Title: Does Affect Influence Judgment When Using a Decision Support System Soussan Djamasbi Doctoral Studen
1Does Affect Influence Judgment When Using a
Decision Support System? Soussan
DjamasbiDoctoral Student University of Hawaii
at Manoawww2.hawaii.edu/soussansdjamasbi_at_hpu.e
du Phone(808) 595-8338 Fax(808) 595-8338
Phone During the Conference (808) 258-8604
2Does Affect Influence Performance When Using a
DSS?
- Affect
- Decision Model Judgment
- Performance
- Hypotheses
- Research design
- Mood Manipulation and Check
- Task
- Results
- Conclusion
3Affect Mood vs. Emotion
- Moods and emotions differ on the dimensions of
intensity, pervasiveness, and specificity
(Forgas, 1991 Moore and Isen, 1990 Isen 1984). - Mood refers to a less intense, more diffused
affective state, which is relatively enduring and
does not seem to be directed toward any
particular object, target, or behavior (Moore et
al., 1990 Lazarus, 1991, p. 48 Forgas, 1991). - Specific moods (e.g. sadness, fear, etc.) can be
grouped into more general or global categories
such as positive, neutral, and negative (Clark et
al., 1982 Osgood et al., 1955 Schwartz et al.,
1988). - The effects of positive mood are more consistent
than those of negative mood (Moore et al., 1990
Isen 1993).
4Decision ModelJudgment
- Judgment can be defined as a cognitive process in
which a person draws a conclusion (a judgment)
about something that he or she cannot see (a
criterion) on the basis of a set of data (cues
and or feedback) that he or she can see (Hammond,
1975 Brehmer, 1988).
5Judgment
?
?
Criterion
Feedback
To make a good judgment, the decision maker must
use all the relevant information that is
provided.
6Performance
- In the DSS literature, performance is primary
measured by the amount of information used and
the accuracy of decisions (e.g. Todd and
Benbasat, 1992 Bettman, et al., 1990 Shugan,
1980) - Effort (amount of information used)
- Accuracy (deviation from the normative strategy)
- Decision making literature suggests that
- Human judges tend to use only a small subset of
the cues or information available to them (e.g.
Slovic et al., 1971 Brehmer and Brehmer, 1987
cited in Brehmer and Brehmer, 1988, p. 97). - This seems to be the pattern even when decision
makers are provided with computerized decision
aids (Todd and Benbasat, 1992 Benbasat and Todd,
1996). - Decision makers tend to pay more attention to
effort reduction than to accuracy maximization
(Todd and Benbasat, 1992 Benbasat and Todd,
1996).
7Performance Why Mood Is an Important Variable in
Decision Making?
- The Mood literature suggests that positive
mood - Can influence cognitive context, structure, and
flexibility (Isen, et al., 1978 Isen et al.,
1984). - Can facilitate creative problem solving (Isen et
al., 1983). - Can facilitate efficient decision making (Isen et
al., 1987). - Can promote exploration and variety seeking (Kahn
et al. 1993).
Network Theory of Mood and Memory(Isen, 1984)
8Performance Positive Mood Can Help to Make
Better Decisions
?
?
Cue
?
Cue
Criterion
Cue
Cue
Cue
9Hypotheses
- Subjects in the positive mood group will exhibit
a greater degree of effort than their control
counterparts. That is, they will use a greater
number of cues provided by the DSS to base their
decision upon than their neutral mood control
counterparts. - Subjects in the positive mood group will make
more accurate judgments. That is, the mean
absolute error of the judgments for the subjects
in the positive mood group will be significantly
lower than those of the control group.
10Research Design
- 49 male and female undergraduate business
students were randomly assigned to two groups.
The treatments (experimental and control) were
then randomly assigned to these groups. The
subjects in the experimental group were induced
with positive mood. The subjects' mood in the
control group was not manipulated.
11Mood Manipulation and Check
- ManipulationConsistent with prior research
(Isen et. al., 1978, 1987, 1992) subjects in the
experimental group received a surprise gift of
chocolate and candy wrapped in colorful paper
prior to performing the task. The participants in
the control group did not receive a surprise
gift. - CheckConsistent with prior research (Isen and
Gorgolione, 1983 Kraiger et al., 1989 Elsbach
and Barr, 1999) a self-report survey was used to
measure the feeling state of the subjects.
12TaskBased on Holt, Modigliani, and Muths Model
of the Production-Scheduling Problem (1956)
Demand Current month
?
?
Demand Next month
How many units to produce?
Demand Two months from now
Work force
Inventory
Outcome feedback Percentage of error
13Results
- The results show that
- the mean of the mood scores in the positive mood
group (mean 11.24) was significantly higher
(t1.959, df 47, p0.028) than the mean of the
mood scores in the control group (mean 10.00). - the number of subjects in the positive mood group
who used two or more cues (21 out of 25 subjects)
was significantly higher (z-2.265, p0.012) than
the number of subjects who used the same number
of cues in the control group (13 out of 24
subjects). Similarly, significantly (z-1.729,
p0.042) more subjects in the positive mood group
(11 out of 25 subjects) used three or more cues
than their control counterparts (5 out of 24
subjects). - the mean of the absolute error in the positive
mood group (MAE112.23) was significantly lower
(t 2.246, df 40, p 0.015) than the mean of the
absolute error in the neutral mood group
(MAE127.16).
14Conclusion
- The results of this study show that positive
mood has a significant impact on ones decision
behavior when using a DSS. - Theoretical and practical implications
- This study
- demonstrates the robustness of Isens network
theory since it demonstrates that Isens theory
extends to complex managerial decisions and
judgments. - extends existing theories of behavioral decision
making by establishing the role of mood in these
theories. - shows significant performance improvement in the
measures of effort and accuracy which are the
primary focus of decision behavior studies in the
DSS literature. - provides managers with additional information to
increase productivity by paying attention to
organizational climate. - has important implications for the design of a
DSS. Paying attention to how the DSSs interface
interacts with the users feeling state can
potentially lead to building more effective
systems.
15Does Affect Influence Judgment When Using a
Decision Support System? Soussan
DjamasbiDoctoral Student University of Hawaii
at Manoawww2.hawaii.edu/soussansdjamasbi_at_hpu.e
du Phone(808) 595-8338 Fax(808) 595-8338
Phone During the Conference (808) 258-8604
16Task Scheduling Production Volume to Meet an
Uncertain Demand
- Optimal rules with no uncertainty in the
systemPt ?02 ?12 Wt-1 - ?22 It-1 ?32
E(St) ?42 E(St1) ?52 E(St2) - Optimal rules with noisePt ?02 ?12 Wt-1 -
?22 It-1 ?32 E(St) ?42 E(St1) ?52
E(St2) e