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Does Affect Influence Judgment When Using a Decision Support System Soussan Djamasbi Doctoral Studen

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Title: Does Affect Influence Judgment When Using a Decision Support System Soussan Djamasbi Doctoral Studen


1
Does 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
2
Does Affect Influence Performance When Using a
DSS?
  • Affect
  • Decision Model Judgment
  • Performance
  • Hypotheses
  • Research design
  • Mood Manipulation and Check
  • Task
  • Results
  • Conclusion

3
Affect 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).

4
Decision 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).

5
Judgment
?
?
Criterion
Feedback
To make a good judgment, the decision maker must
use all the relevant information that is
provided.
6
Performance
  • 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).

7
Performance 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)

8
Performance Positive Mood Can Help to Make
Better Decisions
?
?
Cue
?
Cue
Criterion
Cue
Cue
Cue
9
Hypotheses
  • 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.

10
Research 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.

11
Mood 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.

12
TaskBased 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
13
Results
  • 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).

14
Conclusion
  • 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.

15
Does 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
16
Task 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
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