期)2004_(A)Exploring why more communication is not better_ insights from a computational model of...

8/2/2019 )2004_(A)Exploring why more communication is not better_ insights from a computational model of cross-function

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J. Eng. Technol. Manage. 21 (2004) 83114

Exploring why more communication is not better:insights from a computational model of

cross-functional teams

Ralitza R. Patrashkova, Sara A. McComb

Isenberg School of Management, University of Massachusetts Amherst, Amherst, MA 01003, USA

Abstract

Recent evidence suggests that communication and performance in cross-functional new product

development (NPD) teams are curvilinearly related, but fails to pinpoint the reasons for this relation-

ship. We developed a computational model to study the communication activities of cross-functional

new product development teams. Our simulation confirms therecent evidence and offers insights into

the underlying reasons for the curvilinearity. We provide guidelines regarding when the top perfor-mance occurs, for both frequency and duration of synchronous and asynchronous communication.

Further, we perform a series of post-hoc analyses to examine the reasons for the curvilinearity of the

communicationperformance relationship. The work concludes with a discussion of the theoretical

and practical applications of the results.

2004 Elsevier B.V. All rights reserved.

JEL classification: C63; O31

Keywords: Communication; Performance; New product development; Cross-functional teams; Simulation

1. Introduction

Communication is an essential component of the new product development (NPD)

process (Brown and Eisenhardt, 1995). The challenge for cross-functional teams (CFT),

routinely used for NPD (Denison et al., 1996), is to ascertain the level of information

exchange among team members that will allow them to optimize their performance. Com-

munication frequency is often explored with the assumption that it is linearly related

to performance (Allen, 1977; Katz and Tushman, 1981; Ancona and Caldwell, 1992;

Corresponding author. Tel.:+1-413-545-5681; fax:+1-413-545-3858.

E-mail address: [email protected] (S.A. McComb).

0923-4748/$ see front matter 2004 Elsevier B.V. All rights reserved.

doi:10.1016/j.jengtecman.2003.12.005


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84 R.R. Patrashkova, S.A. McComb / J. Eng. Technol. Manage. 21 (2004) 83114

Smith et al., 1994). Recently, evidence has shown, however, that both high and low lev-

els of communication can impede team performance, thus suggesting a curvilinear re-

lationship between performance and team communication (Hutchins, 1995; Patrashkova

et al., 2003). One possible explanation for these results is offered by the research oninformation processing. Team members have limits on the amount of information they

can process (Boisot, 1995), as too much information overloads the capabilities of team

members and inhibits their performance (Goodman et al., 1986). At the same time, infre-

quent communication cannot supply the necessary information, which also leads to low

performance.

The main objective of this research is to provide insights into how communication relates

to performance and why this relationship holds. In order to obtain this goal, we develop and

test a computational model of communication in CFT. Computational modeling refers to

incorporating mathematical and theoretical models into computer simulations (Hulin and

Ilgen, 2000; Zeigler, 1976). We use computational modeling as a primary research tool,

because when using it the researcher is able to control the variables under consideration,

manipulate them and examine all possible combinations and interactions (Lant, 1994).

The model we derive, formalize and code, is based on theoretical assumptions stemming

from extant theory and empirical results. We use the model to verify earlier research by

assessing whether too much, or too little, communication among team members impedes

performance. Further, after establishing the relationship, we perform a series of post-hoc

analyses to gain a better understanding of communication in teams. Specifically, we explore

the effects of information content, team members expertise and project complexity on the

communication/performance relationship. Comparing results allows us to isolate the impactof these variables.

This work makes several contributions to research and practice. To our knowledge, this

is the first computational model of CFT communication processes. Designing the model

required us to precisely specify many relationships among variables that are implied, but

not quantified, by theory and to formalize a team interaction procedure. The existence of

such a computational model allows us to move beyond confirming a relationship between

variables to an explicit understanding of the nature of that relationship. Consequently, our

findings are a verification and extension of earlier work in this area. Finally, the results we

obtained give clear guidance about identifying the level of communication corresponding

to tops level of performance.

2. Communication in cross-functional teams

Communication is the primary means through which CFTs collaborate. In her study of

NPD teams, Dougherty (1992) observed how difficult, yet essential, it is for team members

to effectively collaborate, and therefore, effectively communicate. Team members are typ-

ically drawn from many different functional areas within an organization and they bring

their unique perspectives, or thought worlds to the team. As they exchange information

through communication, the team members may have a difficult time collaborating if theydo not compensate for their different perspectives regarding the teams work. Too little

information exchanged will result in confusion and misunderstandings. Alternatively, too


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R.R. Patrashkova, S.A. McComb / J. Eng. Technol. Manage. 21 (2004) 83114 85

much new information may tax the information processing capabilities of the team members

(Boisot, 1995; Goodman et al., 1986). Further, if they select an inappropriate medium for

these information exchanges, misinterpretations can also result (Carlson and Zmud, 1999).

Effective communication, therefore, requires that team members select the most appropriatemedium for the information transfer and communicate the optimal amount of information

in order to achieve top performance.

Media selection refers to the communication medium (e.g., telephone, email) chosen to

transfer information (Daft and Lengel, 1986). When selecting media, team members must

decide how best to communicate the requisite information. Two theoretically different me-

dia are available: synchronous or asynchronous. Synchronous communication media (e.g.,

face-to-face meetings, telephone conversations) are employed when two or more members

engage at the same time in the communication act, whereas asynchronous communication

(e.g., electronic mail, written communication) occurs when the members do not engage

in communication at the same time (Levitt et al., 1994). Synchronous and asynchronous

communication media have different capabilities to transfer information (Daft and Lengel,

1986). Specifically, synchronous media are able to transfer more information per message

than asynchronous media, because it utilizes more channels (e.g., facial expressions, into-

nation) for information transfer. For example, in face-to-face communication the tone of

the voice, the context and the facial expressions are used as additional cues clarifying and

supplementing the information content of the message. Asynchronous media lack these

additional channels for information transfer.

We quantify the amount of communication that occurs during information exchange by

measuring both the frequency and duration of the interactions. The majority of researchhas assessed amount of communication by measuring communication frequency (number

of messages exchanged) (e.g., Ancona and Caldwell, 1992; Patrashkova et al., 2003; Smith

et al., 1994). Frequency, however, does not distinguish between long information intensive

meetings and short emails asking for a small amount of information. Communication dura-

tion (the time in which team members are engaged in communication) has not been used as

often to capture the amount of communication transpiring among individuals (e.g., Kraut

et al., 1990). We include duration because it provides a more comprehensive depiction of

the communication activities of the team.

Much of the past research on team communication presumes that more communica-

tion among team members will lead to higher performance (Allen, 1977; Katz andTushman, 1981). Recently, communication has been shown to be curvilinearly related to

performance (Hutchins, 1995; Patrashkova et al., 2003). Hutchins (1995), using compu-

tational modeling, compared the development of cognitive maps of team members based

on their frequency of communication. His results show that more communication is not

always better. When members of a group exchange too much information their cogni-

tive maps become too similar, and the group is assumed to be incapable of innovation.

Too little communication, conversely, will not bring the cognitive maps close enough for

a mutual understanding. These results give insights into what happens in a group when

its members communicate, however, they do not explain how communication relates to

performance.The study by Patrashkova et al. (2003) is a cross-sectional investigation of the relationship

between communication frequency and team performance. Using a sample of 60 project


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teams, they found that performance decreases after some peak communication frequency

is reached. The empirically established curvilinear relationship holds for several types of

performance (goal achievement, project efficiency, and cohesion) and two communication

media (face-to-face and e-mail). The relationship is particularly pronounced for e-mailcommunication. These results empirically establish the curvilinear relationship between

communication and performance, but, because of the research design, no possible causes

could be explored.

Based on the results of Hutchins (1995) and Patrashkova et al. (2003), we suggest that

the relationship between communication frequency and performance will be curvilinear.

Further, because Patrashkova et al. (2003) found unique curvilinear relationships for syn-

chronous and asynchronous communication media, we examine them separately. Thus, we

hypothesize:

Hypothesis 1. Team performance will have a curvilinear relationship with team syn-

chronous communication frequency, such that low and high communication frequencies

will be associated with lower levels of team performance while moderate levels of commu-

nication will be associated with high levels of team performance.

Hypothesis 2. Team performance will have a curvilinear relationship with team asyn-

chronous communication frequency, such that low and high communication frequencies

will be associated with lower levels of team performance while moderate levels of commu-

nication will be associated with high levels of team performance.

We also extend previous research by including communication duration as an alternative

measure of the communication activities of the team. To our knowledge, no other work has

compared the measures of communication frequency and duration, nor have they attempted

to study the relationship between duration and performance. We, therefore, rely on logic

to propose that communication duration will behave similarly to communication frequency

with respect to team performance. Thus, we proffer the following hypotheses:

Hypothesis 3. Team performance will have a curvilinear relationship with team syn-

chronous communication duration, such that low and high communication duration will

be associated with lower levels of team performance while moderate levels of communica-tion will be associated with high levels of team performance.

Hypothesis 4. Team performance will have a curvilinear relationship with team asyn-

chronous communication duration, such that low and high communication duration will be

associated with lower levels of team performance while moderate levels of communication

will be associated with high levels of team performance.

Our methodology provides us with the opportunity to further extend our knowledge about

the relationship between communication and performance by examining why the results

occur. We accomplish this task by conducting post-hoc analyses where certain variables ofinterest are held constant to determine their effects. Additional experiments, such as these,

are easily conducted using computational modeling and simulation.


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3. Research approach

To extend our understanding of the team communication process, we utilize a compu-

tational modeling approach. Computational modeling and simulation refer to an approachwhere the researcher develops a theoretical model of the system of interest, formalizes

this model by developing an algorithm of system behavior, codes the model in a computer

programming language and subsequently executes the code so data about the behavior of

the system are obtained (Law and Kelton, 2000; Zeigler, 1976). Computational modeling

has been successfully used in research to represent many organizational constructs, includ-

ing withdrawal (Hanish, 2000), training and turnover (Glance et al., 1997), organizational

learning (Lant and Mezias, 1992), cultural transmission (Harrison and Carroll, 1991), and

team decision making (Kang et al., 1998).

The computational modeling process begins when the researcher develops a theoretical

model as a series of decision rules that represent theories of human behavior. The theoreti-

cal model is used as a basis upon which a computer algorithm is generated. This computer

algorithm formalizes the behaviors of the elements of the system and determines the output

(Hulin and Ilgen, 2000). The computer algorithm needs to be coded in a high level program-

ming language, such as C, C++, Pascal or the like. The modeled system is simulated when

the resulting code is executed, or run. Each run gives a single observation of the system

behavior.

The development of a computational model requires both an explicit quantification of

the variables included and a detailed specification of the relationships among the variables.

Thus, the design process of a computational model forces the researcher to be systematic andspecific in the behavior description (Kang et al., 1998). The resulting model is a simplified

reality, allowing reliable causal relationships to be established. Computational models are

developed to address the functioning of complex systems and the behavior of individuals

in such systems by focusing on what if? questions. Although it is a simplified reality,

the model is complex enough to adequately test theoretical assumptions (Bendor and Moe,

1992).

Computational modeling and simulation are an especially appropriate methodology for

studying teamwork and quite suitable for representation of human information processing

activities necessary for effective team communication (Kang et al., 1998). Further, the

combination of modeling and simulation gives the researcher much flexibility. Specifically,modeling provides the ability to investigate the effects of individual variables by keeping

them constant. Simulation also provides large samples through the possibility to run the code

as many times as required (Taber and Timpone, 1996). In sum, computational modeling

can, and should, be used as a research tool (Bendor and Moe, 1992).

4. Computational model description

The melding of perspectives required in a CFT is achieved through exchanging and

processing the information the team members possess (Hinsz et al., 1997). We, there-fore, base our computational model on information processing theory (see Schroder et al.,

1967; Streufert and Streufert, 1978; Streufert and Swezey, 1986). NPD is a process through


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which information, in the form of requirements, is converted into information that describes

the final product. This transformation is an information processing activity (Safoutin and

Thurston, 1993). In our model the specific information each team member possesses is

transferred to the team through communication. This received information is subsequentlyprocessed. This process is repeated until all the necessary information is exchanged.

Fig. 1 highlights the variables and decision rules in our model. The simulation begins by

generating a project. The project is used to determine the information content requirements,

which quantify the task and establish the project schedule. Next, a team is generated based

on the information content requirements of the project. In the third phase of the computa-

tional model, the team members communicate with each other until the information content

Fig. 1. Structure of the model.


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requirements are fulfilled. At the end of the simulation, communication frequency, com-

munication duration and on-schedule performance are calculated. Each of these phases is

discussed in the subsequent paragraphs. Details regarding specific variable generation and

decision rules are provided in Appendix A.

4.1. The project

The project is the assignment for which a CFT is created and about which the team

members must communicate. In order to develop a high level formalization of the project,

we model it as information units. This approach allows us to use information as both a

knowledge objective and a unit of communication. Specifically, knowledge intensive tasks

can be expressed as a number of information units (Streufert and Streufert, 1978). Moreover,

information can also represent the tacit knowledge exchanged among team members to

accomplish their assignment (Boisot, 1995). Taken together, information provides us with

a means of initializing the scope of the project and assessing when enough information has

been exchanged to consider the project complete. This representation allows us to cover

a wide range of projects, thereby achieving greater generality, greater realism and greater

explanatory power (Boisot, 1995).

To generate a project, we begin with the establishment of project parameters, particularly

cost and technical objectives, as is typical in NPD projects (Bowen et al., 1994). In our

model, the cost parameter is randomly generated. We then use cost to determine the techni-

cal objectives. Organizational capability is also important to determine the amount of work

required to complete any project, therefore we include this parameter and randomly gener-ate a value for it. Organizational capability and the technical objectives are used to establish

the information content requirements. Information content requirements refer to the overall

number of information units a specific project needs to be completed. Each project is also

represented as a structured sequence of events, shown in Table 1 (Jones, 1997), that does

not change from one simulation run to the next. We use this structure and the information

content requirements to determine the project schedule (i.e. the amount of time allocated

to each sub-phase) as well as the information content requirements for each sub-phase

by functional area. In sum, through this portion of the model, the project parameters are

transferred from a high level objective (cost and technical objectives) to specific informa-

tion content requirements and timeframes associated with each sub-phase by functionalarea.

4.2. The team

The team is created to communicate and process the information needed to complete the

initialized project. The process of establishing the team begins with the determination of

the team size, based on the information content requirements. Table 1 shows the functional

composition of the team, delineating which functional areas participate in each of the

seventeen sub-phases. We include eleven functional areas previous research has identified

as critical for effective NPD (Brown and and Eisenhardt, 1995; Sethi et al., 2001) anddistinguish between the dominant and participating functional areas for each sub-phase

(Jones, 1997). The specific information content requirements are used to determine how


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Table 1

Seventeen project phases and the corresponding functional representation

Customer Marketing R&D Engineering Manufacturing Sales Quality Finance

Predevelopment

(1) New product opportunity

examined

(2) Need identified

(3) Ideas generated

(4) Ideas assessed

(5) Project planned

Development

(6) Concept defined

(7) Design established

(8) Ideas developed

(9) Ideas modeled

Execution/implementation

(10) Technical requirements

detailed

(11) Work schedules

executed

(12) Prototype tested

(13) Product developed

Termination

(14) Product finalized

(15) Product reviewed andaccepted

(16) Manufacturing started

(17) Project evaluated

(): dominant functional area, (): participating functional area.


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many team members from each functional area will participate in the team. Thus, the team

size varies from project to project.

After the team size, and corresponding functional composition, has been determined, each

team member is given a set of personal characteristics identified as important to effectiveteam functioning. The characteristics are functional expertise, communication potential,

cohesion and ability to process information (Gladstein, 1984). Together, these charac-

teristics are used to determine how effectively the team members exchange and process

information.

4.3. Team collaboration process

During the team process, team members communicate with each other. The communi-

cation process, as modeled in this work, consists of: identifying a need for information,

generating a message, communicating the message, and processing the received informa-

tion. Specifically, each team member is able to detect a need for information based on

the difference between the information content requirements and the information s/he pos-

sesses. Further, the team member can find the appropriate source for getting this informa-

tion (within or outside the team, as boundary spanning is important for team performance

(Ancona and Caldwell, 1992)) and communicate his/her need to the source. The source

generates a message containing information in a response. The message is characterized

by duration, information content, relevance, complexity and ambiguity (Boisot, 1995). The

receiver of the message then processes the information and determines if more information

is needed. This cycle continues until each individual team member has satisfied his/herinformation content requirements.

The generated message is sent to the requester either synchronously or asynchronously.

The selection of the appropriate medium is done based on the ability of the medium to

transfer information (Daft and Lengel, 1986). Therefore, team members choose a com-

munication medium depending upon the ambiguity of the information that needs to be

transferred. Highly ambiguous information is transmitted via synchronous medium, while

asynchronous communication is used when the information communicated has lower am-

biguity.

4.4. Team output: team performance

In this research, we focus on team-level on-schedule performance. One of the main

reasons cross-functional teams are assembled is the ability of a CFT to reduce product

development time (Cardinal and Lei, 2000). Further, as these reductions are the result

of a participative (i.e. collective) process, instead of a linear, sequential, individualized

process (Jassawalla and Sashittal, 2000), we consider on-schedule performance a valu-

able indicator of team performance. Team performance is evaluated at the end of the

simulation using the earned value procedure, which allows for comparisons across sim-

ulations by calculating a ratio of budgeted time (project schedule) to actual time (cu-

mulative time required for message request, message generation, information exchange,and processing). For a detailed description of the earned value procedure, see Kerzner

(2001).


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4.5. Team output: team communication

At the end of the simulation, communication frequency and duration are calculated.

Communication frequency is a count of the number of messages exchanged throughout thewhole project. Communication duration is the sum of the duration of all messages exchanged

throughout the project. We calculate frequency and duration separately for synchronous and

asynchronous media.

4.6. Simulation experiments

The computational model is coded in Visual C++ computer programming language.

A total of 10,000 projects, employing the decision rules described, were simulated. This

number of simulations provides results with an absolute error of 0.01 for on-schedule

performance with 95% precision (Law and Kelton, 2000).

5. Results

Graphical summaries of the simulation results are shown in Figs. 2 and 3. Each data

point represents the relationship between on-schedule performance (on the Y-axis) and

communication (on the X-axis) for a single simulation run. Thus, each point represents the

relationship between performance and the communication (synchronous or asynchronous,

frequency or duration) for a unique project with a random set of parameters. As can be seenfrom the figures, our hypotheses are supported for all conditions.

5.1. Communication frequency and on-schedule performance

We examine the relationships between communication frequency and performance for

synchronous and asynchronous communication. The relationship between synchronous

communication frequency and on-schedule performance is given in Fig. 2a. The resulting

curve confirms the curvilinear form of the communication/performance relationship. When

a small number of synchronous communications occur, the performance varies from very

low to very high and does not show any systematic relationship.Fig. 2b presents a view of the relationship between synchronous communication fre-

quency and on-schedule performance in order to better observe the optimal peak. After

analyzing this figure and reviewing the raw data, we determined that the best performance

is achieved for frequencies between about 10 and 75 communications. The performance

rapidly decreases when more than 75 communications occur. We utilized the same proce-

dure to ascertain the levels of communication associated with peak performance reported

herein.

The relationship between frequency of asynchronous communication and performance

shows a similar trend (see Fig. 2c). Communication frequencies below 5 do not affect

performance in any systematic way. Peak performance occurs at about 40 communicationsand decreases after about 140 communications, though not as steeply as for synchronous

communication.


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Fig. 2. (Continued).

peak occurs at approximately 120 h for synchronous communication and at approximately

70 h for asynchronous.

5.3. Post-hoc analyses

One of the primary benefits of computational modeling and simulation as research tools is

that they provide the researcher with the ability to conduct post-hoc analyses that confirm or

reject theoretical explanations of the observed behavior. The theoretical model exists as a set

of decision and variable generation rules. These rules can be easily modified to pinpoint their

effect on the system. We performed several post-hoc analyses to deepen our understanding

of the communicationperformance relationship and confirm the theoretical explanations

we are offering. For each of these post-hoc analyses a variable or set of variables were held

constant as a control. In each case, we performed an additional 2000 simulation runs. Thespecific analyses and the results obtained are described in the following paragraphs.

5.4. Effects of project complexity

We conducted a post-hoc analysis in which we controlled for project complexity. This

analysis was necessary to ensure that the results from the original computational model

were not unduly influenced by the project. In other words, we wanted to examine if all

projects resulting in low levels of communication were also of low complexity, and vice

versa. To this end, we set project complexity at a medium level, which results in the same

project being conducted in all simulation runs. All other parameters were allowed to varyas per the original design. Controlling for project complexity in this manner ensures that

performance is dependent only upon communication and the respective characteristics of


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Fig. 3. Relationship between (a) synchronous and (b) asynchronous communication duration and performance.

the team members. The results are presented in Fig. 4a and b. The peak performance is

achieved for about 2040 synchronous communications, while peak performance for asyn-

chronous communication is achieved for about 7090 communications. Both relationships

were curvilinear as expected and, therefore, provide further confirmation of our hypotheses.The results also validate that our original results were not a function of the complexity of

the projects being undertaken in each simulation run.


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Fig. 4. Relationship between (a) synchronous and (b) asynchronous communication frequency/performance for

single project.

5.5. Efficiency of communication media

In order to test whether asynchronous communication is less efficient than synchronous

media, we performed the second post-hoc analysis. Specifically, we reduced the informa-

tion content being transferred and increased the duration required for the exchange when

generating asynchronous messages. For example, if in the original model e-mail carried

5 units of information and took 20 min to be understood, in the post-hoc analysis the same

e-mail carried 3 units of information and took 25 min to be understood. The relationships

are given in Fig. 5a and b. The resulting curve confirms the curvilinear form of the com-munication/performance relationship. After analyzing the data, we determined that the

best performance for synchronous communication appears to be achieved for frequencies


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low information content.

between about 130 and 300 communications. For asynchronous communication the best

performance is achieved for about 7590 communications.

5.6. Effects of team member skills

In the third post-hoc analysis, we simulated team performance where all team members

were generated with lower levels of expertise than in the original model, to see whetherthe skills of team members affect the communication/performance relationship. Our re-

sults (Fig. 6a and b) show a curvilinear relationship with best performance achieved for


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low team member skills.

synchronous communication between about 90 and 140 communications and for asyn-

chronous about 1300 communications. An interesting phenomenon that can be observed in

these figures is the almost complete lack of an unsystematic relationship at low levels of

communication.

5.7. Amount of information exchanged

In our final post-hoc analysis, we explored the relationship between the amount of in-

formation exchanged and team performance. To accomplish this post-hoc analysis, we


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Fig. 7. Relationship between amount of information exchanged and performance.

used the earned value procedure (Kerzner, 2001) to calculate the ratio of effective infor-

mation exchange. Specifically, we divided what the team members actually communicated

(quantity of information) by what they needed to communicate (information content re-

quirements). A ratio of one indicates that exactly the right amount of information was

exchanged. If the ratio is greater than one, excessive amounts of information were com-

municated, and vice versa. Using the data from the original simulation runs, we plot-

ted effective information exchange versus the performance achieved for each project

(Fig. 7).

6. Discussion

We constructed a computational model to formalize and increase our understanding of

the relationship between communication and on-schedule performance. The results con-

form to our expectations that a curvilinear relationship exists between communication

and on-schedule performance. Regardless of the nature of communication (synchronous/

asynchronous) and the measurement system used (frequency/duration), the results follow

a similar pattern. Furthermore, as demonstrated in our first post-hoc analysis, these results

do not appear to be a function of the project about which the team is communicating. Over-

all, low levels of communication result in sporadic performance, the relationship peaks at

a mid-level of communication and tapers off as communication increases. In addition to

confirming our hypotheses, our results show a striking similarity between the behavior ofcommunication frequency and duration. Both measures exhibit nearly the same curvilinear

relationships. We conclude, therefore, that communication frequency, despite its deficien-


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always beneficial. Our findings suggest that instead of exchanging every available piece

of information, performance improves when teams focus on exchanging only the requisite

pieces of information. We can see that very high ratios, which represent too much informa-

tion being transferred, result in extremely low performance. When too much informationis transmitted, individuals must sift through everything in order to find the relevant pieces,

thereby using their limited time to process unnecessary information. In practice, team lead-

ers should monitor not only the time team members are engaged in communication, but also

the quantity of transferred information. Finding the proper balance is not easy, but is worth

the effort. Our results provide proof that there is an optimal level of information exchange.

In sum, to achieve the best performance not all of the available information needs to be

shared, only the requisite units.

Like all research, the present study has some limitations that need to be considered. We

begin with two general limitations of computational modeling that we could not avoid.

First, although computational models are not simple, they are a simplified representation

of human-to-human interactions. In realistic cases, the phenomenon under investigation is

too complicated to be adequately modeled and simplification is required (Zeigler, 1976).

Such simplifications produce valid models, but decrease the generalizability of the results

obtained. Our computational model, therefore, is valid only for cross-functional teams with

similar project structures. A second limitation of the model stems from a general weakness

of computational models: the results we obtained are not based on observed behavior, but on

inputs of a mathematical model. We put significant effort into formalizing the relationships

according to the existing theory and generating representative parameters, but the results

we obtained should be considered as guidelines.Two other limitations stem from the rules we used while developing the computational

model. First, we assumed that the skills and communication potential of the team members

were constant throughout the project. These rules helped us to clearly depict the relationship

between communication and performance, but prevented us from a full analysis of CFT

interaction. Second, we intentionally did not include product quality as a performance

measure, because CFT are assembled, primarily, to decrease development time (Cardinal

and Lei, 2000). The relative obstacles in representing mathematically the quality of the

finished product also played a role in our decision, because it would be challenging to

mathematically represent quality with acceptable accuracy.

Future research can extend the present computational model in two possible ways. Thefirst type of extension is to perform additional post-hoc analyses using the current model.

In this work, our main goals were to establish the relationship between communication and

performance and to prove that the amount of information communicated and processed is

the main cause of the curvilinearity. As information exchange and processing are the main

focus of this work, we excluded some potentially insightful, but unrelated, post-hoc analyses.

For example, the role of functional team composition and the boundary spanning of team

members can be examined. In this model, we assumed that the organization would have

enough capacity to always provide the required number of team members. Without changing

the model, it is possible to impose a limit on the team members available and to explore

whether the curvilinearity between communication and performance will be maintained. Asecond feature included in the model is the possibility for team members to seek outside

information when needed. Evidence suggests that teams who seek information through


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boundary spanning achieve higher team performance (Ancona and Caldwell, 1992). Future

research could explore how boundary spanning impacts the communicationperformance

relationship in a number of ways.

The second type of extension is through refinements of the theoretical model. Our com-putational model can be further developed by incorporating additional aspects of team

processes to it. We propose three possible extensions. Future models could include team

member learning related both to communication potential and skills. Learning may alter

the performance curves slightly, because team members will be able to transfer more in-

formation via fewer messages if they become more skilled communicators. Under these

conditions, team members may reach their overload potential more quickly, but if they are

more skillful, with respect to their domain, they can process the information faster. Another

possible extension is the creation of an explicit mechanism through which team members

can control the level of information they are transferring. In the current model, the members

are required to exchange information until they satisfy the information content require-

ments. They cannot stop this exchange when they start falling behind schedule. If they

stop the communication process before fulfilling the requirements, a lower quality outcome

may be obtained, but the on-schedule performance should be better. Inclusion of a set of

rules allowing tradeoffs between quality and time will give us insights into how to improve

control of the information exchange process. Finally, the inclusion of other performance

measures, such as attaining technical objectives and product quality standards, should be

examined.

In conclusion, we present a computational model that explores the relationship between

communication and on-schedule performance. Our results contribute to the extant literatureon project team communication in a number of ways. First, we confirm the curvilinear rela-

tionship between communication and performance. Too much, as well as too little, commu-

nication causes low performance for both synchronous and asynchronous communication.

Second, we provide a theoretical explanation for the curvilinear relationship by showing

that the quantity of information exchanged reaches a point of diminishing returns, contrary

to earlier research. Third, we show that communication frequency is a viable approximation

for measurement of communication activities of the team, because it behaves similarly to

communication duration. Fourth, our computational model augments our understanding of

CFT communication. In particular, we explicitly include information processing time, in

addition to the time required to conduct the communication exchange. This expands onprevious work in this area and provides us with a deeper understanding of the informa-

tion transfer process. Finally, the model can serve as a foundation for future computational

models that explore other team processes and performance measures.

Our results also have implications for practitioners. First, the identification of the op-

timum levels of synchronous and asynchronous communication, associated with effective

information exchange among team members, provides guidance for team leaders attempt-

ing to achieve top performance. Second, training programs can be developed based on our

results. Potential team members need to understand that not all information they possess

has to be communicated to the team, but only the requisite units. Lastly, we provide in-

sights into how the communication processes of teams should be managed. Our resultsunderscore that more communication is not better and that quantity, as well as quality, of

communication should be monitored.


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Appendix A

Appendix A provides details regarding variable generation and decision rules. We begin

with a brief overview of the theoretical background on parameter distribution and parameterscales. The remainder of Appendix A is devoted to a complete, phase-by-phase list of

the variables used in the model. For each phase of the model (Fig. 1), we describe the

purpose of the phase and the variables included. Next, we give the theoretical rationale for

each variable and the generation rules. We conclude each section with a description of the

programming sequence. If further detail is required, the simulation code is available from

the corresponding author.

Computational modeling of social processes requires that some parameters in the model

be determined by random variables. If such random parameters are used, they can be gener-

ated from either the normal or the uniform distribution (Taber and Timpone, 1996). When

the underlying distribution is unknown, as it is for our parameters, the uniform distribution

should be used because it assigns equal probability to all possible outcomes (Whicker and

Sigelman, 1991). The ranges from which the random numbers will be drawn are induc-

tively determined and intended to be reasonable (Koput, 1997). As we are not concerned

with absolute numerical values, but with the overall behavioral pattern (Dutta, 2001), we

standardized the majority of variables used in the model on an integer scale from 0 to 5,

where 0 indicates low and 5 indicates high. For the variables that are not measured on a 0 to

5 scale, we provide the rationale behind our decision. To ensure that the results are not an

artifact of a particular combination of initial values and parameter settings, the variables are

randomly varied over the ranges described from simulation to simulation (Koput, 1997).

A.1. The project

The project phase of the model is used to transform the project parameters into the specific

information content requirements and a project schedule. The variables included in this

phase of the model are: project parameters (cost and technical objectives), organizational

capability, information content requirements, information content requirements for each

sub-phase, information content requirements for each functional area, project structure,

project complexity, project schedule per sub-phase, and overall project duration.

A.2. Project parameters

The project is the assignment for which the CFT is created and about which the team

members must communicate. Initially the project is represented as a vector with the fol-

lowing parameters (Srinivasan et al., 1997):

Project = (cost, technical objectives).

The cost is the first parameter to be generated from an discrete uniform distribution. It

ranges between 0 (low cost) and 25 (high cost). Our goal was to generate a large varietyof projects in order to avoid results that stem from a particular combination of parameters.

Thus, instead of generating the project cost on a scale of 05, we chose a larger scale


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(025), with the assumption that it will provide a wider range of projects for the simulation

runs. As the initial two parameters are highly interrelated (Pinto and Kharbanda, 1995),

they should be correlated. Because we must use the uniform distribution, we are not able

to generate correlates of the cost variable. Therefore, to obtain interrelated values, we usecost to establish the ranges in which technical objectives will be generated. In this way,

the resulting variable depends on cost. Technical objectives are calculated as follows: to

a discrete uniform number from one to the value of cost is added the value of cost. For

example, if the generated value of cost is 15, technical objectives equals 15 + a random

number between 1 and 15. If the next number generated is 8, technical objectives will be

set at 23. Technical objectives are used to determine the information content required to

complete the project.

A.3. Organizational capability

Organizational capability represents the knowledge and resources an organization pos-

sesses that impact the amount of work required to complete any project (Bowen et al., 1994).

High organizational capability indicates that the organization members assigned to the team

possess high levels of tacit knowledge about the project and are able to proceed faster and

easier while performing the actual work. The variable is generated as a uniform random

number between 0 and 5, with 0 representing minimal organizational capability relating

to the project and 5 representing maximal organizational capability. We use organizational

capability to help determine information content requirements.

A.4. Information content requirements

The amount of information the simulated team members need to communicate and pro-

cess in order to meet the projects objectives is given by the information content require-

ments. The information content requirements are determined from the technical objectives

and the organizational capabilities. One of the decisions a computational modeler has to

make is how to achieve the right level of detail (Zeigler, 1976). If there is too much detail,

a lot of time and effort will be lost during the simulation. If there is not enough detail, the

model will not be a reliable representation of a true phenomenon. Thus, we developed the

scheme outlined here to achieve a reasonable level of detail for this work. We apply thefollowing rule: the range for technical objectives is divided into five mutually exclusive

intervals. The range of values is 050 because the maximum value technical objectives can

take is 50, (the maximum value of cost is 25 that is added to a random number between 0

and 25). The intervals, therefore, are: 010, 1120, 2130, 3140, 4150. Organizational

capability is divided into two mutually exclusive intervals: 02, representing low capability

and 35, representing high capability. The cross-product of these intervals gives us 10 mu-

tually exclusive intervals (e.g. technical objectives 110 andorganizational capability 02;

technical objectives 110 and organizational capability 35, etc.). All intervals are given

in Table A.1. To each one of these intervals is assigned a range of values for information

content requirements per project. These ranges are also mutually exclusive. Each range is3500 units, as the maximum value for information content is set to 35,000 (this number was

chosen, as it is close to the maximum integer that a standard C++ compiler can generate


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Table A.1

Information content requirements and project complexity

Organization capabilities 2 Organization capabilities 3

Technical objectives [0,10] Information content [1000, 3500] Information content [3500, 7000]

Complexity = 0 Complexity = 1









and provides an acceptable level of detail for the information requirements per project).

Depending on the technical objectives and organizational capabilities, the information con-

tent is generated in the corresponding interval. For example, if technical objectives are 23

and organizational capability is 4, a random number between 17,500 and 21,000 represents

information content requirements of the project. Information content requirements are sub-

sequently used to determine the information content per sub-phase and the schedules for

the sub-phases.

A.5. Information content per sub-phase

The information content per sub-phase represents the portion of the information content

requirements that needs to be communicated and processed during a given sub-phase. Thus,

the information content requirements for the project must be divided into information con-

tent per sub-phase. When developing the model we set the information content per sub-phase

to vary from sub-phase to sub-phase (i.e. all seventeen sub-phases require different amounts

of information to be communicated and processed) to create a realistic representation of

the events in a NPD project. We use a binary search procedure to emulate the negotiation

process that occurs among team members as they establish the requirements for each phaseof the project. The binary search procedure ensures that each sub-phase will have different

requirements and the sum of these requirements will not exceed the total information con-

tent requirements. We proceed in the following manner. First, the total information content

requirements are divided by 17 (the number of sub-phases), so an average requirement per

project is obtained. Next, the specific requirements for each sub-phase are negotiated. The

process begins when eleven (one for each functional area represented on the team) random

estimations between one and the average requirement are generated. The highest and the

lowest of these estimations determine the new range in which the next random estimations

of the information requirements per sub-phase will be generated. Repeating this procedure

gradually decreases the range of generation until only one discrete number can be generated.This number is the information content requirements for the given sub-phase. The binary

search is repeated for all 17 sub-phases.


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The information content requirements per sub-phase represent the number of information

units that must be exchanged during a given sub-phase, regardless of functional area. In

order to identify where the information can be acquired, the information content require-

ments per sub-phase are further divided into the requirements for each functional area. Therequirements for each functional area are allocated based on estimates of cross-functional

participation determined by Dooley et al. (2000). They performed a study using survey

questions and constructed an event history file to determine the role each functional area

is expected to play in a cross-functional team. Their results show that the dominant team

members contribute between 30 and 60% of the work. Thus, in ourmodel, the dominant func-

tional area (specified in Table 1) is randomly assigned 3060% of the information content

requirements. The remaining information content requirements are distributed among the

participating functional areas, with each area responsible for a randomly assigned 1020%.

These percentages represent a small, but reasonable, number of information units. The al-

gorithm allows for overlap (the percentages can sum to over 100). This overlap ensures that

no information content is lost because of the binary search and the allocation process.

The procedure justdescribed establishes the number of information units required for each

sub-phase by each functional area. The next step is to assign each team member this identical

set of information content requirements. These requirements represent the information units

that the team member must receive and process from each functional area (except his/her

own) before the team can proceed to the next sub-phase. The information is received from

his/her team members, or an outside source. Team members can only provide information

regarding their functional area, and the amount of information they can provide is limited by

the amount of time they have available to work on the project (the establishment of which isdescribed in a subsequent section). Creating separate requirements for each team member

in this manner allows us to ensure that all team members participate in the information

exchange process.

A.6. Project complexity

We use project complexity to describe the level of innovativeness and creativity required

for the project. We assign one value of project complexity to each of the 10 intervals in

Table A.1, thus generating the variable as a uniform discrete number from 0 to 9. This way

projects with high information content will have higher complexity. Project complexityis used to determine the time required for the project, as we assume that highly complex

projects will require more time to complete than projects with low complexity.

A.7. Project schedule

The schedule for each sub-phase is determined based on the information content require-

ments and the project complexity. The information content per project is divided into seven

intervals, each with an increment of 5000 (35,000 is the maximum possible information

content requirement). Project complexity is divided into two intervals (04 as low and 59

as high). The cross-product of these intervals results in 14 intervals, as shown in Table A.2.To each of these intervals we assigned a range (see Table A.2) with which a random number

is generated to represent the timeframe per sub-phase. We assume that the timeframe is in


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Table A.2

Project schedule

Project complexity 4 Project complexity 5

Information content [1000, 5000] Time per sub-phase = 1 Time per sub-phase [1,2]

Information content [5000, 10000] Time per sub-phase [1,2] Time per sub-phase [1,3]






whole weeks. For example, if project complexity is 6, and information content per project

is 18,500, timeframe per sub-phase will be randomly generated as 3 or 4 weeks. The sumof the resulting timeframes is the total time for the project. The total time for the project is

used to calculate the on-schedule performance at the end of the simulation.

A.8. Programming sequence

The simulation proceeds as follows. First, the project parameters are generated. Second,

organizational capabilities are initialized. Both project parameters and organization capa-

bilities are used to determine the information content requirements. The information content

requirements and the project structure (see Table 1) are used to determine the information

content requirements for each sub-phase. Next, the information content requirements for

each sub-phase are divided into information content requirements for each functional area.

Finally, the project schedule is established. The project schedule is created in the following

manner. First, project complexity is determined from project parameters and organization

capabilities. Next, the information content requirements, project complexity and project

structure are used to determine the project timeframes per sub-phase. These timeframes

then are summed to determine the expected overall project duration.

A.9. The team

The team is the entity responsible for completing the project through collaboration. The

team consists of the team members determined by the information content requirements. In

addition to the information content requirements previously discussed, each team member

is assigned a functional area of expertise, time requirements for each sub-phase and personal

characteristics.

A.10. Team composition

Team composition describes the number of team members and the functional area each

team member represents. Each team member in this model can belong to exactly onefunctional area. In Table 1, we identify eleven functional areas considered to be impor-

tant for effective collaboration on NPD projects (Brown and and Eisenhardt, 1995; Sethi


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et al., 2001), distinguishing between the dominant and participating functional areas for

each sub-phase (Jones, 1997). First, one team member from each area is initialized. If,

after the information content requirements are allocated, more team members are needed,

they are initialized in the requisite area. The procedure for this allocation is discussed inSection A.11.

A.11. Time per team member

Team members have work-hours assigned for the sub-phases of the project in which

they participate. They can participate in the communication and information processing

activities of the team only during their work-hours. The work-hours are determined by the

information content requirements per sub-phase and the project complexity. For complex

projects (complexity > 2), the work-hours per unit of information required are randomly setbetween 4 and 6, and for less complex projects (complexity 2) between 1 and 3. Thus, to

calculate the work-hours, we multiply the work-hours by the number of information units.

For example, if a team member has an information content requirement of 50 and project

complexity of 5, the time will be a random number (4, 5 or 6) multiplied by 50. If the resulting

work-hours are more than 40 h per person per simulated week, we generate additional team

members in the affected functional areas by dividing the total work-hours for a given week

by 40 h per week. Any fractional values represent part-time worker requirements. If, for

example, there is a 60 h requirement for a given functional area, there will be two members

from this area: one will work 40 h and the second one 20 h.

A.12. Team member characteristics

The next four parameters represent the personal knowledge, skills and abilities of the

team members. The members must possess several personal characteristics that have been

identified to be important for effective team performance. In this research, we focus on

cohesion, functional expertise, communication potential and information processing ability

(Gladstein, 1984).

Cohesion assesses whether team members feel they make a contribution to the team,

because employees need to feel proactive towards their work situations so that they can

exhibit their skills and abilities (Dubinsky et al., 1986, p. 196). Cohesion is set initially to

0.01, because we assume all team members are initially strangers. As they interact, however,

their cohesiveness increases linearly according to the expression:

Cohesiont = cohesiont1 + 0.3personal communication frequency

In setting cohesion to increase with the level of personal communication, we modeled the

way a more cohesive team will emerge. The rationale for this decision stems from the results

ofStewart and Barricks (2000) study. They reported that teams with high communication

frequencies are invariably more cohesive than teams with low communication frequencies.

As one of our reviewers pointed out, however, this may not always be the case and conflictcan emerge that will prevent a team from being cohesive. To explore this alternative, we

ran 2000 simulations in which cohesion increased, stayed the same and decreased with


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equal probability. The results indicate that the maximum achieved on-schedule performance

is significantly lower than in simulations without conflict, but the curvilinear form of the

relationship between communication and performance is preserved. We, therefore, maintain

the rationale that the team member cohesion increases with communication for our purposesin this research, but acknowledge that this area may be a fruitful domain for future research.

Functional expertise represents the skills that team members bring to the CFT. Evidence

has shown that skills possessed by team members can affect team performance (Rulke and

Galaskiewicz, 2000). We randomly generated each team members functional expertise level

on a scale of 05, with 0 meaning low expertise and 5 meaning high expertise. Functional

expertise is held constant through each simulation run, as we assume that members do not

learn throughout the project.

Communication potential refers to the ability of a team member to effectively exchange

information. The ability of each team member to adequately communicate with others is

important, especially in a CFT domain (Safoutin and Thurston, 1993). Similarly to skills,

we initialized team members communication potentials on a scale of 05 and held them

constant.

Information processing time is the amount of time a team member will need to adequately

process information. Information processing refers to the ability of a person to code, or

classify the incoming information into their already possessed knowledge scheme (Boisot,

1995). A team members coding abilities can be overwhelmed easily by complex, new

information and thus, classification of the incoming new information can take a significant

amount of time. Hicks (1952) demonstrated that performing a task with several choices,

the time decisions require increases as the complexity of incoming information increases.He constructed the following equation representing the time necessary to process incoming

information:

Information processing time = k log (N)

where k is a scaling constant based on the skills of the team member and Nthe distinct

number of information units a team member must process. In our model, the constant kis

set to 1.5 for a team member with high skills and to 3 for a team member with low skills.

These values for kwere derived based Hicks results. He established this relationship with

an experiment where the unit of information was a Morse code. Depending on the skill of

the subjects, he obtained values for kof 0.5 and 0.9. As we assumed that the project in our

model is more complex than deciphering Morse code, we used values for kapproximately

three times higher than Hicks.

A.13. Programming sequence

The team is generated in the following sequence. First, one team member from each

functional area is generated. Next, work-hour requirements for each area are calculated from

the information content requirements. Based on the work-hour requirements, additional

team members are initialized (if necessary) and the time available for collaboration isassigned to each team member. Third, the personal characteristics of each team member are

generated.


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A.14. Team communication process

The communication process represents the way in which the team members collaborate.

Collaboration is achieved through message exchange. The variables used in this section are:message generation and media choice.

A.15. Message generation

Each time a team member receives a request for information, a message is generated.

A request for information is generated when a team member determines that s/he has a

lower number of information units than his/her information content requirements in the

current sub-phase for a given functional area. When a deficiency is established, the team

member sends a request to the individual who possesses the needed information, i.e. a team

member from the respective functional area. If no team member can provide the information

due to a lack of availability, it is sought from an external source. As a response to the

request for information, the initiating team member receives a message from the individual

possessing the requisite information with the following characteristics: information content,

complexity, relevance, ambiguity, and duration (Boisot, 1995). These characteristics depict

how well the message transfers information.

Information content is the amount of information a message contains. It is determined

by the functional expertise and cohesion of the sender. Content is randomly set between

0 and 2 if functional expertise and/or cohesion are low (between 0 and 2) and between 3

and 5 if functional expertise and cohesion are high (between 3 and 5). The logic behindthe numbers assigned is the following: when team members are highly competent and feel

devoted to the team, they will give as much information as possible. If they lack knowl-

edge and/or do not feel comfortable with the team, the maximum information will not be

supplied.

Complexity is an assessment of how dense the information included in the message is. It is

based on the communication potential and the functional expertise of the sender. Complexity

is randomly set between 0 and 2 if functional expertise and/or communication potential are

low (between 0 and 2) and between 3 and 5 if functional expertise and communication

potential are high (between 3 and 5). In this case, team members lacking knowledge and

communication abilities will not be able to generate highly complex messages that aredifficult to understand.

Relevance is an indicatorof the senders ability to provide the exact information requested.

It is based on the functional expertise and cohesiveness of the sender. Relevance is randomly

set between 0 and 2 if functional expertise and/or cohesiveness are low (between 0 and 2)

and between 3 and 5 if functional expertise andcohesiveness are high (between 3 and 5).

We assumed that team members who do not feel as if they make a contribution to the team

and who do not possess enough knowledge in their respective area will be less likely to give

information that is pertinent.

Ambiguity refers to the clarity with which the message is sent. It is dependent upon the

senders communication potential. If the senders potential is high, then the ambiguity is low(randomly generated between 0 and 2). If the senders potential is low, then the ambiguity

is high (between 3 and 5).


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Duration characterizes the time required to send and receive the message, so both sender

and receiver are involved. Duration depends upon the complexity of the message. For mes-

sages with low complexity, the duration is randomly set from 15 to 45 min. The time required

for highly complex messages ranges from 46 to 120 min. These values were derived basedon the findings of Kraut et al. (1990). Their results suggest that scheduled conversations

have durations between 15 and 60 min. We extended the time range to 120 min, because

when collecting their data, Kraut et al. (1990) collapsed all conversations that lasted longer

than 60 min into the 60 min category to remove outliers. As it is not reasonable to assume

that all meetings last


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all the information content requirements for the current phase are met. If they are not met,

the team must continue working until they are, thereby lengthening the project duration. If,

however, some information content requirements are outstanding at the end of a sub-phase,

they can be carried over into the next sub-phase in an effort to keep the project on-schedule.For example, if a team has not completed the predevelopment process (a project phase),

they cannot begin development. But, if the team has not fully completed idea generation

(a project sub-phase), they can begin to assess the various ideas without jeopardizing the

integrity of the project.

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