PRIMING THE SYSTEM: BUILDING IMMUNITY TO THE NEGATIVE EFFECTS OF
STRESS IN TEAMS THROUGH CROSS-TRAINING
ALEKSANDER P.J. ELLIS
The University of Arizona
The Eller College of Management
McClelland Hall, 405T
Tucson, AZ, 85721-0108
Tel: (520) 621-7461
Fax: (520) 621-4171
e-mail: ellis@eller.arizona.edu
BRADLEY J. WEST
University of Nebraska
Department of Management
Gallup Leadership Institute
P.O. Box 880491
University of Nebraska-Lincoln
Lincoln, NE 68588-0491
Phone: (402) 472-8885
Fax: (402) 472-3189
e-mail: bwest4@unl.edu
MATTHEW J. PEARSALL
The University of Arizona
The Eller College of Management
McClelland Hall, 405T
Tucson, AZ, 85721-0108
Tel: (520) 621-7461
Fax: (520) 621-4171
e-mail: mpearsal@email.arizona.edu
Author note: This research was supported in part by Grant N00014-96-1-0983 from the
Cognitive and Neural Sciences Division of the Office of Naval Research and by contract
F49620-03-1-0377 from the AFOSR/MURI to the University of Arizona. Although support for
this work is gratefully acknowledged, the ideas expressed herein are those of the authors and not
necessarily endorsed by the funding agencies.
ABSTRACT
The purpose of this study was to examine the role of cross-training in the relationship
between stress, mental models, and transactive memory. Results from 54 teams working on a
command and control simulation indicated that cross-training exhibited significant direct effects
on mental models and transactive memory. Cross-training also exhibited significant interactive
effects, reducing the negative effects of stress on mental models and transactive memory.
Finally, mental models and transactive memory were significantly related to team viability.
Theoretical and practical implications are discussed, as well as possible limitations and
directions for future research.
As organizations have structured work around teams rather than individuals (e.g.,
Kozlowski & Bell, 2003) and the amount of stress placed on employees has increased (see
Barling, Kelloway, & Frone, 2005), teams are frequently forced to face the barriers associated
with stress, often at their own peril. The Hubble Space Telescope fiasco, the USS Vincennes
disaster, and the crash of United Airlines Flight 173 are all examples of teams catastrophically
failing under stress (see Cannon-Bowers & Salas, 1998; Driskell, Salas, & Johnston, 1999; Ellis,
in press), where “certain environmental demands . . . . evoke an appraisal process in which
perceived demand exceeds resources and results in undesirable physiological, psychological,
behavioral, or social outcomes” (Salas, Driskell, & Hughes, 1996: 6).
Given the impact of stress in teams, researchers have begun to examine the issue more
closely. Results from a number of studies confirm the anecdotal evidence - teams perform more
poorly under stress (e.g., Cannon-Bowers & Salas, 1998; Driskell & Salas, 1991; Driskell et al.,
1999; Ellis, in press). However, researchers recognize that, in order for organizations to design
interventions aimed at reducing the negative effects of stress in teams, they need to possess “a
clear understanding of the processes by which this occurs” (Driskell et al., 1999: 299); an issue
that was recently addressed by Ellis (in press). Ellis found that stress did not simply reduce the
amount of communication within teams. Rather, stress disrupted mental models, defined as
organized mental representations of knowledge regarding critical components of the team’s task
environment (Klimoski & Muhammed, 1994), and transactive memory, defined as a cooperative
division of labor for learning, remembering, and communicating relevant team knowledge (e.g.,
Wegner, 1987).
Despite significantly advancing our knowledge, one critical issue remains to be resolved:
what can organizations do to reduce those effects? Several researchers have suggested that the
introduction of a team training program may provide team members with the knowledge and
skill they need to successfully weather stressful situations (e.g., Driskell & Salas, 1991; Driskell
et al., 1999; Serfaty, Entin, & Johnston, 1998). Cross-training, defined as “an instructional
strategy in which each team member is trained in the duties of his or her teammates” (Volpe,
Cannon-Bowers, Salas, & Spector, 1996: 87), may be particularly influential (Blickensderfer,
Cannon-Bowers, & Salas, 1998; Ellis, in press).
Therefore, the purpose of this study is to examine cross-training as a potential moderator
of the negative effects of stress in teams. Given that information processing theory represents a
viable framework that can help add to our understanding of the effects of stress in teams (e.g.,
Ellis, in press; Gladstein & Reilly, 1985; Staw, Sandelands, & Dutton, 1981), we utilize it as an
explanatory framework and link cross-training to the relationship between stress, mental models,
and transactive memory in two ways. First, we expect cross-training to exhibit direct, positive
effects on mental models and transactive memory by focusing team members’ attention on teamrelated aspects of the task. Second, we expect that, through priming, cross-training will help
immunize team members against a shift in attention away from their collective encoding,
storage, and retrieval duties, reducing the negative effects of stress on mental models and
transactive memory. We then examine the importance of these effects in terms of team viability,
suggesting that teams will evidence less task withdrawal when their information processing
system is operating efficiently and effectively.
LITERATURE REVIEW AND HYPOTHESES
The Effects of Stress in Teams
Researchers have consistently found that stress negatively affects information processing
at the individual level by narrowing attention to sources of information that are considered to be
a priority (see Gladstein & Reilly, 1985; Staw et al., 1981). Similar results have been found in
teams, where information processing represents “the degree to which information, ideas, or
cognitive processes are shared, and are being shared, among the group members” (Hinsz et al.,
1997: 53). However, instead of ignoring peripheral tasks, team members tend to focus more on
the self and less on the team. This shift in attention away from the team disrupts the
interdependent nature of each of the components of the team’s information processing system
(Driskell et al., 1999), including mental models and transactive memory.
During encoding, team members’ individual mental representations of information are
combined into a representation for the entire team (Wilson & Canter, 1993). Mental models
provide team members with a psychological map, or organized structure of knowledge, depicting
how the characteristics, duties, and needs of their teammates fit with one another (e.g.,
Mohammed, Klimoski, & Rentsch, 2000). This emergent cognitive state (see Marks, Mathieu, &
Zaccaro, 2001) taps qualities of the team that represent member cognitions and provides team
members with a heuristic that can help them interpret information in a similar manner (Hinsz et
al., 1997). The efficiency and effectiveness of mental models as encoding devices are determined
by the degree of similarity and accuracy across team members (e.g., Smith-Jentsch, Campbell,
Milanovich, & Reynolds, 2001).
During storage and retrieval, transactive memory provides the team with a system for
distributing and retrieving specific information based on team member expertise (Hinsz et al,
1997). The system represents a blend of both cognitive and behavioral components that develops
as team members’ knowledge repositories deepen and integrative processes improve (e.g., Ellis,
in press; Lewis, 2004; Mohammed & Dumville, 2001). However, because the cognitive
components tend to emerge more slowly and may exert less of an impact in short-term teams
(see Ellis, in press; Lewis, 2004), this study focuses on transactive memory behavior, which is
comprised of directory updating, information allocation, and retrieval coordination (e.g.,
Wegner, 1987; Wegner, Erber, & Raymond, 1991). Through directory updating, team members
learn about each other’s areas of expertise by sharing or requesting information about “who
knows what.” Through information allocation, team members communicate specific
information to the team member that possesses the relevant area of expertise. Through retrieval
coordination, team members use their “directory of directories” to request specific information
that they need from the team member with the proper area of expertise (e.g., Ellis, in press;
Hollingshead, 1998a, 1998b).
Although Ellis (in press) found that mental models and transactive memory represent
interrelated components of the team’s information processing system, researchers have
theoretically and empirically distinguished the two constructs (e.g., Austin, 2003; Lewis, 2003;
Mohammed and Dumville, 2001). Team-interaction mental models do not reflect whether team
members choose to draw upon and combine their teammates’ knowledge by allocating
information to, and retrieving information from, the correct team members (e.g., Lewis, 2004).
Mental models represent an integrative function, serving to integrate team members’ perceptions,
while transactive memory represents a differentiation function, serving to capitalize on
differences between team members’ roles and responsibilities (Ellis, in press).
Because stress shifts attention away from the team, Ellis (in press) found that stress
negatively affected mental models and transactive memory. More specifically, stress led to
deficiencies in the similarity and accuracy of team-interaction mental models, which focus
primarily on team-related aspects of the situation (see Mathieu, Heffner, Goodwin, Salas, &
Cannon-Bowers, 2000), and reduced the level of directory updating, information allocation, and
retrieval coordination within teams.
The Effects of Cross-Training
While stress shifts team members’ attention away from the team, cross-training likely has
the opposite effect. Teams allocate attention according to the information they receive (Hinsz et
al. 1997). Cross-training “focuses specifically on providing members with information about
others’ roles and how team members combine their responsibilities to accomplish collective
tasks” (Marks, Sabella, Burke, & Zaccaro, 2002: 5). Researchers suggest that this type of
information expands team members’ focus outside of their individual roles and responsibilities to
include team-related aspects of the task, influencing encoding, storage, and retrieval processes
(Ellis & Bell, 2005).
Regarding mental models, researchers argue that cross-training increases team members’
interpositional knowledge, which represents a necessary condition for team-interaction mental
model development (e.g., Mathieu et al., 2000). In support of these propositions, Marks et al.
(2002) found that cross-training through positional clarification and positional modeling
significantly improved team-interaction mental model similarity among team members
participating in a military simulation. Although Marks et al. examined cross-training techniques
that were less intensive than the technique examined in this study, they argue that the effects
should increase with training intensity. In addition, although there is no specific evidence linking
cross-training with team-interaction mental model accuracy, Cannon-Bowers, Salas,
Blickensderfer, and Bowers (1998) found that cross-trained team members were more
knowledgeable of their teammates’ tasks. Thus, we hypothesize the following:
Hypothesis 1. Cross-training will positively affect team-interaction mental model
similarity and accuracy.
Regarding transactive memory, researchers suggest that the knowledge gained through
cross-training can improve communication, coordination, and regulation between team members
(Blickensderfer et al., 1998). For example, Volpe et al. (1996) and Cannon-Bowers et al. (1998)
found that cross-trained team members volunteered more information before it was requested by
their teammates. In addition, Marks et al. (2002) found that cross-training affected coordination
behavior, albeit indirectly, allowing them to better orchestrate the sequence and timing of their
interdependent actions (e.g., “the radar specialist provided the pilot with navigational
information”: 7). While transactive memory does not involve the timing of specific behaviors, it
does relate to the management of synchronous activities and the exchange of information within
the team (Hinsz et al., 1997). By encouraging team members to learn and understand the
operations of their teammates, cross-training may act as a mechanism to stimulate behaviors such
as directory updating, information allocation, and retrieval coordination (Lawler, 1982).
Therefore, we hypothesize that:
Hypothesis 2. Cross-training will positively affect transactive memory through the use of
directory updating, information allocation, and retrieval coordination.
While we expect cross-training to directly affect mental models and transactive memory,
we also expect that, through priming, cross-training will help immunize team members against a
shift in attention away from their collective encoding, storage, and retrieval duties during
stressful situations. Priming occurs when the presentation of a particular set of task-related
information early in a team’s life cycle facilitates its use later on, even following shifts in the
task environment (Hinsz et al., 1997). Through priming, teams develop a certain set of selfreinforcing norms and expectations regarding how to approach and complete a task
(Bettenhausen & Murnighan, 1991). These norms and expectations regarding the allocation of
processing resources become entrained in the system (Ancona & Chong, 1996), resisting
exogenous threats and providing meaning in threatening situations (Raven & Rubin, 1976).
Team members that undergo cross-training must align their roles and responsibilities with the
roles and responsibilities of their teammates, forcing them to focus on “the big picture” rather
than on their own division of the task. This primes team members in team-related aspects of the
task, which may prevent regression into their individual roles and responsibilities under stress.
A number of studies have shown that priming team members regarding role
interdependencies develops team-focused norms and expectations that are immune to
environmental shifts (e.g., Bettenhausen & Murnighan, 1985, 1991; Gersick & Hackman, 1990).
For example, Johnson, Hollenbeck, Ilgen, Humphrey, Meyer, and Jundt (in press) found that
teams were better able to shift from a situation where their reward was contingent on cooperation
to a situation where their reward was contingent on competition. These teams, labeled “friendly
competition” teams, originally focused their attention on the interdependent nature of their roles
and responsibilities, and that continued into later stages of the team’s life cycle. After undergoing
the shift, friendly competition team members kept sharing information even though such
behavior was discouraged. Similarly, Moon et al. (2004) examined teams going through a
structural shift and found that, when the team’s initial structure required high levels of
coordination, team members continued to focus on team-related aspects of the task, supporting
one another and distributing needed information, even when the new structure promoted
independent activity.
Based on the above arguments, we expect that priming team members through crosstraining will help them develop a resistance to shifts in attention away from the team under stress
and hypothesize the following:
Hypothesis 3. The effects of stress on team-interaction mental model similarity and
accuracy will be less negative for cross-trained teams.
Hypothesis 4. The effects of stress on transactive memory through the use of directory
updating, information allocation, and retrieval coordination will be less negative for
cross-trained teams.
Mental Models, Transactive Memory, and Team Viability
Researchers have suggested that team members’ information processing capabilities
represent crucial components of team effectiveness (Hinsz et al., 1997). As evidence, a number
of studies have clearly established a positive relationship between transactive memory, mental
models, and team performance (e.g., Austin, 2003; Ellis, in press; Lewis, 2003; Marks et al.,
2002; Marks, Zaccaro, & Mathieu, 2000; Mathieu et al., 2000).
However, a comprehensive assessment of team effectiveness must capture both current
team effectiveness (i.e., present performance) and future team effectiveness (i.e., team viability)
(e.g., Hackman, 1987). With the exception of Lewis (2004), researchers have yet to examine the
relationship between mental models, transactive memory, and team viability, or the team’s
capability to continue functioning as a unit (e.g., Phillips, 2001).
While there are a number of components of team viability, task withdrawal is particularly
important given the interdependent nature of teamwork (Roberson & Colquitt, 2005). Task
withdrawal can be defined as team members’ efforts to physically and psychologically remove
themselves from their work environment by reducing the amount and frequency of their
participation in the team (Hulin, 1991). Because task withdrawal represents a shift in attention
away from team-related duties, it is likely negatively related to mental models and transactive
memory.
Regarding mental models, researchers have shown that, when individuals are unclear as
to how their role fits in with other employees, they become confused and dissatisfied and tend to
withdraw from the situation altogether (Miller & Jablin, 1991). Individuals also feel a desire to
withdraw when they have failed to master their tasks (Judge, Thoresen, Bono, & Patton, 2001).
When team members hold similar and accurate team-interaction mental models, they are clear
about their roles and responsibilities and have mastered task interdependencies (e.g., Marks et
al., 2002). Therefore, we suggest that, if teams possess similar and accurate team-interaction
mental models, they will also evidence low levels of task withdrawal, leading to the following
hypothesis:
Hypothesis 5. Team-interaction mental model similarity and accuracy will be negatively
correlated with task withdrawal.
Regarding transactive memory, researchers have shown that employees feel rejected and
withdraw when they are not included in social activities (Kammeyer-Mueller & Wanberg, 2003).
The less team members stay actively engaged in the task, and the less they feel their input is
important and valued within the team, the more they withdraw (Foushee, 1984; Phillips, 2001).
Through the use of directory updating, information allocation, and retrieval coordination, team
members actively distribute essential task information, establishing connections within the team
(e.g., Ellis, in press). As a result, we argue that high levels of transactive memory behavior and
high levels of task withdrawal are unlikely to co-exist within teams, leading to the following
hypothesis:
Hypothesis 6. Transactive memory through the use of directory updating, information
allocation, and retrieval coordination will be negatively correlated with task withdrawal.
METHODS
Participants
Participants included 216 students from an introductory management course at a large
Midwestern University who were arrayed into 54 four-person teams. Out of the 216 students,
120 (55.6%) were male and 202 (93.5%) were white with an average age of 21. In exchange for
their participation, each earned class credit and all were eligible for cash prizes ($400 per team)
based upon the team’s performance. This study was a 2 (stress vs. no stress) by 2 (cross-training
vs. no cross-training) factorial design and teams were randomly assigned to one of the four
conditions. Out of the 54 teams, 27 encountered the stress manipulation, 30 encountered the
cross-training manipulation, 15 encountered both, and 12 encountered neither.
Task
Participants engaged in a modified version of the Distributed Dynamic Decision-making
(DDD) Simulation (see Miller, Young, Kleinman, & Serfaty, 1998). The DDD is a
computerized, dynamic command and control simulation requiring team members to monitor a
geographic region and defend it against invasion from unfriendly tracks (i.e., radar
representations of unfriendly forces moving through the region). The objective is to maximize
the number of team points, which can be accomplished by identifying tracks, determining
whether they are friendly or unfriendly, and, if unfriendly, keeping them out of restricted areas.
The geographic region is partitioned into four quadrants of equal size (one per team
member). In the center of the computer screen is a 4 by 4 square designated as the “highly
restricted zone” which is nested within a larger 12 by 12 square called the “restricted zone.”
Outside the restricted zone is neutral space.
In terms of monitoring the geographic region, each team member has a base of operations
surrounded by a detection ring that detects the presence or absence of tracks within its radius. To
detect tracks outside of their bases’ detection rings, team members can rely on their teammates or
they can rely on the vehicles located at their base. Vehicles are semi-intelligent agents that can
automatically perform certain functions during the task. As shown in Table 1, there are four
different types of vehicles; (a) AWACS (surveillance) planes, (b) tanks, (c) helicopters, and (d)
jets. Although each of the vehicles can detect targets, they vary in capability across five major
dimensions; (a) range of vision, (b) speed of movement, (c) duration of operability, (d) weapons
capacity, and (e) identification capacity. Capabilities are distributed among the vehicles so that
each has both strengths and weaknesses.
---------------------------------Insert Table 1 about here
---------------------------------When tracks enter a detection ring, they show up as unidentified. Once the track is
identified using an AWACS plane, a team member can engage it with a tank, helicopter, or jet. If
the vehicle has the correct level of power, the track can be disabled. In this study, teams faced
four different types of tracks: A, B, C, and D. As shown in Table 1, each track had a power of 0
(friendly), 1, 3, or 5, depending on whether it appeared during DDD training or the experimental
task. During DDD training, team members did not have specific areas of expertise. Each team
member owned a tank, an AWACS plane, a helicopter, and a jet and knew that the A, B, C, and
D tracks corresponded to power 0, 1, 3, and 5, respectively. During the experimental task, team
members did have specific areas of expertise. Specific areas of expertise were created by
splitting up knowledge regarding the tracks and possession of the four different vehicles. Each
team member knew the power level of one track and was responsible for one type of vehicle.
DM4 knew that track A had a power of 1 and had four jets, DM3 knew that track C had a power
of 3 and had four helicopters, DM2 knew that track B had a power of 5 and had four tanks, and
DM1 knew that track D had a power of 0 and had four AWACS planes. Each team member was
only aware of his or her area of expertise at the start of the experimental task.
Procedure
Team members were first trained on declarative and procedural knowledge regarding the
various aspects of the DDD for approximately 60 minutes. Participants then played a 45-minute
DDD training task, where they learned how to launch and move vehicles, identify tracks, and
engage tracks without specific areas of expertise. After DDD training, team members performed
a 45-minute experimental task and a 15-minute practice or cross-training task with specific areas
of expertise. Each team encountered 140 tracks consisting of 35 of each of the four types (A, B,
C, and D) evenly distributed across the experimental task. Each team encountered 60 tracks
consisting of 15 of each of the four types (A, B, C, and D) evenly distributed across the practice
or cross-training task. Participants were only allowed to communicate verbally with one another.
Teams in all four conditions completed 15-minutes of the experimental task. At the 15minute mark, teams in the cross-training condition received 15 minutes of cross-training while
other teams continued to practice for 15 minutes at their own stations (see below). After the
cross-training or practice task, teams in the stress condition were introduced to the stress
manipulation. Teams then completed the final 30-minutes of the experimental task. Regarding
measures, transactive memory was measured during the final 30-minutes of the experimental
task. The manipulation check, the team-interaction mental models measure, and the task
withdrawal measure were completed following the experimental task.
Manipulations
Cross-training. Blickensderfer, et al. (1998) identified three different cross-training
methods: positional clarification, positional modeling, and positional rotation. Positional
clarification offers information about each role through a brief presentation, positional modeling
employs both discussion and observation of each role in action, and positional rotation provides
hand-on training in each role. While all three cross-training methods have been shown to be
effective, researchers suggest that positional rotation, which was utilized in this study, is more
in-depth than positional clarification or positional modeling (Marks et al., 2002).
Team members in the control condition remained at their original positions and were told
they would have 15 minutes to continue to practice their skills. Team members in the crosstraining condition were also told they would have 15 minutes of additional practice. However,
during the task they engaged in positional rotation where they experienced the task from their
teammates’ perspectives by switching positions every five minutes. For example, DM2 began at
DM3’s position, moved to DM4’s position at the 5-minute mark, and moved to DM1’s position
at the 10-minute mark. Therefore, team members were able to gain experience carrying out the
duties of their teammates through active participation.
Stress. The stress manipulation, adapted from previous research (see Driskell et al., 1999;
Ellis, in press; Turner, Pratkanis, Probasco, & Leve, 1992), combined time pressure and threat to
achieve an exponentially greater response (Kahn & Byosiere, 1992). Both time pressure and
threat were introduced after the 15-minute training or practice session. Time pressure was
manipulated by warning team members at 5-minute intervals during the task that “You now have
only XX minutes left to work on the task, which is not a lot of time. In order to perform well,
you need to hurry up and work harder at keeping the restricted zones free from enemy tracks.”
Threat was manipulated by setting up a videocamera and telling team members that, in their class
of 600 students, “If your team is one of the three lowest performers, your professor will show the
tape to the entire class the last week of the semester as an example of ineffective teamwork.”
Measures
Transactive memory. To measure transactive memory, this study employed direct
measures of verbal behavior based on team members’ areas of expertise (see Ellis, in press;
Hollingshead 1998a; 1998b). Because variance was of no theoretical or operational concern, we
utilized an additive index (i.e., sum) to represent directory updating, information allocation, and
retrieval coordination at the team level (Chan, 1998). Directory updating occurred when team
members shared expertise with, or requested expertise from, their teammates (e.g., “I’m DM2
and I have the tanks” or “Who knows what the D track is?”). Information allocation occurred
when team members sent information to the person with the correct track or vehicle specialty
(e.g., “DM3, I have several C tracks in my restricted zone”). Retrieval coordination occurred
when team members requested information known to be part of someone’s track or vehicle
specialty (e.g., “DM3, what is the C track again?”).
Two experimenters were in charge of coding. To ensure that the coding was accurate and
consistent, both experimenters participated in a two-hour training session, which included a
review of the construct definitions for each dimension as well as the coding of two practice
teams. The experimenters then coded 7 (13%) of the teams together. Cohen’s (1960)  provided
an index of interrater agreement. In this study,  = .78 for directory updating,  = .77 for
information allocation,  = .79 for retrieval coordination, which indicated acceptable levels of
agreement (see Landis & Koch, 1977). The remaining 47 teams were divided between the two
experimenters, with one experimenter coding 24 teams and the other coding 23 teams.
Team-interaction mental models. Team-interaction mental models represent a configural
construct whose properties capture an array of divergent contributions to the whole (Kozlowski
and Klein, 2000). While several measurement techniques have been developed (see Mohammed
et al., 2000), this study utilized concept mapping (see Ellis, in press; Marks et al., 2000). Team
members were given two separate task scenarios focusing on team members’ roles and
responsibilities and interaction patterns within the team (see Figure 1). Each task scenario was
accompanied by two columns and four rows of blank spaces. Participants were then given nine
behavioral options and needed to place behaviors for each team member that best represented
their actions in sequence in the blank spaces.
---------------------------------Insert Figure 1 about here
---------------------------------To calculate the similarity between team members’ concept maps, one point was given
when two participants agreed on the sequence of behavior for one of the team members, three
points were given when three team members agreed, and nine points were given when all four
team members agreed. Because four team members were included in each map, scores could
range from 0 to 36. Team similarity scores were formed by summing together the number of
points teams garnered in each concept map.
Accuracy scores were assessed by two judges who were subject matter experts in the
DDD command and control simulation. Each team member’s concept map was rated from 1
(inaccurate) to 7 (highly accurate). Judges paid particular attention to (1) the critical DDD
functions, (2) the appropriate role assignments for each team member, and (3) a reasonable
sequence of actions for successful completion of the task. The two judges’ evaluations were
highly correlated for both the first (r = .92, p< .01) and the second (r = .89, p < .01) concept
maps. As a result, team accuracy scores were formed by averaging the two judges’ ratings for
each team member’s map and then summing those four values together.
Task withdrawal. Task withdrawal was measured with a 9-item Likert scale adapted
from Phillips (2001). An example item read “I often daydreamed while working on this task.”
The coefficient alpha reliability of this scale was .83. An ICC(1) of .21 and an ICC(2) of .72
suggested that member level scores could be aggregated to the team level using the average of
the four team members’ scores.
RESULTS
Manipulation Check
To examine the effectiveness of the stress manipulation, participants completed a fouritem scale adapted from Ellis (in press). Some example items were “I felt stressed because there
was not enough time to complete the task” and “I felt a lot of pressure to perform well on this
task because there was a chance that others could observe my behavior.” Participants responded
on a scale from 1- Not at all true to 7- Very true. Coefficient alpha reached .81. Individuals in the
experimental conditions reported higher levels of stress (M = 3.68, SD = 1.47) than individuals
in the control condition (M = 3.09, SD = 1.24), t(213) = 3.18, p < .01.
Tests of Hypotheses
Means, standard deviations, and intercorrelations among all the variables included in the
hypotheses tests are included in Table 2. We subjected team-interaction mental model similarity
and accuracy and directory updating, information allocation, and retrieval coordination to a 2 X 2
MANOVA comparing the stress and cross-training manipulations. Significant multivariate
effects emerged for the stress manipulation [F(5,46) = 7.43, p<.01, partial eta2 = .45], the cross-
training manipulation F(5,46) = 4.40, p<.01, partial eta2 = .32], and their interaction F(5,46) =
2.92, p<.05, partial eta2 = .24], allowing us to conduct a series of univariate follow-up ANOVAs.
Supporting the results of Ellis (in press), results indicated that stress negatively affected teaminteraction mental model similarity [F(1,53) = 10.86, p<.01, partial eta2 = .17] and accuracy
[F(1,53) = 9.05, p<.01, partial eta2 = .15], directory updating [F(1,53) = 5.18, p<.05, partial eta2
= .09], information allocation [F(1,53) = 16.32, p<.01, partial eta2 = .24], and retrieval
coordination [F(1,53) = 13.52, p<.01, partial eta2 = .21].
---------------------------------Insert Table 2 about here
---------------------------------The first hypothesis proposed that cross-training would positively affect mental models.
Results indicated that cross-training positively affected team-interaction mental model similarity
[F(1,53) = 2.91, p<.10, partial eta2 = .05] and accuracy [F(1,53) = 6.16, p<.05, partial eta2 = .11],
supporting Hypothesis 1.
The second hypothesis proposed that cross-training would positively affect transactive
memory. Results indicated that cross-training negatively affected directory updating [F(1,53) =
6.01, p<.05, partial eta2 = .10] and retrieval coordination [F(1,53) = 2.88, p<.10, partial eta2 =
.05] and exhibited no effects on information allocation [F(1,53) = .00, ns, partial eta2 = .00].
Thus, Hypothesis 2 was not supported.
The third hypothesis proposed that the effects of stress on team-interaction mental models
would be less negative for cross-trained teams. Results indicated that the interaction between
stress and cross-training explained an insignificant amount of variance in team-interaction
mental model similarity [F(3,53) = 2.31, ns, partial eta2 = .04]. However, the interaction between
stress and cross-training explained a significant amount of variance in team-interaction mental
model accuracy [F(3,53) = 5.78, p<.05, partial eta2 = .10]. A simple slopes test (Aiken & West,
1991) indicated that teams resisted the negative effects of stress on team-interaction mental
model accuracy in the cross-training condition [F(1,29) = .76, ns, partial eta2 = .03] but not in the
control condition [F(1,23) = 16.33, p<.01, partial eta2 = .43], as shown in Figure 2. These results
offer partial support for Hypothesis 3.
---------------------------------Insert Figure 2 about here
---------------------------------The fourth hypothesis proposed that the effects of stress on transactive memory would be
less negative for cross-trained teams. Results indicated that the interaction between stress and
cross-training explained an insignificant amount of variance in directory updating [F(3,53) = .00,
ns, partial eta2 = .00]. However, the interaction between stress and cross-training explained a
significant amount of variance in information allocation [F(3,53) = 4.68, p<.05, partial eta2 =
.09] and retrieval coordination [F(3,53) = 4.60, p<.05, partial eta2 = .08]. As shown in Figure 3,
teams resisted the negative effects of stress on information allocation in the cross-training
condition [F(1,29) = 2.34, ns, partial eta2 = .08] but not in the control condition [F(1,23) = 23.85,
p<.01, partial eta2 = .52]. Teams also resisted the negative effects of stress on retrieval
coordination in the cross-training condition [F(1,29) = 3.20, ns, partial eta2 = .10] but not in the
control condition [F(1,23) = 12.54, p<.01, partial eta2 = .36]. In sum, these results provide partial
support for Hypothesis 4.
---------------------------------Insert Figure 3 about here
---------------------------------The fifth hypothesis proposed that team-interaction mental models would be negatively
correlated with task withdrawal. Multivariate tests indicated that task withdrawal was
significantly and negatively correlated with team-interaction mental model similarity [F(1,53) =
4.50, p<.05, partial eta2 = .08] and team-interaction mental model accuracy [F(1,53) = 3.62,
p<.10, partial eta2 = .07]. Thus, Hypothesis 5 was supported.
The sixth and final hypothesis proposed that transactive memory would be negatively
correlated with task withdrawal. Results indicated that task withdrawal was significantly and
negatively correlated with information allocation [F(1,53) = 10.09, p<.10, partial eta2 = .16] and
retrieval coordination [F(1,53) = 4.55, p<.05, partial eta2 = .08]. However, task withdrawal was
not significantly correlated with directory updating [F(1,53) = .60, ns, partial eta2 = .01].
Therefore, Hypothesis 6 was partially supported.
DISCUSSION
Although stress often leads to disaster for teams, the crew of a recent Air France flight
managed to overcome the odds. On August 2, 2005, Air France Flight 358 left Paris for Toronto
carrying 297 passengers and 12 crew members. When attempting to land at Toronto’s Pearson
International Airport, the plane overshot the runway by 200 yards, sliding into a ravine and
breaking into pieces. Remarkably, due to the efficiency of the cabin crew and their coordination
with local fire and rescue crews, all 309 people on board managed to escape moments before the
plane burst into flames. While some called the evacuation “miraculous,” local officials and other
flight experts attributed the success of the crew to their superior training (e.g., Blenford, 2005;
Marks, 2005).
The rarity of the Flight 358 example reflects the growing need for research aimed at
identifying methods to help teams fight the negative effects of stress (e.g., Cannon-Bowers &
Salas, 1998; Ellis, in press). Utilizing information processing theory as an explanatory concept,
this study examined the role of cross-training. By providing knowledge regarding team
members’ roles and responsibilities, we expected cross-training to focus team members’
attention on the team, directly affecting mental models and transactive memory. We also
expected that, through priming, cross-training would build team members’ immunity to the
negative effects of stress. We found partial support for our hypotheses. Regarding direct effects,
we found that cross-training positively affected team-interaction mental model similarity and
accuracy and negatively affected directory updating and retrieval coordination. Regarding
interactive effects, we found that the effects of stress on team-interaction mental model accuracy,
information allocation, and retrieval coordination were less negative for cross-trained teams.
Finally, we found that team-interaction mental model similarity and accuracy, information
allocation, and retrieval coordination were negatively related to task withdrawal.
Theoretical Implications
Cross-training focuses on providing team members with knowledge regarding their
teammates’ roles and responsibilities, not on providing skill in efficiently and effectively
distributing information (e.g., Marks et al., 2002). Based on the results of this study, the direct
effects of cross-training on team members’ information processing capabilities may be fairly
localized. Our results suggest that cross-training positively affects encoding, which focuses on
the cognitive representations of roles and responsibilities within the team, rather than storage and
retrieval, which focus on distributing and retrieving information between team members. In fact,
as shown in Figure 3, cross-trained team members appeared overconfident, feeling that their
newly acquired knowledge precluded the need to engage in behaviors such as directory updating
and retrieval coordination.
Cross-training may also exert a positive impact on transactive memory through its effects
on mental models. Researchers suggest that encoding prepares information for storage and
retrieval (Hinsz et al., 1997). The same process occurs during training, where trainees translate
declarative knowledge into procedural knowledge in the initial stages of skill acquisition (Neves
& Anderson, 1981). As evidence, Marks et al. (2002) found that cross-training exhibited effects
on mental models, which then influenced coordination and backup behaviors. However, given
the design of this study, the issue needs direct a priori confirmation from future research.
Furthermore, the direct effects of cross-training on mental models and transactive
memory supports the idea that the two represent distinct constructs. While this study provided
additional evidence that mental models and transactive memory represent complementary
components of the team’s information processing system, sharing as much as 35% of their
variance, the system is not completely fluid. The two constructs are clearly influenced by
different antecedents, suggesting that the components can act independently of one another.
More widespread support was found for the interactive effects of cross-training,
indicating that team members can be immunized against the negative effects of stress on both
mental models and transactive memory. After being primed through cross-training, team
members persisted in allocating processing resources to team-related aspects of the task under
stress, suggesting that established norms and expectations regarding role interdependencies
continued despite radical environmental shifts. However, cross-trained team members were not
fully immune to the negative effects of stress. Cross-training did not reduce the negative effects
of stress on directory updating. Although these results were unexpected, it is possible that,
because cross-training primed team members regarding the roles and responsibilities of their
teammates, they saw less of a need to further expend their limited resources updating their
directories, choosing instead to concentrate efforts on continuing to engage in retrieval
coordination and information allocation. Cross-training also failed to moderate the relationship
between stress and team-interaction mental model similarity. Despite the fact that the interaction
failed to reach statistically significant levels, these results were likely a function of sample size.
As shown in Figure 2, the pattern of results closely mirrored those found for team-interaction
mental model accuracy.
The results regarding the immunizing capabilities of cross-training significantly expand
our understanding of the nomological network surrounding the stress process in teams. In the
stress literature, the general work stress health model proposes that, at the individual level,
objective work conditions lead to job-related strains, and those effects are moderated by certain
situational characteristics. While the same model may operate in teams, researchers have yet to
uncover the crucial team-level variables that act as stressors, strains, or moderators (Cooper,
Dewe, & O’Driscoll, 2001). In an effort to advance our understanding, Ellis (in press) identified
mental models and transactive memory as team-level outcomes of stress. In this study, we
identified cross-training as a team-level moderator of the negative effects of stress on mental
models and transactive memory. Similar to Ellis (in press), our results support the use of
information processing theory as a viable framework in the investigation of the effects of stress
in teams (e.g., Gladstein & Reilly, 1985; Staw et al., 1981) and add to our understanding of the
work stress health model at the team level.
Finally, while information processing capabilities are thought to be a crucial component
of team effectiveness (Hinsz et al., 1997), researchers have focused primarily on team
performance (e.g., Ellis, in press; Lewis, 2003). Supporting the findings of Lewis (2004), we
found that the importance of mental models and transactive memory extends to other dimensions
of effectiveness; namely, team viability. Based on our results, mental models and transactive
memory are associated with lower levels of task withdrawal in teams. Only directory updating
failed to exhibit a significant relationship with task withdrawal. However, consistent with
Wegner’s (1987) initial conceptualization of the construct, the role of directory updating in the
team’s information processing system may wane as team members become familiar with their
teammates’ roles and responsibilities.
Practical Implications
The results of this study suggest that organizations can prepare teams for stressful
situations through cross-training. However, three questions remain: (a) what form of crosstraining should organizations use, (b) when should cross-training be given, and (c) who should be
cross-trained?
While this study examined the positional rotation form of cross-training, there are a
number of different cross-training techniques available to organizations. The techniques differ in
the degree of immersion in the task. Positional rotation is the most intense, followed by
positional modeling, which visually demonstrates team members’ roles and responsibilities, and
positional clarification, which verbally demonstrates team members’ roles and responsibilities.
While the type of team and task examined in this study allowed for the use of all three forms of
cross-training, options can be much more limited. For example, a surgical team comprised of
doctors and nurses would likely be unable to allow nurses to gain “hands-on” experience.
However, given that the effects of all three methods appear to be fairly similar (Marks et al.,
2002), it may be possible for organizations to similarly reduce the negative effects of stress in
teams utilizing a less intense form of cross-training.
The timing of cross-training is also an important issue. Although the majority of team
training research has focused on providing remedial training to mature teams (Kozlowski & Bell,
2003), researchers suggest that team training content needs to match the competencies required
at a specific developmental phase (Kozlowski, Gully, Nason, and Smith, 1999). During team
formation, the team is merely a loose collection of individuals trying to make sense of their
environment amidst high levels of social uncertainty. That is the phase of team development
where cross-training has the potential to exert a significant priming impact, as team members
search for information regarding each other’s roles and responsibilities. Cross-training would
likely have less impact as a remedial training strategy at later stages such as team compilation,
where team members begin to explore adaptive transaction alternatives based on team members’
location within the social network.
Regarding who should be cross-trained, given the type of team and task examined in this
study, organizations should concentrate their efforts on cross-training temporary project or action
teams with high levels of within team specialization that will likely encounter stressful situations.
Examples include surgery teams, rescue units, cockpit crews, military units, engineering teams,
and programming teams (see Sundstrom, 1999). Given the breadth of cross-training options,
organizations could efficiently and effectively prime such team members regarding their
teammates’ roles and responsibilities during team formation.
Finally, even though cross-trained teams were generally more resistant to the negative
effects of stress, they were still not able to improve their mental models and transactive memory.
Given the fact that cross-training failed to exhibit positive direct effects on transactive memory,
the benefits gained from cross-training in times of stress were limited (see Figure 3). To further
immunize team members, we suggest combining cross-training with a team training program
designed to enhance behavioral skills such as information sharing. For example, Ellis, Bell,
Ployhart, Hollenbeck, and Ilgen (2005) examined the impact of generic teamwork skills training
and found that it enhanced information sharing and the utilization of communication networks.
While generic teamwork skills training is one of a number of options (see Kozlowski & Bell,
2003), we believe team members’ resistance would be further strengthened by learning skills that
can help them efficiently and effectively utilize their teammates’ roles and responsibilities.
Limitations
A few limitations should be highlighted. First, the examination of stress has primarily
utilized temporary teams with high levels of within-team specialization (e.g., Ellis, in press).
While these types of teams remain popular choices for organizations, the characteristics inherent
in other types of teams (see Sundstrom, 1999) may influence the results found in this study. For
example, service teams vary in terms of within-team specialization, which could influence the
importance of mental models and transactive memory in the relationship between stress and team
performance and limit the impact of cross-training interventions. For example, cross-training
would likely have no effect on a telecommunications sales team consisting of members who are
doing very similar tasks and exhibiting low levels of interdependence because the information
provided would be entirely redundant. Service teams also tend to be more permanent. It is
unclear whether priming will continue to exert an effect as the time lag between cross-training
and stress increases. In later stages of development, teams require training on a different set of
competencies, such as adaptive skill (Kozlowski et al., 1999).
Second, based on our results regarding directory updating, there may be limits regarding
the effectiveness of transactive memory behavior. Team members clearly need to engage in
directory updating, information allocation, and retrieval coordination. However, the effects may
be curvilinear. Once team members reach the crest of the curve, additional interaction may be
counteractive, blocking communication channels within the team. Although we failed to find any
curvilinear effects in this study, our sample size was limited and the teams only remained
together for a short period of time, leaving direct a priori confirmation of the issue for future
research.
Third, although researchers and practitioners need to keep in mind that this study was
conducted in a laboratory context, we believe that the venue was appropriate given the design of
the study and the research questions involved. When investigating the potentially harmful effects
of stress, a laboratory setting where the actual “damage” is only simulated is ideal. Furthermore,
we are concerned more with utilizing information processing theory as an explanatory
framework to test the role of certain variables in the stress process in teams than with the
command and control simulation itself. Because there is no reason to think that information
processing theory would not be applicable, this context serves as a meaningful venue for testing
our hypotheses (see Humphrey, Hollenbeck, Ilgen, & Moon, 2004).
Conclusion
The results of this study indicate that cross-training plays a multi-faceted role in the
relationship between stress, mental models, and transactive memory. These findings extend
previous research regarding the negative effects of stress in teams and provide precise
prescriptive information to organizations interested in developing interventions aimed at
reducing those effects.
REFERENCES
Aiken, L. S., & West, S. G. 1991. Multiple regression: Testing and interpreting interactions.
Newbury Park, CA: Sage.
Ancona, D. G., & Chong, C. L. 1996. Entrainment: Pace, cycle, and rhythm in organizational
behavior. In B. M. Staw (Ed.), Research in Organizational Behavior (Vol. 18): 251-284.
Greenwich, CT: JAI Press.
Austin, J. R. 2003. Transactive memory in organizational groups: The effects of content,
consensus, specialization, and accuracy on group performance. Journal of Applied
Psychology, 88: 866-878.
Barling, J., Kelloway, E. K., & Frone, M. R. 2005. Handbook of work stress. Thousand Oaks,
CA: Sage.
Bettenhausen, K. L., & Murnighan, J. K. 1985. The emergence of norms in competitive decision
making groups. Administrative Science Quarterly, 30: 350-372.
Bettenhausen, K. L., & Murnighan, J. K. 1991. The development of an intragroup norm and the
effects of interpersonal and structural challenges. Administrative Science Quarterly, 36: 2035.
Blenford, A. 2005. Rescue plans stand Toronto test. BBC News website.
http://news.bbc.co.uk/go/pr/fr/-/1/hi/world/Americas/4742669.stm. 8/3/05.
Blickensderfer, E., Cannon-Bowers, J. A., & Salas, E. 1998. Cross-training and team
performance. In J. A. Cannon-Bowers & E. Salas (Eds.), Making decisions under stress:
Implications for individual and team training: 299-311. Washington, DC: American
Psychological Association.
Cannon-Bowers, J. A., & Salas, E. 1998. Making decisions under stress: Implications for
individual and team training. Washington, DC: APA Press.
Cannon-Bowers, J. A., Salas, E., Blickensderfer, E., & Bowers, C. A. 1998. The impact of crosstraining and workload on team functioning: A replication and extension of initial findings.
Human Factors, 40: 92-101.
Chan, D. 1998. Functional relations among constructs in the same content domain at different
levels of analysis: A typology of composition models. Journal of Applied Psychology, 83:
234-246.
Cohen, J. 1960. A coefficient of agreement for nominal scales. Educational and Psychological
Measurement, 20: 37-46.
Cooper, C. L., Dewe, P. J., & O’Driscoll, M. P. 2001. Organizational stress: A review and
critique of theory, research, and applications. Thousand Oaks, CA: Sage Publications, Inc.
Driskell, J.E., & Salas, E. 1991. Group decision making under stress. Journal of Applied
Psychology, 76, 473-478.
Driskell, J. E., Salas, E., & Johnston, J. 1999. Does stress lead to a loss of team perspective?
Group Dynamics: Theory, Research, and Practice, 3: 291-302.
Ellis, A. P. J. in press. System breakdown: The role of mental models and transactive memory in
the relationship between acute stress and tem performance. Academy of Management
Journal.
Ellis, A. P. J, & Bell, B. S. 2005. Team learning: An information processing perspective. In C.
Schrieshiem & L. L. Neider (Eds.), Understanding Teams: 1-26. Greenwich, CT:
Information Age Publishing.
Ellis, A. P. J., Bell, B. S., Ployhart, R. E., Hollenbeck, D. R., Ilgen, D. R. 2005. An evaluation of
generic teamwork skills training with action teams: Effects on cognitive and skill-based
outcomes. Personnel Psychology, 58: 641-672
Foushee, H. C. 1984. Dyads and Triads at 35,000 feet: Factors affecting group processes and
aircrew performance. American Psychologist, 39: 885-893.
Gersick, C., & Hackman, R. 1990. Habitual routines in task-performing groups. Organizational
Behavior and Human Decision Processes, 47: 65-97.
Gladstein, D. L., & Reilly, N. P. 1985. Group decision making under threat: The tycoon game.
Academy of Management Journal, 28: 613-627.
Hackman, J. R. 1987. The design of work teams. In J. W. Lorsch (Ed.), Handbook of
organizational behavior: 315–342. Englewood Cliffs, NJ: Prentice Hall
Hinsz, V. B., Tindale, R. S., & Vollrath, D. A. 1997. The emerging conceptualization of groups
as information processors. Psychological Bulletin, 121: 43-64.
Hollingshead, A. B. 1998a. Communication, learning, and retrieval in transactive memory
systems. Journal of Experimental Social Psychology, 34: 423-442.
Hollingshead, A. B. 1998b. Retrieval processes in transactive memory systems. Journal of
Personality and Social Psychology, 74: 659-671.
Hulin, C. L. 1991. Adaptation, persistence, and commitment in organizations. In M. D.
Dunnette, & L. M. Hough (Eds.), Handbook of industrial organizational psychology (Vol
2): 435-505. New York: Wiley.
Humphrey, S. E., Hollenbeck, J. R., Ilgen, D. R., Moon, H. 2004. The changing shape of largescale programs of research: MSU-DDD as an illustrative example. In S G. Schiflett, Elliott,
L. R., Salas, E., & Coovert, M. D. (Eds.), Scaled worlds: Development, validation and
applications: 200-219. Burlington, VT: Ashgate.
Johnson, M. D., Hollenbeck, J. R., Ilgen, D. R., Humphrey, S. E., Meyer, C. J., & Jundt, D. K. in
press. Cutthroat cooperation: Asymmetrical adaptation of team reward structures. Academy
of Management Journal.
Judge, T. A., Thoresen, C. J., Bono, J. E., & Patton, G. K. 2001. The job satisfaction-job
performance relationship: A qualitative and quantitative review. Psychological Bulletin, 127:
376-407.
Kahn, R. L., & Byosiere, P. 1992. Stress in organizations. In M. D. Dunnette & L.M. Hough
(Eds.), Handbook of industrial and organizational psychology (2nd ed.), vol. 2: 571-650.
Palo Alto, CA: Consulting Psychologists Press.
Kammeyer-Mueller, J. & Wanberg, C. R. 2003. Unwrapping the organizational entry process:
Disentangling multiple antecedents and their pathways to adjustment. Journal of Applied
Psychology, 88: 779-794.
Klimoski, R. J., & Muhammed, S. 1994. Team mental model: Construct or metaphor? Journal
of Management, 20: 403-437.
Kozlowski, S. J. W., & Bell, B. S. 2003. Work groups and teams in organizations. In W. C.
Borman, D. R. Ilgen, & R. Klimoski (Eds.), Comprehensive handbook of psychology (Vol.
12): Industrial and organizational psychology: 333-376. New York: Wiley.
Kozlowski, S. W. J., Gully, S. M., Nason, E. R., & Smith, E. M. 1999. Developing adaptive
teams: A theory of compilation and performance across levels and time. In D. R. Ilgen & E.
D. Pulakos (Eds.), The changing nature of performance: Implications for staffing,
motivation, and development: 240-292. San Francisco, CA: Jossey-Bass.
Kozlowski, S. W. J., & Klein, K. J. 2000. A multilevel approach to theory and research in
organizations: Contextual, temporal, and emergent processes. In K. J. Klein & S. J. W.
Kozlowski (Eds.), Multilevel theory, research, and methods in organizations: Foundations,
extensions, and new directions: 3-90. San Francisco: Jossey-Bass.
Landis, J., & Koch, G. G. 1977. The measurement of observer agreement for categorical data.
Biometrics, 33: 159-174.
Lawler, E. E., III. 1982. Increasing worker involvement to enhance organizational effectiveness.
In P. S. Goodman (Ed.), Change in organizations: 280-315. San Francisco: Jossey-Bass.
Lewis, K. 2003. Measuring transactive memory systems in the field: Scale Development and
validation. Journal of Applied Psychology, 88: 587-604.
Lewis, K. 2004. Knowledge and performance in knowledge-worker teams, A longitudinal study
of transactive memory systems. Management Science, 11: 1519-1533.
Marks, A. Why flying is safer now. USA Today, Aug 4, 2005: T4.
Marks, M. A., Mathieu, J. E., & Zaccaro, S. J. 2001. A temporally based framework and
taxonomy of team processes. Academy of Management Review, 26: 356-376.
Marks, M. A., Sabella, M. J., Burke, C. S., & Zaccaro, S.J. 2002. The impact of cross-training on
team effectiveness. Journal of Applied Psychology, 87: 3-13.
Marks, M. A., Zaccaro, S. J., & Mathieu, J. E. 2000. Performance implications of leader
briefings and team-interaction training for team adaptation to novel environments. Journal of
Applied Psychology, 85: 971-986.
Mathieu, J. E., Heffner, T. S., Goodwin, G. F., Salas, E., & Cannon-Bowers, J. A. 2000. The
influence of shared mental models on team process and performance. Journal of Applied
Psychology, 85: 273-283.
Miller, V. D., & Jablin, F. M. 1991. Information seeking during organizational entry: Influences,
tactics, and a model of the process. Academy of Management Review, 16: 92-120.
Miller, D. L., Young, P., Kleinman, D., & Serfaty, D. 1998. Distributed dynamic decisionmaking simulation phase I: Release notes and user’s manual. Woburn, MA: Aptima, Inc.
Mohammed, S., & Dumville, B. 2001. Team mental models: Expanding theory and measurement
through cross-disciplinary boundaries. Journal of Organizational Behavior, 22: 89-106.
Mohammed, S., Klimoski, R., & Rentsch, J. R. 2000. The measurement of team mental models:
We have no shared schema. Organizational Research Methods, 3: 123-165.
Moon, H., Hollenbeck, J. R., Humphrey, S. E., Ilgen, D. R., West, B., Ellis, A. P. J. and Porter,
C. O. L. H. (2004) Asymmetrical adaptability: Dynamic structures as one-way streets.
Academy of Management Journal, 47: 681-696.
Neves, D., & Anderson, J. R. 1981. Knowledge compilation: Mechanisms for the automatization
of cognitive skills. In J. Anderson (Ed.), The acquisition of cognitive skill: 57-84.
Hillsdale, NJ: Lawrence Erlbaum.
Phillips, J. M. 2001. The role of decision influence and team performance in member selfefficacy, withdrawal, satisfaction with the leader, and willingness to return. Organizational
Behavior and Human Decision Processes, 84: 122-147.
Raven, B., & Rubin, J. Z. 1976. Social psychology: People in groups. New York: Wiley.
Roberson, Q. M., & Colquitt, J. A. 2005. Shared and configural justice: A social network model
of justice in teams. Academy of Management Review, 30: 595-607.
Salas, E., Driskell, J. E., & Hughes, S. 1996. Introduction: The study of stress and human
performance. In J. E. Driskell & E. Salas (Eds.), Stress and human performance: 1-46.
Mahwah, NJ: Erlbaum.
Serfaty, D., Entin, E. E., & Johnston, J. H. 1998. Team coordination training. In J. A. CannonBowers & E. Salas (Eds.), Making decisions under stress: Implications for individual and
team training: 221-245. Washington, DC: American Psychological Association.
Smith-Jentsch, K. A., Campbell, G. E., Milanovich, D. M., & Reynolds, A. M. 2001. Measuring
teamwork mental models to support training needs assessment, development, and evaluation:
Two empirical studies. Journal of Organizational Behavior, 22: 179-194.
Staw, B. M., Sandelands, L. E., & Dutton, J. E. 1981. Threat-rigidity effects in organizational
behavior: A multilevel analysis. Administrative Science Quarterly, 26: 501-524.
Sundstrom, E. 1999. The challenges of supporting work team effectiveness. In E. Sundstrom and
Associates (Eds.), Supporting work team effectiveness: 3-23. San Francisco: Jossey-Bass.
Turner, M. E., Pratkanis, A. R., Probasco, P., & Leve, C. 1992. Threat, cohesion, and group
effectiveness: Testing a social identity maintenance perspective on groupthink. Journal of
Personality and Social Psychology, 63: 781-796.
Volpe, C. E., Cannon-Bowers, J. A., Salas, E., & Spector, P. E. 1996. The impact of crosstraining on team functioning: An empirical investigation. Human Factors, 38: 87-100.
Wegner, D. M. 1987. Transactive memory: A contemporary analysis of the group mind. In B.
Mullen & G. R. Goethals (Eds.), Theories of group behavior: 185-208. New York: SpringerVerlag.
Wegner, D. M., Erber, R., & Raymond, P. 1991. Transactive memory in close relationships.
Journal of Personality and Social Psychology, 61: 923-929.
Wilson, M., & Canter, D. 1993. Shared concepts in group decision making: A model for
decisions based on qualitative data. British Journal of Social Psychology, 32: 159-172.
TABLE 1
Summary of Vehicles and Tracks
Vehicles
Tracks
Duration
(in min.)
Speed
Vision
Power?
Identify
ring?
Vehicles
Tank
8:00
slow
yes (5)
no
Helicopter
Jet
AWACs
4:00
2:00
6:00
medium
very fast
fast
very
limited
limited
far
very
far
yes (3)
yes (1)
no
no
no
yes
Tracks
A
B
C
D
Speed
Power
(Exp.
Task)
Power
(DDD
Training)
Nature
Slow
Slow
Slow
Slow
low (1)
high (5)
med. (3)
none
none
low (1)
med. (3)
high (5)
Enemy
Enemy
Enemy
Friendly
Note: For vehicles: duration = amount of time a vehicle may stay away from the base before
needing to refuel, speed = how fast the vehicle travels across the task screen, vision = refers to
the range of vision the vehicle has to both see and identify tracks, power = the ability of the
vehicle to engage enemy tracks, identify ring = the ability of the vehicle to identify tracks. For
tracks: speed = how fast the track travels across the task screen, power = the level of power
needed to successfully engage the tracks, nature = whether the track is an enemy or friend.
TABLE 2
Means, standard deviations, and intercorrelations among variables of interest
Variable
1. Stress
1
2
3
2. Cross-Training
-.00
3. Directory Updating
4. Information Allocation
5. Retrieval Coordination
-.30* -.32** --.49** .00
.21†
-.45** -.23* .42**
5
6
7
8
--
6. Team-Interaction Mental -.42** .23*
Model Similarity
7. Team-Interaction Mental -.39** .33*
Model Accuracy
8. Withdrawal
.10
.06
Descriptive Statistics
Totals
M
SD
Control Condition
M
SD
No Stress / Cross-training
M
Sd
Stress / No Cross-training
M
Sd
Stress / Cross-training
M
Sd
4
-.41**
--
.13
.18
†
.59**
--
.10
.21†
.36**
.84**
--
-.11
-.40** -.28*
-.28*
-.26*
--
.50
.50
.56
.50
13.93
7.45
13.15
7.50
1.09
1.61
37.59
20.95
44.10
7.16
2.34
.34
.00
.00
.00
.00
18.75
6.86
18.92
5.87
2.67
2.23
45.17
23.95
46.46
4.90
2.23
.30
.00
.00
1.00
.00
14.10
6.35
15.10
6.34
1.13
1.46
47.10
20.17
47.10
5.22
2.37
.40
1.00
.00
.00
.00
14.42
5.63
7.42
5.66
.33
.49
19.33
11.90
36.58
6.90
2.41
.32
1.00
.00
1.00
.00
9.53
8.19
11.20
7.46
.40
.63
36.67
16.17
45.2
6.81
2.35
.32
Note: N=54. (control condition: n = 12, stress condition: n = 27, cross-training condition: n = 30,
stress and cross-training condition: n = 15). Stress was coded 0 for no stress and 1 for stress.
Cross-training was coded 0 for no training and 1 for training. †p<.10 *p<.05 **p < .01. Onetailed tests.
FIGURE 1
Example Concept Map
DM1 is getting attacked by a wave of eight tracks. All eight ground tracks slowly move through
DM1’s forbidden zone and stop inside DM1’s highly forbidden zone (i.e., the red box). They are
the only tracks on the screen and DM1’s defensive score is rapidly decreasing. What are the
actions that each team member needs to go through in order to clear any enemy wave tracks
from the forbidden zones?
DM1
Enemy
wave
tracks
cleared
from
the
forbidden
zones
DM2
DM3
DM4
Identify
Tracks
Send
Helicopters
Send
Tanks
Send
Engage
Engage
Jets
Any A Tracks
Any B Tracks
Send
Engage
Engage
AWACS Planes
Any D Tracks
Any C Tracks
FIGURE 2
The Effects of Stress and Cross-Training on Mental Models
0.3
0
-0.3
Team-Interaction Mental
Model Similarity
-0.6
Team-Interaction Mental
Model Accuracy
-0.9
Condition
Stress/CrossTraining
Stress/No
CrossTraining
No
Stress/CrossTraining
Control
-1.2
FIGURE 3
The Effects of Stress and Cross-Training on Transactive Memory
1
0.5
Directory Updating
0
Information Allocation
Retrieval Coordination
-0.5
Condition
Stress/CrossTraining
Stress/No
CrossTraining
No
Stress/CrossTraining
Control
-1