دخول وخروج القرارات في الفرق المرنة
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Transcript of دخول وخروج القرارات في الفرق المرنة
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ENTRY AND EXIT DECISIONS IN FLEXIBLE TEAMS
For the Journal of International Business Studies
Abstract The present study identifies a major limitation of current research on multinational corporations (MNCs). Joint
decision making in a distributed setting is of critical importance in practice, but has received little attention in
our theories. To address this gap in the knowledge, we examine the effects of flexible decision teams when
MNCs assess turbulent markets. Remarkably, flexible teams comprised of fallible evaluators can outperform
what is usually thought of as an optimal decision. Our main result supports the claims advanced in recent
empirical studies. Structural flexibility can help MNCs achieve high levels of performance even in conditions of
turbulence.
Keywords: Organization Theory, Decision-Taking Structures, Industrial Organization, Entry and Exit
Decisions, Market Turmoil, Flexible Teams
Acknowledgements: This paper received the best paper award at the 3rd annual JIBS/AIB/CIBER invitational conference, September
2005. Comments from the editor Arie Y. Lewin, two anonymous reviewers and Bill McKelvey, Paola Perez-Aleman, Phanish Puranam,
Nils Stieglitz, Kannan Srikanth, and Henk W. Volberda are gratefully acknowledged. Comments from participants at the 3rd annual
JIBS/AIB/CIBER invitational conference and support from the Strategic Organization Design Unit (http://www.sdu.dk/sod), University
of Southern Denmark is also acknowledged.
*Corresponding author. Email: [email protected], Tel: +45-65501000, Fax: +45-66155129.
Thorbjrn Knudsen*
Strategic Organization Design Unit (SOD),
Department of Marketing & Management
University of Southern Denmark
Campusvej 55,
DK-5230 Odense M,
Denmark
Phone: +45 - 6550 1000
Fax: +45 - 6615 5129
Email: [email protected]
Michael Christensen
Strategic Organization Design Unit (SOD),
Department of Marketing & Management
University of Southern Denmark
Campusvej 55,
DK-5230 Odense M,
Denmark
Phone: +45 - 6550 1000
Fax: +45 - 6615 5129
Email: [email protected]
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ENTRY AND EXIT DECISIONS IN FLEXIBLE TEAMS
1. INTRODUCTION
The present article is concerned with organizational flexibility in multinational corporations (MNCs), i.e. large
firms that operate in multiple national markets. While the notion of organizational flexibility includes many
dimensions, we choose to focus on flexible decision teams. Our teams are flexible because they switch from
centralized to decentralized modes of making decisions. Contrasting prior research on entry modes (e.g. joint
ventures, greenfield investments, acquisitions), the present article examines the way such flexible decision teams
can influence entry and exit decisions.
Recent empirical studies on new organizational forms broadly support the claim that MNCs
experiment with flexible organizational structures in response to increased levels of turbulence in international
market (Ilinitch, DAveni, & Lewin, 1996; Lewin & Volberda, 1999). However, these advances in description of
flexible organization structures have been largely ignored in our theories of decision making in MNCs (Delios &
Henisz, 2003; Kogut & Singh, 1988; Reuer, 2000). It appears to be a stylized fact that flexibility is beneficial in
turbulent markets, but we do not really know why. To address this gap in our knowledge, we examine whether
flexible decision teams can be designed to reduce mistakes and thus increase profits, as MNCs decide to enter
and exit turbulent markets.
A critical issue is the problem of entry and exit in turbulent international markets, whether via
joint ventures, greenfield investments or acquisitions (Dixit & Pindyck, 1994; Kogut & Singh, 1988; Reuer,
2000). Sometimes costly mistakes get made,i for instance, when MNCs enter markets that result in losses and
ignore markets that would have become the sources of gains. Similarly, deciding when to exit can also be
problematic. Under turbulent conditions, a promising market opportunity can turn sour. Sometimes, MNCs exit
markets when their continued presence would have led to gains, or fail to exit markets where continued presence
can only lead to further losses.
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Recent studies highlight the challenge of making entry and exit decisions in turbulent
international markets (Delios & Henisz, 2003; Kogut & Singh, 1988; Reuer, 2000). Delios & Henisz (2003)
found that changes in investment sequences occur because firms shift from an emphasis on developing
knowledge about international markets and consumers in stable environments, to an international expansion
strategy in more turbulent policy environments. Increased market turbulence tends to exacerbate the frequency
and severity of bad decisions. Thus, the evaluation of market opportunities in turbulent policy environments is a
critical determinant of entry and exit. While research into the efficacy of alternative modes of entry and exit into
international markets has a long pedigree, there has been little research into the ways that evaluations of market
opportunities influence entry and exit in international markets (exceptions are Delios & Henisz, 2003; Reuer,
2000). The present article offers a framework that enables us to consider these issues in a systematic way.
We develop a modeling structure that allows comparison of industry entry and exit under
alternative assumptions of managerial ability, and different levels of market turbulence. Three types of decision
maker the optimizer, the local searcher, and the fallible evaluator are compared in a generic entry-exit model
where turbulent conditions in an international market are modeled as fluctuating, short-run profits. Two extreme
forms of decision teams the centralized hierarchy and the decentralized polyarchy (the flat team) are
considered. The aim of the model is to examine whether teams of fallible evaluators can benefit from being
located in flexible decision teams, e.g. from shifting between hierarchical and polyarchical modes of
organization.
Our most important finding was that flexible decision teams comprising simple fallible agents
were superior to infallible agents able to solve a complicated dynamic programming problem. This result is quite
remarkable and also very encouraging from a behavioral perspective since it highlights the power of realistic
assumptions in our theories.
The article is organized as follows. Section 2 reviews the literature on new organizational forms.
Section 3 develops a modeling structure that allows comparison of industry entry and exit under alternative
assumptions of managerial ability, and different levels of market turbulence. We also examine how flexible
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teams can improve entry and exit decisions in turbulent markets and why they have such remarkable properties.
Section 4 considers the implications of this study and concludes the article.
2. LITERATURE REVIEW
The last decades research on organizational forms has made headway, in converging upon a few particularly
important causes of the new forms that have been observed. Reflecting the wide agreement in the organization
literature, Child and McGrath (2001) in their introduction to a collection of nine articles in a special research
forum on new organizational forms, published in the Academy of Management Journal, point to increased
information intensity and internationalization as the main reasons for the emergence of these new forms.
The challenges referred to can be grouped into four broad categories: (1) increased
interdependence in interactions among organizations; (2) the possibility of performance disembodied from asset
ownership; (3) higher velocity characterizing almost all aspects of organizational functioning; and (4) changes in
power, in terms of a shift from a power base of tangible assets and inputs to power derived from the possession
of knowledge and information. The widely observed new organizational forms, it is argued, have emerged in
response to these challenges. According to Child and McGrath (2001), the objective of the new forms is to
delegate decision rights to where the relevant knowledge and information reside, and then to use information
technology (IT) for support.
In contract economics, this move of decision rights to be closer to those with information, has
been referred to as the organization redesign solution, as opposed to the traditional management information
systems (MIS) solution, according to which it is information that is moved closer to the decision maker
(Brynjolfsson & Mendelson, 1993; Nault, 1998). Fundamental advances in IT and measurement technologies
have facilitated a number of experiments with organizational forms (Nault, 1998; Zenger & Hesterly 1997) that
are often referred to by the notion of new organizational form (Daft & Lewin 1993). A common characteristic
of these experiments is the use of IT in hierarchies to achieve a decentralization of decision rights (Child &
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McGrath, 2001; Nault, 1998). Whereas advances in IT are commonly viewed as the facilitator of new forms,
hypercompetition is seen as the primary cause of their emergence since traditional forms are considered
maladaptive when high turbulence is the norm (Child & McGrath, 2001; Daft & Lewin, 1993; Volberda, 1996).
Hypercompetition refers to the shift in the rules of competition that was observed through the
1990s (Ilinitch, DAveni & Lewin, 1996; Volberda, 1996). Technological change, the shortening of product life
cycles, and the increasing aggressiveness of competitors have led to shorter and shorter periods of competitive
advantage, punctuated by frequent disruptions. That is, hypercompetition and market turbulence go hand in
hand. This development associates turbulence with flexible organizational arrangements as in Volberdas
proposition: In a fundamentally unpredictable environment which may also be dynamic and complex
(hypercompetitive), the optimal form employs a broad flexibility mix dominated by structural and strategic
flexibility and has a nonroutine technology, an organic structure, and an innovative culture (1996: 366-367).
Volberda (1996) nicely sums up the argument for flexibility that has been forwarded in much of the literature on
new organizational forms: In the new mode of hypercompetition rents do not derive from specialized routines
but from adaptive capability. The reason is that, with hypercompetition, competitive change cannot be predicted
but only responded to more or less efficiently, ex post (1996: 360).
The identified imperative of firm-level flexibility, in turn, stresses the need to maintain some level
of organizational consistency. Thus, it has been emphasized that organizations must respond to the twin
pressures of continuity and change. That is, organizations need to exhibit increasing flexibility while maintaining
consistency and reliability (Bartlett & Ghoshal, 1998; Volberda, 1996, 1998).
A broad empirical literature supports the argument that flexibility outperforms rigidity under
conditions of turbulence.ii If we examine the observed changes in organizational characteristics in more detail, an
impressionistic picture with marked features appears, including downsizing (Bowman, Singh, Useem &
Bhadury, 1999), a trend toward more collaborative business relationships (Cannon & Homburg, 2001; Nault &
Tyagi, 2001), network organizations (Achrol, 1997), flexibility achieved by more organic and temporary work
arrangements (Bigley & Roberts, 2001; Child, 1997; Heydebrand, 1989; Ilinitch, DAveni, & Lewin, 1996;
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Lewin & Volberda, 1999; Zahra & ONeill, 1998), more nimble governance structures (Cecil, Ciccotello &
Terry, 1995; Kole & Lehn, 1997), decentralization of decision rights (Moller & Rajala, 1999), and new
employment contracts characterized by less commitment between employer and employee (Charness & Levine,
2002). While flexibility spans a number of meanings in these studies, it is generally thought to be beneficial in
turbulent markets. However, we do not really know why.
To address this neglect, the present study aims to direct attention to the role of flexible decision
teams in MNCs evaluation of market opportunities. To stimulate further research on this important topic, we
develop a general modeling structure from which testable propositions can be drawn. This effort adds to prior
research on entry and exit decisions in MNCs, including studies on the evaluation of foreign opportunities
(Cavusgil, Kiyak & Yeniyurt 2004; Schooler, 1974). By examining flexible evaluation structures, we also add to
prior studies that examine the effect of voting rules in stable evaluation structures (Birnberg, Pondy & Davis,
1970).
The present article examines the effects of flexible decision teams when MNCs are making entry
and exit decisions in turbulent international markets. A critical issue is the assessment of market opportunities in
the face of irrecoverable entry and exit costs. Using the common analogy from statistical inference, MNCs make
errors of omission (Type I errors) and commission (Type II errors). MNCs enter markets that offer losses
(commission errors) and steer clear of markets that would have been sources of gains (omission errors). MNCs
face the poker players problem of deciding when to enter the game and when it is time to exit. Under turbulent
conditions, a promising market opportunity can turn sour. Mistakes occur when MNCs leave an international
market too early, or too late. Such mistakes can be costly, and increased market turbulence can lead to both more
frequent and even more severe mistakes. We examine whether flexible decision teams can somehow be designed
to reduce the frequency of such mistakes and thus increase profits.
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3. THE MODEL
We develop a modeling structure that allows comparison of industry entry (exit) decisions under alternative
assumptions of managerial ability and different levels of market turbulence. Three types of decision makers the
optimizer, the local searcher, and the fallible evaluator are compared in an entry-exit model where turbulent
market conditions are modeled as a fluctuating short-run profit.
Individual evaluators: optimizer, local searcher, fallible evaluator. The MNC operates in a
turbulent international market. We invoke the following standard assumptions from dynamic industry entry-exit
models. The MNC observes the next periods potential operating profit (t+1), earned in case that the MNC is
active (an inactive business unit earns an operating profit of zero). Based on this information, the decision maker
estimates critical levels of operating profit at which an inactive business unit should enter, and an active business
unit should exit. The optimizer uses a dynamic programming approach to extract the optimal critical levels of
operating profit from the Bellman equation.iii Individual agents capable of using such methods can identify and
use a proposed optimal profit level to trigger industry entry and exit. These would be very close to perfection,
but real-world decision makers are unlikely to achieve such high standards. The optimizer is an unrealistic, but
useful theoretical comparison point against which the performance of fallible decision makers can be assessed.
The second type of decision maker is the local searcher, familiar in behavioral theories of the firm
(Cyert & March, 1963; Knudsen & Levinthal, 2007; Levinthal & March, 1981; Nelson and Winter, 1982). The
set of alternative actions is not presumed to be laid out in its entirety ex-ante, but must be discovered or searched
for. In the present context, the local searcher enters a new market if the operating profit is at least equal to zero
(or some other trigger point) and exits if the short-run profit becomes negative. In this way, such agents search
for new markets that satisfy some minimum performance criteria (e.g. potential profit should be at least zero).
However, alternatives, once identified, must also be evaluated (Knudsen & Levinthal, 2007). While the local
searcher is usually portrayed as being capable of perfect evaluation, the fallible evaluator offers a more realistic
characterization of decision makers than does the local searcher.
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The third type of decision maker, the fallible evaluator, has imperfect discriminating ability. This
type of decision maker behaves like the local searcher, but is not capable of making sharp evaluations of short-
run profit for the reasons proffered in the literature on new organizational forms and elsewhere (ambiguity,
complex interactions, limited information, limited computation power, etc.). That is, our fallible evaluator
behaves in accordance with the stylized description of real-world decision makers offered in much of the
organization and management literature (Knudsen & Levinthal, 2007).
The discriminating ability of the fallible evaluator is modeled as a linear screening function.iv
Figure 1 presents five examples of the fallible agent. These include the agent who is virtually unable to
distinguish whether the currently observed profit is a good indicator of future industry profits (= 0.01) as well
as the agent who has a much better idea about the relation between observed profit and post-entry performance
(= 0.50). The fallible agent becomes more able as the inclination of the screening function increases. Suppose
the ability of an agent is captured by a screening function with slope = 0.05. If the currently observed profit is
5, we can read the probability on the y-axis that an entry decision will be made. As shown in Figure 1, the agent
in our example would enter the market with probability 0.75.
It is apparent from Figure 1 that a steeper slope (higher value of ) centers the zone of uncertainty
on a more narrow range of values for observed profit. By contrast, a lower value of spans a wider range of
uncertainty where observed profit maps onto a probabilistic entry decision. The slope of the screening function
captures fallibility, and fallibility translates into a wider zone of uncertainty. The lower the slope, the more
fallible is the decision maker. At the extreme, the agent with = 0 is a coin flipper who would enter (or exit) the
market with probability 0.5 no matter the level of observed profit. A more fallible agent also has a wider zone of
uncertainty within which costly reversals of prior decisions can occur.
FIGURE 1
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Figure 1 also depicts the local searcher ( ). In our example, the local searcher would enter
with probability 1 for any non-negative level of observed profit and this agent would exit with probability 1 as
soon as negative profits were observed. The local searcher is unable to shift between multiple decision criteria
even though such an agent is infallible as regards quality discrimination. In our case, the observed levels of profit
that lead to successful decisions are far apart when firms pay an irrecoverable fee to enter and exit. Costly entry
and exit decisions introduce multiple decision criteria because they require consideration of two trigger points
for observed profit. In a sense there is a trade-off between treatment of multiple decision criteria and certain
quality discrimination. The local searcher can make extremely sharp judgments about quality but this virtue goes
hand in hand with a zone of uncertainty that shrinks to a single point, i.e. the local searcher is unable to consider
more than one trigger point.
It is disturbing that the characterization of the local searcher in most of our formal models does
not match the empirical reality in entry and exit decisions. To illustrate this point, Figure 1 provides an example
of optimal entry and exit points computed by dynamic programming. Only if observed profit is at least 9.89
would the firm enter the market and become active. Once active, it would only exit if observed profit fell below -
9.90. Our fallible evaluator, by contrast, has a zone of uncertainty that can be made wide enough to span the two
optimal points for entry and exit. As the two optimal points for entry and exit become farther removed from each
other, however, the slope of the screening function must decline. In Figure 1, the fallible agent with a slope of
about = 0.05 spans the two optimal points for entry and exit. Unfortunately, a lower slope implies that more
decisions will be reversed. The role of the organizational structure in this case, is to remedy the unfortunate and
costly tendency to reverse decisions while maintaining the span of difference between trigger points for entry
and exit. As sunk costs increase, the individual agent is less able to handle the conflicting demands of multiple
decision criteria and certain quality discrimination. The way out is to structure costly entry decisions in decision
teams.v
Decision teams. We use Christensen & Knudsens (2007) recent extension of the Sah & Stiglitz
(1985, 1986, 1988) characterization of organizational architectures to model evaluation in teams. This modeling
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approach was recently extended to the study of imperfect evaluation within the context of NK models (Knudsen
& Levinthal, 2007). The problem we are considering in the present article, however, is different from those
problems for which the NK model is best suited. The NK model has become the standard tool for organizational
analysis of complex spaces where alternatives are hard to locate even though the payoff for each particular
configuration remains fixed, once and for all. In contrast, the problem we are addressing relates to fluctuating
payoffs.
The intuition in what follows is that fallible evaluators can (always) benefit from being placed in
flexible teams whereas local searchers are beyond help because they discriminate perfectly. Ironically, imperfect
discrimination of fallible agents is a source of flexibility that can be utilized by designing appropriate decision
teams. As we shall see, this gives rise to the surprising result that flexible teams with relatively few fallible
decision makers can outdo the so-called optimizer. Notably, the fallible decision makers in our model are
modeled as local searchers and thus, in accordance with much of the literature on organizations and
management, are quite realistic.
Local searchers are limited in their ability to search for new alternatives, but once identified, they
are capable of perfect discrimination. Within the present context, local search approaches optimality when the
critical levels of operating profit become a single point say, optimal entry (exit) for non-negative (negative)
operating profit. This happens when entry and exit costs go to zero.
In contrast to local searchers, fallible agents are not capable of perfect discrimination. It can be
shown, however, that fallible evaluators can be placed in teams such that their joint screening function comes
arbitrarily close to perfect discrimination (Christensen & Knudsen, 2007). Then, under the assumption of (very)
low costs of entry and exit, fallible evaluators could always gain from being located in fixed organization
structures.
However, we are interested here in the much more realistic case of significant exit and entry costs.
In a turbulent market with significant entry (and exit) costs, the optimal points of entry and exit are located far
apart (as illustrated in Figure 1), i.e. firms only enter if operating profits are (very) high and they only exit if
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operating profits are (very) low. To help fallible agents overcome the inbuilt rigidity that narrows their focus to a
single point of operating profit, we are led to consider the possible advantage of using flexible evaluation
structures. In particular, we consider two extreme forms of evaluation structure. One is the hierarchy, in which a
proposal to enter (or exit) a market is validated at successively higher levels in the team. Only if the proposal is
accepted at each level, will the MNC enter a new market. The second form is a polyarchy, a flat, decentralized
structure in which acceptance by any one actor is sufficient for the proposal to be approved. A flexible decision
team is modeled by shifting between the two forms of organization.
The effect of locating fallible evaluators (e.g. = 0.05) in a hierarchical form is shown in Figure
2. In what we term a hierarchy, the short-run profit is initially considered by a member of the decision team. If
the proposal is rejected by that team member, it is eliminated from further consideration and the business unit
discontinues its activities (or remains inactive). Alternatively, if the proposal is approved by that decision maker,
then it is passed on to the next decision maker in the chain of command. A proposal is acted upon only if it has
been positively vetted by all of the evaluators in the team. Formally, sequential decision making in a hierarchy
corresponds to joint decision making in a team (or committee) where a proposal of entry is only accepted if each
member accepts it.
Figure 2 compares the effect of locating fallible agents in hierarchical decision teams with two
(H2), five (H5) or ten (H10) members, and also compares these structures to the optimizer and the local searcher.
The single fallible agent is represented by a dashed line and we use a linear screening function with a slope of =
0.05 for the purpose of illustration. H5 is a hierarchy employing five such agents. We can read off the probability
of entry for the hierarchical teams on the y-axis and compare these to the single fallible agent, the optimizer and
the local searcher. Suppose the observed profit is 5. In that case, the local searcher enters with probability p=1,
the single fallible agent enters with probability p=0.75, and H5 enters with a probability of p=0.24. The
hierarchy is a skeptical organizational form that tends to tone down a positive vetting made by a single fallible
team member (reducing the entry probability from 0.75 to 0.24). The effect is to reinforce the status quo unless a
very promising observation is made. As the size of the hierarchy increases, its screening function approaches a
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vertical line. Figure 2 illustrates this effect by mapping out the change in screening functions from H2 over H5 to
H10.
FIGURE 2
The effect of locating fallible evaluators (e.g. = 0.05) in a flat team, also known as a polyarchy,
is exactly the mirror image of Figure 2 and therefore not shown here. In the flat team, a proposed alternative can
be adopted by any of the members of the decision team. Only if all decision makers in succession reject an
alternative is it dismissed. Formally, sequential decision making in a polyarchy corresponds to joint decision
making in a team (or committee) where a proposal of entry is accepted if only a single member accepts it. As the
mirror image of the hierarchy, the polyarchy is an optimistic organizational form that tends to promote change.
Flexible decision teams. The aim of our model is to examine whether teams of fallible evaluators
can benefit from being located in flexible organizations, e.g., from shifting between hierarchical and flat,
polyarchical modes of organization. This case could also be viewed as the consistent use of a hierarchical form
with shifting targets (accepting entry versus accepting exit). In joint decision making, this is equivalent to
shifting from a rule requiring that each member of an decision team must accept entry to a new rule, requiring
that only one member accepts continued presence, after entry.
Note how the local searcher in Figure 2 is represented by a single vertical line ( ) while the
optimizer is represented by two vertical lines computed by dynamic programming. Costly entry and exit
decisions require consideration of two trigger points for observed profit. These two points represent the
challenge of balancing two kinds of error. One is the error of staying in the market despite a downturn in the
business cycle and the second is the error of missing an upturn in the business cycle by remaining inactive.
A flexible use of teams that shifts between entry decisions in hierarchical teams and exit decisions
in flat, polyarchical teams will effectively mimic the optimizers use of two decision criteria. For example, using
H5 in Figure 2 in the case of an entry decision and then delegating the exit decision to a flat, polyarchical, team
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would shift between the H5 curve and its mirror image, a curve for a 5-member polyarchy (not shown here). As
reported below, this leads to the surprising result that simple flexible decision structures comprising 4-5
imperfect people can outdo the entirely superhuman performance of the "optimal" agent (an encouraging result
as regards realism).
It also turns out that the organizational structure that best helps the myopic decision maker in our
model is a flexible team of quite realistic size (4-5 members). The reason that larger teams, such as H10 in
Figure 2, are inferior is that they span a narrower zone of uncertainty. In Figure 2, H10 is closer to the straight
line of the perfect agent than H5. More generally, as more agents are included in the decision team, the hierarchy
(and polyarchy) approaches a straight line. However, our results show that in turbulent markets, some zone of
uncertainty, as represented by a non-linear trigger point in Figure 2, is superior to perfect discrimination
represented by a straight line.
The nature of the environment. The local environment for the MNC subsidiary is more or less
turbulent and the MNC considers whether its subsidiary should operate. More precisely, short-run profit is a
random walk, modeled as an exogenous Markov process.vi The MNC observes the short-run profit of the next
period and decides whether it should enter the market. The next periods potential profit is the exogenous
Markov process:
t+1 = h(t , t+1) = AVG + (t AVG) + t+1 (1)
where the random shocks are i.i.d. and normally distributed, N(, 2), with mean = 0 and variance
2. The
parameter is the autoregressive coefficient, which determines the rate of reversal towards the mean AVG. The
following analyses set AVG = 0 and = 0.90, but vary the volatility parameter in order to examine alternative
levels of turbulence in international markets. Higher volatility increases turbulence within the environmental
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conditions defined by . This is shown in Figure 3, where the lower panel illustrates a highly turbulent task
environment (=10), while the upper panel illustrates a less turbulent environment (=4).
FIGURE 3
Figure 3 shows the optimal entry and exit points computed by dynamic programming (these
points are identical to the examples from Figures 1 and 2). Only if observed profit is at least 9.89 does the
optimizer enter the market and become active. Once active, the optimizer exits only if observed profit falls
below -9.90. The actual entry and exit decisions for the optimizer are shown in the panels of Figure 3. The upper
panel of Figure 3 also shows a period where H5 has comparative advantage over the optimizer (from t=13 to
t=36). The underlying reasoning is subtle. As depicted in Figure 2, the optimal evaluator uses a single linear
trigger point, but the organization comprising fallible agents has a curvilinear set of trigger points. This
curvilinear set of trigger points can effectively work as a confidence interval around the exit and entry points
used by the optimizer. Supposing the observed profit is positive, H5 enters with a positive probability. On
average, therefore, H5 would be active during some of the periods between t=13 and t=36 (upper panel of Figure
3). During this time, the net profit is positive. Therefore, H5 earns an expected positive profit. By contrast, the
optimizer earns a profit of zero during the same period because it remains inactive (until t=36).
But why is H5 superior to the fallible agent? Again, it is useful to refer back to Figure 2. If the
observed profit is 5, the single fallible agent enters with probability p=0.75 and H5 enters with a probability of
p=0.24. The single fallible agent is quick to enter and once active in the market, it is also quick to reverse the
decision, and exit. As profits fluctuate the single fallible agent will make many reversals of prior decisions.
When exit and entry are costly, the net result is diminished profits when compared to the steadier course of H5.
Some restraint is required before the firm enters and that is exactly what H5 provides. If more agents were
included, however, the restraint would become excessive. The move from H5 to H10 in Figure 2 illustrates this
effect.
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Our results generally indicate that H5 is superior to larger or smaller decision teams as well as the
optimizer (see Figure 5, below). However, the advantage shrinks as volatility increases from =4 to =10. The
lower panel of Figure 3 illustrates a task environment with =10. In such a highly volatile environment, the
shifts from low profits to high profit peaks are much sharper. As volatility increases to a level of =10, the shifts
that trigger entry and exit will therefore tend to be the same for the optimizer and H5. In consequence, the
advantage of H5 over the optimizer shrinks (as shown in Figure 5).
The decision to enter and become active. The state variables are the short-run profit, , and the
current operational status, o, of the MNC in the international market.vii
If the MNC is currently operating in the
international market, o is 1. If the MNC does not operate, o is zero. At the beginning of each period, the MNC
makes an operating decision, a, for the next period. If the MNC operates in the next period, a is 1. If the MNC
does not operate in the next period, a is zero.
That is, the MNC observes the next periods potential profit () and its current operational status
(o). It then takes an action (a), about whether it should operate in the next period. In consequence, the MNC
earns a reward f(, o, a) that depends on the current state of the economic system, the current operational state of
the MNC, and the action taken. In addition, an unknown exogenous shock influences the next periods potential
profit. The state transition function is (see e.g. Miranda & Fackler, 2002):
g(t+1, o, a, ) = (h(, ), a) (2)
If the MNC is not operating and decides to enter the international market under consideration, it pays a fixed
entry cost, KEntry. Should the MNC be operating in an international market and then decide to exit, there is a
shutdown cost of KExit. In all simulations, KEntry was set at 10 and KExit was set at 10 (our results hold for a broad
range of these values). The reward function is:
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f(, o, a ) = a KEntry (1 o)a KExit (1 a)o (3)
Estimation of critical levels of short-run profit. The optimal value of the business unit, V(, o),
given an observed short-run profit and operational status o, is estimated by the Bellman equation. We use
numerical methods provided by Miranda & Fackler (2002) to approximate the value function V(, o):
V(, o) = max{ f(, o, a) + E V(h(, ), a)} (4)
The functions, f() and h() are given by eq. 2 and 3, is the discount rate.viii
Without loss of
generality was set at 0.10 (additional analyses show that alternative values provide qualitatively similar
results). We extract, by numerical methods, the critical levels of operating profit that maximize the value
function (eq. 4). These provide simple criteria for optimal entry and exit. In addition, note that the two
components of the value function express a trade-off between immediate rewards f(, o, a) and the expected
future gains following the immediate action E V(h(,),a).
The unrealistic dynamic programming approach described so far is only available to the
optimizer. The local searcher and the fallible agents simply compare short run to a criterion for satisficing
performance. If short run profits are too low, it will stay out (or exit in the case that it is active). The fallible
decision maker behaves exactly like the local searcher. The only difference is that the fallible decision maker
sometimes makes a wrong decision, i.e. exits when the firm should have remained active according to the
satisficing criterion.
The critical profit levels extracted for a particular example of = 4, and = 0.90 are shown in
Figures 1 and 2. Figure 1 compares the critical profits levels to alternative levels of discriminating ability in a
fallible evaluator, and Figure 2 shows the effect of placing a fallible evaluator in a hierarchy (using a
polyarchical structure gives the mirror image of Figure 2). Figure 4 shows the results obtained for this particular
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16
example, with = 4, and = 0.90. The results for a broad range of parameter values are reported in Figure 5.
The configuration space included a full range of parameter values: = 0.01, 0.05, 0.10, 0.50, = 1, ..., 10, and
= 90 (additional simulations were conducted to assess robustness). We set entry costs, KEntry, at 10 and shutdown
costs, KExit, at 10. A random draw determines the initial operating status, o, of the business unit.
A business unit optimally operates during the next period if the short run profit for that period is
above some level, H, and a business unit optimally shuts down if the profit is lower than L. Given entry and
exit costs, L < 0 < H. Once the MNC has accepted that one of its units enters a new market, it is often optimal
to continue operating despite periods of negative operating profits.
The estimation of optimal actions provides a useful comparison point for assessment of the
performance of decision makers with much less information and computational power than required to solve eq.
4. As previously explained, the performances of three types of decision makers are examined. The first type is
the optimizer who is capable of finding the optimal decision criteria in terms of unique trigger points (as shown
in Figure 1-2). Given information about the systems state transition function, the optimizer solves the dynamic
programming problem implied by the corresponding Bellman equation. The second type of decision maker is the
local searcher who enters if the short-run profit is at least zero and exits if short-run profit is below zero (other
trigger points were examined to assess robustness). The third type of decision maker is the imperfect evaluator
(we examine different levels of evaluation capability as shown in Figure 2).
The MNC uses a flexible organizational form to evaluate market opportunities. Prior to entry, the
MNC uses the skeptical hierarchical form that allows entry only after validation at a number of successive
levels. After entry, decisions are delegated; the decision team shifts to an optimistic polyarchical mode where
approval of the market conditions by any one actor is sufficient to stay in the market. Only if everybody in the
decision team disapproves of the market conditions, will the MNC exit. Our choice of organization structures is
kept simple for illustrative purposes. The results are not sensitive to this choice. In the general case, similar
results are obtained when entry decisions take place in a structure that is more centralized than the exit decision
(which is often the case as decision power is usually delegated to a subsidiary).
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17
Results
We compare the performances of the three types of decision makers in a horse-race over 1000 periods. On the
basis of 1000 samples, we find that optimizers always outperform local searchers and single fallible evaluators.ix
Generally, local searchers, who are perfect evaluators using a wrong decision rule (critical profit level of = 0,
in the case of both entry and exit), also outperform single fallible evaluators (as shown in Figures 4 and 5). The
single fallible evaluator does not perform well, tending to promote frequent (and costly) reversals of prior
decisions. Since entry and exit, in each case, are associated with a non-recoverable cost, the many mistakes
impose a considerable cumulative cost.
The disadvantage of the local searcher and the single fallible evaluator is ameliorated in a
turbulent environment (Figure 5). With higher turbulence, the range between the values of short run profit that
are optimal for entry and exit widens (holding entry and exit costs constant). Thus, a high results in
pronounced, long periods of negative and positive profit that are visible to the more simple-minded local
searcher.x The performance of the local searcher shown in Figure 4 is markedly higher than the performance of
the single fallible decision maker and even a flexible team comprising two fallible decision makers. As more
decision makers are added to the team, the quality of the decisions further improves until it comprises five
members. As further members are added, however, there would be a marginal decline in performance.xi
FIGURE 4
The critical values of short run profit used in the simulation reported in Figure 4 are the same as
those in Figures 1 and 2. From Figure 2, it can be seen that the effect of adding members to the hierarchical
decision team is to push a portion of the screening function to the south-east of the critical entry value. Adding
members implies that the screening function of the decision team begins to approximate the optimal entry level.
At some point, however, the screening function is pushed too far to the east, beyond the optimal level. The exit
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18
case is perfectly symmetrical to the case of entry and therefore not shown here. By switching between the
hierarchy when entry opportunities are evaluated and the flat, polyarchy when exit options are considered, a
team of fallible evaluators can approximate the two optimal values of entry and exit.
Rather surprisingly, and consistent with the conjectures in the literature (Bartlett & Ghoshal,
1998; Child & McGrath, 2001; Lewin & Volberda, 1999; Volberda, 1996, 1998), it turns out that flexible
evaluation structures can dramatically improve the performance of the fallible evaluator. We show that, rather
remarkably, MNCs employing flexible teams of fallible evaluators can obtain higher levels of profits than the
levels obtained by the optimizer (Figure 5). This result is obtained if the MNC uses a centralized decision
making structure (a hierarchy) prior to entry and then switches to a flat (polyarchical), decentralized structure
after its business unit has entered the international market.
FIGURE 5
Considering the results depicted in Figure 5, it is clear that a team of fallible agents generally
outperforms a single fallible evaluator. Moreover, the gain in performance occurs for very moderate team sizes
of about five members. Using teams of five in the way described here, rather than relying on single evaluators,
will generally lead to improved decisions. The most surprising result reproduced in Figure 5 is that fallible
decision makers who use flexible decision making structures are generally superior to the so-called optimal
decisions based on a dynamic programming approach.xii
Figure 2 shows the cause of this surprising result: the
optimal evaluator uses a single linear trigger point, but the organization comprising fallible agents has a
curvilinear set of trigger points. The set of curvilinear trigger points effectively works as a confidence interval
around the optimizers exit and entry point. A team of fallible decision makers would exit before the operating
profit plunges to the negative level required by an optimizer.
So, it is the non-linearity induced by the joint effect of individual evaluators that produces
curvilinear trigger points. This non-linearity gives our small teams an edge. Thus, flexible teams of fallible
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19
agents become a source of competitive advantage in a turbulent environment.xiii
The effect is most pronounced in
moderately volatile task environments. In a highly volatile environment, the shifts from low profits to high profit
peaks are much sharper (see Figure 3) and the shifts that trigger entry and exit will therefore tend to be the same
for the optimizer and H5. In consequence, the advantage of H5 over the optimizer shrinks as volatility increases
from =4 to =10. The only exception to the surprising advantage of H5 over the optimizer is a very low
volatility of =1. In that case, the optimizer will never enter (since entry and exit costs are substantial at KExit=10
and KEntry=10). By contrast, H5 will on average earn negative profits because it occasionally will enter and then
discover that the operating profits cannot cover the entry cost. However, our flexible teams of simple fallible
agents are generally superior to the so-called optimizer when task environments become more volatile (>1). In
a world with increasingly turbulent international markets, this result points to the advantage of small flexible
decision teams.
4. CONCLUSION
This study investigates entry and exit decisions in turbulent markets with fluctuating short-run profits. It
complements prior efforts by examining the viability of flexible teams, e.g. shifts between centralized and
decentralized modes of decision making. We found that fairly small teams generally outperform a single
decision maker. This result reflects the properties of a centralized, hierarchical structure, which reduces
commission errors (accepting a bad proposition) whereas as a decentralized, polyarchical structure reduces
omission errors (rejecting a good proposition). As a firm uses a centralized structure when considering entry into
a new international market and then leaves the choice of continued operation to a decentralized local team, it
actually mimics an optimal choice. Indeed, we found that fallible decision makers located in flexible teams were
even able to outperform what is commonly thought to be an optimal decision. That is, our teams of simple
fallible agents were superior to infallible agents who are able to solve a complicated dynamic programming
problem, given all the relevant statistical information and the systems state transition function. This result is
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20
quite remarkable and also very encouraging from a behavioral perspective since it highlights the power of
realistic assumptions in our theories.
Our model is a behavioral extension of classical entry and exit models. The work most closely
related to ours is Dixits (1989) analysis of entry and exit decisions under uncertainty. While this work points to
the benefit of fuzzy trigger points in turbulent markets, our model uncovers the remarkable result that flexible
decision teams are a realistic way for simple-minded managers to realize and obtain the benefits of such fuzzy
trigger points. A number of limitations should be considered when our results are extrapolated to real-world
cases. First, we abstract from a detailed analysis of market structure and industry dynamics and thereby limit our
analysis to established international markets. Second, we only examine shifts between extreme hierarchical and
flat organizational structures. Third, we abstract from multiple goals and assume that profits are a sufficient
summary measure of market attractiveness. Fourth, we abstracted from two of the main aggregation forces in an
organization incentives and coordination. While the first two limitations add important nuances to our
analyses, they do not alter the prediction that fairly small teams, comprising a handful of agents, generally
outperform other modes of decision making when considering costly entry and exit. The third and fourth
limitation, however, define boundary conditions of our model.
These limitations to our study can be overcome in a number of ways. First, the generation of new
markets is to some extent captured in the notion of irrecoverable entry and exit costs. However, we abstract from
other issues relating to a detailed understanding of market structure and industry dynamics. Future research on
these important issues could usefully draw on the extension of the NK-modeling framework developed by
Lenox, Rockart & Lewin (2006, 2007). Two features of our model should be integrated into the analysis. We
were addressing a problem relating to random walks in payoffs rather than ruggedness stemming from
complexity within a context where payoffs are fixed, once and for all. In order to overcome this limitation,
fluctuating payoffs should be included in the NK model. Another limitation of most prior NK models is that they
do not extend to analyses of teams. Knudsen and Levinthal (2007) recently pointed the way to such an extension.
Second, the limitation relating to the range of organizational structures being studied can be overcome by the
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21
methods offered by Christensen and Knudsen (2007) and applied within the NK-modeling framework by
Knudsen and Levinthal (2007). While adding nuances, such analysis would not alter our main results because we
have used the organizational structures with the most extreme screening properties (Christensen & Knudsen,
2007). Third, as regards a more detailed analysis of multiple goals, recent work on the NK model seems
promising (Ethiraj and Levinthal, 2007). Finally, the consideration of incentives and coordination adds layers of
complexity to the analysis, which are likely to uncover interesting contingencies, albeit none that we expect to
contradict the advantage of flexible teams when considering entry and exit decision. Overall, agent based
approaches seem promising in reducing some of the limitations of the present study, albeit at the cost of a more
involved model. As researchers begin to consider joint decision making, the need will become more pressing to
include wider issues pertaining to incentive alignment and coordination of activities in a distributed setting.
Our contribution is to provide a simple model of flexible teams composed of decision makers
whose behavioral and cognitive abilities appear quite realistic. Because our decision makers are fallible, they can
gain much from the structural support of the organization within which they are located. Our theory is also in
accordance with realistic assumptions about the size of management teams as it predicts that teams with four to
five members are superior both to smaller and larger teams. Briefly, the intuition for our claim is as follows.
Very small teams of fallible decision makers will make erroneous estimates and therefore experience many
periods with negative profits. In contrast, too large teams will tend to wait too long before they leap and thereby
miss many opportunities to gain from periods of mainly positive profits. In essence, our results highlight that the
challenge presented by turbulent market conditions can be met by fairly small flexible teams. With increases in
environmental turbulence from global competition we would expect future empirical studies to highlight the
prevalence and efficacy of flexible decision making structures.
In order to verify our claims, we need more data on the relation between organization structures
(or voting rules) and the quality of the decisions that are made. Why do MNCs pursue international expansion in
turbulent policy environments where opportunities are notoriously hard to evaluate? Why do they enter a
turbulent market in the first place? Our theory highlights that it would be important to understand how decisions
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22
are actually made in such circumstances. Are decisions about entry and exit made by individual managers or by
teams? To the extent that teams are involved, what are the characteristics of the decision making structures (size,
flexibility, distribution of authority)? How do these characteristics impact on the quality of the decisions taken?
Despite much prior research on entry and exit decisions in MNCs, we lack evidence on the way joint decisions
are structured. The many studies of MNCs, including those on entry and exit decisions, have given scant
attention to the role of decision making structures. This is surprising since many decisions are made in teams
(such as boards, top management teams, ad hoc committees). An early experiment by Birnberg, Pondy & Davis
(1970) examined the effect of voting rules in stable teams and found that a simple majority rule outperformed
unanimity (equivalent to hierarchy) and veto (equivalent to polyarchy). However, we have very little knowledge
about the actual structuring of joint decision making in MNCs. More generally, the study of evaluation in
(business) organizations is an important but rather neglected topic (Knudsen & Levinthal, 2007).
The present study has identified a major limitation for current research on MNCs. Joint decision
making is of critical importance in practice, but has not received much attention in our theories. Our modeling
structure begins to fill this gap as it can be used to understand why alternative ways of structuring joint decisions
have an impact on performance. While our model begins to unravel how individuals decide in contexts defined
by other individuals on whom they are interdependent, there remains a huge gap in theories of organization
design. When MNCs and other organizations pursue strategic objectives, they do so by virtue of the way
individuals jointly generate, evaluate, and implement alternatives. Yet, theories of organization design seem to
take little account of the micro-foundations: of how individuals decide, act, or learn in a context defined by other
individuals with whom they have interdependence. A theory that predicts how individual choices are aggregated
and become stable would be a major contribution to the literature on international business and organization
theory more generally.
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23
REFERENCES
Achrol, R.S. 1997. Changes in the theory of interorganizational relations in marketing: Toward a network
paradigm. Academy of Marketing Science, 25(1): 56-71.
Allen, L. & Pantzalis, C. 1996. Valuation of the operating flexibility of multinational corporations. Journal of
International Business Studies, 27(4): 633-653.
Bartlett, C.A., & Ghoshal, S. 1998. Managing across borders: The multinational solution (2nd ed.). Harvard
Business School Press: Boston.
Becker, M.C., & Knudsen, T. 2004. The role of routines in reducing pervasive uncertainty. Journal of Business
Research, 58(6): 746-757.
Bellman, R. E. 1957. Dynamic programming. Princeton University Press: Princeton, NJ.
Bigley, G.A. & Roberts, K.H. 2001. The incident command system: High-reliability organizing for complex and
volatile task environments. Academy of Management Journal, 44(6): 1281-1299.
Birnberg, J.G., Pondy, L.R. & Davis, C.L. 1970. Effect of three voting rules on resource allocation decisions.
Management Science, 16(6): B356-B372.
Bowman, E.H., Singh, H., Useem, M., & Bhadury, R. 1999. When does restructuring improve economic
performance? California Management Review, 41(2): 33-54.
-
24
Brynjolfsson, E. & Mendelson, H. 1993. Information systems and the organization of modern enterprise. Journal
of Organizational Computing, 3(4): 245-255.
Cannon, J.P. & Homburg, C. 2001. Buyers-supplier relationships and customer firm costs. Journal of Marketing,
65(1): 29-43.
Cavusgil, S.T., Kiyak, T., & Yeniyurt, S. 2004. Complementary approaches to preliminary foreign market
opportunity assessment: Country clustering and country ranking. Industrial marketing management, 33(7): 607-
617.
Cecil, H.W., Ciccotello, C.S., Grant, C.T. 1995. The choice of organizational form. Journal of Accountancy,
180(6): 45.
Charness, G. & Levine, D.I. 2002. Changes in the employment contract? Evidence from a quasi-experiment.
Journal of Economic Behavior & Organization, 47(4): 391-405.
Child, J. 1997. Strategic choice in the analysis of action, structure, organizations and environment: Retrospect
and prospect. Organization Studies, 18(1): 43-76.
Child, J. & McGrath, R.G. 2001. Organizations unfettered: Organizational form in an information-intensive
economy. Academy of Management Journal, 44(6): 1135-1148.
Christensen, M. & Knudsen, T. 2007. The human version of Moore-Shannon's Theorem: The design of reliable
economic systems. Available at SSRN: http://ssrn.com/abstract=996311. Accessed 24 June 2007.
-
25
Cyert, R.M. & March, J.G. 1963. A behavioral theory of the firm. Prentice Hall: Englewood Cliffs, NJ.
Daft, R. & Lewin, A.Y. 1993. Where are the theories for the new organizational forms? Organization Science,
4(4): i-vi.
Delios, A. & Henisz, W.J. 2003. Policy uncertainty and the sequence of entry by Japanese firms, 1980-1998.
Journal of International Business Studies, 34(3): 227-241.
Denrell, J. 2005. Why most people disapprove of me: Experience sampling in impression formation.
Psychological Review, 112(4): 951978.
Denrell, J., & March, J. G. 2001. Adaptation as information restriction: The hot stove effect. Organization
Science, 12(5): 523538.
Dixit, A. 1989. Entry and exit decisions under uncertainty. The Journal of Political Economy, 97(3): 620-638
Dixit, A. & Pindyck, R. S. 1994. Investment under uncertainty. Princeton University Press: Princeton.
Ethiraj, S.K. & Levinthal, D.A. 2007). Hoping for A to Z while rewarding only A: Complex organizations and
multiple goals, Working paper.
Heydebrand, W.V. 1989) New organizational forms. Work and Occupations, 16: 323-357.
Howard, R. 1960), Dynamic programming and Markov Processes. MIT Press, Cambridge, MA.
-
26
Ilinitch, A.Y., DAveni, R.A., & Lewin, A.Y. 1996. New organizational forms and strategies for managing in
hypercompetitive environments. Organization Science, 7(3): 211-220.
Knudsen, T. and D. A. Levinthal 2007. Two faces of search: Alternative generation and alternative evaluation.
Organization Science, 18(1): 39-54.
Knudsen, T. & Winter, S.G. 2007. An evolutionary model of spatial competition. Paper prepared for the DRUID
conference 2007.
Kogut, B. & Singh, H. 1988. The effect of national culture on the choice of entry mode. Journal of International
Business Studies, 19(3): 411-432.
Kole, S. & Lehn, K. 1997. Deregulation, the evolution of corporate governance structure, and survival. The
American Economic Review, 87(2): 421- 42
Lenox, M.J., S.F. Rockart and A.Y. Lewin 2006. Interdependency, competition, and the distribution of firm and
industry profits. Management Science, 52(5): 757-772.
Lenox, M.J., S.F. Rockart and A.Y. Lewin 2007. Interdependency, competition, and industry dynamics.
Management Science, 53(4): 599615.
Levinthal, D.A. & March, J. G. 1981. A model of adaptive organizational search. Journal of Economic Behavior
& Organization, 2: 307-333.
-
27
Lewin, A.Y., & Volberda, H.W. 1999. Prolegomena on coevolution: A framework for research on strategy and
new organizational forms. Organization Science, 10(5): 519-534.
Miranda, M.J. & Fackler, P.L. 2002. Applied computational economics and finance. The MIT Press: Cambridge,
MA.
Moller, K. & Rajala, A. 1999. Organizing marketing in industrial high-tech firms: The role of internal marketing
relationships. Industrial Marketing Management, 28(5): 521-535.
Nault, B.R. 1998. Information technology and organization design: Locating decisions and information.
Management Science, 44(10): 1321-1335.
Nault, B.R. & Tyagi, R.K. 2001. Implementable mechanisms to coordinate horizontal alliances. Management
Science, 47(6): 787-799.
Nelson, R.R. & Winter, S.G. 1982. An evolutionary theory of economic change. Harvard University Press:
Cambridge, MA.
Reuer, J.J. 2000. Parent firm performance across international joint venture life-cycle stages. Journal of
International Business Studies, 31(1): 1-20.
Rivkin, J. W. 2000. Imitation of complex strategies. Management Science, 46(6): 824844.
Schooler, R.D. 1974. The determinants of foreign opportunity attractiveness. Management International Review,
14(4/5): 115-127.
-
28
Sah, R. & Stiglitz, J. 1985. The theory of economic systems: Human fallibility and economic organization.
American Economic Review, Papers and Proceedings, 75(2): 292-297.
Sah, R. & Stiglitz, J. 1986. The architecture of economic systems: Hierarchies and polyarchies. American
Economic Review, 76(4): 716-727.
Sah, R. & Stiglitz, J. 1988. Committees, hierarchies and polyarchies. Economic Journal, 98(391): 451-470.
Volberda, H.W. 1996. Toward the flexible form: How to remain vital in hypercompetitive environments.
Organization Science, 7(4): 359-374.
Volberda, H.W. 1998. Building the flexible firm. Oxford University Press: Oxford, England.
Zahra, S.A. & ONeill, H.M. 1998. Charting the landscape of global competition: Reflections on emerging
organizational challenges and their implications for senior executives. Academy of Management Executive,
12(4): 13-21.
Zenger, T.R. & Hesterly, W.S. 1997. The disaggregation of corporations: Selective intervention, high-powered
incentives, and molecular units. Organization Science, 8(3): 209-222.
-
29
Figures
-10 -5 0 5 100
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Observed profit
Pro
ba
bili
ty o
f e
ntr
y
= 0.01
= 0
= 0.05
= 0.10 = 0.50
Optimal exit
Optimal entry
Local searcher
Figure 1: Levels of ability of fallible evaluator (=0.00, 0.01, 0.05, 0.10, 0.50). The five examples include an
agent who is virtually unable to distinguish whether observed profit should lead to entry (= 0.01), as well as an
agent who is much more certain about the relation between observed profit and the promise of entry (= 0.50).
The fallible agent becomes more able as the slope of the screening function increases. Figure 1 also shows the
local searcher ( ) and the two optimal points for entry and exit computed by dynamic programming.
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30
-10 -5 0 5 100
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Observed profit
Pro
ba
bili
ty o
f e
ntr
y
H2 H5 H10 Optimizer
Single fallible:p(entry) = 0.75
H5:p(entry) = 0.24
Local searcher
Singlefallible
Local searcher:p(entry) = 1
Optimizer:p(entry) = 0
Figure 2: Levels of ability for hierarchical decision teams with two (H2), five (H5) or ten (H10) fallible
members compared to the optimizer and the local searcher. The teams employ fallible agents who are
represented with a dashed line in our example (=0.05). H5 is a hierarchy with five such employees. We can
read off the probability of entry at the y-axis for hierarchical teams and then compare these to the single
fallible agent, the optimizer and the local searcher. For example, the optimal point for entry is shown when
= 4, = 0.90, KExit=10, and KEntry=10. If observed profit is 5, then the optimizer will not enter the market. By
contrast, the single fallible agent would enter with p=0.24, H5 with p= 0.75 and the local searcher with p=1.
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31
0 10 20 30 40 50 60 70 80 90 100
-50
0
50
Volatility, =4
Op
era
ting
Pro
fit
0 10 20 30 40 50 60 70 80 90 100
-50
0
50
Volatility, =10
Op
era
ting
Pro
fit
Optimizer entry
Optimizer exitOptimizer exit
Optimizer exit
Optimizer entryOptimizer entry
Optimizer entry Optimizer entry
Advantage for H5
Figure 3: Examples of profit distributions with low (= 4) and high volatility (= 10). In both cases, the
autoregression, is 0.90. Optimal entry points for KExit=10, KEntry=10 are also shown. The entry and exit
decisions for the optimizer are shown in both panels. The upper panel also shows a period where H5 has a
comparative advantage over the optimizer. In this period, H5 would earn a positive profit whereas the optimizer
is inactive an earns a profit of 0.
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32
0 100 200 300 400 500 600 700 800 900 1000-6000
-4000
-2000
0
2000
5
Time steps
Cu
mu
lative
We
alth
2
1
L
O*
Figure 4: Performance of the optimizer, the local searcher and a single fallible evaluator (= 0.10), compared
with the performance of flexible decision teams with two or five fallible members. Example for = 4, = 0.90,
KExit=10, KEntry=10.
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33
1 2 3 4 5 6 7 8 9 10-10
0
10
Volatility,
Ave
rag
e p
rofit
5
2
1
L
Optimizer
Figure 5: Performance of the optimizer, the local searcher and a single fallible evaluator (= 0.10), compared
with the performance of flexible decision teams with two or five fallible members. Example for = 110, =
0.90, KExit=10, KEntry=10.
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34
ENDNOTES
i By turbulence we mean the frequency of events and their magnitude. A highly turbulent environment has rapid variation in
profit levels as well as high volatility of profit levels. ii An exception is Becker and Knudsen (2004) who find that routinization can help managers focus on issues that can be
solved, as opposed to wasting resources in the pursuit of problems that are beyond solution. iii
The Bellman equation is a common numerical method used to extract information on optimal expected rewards from a
Markov decision process such as the one considered here. This method, also known as dynamic programming, was
developed by Bellman (1957) and Howard (1960). iv
We use an approach that is similar to Knudsen & Levinthal (2007). v More generally, the structuring of decision flows among multiple agents becomes a critical issue when imperfect agents
face a difficult trade-off between treatment of multiple decision criteria and certain quality discrimination. A more
comprehensive guide to designing decision structures is provided in Christensen & Knudsen (2007). vi
In the Behavioral Theory of the Firm tradition, random walk models of the type used here are commonly used to capture
stochastic payoffs (Cyert & March, 1963; Denrell, 2005; Denrell & March, 2001; Levinthal & March, 1981; Nelson and
Winter, 1982). In order to provide a common reference point, the specification of the industry entry-exit model follows
closely the simple generic model provided by Miranda and Fackler (2002), ch. 8 and 9. The classical treatment in
economics is Dixit (1989) who offers a more advanced treatment that foreshadows our results. vii
Time subscripts should be self-explanatory and are therefore omitted in the remainder of the text in order to simplify the
exposition. viii
In solving for the Bellman equation, the expectation E, is represented by weighted discrete shocks with std. dev. . ix
As described below in the text, all results reported here were confirmed through comprehensive additional tests. x We analyse a situation with symmetrical entry and exit costs. If they were asymmetrical (e.g. lower exit costs), the trigger
points for entry and exit would also be asymmetric in which case the local searcher would be at more disadvantage. xi
Results are not shown here, but available upon request. xii
The so-called optimal decisions based on a dynamic programming approach is superior only if there is very modest
turbulence or very low entry/ exit costs. xiii
We use the hierarchy and the flat structure to simplify the analysis, but the results can be obtained for any flexible
decision structure that changes from more to less centralized. This is shown in additional robustness checks available
upon request. This proposition is valid within the modelling framework suggested here. It is due to the general property that
centralized decision structures are more conservative and decentralized decision structures more optimistic.