cases illustrate, economic per-

formance of the venture need not exclusively determine sur-

vival. Rather,

it is economic performance relative

to the

threshold

that drives the exit decision.

Hypotheses

From the threshold model illustrated in figure 1, we develop

hypotheses that predict

how four important

dimensions of

the human

capital

of an entrepreneur-general human

capital

(x1), human capital specific to the current

venture (x2),

psy-

chic income from entrepreneurship (X4), and switching costs

(x6)-influence entrepreneurial

exit by their separate effects

on economic performance

and threshold

of performance.

We do not develop hypotheses about the effects of human

capital specific to alternative occupations (X3) or psychic in-

come from alternative

occupations (X5), since we cannot de-

termine a priori

what those alternatives

are. Alternative em-

ployment opportunities are nearly infinite and may include

wage-earning positions, other self-employment activities, or

leisure activity.

To the extent that these alternative employ-

ment opportunities cannot be fully specified ex ante, we as-

sume a generic alternative, letting

the effect of (X3) and (X5)

be included

in the error

term of the threshold

equation.

General human capital. The simple model presented in

equation (4) generates several theoretical

insights and em-

pirical predictions. General human capital (x1), as measured

by such constructs as formal

education

and the prior

work

experience of the entrepreneur, may lead to skills

that are

useful across a wide range of occupational

alternatives

(Becker, 1975). Work

experience is commonly measured as

the number

of years of experience but may also be signaled

by achievement levels in employment, such as management

or supervisory experience (Bates, 1990). While increasing

levels of education and experience are likely

to elevate eco-

nomic performance (EPE), they will also broaden the opportu-

nity set of the entrepreneurs and raise their expected in-

come from alternative

employment (EPA). Thus, while

entrepreneurs

with general skills may perform

better in self-

employment, they would also have higher performance

re-

quirements

to remain in business. How general human capi-

tal influences survival will depend on its relative

payoff in the

venture versus alternative

employment. Evans

and Leighton

(1989) found that business experience had about the same

returns

in wage work and self-employment,

while education

had greater returns

in self-employment.

A study by Fujii

and

Hawley (1991) revealed

that self-employment had slightly

lower returns associated with both experience and education

than wage work, but they did not test to see if these differ-

ences were significant.

Because there is a lack

of consensus

756/ASQ, December 1997

Survival of the Fittest?

Figure 1. Threshold model of entrepreneurial exit: Human capital considerations.

General human capital (x1) +

K ~+

\_ / \ ~~~~~~~~~~~~~ECONOMIC

\ ~~~~~~~~~PERFORMANCE

\ ~~~~~~~~* {~~~~~~EPE)\

Human capital specific to

current venture (x2)

EXIT FROM

BUSINESS

Human capital specific to +

alternative occupations Income available in other

employment (EPA)

+~~~~~~

r \ I \+ l~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Psychic

income from \

entrepreneurship (X4)+| \ .. .._......./

entrepreneurship /x4) I I Difference in psychic I|

income between self- ' i UNOBSERVED

employment and [-p( THRESHOLD LEVEL I

Psychic income from alternative employment OF PERFORMANCE I

Psychic income from ' I i

(PIPEPIA) i (TE) I

alternative occupations (x5) - I [ .

g > + | [ Cost of switching -

Switching costs (x6) + occupations (SC)

<~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.

} ...... .... ........ ........ .... . . . ..

...........................I

.............. ..........

... .. ... . ..... ............... .. .......... .

. . ....... .......... ... ... ....... .

....... . ...... . ........_.

on the relative payoff of work experience and education, we

hypothesize that the net effect of general human capital on

entrepreneurial exit is indeterminate a priori:

Hypothesis 1 (H1):

General

human

capital

will be positively

to the economic performance

of the venture and also to the entre-

preneur's

threshold

level of performance.

Hence, a priori,

general

human

capital

has an indeterminate

effect on the likelihood

of exit.

Human capital specific to the current venture. Specific

human capital results from education, training, or experience

that has a limited scope of applicability. Investments in spe-

cific human capital create value in a particular

business con-

text but do not have relevance in alternative occupations.

Therefore, while human capital specific to the venture raises

performance, it has no influence on entrepreneurs' threshold

of performance. The implication is that individuals whose

human capital is more specific to the venture would be less

mobile (Becker, 1975). A measure of specific human capital

is an entrepreneur's knowledge of customers, suppliers,

products, and services within the context of the venture (x2).

This should be directly related to the degree of similarity be-

tween the new venture and the organization where the en-

trepreneur had previously worked. This knowledge may be

critical to success, conferring a favorable asymmetry be-

tween the entrepreneurs who have been exposed to it and

those who have not (Sandberg, 1986; Cooper, Gimeno, and

Woo, 1994). In addition, similarity between the new venture

and the prior experience may mean that the entrepreneur

can build on prior relationships with relevant stakeholders

and thus minimize the "liability

of newness" (Stinchcombe,

1965; Aldrich and Auster, 1986). Yet such knowledge and

ties largely lose their value outside of their original context.

757/ASQ,

December

1997

To the extent that human capital specific to the venture

raises performance (EPE ) but has no effect on performance

in alternative occupations (EPA), and therefore on the thresh-

old (TE), it should be negatively related to entrepreneurial

exit:

Hypothesis 2 (H2): Specific human capital will be positively related

to the economic performance of the venture but should have no

influence on the entrepreneur's threshold level of performance.

a result, specific human

capital

should be negatively

related to the

likelihood of exit.

Hypotheses 1 and 2 assume that specific and general hu-

man capital

can be measured separately. In other contexts, it

may be easier to measure an individual's overall level of hu-

man capital

and the degree to which such overall

capital

general or specific, maybe as a ratio

or a subjective evalua-

tion. In those cases, we would expect lower thresholds for

entrepreneurs

with a greater ratio

of specificity in their hu-

man capital,

since general human

capital

increases thresh-

olds while specific human

capital

does not. This prediction

comparable

to our earlier

argument

that organizations (entre-

preneurs) with less mobile resources (human capital) should

be more willing

to withstand low performance.

Psychic income from entrepreneurship. The probability

exit will also be negatively related to the psychic income, or

personal satisfaction

the entrepreneur

derives from self-em-

ployment (X4). Considerable research indicates that many

entrepreneurs

are motivated,

at least in part, by noneco-

nomic goals, including

satisfaction

from the autonomy

self-employment

or from doing the type of work they like

(Smith and Miner, 1983; Lafuente and Salas, 1989). Entre-

preneurs may also be personally attached to entrepreneurial

activities if their parents were self-employed (Evans

and

Leighton, 1989; Bruderl, Preisend6rfer,

and Ziegler,

1992).

Since parents are seen as role models, it has been reasoned

that people growing up in such families perceive entrepre-

neurship

to be a more viable career than those without such

a family background (Shapero

and Giglierano, 1982). There-

fore, entrepreneurs

who have an intrinsic

motivation for the

activity

or who come from entrepreneurial

families are likely

to obtain a higher psychic income from entrepreneurship

(PIE) than those who do not have those backgrounds

and

motivations.

Accordingly,

the threshold level of performance

(TE)

is lower for such entrepreneurs, indicating

that they may

be willing

to accept lower economic returns

to gain personal

satisfaction

from the venture. Because a psychic attachment

to the venture lowers the threshold level of performance,

while having

no apparent

effect on the economic perfor-

mance of the venture, we expect these factors to have a

negative effect on the likelihood of exit:

Hypothesis 3 (H3): High psychic income from entrepreneurship

should have no influence on the economic performance

of the ven-

ture but should decrease the entrepreneur's

threshold

level of per-

formance.

As a result, higher

levels of psychic income from entre-

preneurship

should be negatively

related to the likelihood

of exit.

Switching costs. Finally,

the probability

of entrepreneurial

exit will be negatively

related to the costs of switching to

new employment (x6).

These costs are defined as the costs

inherent in the act of switching between two alternative

oc-

758/ASQ, December 1997

Survival of the Fittest?

cupations and do not include the difference in returns in

those alternative occupations. This definition reflects the ef-

forts and expenses the individual

would need to undertake in

job searches and retraining, as well as the psychic costs of

not knowing whether a suitable job can be found. Older

people have less time to recoup the costs associated with

switching jobs and thus are likely to perceive lower benefits

from switching. Consistent with this view, Mincer (1974)

found a tendency for investments in training to be concen-

trated at younger ages and to continue at a diminishing rate

throughout much of a person's working life. Since employers

may need to train and develop their employees, they are

also more likely to prefer younger candidates, to maximize

the return from their investment. Evidence suggests that

older job seekers are more likely than younger ones to take

more time to find a job (Shrieves, 1995) and less likely to

find jobs eventually (Bortnick and Ports, 1993). For these rea-

sons, it is expected that older entrepreneurs will be less will-

ing to switch occupations. The existence of switching costs

should decrease the threshold level of performance (TE)

and

therefore decrease the likelihood of exit. This expectation

was supported by the in-depth case studies of Mayer and

Goldstein (1961), who found that concern about job seeking

at an older age was a major reason why older entrepreneurs

would continue their involvement with new businesses with

marginal economic performance.

By serving as a proxy for general human capital, age may

also be linked to the monetary performance of the venture

(Preisend6rfer and Voss, 1990). After controlling for general

and specific human capital levels (hypotheses 1 and 2), how-

ever, we do not expect older entrepreneurs to perform dif-

ferently from younger ones. We expect age to affect exit

only through its influence on the threshold level of perfor-

mance, not through economic performance:

Hypothesis 4 (H4):

Factors

associated with an entrepreneur's

cost

of switching

to alternative

occupations

should have no influence on

economic performance

of the venture but should decrease the en-

trepreneur's

threshold level of performance.

As a result, higher

lev-

els of switching costs should be negatively

to the likelihood

of exit.

Other influences. There are some important human capital

variables that may measure a combination of general human

capital, specific human capital, psychic income, or switching

costs. For example, entrepreneurs with previous venture

start-up or ownership experience may be endowed with hu-

man capital that is valuable in new venture situations be-

cause they have experience in the start-up process and in

running their own business. This experience may not be as

valuable in alternatives that include work in established

firms. At the same time, owners with prior entrepreneurial

experience may be psychologically attracted by the thrill of

start-up and thus may decide to quit and start another ven-

ture unless current performance is high.

Another important characteristic of entrepreneurs is the

number of jobs previously held. People who have been in

more job settings are likely to gain general human capital

that can be applied across a number

of alternatives.

At the

same time, however, many job changes may signal that the

759/ASQ, December 1997

Table

Hypothesized Impact of Independent Variables on Performance, Threshold of Performance, and Exit

Threshold of Performance

Impact on

economic Impact on

performance in psychic income Impact on Overall impact

Economic alternative from cost of on

Variable Performance employment entrepreneurship switching threshold Exit

General

human

capital

(H1)

Formal education + + 0 0 + ?

Management experience + + 0 0 + ?

Supervisory experience + + 0 0 + ?

Specific human

capital

(H2)

Similar

business + 0 0 0 0

Psychic

income (H3)

Intrinsic motivation 0 0 + 0 -

Parents owned a business 0 0 + 0

Switching

costs (H4)

Age of entrepreneur 0 0 0 +

individual

was forced out of those jobs because of poor per-

formance or low degrees of general human

capital.

Thus, a

moderate number of jobs may be the strongest indicator

general human

capital.

addition,

entrepreneurs

who have

changed jobs frequently in the past may face lower switch-

ing costs. While job switching is associated with substantial

stress (Mobley, 1977), those who have experienced it in the

past may be better able to draw on "scripted

behavior"

that

enables them to deal with the stress of job search (Lee and

Mitchell,

1994). Thus, the number of jobs previously

held by

the entrepreneur

may signal both general human capital

and

switching costs.

Our

hypotheses are summarized

in table 1, which shows the

expected effects of the variables

included

in our model.

RESEARCH

DESIGN

Testing the effect of human

capital

on thresholds of perfor-

mance in new entrepreneurial

ventures requires

finding

sample of new ventures in which the entrepreneurs

are

making

substantial

commitments, so that the decision to exit

is not trivial.

This suggests that the ventures should be pri-

marily

full-time,

or with the potential

to become full-time

businesses, and that there should be significant

investment

involved,

so that the decision to exit may involve a careful

consideration of the income available

from different

alterna-

tives and not merely a reaction

to immediate pressures.

The samples used in the studies noted in the literature re-

view are not adequate for testing our theory. The 1982 Char-

acteristics of Business Owners (CBO)

sample (Bates, 1990)

and the Munich

Founder

Study sample (Briderl, Preisend6r-

fer, and Ziegler,

1992) lack information

about specific human

capital

and performance,

respectively,

which are critical

vari-

ables in our study. The National

Longitudinal

Survey

Young Men (Evans

and Leighton,

1989) lacks information

about the business owned by the entrepreneur

and provides

a large enough sample size to study determinants

of entre-

preneurial

entry, but not exit.

76OIASQ,

December 1997

Survival of the Fittest?

The sample we used includes new members of the National

Federation of Independent

Business (NFIB), the largest U.S.

small business trade association. NFIB

members tend to

have organizations

with separate business addresses, full-

time founders, and significant

investment. For

the sample,

82.2 percent of all entrepreneurs had no part-time

or full-

time jobs outside of the venture, and virtually all put in more

than 30 hours per week and grossed more than $25,000 in

the first year. The organizations

sampled at the time of the

initial

survey had a mean of 5.04 employees (median of

three employees), and only 13.2 percent had one full-time

employee or fewer (including the entrepreneur). Their me-

dian and modal investments at the time of their first sales

were between $20,000 and $50,000.

Although

sampling

from a trade association with voluntary

membership raises concerns of self-selection and represen-

tativeness of the population,

representativeness is a slippery

concept when new ventures might be defined and identified

in different

ways (Birley,

1984; Aldrich

et al., 1989). In

a pre-

liminary

comparison of our sample with other probability

samples (1982 CBO, Munich

Founder

Study data, 1985 Sta-

tistics of Income),

we found no fatal biases in the distribu-

tion of revenues, employment size, industry

membership,

and path to ownership, although

it was apparent that the

NFIB

sample included fewer part-time

entrepreneurs than

either the 1982 CBO or the Munich data. Whether the

sample is representative

of full-time

new businesses in the

United

States is difficult to discern, because there are no

comparable data on the population

of all new full-time

busi-

nesses. The fruitfulness

of this study, however, does not

depend on whether the sample is fully representative. The

aim is not to try to determine the precise mortality

rate for

all new firms but, rather,

to examine the effect and implica-

tions of firm

thresholds on organizational

survival.

We view

the current

analysis as illustrative

rather

than definitive

(Sut-

ton and Staw, 1995) and encourage further

replication

samples specifically

collected for this purpose.

Sample

In May 1985, we sent approximately

13,000 questionnaires

to members of the National

Federation of Independent

Busi-

ness (using the NFIB

address lists) who reported

that they

had "been in business" for eighteen months or less. We

focused on entrepreneurs

who had recently become owner-

managers, so that relevant

facts associated with start-up

would still be fresh in their minds. We had previously pre-

tested the survey instrument with 154 members of the NFIB

and altered it to improve

clarity.

We received completed sur-

veys from 4,814 entrepreneurs

(37 percent response rate),

and we sent them follow-up questionnaires

in May 1986 and

May 1987. In the three years of the study, each survey

round

involved an initial

mailing

and two follow-up

remind-

ers. For the second and third

years the response rates, cal-

culated as a percentage of those businesses not known to

have been discontinued or sold, were 47 and 39 percent,

respectively.

The 4,814 firms represent a broad range of in-

dustries and all geographic

areas of the United

States.

761/ASQ, December 1997

Determining

the status (surviving,

sold, or discontinued)

of a

firm involved

several steps. If a questionnaire

was not re-

turned in the second or third years, we sent a letter to the

business with an enclosed postcard,

asking the owner to

indicate whether the business was still in operation,

was

sold, or was discontinued. If we received no response, we

consulted the NFIB

membership records, in which the field

agents of the NFIB

report

whether businesses (whether they

continued as NFIB

members or not) had discontinued

or sur-

vived. We did not use the NFIB

records as our primary data

source, however, because the field representatives

visit

each business only once each year, so that, on the average,

the data are six months old. Finally,

if none of the above

sources indicated the status of the firm,

we noted if the

post office reported that the mail could not be delivered.

The responses to the first questionnaire

indicated that some

of the businesses were, in fact, older than the NFIB

records

indicated,

with some having

been started or acquired

1982 or earlier. For the 4,814 respondents, we were able to

determine the month in which they had become owner-man-

agers for 1983, 1984, and 1985 (4,103 ventures), while we

could not verify

the exact year of founding

for those organi-

zations started in "1982 or before" (711 ventures). Because

our original

sample had targeted new businesses, we elimi-

nated those older businesses that had been erroneously

sampled. We also eliminated from the sample those with

missing or nonvalid

values for the independent

variables

for

1985 (488 ventures). This left 3,615 firms with valid re-

sponses to the first questionnaire

in the sample.

A second round of sample selection was based on the data

available at the end of the third year. We maintained

in the

sample those firms that either had survived and responded

to the third-year

survey (936 observations)

or were known to

have discontinued

by that time (611 observations).

We had

to eliminate those ventures that did not return the third-year

survey and could not be identified as discontinued or sold

(1,897 firms).

We also eliminated those firms that had been

sold by the third

year (171 ventures). Thus, the final

sample

consists of 1,547 firms.

The decision to eliminate sold businesses was both theoreti-

cally grounded

and supported by substantial

sensitivity analy-

sis. Sold businesses differ from discontinued businesses in

that the owner may receive a premium

over the liquidation

value of the firm.

Thus, we believe that the choice to sell

may be different

from discontinuing

and that the two

choices should not be pooled. We conducted two statistical

tests based on a multinomial

logit specification

of the choice

problem (survived,

discontinued, sold) to determine whether

it was appropriate

either to (a) pool sold firms with those

that discontinued

or (b)

eliminate sold firms from the

sample. To examine (a),

we used a log-likelihood

ratio test to

compare whether the vector of coefficients specific to the

discontinued choice (relative

to survived)

were equal to the

vector of coefficients specific to the sold choice (relative

survived).

This test revealed that there was a statistically sig-

nificant difference between the vectors of coefficients

(X2

= 76.594; d.f. = 33; p < .0001) and implied

that discontin-

ued and sold firms should not be pooled. The Small and

762/ASQ, December 1997

Survival of the Fittest?

Hsiao (1985) Independence of Irrelevant Alternatives (IIA)

test showed that the coefficients specific to the discontin-

ued and survived choices were unaffected by eliminating

sold firms (X2

= 1.7748; d.f. = 33; p <.9999). Given

the re-

sults in both tests, and to preserve consistency with our

theoretical

model, we felt justified

in eliminating sold firms

from the analysis.

We examined the possibility

of sample selection bias by

comparing our final sample (1,547 observations) with those

observations

that we eliminated

because they were sold or

did not respond to the final survey (2,068 observations). We

compared independent and control variables across these

sets of firms using tests of differences of means (t-tests) for

continuous variables and cross-tabs tests of independence

(chi-squared tests) for categorical variables. The only signifi-

cant differences between these groups were that entrepre-

neurs in the final sample had more education (p < .001), op-

erated businesses of larger scope, were more likely

to be in

professional service industries,

and were less likely

to be in

personal

service industries.

While this may indicate a slight

sample selection bias, the magnitude

of this problem

does

not seem to be great.

The period of time when the businesses in the sample were

initiated

(1983 to 1985) was characterized by a slightly

lower

index of net new business formations

in the U.S. than for

the whole decade of the 1980s (119.9 vs. 122.1, in an index

for which 1967 = 100). Net formations

hit their decade low

in 1982 and were slowly on the rebound. The period

of time

for which we recorded the exits from these businesses

(1986 and 1987) was characterized by a relatively high failure

rate (per 10,000 concerns) compared

with the whole decade

of the 1980s (111 vs. 91), with 1986 being the peak of busi-

ness failures for the decade (U.S. Bureau of the Census,

1996: 543). Thus, the study period might be characterized as

a slightly

unfavorable

period

for entrepreneurs, which, while

it may increase the baseline rate of exit in our sample,

should not affect the coefficients of the independent

vari-

ables in any systematic way.

Dependent Variables

The theoretical

model posits that the venture's economic

performance

and the entrepreneur's

threshold

jointly

deter-

mine entrepreneurial

exit. While threshold is not observable,

it can be derived

from comparisons

of two observable out-

comes: economic performance

and venture discontinuance.

When firms have equal economic performance,

the inci-

dence of exit can be attributed

to differences in thresholds.

Thus, economic performance

and exit constitute the ob-

served dependent variables in the empirical analysis.

Exit, a binary variable, represents the exit decision of firm n

(0 if the firm

continued, 1 if it was discontinued).

For those

firms that survived and returned the 1987 questionnaire,

economic performance

is represented by the amount of

money (in

the form of salaries, perquisites,

and dividends)

the entrepreneur

withdrew

from the venture during

the third

year. We lost a large number

of observations as a result of

non response to the 1987 questionnaire, and data were not

gathered on money withdrawn during either of the first two

763/ASQ, December 1997

years. For firms in the final

sample, however, money taken

out represents the overall

returns

to the entrepreneur

for

both the financial and human

capital invested in the venture

and therefore is consistent with our theoretical focus on the

economic performance for the owner. Money taken out was

reported as being within a range between predetermined

bounds (e.g., from $10,000 to $15,000).

Money taken out has the disadvantage

of not reflecting

the

extent to which an entrepreneur

may choose to accept

lower current

benefits to support greater organizational

growth. In practice,

distinguishing between economic re-

turns to investment (financial

and human

capital)

and rein-

vestment intensity is problematic

in the study of new ven-

tures because this distinction

hinges on sophisticated

accounting

concepts (accounting profits,

depreciation,

invest-

ment versus expense, retained

earnings) seldom used in

small businesses, which tend to use the cash method. It

may be that reinvestment intensities substantially

differ

industry,

in which case the industry

control dummies may

partial

out this effect. Below, we discuss the results in light

of this potential

weakness and conclude that the results do

not appear to be biased by it.

Independent Variables

Measures for the independent

variables

introduced

in earlier

discussions are listed in Appendix

A, while table 2 presents

descriptive statistics and correlations for the independent

and control variables in the entire sample. Because we could

only identify

the level of education (nine categories) attained

by an entrepreneur,

formal

education

was measured as the

percentage of people in the sample with lower levels of edu-

cation than the entrepreneur

in the observation.

This variable

ranges from 0 to 1 and is scaled based on the empirical

dis-

tribution

of education. While this continuous measure has

been seldom used, it may be a better metric

than "years of

education"

for several reasons. First,

the productivity

and

earnings effects of years of education are very different

for

different

stages of education. For

instance, two years of

education between high school and an associate's degree

may have different effects than two years between a bach-

elor's degree and an MBA.

Also, years of education does not

take into account that earnings

from education

are partly

de-

termined by the empirical

distribution of level of education in

the labor

supply.

While prior

research has tended to operationalize

work expe-

rience in terms of the number

of years of work experience

(Evans and Leighton, 1989; Bruderl,

Preisond6rfer,

and Zie-

gler, 1992), such a variable was not included

in our surveys.

While this could be viewed as a weakness of our study,

years of experience may not closely reflect skills and knowl-

edge developed. We used alternative

operationalizations,

meant to capture

work experience through

achievement

level attained

by the entrepreneur.

We obtained

three mea-

sures of attainment

from one question in the survey, asking

whether the highest level of management experience

achieved was "supervised managers," "supervised

others,"

"managed own business," or "supervised

no one." With

"supervised no one" as the reference group, management

764/ASQ, December 1997

Survival of the Fittest?

Table 2

Descriptive Statistics and Pearson Correlation Coefficients*

Variable Mean S.D. 1 2 3 4 5 6 7 8 9

1. Formal education 0.42 0.31

2. Management experience 0.13 0.33 .17

3. Supervisory experience 0.40 0.49 -.03 -.31

4. Similar

business 0.46 0.39 -.03 -.01 .02

5. Intrinsic motivation 0.01 0.99 .03 -.03 .07 -.05

6. Parents owned business 0.45 0.50 .05 -.02 -.02 -.01 .00

7. Age of entrepreneur 36.65 9.35 -.02 .13 -.14 -.09 .08 -.06

8. 1 Prior job (vs. none) 0.09 0.29 .10 .04 .01 .05 -.02 .02 -.16

9. 2 Prior jobs (vs. none) 0.17 0.37 .06 .00 -.02 .03 .04 .02 -.08 -.14

10. 3 or 4 Prior jobs (vs. none) 0.34 0.47 .03 .05 .03 -.02 -.05 -.04 -.02 -.23 -.32

11. 5+ Prior jobs (vs. none) 0.36 0.48 -.15 -.06 .00 .01 .05 .01 .22 -.24 -.33

12. Entrepreneurial experience 0.25 0.44 -.04 -.22 -.48 .09 -.07 .07 .16 -.07 .01

13. Hours worked per week 56.83 16.25 -.07 .04 -.01 .11 -.03 .01 -.05 -.07 .00

14. Outside job 0.14 0.30 -.08 -.04 .04 -.17 .00 .03 .00 .04 -.04

15. Initial capital (log) 10.04 1.21 .14 .14 -.11 -.03 -.07 .00 .12 .02 .01

16. Number of employees (log) 1.16 0.85 .11 .18 -.08 .12 -.17 -.01 .07 -.03 -.01

17. Acquired business 0.29 0.45 -.01 .01 -.04 -.11 .01 .03 .05 .00 .01

18. Inherited business 0.02 0.13 .03 -.05 .05 -.02 -.06 .11 -.08 -.03 .06

19. Radius of business sales 22.39 27.44 .05 .06 -.03 .11 -.02 .03 .09 -.06 .01

20. Months

in business (log) 2.55 0.56 .05 .05 -.01 .06 .01 .01 .04 .00 .02

21. Informational ties 0.49 0.20 -.02 .00 .03 .10 -.07 .07 -.09 .00 .03

22. Industry-construction 0.09 0.29 -.03 .01 .01 .16 -.05 .02 -.03 -.04 -.01

23. Industry-manufacturing 0.08 0.28 .00 .03 .02 .05 -.01 .01 .04 .00 -.02

24. Industry-transportation 0.02 0.15 .00 .01 .01 .03 -.03 .03 .01 .00 -.02

25. Industry-wholesale 0.05 0.21 .02 .00 -.01 .03 -.05 .00 .04 -.04 .02

26. Industry-agriculture 0.02 0.15 .11 -.01 -.03 .05 -.03 .09 -.04 .11 .01

27. Industry-financial services 0.05 0.21 .07 .01 -.03 .06 .01 .01 .03 .01 .09

28. Industry-personal services 0.17 0.38 -.13 -.02 -.03 -.04 .02 .01 -.01 -.02 -.01

29. Industry-professional services 0.07 0.26 .32 .03 .02 .10 .14 -.06 -.04 .04 .01

30. Environmental

dynamism 0.74 1.07 .04 .06 -.01 .05 -.03 .03 -.06 -.02 -.03

31. Growth

in GSP 0.08 0.11 -.06 .00 -.02 -.01 -.03 -.05 .01 -.02 -.01

32. Change

in competitors 0.51 0.92 .11 .03 .01 .02 .00 .02 .02 -.01 .00

Variable 10 11 12 13 14 15 16 17 18 19 20

11. 5+ Prior

jobs (vs. none) -.54

12. Entrepreneurial experience -.05 .10

13. Hours worked

per week -.05 .10 .07

14. Outside

job .00 .01 -.04 -.24

15. Initial

capital (log) .02 -.05 .09 .12 -.10

16. Number of employees (log) .06 -.03 .14 .17 -.12 .37

17. Acquired business -.02 .00 .03 -.01 .00 .22 .04

18. Inherited business -.01 -.04 -.03 .00 -.03 .01 .05 -.08

19. Radius

of business sales .01 .02 .08 .00 .01 .05 .19 -.11 .07

20. Months

in business (log) .02 -.03 -.01 .01 -.02 .00 .14 -.02 .01 .07

-21. Informational

ties .06 -.07 -.03 .01 -.03 .13 .12 .03 .03 .03 -.03

22. I

ndustry-construction .08 -.05 .04 .00 -.05 -.09 .15 -.09 -.01 .04 .07

23. Industry-manufacturing -.03 .06 .02 .01 .02 .06 .15 -.03 .01 .21 .01

24. Industry-transportation -.01 .02 .03 .00 .01 .04 .09 -.02 .05 .11 .01

25. Industry-wholesale .02 .01 .05 -.01 .01 .06 .04 .00 -.03 .19 .01

26. Industry-agriculture -.03 -.05 .01 .04 -.02 .07 -.02 .02 .11 .01 .02

27. Industry-financial

services -.04 -.02 -.02 .00 -.06 -.09 -.01 -.04 .02 .00 .01

28. Industry-personal

services -.04 .04 -.01 -.03 .06 -.16 -.11 -.05 .00 -.04 -.03

29. Industry-professional services .00 -.04 -.05 -.12 -.05 -.03 -.03 -.07 -.02 .02 .05

30. Environmental

dynamism .02 .02 .03 .13 -.09 .00 .11 -.04 -.01 .02 .01

31. Growth

in GSP .04 -.01 .03 .00 .00 -.02 .01 -.05 .04 -.03 -.01

32. Change

in competitors -.03 .03 .01 .01 .02 -.05 .05 -.09 -.01 .01 -.01

Variable 21 22 23 24 25 26 27 28 29 30 31

22. Industry-construction .05

23. Industry-manufacturing -.03 -.09

24. Industry-transportation .02 -.05 -.05

25. Industry-wholesale .01 -.07 -.07 -.03

26. Industry-agriculture -.04 -.05 -.05 -.02 -.03

27. Industry-financial

services -.03 -.07 -.07 -.03 -.05 -.04

28. Industry-personal

services -.09 -.14 -.14 -.07 -.10 -.07 -.10

29. Industry-professional

services -.04 -.09 -.09 -.04 -.06 -.04 -.06 -.13

30. Environmental

dynamism .11 -.01 .00 .02 .06 .03 .11 -.05 .00

31. Growth

in GSP -.02 .06 .02 -.01 -.02 -.03 -.06 .02 -.01 .05

32. Change

in competitors .02 .03 .00 .02 -.01 -.02 .05 .04 .09 .07 .03

* N = 1,457; correlations

greater

than ?.07 are significant

at p < .01.

765/ASQ, December 1997

We also considered a quadratic specifica-

tion, but the nonsymmetrical distribution

of the variable, together with the fact

that most observations fall in a relatively

small number of cells (75 percent of en-

trepreneurs had five prior jobs or fewer),

led us to prefer this discrete specifica-

tion. We also ran the model with a qua-

dratic specification but preferred the dis-

crete stepwise operationalization when

we compared the results using Akaike's

Information Criterion (AIC).

experience was coded 1 if the entrepreneurs

had "super-

vised managers,"

supervisory

experience was coded 1 if the

entrepreneur

had "supervised

others," and entrepreneurial

experience was coded 1 if the entrepreneur

had "managed

own business." The survey also contained information

about

the number of prior

full-time jobs held by the entrepreneur,

which is likely

to be somewhat correlated

with years of work

experience. To account for potential

nonlinear

effects, we

segmented the variable

describing

the number of prior

jobs

into five discrete segments (0, 1, 2, 3 or 4, or 5 or more

jobs).2

Our

measure of specific human

capital

is an entrepreneur's

experience with (a) customers, (b) suppliers,

and (c)

products

and services, each of which we measured individu-

ally on a 5-point

Likert scale and then combined (Cronbach

alpha

= .8723) to create the variable

similar

business (re-

coded to the 0-1 range). Intrinsic

motivation

was coded 1 if

the entrepreneur's

most important

goal in starting

a new

venture was "to let you do the kind

of work you wanted to

do" or "avoid

working

for others"; it was coded -1 if the

entrepreneur

responded "to make more money than you

would have otherwise" or "to build

a successful organiza-

tion," and 0 if the entrepreneur's

goal was "other."

Parents

owned a business is a dummy variable

that takes into ac-

count the parents' history

in an entrepreneurial

venture. Fi-

nally,

the age of the entrepreneur

is the age recorded

at the

time of the first questionnaire.

To avoid multicollinearity

be-

tween age and age2, age was operationalized as deviation

from the mean (36 years) and age2 as the square of such

deviation (Aiken

and West, 1991: 35).

Control Variables

There are well-researched

factors, unrelated

to human

capi-

tal, that may affect performance,

threshold

of performance,

or entrepreneurial

exit. These factors can be roughly

classi-

fied as characteristics

of the entrepreneur,

the firm,

and the

environment.

An important

characteristic

of the entrepreneur

is the amount of hours worked

in the venture. Entrepreneurs

working

more hours may perform

better and also expect

more from their ventures, while an entrepreneur's

desire to

readjust

work hours (Mincer,

1986) to maximize the payoff

to general human

capital,

or psychological

capital,

may lead

to exit. Entrepreneurs

who work longer hours may therefore

have a higher

threshold, being less willing

to accept a lower

level of performance.

The entrepreneur's

decision to main-

tain a full-time

or part-time

job outside the venture can also

influence

the performance

and survival

of the venture by

drawing

energy and attention

from the venture and may sig-

nal the entrepreneur's

lack of commitment, leading

to poor

performance.

The effect of outside job on threshold is un-

clear. Entrepreneurs

with outside employment may have

lower performance

thresholds because they have supple-

mentary

income to offset low performance,

or they may

have higher

thresholds because of lower switching costs.

Controls

for firm characteristics

include initial

capital

invest-

ment, firm's size, path of ownership (whether

the business

was started, acquired,

or inherited),

breadth

of geographical

niche, informational

ties, and age of the venture. Both firm

766/ASQ, December 1997

Recent ecological

findings

cast some

doubt

about the specific form

of age de-

pendence that firms

experience

and the

liability

of newness in particular.

These

findings

suggest that, after

controlling

for

firm size, organizational

age may have a

negative

effect on survival,

or a liability

obsolescence or aging (Ranger-Moore,

1991; Barron,

West, and Hannan,

1994;

Baum,

1996). Given

the restricted

range

of organizational

age in our sample (the

oldest firm

in our

sample, as of the third

year,

was only

five years and four

months

old),

we cannot

enter this de-

bate. To the extent that there may be

unobserved

heterogeneity

in the sample,

we expect greater

mortality

of younger

firms during

these early stages of the

venture.

Survival of the Fittest?

size (defined

as number

of employees) and initial

capital

may

improve

efficiency and reduce the liability

of smallness (Han-

nan and Freeman, 1984; Aldrich

and Auster, 1986), while

initial capital

investment may also provide

a liquidity

buffer

for the firm

to survive under

conditions

of low performance

(BrOderl

and SchOssler,

1990; Levinthal,

1991; Fichman

and

Levinthal,

1991). Firms

that were started by the owner are

more likely

to experience a higher

liability

than acquired

inherited

businesses, since completely new roles and ties

must be developed (Stinchcombe,

1965). Inherited

busi-

nesses may also experience different

performance

and sur-

vival

dynamics because the owner may have developed prior

firm-specific

human

capital

while working

for the family

firm

and a possible psychic attachment

to the firm. Radius

business sales reflects the breadth

of the geographical

mar-

ket niche, a critical

dimension of the firm's strategy that is

likely to influence performance

and survival

(Carroll,

1985;

Briderl, Preisendbrfer,

and Ziegler,

1992). Informational

ties

of the organization,

which can convey relevant

knowledge

and information

to the owner, was measured with an index

of the use and importance

of seven information

and advice

sources (Cronbach

alpha

= .5849). Months in business (age

of the venture) may reflect the liability

of newness (Stinch-

combe, 1965) as well as the extent to which the venture has

experienced the effects of selection (Freeman,

Carroll,

and

Hannan,

1983).3

We controlled

for the age of the venture to

minimize

the potential

for left-censoring

bias (Guo, 1993).

The venture's competitive environment

also has important

influences on economic performance

and survival.

Industry

environments

may differ

in average performance

(Bain,

1956), reinvestment intensity,

sunk costs, and barriers

exit (Caves and Porter,

1976; Porter,

1976). We controlled

for nine industry

classifications

using eight dummy variables

(construction,

manufacturing,

transportation,

wholesale, agri-

culture,

financial

services, personal

services, and profes-

sional services), with retail

as the reference group.

The in-

tensity of competition

is also a function

of the changes in

the munificence

of the environment.

Markets

that are grow-

ing may experience less intense competition

for resources,

and vice versa. We controlled

for such munificence by in-

cluding

the growth of gross state product

(GSP)

between

1985 and 1987. We expected high growth of GSP to be as-

sociated with higher

performance

and survival.

Because our

ability

to control

for objective dimensions of the competitive

and institutional

environment

was very limited,

especially if

compared

with the industry-specific

samples often used in

population

ecology studies, we used two perceptual

vari-

ables to describe the environment:

the entrepreneur's

per-

ceptions about the expected change in (number

of) competi-

tors in the next five years and how rapidly

the business is

changing (environmental

dynamism).

While these perceptual

measures may be quite unreliable,

they help minimize

the

data's weaknesses in terms of omitted competitive vari-

ables.

Statistical Methods

From the theoretical

model formulated

above, if a venture's

economic performance

exceeds the entrepreneur's

threshold

level of performance

we expect the entrepreneur

to con-

767/ASQ, December 1997

tinue with the venture. Conversely, if economic performance

is less than the threshold level, the entrepreneur is expected

to exit the venture. Thus, the decision to continue with or

exit from venture n is endogenously determined by the eco-

nomic performance

of the venture and the entrepreneur's

threshold level of performance,

following the decision rule:

EX11T

{

0 i.e., venture

continues, if EPn*(Xn)

> Tn*(Xn)

n l 1 i.e., venture is discontinued, if EPn*(Xn)

< Tn*(Xn).

(5)

EPn*

is a latent variable that represents economic perfor-

mance as of the third

year. When the venture is discontin-

ued, economic performance

(EPn*)

is completely unobserva-

ble. When the venture survives, we observe, instead, money

taken outn,

which indicates whether EPn*

falls between two

known bounds. Tn*

is the latent construct of threshold per-

formance, which is never directly

observed. We seek to esti-

mate the effect of the independent variables

(Xn)

on eco-

nomic performance

and threshold level of performance.

This

estimation presents four methodological

challenges: (1) the

unobservability

of economic performance in the case of exit,

(2) the endogenous nature of the exit decision, (3) the total

unobservability

of the threshold of performance,

and (4) the

ordinal

nature of our measure for economic performance

for

those ventures that continued.

Our

approach

combines two well-known

methodologies in

the econometric literature

involving studies of discrete and

limited

dependent variables

(Maddala,

1983). The first meth-

odology, censored regression (or

tobit) model with unob-

served stochastic thresholds (Nelson, 1977; Smith, 1980;

Maddala,

1983: 174-178), is appropriate

when the depen-

dent variable is only observed when it falls above a particular

level or threshold,

and this threshold varies from observation

to observation

as a function of some independent

variables.

Thus, this methodology deals with the first three challenges

highlighted

above. This method has been used to estimate

the determinants

of female labor

supply (Nelson, 1977) and

the predictors

of market

transaction

costs and internal

orga-

nizational costs (Masten, Meehan, and Snyder, 1991). This

methodology is also useful for avoiding

potential

problems of

self-selection bias (Heckman,

1979). When the observability

of a dependent variable

(in our case, economic performance)

is endogenously determined

by a decision (exit) in which the

economic performance is itself an important

factor, missing

observations (on those firms that have exited) are not ran-

dom. Rather,

they are self-selected based on their lower

economic performance,

higher threshold of performance,

both, thus creating

a problem

of selectivity bias that could

make the coefficients unreliable.

The second methodology, grouped data regression (Stewart,

1983; Greene, 1990), attends to the final

challenge identified

above. It is useful when the exact value of the dependent

variable

(in our case, economic performance,

EPn*

is not ob-

served but is known to be in a range between two known

values. It has been applied

to the analysis of income data

from surveys (like

ours) in which the respondent is asked

whether his or her income is between some prespecified

dollar

amounts.

768/ASQ, December 1997

The probit equation could be estimated

jointly with a performance equation to

increase the efficiency of the exit param-

eter estimates. We present the single-

equation probit here because it focuses

on mortality alone and is therefore com-

parable to prior research on mortality (no

prior research that we know of has used

joint estimation of exit and performance).

A comparison of coefficients derived

from joint estimation suggests that they

are not different in any meaningful way

from those derived in the single-equation

probit illustrated in column 3.

Survival of the Fittest?

We combined both methodologies here. As in censored re-

gression with stochastic thresholds, the decision to continue

or exit is based on the comparison

of the two latent con-

structs of economic performance

and the threshold (see

equation

4), which are specified as a linear

function

of the

observable independent

variables,

as follows:

EPn = Xb1 Xin

+ e1n (6a)

Tn= Yb *Xin

+ e2n, (6b)

where Xn

is the vector of independent

and control

variables,

b1 and b2 are vectors of regression coefficients, and e1 and

e2 are random

disturbances.

We estimated the censored regression model on economic

performance

and the tobit model on threshold performance

simultaneously

using a maximum

likelihood

procedure.

Since

there is no standard

program

that produces maximum

likeli-

hood estimates for this combination

of models, we math-

ematically

derived

the likelihood

function

and maximized

using LIMDEP

7.0. Details about this maximum

likelihood

estimation method are provided

in Appendix

RESULTS

Model Significance

We tested the significance

of the human

capital

variables

the threshold model (columns 1 and 2 of table 3) by examin-

ing whether the addition

of these variables

significantly

im-

proved

the ability

to explain

exit through

economic perfor-

mance and performance

threshold.

We used a log-likelihood

ratio

test to compare the full model with two nested naive

models (not shown); the first with only constants for eco-

nomic performance

and performance

threshold,

the second

including

only control

variables.

The first test produced

a chi-

square value of 692.85 (66 d.f.), while the second had a

value of 154.09 (26 d.f.). Both tests were significant

(p < .0001). The first test reflects the overall

significance

the model, while the second indicates the joint significance

of the independent (human

capital)

variables

of the model.

Hypothesis Testing

The results for the independent

variables

generally

confirm

the separate effects of both economic performance

and

threshold

on the entrepreneur's

likelihood

of exit. The results

from the full model are presented in table 3. Columns 1 and

2 present the coefficients relating

to economic performance

and threshold, respectively. Column

3 presents the coeffi-

cients of a probit

model on exit.4 The hypotheses were

tested by examining

these three columns. Because we had

prior

expectations about the direction

of hypothesized rela-

tionships, we used a one-tailed

test where appropriate.

Simi-

lar to column 1, column 4 presents the results from a re-

gression on economic performance

but differs in that it does

not control

for the self-selection problem.

Although

column 4

is not directly

to our hypothesis testing, we introduce

it to evaluate the effect of self-selection bias in estimating

performance.

In general, hypothesis 1 (relationships

regard-

ing general human capital)

receives partial

support,

while hy-

potheses 2 through

4 (relationships

regarding

specific human

769/ASQ, December 1997

Table 3

Parameter Estimates of Economic Performance, Threshold of Performance, and Exit

Joint Maximum Likelihood Model

Economic Threshold of Binomial Non-censored

Performance Performance Probit Regression on

Equation* Equation on Exit Economic Performance*

(1) (2) (3) (4)

Variables Coeff. S.E. Coeff. S.E. Coeff. S.E. Coeff. S.E.

Formal

education 6.84-t (2.72) .03t (2.79) -.21- (.13) 4.95 (2.37)

Management

experience 3.75-t (2.75) 6.05t (2.97) .07 (.13) 4.43- (2.48)

Supervisory

experience 5.05Ot (2.06) 1.25t (2.19) -.18- (.09) 3.34- (1.89)

Similar

business 8.89-t (2.12) .04 (2.25) -.38"t (.10) 5.21 (1.85)

Intrinsic

motivation -.39 (.77) -1.87mt (.79) -.07t (.04) -1.15- (.69)

Parents

owned business -.19 (1.53) -3.20"t (1.63) -.1 5t (.07) -2.27- (1.34)

Age of entrepreneur .14 (.10) -.49-t (.11) -.02't (.00) -.10 (.09)

Age2 .00 (.01) .00 (.01) .00 (.00) .00 (.01)

1 Prior

job (vs. none) 5.72 (5.28) -7.30 (5.18) -.55m (.21) -.17 (4.32)

2 Prior

jobs (vs. none) 8.59' (4.77) -2.42 (4.63) -.42" (.20) 4.15 (4.11)

3 or 4 Prior

jobs (vs. none) 6.75 (4.68) -2.05 (4.43) -.30 (.19) 3.71 (4.00)

5+ Prior

jobs (vs. none) .67 (4.69) 1.15 (4.41) .06 (.19) 1.11 (4.06)

Entrepreneurial

experience 4.07- (2.36) 6.45" (2.47) .07 (.11) 5.04 (2.14)

Hours

worked -.01 (.05) -.04 (.05) .00 (.00) -.05 (.05)

Outside

job -11.68-- (2.76) -7.34 (2.90) .16 (.12) -10.82-- (2.63)

Initial

capital

(log) 2.76-- (.71) -1.52" (.77) -.17-- (.03) 1.12- (.61)

Number

of employees (log) 9.07---- (1.01) 3.52-. (1.07) -.16" (.05) 8.30w (.92)

Acquired

business 1.06 (1.79) -1.35 (1.88) -.17 (.08) -1.24 (1.50)

Inherited

business 2.85 (5.61) -11.02 (7.05) -.63 (.30) -3.56 (4.51)

Radius

of business sales -.03 (.03) .100 (.03) .01w (.00) .03 (.03)

Months

in business (log) 7.45-- (1.45) -.59 (1.39) -.35w- (.06) 3.57--- (1.30)

Informational

ties 1.78 (3.92) 1.54 (4.06) -.09 (.18) .25 (3.42)

Industry-construction 12.22--- (2.70) 3.12 (2.77) -.32 (.14) 9.75w (2.45)

Industry-manufacturing 11.11-- (2.79) 3.87 (3.11) -.34w (.14) 8.19- (2.53)

Industry-transportation 9.17- (4.92) 5.80 (4.91) -.09 (.24) 8.88- (4.62)

Industry-wholesale 11.62--- (3.57) 4.73 (3.91) -.29- (.18) 9.650 (3.20)

Industry-agriculture 10.52" (4.78) -.74 (6.56) -.55w (.26) 6.46 (3.96)

Industry-financial

services 25.13w (3.87) 11.66' (4.37) -.71w (.18) 18.90w (2.93)

Industry-personal

services 6.28- (2.24) -.48 (2.31) -.28m (.10) 3.96-- (2.01)

Industry-professional

serv. 23.77w (2.88) 6.08- (3.18) -.69m. (.16) 17.85"--- (2.66)

Environmental

dynamism .44 (.73) .04 (.77) -.03 (.03) .09 (.65)

Growth

in GSP 34.01---- (7.23) 6.74 (7.53) -1.20w (.32) 21.23w (6.36)

Change

in competitors -.48 (.77) .24 (.81) .05 (.04) .08 (.74)

Log-likelihood -2,433.50 -890.64 -1,558.11

N 1547 1547 936

* p < .10; *-p < .05; Up < .01; Amp < .001; two-tailed

Wald test unless otherwise indicated.

* Dependent

variable

uses grouped data methodology.

t One-tailed

Wald

test for hypothesized

relationships.

capital,

psychic income, and switching costs) receive rela-

tively strong support.

General human capital. Measures of general human

capital

should raise the economic performance

of the venture and

the expected returns

outside the venture. Because of these

offsetting factors, we cannot determine the relationship

general human capital

to survival a priori;

it depends on the

relative

payoff of human

capital

in the venture versus out-

side the venture. Hypothesis 1 received mixed support.

expected, education, management experience, and supervi-

sory experience are positively

to the economic per-

formance of the venture, but only management experience

has the expected positive relationship

with threshold. The

insignificant

effect of management experience on exit

seems largely

driven

by the offsetting influence of this vari-

able on both performance

and threshold. In contrast, it ap-

pears that the negative relationship

of supervisory

experi-

ence and education

to exit is largely

driven

by a greater

770/ASQ, December 1997

Survival of the Fittest?

payoff to these forms of human capital

in the venture. A

two-tailed

Wald

test shows that supervisory

experience has

a significantly

lower payoff (one-tailed

Wald

test: p < .10) in

other alternatives

(a lower threshold)

than does management

experience.

While not reported

here, we explored

further

the effect of

education

on performance and threshold

by operationalizing

education as a set of indicator

variables for five different

lev-

els of educational

attainment.

Consistent with the linear re-

sults, entrepreneurs with higher

education (with bachelor's

or graduate

degrees) have significantly

higher

performance

than those with medium levels of education (high school

graduates or some college), even though those without a

high school diploma

do not necessarily perform

worse than

the rest. Paradoxically,

entrepreneurs with a high school di-

ploma or with a bachelor's

degree have a lower threshold

than those who did not attain

their high school diploma and

those who went to college but did not obtain a bachelor's

degree. This nonmonotonic

effect may explain the nonsignifi-

cant threshold

effect of the linear

operationalization

of edu-

cation. This result may suggest that those entrepreneurs

who persisted in their studies until

they obtained the degree

are also more likely

to persist in their business.

Human capital specific to the venture. Hypothesis 2 pre-

dicted that because an entrepreneur's

specific human capital

will have little value outside the venture, the decision to exit

will be influenced

primarily by the venture's performance.

Since entrepreneurs

with higher

degrees of specific human

capital

are expected to have better performing

ventures, we

expect them to be less likely

to exit. Our

measure of spe-

cific human

capital,

similar

business, is related

as expected.

As depicted in column 3, the variable

has a strong, negative

relationship

to exit (one-tailed

Wald

test: p < .001). That rela-

tionship is driven

by the very strong and positive impact

economic performance

(one-tailed

Wald

test: p < .001) and

the insignificant

effect on threshold.

Psychic income from entrepreneurship. Hypothesis 3 ar-

gued that individuals

who attach high psychic income to en-

trepreneurship

are expected to accept a lower level of per-

formance before exiting

their ventures. While they are not

necessarily performing

better, we expect them to be less

likely

to exit their business. This hypothesis received strong

support. Our

measures of psychic income, intrinsic

motiva-

tion (one-tailed

Wald

test: p < .01) and parents owned a busi-

ness (one-tailed

Wald test: p < .05), are both negatively

re-

lated to threshold,

while having no statistical

relationship

with economic performance. This explains

the negative and

significant

effect of both variables

on exit. It seems that en-

trepreneurs

who are more intrinsically

motivated and have a

family

history

in entrepreneurship

are simply more likely

accept a lower level of economic performance

to remain

business.

Switching costs. With

age of the entrepreneur

as a mea-

sure of switching costs, our results support hypothesis 4.

The persistence of older entrepreneurs

is due to their

will-

ingness to accept a lower return,

i.e., their lower threshold

(one-tailed

Wald

test: p < .001), since they do not seem to

771 /ASQ, December 1997

perform

significantly differently

than younger entrepreneurs.

The main effect of age on performance

is significant

in a

one-tailed Wald

test at the p < .10 level, but not in a two-

tailed test. Since we do not have initial

theoretical

expecta-

tions about the effects of age on performance,

we believe

the two-tailed test to be appropriate,

but this result may indi-

cate that age is picking up some omitted variables measur-

ing the effect of human capital, such as years of work expe-

rience. Age2 had no significant

effects on either performance

or threshold.

Other influences. Entrepreneurial

experience has a positive

and significant effect on economic performance

(two-tailed

Wald test: p < .10) and threshold (two-tailed

Wald test:

p < .01). It appears that the effect on threshold is greatest.

Consistent with the view that too many or too few jobs may

indicate low general human

capital, we found that entrepre-

neurs with two prior jobs perform better than those with no

previous work experience (two-tailed Wald

test: p < .10) and

than those with five or more prior

jobs (two-tailed

Wald test:

p < .001); and entrepreneurs

with three or four prior jobs

perform

better than those with five or more prior jobs (two-

tailed Wald test: p < .001). The same result does not hold for

the threshold

equation. Instead, entrepreneurs

having held

five or more prior

jobs have higher

thresholds than entrepre-

neurs with only one prior job (two-tailed Wald test: p < .05).

This suggests that entrepreneurs who have held many jobs

are less willing

to tolerate low performance,

perhaps be-

cause they have lower economic or psychic switching costs.

Control variables. The results in table 3 also illustrate

sev-

eral interesting

relationships

for the control

variables. Of the

remaining

variables

related to the entrepreneur,

hours

worked was not related

to any of the dependent variables.

As expected, the economic returns to entrepreneurs with an

outside job was significantly

lower than those concentrating

solely on the venture. Interestingly,

outside job was also re-

lated negatively with threshold, presumably

because entre-

preneurs

could afford to accept lower performance.

Several of the firm-level

control

variables had important

influ-

ences on economic performance, threshold,

and exit. Initial

capital,

number of employees, and months in business had

significant positive effects (two-tailed

Wald test: p < .001) on

economic performance,

suggesting that better capitalized,

larger,

and older firms were better performers.

This explains

the strong and significant negative effects of these variables

on the probability

of exit (two-tailed

Wald

test: p < .001). The

variables,

however, differed in their effect on threshold of

performance.

Initial

capital

had a significant

negative effect

on threshold (two-tailed

Wald test: p < .05), number of em-

ployees had a significant positive effect (two-tailed

Wald

test: p < .001), and months in business had no significant

effect on threshold.

The path to ownership of the venture,

whether the firm

was acquired, inherited,

or developed,

seemed to have no significant

bearing

on either performance

or threshold,

although

owners who inherited their busi-

nesses seemed to be willing

to accept lower performance

(two-tailed

Wald test: p < .12) than those who started their

firms. Both acquired businesses and inherited businesses

were less likely

to exit than start-up

firms. While firms with a

772/ASQ, December 1997

Survival of the Fittest?

wider radius

of business sales tended to exit more often,

this result was due primarily

to the firms' higher

thresholds,

because there were no significant

performance

differences.

Information

ties did not show significant

effects in our analy-

sIs.

Several industry

dummies also had significant

effects on per-

formance, threshold,

and exit. All industries

were compared

against the largest industry group-retailing-and all showed

higher

economic performance

than that group. Firms

in fi-

nancial

and professional

industries

tended to perform

better

and exit less frequently.

As expected, economic returns

the entrepreneur

were greater when growth in GSP was

higher.

Our

measures of environmental

change and change

in competitors had no significant

influence on performance,

threshold,

or exit.

Effects of Self-Selection Bias

A methodological

problem

of studying determinants

of eco-

nomic performance

in businesses with high attrition

rates is

that the dependent variable is only available

for firms that

survive, yet survival is more likely

for firms with high perfor-

mance. This creates a self-selection problem.

To highlight

the effects of self-selection bias on the analysis of economic

performance,

we compare column 4, a simple grouped data

regression model without correction

for self-selection bias,

with column 1, which corrects for this bias. The magnitude

of the self-selection problem

will differ for each predictor

variable,

depending on whether the variable

is expected to

be related to the selection function, in this case survival

(Heckman,

1979). Variables that have a strong effect on sur-

vival

suffer more from self-selection, which biases down-

ward their coefficients on performance,

because firms that

fail are more likely

to have lower values of these variables.

By modeling performance

effects with only surviving

firms,

the distributions

of affected variables

fall within a narrower

range, creating

effects that are less significant.

In some cases self-selection bias can make insignificant

co-

efficients appear

to be significant

in the performance

equa-

tion. This bias was reflected most visibly

in our measures of

psychic income: intrinsic motivation and parents owned a

business. From

column 4, it appears that entrepreneurs

who

were intrinsically

motivated

or whose parents owned a busi-

ness have lower performance,

but this conclusion reflects

the potentially

incorrect

interpretations

of biased results. Our

earlier discussion suggested that these entrepreneurs

were

more likely

to survive simply because they were willing

accept lower levels of performance

to stay in business. This

selection mechanism led to the spurious

observation that

surviving

"unsuccessful" entrepreneurs

were more likely

have intrinsic motivation

or family

ties to entrepreneurship,

even though the relationship

was not causally

to a

lack of economic success. Another

interesting

observation

that the coefficient of months in business, one of the strong-

est effects on performance,

became strongly

biased down-

ward (to less than half of its unbiased

value) if the estima-

tion of the effect was only based on surviving

firms. This

result suggests that the apparent

lack

of consistency be-

tween the effect of venture age on survival

and perfor-

773/ASQ, December 1997

mance-an observation that motivated Meyer and Zucker's

(1989: 19) theory of permanently failing organizations-may

be partly due to self-selection bias. These examples empha-

size the importance of controlling for self-selection bias and

are consistent with Barnett, Greve, and Park (1994), who

also found that controlling for selection bias substantially

in-

fluenced the statistical effects of variables on performance.

DISCUSSION

Assumptions about the relationship between performance

and survival are so entrenched in social science research

that little work has investigated

them empirically.

In some

perspectives, those with Panglossian

overtones, it is as-

sumed that the efficiency of markets

will lead to the "sur-

vival of the fittest." This rhetoric implies unidimensionality

between performance and survival: the lowest performing

organizations

are also the least likely

to survive. Scholars es-

pousing other perspectives recognize that selection does not

necessarily favor

the best performing organizations

(Hannan

and Freeman, 1977; Meyer and Rowan, 1977) but have

failed to study the causal relationships

between these con-

structs (Meyer

and Zucker, 1989; Amburgey

and Rao, 1996).

Our theoretical model and empirical

results suggest that sur-

vival is enhanced by economic performance but not uniquely

determined by it. Rather, organizations

have different re-

quired thresholds of performance,

and survival

(or exit) is

determined by whether performance

falls above (or below)

the threshold. In small and new ventures, the threshold

performance

is fundamentally

influenced

by the human

capi-

tal characteristics of the entrepreneur,

including

the value of

this capital

in alternative uses, psychic income, and switch-

ing costs. This paper is the first, we believe, to provide

em-

pirical support

for the assertion that thresholds of perfor-

mance differ

systematically

across firms and play

important

role in determining

firm survival.

Several of the

results are worth highlighting.

First,

we found that entrepreneurs

with more general human

capital perform

better but do not necessarily survive more

frequently.

We had expected that entrepreneurs

endowed

with general human

capital

would have higher

performance

requirements

for their businesses and might quit if these

requirements

were not met. This expectation was only par-

tially supported by our results. We found that general man-

agement experience (having managed managers)

influences

an entrepreneur's

threshold

of performance,

while neither

education nor supervisory experience are related.

This sug-

gests that the value of general management experience out-

side the venture may be comparable

to its value in entrepre-

neurship,

while returns

to education and supervisory

experience may be somewhat better in self-employment,

findings

consistent with Evans and Leighton (1989). The ap-

parently divergent payoffs of management experience and

supervisory experience are especially intriguing.

While man-

agement experience may be more generally

applied,

it may

also be that having experience "managing

managers" is

more valuable in larger organizations

with more formal struc-

tures.

Second, we found that entrepreneurial

skills that have little

use outside of the venture, such as prior experience with

774/ASQ, December 1997

Survival of the Fittest?

the venture's customers, suppliers,

products,

and services

are related to both performance

and survival.

Furthermore,

by demonstrating no significant

relationship with threshold,

our results support the expectation that human

capital

that is

largely

specific to the venture context should have little or

no bearing

on returns outside of the venture. Apparently,

specific human

capital influences survival

by increasing

the

gap between performance and threshold (i.e., increasing

per-

formance without raising

the threshold).

Third, our findings suggest that some dimensions of human

capital

have important

effects on persistence, even when

they do not influence performance. Factors such as the en-

trepreneur's

age, family experience with entrepreneurship,

or intrinsic motivation

do not have any tangible

effect on per-

formance. Yet entrepreneurs

with a higher level of these at-

tributes are willing

to accept a lower level of performance to

survive. Entrepreneurs

who have inherited businesses are

also more likely

to continue, apparently because of their de-

sire to sustain the family

business (lower threshold)

and not

because of superior performance

associated with any "ex-

tra" knowledge passed on to them.

These findings provide strong support for our threshold

model of entrepreneurial exit and reconcile many of the in-

consistent relationships previously

found in entrepreneurial

performance

and survival research: some human

capital

vari-

ables significantly

influence both performance and survival

(specific human

capital),

some influence performance

than survival

(general

human

capital),

and some influence

survival but not performance

(switching costs, psychic in-

come). This evidence suggests that there are differences in

determinants of performance

and survival

and that research

agendas ignoring

the entrepreneur's

choice to accept a given

level of performance are incomplete.

A somewhat surprising result is that entrepreneurial experi-

ence seems to have at least as much effect on threshold as

it does on economic performance

of the venture. If entrepre-

neurial

experience constitutes human

capital

that is specific

to the venture, we would expect its effect to be greater in-

side the venture than outside it. We interpret

the higher

threshold

effect as suggesting that experienced entrepre-

neurs have reduced switching costs into alternative

employ-

ment (maybe trying again with another

venture), perhaps be-

cause they have developed networks or familiarity

with the

routine of starting

businesses. Experienced

entrepreneurs

may also gain a certain "thrill" from the start-up process and

thus experience a negative psychic income once the venture

becomes stable.

As we might expect, entrepreneurs'

history

of previous jobs

seems to reflect their general human capital.

Our

findings

indicate that a low number

of prior

jobs may be associated

with a lack of outside alternatives,

while a high number of

prior jobs may suggest an inability

to perform jobs satisfacto-

rily. Thus, both a low and high number of jobs suggest low

degrees of human

capital

and poor performance

in the ven-

ture. At the same time, we found that a large number of

prior

jobs increases an entrepreneur's threshold, suggesting

that these entrepreneurs are less willing to tolerate low per-

775/ASQ, December 1997

formance. This is likely due to low economic or psychic

switching costs.

By incorporating the threshold construct, our analysis also

provides insight into the effects of some well-studied

factors

bearing on organizational

mortality: initial

capital, size, age,

and strategy of the venture. The first three variables are

strongly related

to performance;

both initial

capital

and size

also influence survival

through

their impact

on threshold.

The

negative effect of initial

capital on threshold may support the

view that initial

capital

provides the entrepreneur a buffer to

withstand poor performance

and liquidity

problems

without

having to exit the business (BrOderl and SchOssler,

1990;

Levinthal,

1991; Fichman

and Levinthal,

1991). It may also

suggest that financial

investments are largely

irreversible.

contrast, the positive effect of number of employees (size)

on threshold

suggests that entrepreneurs

of larger organiza-

tions require

higher

performance, perhaps because they can

easily disband if they do not obtain such performance.

This

is assisted by a labor

market that facilitates mobility.

The

lack of any significant

relationship between age of the ven-

ture and threshold suggests that the processes underlying

the "liability

of newness" phenomenon tend to influence

exit mainly

through

the performance

component. Our

finding

that firms with broader

scope exit more frequently

is consis-

tent with Bruderl,

Preisend6rfer, and Ziegler

(1992). We

found that this effect is mainly

due to their higher

threshold

of performance.

The environmental

controls show that industries differ in

both their performance and threshold effects. Financial and

professional

services industries

tend to perform

better but

also to have higher

thresholds, while personal

services and

retail

industries

are characterized

by both low performance

and low thresholds. These patterns may reflect broad differ-

ences in the general human

capital

of entrepreneurs

in those

industries.

Finally,

environmental munificence has a positive

effect on performance

and exit. While it might have been

expected that a good economic environment would also in-

crease the income in alternative

occupations, and thus in-

crease threshold,

the coefficient of munificence on threshold

is positive but not statistically significant.

Alternative Explanations and Limitations

Our

empirical

findings may have alternative

explanations.

could be that entrepreneurs

differ in their decision-making

speed and rationality

when faced with poor performance.

Entrepreneurs

who simply delay the exit decision or experi-

ence a psychological

escalation of commitment (Staw,

1981), perhaps because of less training,

would appear

in our

results as having

lower thresholds. It may be that formal

education does not increase underlying

ability

but that it has

value as a market

signal that allows employers to segregate

people of higher

ability

(Spence, 1974). Alternatively,

an en-

trepreneur's

high level of formal

education may increase the

legitimacy

of a small business, facilitating

access to better

trading partners

and leading

to improved

performance

(Meyer

and Rowan, 1977). These alternative theoretical

justi-

fications for our findings should be considered in future stud-

ies, as should factors beyond the scope of our model that

776/ASQ, December 1997

Survival of the Fittest?

are examined in prior

research. Venture performance

has

been linked

to network

formation

(Aldrich, Rosen, and

Woodward,

1987), environments

(Carroll,

1987), and strate-

gies (McDougall,

Robinson,

and DeNisi, 1992), as well as

interactions

between environments

and strategies (Ro-

manelli,

1989; Eisenhardt

and Schoonhoven, 1990). A

broader

array

of perspectives will undoubtedly

increase our

insight into new venture thresholds.

There are also some empirical

limitations

to our research,

mainly

because of shortcomings in data availability

and mea-

surement. Because we chose to make the sample as broad

as possible, we lacked

fine-grained

measures of the institu-

tional

dimensions of the environment.

It would be illuminat-

ing to replicate

this study in the context of a single industry

or population

to control

more aptly

for environmental

dimen-

sions. Our

reliance on demographic

variables

(age, parents

who owned a business) as proxies for psychological

con-

structs (psychic income) or switching costs is also another

limitation

that could be overcome with better measures in

future

work. The sociological

and psychological

literatures

may contain useful insights into how to extend our opera-

tionalization

of these important

constructs. Further,

the lack

of available information

on the alternatives

under

consider-

ation by entrepreneurs

did not allow us to specify fully

our

theoretical model, which may therefore be affected by un-

measured heterogeneity. Information on liquidation

values

for discontinued

businesses could have also helped specify

the exit choice more accurately.

Another

potential

limitation

is our measurement of economic

performance

as current

monetary

outlays. Monetary

outlays

to the entrepreneur

may not distinguish

between firms with

low performance

and firms with high reinvestment intensity

or bright

prospects. Firms

with low current

monetary

outlays

may still survive if the expected return to reinvestment activ-

ity is high, a condition

that may be incorrectly

reflected as a

low threshold

effect. Examination

of our results, however,

shows that lower thresholds are associated with entrepre-

neurs who are older, motivated by independence rather

than

growth or financial

goals, who have little prior

management

or entrepreneurial

experience, and who are not fully

commit-

ted to the venture. This description

does not fit the profile

entrepreneurs

with high prospects willing

to reinvest aggres-

sively but, rather,

of those with limited alternatives

and posi-

tive psychic income from independence.

Implications for Entrepreneurship and Beyond

This research highlights

the importance

of considering

the

human

capital

characteristics

of the entrepreneur

in the sur-

vival of new ventures. Despite recent criticisms

of inconsis-

tent findings in the "trait"

literature

(Aldrich,

1990: 8), the

multiple

and critically

important

roles of the entrepre-

neur-as chief strategist and decision maker,

as repository

of much of the knowledge and skills that make up the intan-

gible assets of the firm,

as the person who develops the

contacts and networks upon which the new venture de-

pends-cannot be ignored. Our

study suggests that the ef-

fects of these entrepreneurial

characteristics

on entrepre-

neurial thresholds are important

for survival

and can explain

777/ASQ, December 1997

prior

inconsistent findings.

The implication

for entrepreneur-

ship research is that more attention is due to the outside

opportunities

of entrepreneurs,

their psychic income, and

switching costs, even if those variables

do not directly

influ-

ence entrepreneurial

performance.

Prior

research has shown

that entry into entrepreneurship

may be more likely for those

with reduced options elsewhere (Fuchs, 1982; Borjas,

1986;

Brittain and Freeman, 1986; Carroll

and Mosakowski, 1987).

This research showed that those entrepreneurs

are also

more likely

to survive, independent

of performance.

Thus, a

clear appreciation

of thresholds is necessary to understand

the entrepreneurial

process.

The concept of the threshold

of performance

outlined

in this

study can serve as an integrating

construct for understand-

ing performance

and survival

for all organizations,

not merely

new ventures. The question then becomes, "Why

do organi-

zations differ

in their thresholds?" Perhaps the answer to

this question must come from theoretical

perspectives be-

yond human capital

theory. Future research could focus on

the role of governance structures,

coalition

power, and in-

trafirm

conflict (Meyer

and Zucker,

1989), which may limit

the ability

of owners to influence their organizations

and

therefore be associated with persistence under low perfor-

mance. Other research could articulate

thresholds as a func-

tion of slack (Cyert

and March,

1963), initial

resource endow-

ments, or established relationships (Bruderl

and Schissler,

1990; Miner,

Amburgey,

and Stearns, 1990; Levinthal,

1991;

Fichman and Levinthal,

1991) and disentangle the perfor-

mance and threshold effects of these important

constructs.

The nature

of resource commitment (whether

the resources

are sunk or highly

mobile)

would also influence both perfor-

mance (Ghemawat,

1991) and exit barriers

(Caves and Por-

ter, 1976) and may constitute an interesting

area for future

research.

These multiple

theoretical lenses can be brought

to bear in understanding

cross-sectional and longitudinal

dif-

ferences in thresholds of both new and established organiza-

tions. The concept of threshold may also be applied,

within

the firm,

to determine the minimum level of performance

that a multidivisional

organization

requires

to maintain

an ac-

tive division.

Proponents

of the unidimensional

view might argue that

thresholds are simply

temporary

"buffers"

against the inevi-

table success of better performing

firms. While initial

re-

sources or decision-making lags may delay exit, those buff-

ers may ultimately

be depleted by continued low

performance

(Bruderl

and SchOssler,

1990; Levinthal,

1991;

Fichman and Levinthal,

1991), leaving intact the long-term,

unidimensional

relationship

between performance

and exit.

Even if thresholds are temporary,

however, they may be an

important

initial condition

that has long-term

implications

for

performance

and survival

by providing

a buffer

at a crucial

stage of the organization's

development (Eisenhardt

and

Schoonhoven, 1990; Levinthal,

1991). We believe, however,

that thresholds persist over time and that they reflect a will-

ingness to accept lower performance

for the reasons high-

lighted in this paper.

Whether a temporary

buffer or an en-

during quality,

thresholds will influence the long-term

selection processes in populations

of organizations.

If perfor-

778/ASQ, December 1997

Survival of the Fittest?

mance is stochastic (as in Levinthal's model of random

walks), lower initial

thresholds allow firms to survive

during

runs of bad performance

until performance

improves and,

therefore, have a path-dependent effect on selection out-

comes. Even if interfirm

performance differences are rela-

tively deterministic

and difficult to change, organizations

that

are willing to remain

in business at low levels of economic

performance

(those having low thresholds, perhaps because

of low resource mobility)

may increase competition and have

a "crowding

out" effect on better performing

but more mo-

bile firms. These arguments suggest that it is difficult to de-

termine a priori

to what extent selection outcomes in a

population

reflect differences in efficiency and performance

or differences in thresholds. Thus, our theoretical model cau-

tions against the use of survival as a measure of perfor-

mance in empirical

studies, since the "survival

of the fit-

test," as this paper shows, cannot be assumed.

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APPENDIX A: Variable Definitions

Dependent variables Measure

Exitn 1 = firm discontinued; 0 = firm

survived.

Money taken outn Amount (in thousand $) of money (including

salary, draw, dividends, etc.) that

the entrepreneur was able to take out in the previous 12 months. The

variable is ordinal with 7 values: (1) under $1 OK, (2) $1 OK to $15K, (3) $15K

$25K, (4) $25K to $35K, (5) $35K to $50K, (6) $50K to $75K,

and (7) over

$75K.

Independent and control variables

Attainment

in work experience Highest level of management experience achieved by owner (multinomial

categorical variable). Supervisory experience

equals 1 if "supervised

others";

0 otherwise. Management experience

equals 1 if "supervised managers";

otherwise. Entrepreneurial experience equals 1 if "managed

own business";

0 otherwise. Reference group

is "supervised

no one."

Formal

education Percentage of observations

in sample with less formal education than

entrepreneur.

Similar

business Index of similarity between present business and previous organization

in (a)

products or services, (b) customers, and (c) suppliers (1 = very similar, 0 =

very different) (Cronbach alpha

= .8734).

Intrinsic motivation 1 = the entrepreneur's most important

goal in starting

a new venture

is to "let

you do the kind of work

you wanted to do" or "avoid working for others."

-1 = most important goal in starting

a new venture is to "make more money

than you would have otherwise" or "build

a successful organization."

0 = most important goal in starting a new venture is "other."

Parents owned business 1 = parents

owned a business; 0 = otherwise.

Age of entrepreneur Age of entrepreneur

at the time of first questionnaire.

Number of prior jobs Total number of full-time jobs prior

to venture. Transformed to multiple binary

variables measuring whether there were 1 prior

job, 2 prior

jobs, 3 or 4 prior

jobs, and 5 or more prior jobs. Reference group

is no prior jobs.

Hours worked Total

number

of hours

worked per week by the entrepreneur.

Outside

job 1 = entrepreneur

had a full-time

job outside the venture;

.5 = entrepreneur

had

a part-time

or irregular job outside the venture;

0 = entrepreneur

had no job

outside the venture.

Initial

capital (log) Natural

logarithm

of the amount

of capital (in

thousand

$) invested by the time

of first sale (ordinal,

8 brackets).

Number of employees (log) Natural

logarithm

of the number

of full-time and part-time (.5) employees

(including the owner) at the time of the first questionnaire.

Path

to ownership How entrepreneur became owner of present business (multinominal categorical

variable). Acquired business equals 1 if "purchased it";

0 otherwise. Inherited

business equals 1 if "inherited

it";

0 otherwise. Reference

group

is "started

it."

Radius of business sales Radius

of area in which 80% of customers are located.

Months

in business (log) Natural

logarithm

of the number

of months since business registered

its first

sale at the time of the first questionnaire.

Informational

ties Composite index of use and importance

of seven information sources: (a)

accountant, bookkeeper, (b)

friends or relatives, (c) other business owners,

(d) bankers, (e) trade organizations,

(f) lawyers, attorneys,

and (g) franchisers

or suppliers (1 = very important;

0 = not used) (Cronbach alpha

= .5849).

Industry Eight control variables

for nine different

industries, including construction,

manufacturing, transportation, wholesaling, agriculture, financial services,

personal services, professional services. Reference group is retail industry.

Environmental

dynamism Likert scale agreement with statement "My

business is changing rapidly" (2 =

strongly agree, -2 = strongly disagree).

Growth

in GSP Change

in Gross State Product

from 1985 to 1987 in the U.S. state where the

business is located.

Change

in competitors Expected change per year over next five years in number

of competitors:

Increase over 20% (=3), increase 11 %-20% (=2), increase 3%-1

0% (=1),

unchanged -3%-3% (=0), decrease (=-1).

782/ASQ, December 1997

Survival of the Fittest?

APPENDIX

B: Maximum Likelihood Estimation of Model

The likelihood function

represents the probability that a sample was ob-

tained

from a statistical model with given parameters.

By maximizing

the

logarithm of the likelihood function with respect to these parameters,

are able to obtain maximum likelihood

estimates of the coefficients in equa-

tions (6a)

and (6b).

For

a given observation

the sample,

we observe

exitn

(the

binary variable

representing exit),

money taken

out, (the

ordinal

variable

representing

the

range of economic

performance, only

observed

if exitn = 0), and

(the inde-

pendent

variables).

The relevant

parameters

of the model

are b1,

the coeffi-

cients of the independent

variables

on economic

performance, b2,

the coeffi-

cients of the independent

variables

on the threshold,

sl, the standard

deviation

of the disturbance

of the economic performance

equation

(e1),

which

is as-

sumed to be normally

distributed,

and s, the standard deviation of the differ-

ence of the disturbances

of the threshold

equation

and the economic

perfor-

mance, e2 - e1. A required assumption for the full identification of the model is

the assumption

that

e1 and e2 are independent

(Nelson, 1977).

The likelihood

function,

represented by L(b1,b2,s1,s

I exitn, money

taken

outn,Xn),

captures

the

probability

that some given

sample

values (exitn, money

taken

outn,Xn)

were

obtained if the parameter

values were b1, b2,

sj, and s.

For a given observation,

the probability

of observing

exit is:

Prob(exitn

= 1) = Prob(EP*

< T*)

= Prob(Ib1l Xi

+ e1 5 1b2i *

+ e2)

= Prob(e1 - e2 c (b2i - b1;) Xi

= (b2i - b1i) i),

where F(z) represents the normal cumulative

distribution

function.

For a given observation,

the probability

of observing

continuation

and obtain-

ing a value of k for money taken outn (which

means that the exact eco-

nomic performance

is between a known lower bound

Lk and a known upper

bound Uk)

is:

Prob

(exitn

= 0 and money taken outn = k)

= Prob

(T*

< EP* and

Lk < EP*

< Uk)

= Prob(Xb2i

Xi + e2 <

Xbli *

+ e1 and Lk < Xbli *

+ e1 < Uk)

= Prob(e2

- e1 < 1(b1 - b2i) *

and Lk - b1 Xi

< el < Uk - Xb1 (Xi)

= Prob(e2

- e1 < J(b1i

- b2i) *

and el < Uk - Xb1,

j Xi)

- Prob(e2

- e1 <

X(b1

i - b2i) *

and el < Lk - Xb1

i *

Xi).

Each of the probabilities

in this equation

can be represented

as cumulative

distribution functions

of a standard

bivariate

normal, F2(z1,z2,r).

this case,

the first

variable

is z1 = (e2 - e1)/s, and the second is z2=(e1/sl),

while the

correlation of the variables

equals r=(-s1/s)

Thus, we can write Prob

(exitn

= 0 and money taken outn = k)

4(b

( -

Sb2i) Xi ),(Uk -bli Xi ) s )

- (( ( - b2i) i ),(Lk lbl i *Xi _ sl

The likelihood function

aggregates these probabilities

by multiplying

them

over all of the observations

in the sample. By taking

the logarithmic

transfor-

mation of this likelihood

function,

we then obtain the log-likelihood

function,

which can be written as:

InL(bj,b2,s1,s

exitn,

money taken

outn,

Xn)

1:I ( (b2i - blij) *

= , lnF(S " X'

exitn=1 /

~e~~c

ln[1U( (Y(bi i - b2j)

' Xi) (Uk- b1i' Xi )5)

carried out in two steps. First,

we use LIMDEP

7.0's grouped

data regres-

sion with sample selection procedure

to obtain

performance

and exit param-

eters, and from those we obtain

initial

estimates of b1, b2, s1, and s follow-

ing the procedure

outlined

by Maddala

(1983: 228-230). We then use those

initial

estimates for the maximization of the log-likelihood

function using a

Davidon-Fletcher-Powell

optimization

algorithm

(Greene, 1990), also available

in LIMDEP.

783/ASQ, December 1997

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Organizational niches and the dynamics of organizational mortality

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Full-text available

Sep 1994

Permanently Failing Organizations.

Article

Jun 1991

Institutionalized organizations: Formal structure as myth and ceremony

Article

Jan 1983

Variable risk preferences and the focus of attention.

Article

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Empirical investigations of decision making indicate that the level of individual or organizational risk taking is responsive to a risk taker's changing fortune. Several nonstationary random-walk models of risk taking are developed to describe this phenomenon. The models portray a risk taker's history as the cumulated realizations of a series of independent draws from a normal probability distribution of possible outcomes. This performance distribution is assumed to have an unchanging mean and a variance (risk) that changes

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Aug 1984

Sue Birley

This paper emanates from a study of the job generation effect of new firms in St. Joseph County, Indiana. It discusses the methodological problems associated with identifying the new firm and compares the results of a search from three sources, the County Unemployment ES202 Files, the Telephone Directory, and the Dun and Bradstreet Market Identifier Files (DMI). Significant differences emerge. If DMI is used, the basis of the Birch Report, 96% of the new firms found in ES202 would fail to be recorded. The paper concludes that the wide variations found suggest that the data base chosen can significantly affect the results of any study of new firms.

Exits and entries: A study in yellow pages journalism

Article

Jan 1982

The Costs of Organization

Article

Mar 1991
J LAW ECON ORGAN

Interorganizational Linkages and Population Dynamics: Buffering and Transformational Shields

Article

Dec 1990

This paper examines evidence concerning the existence of organizational buffers, which insulate an organization from environmental disturbances, and transformational shields, which insulate an organization from the risk of failure due to transformation. It proposes that interorganizational linkages in particular can (1) buffer organizations from failure, (2) affect the likelihood of organizational transformation, and (3) modify the effect of organizational transformation on failure. These propositions are examined using event-history analysis of data on approximately 1,000 Finnish newspapers over a 200-year period. In this population, linkages reduced failure, increased organizational transformations, and altered the chances for failure after transformation. More generally, the findings support the existence of organizational buffers and transformational shields, suggesting further work on their role in population dynamics.

The Escalation of Commitment To a Course of Action

Article

Oct 1981

Barry M. Staw

There are many instances in which individuals can become locked into a costly course of action. Because it is often possible for persons who have suffered a setback to recoup their losses through an even greater commitment of resources to the same course of action, a cycle of escalating commitment can be produced. In this paper, I review recent research on the escalation of commitment and try to integrate its complex and often conflicting determinants.

Multiple Regression: Testing and Interpreting Interactions

Article

Jan 1994
J OPER RES SOC

Survival of the Fittest? Entrepreneurial Human Capital and the Persistence of Underperforming Firms Author(s)

Abstract and Figures

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