Motivation and Performance in Complex Problem Solving
Annette Feuchter
Abstract
Previous research has suggested that individuals tend to engage in social loafing, i.e. they
reduce their motivation and their effort, when working collectively on simple tasks, while
individuals working on complex and difficult tasks do not: participants working collectively
work as hard as participants working co-actively. On the other hand, findings show that social
loafing occurs in complex tasks, too. There is also some evidence that social loafing groups
under these conditions outperform co-active groups.
The results of the research presented here gives new evidence that social loafing does occur in
groups working on complex problem solving tasks. Furthermore it is shown that co-active
groups outperform collective groups in working on simple tasks. On complex tasks social
loafing groups however outperform co-active groups.
Introduction
In most situations of our daily life, at university, in politics, in economics, and even in leisure
activities such as sports we are working together with other people to reach a common goal.
In these situations we often combine our efforts to produce one single product. Most of the
situations we are involved in are rather complex and dynamic. Therefore it is interesting to
find out whether social loafing, i.e. a reduction of motivation and effort which occurs when
people work collectively (i.e. when they have a collective responsibility for a common group
product) compared to when they work co-actively (i.e. when the common product is divided
and individuals are only responsible for a part of the product), has an influence on the performance in these complex and dynamic situations.
Working collectively could lead to a reduction of responsibility for the group product and
therefore to a reduction of effort due to the fact that actors expect their contributions to be
dispensable. Since their inputs are pooled into one group product, they expected their individual contributions will get lost in the crowd.
Social loafing is a type of social disease, having negative consequences for individuals, social
institutions and societies. This was pointed out by Bibb Latané, Kip Williams, and Stephen
Harkins in 1979 when the slogan "social loafing" was coined for the first time.
Because of the negative evaluation of social loafing, most of the research has been focussed
on conditions under which social loafing can be reduced. According to the empirical findings
social loafing can be lessened
-
if subjects can identify their individual contributions,
-
if their contributions can be evaluated by the experimenter or by their co-workers,
-
if participants highly value the task or the group, and,
-
if individual contributions are unique.
There is also some evidence that individuals who are working on challenging and complex
group tasks do not loaf compared to individuals working on simple group tasks.1
Apart from a few studies in which complex tasks have been considered, the bulk of research
in this field has concentrated on simple tasks. In these simple tasks, a reduction of effort in
collective groups due to social loafing leads to poorer performance. Some of the available
research comparing simple and complex group tasks has shown that members in collective
groups, compared to those of co-active groups, spend less effort working on complex tasks
but obtain a higher performance than co-active groups. That means that subjects perform better collectively than co-actively on complex tasks.2 This finding is particularly interesting
because it contradicts the assumption that an increased level of effort leads to an increased
level of performance.
In my research I have tested the following hypotheses in two experiments:
H 1: In problem solving groups the effort in co-active working conditions is higher compared
to collective working conditions.
1
Amabile, 1979; Bartis, Szymanski & Harkins, 1988; Brickner, Harkins & Ostrom, 1986; Geen, 1989,
1991; Kerr & Bruun, 1983; Paulus, 1983; Zaccaro, 1984
2
H 2: The higher level of effort in co-active groups (E1) leads to higher performance in simple
tasks (P) compared to the lower level of effort in collective groups (E2)
=> simple tasks: E1 > E2  P1 > P2
H 3: The higher level of effort in co-active groups (E1) leads to lower performance in complex
tasks (P) compared to the lower level of effort in collective groups (E2)
=> complex tasks: E1 > E2  P1 < P2
The results of the two experiments indicate a significant social loafing effect and substantiate
the finding that lower levels of effort lead to better performances in groups working on complex tasks.
Theoretical Background
In the social loafing literature there are many different theoretical explanations for social
loafing. These explanations can be divided into situational and task specific factors. Those
factors concerning situation are, the lack of responsibility for the group product, the lack of
identification of individual contribution, and the lack of evaluation. Various research has
shown that these are indeed valid factors.3
Theorists who were interested in the task related influence of social loafing indicated that the
uniqueness of individual contributions as well as the task difficulty have a negative influence
on social loafing. The research in this field comes to the conclusion, that participants who
have the impression that their contribution to the group product is not dispensable do not loaf
as much as participants who do not have this impression. Other results show that people
working on difficult and intrinsically motivating tasks do not loaf either.
Karau & Williams state that the different factors described act as mediators for explaining
social loafing. There is however still no model available which permits a prediction of which
factors will lead to an increase or a decline of social loafing under different conditions. This is
due to the fact that the moderating variables are associated with heterogeneous theories,
which allow only one of several causal mechanisms at a time.
2
Bartis, Szymanski & Harkins, 1988; Griffith, Fichman & Moreland, 1989; Harkins & Petty, 1982;
Jackson & Williams 1985; Petty et al. 1985
3
In their analyses Karau & Williams try to integrate all feasible theoretical approaches and all
heterogeneous factors on social loafing into one model, the collective effort model (CEM).4
This model adapts individual-level expectancy-value models of effort to collective contexts.
In addition it uses recent theories of self-evaluation in group contexts to clarify which outcomes are likely to be valued by individuals when working collectively.5
Karau & Williams have suggested expectancy-value models of effort be extended to collective contexts by specifying the group influences on individuals' perceptions of the relationship
between their effort and their expected outcome. They believe that social loafing occurs
because there is usually a stronger perceived contingency between individual effort and valued outcomes when working individually. When working collectively, factors other than the
individual's effort frequently determine performance, and valued outcomes are often divided
among other people.
Like traditional expectancy-value models of effort, the CEM assumes that individuals behave
hedonistically and try to maximise the expected utility of their actions.
Accordingly to Vroom (1964) individuals' motivational force is dependent on three factors:
1. expectancy, or the degree to which high levels of effort are expected to lead to high levels
of performance,
2. instrumentality, or the degree to which high-quality performance is perceived as instrumental in obtaining an outcome, and,
3. valence of outcome, or the degree to which the outcome is viewed as desirable.
The CEM expands on this logic by specifying that instrumentality on collective tasks is
determined by three factors:
a) the perceived relationship between individual performance and group performance,
b) the perceived relationship between group performance and group outcomes, and,
c) the perceived relationship between group outcomes and individual outcomes.
3
Davis, 1969; Ingham et al., 1974; Latané et al., 1979; Harkins & Szymanski, 1987; 1989; Szymanski
& Harkins, 1987; Williams et al., 1981
4
Karau & Williams 1993, 1995; Shepperd & Taylor, 1999
5
It is also possible that individuals who highly evaluate their groups or the group product spend even
more effort in the collective situation compared to the co-active situation. See Karau & Hart 1998;
Williams, Karau & Bourgeois 1993
4
The model suggests that working on a collective task introduces additional contingencies
between individuals' efforts and their outcomes. This can lead to decreased perceived instrumentality and therefore to decreased motivation and effort. Similarly participants do not exert
much effort if the potential outcome is not desirable, even if they are immediately related to
individual performances.
Even if the collective effort model provides a suitable framework for my research, it does not
allow to explain the inverse relationship between effort and performance in groups working
on complex tasks.
An explanation for this inverse relationship is offered by the arousal reduction theory.6 Jackson & Williams (1985) succeeded to show that on simple tasks subjects performed better coactively than collectively. On complex tasks, however, subjects performed better collectively
than co-actively. The authors argued that working collectively led to reduced drive and effort,
resulting in decreased performance on simple tasks where the response is well-defined and
likely to be correct, and, increased performance on novel, difficult tasks where the response is
not well-defined and therefore more likely to be wrong.
Another theoretical framework was given by Paulus' cognitive-motivational model (CMM).7
According to this model the motivation in co-active situations is higher because the consequences for the individuals are more important. These consequences can either be positive,
e.g. rewards, or negative, e.g. being afraid of negative evaluation. High positive consequences
lead to higher effort and high negative consequences lead to higher arousal, to higher effort
and to higher task irrelevant information processing (i.e. information processing which is not
task-related).
In working on simple tasks the irrelevant information processing does not affect the performance negatively whereas in working on complex tasks the irrelevant information processing
has detrimental effects on the performance.
To summarise: The CEM as an integrated model allows to make predictions about the social
loafing effect but it would not suffice to explain an inverse relationship between social loafing
6
7
See Schachter, 1959.
Geen, 1989; Griffith, Fichman & Moreland 1989; Karau & Williams 1993; Paulus, 1983
5
and performance. Hence the arousal reduction theory and the CMM are used to complement
the CEM.
Experiments
In my research I have examined the impact of task complexity on social loafing in two
experiments. For this purpose I tested its influence in different group settings.
Therefore it is necessary to describe the features of complex tasks. Following Dietrich Dörner
we can mention five criteria:
1. complexity, in terms of different variables. This requires a high capacity for information
processing from the problem solver.
2. connectivity, in terms of connected variables. The problem solver has to find out how the
variables depend on each other.
3. dynamic, which means that the system or task develops without intervention of the problem solver.
4. obscurity, which means that the problem solver has no immediate access to all information necessary for the next decision, and
5. multiple goals, which means that there are different goals to be reached at the same time.
The problem solver has to set up priorities and has to determine which are the most
important goals.8
Having defined these features I am able to describe the manipulation of different levels of task
complexity. This description of different levels of task complexity remains in previous
research often unclear.
Figure 1: Fire Chief Scenario
8
Dörner et al., 1983; Frensch & Funke, 1995; Funke, 1999
6
The task I have chosen is the Networked Fire Chief program, developed by Mary Omodei,
Alex Wearing, and Peter Taronto.9 Networked Fire Chief is a computer simulated microworld
in which subjects are required to adopt the role of a fire chief responsible for fire control in a
specific area comprising various types of landscape elements and fire fighting appliances like
fire trucks and helicopters. The subjects are able to control the spread of the simulated fires by
dispatching the fire fighting appliances to drop water on the fires. Once the fires start they
spread to adjacent unburned areas at a rate which depends on varying wind strength and wind
direction. Additional complexity is achieved by having fire trucks and helicopters moving at
different speed and dropping varying amounts of water.
9
Omodei & Wearing 1993, 1995b; Omodei et al., 1998
7
In both experiments subjects fight fire in groups of three people and every subject is responsible to fight the fire in one of the three zones. In the first experiment they have no access to
the other zones while in the second experiment the subjects can fight the fire in all three
zones, even if they remain responsible for one zone. Hence the screen surface which is
depicted below (see figure 1) is divided into three zones which are of equal size. They each
contain equal amount of consumable landscape elements, water filled dams, petrol filled petrol stations, and, fire fighting appliances.
An overall performance score as well as three zone dependent performance scores are automatically generated by the Fire Chief program. These scores are measured on the basis of the
unburned landscape elements. Furthermore the program generates the level of effort, which is
measured by the number of commands made by each subject.
While previous research often did not control the difference between effort and performance it
is advantageous that the Networked Fire Chief simulation allows to distinguish these two
variables which is necessary to test whether social loafing occurs in groups working on complex tasks and whether this leads to good or poor performance.
Experiment 1
The experiment examines the influence of different working conditions and of different levels
of task complexity on effort and performance. Therefore I used a 2 (co-active vs. collective) *
2 (simple vs. complex problem) factorial between subject design.
Method
60 subjects participated in the experiment. Three participants were randomly assigned to one
of the four experimental situations.
The independent variables are the working conditions (collectively vs. co-actively) and the
complexity of the problem. In the experiment the effect of these independent variables on the
effort and on the performance was tested. The calculation of the relation between the two
dependent variables, performance and effort, determines an efficiency coefficient for the four
experimental conditions.
Manipulation Checks
8
After each trial (every group passed three experimental trials) participants were asked to
answer a questionnaire concerning their individual disposition,10 task relevant and task irrelevant information processing, outcome valence (i.e. evaluation of the pertinence of the task),
and, perceived instrumentality.
Experimental manipulation
The manipulation of the working conditions consisted in the different tasks for the respective
groups. During the introduction to the program it was indicated to the collective groups that
their individual contribution could not be identified and that they should try to achieve a high
collective performance. Therefore the task in these groups was to fight the fires in the tripartite group as efficiently as possible even if each player only had access to one field.
In the nominal groups it was indicated that the program records the performances of the individual players and that therefore every player should perform as well as possible.
The manipulation of the task complexity was made by introducing a further resource necessary for moving the appliances. In the simple scenarios the appliances only required water for
fire fighting and in the complex scenario the appliances also required petrol.
According to Dörners taxonomy the variation of task complexity by introducing a further
resource leads to an increase of task complexity, an increase in the connectivity of the variables, and, an increase of obscurity.
Results
Effort measures. Overall there was a significant loafing effect, with participants giving more
commands while working co-actively (M=111,70, SD=19,38) than collectively (M=99,86,
SD=14,77), F(1,56)=7,61, p<0.01.
The variance analysis (MANOVA) shows that the independent variable working condition
has a positive influence on the effort whereas the complexity of the task has a negative influence on the effort, F(1,56)=5,94, p<0,02.
Table 1: Commands
Co-active groups
Collective groups
Mtotal
Simple Problem
10
Of main interest were the two factors stress and arousal, which were measured using selected
adjectives from the 18 items of the Stress and Arousal Checklist. See Cox & Mackay, 1995; King,
Burrows & Stanley, 1983
9
M
N
SD
Complex Problem
M
N
SD
115,73
15
17,44
106,29
15
10,98
222
30
107,67
15
20,94
93,42
15
15,58
201
30
Mtotal
N
223,4
30
199,7
30
Some of the previous research has shown that effort increases when working on complex
tasks compared to working on simple tasks. Furthermore this research has pointed out that
social loafing occurred less when working on complex tasks.
Figure 2: Co-active and Collective Groups
120
110
100
90
80
M
E
A
N
Performanc
e
70
60
coac
t
coll
Commands
AND
Efficienc
y
Working Condition
The present results contradict these findings. The results indicate that social loafing occurred
in working on complex tasks, and, on the other hand, it has been shown that during the complex task situations less commands were given by the subjects compared to the simple task
situation. This previous finding is astonishing because it contradicts the expectation that subjects spend more effort on complex tasks.
An explanation for the difference between the two effort levels in the complex and the less
complex situation concerns the experimental setting: in the complex situation the appliances
need one further resource (petrol) and, therefore, the appliances could not be moved that
much.
10
During the time that the appliances are being supplied with resources they can not be activated by the subjects. This can explain why subjects delivered fewer commands in the more
complex scenario than in the other scenario.
If we analyse the data of the complex and simple task separately, we can find a significant
social loafing effect in the complex task situation. Subjects in the co-active groups
(M=107,67, SD=20,94) spend significantly more commands compared to subjects in the collective groups (M=93,42, SD=15,58), F(1,28)=4,466, p<0.05. This difference is also validated
by the apriori contrast analysis, t(1,28)=-2,113, p<0,05. In the simple task situation the difference between the two groups is not significant. But as expected, co-active group members
(M=115,73, SD=17,44) spend more effort compared to the collective groups (M=106,29,
SD=10,98), F(1,28)=3,15, n.s. In the apriori contrast analysis there is also no significant difference, t(1,28)=-1,775, n.s.
Table 2: Performance
Simple Problem
M
N
SD
Complex Problem
M
N
SD
Mtotal
N
Co-active groups
Collective groups
Mtotal
88,36
15
4,55
87,02
15
4,99
175,4
30
72,2
15
5,52
77,22
15
4,72
149,4
30
160,6
30
164,2
30
Performance measure. We can find no main effect of the working condition on the performances of the two groups. The influence on the differences in performance between co-active
(M=80,28, SD=9,6) and collective groups (M=82,12, SD=6,9) are not significant,
F(1,56)=2,08, n.s.
The analysis of variance (MANOVA) indicates a strong interaction effect between working
condition and task complexity, F(1,56)=6,16, p<0.02. Furthermore we can find the largest
effect between the different levels of task complexity, F(1,56)=102,78, p<0,001.
11
If we have a closer look at the groups in the complex task situation, we can find a significant
difference between the co-active (M=72,2, SD=5,52) and the collective groups (M=77,22,
SD=4,72). This is validated by the apriori contrast analysis, t(1,28)=2,678, p<0.02. For the
simple task situations on the other hand, we can find no difference in the performance
between the co-active (M=88,36, SD=4,55) and the collective situation (M=87,02, SD=4,99).
The apriori contrast analysis also indicates this weak difference, t(1,28)=-0,765, n.s.
Figure 3: Differences between the four experimental conditions
120
110
100
90
80
M
E
A
N
Performance
70
Commands
60
Efficiency
coac-sim
coll-sim
coac-com
coll-com
Working Condition
In the following I will shed a light on the question whether there are differences between the
two groups with regard to efficiency. Efficiency is measured as the relationship between performance and effort. In the collective working condition the efficiency (M=0,839, SD=0,13)
is higher compared to the co-active working condition (M=0,736, SD=0,13), F(1,58)=8,807,
p<0.01.
The results of the apriori contrast analyses show that the efficiency in the co-active-complex
groups (M=0,694, SD=0,14) is lower than that of the collective-complex groups (M=0,849,
SD=0,15), t(1,28)=2,902, p<0,01.
Discussion. According to the theoretical framework developed earlier in this paper I find
some support for the Collective Effort Model. For both levels of complexity we can find a
higher score for the factor outcome valence and for the factor perceived instrumentality,
12
which could explain the higher level of effort in the co-active groups. This means that the
CEM model provides a good framework for explaining social loafing effects.
As discussed before the CEM can not explain the finding of the inverse relationship between
effort and performance, therefore, it is necessary to integrate other explanations given by the
arousal reduction theory and Cognitive-Motivational Model.
The empirical results validate the inverse relationship between effort and performance in the
collective situation of the complex task. According to the CMM we find that the task irrelevant information processing in the co-active situation is significantly higher than in the collective situation.
When working on simple tasks it was predicted that task irrelevant information processing has
no negative influence on the performance and when working on complex tasks it impaired
performance. The empirical findings indicate that there was no negative impact of the irrelevant information processing on the performance of the subjects of the simple task groups. In
the complex task groups we can find a positive influence of the task irrelevant information
processing on the effort and a negative influence on the performance.
In addition we can find that the factor stress correlates positively with task irrelevant information processing and that it correlates negatively with task relevant information processing.
There is also a significant difference between the higher level of stress in the co-active and the
lower level of stress in the collective situation.
Concerning the arousal reduction theory there is no evidence that the arousal has an influence
on the performance. We can find no differences between the two working conditions for both
levels of complexity. Hence, we can say that the integration of the CMM into the CEM can be
a further step to develop this theoretical model.
Experiment 2
In this, as in the first, experiment the influence of different working conditions and of
different levels of task complexity on effort and performance is examined. I used a 2 (coactive vs. collective) * 2 (simple vs. complex problem) factorial between subject design.
13
Method
In the experiment 60 subjects participated. Three participants were randomly assigned to one
of the four experimental situations. The independent variables are the same as in the first
experiment, their influence on effort and on performance was tested.
Contrary to the first experiment where subjects could only work in their zones and were not
allowed to help each other, in the second experiment there were no limitations and consequently subjects were allowed to fight the fires in all three zones.
Manipulation Checks
After each trial (every group passes three experimental trials) participants were asked to
answer a questionnaire concerning individual dispositions, task relevant and task irrelevant
information processing, outcome valence, perceived instrumentality, individual satisfaction
with respect to their own results and the results of their groups, sense of rivalry, and, evaluation of the group.
Experimental manipulation
During the introduction to the program it was indicated to the collective groups that their
individual contribution could not be identified and that they should try to achieve a high collective performance. Thus the task in these groups was to fight the fires in the tripartite group
as efficiently as possible. In the nominal groups it was indicated that the program records the
performances of the individual players and that therefore every player should perform as good
as possible. The manipulation of the task complexity remains the same as in the first experiment.
To summarise: The main difference between the two experiments consists in having access to
the other fields in the second experiment compared to the first experiment.
Results
Effort measures. Overall there was a significant loafing effect, with participants giving more
commands if working co-actively (M=107,84, SD=12,26) compared to participants working
collectively (M=100,34, SD=14,31), F(1,56)=4,81, p<0.05.
The variance analysis (MANOVA) shows that the independent variable working-condition
has a positive influence on the effort whereas the complexity of the task in this experiment
has no significant influence on the effort, F(1,56)=2,59, n.s.
14
Table 3: Commands
Simple Problem
M
N
SD
Complex Problem
M
N
SD
Mtotal
N
Co-active groups
Collective groups
Mtotal
109,84
15
13,34
103,84
15
18,31
213,68
30
105,84
15
11,18
96,84
15
7,92
202,68
30
215,68
30
200,68
30
As in the first experiment we can find that subjects in the complex task situation gave fewer
commands compared to those in the simpler task situation. The reasons for this finding was
already discussed in Experiment 1.
Figure 4: Co-active and Collective Groups
110
100
90
80
M
E
A
N
Perform
70
coac
coll
iveko
vek
Commands
MAND
Efficiency
ZIEN
If we analyse thetivect
data of the complexectiand simple task situation separately we can find a sigll
Working Condition oll
nificant social loafing
effect in the complex task situation. Members in the co-active groups
(M=105,84, SD=11,18) invest significantly more commands compared to those in the collective groups (M=96,84, SD=7,92), F(1,28)=6,473, p<0.05. This difference is also validated by
the apriori contrast analysis, t(1,28)=-2,544, p<0,05. In the simple task situation the difference
between the two groups is not significant. But as expected, co-active group members
(M=109,84, SD=13,34) dedicate more effort compared to the collective group members
15
(M=103,84, SD=18,31), F(1,28)=1,052, n.s. In the apriori contrast analysis there is also no
significant difference, t(1,28)=-1,026, n.s.
Table 4: Performance
Simple Problem
M
N
SD
Complex Problem
M
N
SD
Mtotal
N
Co-active groups
Collective groups
Mtotal
88,36
15
4,55
87,02
15
4,99
175,4
30
72,2
15
5,52
77,22
15
4,72
149,4
30
160,6
30
164,2
30
Performance measure. With regard to the performance we can find one main effect of the
two working conditions and another main effect between the two levels of task complexity.
The analysis of variance (MANOVA) shows that the working conditions have a significant
influence on the performance in co-active (M=79,27, SD=8,77) and collective groups
(M=82,46, SD=8,2), F(1,56)=5,71, p<0,05. Furthermore the analysis of variance indicates a
strong effect for the task complexity, F(1,56)=100,05, p<0,01.
If we take a closer look at the groups in the complex task situation we can find a large discrepancy between the co-active (M=72,11, SD=5,78) and the collective groups (M=76,27,
SD=6,2), F (1,28)=3,407, p<0,07. For the simple task situations, we can find no significant
difference in the performance between the co-active (M=86,42, SD=4,02) and the collective
situation (M=88,64, SD=4,34).
Figure 5: Differences between the four experimental conditions
120
110
100
90
80
M
E
A
N
Performance
70
Commands
16
The results in this experiments show that the performances on both levels of complexity are
higher in the loafing groups, i.e. the collective groups, compared to the co-active groups. This
contradicts the assumption that in the simpler task situations the performances should be
higher compared to the complex task situations.
How can we explain this result? One explanation is that in this experiment subjects were
allowed to help each other, and, there is some evidence that this took place. On the one hand,
it was observed by the experimenter that in collective groups they helped each other more
often compared to the co-active condition. On the other hand, the answers in the questionnaire
show that the collective group members claim more often that they helped other players and
that they received help from the other players more often.
Efficiency measure. In the following I will shed a light on the question as to whether or not
there are differences between the two groups with regard to efficiency. Efficiency is measured
as relationship between performance and effort. In the collective working condition the efficiency (M=0,836, SD=0,127) is significantly higher compared to the co-active working condition (M=0,74, SD=0,1), F(1,58)=10,15, p<0.01.
Discussion. In this experiment there is an interesting finding concerning the Collective Effort
Model. For the simpler task I found a higher score for the factor outcome valence and for the
factor perceived instrumentality in the collective situations whereas for the complex task the
factors outcome valence and perceived instrumentality are higher in the co-active groups
compared to the collective groups.
This finding explains the significant higher effort levels in the complex co-active situation
compared to the complex collective situation, but there is a problem of interpretation for the
simple task situation, because the higher scores of perceived instrumentality and outcome
valence does not lead to higher effort in the simple task situation. (In the first experiment we
17
found higher values in the co-active situations for perceived instrumentality and outcome
valence in both levels of task complexity.)
In this experiment for both tasks the collective groups received higher performances compared to the co-active groups. This means that the hypothesis that in simpler tasks higher
levels of effort lead to better performances could not be validated. The difference is very
slight in the simple task situation but if we have a look at the complex task situation we can
validate the results of the first experiment and we can find that collective groups perform significantly better compared to co-active groups.
As in the previous experiment we still have to test the predictions of the arousal reduction
theory as well as the cognitive-motivational model made in order to be able to explain the
inverse relationship between effort and performance. In this experiment we did not find differences between the levels of arousal in the two working conditions either. According to the
CMM our results shows that the task irrelevant information processing is in total higher in the
co-active compared to the collective situation. Therefore we can validate the finding of the
first experiment.
Summary
It was shown that social loafing as a reduction of effort can be transferred to complex problem
solving in social situations and that the CEM is a suitable model to predict this loafing effect.
Furthermore we have seen that collective groups performed better compared to co-active
groups in complex tasks which can not be explained by the CEM. Hence we tested two more
models, the arousal reduction theory and the CMM, in order to explain this inverse relation
ship. There were reasons to reject the arousal reduction theory and to adopt the CMM as an
additional model for better explaining our findings. In sum: we have to supplement the CEM
with aspects of the CMM in order to explain the relationship between effort and performance
in complex task situations.
Literature
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Bartis, S., Szymanski, K. & Harkins, S. G. (1988). Evaluation and performance: A two-edged
knife. In: Personality and Social Psychology Bulletin, 14, 242-251.
Brehmer, B. (1992). Dynamic decision making: Human control of complex systems. In: Acta
Psychologica, 81, 211-241.
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