Managerial Decision-Making and Escalation of Commitment

Bazerman, M., & Moore, D. (2013) Judgment in managerial
decision making
Chapter 6, 7
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GEO 207: Global Geography
Module 4 Homework Assignment

PART I: answer with 100 words or more.
Since 2001, the role of the United States in the world has been
at times championed as well as criticized; we are either loved or
hated, so it seems. Speculate on the “standing” of America in
the world today: are we still a superpower or have we lost our
strength? Consider such important events as the September 11,
2001 Terrorist Attacks, the Wars in Afghanistan and Iraq, issues
pertaining to illegal immigration, the North American Free
Trade Agreement (NAFTA), and our response to ISIS as well as
Russian aggression in the Ukraine.
PART II – SHORT RESEARCH PAPER
Directions: Please search for two research articles that will
serve as the basis for your homework this week.
Research the history of your family as far back as you can.
Determine at what point your family moved to the United States
and for what reasons (which is hallmark of cultural geography).
Include the following:
When did your family originally arrive in the United States?
Where did they settle?
What reasons do you know or think that they settled for?
What social processes from your ancestors’ home country (e.g.,
unemployment, religious persecution) drove them to the United
States? Did any other factors compel your family to move to
the United States (e.g., poor economic conditions in the home
country, frequent natural disasters, etc).
Your essay should be 2-3 pages, double-spaced, and APA-
formatted.

My last name is DeWindt. It is Dutch origin. The rest you can
research or make up as long as it is realistic.
191
how selF-JusTiFicaTion indirecTlY drives escalaTion oF
commiTmenT
sbr 66 april 2014 191–222
dominik steinkühler/matthias d. mahlendorf/malte brettel*
how self-JusTifiCaTion indireCTly drives esCalaTion of
CommiTmenT – a moTivaTional perspeCTive**
absTraCT
self-justification is the most examined and empirically
supported explanation of esca-
lation of commitment. based on motivated reasoning theory, we
argue that the need
for self-justification affects escalation of commitment indirectly
via other cognitive pro-
cesses. we suggest that the need for self-justification represents
a strong motivation
for the continuation of a failing project. Thus, it influences the
decision maker’s selec-
tive perception, sunk cost effect, and overoptimism, which in
turn foster escalation of
commitment. we investigate escalation in the venture capital
industry and thereby – in
addition to our theoretical contribution – strengthen the external

validity of previous
studies in the laboratory.
Jel classification: m10, G24, G30.
Keywords: escalation of commitment; motivated reasoning;
overoptimism; selec-
tive perception; self-Justification; sunk costs.
1 inTroduCTion
Some of the most difficult decisions that executives face
involve decisions on whether
to abandon or continue key initiatives that are not succeeding.
When decision makers
are asked to re-evaluate a previously chosen course of action,
they often tend to remain
committed to it even when new information indicates that it
should be terminated. This
phenomenon, known as escalation of commitment, is defined as
a “…tendency to be-
* Prof. Dr. Matthias D. Mahlendorf, Frankfurt School of
Finance & Management, Sonnemannstraße 9–11, 60314
Frankfurt am Main, Phone: +49 (69) 154008-837, Fax: +49 (69)
154008-4837, E-Mail: [email protected];
Dr. Dominik Steinkühler, RWTH Aachen, Templergraben 64,
52056 Aachen, Germany, E-Mail: [email protected]
win.rwth-aachen.de; Prof. Dr. Malte Brettel, Professor, RWTH
Aachen, Templergraben 64, 52056 Aachen,
Germany, E-Mail: [email protected]
** This paper is based on Dominik Steinkühler’s PhD thesis,
completed at RWTH Aachen. We would like to thank
the editor, one anonymous reviewer, and the participants of the
session at the Academy of Management Confer-
ence 2009 in Chicago for their valuable feedback.

d. sTeinKühler/m. mahlendorF/m. breTTel
192 sbr 66 april 2014 191–222
come locked in to a course of action, throwing good money after
bad or committing new
resources to a losing course of action” (Staw (1981)). Thus,
instead of terminating failing
projects, decision makers escalate their commitment by
continuing to invest resources
(money, time, work, etc.) and “…persist beyond an
economically rational point” (Staw
and Ross (1987, 41)). The phenomenon of escalation of
commitment is not limited to
any particular area; it has been well documented in a wide
variety of contexts, including
information technology projects (Keil (1995); Keil, Mann, and
Rai (2000); Montealegre
and Keil (2000)), large-scale construction projects (Ross and
Staw (1993)), banking (Mc-
Namara, Moon, and Bromiley (2002); Staw, Barsade, and Koput
(1997)), employee per-
formance appraisal (Bazerman, Beekun, and Schoorman (1982)),
sports events (Camerer
and Weber (1999); Staw and Hoang (1995)), and new product
development (Boulding,
Morgan, and Staelin (1997); Schmidt and Calantone (2002)).
In this paper, we make two contributions to the literature on
escalation of commitment.
We provide a new perspective on how self-justification leads to
escalation; and, following
calls in the literature (e.g. Bobocel and Meyer (1994, 363);

Staw (2005, 229)), we add to
the limited number of studies that investigate escalation outside
the laboratory.
Staw (1976) was the first to discuss the escalation of
commitment. Since then, escala-
tion has attracted sustained interest among researchers who,
over time, have proposed
a plethora of drivers of escalation tendencies in decision
making. Self-justification is the
most examined and empirically supported explanation for the
However, many studies investigate only the direct effect
between these two variables. In
contrast, based on motivated reasoning theory (Kunda (1990)),
we argue that the need
for self-justification affects escalation of commitment indirectly
via other cognitive pro-
cesses. We suggest that the need for self-justification represents
a strong motivation for
the continuation of a failing project, and thus influences the
decision maker’s selective
perception, sunk cost effect, and overoptimism, which in turn
foster escalation of com-
mitment. Establishing mediators between the need for self-
justification and escalation
shifts our understanding of the role of self-justification.
Although many studies see the
need for self-justification as a main driver of escalation of
commitment, we argue that
self-justification alone is not sufficient to explain escalation in
professional settings. Con-
tinuing investments because of “…decision makers’
unwillingness to admit that their
prior allocation of resources to the chosen course of action was
in vain” (Brockner (1992,

39)) would not be acceptable for either the organization or for
any decision maker with
a sense of responsibility. Therefore, we suggest that the effect
of the need for self-justifi-
cation on escalation manifests through three other cognitive
processes that we consider
in this study. This perspective moves research beyond the
predominant single-variable
explanations and adds to the understanding of the escalation
phenomenon and the direct
and indirect effects of its drivers.
Most research on the escalation of commitment has used
laboratory experiments. Al-
though there is no doubt that experiments are preferable for
testing causal relationships,
experiments often deviate substantially from real corporate
decisions, for example with
commiTmenT
193sbr 66 april 2014 191–222
respect to the time span of the decision process, the expertise of
the decision maker, his or
her ability to learn, the complexity of the decision task, and
incentives that may mitigate
escalation in organizational decision making (Guler (2007);
Mahlendorf and Wallenburg
(2013)). Hence, Staw (2005, 229) critiques the state of research
by pointing out that “…
there is no guarantee that the variables manipulated in the
laboratory have captured the

reality of escalation”. In contrast, we investigate the escalation
of commitment based on
survey data on real venture capital investments. Doing so allows
us to cross-validate the
results of previous laboratory experiments and to provide
support concerning the external
validity of prior research.
The venture capital industry provides an attractive setting for
studying escalation of com-
mitment because the potential for escalation in this setting is
high (Guler (2007); Ryan
(1994, 1995)). Venture capitalists routinely must decide
whether to continue investing
resources (both monetary and non-monetary) in risky ventures:
“The staging of rounds
of funding and the reinvestment decisions […] provide an ideal
setting to examine the
potential for escalation of commitment” (Birmingham, Busenitz,
and Arthurs (2003,
220)). Additionally, the investment process is relatively
consistent across firms (Gomp-
ers and Lerner (2004)). In our study, we refer to the process of a
venture capitalist firm’s
initial investment in a portfolio company and its subsequent
support and follow-on fi-
nancing rounds as a “project.” In the venture capital context,
escalation of commitment
implies that venture capitalists fail to terminate
underperforming projects in due time.
To study the influence of different escalation drivers, the
empirical design of our study
focuses on the perspectives of individual decision makers and
their involvement in the
decision on whether to continue or terminate a project.

The paper is structured as follows. In Section 2, to provide the
background for our re-
search model we review studies on the escalation of
commitment and the motivational
side of self-justification. In Section 3, we develop the
hypotheses. In Section 4, we outline
our method and describe our sample, measures, and methods of
empirical analysis. In
Section 5, we present the results of our statistical analysis.
Section 6 concludes with a
discussion of our findings, their managerial implications, and
suggestions on possible
avenues for further research and limitations.
2 liTeraTure review
2.1 selF-JusTiFicaTion
Self-justification is the most examined and empirically
supported explanation of escalation
of commitment (Brockner (1992); Sleesman, Conlon,
McNamara, and Miles (2012)).
The mechanism of self-justification, first discussed by Staw
(1976) in the context of es-
calation, is rooted in the theory of cognitive dissonance
(Festinger (1957)). This theory
proposes that people have a strong need to reduce sources of
cognitive inconsistency and
seek to justify the rationality of past decisions both to
themselves and to others. The idea
194 sbr 66 april 2014 191–222

was triggered by the U.S. involvement in Vietnam. However, it
is applicable to many
situations in which people have invested money or effort into a
failing course of action,
as Staw (2005, 218) so cogently summarizes:
“Like the Vietnam War effort, people may invest in stocks,
careers, or even marriages, and
when these investments do not pay off, they may not necessarily
withdraw from the situation.
Instead, people may actually invest further so as to turn the
situation around – to prove that
their prior decision was indeed and accurate ore appropriate
one.”
In other words, the concept of self-justification posits that
decision makers may experi-
ence psychological discomfort from cognitive dissonance as a
direct result of negative
feedback on previous investment decisions (Staw and Ross
(1987)). To rationalize their
past decisions and reaffirm the correctness of their initial
investments, decision makers
will continue to commit resources to a failing course of action
and thus avoid the psycho-
logical costs of failure (Staw and Ross (1978)).
To illustrate this stream of research, we refer to a study
conducted by Zhang and Baumeis-
ter (2006). In their laboratory experiment, undergraduate
students took part in a game
in which they had a chance to win a jackpot. To continue
playing, participants had to
periodically invest more money. The experiment tested the ego-
threat condition and the

control condition. In the ego-threat condition, participants’ need
for self-justification was
increased by telling them “If you’re the kind of person who
usually chokes under pressure
or if you don’t think that you have what it takes to win the
money, then you might want
to play it safe [i.e., quitting the game to avoid escalation]. But
it’s up to you.” The results
indicated that students in the ego-threat condition lost
significantly more money than did
those in the control condition. In other words, in a situation that
increases the need for
self-justification, people have a strong tendency to escalate
their commitment.
2.2 selecTiVe percepTion
The concept of selective perception has long since been
established as a factor that influ-
ences decision-making. As early as 1954, Hastorf and Cantrill
(133) stated that “…out of
all occurrences going on in the environment, a person selects
those that have significance
for him from his own egocentric position.”
In the context of escalation of commitment, Biyalogorsky,
Boulding, and Staelin (2006)
show that one important driver of escalation is biased belief
updates. In their experiment,
MBA students initially receive case information about a new
product, the history of the
market, competitors, etc. Based on this information, they have
to decide whether the or-
ganization should invest $2.5 million to improve and relaunch
the product. In the second
part of the case, students receive negative new information

about the net present value
forecast for the investment. The results of Biyalogorsky et al.
(2006) indicate that the
students do not correctly update their beliefs based on the new
information. Instead, their
commiTmenT
195sbr 66 april 2014 191–222
final evaluation of the investment is strongly influenced by
their initial beliefs. Hence,
selective perception seems to have occurred.
Keil, Depledge, and Rai (2007) conducted a laboratory
experiment with undergraduate
students that investigated selective perception concerning
software quality and its subse-
quent effects on escalation. Those students who were asked to
play the role of marketing
staff showed a considerably lower perception of software
quality problems than did a
comparison group that was assigned the position of quality
assurance staff. This selective
perception led to a higher escalation of commitment, measured
as a single item that asked
the students to recommend whether the project should continue
as planned.
2.3 sunk cosT eFFecT
A considerable amount of evidence shows that individuals take
sunk costs into account

when faced with decisions on continuance or termination of a
project (Arkes and Blumer
(1985); Dilts and Pence (2006); Garland and Newport (1991);
Garland, Sandefur, and
Rogers (1990); Moon (2001)). Sunk costs are defined as
resources that have already been
irrevocably committed and cannot be recovered. According to
classical economic and
normative decision theory, sunk costs are irrelevant for project
continuation decisions,
which should be made only on the basis of incremental gains or
losses (Bonini (1977);
Howe and McCabe (1983); Robichek and Van Horne (1967)).
However, in the context
of escalating projects, the studies mentioned above show that
once resources such as
money, effort, or time have been invested, decision makers
show a greater tendency to
continue a course of action (Arkes and Blumer (1985); Astebro,
Jeffrey, and Adomdza
(2007)). This behavior is referred to as the “sunk cost effect.”
For example, Astebro et al. (2007) investigate data from
investment decisions that re-
ceived feedback from the Inventors’ Assistance Program at the
Canadian Innovation Cen-
tre. This feedback comprised substantiated advice on whether to
continue or terminate
efforts. The Astebro et al. (2007) analysis suggests almost a
one-to-one relation between
resource commitments before receiving the advice to stop and
the resource commitment
afterwards. In other words, those who had already spent, for
example, $10,000, when
receiving the feedback to terminate the investment, spent
another $10,000 after having

received this advice. Thus, sunk costs seem to increase
2.4 oVeropTimism
Overoptimism, i.e., the tendency of decision makers to
systematically overestimate the
probability of good performance and underestimate the
probability of bad performance
(Heaton (2002)), has been demonstrated in several different
areas (Camerer and Lovallo
(1999); Camerer and Weber (1999); De Meza and Southey
(1996); March and Shapira
(1987); Weinstein (1987)) and is accepted as a robust decision
making bias. In the con-
196 sbr 66 april 2014 191–222
text of the escalation of commitment, overoptimism leads
decision makers to systemati-
cally overestimate the probability of success and therefore to
terminate failing projects too
late (Ross and Staw (1993); Staw (1997); Staw and Ross
(1987)).
In the escalation literature, we can distinguish two different
perspectives on optimism.
One perspective is interested in optimism as a personality trait,
i.e., being an optimistic
person; the other perspective focuses on optimism concerning
specific project or busi-
ness outcomes, i.e., being optimistic with regard to a specific

future event. Studies such
as that by Carver, Scheier, and Segerstrom (2010, 879) that take
the first perspective are
interested in the effect of generalized favorable expectancies for
the future on escalation
tendencies. In this vein, Astebro et al. (2007) find that above-
average optimists spend
significantly more time and money on projects on which they
have received advice to
cancel than do below-average optimists. An example for the
second perspective is the
study by Mahlendorf and Wallenburg (2013), which provides
evidence that the relation
between public justification requirements and investments in
failed projects is moderated
by optimistic outcome expectations. The study suggests that,
public justification increases
investments in failing projects in the case of low optimistic
outcome expectations, but not
in the case of highly optimistic outcome expectations.
3 hypoThesis developmenT
From our review of literature, we draw two main conclusions
that pertain to escalation
commitment.
First, when we consider methods, we see a need to complement
the findings from experi-
ments in the laboratory with data from the field. Decision
making in organizations might
be affected differently from individual biases than would be
decisions in the laboratory
(Guler (2007)). For example, organizations can apply a set of
monitoring and incentive
mechanisms that promote economically rational decisions. They

can also hire experts and
establish learning routines that might improve rational behavior
beyond those observed
in the laboratory. And decision makers who are facing
suboptimal performance during
actual practice will usually gather more evidence, such as
expert advice or additional
analysis of performance indicators (Schultze, Pfeiffer, and
Schulz-Hardt (2011)). Taken
together, these arguments suggest that the experimental studies
should be complemented
with evidence from the field.
Bobocel and Meyer (1994, 363) summarize the trade-off that
exemplifies many studies
on escalation conducted with experiments in the laboratory:
“Because our primary aim was to disentangle previously
confounded variables, we sought to
maximize internal validity in the present research. […]
Nevertheless, the precision gained in
the present paradigm comes at the expense of external validity.
It is thus imperative that field
research be conducted to assess the generalizability of our
findings.”
commiTmenT
197sbr 66 april 2014 191–222
Second, we believe that more refined models are necessary to
link the disparate expla-
nations for escalation of commitment. Since Staw’s (1976)

paper, many theories have
been proposed on what drives escalation of commitment
“…almost to the point that any
mechanism used to predict social behavior in general […] has
become a viable candidate
for explaining behavior in escalation situations” (Staw and Ross
(1987, 42)). Most of
the research focuses on the influence of single factors on
escalation. This approach nei-
ther embraces the multidetermined nature of escalation of
commitment (Staw (1997))
nor helps to advance the understanding of the relationships
among escalation drivers. In
fact, “…to further advance our understanding a more integrative
approach is needed to
construct and test a richer model of the [escalation]
phenomenon that includes causal
pathways between constructs” (Keil et al. (2007, 393)).
Motivated reasoning theory suggests that motivation affects
reasoning “…through reli-
ance on a biased set of cognitive processes” (Kunda (1990,
480)). Accordingly, the mo-
tives of a decision maker strongly influence his or her cognitive
processes (Kunda (1987);
Pyszczynski and Greenberg (1987); Sorrentino and Higgins
(1986)). Kunda (1990, 480)
suggests that “…any wish, desire, or preference that concerns
the outcome of a given
reasoning task” may bias “…strategies for accessing,
constructing, and evaluating beliefs”.
For example, defense motives induce selective processing in
favor of the desired conclu-
sion (Ditto and Lopez (1992)). The motivation to invoke a
certain impression will also
bias systematic processing towards satisfying the motivational

goal (Agrawal and Ma-
heswaran (2005)).
Given that cognitive dissonance theory on which self-
justification is based is “fundamen-
tally motivational in nature” (Elliot and Devine (1994, 382)), it
may not be sufficient to
consider just a direct behavioral effect of the need for self-
justification, as has been done
in prior research. In fact, dissonance theory explicitly proposes
that individuals can re-
duce dissonance by changing their cognitions (Festinger
(1957)). Hence, decision makers
who feel the need to justify their failed decisions may attempt
to cope through a variety
of cognitive defense mechanisms (Sivanathan, Molden,
Galinsky, and Ku (2008)). We
propose that the need for self-justification motivates people to
arrive at the conclusion that
the respective project should not be abandoned. Self-
justification will bias their cognitive
processes and thus indirectly influence escalation through
affecting aforementioned cog-
nitive escalation drivers. Figure 1 illustrates our suggested
mediation model.
As noted above, the effect of selective perception on escalation
of commitment has
primarily been investigated in laboratory experiments. Among
the few insights from
the field are the qualitative results from Guler (2007) and Ryan
(1994). In her study
on escalated venture capital projects, Guler (2007, 258) argues
that “…investors who
evaluated a venture early and worked with it over time might
fail to spot critical prob-

lems in a timely manner”. However, the correct perception of
portfolio companies’ per-
formance and challenges is crucial, because these aspects
determine whether a venture
capitalist will continue to support or abandon a portfolio
company (De Clercq and
Sapienza (2006)).
198 sbr 66 april 2014 191–222
Figure 1: Graphical Representation of the Research Model
In a similar vein, describing her case study results, Ryan (1994,
77–78) reports that “…
persistent investors were able to maintain a rigid focus on the
sequence of variables that
they believed would result in the achievement of bright
promise. They did so by selec-
tively and, at times, positively perceiving critical events in the
company’s history. These
biased interpretations in turn reinforced escalaters’ belief in a
successful outcome”. To
cross-validate the results from the laboratory and the qualitative
studies, we reinvestigate
the following hypothesis:
H1. With increasing selective perception, project escalation
increases.
In the venture capital industry, the sunk cost effect often gives
rise to what are called
“protective investments”, which Guler (2007, 259) defines as

those that are made by
investors to recover their initial investments and
“…demonstrate an inability to disregard
sunk investments”. Investors have a strong desire not to walk
away from a company and
take a certain loss but instead will invest further funds to keep
the company alive (Ryan
(1995); Valliere and Peterson (2005)). Building on these
previous studies, we attempt to
cross-validate the following hypothesis:
H2. With an increasing sunk-cost effect, project escalation
increases.
As described in the literature review, some studies on escalation
analyze overoptimism in
the sense of a general personality trait; others focus on project
specific overoptimism. We
take the latter perspective, because, as we note below in
hypotheses 5c and 5d, project-
specific overoptimism can be influenced by motivational
aspects such as self-justification.
Two case studies examine escalated venture capital projects.
Guler (2007) provides qua-
litative evidence that overoptimism occurs as one potential
driver of escalation in the
commiTmenT
199sbr 66 april 2014 191–222
venture capital setting. Guler shows that several interview

partners believe that venture
capital firms can achieve turn arounds with follow-up
investment. In her qualitative field
study Ryan (1994) identifies decision makers’ overoptimism as
a source of enduring per-
sistence and continued financing of failing ventures. Thus, we
reinvestigate the following
hypothesis:
H3. With increasing overoptimism, project escalation increases.
Similar to the other decision-making biases introduced above,
some research suggests that
the need for self-justification influences venture capital
decision makers (Guler (2007);
Ryan (1994, 1995)). According to Ryan (1995, 340), investors
often continue to support
a failing venture “…to buy time [which] is consistent with self-
justification explanations
of escalation behavior”, thus justifying the correctness of their
initial resource allocations.
Apart from this qualitative evidence, most research on self-
justification uses the experi-
mental setup originally introduced by Staw (1976). However, a
study by Schulz-Hardt,
Thurow-Kroning, and Frey (2009) questions whether the effects
of self-justification on
escalation in this type of experiment are driven by preference
for the initial decision.
Hence, it seems desirable to cross-validate the following
hypothesis using a different
method:
H4. With increased need for self-justification, project escalation
increases.

Previous research usually looks at direct effects on escalation of
commitment, but we
propose an indirect relation between the need for self-
justification and escalation of com-
mitment based on motivated reasoning theory (Kunda (1990))
and “…relying on the
prevalent view of motivation as having its effects through
cognitive processes” (Jain and
Maheswaran (2000, 358)).
One way to reduce the cognitive dissonance associated with
failing projects is to avoid
situations in which negative information might become salient.
“[W]hen dissonance is
present, [… the decision maker] will actively avoid situations
and information which
would likely increase the dissonance” (Festinger (1957, 3)).
Prior experimental studies
show that decision makers with a need to self-justify become
increasingly selective in their
search for information and will ignore negative feedback
(Caldwell and O’Reilly (1982);
Conlon and Parks (1987); Schmidt and Calantone (2002); Staw,
Sandelands, and Dut-
ton (1981)). Furthermore, managers who must cope with self-
threats may apply selective
cognitive effort when analyzing information (Ditto, Scepansky,
Munro, Apanovitch, and
Lockhart (1998)), pay selective attention to the reliability of the
received information
(Doosje, Spears, and Koomen (1995)), and discredit the ability
of others who attempt to
communicate information on a failing course of action (Staw
and Ross (1987)). Hence,
we hypothesize that a need for self-justification is positively
related to selective percep-

tion.
H5a. With increasing need for self-justification, selective
perception increases.
200 sbr 66 april 2014 191–222
Defensive motives, such as the need for self-justification,
increase people’s reliance on heu-
ristics to support their desired conclusion (Giner-Sorolla and
Chaiken (1997)). Thus, the
need for self-justification may strengthen “…powerful
judgmental heuristics, such as ‘waste
not…’ on the sunk cost problems” (Klaczynski (2001, 305)).
Additionally, when choosing
between continued investment and project termination, decision
makers may be “…chang-
ing the attractiveness of the alternatives” (Festinger (1957, 42)).
In the context of project
escalation, the perceived attractiveness of alternatives often
inversely relates to the non-
retrievable prior investments (i.e., sunk costs). Thus, decision
makers may pay increased at-
tention to the sunk costs of the project to lessen the
“attractiveness” of a termination (Arkes
and Blumer (1985); Conlon and Leatherwood (1989)).
Therefore, we hypothesize:
H5b. With increasing need for self-justification, the sunk-cost
effect increases.
In addition to reducing the attractiveness of termination,

decision makers can also in-
crease the attractiveness of continuation, thus mitigating
cognitive dissonance. They
could, for example, consider any setbacks as only temporary
and “…continue their belief
in the bright promise of future gains” (Ryan (1994, 105)), i.e.,
engage in overoptimism.
More generally, the need for self-justification increases the
desirability of a positive project
outcome. This effect in turn increases desirability bias, which is
one cause for overopti-
mism (Krizan and Windschitl (2007)). Accordingly, we expect
that the need for self-
justification affects overoptimism. We express this conjecture
in the following hypothesis:
H5c. With increasing need for self-justification, overoptimism
increases.
So far, few studies have rigorously addressed the influence of
self-justification on the
cognitive processes of decision makers who escalate
commitment, but the considerations
we mention above suggest potentially important relationships.
In fact, re-examining the
roots of self-justification in cognitive dissonance theory and its
motivational relation to
a decision maker’s cognitive processes may even open up an
entirely new perspective on
the mechanism through which the need for self-justification
exerts an influence on esca-
lation of commitment. The key tenet of self-justification
reflected in Hypothesis 4 is that
decision makers will actively, and therefore consciously,
escalate their commitment to
rationalize past decisions, as classically conceptualized in the

respective literature. How-
ever, reinterpretations of cognitive dissonance theory (for
reviews see Cooper (2007);
Harmon-Jones and Harmon-Jones (2007)) on which self-
justification is based may cast
doubts on this direct causal effect. For example, the self-
consistency model of cognitive
dissonance proposes that people hold expectations for
competence and morality derived
from “…the conventional morals and prevailing values of
society” (Thibodeau and Ar-
onson (1992, 592)). Dissonance is aroused when a discrepancy
is detected between those
self-expectancies and a behavior. Because people can generally
be expected to hold posi-
tive self-expectancies, incompetent, immoral, or irrational
behavior will thus create disso-
nance (Harmon-Jones and Harmon-Jones (2007)). Similarly,
without including concepts
of the self, the “New Look” model of cognitive dissonance
proposes that dissonance is
triggered whenever behavior is inconsistent with general
societal or normative standards
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201sbr 66 april 2014 191–222
of competent or moral behavior (Cooper and Fazio (1984)). The
self-standards model,
which Maertz, Hassan, and Magnusson (2009, 68) describe as
“…the most comprehen-
sive [and] integrative view of cognitive dissonance to date”,

adopts those views and pro-
poses that any behavioral inconsistency can provoke dissonance
based on either societal
normative standards or uniquely personal standards of
competence and morality (Stone
and Cooper (2001)).
Considering these advances of cognitive dissonance theory and
motivated reasoning, the
classic conceptualization of self-justification seems
questionable. Simply yielding to the
need for self-justification and actively escalating commitment
to a losing course of action
clearly constitutes an irrational and non-normative act, which
would thus arouse further
dissonance. Since, according to the theory, the decision maker
will avoid situations that
increase dissonance, he or she should not be expected to behave
in this manner. However,
a different mechanism may account for the influence of the need
for self-justification
on escalation of commitment. This mechanism may have gone
unnoticed so far due to
limitations of prior studies, which do not examine potential
causal relationships between
different drivers of escalation. According to classic cognitive
dissonance theory, a deci-
sion maker may adapt cognitions to reduce dissonance
(Festinger (1957)). Therefore,
the cognitive “defense mechanisms” described in Hypotheses
5a-c may actually transmit
the influence of the need for self-justification on project
escalation, i.e., the need for
self-justification influences project escalation through the
cognitive biases introduced in
this study. In this respect, Kunda (1990, 480) states that “…this

view of motivation as
cognitively mediated has always been integral to the
understanding of dissonance reduc-
tion”. Research on motivated reasoning, which frequently
proposes that motivation trans-
mits its influence through cognitive processes by determining
which cognitive processes
will be used on a given occasion in order to arrive at a desired
conclusion, supports this
perspective (Jain and Maheswaran (2000); Kunda (1987);
Molden and Higgins (2005);
Pyszczynski and Greenberg (1987); Sorrentino and Higgins
(1986)). These mechanisms
allow decision makers to maintain what Kunda (1990, 483) calls
the “illusion of objectiv-
ity,” which describes their personal belief that the conclusion at
which they arrived (in the
present case, to continue a project) is objectively the right one,
even though the cognitive
processes that led to it were actually biased. We summarize the
theoretical considerations
in the following hypothesis:
H5d. The cognitive processes selective perception, sunk cost
effect, and overoptimism (at least
partially) mediate the effect of the need for self-justification on
project escalation.
4 meThod
4.1 sample and daTa collecTion
Sample. To test the proposed model, we conducted an online
survey among European
venture capital professionals. In the first step, we chose
companies active in venture capi-

202 sbr 66 april 2014 191–222
tal financing located in Europe based on the member directories
of the European Private
Equity and Venture Capital Association (EVCA) as the target
population. Our second
step was to study the homepage of each venture capital firm as
well as press announce-
ments on portfolio investments. Doing so enabled us to identify
the most appropriate
executives within each company who were involved in making
the investment decisions
and who were thus in a position to provide meaningful
information on the decision pro-
cesses (Campbell (1955)). This procedure eventually led to a
contact database of 2,058
venture capital professionals.
Survey design and administration. To develop our multiple
item-based measurement in-
strument, we followed the recommendations of Churchill
(1979), DeVellis (2003), Dia-
mantopoulos (2005), Nunnally (1978), and Rossiter (2002).
We conducted an extensive literature review to identify,
wherever possible, measures that
would appropriately capture the constructs we wished to include
in our study. For several
constructs, we had to develop new scales. In the development of
an initial item pool, we
also relied on prior case studies on escalation behavior and used

content analysis to detect
phrases frequently used by decision makers in relation to the
escalation mechanisms stud-
ied in this paper. We conducted several pretests to improve the
measurement instruments.
These tests included an item-sorting procedure (Anderson and
Gerbing (1991); Hinkin
(1995)) performed by 17 raters; content validity ratings
(Lichtenstein, Netemeyer, and
Burton (1990); Zaichkowsky (1985)) by 24 raters; expert
interviews with six academics
and professionals; a large scale pretest of the survey with 287
German technical manag-
ers that we used to validate and purify the measurement models
(Homburg and Giering
(1996)); and seven additional interviews with venture capital
professionals to assess the
adequacy of the item formulations in the venture capital
context.
During data collection, we followed the procedures prescribed
by Dillmann (2000). We
gathered our data by sending out an email containing a
personalized link to our online
survey instrument to 2,058 participants in our contact database.
We instructed respon-
dents to complete the questionnaire for a single project that
failed (for a similar ap-
proach see Dilts and Pence (2006)). Our reason for focusing on
failed projects was that
escalation of commitment is explicitly defined as continuing a
failing course of action
(Staw (1976)). This approach produced 182 valid responses
after the initial email and
two subsequent reminders. An additional 300 respondents stated
either that company

policies prohibited the participation in surveys or that they had
no experience with a
failed venture capital investment. Because of missing values,
we excluded five data sets
from the further analysis, leaving 177 valid responses in our
sample, which implies a net
response rate of 8.6%.
We checked for nonresponse bias by comparing early and late
respondents using
Mann-Whitney U Tests (Armstrong and Overton (1977)). We
found no significant
differences.
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203sbr 66 april 2014 191–222
4.2 measures
We measure the variables in this study as latent variables with
multiple indicators. Unless
otherwise indicated, all items are measured on a 7-point Likert
scale. Based on the crite-
ria proposed by Jarvis, MacKenzie, and Podsakoff (2003), all
constructs in this study are
specified as reflective. Appendix 1 presents details of all
measures.
Selective perception. Selective perception describes a
systematic tendency of decision mak-
ers to filter out or downplay negative incoming information and
to focus on feedback that

conforms to their positive thinking. Similar to Keil et al.
(2007), we develop four items
that measure whether decision makers focus their attention on
certain areas of informa-
tion, neglect information that contradicts their views, or both.
Sunk-cost effect. The sunk-cost effect describes the effect of
making the decision to con-
tinue a project based on investments made in the past. To
measure this construct we use
five items that capture the extent to which the presence of sunk
costs opposes project
termination in the perception of the decision maker. Our items
draw specifically on the
operationalization of the sunk-cost effect in Keil et al. (2000)
and on phrases that other
researchers, in their studies on the sunk-cost effect, find are
typical for decision makers
(Arkes and Blumer (1985); Keil, Truex, and Mixon (1995)).
Based on Guler (2007), we
include an additional item to capture the notion of protective
investments in the venture
capital industry.
Overoptimism. The effect of overoptimism leads decision
makers to systematically over-
estimate the probability of success and thus believe in an
ultimately positive outcome.
Hence, negative developments are considered, unwarrantedly, as
only temporary or man-
ageable, and decision makers believe that the project will
eventually develop as expected.
We develop five items to tap into those notions and capture the
decision makers’ attitudes
towards negative events as well as their outcome expectancy for
the project.

Need for Self-justification. According to Keil et al. (2000), the
direct assessment of psycho-
logical self-justification is difficult. Therefore, we follow their
advice and limit ourselves
to the assessment of “…observable behaviors that could serve as
reliable indicators of the
[...] need to self-justify” (Keil et al. (2000, 641)) and measure
the factors that influence
the perceived need of the decision maker to justify her/his
decisions. For our five item
scale, we adapt one item from Keil et al.’s (2000) need for
social self-justification scale and
leverage the ten-item measure of decision involvement from
Biyalogorsky et al. (2006).
Project escalation. Project escalation is characterized by two
key features: the continued
investment of resources in a failing project that leads to
economically irrational delayed
project terminations. Our operationalization of project
escalation evolves around these
two characteristics. Due to the complexity of the construct, we
operationalize project
escalation as a second-order construct of Perceived Project
Escalation, Perceived Resource
Investment, and Invested Resources.
204 sbr 66 april 2014 191–222
Perceived Project Escalation taps directly into the key
escalation characteristic of delayed

project termination. Hence, it captures whether the project went
on for too long. We
measure this feature by using a three-item scale that reflects the
ex-post assessment of
the decision maker about the delay of the project termination
(Mahlendorf (2013)). A
sample item is “It was held on to the project too insistently.”
The two other constructs,
Perceived Resource Investment and Invested Resources, relate
to the other key characteris-
tic of escalation, the continued investment of resources that
should have been avoided.
Consistent with our understanding of escalation, delayed project
termination should be
manifested in a “growth in the cumulative amount of resources
invested over time” (Keil
et al. (1995, 372)). Therefore, borrowing from project
management literature (Ashley,
Lurie, and Jaselskis (1987); Dvir, Raz, and Shenhar (2003);
Pinto and Slevin (1988)), the
three items assess Perceived Resource Investment as the
cumulated overinvestment of the
relevant resources time, work effort, and money over the course
of the project as perceived
by the decision maker. A sample item is “Too many financial
resources were invested in
the project.” Additionally, building on Dilts and Pence (2006)
and Mahlendorf and Wal-
lenburg (2013), we also measure Invested Resources, a more
objective measure of project
escalation, by using three items, and compare the actual
resource investment (time, work
effort, and money) to the initial investment planned at the start
of the project. This ap-
proach follows the logic that the initial decision to invest was
made based on a certain

return calculation and that escalating projects will show
increasing budget overruns, mak-
ing continued investment uneconomical. Budget overruns are an
indicator of negative
developments. Hence, the items capture investments despite
negative information, which
is one of the key characteristics of escalation of commitment.
The formulation is “How
do the resources invested into the project until its termination
and/or exit compare to the
expenditures expected at the initiation of the project?” with
separate answer options for
man-days, costs, and length of the project ranging from “1 =
much less than expected” to
“7 = much more than expected”.
In the absence of an objective measure of escalation of
commitment in real investment
projects, we believe that this second-order construct is a useful
proxy for escalation of
commitment in failed projects. Moreover, we believe that this
construct addresses esca-
lation in greater detail than do prior measures (e.g., Armstrong,
Williams, and Barrett
(2004); Ku (2008); Ku, Malhotra, and Murnighan (2005)).
Control Variables. Our analysis includes six control variables.
We include the relative size
of the investment compared to the average investment per
portfolio company to control
for possible effects of financing size on the level of
commitment of the venture capital
firm (Dilts and Pence (2006)). In addition, to capture potential
effects of hierarchical
position, we use a dummy variable that takes the value of one if
the respondents posi-

tion is at the top level. We also use age of the venture capital
firm as a control variable,
because control systems develop over time (Aerts (2005);
Davila (2005); Davila and Fos-
ter (2007)) and might reduce escalation behavior. Further, since
larger venture capital
companies might apply more formal controls, so we include
company size measured as
capital under management (cumulated over all funds, in million
euros). Additionally, we
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205sbr 66 april 2014 191–222
included an industry group dummy variable for the portfolio
company (1 = manufactur-
ing/production; 0 = services and other) to capture general
industry effects (Birmingham
et al. (2003)). And we control for the role of the venture capital
firm (non-lead investor,
co-lead investor, or lead investor) to control for syndication
effects that may bias the re-
sults (Birmingham et al. (2003)).
4.3 meThod oF analysis
To test the hypothesized relationships depicted in Figure 1, we
use the partial least squares
(PLS) technique (cp. e.g. Berson, Oreg, and Dvir (2008);
Cording, Christmann, and
King (2008); Cron, Gilly, Graham, and Slocum Jr. (2009)),
using SmartPLS 2.0 M3

(Ringle, Wende, and Will (2005)). To evaluate the significance
of parameter estimates,
we implement a bootstrapping procedure with 1,000 samples, as
recommended (Chin
(1998)).
5 resulTs
5.1 eValuaTion oF The measuremenT model
Table 1 provides a descriptive analysis of the means, standard
deviations, ranges of the
constructs, and the correlations between them.
We assess the reliability and validity of the measurement
models following the procedures
for PLS analysis recommended by Chin (1998). Out of all items,
we eliminate only two,
due to low factor loading. The subsequent calculation of
Cronbach’s alpha (threshold
0.7), composite reliability (threshold 0.7), and average variance
extracted (threshold 0.5)
shows that the constructs exceed the thresholds for all criteria
(see Appendix 1), with
only one exception (i.e., the construct invested resources
slightly misses the threshold for
Cronbach’s alpha with a value of 0.62). Overall, our results
indicate satisfactory reliability
for the constructs.
We also analyze the discriminant validity on the item and the
construct level. We follow
Fornell and Larcker (1981), who argue that a construct shows
sufficient discriminant va-
lidity when it shares more variance with its measures than with
other constructs. Table 1

suggests satisfactory discriminant validity for all constructs. We
then test item discrimi-
nant validity by examining whether all items share more
variance with their own con-
struct than with any other construct. The matrix of factor cross-
loadings in Appendix 2
shows that each item’s loading is higher on its assigned
construct than on the other con-
structs, which supports adequate discriminant validity.
206 sbr 66 april 2014 191–222
Table 1: Descriptive Statistics and Construct-Level Correlations
Correlations and Square Root of Average
Variance Extracted (AVE)
Variables 1 2 3 4 5 6 7
1. perceived project escalation 0.91
2. perceived resource investment 0.62 0.81
3. invested resources 0.35 0.59 0.75
4. selective perception 0.23 0.20 0.20 0.83
5. sunk cost effect 0.16 0.25 0.26 0.28 0.82
6. overoptimism 0.19 0.20 0.21 0.32 0.41 0.83
7. need for self-Justification 0.20 0.16 0.07 0.20 0.32 0.25 0.85

Summary Statistics
Number of items 3 3 3 4 4 4 5
Mean 4.75 4.62 4.97 3.40 3.42 4.36 3.02
standard deviation 1.85 1.90 1.40 1.60 1.82 1.48 1.83
Min 1 1 1 1 1 1 1
Max 7 7 7 7 7 7 7
note: Table depicts square root of ave on the diagonal and
correlations on off-diagonal.
5.2 common meThod Bias consideraTions
Although the use of key informants is common in research, self-
reported data are poten-
tially vulnerable to common method biases (Podsakoff and
Organ (1986)). Surveying
decision makers on failed projects is an emotionally charged
topic that could be subject to
social desirability bias. We attempt to reduce this problem by
implementing some of the
procedural remedies (e.g., response anonymity) recommended
by Podsakoff, MacKenzie,
Lee, and Podsakoff (2003). Additionally, we collect data from
two particular groups of
informants, key decision makers who were personally involved
in the decisions about
project continuation and termination and thus evaluated their
own decisions, and from
non-decision makers who evaluated the decisions of another
key-decision maker. This

procedure makes it possible to compare the answers of the two
groups of respondents
and thus detect significant differences between the informants.
This classification led to
130 key-decision makers and 47 non-decision makers. We
conduct an ANOVA for all
items, which shows that the groups of informants differ
significantly only on one out of
28 indicators. In addition, we conduct statistical analyses to
assess the potential influence
of common method bias. First, we perform Harman’s single-
factor test (Podsakoff and
Organ (1986)). The results of the principal component factor
analysis show that no single
factor emerges and no single factor accounts for the majority of
the covariance among
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207sbr 66 april 2014 191–222
the measures. Additionally, following Podsakoff et al. (2003)
and Williams, Edwards, and
Vandenberg (2003), we include a common method factor in the
PLS model (see Liang,
Saraf, Hu, and Xue (2007) for a detailed description). Appendix
3 shows that the average
variance explained by the substantive factors is 0.698, while the
average variance ex-
plained by the method factor is 0.005. Hence, the ratio of
substantive to method variance
is about 137:1. Given the invariant answering behavior of key-
decision makers and non-

decision makers, as well as the small magnitude and
nonsignificance of method variance,
we contend that common method bias is unlikely to be a serious
concern for this study.
5.3 eValuaTion oF The sTrucTural model and TesTing oF
hypoTheses
To evaluate the structural model, we estimate the coefficient of
determination R², the
Stone-Geisser-Criterion Q² (Geisser (1975)), and the path
coefficients with their respec-
tive t-values, in PLS. Table 2 presents the results for the overall
sample.
Table 2: Results of PLS Analysis
Hypothesized Path Standardized Path Coefficient t-value
selective perception → project escalation 0.14 1.86**
sunk cost effect → project escalation 0.14 1.71**
overoptimism → project escalation 0.10 1.46*
need for self-justification → project escalation 0.07 1.18
need for self-justification → selective perception 0.20 2.46***
need for self-justification → sunk cost effect 0.32 4.23***
need for self-justification → overoptimism 0.25 4.02***
Controls
relative investment size → project escalation 0.09 1.42*

role of venture capital firm → project escalation –0.08 1.42*
hierarchical level → project escalation 0.01 0.22
industry → project escalation 0.03 0.64
Venture capital firm size → project escalation –0.12 1.66**
Venture capital firm age → project escalation 0.08 0.47
* p < 0.10, ** p < 0.05, *** p < 0.01 (one-tailed).
Considering the fact that the escalation mechanisms discussed
in our paper are only just
a few among the many factors influencing project escalation
(other factors include, for
example, organizational and external effects, see Staw (1997,
2005)), and that the empiri-
cal design of our study focuses on individual decision makers,
leaving aside the influence
208 sbr 66 april 2014 191–222
of the decisions of other people involved in the project, the R²
value for project escalation
of 0.16 indicates satisfactory explanatory power of our model.
Thus, our data substantiate
the importance of the escalation mechanisms. In addition, the
positive Q² value of 0.06
suggests that our model has predictive relevance (Chin (1998)),
lending further support

to the important role of the escalation mechanisms.
Our data support seven out of the eight research hypotheses
proposed in the paper. All
three cognitive escalation drivers, selective perception, the
sunk-cost effect, and overop-
timism, are significantly related to project escalation, thus
supporting Hypotheses 1 to 3.
Hypothesis 4, which postulates a direct influence of the need for
self-justification on pro-
ject escalation in addition to the mediating effects, is not
supported. However, Hypo-
theses 5a-5c, which postulate the indirect influences of the need
for self-justification, are
strongly supported. To further explore this mediated
relationship between the need for
self-justification and project escalation, we utilize the
recommendations by Preacher and
Hayes (2008) for assessing indirect effects in multiple mediator
models and also fol-
low the procedure laid out by Iacobucci, Saldanha, and Deng
(2007) for the analysis of
mediated relationships in structural equation models using the
product-of-coefficients
approach.
As can be seen from the z tests (Sobel (1982)) in Table 3, the
indirect effects of the need
for self-justification on project escalation are significant. Given
that the direct effect of
the need for self-justification on project escalation is not
significant, this result indicates
that its influence is fully mediated by the three cognitive
processes selective perception,
sunk-cost effect, and overoptimism. Hence, Hypothesis 5d is

supported.
We also test for mediation by following the causal-steps
approach recommended by
Baron and Kenny (1986), and find the same results. In the
direct-effect model (with-
out mediators), the need for self-justification is significantly
related to project escalation
(path coefficient = 0.19; p < 0.01), fulfilling the first condition
according to Baron and
Kenny. As Table 3 shows, the other two conditions of a
significant relation between the
independent and the intervening variables, and between the
intervening variables and the
dependent variable, are also fulfilled. Full mediation occurs
because after introduction of
the intervening variables the need for self-justification has no
significant effect on project
escalation.
To test the robustness of our results, we calculate three
additional models. In each of these
models, we drop one of the three mediators (selective
perception, sunk-cost effect, and
overoptimism) from the analysis. In all three models, the
significance of the effects of the
two remaining mediators on project escalation becomes
stronger. Moreover, the direct
effect of need for self-justification on project escalation
becomes marginally significant
(p < 0.1, one-tailed), indicating partial mediation instead of full
mediation. These results
provide support for the robustness of our model and suggest that
all three mediators are
relevant.

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209sbr 66 april 2014 191–222
Table 3: Results of Mediation Analysis
Mediated Relationship Mediator z-value
need for self-Justification → project escalation
selective perception 1.38*
sunk cost effect 1.65**
overoptimism 1.40*
* p < 0.10, ** p < 0.05, *** p < 0.01 (one-tailed).
We find the following results for the control variables. Venture
capital firm size has a
significant negative relation with project escalation (path
coefficient = –0.12, p < 0.05,
one-tailed). Hence, larger companies might have more effective
mechanisms to reduce
escalation; for example, more formal project evaluation
procedures. Relative investment
size is marginally and significantly related to project escalation
(path coefficient = 0.09;
p < 0.1, one-tailed). This result is in line with the sunk-cost
effect and supports the no-
tion that the larger the size of invested resources, the higher the
tendency to terminate a
project too late. The role of the venture capital firm has a

negative marginally significant
relation with project escalation (path coefficient = –0.08; p <
0.1, one-tailed). This result
suggests that lead investors are somewhat less susceptible to
escalation compared to non-
lead investors. All other control variables (i.e., hierarchical
level, industry and venture
capital firm age) do not have a significant impact on project
escalation.
6 disCussion
Our study examines the relationship between project escalation
and several factors that
prior literature identifies as important drivers of escalation.
Analyzing the effects of dif-
ferent escalation drivers simultaneously, we address the
multidetermined nature of esca-
lation of commitment. We are able to assess their direct and
indirect effects and move
beyond the predominant single variable explanations of
escalation. Based on motivated
reasoning theory (Kunda (1990)), in this study we provide what
we believe is a significant
conceptual contribution: we take a motivational perspective on
the influence of the need
for self-justification on escalation of commitment and propose a
mediation by cognitive
processes.
Our tests support our theoretical predictions and results from
laboratory experiments
on the direct effects of selective perception, sunk-cost effect,
and overoptimism on es-
calation. Hence, the empirical data from this survey in the
venture capital industry

provide evidence on the external validity of the former research
results. This finding is
important, because it suggests that companies are not yet able to
overcome individual
biases, for example, by means of monitoring, incentives, and
employing experts. Fur-
thermore, our data support the hypothesis that the need for self-
justification strongly
influences the three cognitive escalation drivers included in our
research model. Although
self-justification has long been considered the main driver of
escalation of commitment
210 sbr 66 april 2014 191–222
(Brockner (1992)), we find no direct effect on project
escalation. Instead, our results
suggest that cognitive processes fully mediate the influence of
self-justification. Thus, our
empirical data support the predictions derived from motivated
reasoning theory. Thus,
the need for self-justification represents a motivation to
continue an ambiguous project.
This motivation in turn affects reasoning “…through reliance on
a biased set of cognitive
processes” (Kunda (1990, 480)). Selective perception, the sunk-
cost effect, and overop-
timism are powerful mechanisms through which the need for
self-justification indirectly
drives escalation of commitment. To satisfy their need for self-
justification, decision mak-
ers utilize cognitive defense mechanisms that distort their

assessment of the situation and
eventually lead them to believe that continued investment is the
appropriate decision.
Recent findings support the view that escalation is strongly
rooted in individual percep-
tion (Biyalogorsky et al. (2006); Keil et al. (2007)), which is
also consistent with classic
dissonance theory: a decision maker with a need to self-justify
will reduce dissonance by
adapting his or her cognitions (Festinger (1957)).
6.1 implicaTions For managemenT
Our study has implications for management practices. First, our
results support earlier
suggestions that managers should implement practices to reduce
the need for self-justi-
fication. Self-justification might be even more important than
previously assumed, be-
cause our analysis suggests undesirable effects of self-
justification on selective perception,
sunk-cost effects, and overoptimism. Hence, reducing the need
for self-justification is key
for avoiding not only deliberate opportunistic behavior, but also
subconscious cognitive
coping strategies. For example, to reduce the need for self-
justification, managers might
consider leadership rotation (Boulding et al. (1997); Schmidt
and Calantone (2002)).
This procedure could be helpful, because a newly assigned
leader has no share of the
responsibility for initiating the original investment and thus has
a lower pressure for self-
justification. (However, we note that sharing decision authority
can also lead to greater
levels of escalation under certain conditions (Sleesman et al.

(2012)).)
The indirect mechanism proposed by our study has a second
important implication for
de-escalation efforts in managerial practice. Even if the need
for self-justification cannot
be completely avoided, its effects on escalation behavior can be
reduced by decreasing the
mediator‘s selective perception, sunk-cost effect, and
overoptimism. To mitigate selective
perception, managers should receive explicit advice on how to
search for disconfirm-
ing evidence, i.e., information that suggests it would be wise to
cancel the investment
(Kadous and Sedor (2004)). To reduce the sunk-cost effect,
alternative investment pos-
sibilities should be highlighted (Keil et al. (1995)). To avoid
overoptimism, (Lovallo and
Kahneman (2003)) suggest that managers could apply the
advocatus diaboli (Devil’s Ad-
vocate) concept.
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211sbr 66 april 2014 191–222
6.2 implicaTions For FuTure research
Our analysis suggests that cognitive processes mediate the
relationship between the need
for self-justification and escalation of commitment. Future
studies may wish to investi-
gate these mediating effects in more detail, perhaps by going

back to the laboratory to
untangle the interactions. In any case, we can endorse the
skepticism of Staw (2005),
which we introduced at the beginning of this paper, that prior
laboratory research might
have failed to capture the complexity of real-world escalation.
For a better understanding
of the escalation phenomenon, future research might consider
scenarios that are more re-
alistic and which cater to the apparent interactions between
different drivers of escalation.
In addition, while our model is a static one, dynamic approaches
may generate valuable
insights on how the effects of different mechanisms evolve over
time as a function of the
level of commitment.
Schulz-Hardt et al. (2009) find that a decision maker’s
preferences may be an underes-
timated driver of escalation. Since preferences represent one
facet of motivation (Kunda
(1990)), motivated reasoning theory may have to offer
additional insights concerning
Schultz-Hardt et al.’s findings. Like our study, future
investigations might explore wheth-
er cognitive processes, such as selective perception, the sunk-
costs effect, and overopti-
mism also mediate the relation between preferences and
escalation.
6.3 limiTaTions
Although we integrate several explanations of escalation of
commitment into our frame-
work, we cannot claim that our model gives a full understanding
of this complex phe-

nomenon. We limit our model to four mechanisms that have
repeatedly been shown to
influence escalation of commitment in experimental contexts.
However, there are ad-
ditional escalation drivers, and they might be included in
subsequent research. We also
highlight that our model focuses on psychological drivers of
escalation. In real-world set-
tings, a lot of aspects influence termination decisions, e.g.,
organizational determinants,
external factors, etc., and researchers suggest that many of these
promote escalation of
commitment (Staw (1997); Staw and Ross (1987)).
A limitation of this study might be its focus on the venture
capital industry, which is a
highly specialized industry. However, investment decisions are
one of the primary ac-
tivities of venture capital firms (Fenn, Liang, and Prowse
(1997); Gompers and Lerner
(2004)) whose managers have both the experience and
incentives to maximize profits.
Additionally, venture capitalists are usually distanced from the
operations of their in-
vestments and thus are more likely to assess them objectively
compared to managers in
other organizations. Hence, the results of our study may
represent the upper bound on
the quality of termination decisions and should, therefore, be
generalizable beyond the
venture capital industry.

212 sbr 66 april 2014 191–222
Another limitation of our study might be its focus on the
perspective of an individual
decision maker. To the extent that the decision to continue or
terminate a project is made
by a group, there may be other escalation mechanisms besides
the individual cognitive
processes included in our research model (Bazerman, Giuliano,
and Appelman (1984)).
However, in group decision-making, it still is important to pay
attention to the individual
decision makers, as the decision of the group reflects the
characteristics and biases of its
members (Hambrick and Mason (1984)). Even more important,
in our venture capital
context, investment decisions are typically made in an
investment committee in which
each partner can veto any deal (Guler (2007)), so that each
decision maker needs to
make an individual decision on whether to continue or terminate
a project. Still, group
effects in escalation situations are a promising avenue for
further research, and one which
could provide insights into the discussion on whether the
individual biases are reinforced
(Whyte (1993)) or negated within the group (Smith, Tindale,
and Steiner (1998)).
appendix 1
Construct Measurement
Constructs and their respective indicators Cronbach’s
Alpha

Compo-
site Reli-
ability
AVE Load-
ing
Selective Perception 0.85 0.90 0.69
if new information on the project appeared, …
1. … i paid more attention to information, which supported
my opinion on the project.
0.76
2. … i paid less attention to information that contradicted
my view.
0.88
3. … i only turned my attention to certain information (i.e.,
i was subject to selective perception).
0.86
4. … i focused my attention on my“pet subjects”. 0.82
Sunk Cost Effect 0.84 0.89 0.68
1. i viewed termination of the project as a waste of resourc-
es, due to the irrecoverable investments already made.
0.82
2. i argued for the continuation of the project, because if

terminated, the previous investments would have been
in vain.
0.83
3. The more resources had been invested, the more expen-
sive a termination of the project seemed to me.
elimi-
nated
4. by continuing the project, i wanted to protect my previ-
ous investments.
0.82
5. i regarded a termination of the project as a destruction of
prior invested resources.
0.81
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