organizational performance in hierarchies and communities of practice

8/13/2019 Organizational performance in hierarchies and communities of practice

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Journal of Economic Behavior & Organization

Vol. 61 (2006) 668690

Organizational performance in hierarchies andcommunities of practice

Olivier Dupouet a, Murat Yldzoglu b,

a BETA (UMR CNRS 752) Universite Louis Pasteur, 61, Avenue de la Foret Noire,67000 Strasbourg, Franceb E3i, IFReDE-GRES, Universite Montesquieu Bordeaux IV, Avenue Leon Duguit, 33608 Pessac, France

Received 26 October 2003; accepted 21 July 2004

Available online 24 July 2006

Abstract

In an earlier article we studied the Communities of Practice and their conditions of emergence using an

Agent based model with a set of agents facing a continuous flow of problems. We now center our analysis on

the performance of this organizational structure compared to a two-level hierarchical delegation structure.

Our results show the crucial role played by the communication and the specialisation of agents; especially that

community structures are efficient for competence building and learning in the long term. This paper backsthe claim made by (Bowles, S., Gintis, H., 2002. Social capital and community governance. The Economic

Journal112, 419437.) that hierarchy and communities are complementary modes of governance.

2006 Elsevier B.V. All rights reserved.

JEL classification: D2; D83; L2

Keywords: Communities of practice; Learning; Emergence of networks; Organisational efficiency; Hierarchy

1. Introduction

The idea that informal social structures play an important role in the behavior and capabilities of

organizations is a long-standing idea in disciplines such as the sociology of organizations (Crozier

and Friedberg, 1977)or management studies (Brown and Duguid, 1991, 1998).The studies of

such informal structures have attained a growing interest with the recent emphasis put on the

knowledge-based economy and the collective learning processes that become the key factor for

success of firms in such a framework. Indeed, an important part of the learning processes within

organizations are increasingly seen as taking place within the informal networks nested in them.

Corresponding author. Tel.: +33 556 84 54 53; fax: +33 556 84 86 47.

E-mail addresses:[email protected](O. Dupouet),[email protected](M. Yldzoglu).

0167-2681/$ see front matter 2006 Elsevier B.V. All rights reserved.

doi:10.1016/j.jebo.2004.07.011
mailto:[email protected]:[email protected]://localhost/var/www/apps/conversion/tmp/scratch_9/dx.doi.org/10.1016/j.jebo.2004.07.011http://localhost/var/www/apps/conversion/tmp/scratch_9/dx.doi.org/10.1016/j.jebo.2004.07.011mailto:[email protected]:[email protected]


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O. Dupouet, M. Yldzoglu / J. of Economic Behavior & Org. 61 (2006) 668690 669

In particular, the concept of community of practice is deemed particularly useful by a number

of scholars to account for the learning processes taking place within an organization (Brown

and Duguid, 1991, 1998; Wenger, 1998).The concept of community of practice was introduced

by Lave and Wenger (1990) who, by focusing on individuals practices, identified groups of

persons engaged in the same practice communicating regularly with one another about theiractivities. Members of a community of practice essentially seek to develop their competencies

in the considered practice. Communities of practice can then be seen as a means to enhance

individual competencies; they are oriented toward their members (Lave and Wenger, 1990; Brown

and Duguid, 1991). This goal is reached through the construction, the exchange and the sharing of

a common repertoire of resources (Wenger). According to this definition, communities of practice

appear as important loci for competence building within firms. As Wenger puts it, communities

of practice are elementary regimes of competence.Amin and Cohendet (2003, 74)note These

communities might be found in traditional work divisions and departments, but they also cut across

functional divisions, spill over into after-work or project-based teams, and straddle networks of

cross-corporate and professional ties. For example, within firms, classical communities includefunctional groups of employees who share a particular specialisation corresponding to the classical

division of labor (e.g. marketing or accounting). They also include teams of employees with

heterogeneous skills and qualifications, often coordinated by team leaders and put together to

achieve a particular goal in a given period of time.

However, although some works, either empirical or theoretical, investigate the impact of com-

munities of practice upon the performance of the organization as a whole (Orr, 1990; Huberman

and Hogg, 1995),such studies remain rare. More precisely, some measures of performance of

communities themselves would be desirable. Moreover, the interplay between communities and

hierarchy and the output of this interplay deserves deeper analysis. These issues are difficult to

tackle in traditional ways since the impact of communities upon hierarchical structures and their

potential benefits in terms of performance remain largely invisible in case studies. Their existence

can be evidenced, but the true input of their activities for the firm are hard to assess. Besides, most

of the theoretical work on communities of practice leaves some questions (such as the bound-

ary of communities, for instance) unanswered. The concept is thus difficult to formalize using,

for example, a mathematical apparatus. In a previous work (Dupouet et al., 2003),we explored

the emergence of communities of practice from a collection of individuals engaged in problem-

solving activities. We evidenced sufficient conditions for the emergence of such social structures

and show the usefulness of some indicators to characterize networks of communities of practice.

This paper goes one step further and, using computational methods, seeks to shed some light onthe role of communities in the performance of organizations, using computational methods.

Even if this articles main contribution concerns the emergence and the efficiency of the com-

munities of practice, we use the hierarchy as a benchmark case. As a consequence this article

is somewhat connected to the literature that has been mainly developed following the seminal

article ofBolton and Dewatripoint (1994).This is not the place to review this quite rich literature,

but we can emphasize the complementarity that exists between our work and these articles on

the efficiency of hierarchy. Bolton and Dewatripoint consider the efficiency of different struc-

tures for the communication networks in an organization that must handle a continuous flow of

information. They study the trade-off that must be established between the specialisation of the

agents and the cost of communications under differentgivennetwork structures and character-ize the conditions under which a pyramidal structure can become the most efficient one. Even if

the main problem of this article is not very far from some of the points we study in our model,

the structure of these models are very different. In our case the structure of the communication


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processes, it refuses the problem and passes it to the next agent in a randomly constructed list

of agents. Otherwise it accepts the problem and tries to give an answer using its rule set. If it

does not possess a satisfactory rule, it consults the community that it has constructed over the

past interactions. If the agent does not receive any response from the contacted agents, it passes

the problem to the next agent in the random list. If the last agent in the list cannot provide anyanswer, the problem remains unsolved (the system does not have the competencies to deal with

this specific problem). Such a situation corresponds to a loss for the firm and hence a decrease

in performance. The overall dynamics thus result both from individual learning by doing and

learning by interacting between agents. We will discuss these elements in more detail later in this

section.

The second part of the model consists of an hierarchical structure where amanageris added

to the community of the agents. The role of the manager is to receive the problems from the

environment and to select the agent it deems the most able to solve this problem. Its role is thus

to carry out a division of labor. We called this part of the model hierarchy with delegation (HD,

hereafter) since the manager has to ask its subordinates for advice in order to answer the problemssubmitted by the environment. In addition, we consider two possibilities: either agents are able

to communicate horizontally or not.

The article questions the various dimensions of these different organizational settings with a

particular interest in the role of communities in the global performance of a firm. In the rest of

this section, we present the problem space that organizations are facing, the learning processes of

agents, the implemented communication processes and the role of the manager.

2.1. The environment

The agents in the model face a continuous flow of classification problems. Such problems

are quite common in organizational contexts (cf.for exampleCarley and Lin, 1997,Ouksel and

Vyhmeister, 2000). In this article, the simulation model is inspired by a specific empirical context:

a population of agents whose task consists in allocating financial warranties for bank loans to small

enterprises. For each enterprise, a given agent has to determine a certain amount of warranty by

considering a set of criteria. In their endeavor to determine the optimum level of warranty, agents

can communicate with each other. They can thus exchange the best practices concerning a

particular subspace of the problem space. In this way, communities of practice are susceptible to

emerge.

Formally, we suppose that a project is characterized by two criteria stocked as a eight bits longbinary string. The first four bits code the value of the first criterion and the four last bits the value

of the second one. The problem space is thus a grid, and each binary string represents a particular

situation and occupies a position on that grid. An action is associated with each situation (for the

agent, the various possibilities take the form [condition:action], where the condition is the binary

string and the action is an integer). According to each particular situation, the agent must propose

an amount of warranty (the action): for example, 10, 20, 30, 40, 50 or 75% (respectively coded as

actions 05; seeFig. 1).The linear nature of the complementarity that exists between these two

dimensions of the projects implies smaller surfaces when we get closer to the origin. That could

make the classification more difficult, and one of the environments we have tested corresponds to

rectangular surfaces. The structure we have retained for this article does not imply any noticeabledifficulty for learning in comparison with the rectangular one. The linear complementarity relation

better corresponds to the nature of the problem we study (riskiness vs return of the projects, for

example).


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Fig. 2. Schematic XCS (adapted from Wilson). The Match set is constructed using the rules that contain the signal 0011 intheir condition part. The sign # corresponds to a joker that can either match 0 or 1. This possibility is behind the capacity

of the Classifier System to generalize from observations.

of performance,F, the fitness that measures the accuracy of the rules (a function of the inverse of

the error) and that is used in the genetic algorithm used by the XCS to explore the problem space.

When the genetic algorithm discovers new rules, they are introduced into the population. For each

problem, rules regarding which the condition part fits the signal from the environment are stored in

an array called the match set. A prediction array is then constituted in order to evaluate the reward

associated with each advocated action. This array contains for each possible action the average rel-ative performance (fitness prediction) of the rules that prescribe this action in the match set. If no

rules in the match set prescribe a particular action, the corresponding value in the prediction array

isnil. Based on this array, an action is chosen and triggered in the environment. In our implemen-

tation, the agent can select the action predicting the highest reward from the environment or use a

roulette wheel process to select the action to be fired. A parameterselectAgcontrols the choice of

the selection mode: when set to 1, the best action is chosen; when set to 0, the roulette wheel is used.

In the latter case, each rule has a positive probability of being chosen, and this probability increases

with the performance of the rule. In our setting these two values ofselectAg(0,1) respectively

correspond to agents who favourexploration(0: roulette wheel) orexploitation(1: best action).

The environment then rewards the XCS that uses this reward to adjust the parameters of its rules.Based on the literature on communities of practice, we endow the agents with the following

characteristics. An agent seeks to develop its competencies on a given practice that does not

constitute its entire activity but is nonetheless deemed critical by the agent. Moreover, the learning


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674 O. Dupouet, M. Yldzoglu / J. of Economic Behavior & Org. 61 (2006) 668690

process of each agent is oriented: new rules produced by the genetic algorithm are inserted in

its memory only if they are in its realm of specialisation. The surface of the specialisation is

controlled by the parameter compRange that can rank from 0 to 1. The variable compRange

corresponds to the maximal possible distance between the specialisation area of the agent and

the submitted problem. For each submitted problem, the agent computes the maximal relativedistance3 between the condition part of the best rules in its classifier set (the ones with a fitness

better than the average of its classifier set) and the submitted problem. If this distance is higher

thancompRange, the agent considers that the problem is too far from its specialisation area and

passes the problem to the next agent in a randomly composed list of agents. compRange also

intervenes in the mutations in order to impede their capacity to disturb the learning process of

the agent (the Genetic Algorithm is niche-based in the specialisation area of the agent). When

compRange is equal to 1, the agent is not specialised and can accept all the problems submitted by

the environment, but when it is close to 0, the agent becomes highly specialised. This is intended

to account for the focused learning taking place in communities of practice. An agent has a limited

memory that does not permit it to deal with the whole problem space by itself.

2.3. Learning by interacting

In our implementation, a parameter, communication, determines whether or not the agents

have the ability to communicate. This parameter can take the values 0 or 1; when it is set to 1, the

communication is allowed. We now present the communication process (seeFig. 3).

The agent can have no rule satisfying the signal from the environment or have only a poor

rule (with a prediction of performance below an acceptable threshold, common to all agents). If

an agent does not possess a satisfying rule to answer a problem but the problem lies nonethelessin its realm of specialisation, it engages a communication process by requesting help from other

agents in its community. Each contacted agent returns the best matching rule it has in its classifier

set. The requesting agent collects all answers and evaluates them.

If a received rule offers a better prediction of performance, then it adopts it.

If predictions are equivalent (either several rules it received have identical prediction, or the

rules it received and the ones it already possesses have identical prediction), it compares the

prediction error and opts for the rule having the lowest one.

In case of equivalence between prediction errors, it compares values of fitness and chooses the

rule with the highest fitness. When these parameters are not discriminating, the agent choosesa rule at random.

By accepting a rule, the agent copies it in its memory and uses it to answer the signal from the

environment.

Each agent uses an address book to keep in memory successive communications with different

partners. This address book is originally empty. When an agent first launches a communication

process, it asks a percentage (consultRatio) of the whole population at random. It then stores in

its address book the references of agents answering him (not only the ones giving him the best

answers). In the following periods, the agent will question the agents present in its address book.

3 This distance is normalized by the maximal possible distance in the environment. As a consequence, it belongs to

[0,1] ascompRange.


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Fig. 3. Solving problems in CP. The agents only accept to handle problems that are close to their specialisation area.

If an agent accepts a problem but does not have a solution for this problem, it consults its community (contained in its

addressbook). If a member of its community provides it with a satisfactory solution, this solution is integrated in the

population of rules of the initial agent (the agent learnsthis rule).

If none of these agents answer its enquiry, it will then ask once again consultRatioof the total

population of agents, which can be seen as an exploration of its social environment and a searchfor new partners. In order to avoid the presence of non-relevant agents in the address book, a

counter (exval) is associated with each agent present in a given address book. If an agent does

not answer to exvalconsecutive enquiries, it is removed from the address book. exvalis thus a

parameter indicative of the stability of links established by agent and of the general degree of

trust in the organization.

Hence,

in order to enhance its practice, an agent engages with other agents in the building and the

sharing of a common repertoire of knowledge resources. In particular, this implies that the

agent is always willing to cooperate with other potential members of a community and that

there is no direct cost associated with the establishment of a new relation;

the agent then nurtures this community of practice with its experience and in turn relies on this

community to enhance its practice.

Behaviors of agents are then twofold; they are engaged in a practice involving individual

learning, and they are committed to social interactions. However, these two parts are interrelated

in that communication informs practice and practice fosters exchanges.

2.4. The role of the manager

The second organizational structure implemented consists in the simple addition of a manager

on top of the structure presented so far. The environment remains the same. However, in this new


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Fig. 4. Problem solving procedure of the manager.

organization, the signals from the environment are directly received by the manager. The duty of

the manager is to allocate the task of answering a given signal to the agent most suited for this

specific problem. The role of the manager can then be seen as twofold. On the one hand, it has to

allocate efficiently the tasks as they are submitted by the environment. On the other hand, since

the allocation of tasks made by the manager will influence the learning processes of the agents,it can also be viewed as a guide for the learning of agents.

From the modelling standpoint, the manager is an XCS similar to the ones used for modeling

agents. The condition part of the managers rules corresponds to the signals arising from the

environment. The action parts consist of the references to the agents (i.e. each agent is referenced

to by an integer).

To choose an agent for answering a given problem, the manager proceeds as follow (see Fig. 4).

First, the manager tries to find an agent by selecting in its memory a rule that matches the signal

from the environment. If it does not have any rule, then the manager chooses an agent that is not

often called and that has a specialisation realm close to the signal submitted by the environment. 4

In the latter case, the manager adds in its memory a new rule of which the condition part is thesignal from the environment and whose action part is the reference to the selected agent. As in the

case of agents, the manager can select the rule with the best prediction of performance in its action

set or use a roulette wheel process to chose the action. This is controlled by the parameter select

Man(whenselect Manis 1, the rule with the best prediction is chosen; when it is 0, the roulette

wheel is used). These two values again correspond to a manager who favours exploration (0:

roulette wheel) orexploitation(1: best action). Once an agent has been selected by the manager,

the problem is passed to this agent and it tries to answer it.

4 The fact that the manager only does one random experiment is not really restrictive. When communication is allowed,

several other agents will generally be questioned by the initially chosen agent. We have also done experiments allowing

the manager to call some (variable) number of agents as long as it cannot find an agent that would accept to treat the

problem. The results we obtained are very similar to the ones exposed in the next section.


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To answer the problem, the agent can either solve it alone or ask for help from other agents

(if communication is set to 1). However, the problem is not passed to another agent when the

selected agent cannot answer. In that case, the problem remains unsolved (the organization has

not been able to develop a policy for this problem), and incurring a cost that corresponds to a

missed opportunity for the organization.The selected agent returns its answer directly to the environment that evaluates it and gives a

reward. The reward is then used by the agent and the manager to carry on their learning processes

and refine the parameters associated with their rules.

3. Simulation protocol and main results

3.1. The simulation protocol and methodology

Given the complexity of the interactions we model and the strong non-linearity of the decision

processes of the agents, we adopt a methodology that allows quite a systematic exploration ofthe parameter space of the model. This methodology is close to the Monte-Carlo method. For

each of our two models, we run 500 series of 15,000 problems5 each where the results from each

problem has a probability of 1% of being saved. Thus, for each run we obtain an average number

of 150 randomly chosen observations for all the measured variables. All series are initialized with

a randomly drawn vector of values for the main parameters of the model. As a result, we obtain,

for each model, a set of 75,000 observations covering quite a diversified subset of the parameter

space and the problem space. The values from which different parameters are drawn can be read

in the Appendix A.6 We do not necessarily discuss in the text all the parameters that appear in

this appendix but only the most significant ones. We analyze these random samples using box

plots (giving the four quartiles of the distributions of the variables), Student tests between subsets,

histograms and regression trees. The statistical analysis is conducted using R (see R. Development

Core Team, 2003).

The gross performance ( [1000,1000]) is the most direct indicator of the organizational

efficiency. When the organization faces a problem, it can find a solution and obtain the actual

performance returned by the environment, or it can be unable to provide any solution, in which

case the problem remains unsolved. The performance takes into account this missed opportunity;

it is computed in this case using the negative of the past average performance (the opportunity

that the organization has missed by being unable to solve the problem). This performance criteria

is useful for us in order to distinguish firms that generally can solve problems but miss someopportunities from the ones that are never able to solve problems. Missing opportunities can be

fatal in a competitive environment as is emphasized by Visser. If the organization is a help desk,

not being able to solve the problems of the customers can be very frustrating for them, and they

can be lost to the competition. If the organization is the financial warranty providing firm of our

example, projects that are not handled can be warranted by the competition and the firm can

loose its credibility and its market share. Given that we desire to evaluate the capability of the

organization to handle problems as well as the quality of this treatment, we must qualify this

capability. The relative weight of negative performances in comparison with the positive ones

nicely indicates the capacity of each organization to treat problems.

5 15000 problems correspond to the yearly average number of cases considered by the refinancing company we have

analysed.6 This appendix is downloadable from our web site:http://beagle.u-bordeaux4.fr/yildi/files/appendixhiercp.pdf.
http://beagle.u-bordeaux4.fr/yildi/files/appendixhiercp.pdfhttp://beagle.u-bordeaux4.fr/yildi/files/appendixhiercp.pdf


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Fig. 6. Comparison of gross performance between organizations with (1) and without (0) communication.

with communications (1) and without communications (0). The graphic (a) shows that the com-

munities of practice need communications to better avoid the negative payoffs (due to unsolved

problems), while the hierarchy needs communications for attaining better payoffs (graphic (b)). As

a consequence, communications do not play the same role in these two organizational structures.

The comparison of results between HD and CP shows that CP is able to attain, on average,

better payoffs than HD. The organizational structure plays the main role in the determination

of the performance, but communities with communication (CP(communication = 1)) attain the

highest performances and the complete ordering between these four cases is given by

CP(1) CP(0) HD(1) HD(0). (2)

Student tests between different cases concerning the gross performance give the followingresults:

H1: CP < HD t= 64.2951, df= 149390.3,pvalue = 1

H1: CP (1) < HD(1) t= 51.3433, df= 122334.1,pvalue = 1

H1: CP (1) < CP (0) t= 8.9367, df= 31741.86, p value = 1

H1: CP (0) < HD(1) t= 27.6562, df= 32190.58,pvalue = 1

H1: HD(1) < HD(0) t= 41.9592, df= 12777.20, pvalue = 1

H1: CP (0) < HD(0) t= 55.6133, df= 18648.14,pvalue = 1

InFig. 7,we consider the total number of communications in CP and HD. It clearly shows

that the number of communications is the highest in CP. Moreover,Fig. 6evidences that com-


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Fig. 7. Comparison of the number of communications.

munications significantly enhance the gross performance of the organizations but they could also

be seen as an important source of organizational cost (Ishida and Ohta, 2001; Butler, 2000).

However, this cost cannot be specified without introducing an ad hoc bias. Nonetheless, if this

cost is really consequential, it could eliminate the contribution of communications to gross

performance. Organizations are thus facing a trade-off, having to find a balance between thenumber of communications that would increase their performance and a reasonable cost of these

communications.

In the rest of the article, we will focus the analysis on the settings where communication is

allowed.

3.2.2. Emergence and performance of the communities of practice

The comparative analysis of performance indicates that communications among agents, outside

the hierarchical line, play a role in the behavior of the system as a whole. In our setting, to allow

communication is to allow agents to build relational structures beyond the hierarchy. In otherwords, in our setting, when communications occur, one can hope to observe the emergence of

communities of practice. To account for the emergence of their communities, we here use an

indicator well-known in social network analysis,cliquishness(Newman et al., 2001; Watts, 1999;

Wasserman and Faust, 1994),which captures the idea that two agents connected to another agent

are likely to be connected to one another. It is thus an indicator of the existence of groups of

tightly connected agents within the graph. Given the assumptions we adopt about the agents and

their behaviors, such groups would correspond here to communities of practice.

In a directed graph, cliquishness represents, for a vertex with n neighbors, the fraction of

potential edges that actually exist between them: the full connection of all the nneighbors with

each other would correspond to n(n 1). Cliquishness is the average of this measure over allvertices. IfG(I,d) is a graph, withIbeing the set of the n nodes ofG and dbeing its adjacency

matrix, the average cliquishness ofG can be defined using the following notation. Ifd(i,j) is the

distance between the verticesiandj,Vi= {j|d(i,j) = 1}is the direct neighborhood ofi. Letni= #Vi


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andmi =

j,l

d(j, l)j, lVisuch asd(j,l) = 1. The cliquishness ofG can be computed as

C=

1

ni

mi

ni(ni 1) . (3)

Cliquishness can consequently rank from 0 to 1. When it is equal to 0, no triangles exist within

the graph. When cliquishness equals 1, the graph is made of fully connected subgraphs. Given

that communications in our model create new links, as well as eliminate the existing ones that

become irrelevant, the passage of time alone does not automatically increase cliquishness.

Fig. 8 shows that communities do not emerge solely in the CP model, but also in HD. Nonethe-

less, the highest values ofcliquishness are only reached in CP. As a consequence, we observe in the

latter either a higher number of communities or more tightly knit ones. A potentially interesting

feature of communities is their contribution to the attainment of high performances.

InDupouet et al. (2003),we characterize the emergence of communities of practice in the CP

model. To do so, we use three indicators (betweenness, degreesand cliquishness). We show that

full-fledged communities of practice only emerge for certain values of these indicators. These

configurations correspond to values of cliquishness between 0.2 and 0.3. In order to study the

role of communities in the overall performance, we consider the distribution of cliquishness

in the cases corresponding to the maximal performance. Fig. 9shows that in CP as well as in

HD, the most frequent values of cliquishness, when the performance is maximal, are the ones

that also correspond to the emergence of the communities of practice (the distributions possess a

cumulation around 0.20.3). These observations will be confirmed below in Figs. (12a) and (13a).

These results show that for both types of organizations, the highest performances are attainedmainly in the cases where the communities of practice emerge. Even if it is practically quite

arbitrary to determine a limit value ofcliquishness corresponding to the emergence of communities

of practice, the correspondence between the highest performances and the presence of structures

similar to communities of practice is quite strong.

Fig. 8. Comparison of Cliquishness.


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Fig. 9. Distribution of cliquishness for the cases with the maximal performance.

Our results have so far focused on the most striking results of the model. However, the under-

standing of the mechanisms behind these results requires further explorations.

3.2.3. Behind the curtains: the role of the main parameters

We analyze the role of different parameters of the model using regression trees (Venables and

Ripley, 1999, chapter 10). A regression tree establishes a hierarchy between independent variables

using their contributions to the overall fit (R2) of the regression. More exactly, it splits the set

of observations in sub-classes characterized by their value in terms of their contribution to the

overall fit and of their predictions for the dependent variables (all parameters that are modified

by the Monte Carlo procedure are included as explanatory variables in each of the following

regressions). This value is validated against a fraction (10%) of the sample that is not used during

the estimation. Regression trees are very flexible and powerful in the clarification of the structure

of the observations. We give in the next paragraph a step-by-step interpretation of the regression

tree exposed inFig. 10.We now consider the determinants of the main performance variables of

our model.

Fig. 10. Regression tree for the gross performance in HD.


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Fig. 11. Regression tree for the gross performance in CP.

3.2.3.1. Determinants of the gross performance. TheFigs. 10 and 11exhibit the variables that

contribute more than 1% to the overall fit in both cases (HD and CP) with communications. In

Fig. 10,the first condition (selectMan < 0.5) divides the observations in two subsets. On the left,

we have the observations for which this condition is fulfilled, and on the right, the observations

that correspond to (selectMan 0.5). This is a common feature of all trees; on the left of a

condition we have observations for which the condition is true, and on the right, the ones for

which it is false. The observations on the left, corresponding to (selectMan < 0.5), are again

separated in two sub-groups by another condition concerning the specialization of the agents:

(comprange < 0.5787). The final nodes of the tree indicate the number n of observations concerned

by these conditions and the expected value of the dependent variable (here the gross performance)

in this sample. For example, (selectMan


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Fig. 12. Determinants of gross performance in HD. (a) Role of Cliquishness (b) Role of comprange (c) Role of the

behavioral selectors.

exploitation (they use the best action rule (selectAg = 1) for choosing their actions). When the

manager uses a more explorative strategy (selectMan = 0), the organization is only able to treatproblems if the agents are loosely specialized [Fig. 10].

CP with communications

In the communities, the hierarchy between the determinants is slightly different. Even ifselec-

tAg plays the main role in the segregation of different cases, the specialisation of the agents clearly

appears as the main determinant of the performances. The community can only attain significant

performances if the agents are not too specialised (compRange 0.13). When conditions upon

compRangeand selectAgare fulfilled, then time matters (since the variableperiodcorrespond to

the number of the submitted problems). There is a learning trajectory of the system over time.

Since time does not play this privileged role in HD, it seems that the initial conditions in CP are less

determining than in HD. It is interesting to observe that other dimensions of the communication

(like the consultRatio) play only a marginal role in the determination of the gross performance.

Their role only appears when we consider more qualitative aspects of the performance (Fig. 11).

The boxplots ofFigs. 12 and 13give the distribution of gross performance as a function of

another variable (for example,cliquishness). These values are split in two groups, on the left we

have the values of the performance when the explicative variable is lower than its second quartile

(its median,Q2) and on the right, we have the complementary cases where the variable is higher

than its median.

Fig. 13. Determinants of gross performance in CP. (a) Role of Cliquishness (b) Role ofcomprange(c) Role ofselectAg.


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The comparison ofFig. 12withFig. 13reveals an interesting structural regularity: higher

performance is associated with higher cliquishness and lower specialisation (highercomprange),

and exploitation allows the hierarchy to increase its performance while permitting the commu-

nities to reduce negative performances. This again confirms the positive relationship between

the emergence of the communities of practice and the global performance and explains how thismechanism works: higher cliquishnessincreases the capacity of CP to cover the problem space,

while it increases the accuracy of problem solving by HD. We should not forget that even if

the manager and the agents favour exploitation, some minimal amount of exploration is always

present through the genetic algorithm.

3.2.3.2. Determinants of communications. The number of communications in the organization

will generally imply a cost as we have stressed above (Ishida and Ohta, 2001; Butler, 2000).

Whatever the nature of this cost (linear, non-linear, etc.) it will generally be increasing with the

number of communications. It is legitimate then to ask if it is possible to limit the communications

when their cost is prohibitive. We consider the determinants of the communications inFig. 14inorder to identify the parameters that the organization could weight in order to reduce this cost.

HD with communications

In HD the number of communications remains relatively limited. It is sufficient to limit the

possibility for the agents to consult other members in order to minimize these costs. This is not a

tautology, since the actual number of communications also depends on other variables such as

the specialization of the agents. Even if strongly restricting the possibility of consultation is not

possible, a manager who strongly favours exploitation over exploration in its decisions will also

be able to limit these costs. Last but not least, an intermediate level of specialisation (compRange [0.17,0.63]) of the agents can also be used to contain these costs (Fig. 14a).


In CP, one must definitely avoid a very high specialisation (compRange < 0.1) for the agents,

but this is not enough. Some limitation of the possibilities of consulting other agents is necessary

Fig. 14. Determinants of the number of communications.


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Fig. 16. Regression tree for cliquishness in CP.

HD with communications

Three variables intervene in the emergence of communities in the case HD: consultRatio,

exval and compRange. When consultRatio is low (consultRatio 0.11), then no commu-

nities can emerge and the value of cliquishness is almost one of a random graph. When

0.11 consultRatio < 0.35, thenexval has to be quite high, that is, there must be some kind of

fidelity or trust in relationships between agents in order to observe the emergence of communities.

Even when consultRatio is higher, exval still has to be above 3.5 to obtain the highest values

of cliquishness. In any case, compRange has to be above a certain threshold to have values of

cliquishness corresponding to full-fledged communities of practice (Fig. 15).


The same three parameters are the determinants in the emergence of communities in the case

CP. However,compRange(that is the level of specialisation of agents) plays here a less important

role than in the case HD. As in the previous case, whenconsult Ratiois low, high values ofexval

help reaching reasonably high values of cliquishness (i.e. exval 9.5 implies a cliquishnessof

0.14). The highest values of cliquishnessare attained for high values of consultRatioand exval.

Hence, in order to build communities of practice, agents need both a great ability to screen theirsocial environment and a certain stability in their relationships (Fig. 16).

The main determinant of the importance of communities of practice is therefore the possibility

of communications between agents. This is of course not a very surprising result in itself, but


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given that the conditions for it are quite demanding (consultRatio> 0.35), communications are not

enough; some degree of trust in the organization is also necessary, and communities of practice

are not a simple epiphenomenon.

4. Conclusion

This article analyzes the potential impact that informal social structures, communities of prac-

tice, can have upon the performance of firms or subunits of firms.

The first result of this work is to show that community structures are efficient for compe-

tence building, particularly if one considers learning in the long term (i.e. without strong time

constraints). However, real firms are required to answer demands from the market in a timely

fashion. In these cases, this work tends to show that hierarchy altogether with communities of

practice is the most efficient structure among those we explore. This paper thus backs the claim

made byBowles and Gintis (2002)that hierarchy and communities are complementary ratherthan substitute modes of governance. When the cost of communications are important, HD can

successfully assure a satisfactory trade-off; it implies a very low level of communications and

a reasonable level of performance. When the reliability of the performance is the issue, CP can

assure more systematically positive performances, but it can also incur a tremendously higher

level of communications.

Moreover, the examination of the interrelations between variables reveals that two of them

are especially crucial for the emergence of communities and their performances: the degree

of specialisation of agents and the conditions of communication between them. If the former

clearly depends on the individual agent, the second can be seen as part of the environment in

which communities exist (e.g. the efforts made by the management to ease communication, the

existence of an intranet, etc.). In the case of HD, one must add the behavior of management to

the two previous parameters. The way managers apprehend their task has an influence both on

the conditions of emergence of communities within the firm and on the global performance of the

organization. It is the interplay between agents idiosyncratic capabilities and managers behavior

that ends up in an organizations specific outcome.

However, we are well aware that this can only constitute an exploratory work on these

issues. Regarding communities, the detailed dynamics of their behavior has not been explored.

In particular, the legitimate peripheral participation process that Lave and Wenger put at the

heart of the evolution of communities structure has not been explored. Also, an interestingdevelopment of the model could follow Hart and Moore and allow for a differentiated special-

ization of the agents, some of them being less specialized than others. The particular role that

such agents could play in the network can be very interesting to study. Enriching the possi-

ble structures for the hierarchy could also be very interesting since this would allow a better

comparison of our model with the hierarchy models developed by Bolton and Dewatripoint

or by Garicano. We should not forget that the structure of the communication hierarchy is

endogenous in our model and its structure could be more directly compared with the struc-

tures considered by Bolton and Dewatripoint (conveyor belt or strict hierarchy, for example).

In an article more directly focused on the hierarchy (instead of the communities of practice),

some indicators from the social network analysis literature could be used to characterize thehierarchical structure of the agents communication network. Besides, concerning the incentives

and motivations of agents to enter in a community, we have assumed that the agents are always

willing to enhance their individual competencies by resorting to communities. This assumption


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is common in the literature about communities of practice but would certainly deserve a deeper

exploration.

Our model with hierarchy also focuses on some key aspects and leaves many other features

unaddressed. For instance, the fostering of communities of practice is not considered as a strategic

issue by the manager, whereas this is certainly a main concern in management studies today.Moreover, we here only dealt with a specific form of hierarchy where the manager is engaged

in a learning process. It could also be interesting to investigate a more rigid hierarchy where the

manager strongly prescribes the tasks to agents without seeking to take into account their learning

curves or specialisation (as, for example, in Carley and Lin). More complex problem spaces (as

inRivkin and Siggelkow, 2003)should also be analyzed since they can condition the learning

process of the agents.

All these limitations highlight the work that remains to be done in modeling the behavior and

the role of communities within firms. It is hoped that this work contributes in opening the way to

this future research.

Acknowledgements

We gratefully acknowledge the comments and suggestions of the participants of the SCE Work-

shop on Complex Behavior in Economics and Finance 2003 and of the WEHIA 2003 Workshop.

We are very grateful to two anonymous referees who have considerably contributed to the read-

ability of this article. Financial support of the CCRRDT program of Region Aquitaine is gratefully

acknowledged by Murat Yildizoglu.

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