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T H E U N I V E R S I T Y O F T U L S A
THE GRADUATE SCHOOL
AFFECT AND JOB PERFORMANCE: THE EFFECT OF DAILY MOOD STATES ON
EMPLOYEES OVERALL AND CONTEXTUAL PERFORMANCE
byKevin E. Fox
A dissertation submitted in partial fulfillment of
the requirements for the degree of Doctor of Philosophy
in the discipline of Psychology
The Graduate School
The University of Tulsa
2006
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T H E U N I V E R S I T Y O F T U L S A
THE GRADUATE SCHOOL
AFFECT AND JOB PERFORMANCE: THE EFFECT OF DAILY MOOD STATES ON
EMPLOYEES OVERALL AND CONTEXTUAL PERFORMANCE by
Kevin E. Fox
A DISSERTATION
APPROVED FOR THE DISCIPLINE OF
PSYCHOLOGY
By Dissertation Committee
, Chair Dr. Robert Tett
Dr. Kurt Kraiger
Dr. Deidra Schleicher
Dr. Wendy Casper
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COPYRIGHT STATEMENT
Copyright 2006 by Kevin E. Fox
All rights reserved. No part of this publication may be reproduced, stored in a
retrieval system, or transmitted, in any form or by any means (electronic, mechanical,
photocopying, recording or otherwise) without the prior written permission of the author.
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ABSTRACT
Fox, E. Kevin (Doctor of Philosophy in Psychology)
Affect and Job Performance: The Effect of Daily Mood States on EmployeesOverall and Contextual Performance
Directed by Dr. Robert P. Tett(232 pp. Chapter 5)
(215 words)The purpose of the current study is to further understanding of the role of daily
mood states, and their non-work antecedents, in influencing workplace task and
contextual job performance. Building on recent affective theory (Weiss & Cropanzano,
1996) and research (e.g., Fisher, 2000; Judge & Ilies, 2004) showing strong relationships
between affect and important workplace outcomes such as job attitudes and job
performance, two primary questions are addressed: (1) what is the effect of positive and
negative life events on mood experienced at work; and (2) what is the effect of daily
mood states on job performance. Data was collected using a longitudinal design whereby
74 employees of 7 organizations located in northern Thailand completed daily measures
of mood for 6 consecutive weeks. Supervisors correspondingly rated 5 dimensions of
daily job performance for each employee over the same interval. Analyses of both
within- and between-subjects effects were conducted using Multilevel Random
Coefficient Modeling (MRCM). Results were unexpectedly weak given previous
research findings. A follow-up exploratory survey was administered and exploratory
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analyses performed. Results of moderator analyses suggest the possibility that the effect
of daily mood states on job performance may be idiographic and thus situationally
determined. Findings are discussed regarding their applicability to both the scientific
study of affect at work and applied organizational practices.
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TABLE OF CONTENTS
Page
ABSTRACT............................................................................................................... iv
ACKNOWLEDGEMENTS....................................................................................... vi
TABLE OF CONTENTS........................................................................................... vii
LIST OF TABLES..................................................................................................... ix
LIST OF FIGURES ................................................................................................... xii
CHAPTER 1: INTRODUCTION ........................................................................... 1Emotion and Mood ...................................................................................... 1Emotion Defined ........................................................................................... 2The Structure of Mood ................................................................................ 5Theories of Mood ......................................................................................... 9
Affective Dispositions......................................................................... 10 Affect Infusion Model......................................................................... 12 Affective Events Theory...................................................................... 14
Current Findings in Workplace Affect Research ..................................... 16Stress.................................................................................................. 16 Job Attitudes....................................................................................... 18Withdrawal Behaviors ....................................................................... 21
Helping Behaviors ............................................................................. 22 Job Performance................................................................................ 24Summary ............................................................................................ 28
Measurement ................................................................................................ 28Limitations and Current Research ............................................................. 30The Current Study ....................................................................................... 31
Hypotheses ......................................................................................... 33
CHAPTER 2: METHOD ......................................................................................... 36Research Design ........................................................................................... 36
Sample Size ........................................................................................ 37Participants ................................................................................................... 38Measures ....................................................................................................... 39
Mood .................................................................................................. 39 Life Events.......................................................................................... 39
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Job Performance................................................................................ 40Single-Item Measures......................................................................... 41Translation......................................................................................... 41
Procedure ...................................................................................................... 42 Analyses ........................................................................................................ 42
CHAPTER 3: RESULTS ......................................................................................... 47Data Cleaning & Descriptive Statistics ...................................................... 47Hypotheses Testing ...................................................................................... 48
Between Subjects................................................................................ 48Within (Pooled) Subjects.................................................................... 51
CHAPTER 4: EXPLORATORY INVESTIGATION .......................................... 54Overview ....................................................................................................... 54Participants ................................................................................................... 58Measures ....................................................................................................... 58
Demographics.................................................................................... 58Commitment ....................................................................................... 59 Emotional Labor ................................................................................ 59 Emotional Intelligence....................................................................... 60
Procedure ...................................................................................................... 61Analysis ......................................................................................................... 61Results ........................................................................................................... 61
OLS Regression Moderator Analyses ................................................ 63MRCM Moderator Analyses .............................................................. 66
CHAPTER 5: DISCUSSION ................................................................................... 68Summary of Findings .................................................................................. 69
Hypothesized...................................................................................... 69 Exploratory ........................................................................................ 72
Implications .................................................................................................. 78Limitations .................................................................................................... 80Directions for Future Research .................................................................. 83Conclusions ................................................................................................... 86
REFERENCES .......................................................................................................... 87
APPENDIX A............................................................................................................ 187APPENDIX B ............................................................................................................ 188APPENDIX C ............................................................................................................ 189APPENDIX D............................................................................................................ 190APPENDIX E ............................................................................................................ 215APPENDIX F............................................................................................................. 217
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LIST OF TABLES
Page
Table 1: Summary of Workplace Affect Literature Review.................................... 104
Table 2: Demographic Variable Descriptive Statistics complete sample (N=73) ... 105
Table 3: Demographic Variable Descriptive Statistics final sample (N=50)........... 106
Table 4: Means, Standard Deviations, Alphas, and Intercorrelations BetweenIndividuals (N = 50)................................................................................... 107
Table 5: HLM Estimates of the Effect of Positive Daily Life Events on DailyMood.......................................................................................................... 108
Table 6: HLM Estimates of the Effect of Negative Daily Life Events on DailyMood.......................................................................................................... 109
Table 7: HLM Estimates of the Effect of Positive Daily Life Events on DiscreteAffective States.......................................................................................... 110
Table 8: HLM Estimates of the Effect of Negative Daily Life Events on DiscreteAffective States.......................................................................................... 111
Table 9: HLM Estimates of the Effect of Positive Mood on Discrete AffectiveStates.......................................................................................................... 112
Table 10: HLM Estimates of the Effect of Negative Mood on Discrete AffectiveStates.......................................................................................................... 113
Table 11: HLM Estimates of the Effect of Positive Mood and Affect on JobPerformance ............................................................................................... 114
Table 12: HLM Estimates of the Effect of Negative Mood and Affect on JobPerformance ............................................................................................... 115
Table 13: Means, Standard Deviations, and Intercorrelations Between Individuals(N = 50)...................................................................................................... 116
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Table 14: Moderator Analyses for the Positive Mood Commitment Relationship.... 117
Table 15: Moderator Analyses for the Positive Mood Job Effort Relationship......... 118
Table 16: Moderator Analyses for the Positive Mood Handling Stress
Relationship ............................................................................................... 119
Table 17: Moderator Analyses for the Positive Mood Helping Relationship............ 120
Table 18: Moderator Analyses for the Positive Mood Overall Job PerformanceRelationship ............................................................................................... 121
Table 19: Moderator Analyses for the Negative Mood Commitment Relationship .. 122
Table 20: Moderator Analyses for the Negative Mood Effort Relationship.............. 123
Table 21: Moderator Analyses for the Negative Mood Handling StressRelationship ............................................................................................... 124
Table 22: Moderator Analyses for the Negative Mood Helping Relationship .......... 125
Table 23: Moderator Analyses for the Negative Mood Overall Job PerformanceRelationship ............................................................................................... 126
Table 24: HLM Moderator Analyses for the Positive Mood CommitmentRelationship ............................................................................................... 127
Table 25: HLM Moderator Analyses for the Positive Mood Job EffortRelationship ............................................................................................... 128
Table 26: HLM Moderator Analyses for the Positive Mood Handling StressRelationship ............................................................................................... 129
Table 27: HLM Moderator Analyses for the Positive Mood Helping Relationship .. 130
Table 28: HLM Moderator Analyses for the Positive Mood Overall JobPerformance Relationship.......................................................................... 131
Table 29: HLM Moderator Analyses for the Negative Mood CommitmentRelationship ............................................................................................... 132
Table 30: HLM Moderator Analyses for the Negative Mood Effort Relationship.... 133
Table 31: HLM Moderator Analyses for the Negative Mood Handling StressRelationship ............................................................................................... 134
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Table 32: HLM Moderator Analyses for the Negative Mood HelpingRelationship ............................................................................................... 135
Table 33: HLM Moderator Analyses for the Negative Mood Overall JobPerformance Relationship.......................................................................... 136
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LIST OF FIGURES
Page
Figure 1: The Circumplex of Emotion Valence to Activation Axes and the 45 Rotation with Positive Affect to Negative Affect Axes........................... 137
Figure 2: Affective Events Theory: Macro Structure.............................................. 138
Figure 3: Theoretical Model of Affect in the Workplace........................................ 139
Figure 4: Measured Theoretical Model of Affect in the Workplace ....................... 140
Figure 5: Plot of the OLS Moderator Effect of Affective Commitment on thePositive Mood Commitment Relationship............................................... 141
Figure 6: Plot of the OLS Moderator Effect of Observability on the PositiveMood Effort Relationship ........................................................................ 142
Figure 7: Plot of the OLS Moderator Effect of Emotional Labor on the PositiveMood Effort Relationship ........................................................................ 143
Figure 8: Plot of the OLS Moderator Effect of Age on the Positive MoodHandling Stress Relationship................................................................... 144
Figure 9: Plot of the OLS Moderator Effect of Education on the Positive MoodHandling Stress Relationship................................................................... 145
Figure 10: Plot of the OLS Moderator Effect of Normative Commitment on thePositive Mood Handling Stress Relationship .......................................... 146
Figure 11: Plot of the OLS Moderator Effect of Emotional Labor on the PositiveMood Helping Relationship..................................................................... 147
Figure 12: Plot of the OLS Moderator Effect of Affective Commitment on thePositive Mood Helping Relationship ....................................................... 148
Figure 13: Plot of the OLS Moderator Effect of Sex on the Positive Mood OverallJob Performance Relationship ................................................................. 149
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Figure 14: Plot of the OLS Moderator Effect of Affective Commitment on thePositive Mood Overall Job Performance Relationship............................ 150
Figure 15: Plot of the OLS Moderator Effect of Age on the Negative MoodCommitment Relationship ....................................................................... 151
Figure 16: Plot of the OLS Moderator Effect of Self Emotional Appraisal onthe Negative Mood Commitment Relationship ....................................... 152
Figure 17: Plot of the OLS Moderator Effect of Age on the Negative MoodEffort Relationship................................................................................... 153
Figure 18: Plot of the OLS Moderator Effect of Education on the Negative MoodEffort Relationship................................................................................... 154
Figure 19: Plot of the OLS Moderator Effect of Sex on the Negative Mood
Effort Relationship................................................................................... 155Figure 20: Plot of the OLS Moderator Effect of Overall Quality on the Negative
Mood Effort Relationship ........................................................................ 156
Figure 21: Plot of the OLS Moderator Effect of Continuance Commitment on the Negative Mood Effort Relationship......................................................... 157
Figure 22: Plot of the OLS Moderator Effect of Age on the Negative MoodHandle Stress Relationship ...................................................................... 158
Figure 23: Plot of the OLS Moderator Effect of Education on the NegativeMood Handle Stress Relationship............................................................ 159
Figure 24: Plot of the OLS Moderator Effect of Sex on the Negative MoodHandle Stress Relationship ...................................................................... 160
Figure 25: Plot of the OLS Moderator Effect of Overall Quality on the NegativeMood Handle Stress Relationship............................................................ 161
Figure 26: Plot of the OLS Moderator Effect of Self Emotional Appraisal on the Negative Mood Handle Stress Relationship ............................................ 162
Figure 27: Plot of the OLS Moderator Effect of Continuance Commitment on the Negative Mood Handle Stress Relationship ............................................ 163
Figure 28: Plot of the OLS Moderator Effect of Normative Commitment on the Negative Mood Handle Stress Relationship ............................................ 164
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Figure 29: Plot of the OLS Moderator Effect of Age on the Negative MoodHelping Relationship ............................................................................... 165
Figure 30: Plot of the OLS Moderator Effect of Education on the Negative MoodHelping Relationship ............................................................................... 166
Figure 31: Plot of the OLS Moderator Effect of Sex on the Negative MoodHelping Relationship ............................................................................... 167
Figure 32: Plot of the OLS Moderator Effect of Overall Quality on the NegativeMood Helping Relationship..................................................................... 168
Figure 33: Plot of the OLS Moderator Effect of Continuance Commitment on the Negative Mood Helping Relationship ..................................................... 169
Figure 34: Plot of the OLS Moderator Effect of Continuance Commitment on the
Negative Mood Overall Job Performance Relationship .......................... 170Figure 35: Plot of the HLM Moderator Effect of Age on the Positive Mood
Commitment Relationship ....................................................................... 171
Figure 36: Plot of the HLM Moderator Effect of Income on the Positive MoodCommitment Relationship ....................................................................... 172
Figure 37: Plot of the HLM Moderator Effect of How Well Know on the PositiveMood Commitment Relationship............................................................. 173
Figure 38: Plot of the HLM Moderator Effect of Age on the Positive Mood EffortRelationship ............................................................................................. 174
Figure 39: Plot of the HLM Moderator Effect of Emotional Labor on the PositiveMood Effort Relationship ........................................................................ 175
Figure 40: Plot of the HLM Moderator Effect of Age on the Positive MoodHandle Stress Relationship ...................................................................... 176
Figure 41: Plot of the HLM Moderator Effect of Income on the Positive MoodHelping Relationship ............................................................................... 177
Figure 42: Plot of the HLM Moderator Effect of Income on the Positive MoodOverall Job Performance Relationship .................................................... 178
Figure 43: Plot of the HLM Moderator Effect of Emotional labor on the PositiveMood Overall Job Performance Relationship.......................................... 179
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Figure 44: Plot of the HLM Moderator Effect of Affective Commitment on thePositive Mood Overall Job Performance Relationship............................ 180
Figure 45: Plot of the HLM Moderator Effect of Affective Commitment on the Negative Mood Commitment Relationship ............................................. 181
Figure 46: Plot of the HLM Moderator Effect of Normative Commitment on the Negative Mood Commitment Relationship ............................................. 182
Figure 47: Plot of the HLM Moderator Effect of Age on the Negative MoodEffort Relationship................................................................................... 183
Figure 48: Plot of the HLM Moderator Effect of Overall Quality on the NegativeMood Effort Relationship ........................................................................ 184
Figure 49: Plot of the HLM Moderator Effect of Regulation of Emotions on the
Negative Mood Effort Relationship......................................................... 185Figure 50: Plot of the HLM Moderator Effect of Income on the Negative Mood
Handle Stress Relationship ...................................................................... 186
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CHAPTER 1
INTRODUCTION
Emotion and Mood
The study of emotion in the workplace can currently be described as undergoing a
renaissance, from a position of relative investigative neglect to a major target of
innovative research (Fisher, 2002; Frijda, 1988; Thoresen, Kaplan, Barsky, Warren &
Chermont, 2003; Weiss, 2001). Despite this renewed attention, however, current
research efforts have been hampered by definitional and methodological uncertainties
(Brief & Weiss, 2002). Among these important yet unresolved questions are: What do
labels such as mood, emotion, and affect mean (Russell & Barrett, 1999)? What is the
structure of emotional constructs (Watson, Wiese, Vaidya & Tellegen, 1999)? What
theory best explains how moods and emotions affect behavior and attitudes (George &
Jones, 1997; Rusting, 1998; Weiss & Cropanzano, 1996)? And, finally, what empirical
research has yielded meaningful data relevant to address the critical inquiries of
researchers and practitioners (Fuller, Stanton, Fisher, Spitzmuller, Russell & Smith,
2003; Hirt, Melton, McDonald & Harackiewicz, 1996)? The current study was designed
to advance knowledge of the role of affect, and, more specifically, daily mood states, in
workplace behaviors and attitudes. Each of the preceding questions is addressed, in turn,
as a foundation for the current effort.
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Emotion Defined
As in any research domain, the study of emotionally related constructs calls for
clear definitions of the subject matter. This deceptively simple task has proved daunting
in that there is no universally accepted definition of what an emotion is (Ashkanasy,
Hartel & Zerbe, 2000; Frijda, 1988). Illustrative of this point is a review by Plutchik
(1980), in which 28 different definitions are identified in the psychological literature.
Kleinginna and Kleinginna (1981) identified 92 from a broader collection of sources.
The scientific research of emotion can be traced back 134 years to Darwins (1873)
examination of the adaptive importance of emotional expression. Looking beyond
scientific study, the investigation of emotions by early philosophers predates western
civilization.
Given this long history of study, why is there so much variability among
contemporary researchers regarding the simple definition of something that most lay
people could describe? The problem lies in the types of theories that have spawned the
definitions of emotional constructs (Ashkanasy, et al., 2000). For example, different
theories of emotion have often focused on very specific aspects of emotional function or
classification. Whether it is theories of evolutionary adaptation (Darwin, 1873; Izard,
1971) or physiological function (LeDoux, 1995), great diversity exists among definitions.
Despite this theoretical diversity, Frijda (1988) argues the study of emotion need not be
slowed by this definitional morass. While he acknowledges that researchers might
quarrel endlessly about the word (1988, p. 351), such exercises are largely fruitless and
detract rather than contribute to our understanding of emotions. He suggests instead that
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researchers focus on understanding the communalities of emotions and rigorously define
what they mean in the context of their own research efforts.
The label of emotion encompasses a wide range of phenomena including feelings,
changes in behavior and cognitions, the engagement of involuntary or impulsive behavior
and thoughts, relative tenacity of beliefs, changes in the relationship between a person
and his or her environment, and physiological changes not caused by physical conditions
(Frijda, 2000). As such, Frijda (1993) summarized the current consensus among emotion
researchers by defining emotions as composed of four aspects: (1) the experience of a
subjective valence state (e.g., positive), (2) experience that is related to something else(e.g., person, object, or event), (3) an identifiable, discrete physiological change in the
person, and (4) specific experiences associated with distinct action tendencies (behavioral
or cognitive).
Given this general definition of emotion, it is important to distinguish what
differentiates emotion from other widely used labels such as mood and affect. Weiss
(2002) provides a useful framework for understanding the different constructs that fall
within this broad conceptual domain. In his framework, affective states refer to a family
of related entities he labels as Mood, Stress and Emotion. Under this definition, and as
adopted by others (e.g., Lord & Kanfer, 2002), affect is a general term used to describe
any emotion related term (e.g., mood, stress, and discrete emotions). Of the three
subcategories of affect, emotion and mood are the most closely related. Emotions are a
large class of discrete identifiable states such as anger, fear, and guilt. There are as many
emotions as can be identified by a given language. Russell (1997) suggests that there
may exist as many as 2,000 emotionally descriptive words in the English language alone.
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Stress is distinguished from emotion by Weiss (2002) despite the belief by some that it
should be subsumed under the emotion label (Lazarus, 1993). This distinction of stress
as a separate aspect of emotion is widely endorsed, as evidenced by the proliferation of
stress specific research that makes little if any acknowledgement of a direct link to the
larger area of emotion (e.g., Dormann & Zapf, 2002; Florio, Donnelly & Zevon, 1998).
As a distinct construct, stress can be defined as an immediate negative psychological
and/or aroused physiological state arising from an individuals experience of an aversive
environmental challenge (Jex, 2002; Weiss, 2002).
Moods, as contrasted to stress, are widely seen as being inexorably linked toemotions (Lord & Kanfer, 2002; Plutchik & Conte, 1997; Russell & Barrett, 1999;
Watson, Wiese, Vaidya & Tellegen, 1999; Weiss, 2002). This point is evidenced clearly
among mood researchers by simple examination of a list of mood exemplars: happiness,
anger, fear, sadness, and surprise (Mayer & Gaschke, 1988). While emotions and moods
do share labels, there is a clear distinction between them. Emotions and moods are
primarily distinguished not in content, but rather by their degree of intensity and duration
(Frijda, 1993; Larson, 2000; Weiss, 2002). Relative to emotions, moods are defined as
being more diffuse, more enduring, and less related to specific environmental phenomena
(Lord & Kanfer, 2002). As an example of this distinction, emotion would be evidenced
by the statement Seeing that movie made me angry, while, Im feeling angry today
would be indicative of a mood state. In both cases, anger is the defining affective label;
however, the behavioral and cognitive implications of anger emotion and anger mood are
different. A person feeling the emotion of anger might in a few minutes feel a
completely different emotion such as pleasure during a nice conversation, whereas a
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person in an angry mood is likely to remain feeling angry well after any source of anger-
inciting stimuli has been removed. Thus, the defining distinctions between moods and
discrete emotions are that: (1) moods are more enduring than emotions; (2) moods are not
formed as distinct and immediate reactions to specific objects, people or events; and (3)
moods are more resistant to change than are emotions.
The Structure of Mood
The study of affect has bifurcated into two general perspectives (Larsen, Diener &
Lucas, 2002; Russell, 1997; Weiss, 2002), the Primary or Basic emotions perspective,and the Circumplex perspective. The basic emotions perspective is built around the
notion that a small set of fundamental emotions exist and serve as the foundation on
which all other human emotions develop (Weiss, 2002). An important question that
arises from this perspective regards the determination of which emotions should be
considered basic emotions. A number of criteria have been proposed including three by
Izard (1992): (1) distinct and universally displayed facial expression; (2) innate and
unique neural substrates; and (3) unique feeling states associated with the emotion.
Additional proposed criteria include the display of emotion in primates, and automatic
appraisal (Ekman, 1994). Weiss (2002) notes that only two criteria, universality and
distinct physiology, seem to enjoy consensus as criteria for determining whether or not an
emotion should be considered basic.
Given the ongoing debate over what constitutes a basic emotion, it is not
surprising to find widespread disagreement as to a comprehensive list of basic emotions
appropriate for study. Ekman (1992) offers a list of six emotions (happiness, surprise,
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fear, sadness, anger, and disgust), Russell (1991) offers five (anger, fear, sadness,
happiness, and disgust), and Larsen et al. (2002) summarize that multiple lists of basic
emotions exist, usually numbering between five and nine. Indeed, as if to personally
illustrate this point, Ekman offers a list of 17 emotions that could be classified as being
basic emotions two years after he suggested the above mentioned list of six (Ekman,
1994).
Despite this relative lack of agreement as to a comprehensive list of basic
emotions, the primary emotions view does offer a number of advantages. First, the
distinct nature of basic emotions allows the researcher to select one emotion to target in aresearch effort and to exclude consideration of other emotions as irrelevant to the study at
hand (Larsen, et al., 2002). For example, if a researcher were interested in exploring the
role of fear in the workplace and adopted a basic emotions perspective, there would be no
need to examine the effect of other emotions such as anger or happiness. A second
advantage manifests in the theoretical conciseness of being able to link a single emotion
to specific behaviors or workplace outcomes.
In contrast to this relatively simplistic perspective, a growing body of research
supports a dynamic interplay among multiple affective dimensions (Barrett & Russell,
1998; Feldman, 1995; Plutchik & Conte, 1997; Reisenzein, 1994; Russell & Barrett,
1999; Russell, Lewicka & Niit, 1989; Russell, Weiss & Mendelsohn, 1989; Watson &
Clark, 1994; Watson & Tellegen, 1985; Watson, et al., 1999). Such circumplex models
of emotional structure offer a venerable perspective dating back to the early work of
Schlosberg (1941, 1954), Plutchik (1958), and Russell (1980). This perspective
postulates that the structure of affect is defined by two or three fundamental affective
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dimensions orthogonally oriented to one another, and that all emotions are blends of these
core dimensions. This idea can be readily compared to the creation of colors (Plutchik,
1997), in that there are a limited set of primary colors that cannot be broken down further
into more distinct colors and that all other colors are derived as combinations of these
primary colors (e.g., mixing blue and red makes green, and mixing them in different
quantities produces distinct shades of green). The critical question then becomes: What
are the core dimensions of the circumplex model of affect?
The overwhelming consensus among circumplex researchers is that the two core
dimensions are valence (pleasant to unpleasant) and arousal (high activation to lowactivation) (Reisenzein, 1994; Russell, 1980; Watson & Tellegen, 1985). Watson and
Tellegen (1985) shifted the emphasis of this original model from valence and arousal by
rotating the axes of the Circumplex 45 (Figure 1) and labeled the rotated axes Positive
Affect (PA) and Negative Affect (NA). Their modification subsequently gave rise to the
big two (Larsen, et al., 2002, p. 73) of affect research and formed the basis for nearly
20 years of renewed interest in workplace affect. Using this Circumplex, the specific
emotion of anger would be defined as a combination of high activation and high
unpleasantness, and contentment would be a combination of low activation and high
pleasantness. As such, the circumplex allows for the complete mapping of all emotions
within its circumference.
While the circumplex structure of affect has enjoyed widespread popularity, it hasnot been without its detractors and a number of debates have arisen. Among the
criticisms are those of Izard (1977) and Schimmack, Oishi, Diener, and Suh (2000), who
argue that the circumplex structure of emotion is too broad and that merely summarizing
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specific emotional states along the valence and activation axes ignores the benefits of
specificity offered by a basic emotion approach. Larsen et al. (2002) further expand on
this criticism to note that while it is possible to aggregate from a basic emotions approach
to a circumplex approach, it is not possible to do the reverse. Mitigating this concern,
however, is the practice of researchers to use measures that assess specific basic emotions
and then aggregate scores on those measures to examine the latent dimensions of valence
and activation (e.g., PANAS-X, Watson & Clark, 1994). Yet, this criticism would apply
to measures that directly assess the axes of the circumplex (e.g., Affect Grid, Russell, et
al., 1989) and as such, must be considered in research efforts using them.Another lively debate to emerge over the last decade concerns whether the axes of
PA and NA represent truly independent dimensions as Watson and Tellegen (1985) and
others (Cacioppo, Gardner & Berntson, 1999; Watson, et al., 1999) suggest, or whether
they are bipolar ends of a single dimension (Green, Goldman & Salovey, 1993; Green,
Salovey & Truax, 1999; Russell & Carroll, 1999). At the core of this debate lies the issue
of whether PA and NA best represent the circumplex, or whether it is more appropriate to
use the original axes of valence and activation for defining the circumplex (Russell &
Carroll, 1999; Watson, et al., 1999). Reisenzein (1994) suggests that the activation and
valence dimensions best capture the basic components of emotions, while PA and NA
capture the major groups of affects composed of activation and valence. While Russell
and Barretts (1999) research has comprehensively demonstrated that the activation
valence dimensions are semantically better fits for the primary dimensions of affect, their
case has done little to undermine the value of research that conceptualized affect as PA
and NA. As Watson et al. (1999) acknowledge, PA and NA can be simply renamed as
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Konovsky, 1993; Iverson & Deery, 2001; Watson & Tellegen, 1985). Second, Forgas
(1995; Forgas & George, 2001) developed the Affect Infusion Model (AIM), a theory by
which mood and emotions affect cognitive processes, which, it turn, influence subsequent
attitudes and behaviors. Third, Weiss and Cropanzanos (1996) Affective Events Theory
(AET) has enjoyed considerable attention in recent years, focusing on the immediate
timeframe and reversing the direction of the traditional causal relationship between
emotions and attitudes or behaviors. Each of these theoretical frameworks is compatible
with both the basic and circumplex structure of affect.
Affective Disposition
The affective disposition perspective stresses the critical difference between state
and trait affect as independent constructs playing distinct roles in determining workplace
behavior (Judge & Larsen, 2001; Watson, Clark, & Tellegen, 1988). Larsen et al. (2002)
point out four critical reasons why state and trait affect must be carefully conceptualized
and measured as distinct from one another: (1) mood states are more proximal than trait
affect and as such may have direct effects on specific behaviors and attitudes that trait
affect does not have; (2) the causal direction of mood states may be different from that of
traits (e.g., moods are influenced by events, while traits influence events); (3) because
trait affect is stable, it might allow for prediction of behavior and emotional reactions to
events; (4) researchers often confuse state and trait in the current literature (e.g.,
correlating trait affect and helping behavior, then claiming that people engage in more
helping behavior when they are in a good mood). Avoiding this conceptual confusion is
not difficult, but requires researchers to be mindful of the definition of the construct
under investigation and aligning measurement correspondingly. It should be noted that
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all dispositional approaches to the study of affect must assume a trait perspective. It is
definitionally inconsistent to discuss mood states as being completely dispositional in
origin. While moods are certainly influenced by trait affect, by definition they are
general reactions to environmental events and as such are indivisibly linked to
environmental conditions and events. Thus, the distinction between trait and state affect
is defined by measuring how one feels generally versus how one feels during a specific
and relatively brief moment in time (e.g., today, or this week) (Watson, et al., 1988).
Yet, if PA and NA are to be considered as universal affective traits, evidence must
be provided to support the claim that they are universally prevalent, relatively stableindividual differences. Evidence of a biological origin of PA and NA would provide
strong evidence to support this claim as biological foundations underlie many of our most
widely studied individual differences (e.g., sex, race, ability). Evidence suggesting the
biological origin and stability of trait PA and NA has come from several sources. First,
Russell and Barrett (1999) summarized the neurophysiological research supporting
distinct brain activity differences between those high and low in PA and NA (Heller,
1993; Lang, Greenwald, Bradely & Hamm, 1993; Lane, Reiman, Bradley, Lang, Ahern,
Davidson & Schwartz, 1997). Second, in a compelling study examining the long term
stability of PA and NA over 23 years and crossing four generations in nearly 3,000
subjects, Charles, Reynolds and Gatz (2001) found that NA slowly decreased with age
while PA remained stable until old age when it decreased slightly. Third, Watson et al.
(1999) concluded that PA follows a circadian rhythm while NA remains fairly stable
across time. Taken as a whole, these studies provide evidence to justify the
conceptualization and existence of trait PA and NA as distinct affective traits.
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Additionally, in their review of the extant literature on trait PA and NA Connolly and
Viswesvaran (2000) report that zero order correlations for these two constructs range
from -.05 (Brief & Roberson, 1989) to -.39 (Judge & Locke, 1993), providing empirical
support for the consideration of trait PA and NA as distinct constructs.
Research examining the role of trait affect in predicting workplace behavior has
generally taken one or both of two approaches. The first approach holds that trait affect
influences workplace attitudes and behaviors directly (Iverson & Deery, 2001; Kelley &
Hoffman, 1997; Lee & Allen, 2002), while the second approach suggests that trait affect
may have a more indirect effect by moderating or mediating the relationship betweensituational variables (e.g., job characteristics) and outcomes (e.g., performance, stress)
(Larsen, 2000; Staw, Sutton & Pelled, 1994; Zerbe & Hartel, 2000). Additionally, it is
not uncommon for researchers to adopt both approaches in the same study (e.g.,
Cropanzano et al., 1993). Findings based on both of these perspectives support the view
that trait affect asserts both direct and indirect effects on workplace behaviors and
attitudes and are reviewed in detail in a later section.
Affect Infusion Model
The Affect Infusion Model or AIM (Forgas, 1995) was developed using an
information processing approach to understanding the role of moods in the workplace.
Essentially, AIM postulates that affective states motivate an individual to engage in
specific cognitive strategies and direct attention towards mood reinforcing information,
thus resulting in a specific affect congruent behavior or attitude (Forgas & George, 2001).
Appreciably, AIM offers a specific set of guidelines allowing researchers to identify
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when cognitive processes are likely to be infused with affect and allowing for the
instance when affect may play no role (Erber & Erber, 2001).
Critical to determining the impact of affect on workplace outcomes are the types
of situational demands to which an individual must adapt and decisions that an individual
must make to successfully achieve his or her goals in the workplace. The reason for this
is that different types of cognitive processes allow different amounts of affect infusion
(i.e., less structured or routine cognitive processes allow for more affect infusion than do
more structured or repetitive cognitive tasks). It is essential, then, to understand what
type of situations and decisions result in specific levels of affect infusion.Three characteristics influence the type of processing in which a person is most
likely to engage: (1) personal variables (e.g., personality, intelligence and mood); (2) task
characteristics (e.g., familiarity); and (3) situational features (e.g., level of scrutiny).
Once input is received, one of four types of processing will be engaged: (1) direct access
processing, (2) motivated processing; (3) heuristic processing; or (4) substantive
processing. Direct access processing results when an individual needs to perform a
habitual or routine task that follows a well-established set of actions or decisions (e.g.,
dialing a telephone, preparing a computer for normal use), and allows little if any
affective infusion. Motivated processing occurs when an individual is motivated to make
decisions designed to allow him or her to achieve a predetermined goal. In this case,
there is little opportunity for affect to influence cognitive processes as the individual has
already decided what to do (e.g., helping a supervisor in the month preceding promotion
or pay raise decisions). Heuristic processing is engaged when there are no preexisting
rules or motivational goals regarding a particular action. This processing strategy is most
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commonly engaged when the individual has little or no investment in the outcome of an
action and allows moderate levels of affect infusion. For example, if asked how are you
doing? by an acquaintance, a person may respond positively, neutrally or negatively
depending on his or her current mood state rather than engage in a more cognitively
taxing thought process to determine how one is really doing. Finally, substantive
processing allows the greatest impact of current mood on thought processes. Substantive
processing occurs when individuals must uncover and process new information. This
open processing style frequently causes individuals to attend to affectively primed
information that is mood congruent and is often unconsciously incorporated into judgments and planned behavior (Forgas, 1998). For example, when tasked with
developing recommendations on how to improve an organizations relationship with
employees, an employee might focus his or her work on reducing bureaucracy because of
a recent negative experience with it, as opposed to addressing a potentially more critical
inequity such as below-market compensation.
By determining the personal, situational and task characteristics of an individual,
it is possible using AIM to predict when mood states should be likely to influence
workplace attitudes and behavior. While little research in the workplace to date has
utilized AIM for making predictions, it is clear that AIM provides a robust theoretical
framework for making specific and testable hypotheses about the role of affect in the
workplace (Forgas, 2001).
Affective Events Theory
Weiss and Cropanzanos (1996) Affective Events Theory (AET) is distinct from
previous theories in two critical ways. First, rather than seeing emotions and moods as
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antecedents of behavior and attitudes, it postulates that affect is often a reaction to, rather
than a cause of, workplace events. This is not to say that affect does not have a
subsequent effect on behavior, but rather to emphasize that events and affect interact in a
continuous cycle. Additionally, while trait affect and the AIM (Forgas, 1995) are
primarily assessed using the broad PA and NA constructs, AET has the potential to
further understanding of the role of specific emotional reactions to discrete workplace
events (e.g., being turned down for a promotion leads to the specific emotions of
rejection and anger, but not fear and disgust).
The second innovative aspect of AET is its consideration of the temporal natureof emotion and the inherent ebb and flow of moods and emotions throughout the workday
as individuals are exposed to multiple affective stimulating events. Specifically, AET
builds on the widely accepted definition of emotions as experienced feelings in reaction
to objects, people and events (Frijda, 1993). As such, AET has formed the theoretical
basis for a number of recent workplace studies examining the dynamic relationship
between work events and affect within a single person during a work day (Fisher, 2002;
Fuller, et al., 2003; Ilies & Judge, 2002). This use of experience sampling
methodologies has allowed researchers to examine the independent effects of state affect
beyond trait affect in the context of an actual work sample (e.g., Fisher, 2002).
Finally, AET encompasses within its framework a model for understanding both
affective reactions to workplace events and the effect of trait affective dispositions. In
their original work, Weiss and Cropanzano (1996) offered a model in which workplace
characteristics lead to work events that, in turn, lead to affective reactions. Affective
reactions are moderated by trait affective dispositions, and the effect of trait dispositions
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on work attitudes and behavior is mediated by affective dispositions (Figure 2). In the
original AET model, all affect-driven behaviors were considered the result of affective
reactions. Recently, however, Weiss (2002) added the further distinction that some
behaviors result from the affective state itself while other behaviors result from attempts
to regulate affective states. While not fundamentally altering the original model, this
addition opens the way for affective dispositions to directly influence subsequent affect
regulation behavior as the original model does not (see Figure 2).
Examination of trait affect theory, AIM, and AET provides a rich framework for
developing a theoretical understanding of the mechanisms through which affect influenceworkplace attitudes and behaviors. Additionally, building on our understanding of these
theories, it is possible to develop a set of hypotheses that describe how affect influences
an individuals workplace attitudes and behavior. Before advancing these hypotheses, a
review of the relevant workplace affect literature is offered.
Current Findings in Workplace Affect Research
The following review of the extant literature on affect in the workplace is
organized to address four key areas: (1) outcomes; (2) measurement issues; (3)
limitations of extant research; and (4) remaining questions. The first key area regards
what variables have been studied in the extant literature. Five general types of outcomes
were identified from the literature, as discussed below and summarized in Table 1.
Stress
Researchers examining the relationship between affect and stress/strain generally
focus on two distinct sets of issues. Underlying both of these research streams is the well
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established and substantial correlations (ranging from .31 to .74) between measures of
trait NA and job stress and strain (Brief, Burke, George, Robinson, & Webster, 1988;
Chen & Spector, 1991; Fuller et al., 2003; Schaubroeck, Ganster & Fox, 1992; Spector,
Chen & OConnell, 2000; Watson & Pennebaker, 1989; Williams, Gavin & Williams,
1996). The first and largest of these two lines of research concerns the role of NA as a
third variable or nuisance variable in the relationship between job stressors and
subsequent stress/strain (Brief et al., 1988; Burke, Brief, & George, 1993; Chen &
Spector, 1991). While early research tended to support the need to control NA in stress
related research (e.g., Watson & Pennebaker, 1989), more recent studies have suggestedthat earlier findings were misleading and that NA has a negligible impact on the stress-
strain relationship (Schaubroeck, et al., 1992; Spector, et al., 2000; Williams, et al.,
1996). Summarizing this point, Spector et al. (2000) warn that removing trait NA
variance from the stressor-strain relationship is likely to do more harm than good by
removing valid variance. While interesting in its own right, this debate has served the
secondary purpose of providing a solid research base for establishing the strong and
enduring relationship between trait affect and work stress.
The second line of research, which has emerged more recently, concerns the
investigation of mood states as an affective reaction to the experience of stressful work
events. Three studies using longitudinal designs examined the effect of daily stress on
subsequent mood states in white collar workers (Van Eck, Nicolson & Berkhof, 1998),
accountants (Teuchmann, Totterdell & Parker, 1999) and administrative staff (Fuller et
al., 2003). In all those studies, a strong relationship was observed between current
stressors and mood states. In two of the studies (Teuchmann, et al., 1999; Van Eck, et al.,
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1998), perceptions of controllability over the stressful event or conditions were found to
alleviate the subsequent negative mood states. Additionally, Van Eck et al. (1998)
reported that higher levels of trait NA resulted in increased reactivity of individuals to
stressful events; that is, individuals high in trait NA were more likely to experience
negative mood states when confronted with a stressful event than were individuals low in
trait NA. Taken together, both lines of research establish a reasonable foundation for
understanding the distinct impact of both trait affect and mood states on workplace stress.
Job Attitudes
Two of the most widely examined and well understood workplace attitudes are
job satisfaction and organizational commitment. Job satisfaction is most often defined as
a pleasurable or positive emotional state resulting from the appraisal of ones job or job
experiences (Locke, 1976, p. 1300). However, this definition has created some
controversy as it mixes both a cognitive/evaluative component with an affective reaction
(Brief & Weiss, 2002), which has falsely led many researchers to mislabel job
satisfaction as affect. So pervasive and enduring is this misconception that some have
pleaded that job satisfaction is not affect and it is time we stopped saying it is (Weiss &
Cropanzano, 1996, p. 65). While differences exist as to the usefulness of job satisfaction,
researchers must be clear in their work that it is not the same as affect. Motowidlo (1996)
makes this distinction clear in his definition of job satisfaction as an assessment or
judgment about the favorability of the work environment. Moreover, exploratory and
confirmatory factor analysis research has strongly supported the distinctiveness of trait
PA and NA from job satisfaction (Agho, Price & Mueler, 1992).
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Research between trait PA and NA has developed to the point where meta-
analytic techniques have allowed for aggregation of the available research. Two meta-
analytic studies shed light on the affect job satisfaction relationship. The first study, by
Connolly and Viswesvaran (2000), reported meta-analytic correlations of .49 ( N = 3,326,
k = 15) between PA and job satisfaction and -.33 ( N = 6,233, k = 27) between NA and job
satisfaction. Additionally, five moderator variables (e.g., measure, tenure, organizational
size, organizational size, and age) were found to be nonsignificant. In a more recent
comprehensive effort, Thoresen et al. (2003) examined state and trait affect separately in
relation to job satisfaction. They report that trait PA has a corrected meta-analyticcorrelation of .33 ( N = 22,148, k = 71) with job satisfaction (95% confidence interval =
.29 to .37), while state PA has a corrected meta-analytic correlation of .44 ( N = 1,503, k =
10) with job satisfaction (95% confidence interval = .35 to .54). Trait NA has a corrected
meta-analytic correlation of -.37 ( N = 52,120, k = 145) with job satisfaction (95%
confidence interval = -.36 to -.31), while state NA has a corrected meta-analytic
correlation of -.36 ( N = 9,220, k = 40) with job satisfaction (95% confidence interval = -
.42 to -.31). Interestingly, there is greater variability in findings between state and trait
PA than there are for state and trait NA (as seen in the differences in 95% credibility
intervals between state and trait). This is consistent with prior research suggesting
greater stability for state NA than for state PA (Watson, 2000).
Organizational commitment is another widely studied job attitude referring to an
individuals adoption of organizational goals and values as his or her own, as well as a
general sense of emotional attachment to the organization (Mowday, Porter & Steers,
1982). Increasingly, the most popular conceptualization of organizational commitment
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has been Meyer and Allens (1997) three part model of affective, normative and
continuance commitment. Affective commitment refers to an individuals emotional
identification with their organization and their desire to stay with it. Continuance
commitment focuses on an individuals perception of the relative costs associated with
leaving their organization. Normative commitment reflects an individuals sense of
obligation to stay with an organization. While all three commitment types are interesting
in their own right, affective commitment has enjoyed the majority of attention from
researchers (Wright & Bonett, 2002), and especially so in affect research (Thoresen, et
al., 2003).In the extant literature, all investigations examining the relationship between
affect and organizational commitment have used trait measures of affect with the
exception of a single identified study (Fisher, 2002). Given this paucity, the effects of
mood states on organizational commitment remain an open question. Meta-analytic
investigations of the relationship between trait PA and organizational commitment have
yielded a correlation of .35 ( N = 4,873, k = 15, 95% confidence interval =.25 to .45), and
for trait NA and organizational commitment a correlation of -.27 ( N = 8,040, k = 27, 95%
confidence interval = -.32 to -.22). Results of Thoresen et al.s (2003) meta-analysis
clearly support the relationship between affective organizational commitment and trait
affect.
In the only identified study investigating the role of affective reactions on
affective commitment, Fisher (2002) found support for the relationship using both zero
order correlations ( r = . 28 for PA) and structural equation modeling ( .36 and .37 path
coefficients in an alternative and theoretical model, respectively, for PA). NA, was
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unrelated to affective commitment. This study is of particular interest because it tests
AETs (Weiss & Cropanzano, 1996) hypothesis that affective reactions completely
mediate the relationship between trait affect and job attitudes, for which Fisher found
only partial support. While the relationship between trait affect and organizational
commitment seems fairly well established, much work remains in fully examining how
moods relate to organizational commitment.
Withdrawal Behaviors
Withdrawal behaviors have the potential to have a negative financial impact on
organizations, yet little research has examined the potential for dispositional causes of
withdrawal behavior (Iverson & Deery, 2001). The study of affect and workplace
withdrawal is an exception to this relative neglect, and generally focuses on two types of
withdrawal behaviors: absenteeism and turnover intentions (Brief & Weiss, 2002; Forgs
& George, 2001). Findings from several studies suggest that both trait affect and moods
affect levels of employee absenteeism and tardiness (Forgas & George, 2001). Early
work by George (1989) examining the impact of mood on absenteeism among
salespeople found that positive mood had a negative correlation ( r = -.28) while negative
mood was unrelated ( r = -.03). Trait affect showed an opposite pattern of results in that
NA correlated positively ( r = .25), while PA was uncorrelated ( r = -.01). These findings
were supported in a subsequent study (Iverson & Deery, 2001), where trait NA correlated
.09 with absenteeism and trait PA did not correlate significantly ( r = -.07, n.s.).
Interestingly, Iverson and Deery (2001) followed up this analysis by examining the
incremental impact of trait affect beyond demographic (e.g., sex, age), job related (e.g.,
coworker support, job satisfaction), and environmental variables (e.g., absence culture,
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external responsibilities) and found that PA contributed uniquely ( = -.10), while NA
did not ( = .06, n.s.). Support for the mood relationships was provided by Pelled and
Xin (1999) as they report that positive mood correlated -.36 with absenteeism, while
negative mood correlated less strongly ( r = .17) with absenteeism.
Next to job satisfaction, the turnover intention trait affect relationship is one of
the most widely studied relationships in the affect work outcomes literature. As such,
Thoresen et als (2003) meta-analysis provides an examination of the aggregated findings
from this literature. Trait PA correlates -.17 ( N = 5,327, k = 18, 95% confidence interval
= -.25 to -.09) with turnover intentions. For NA comparisons are available between trait
affect and mood states in their relationships with turnover intentions. Trait NA,
correlates .24 ( N = 6,741, k = 25, 95% confidence interval =.18 to .31) with turnover
intentions, and state NA correlates .42 ( N = 2,041, k = 10, 95% confidence interval =.30
to .54) with turnover intentions. In a follow up analysis, Thoresen et al. (2003) show that
trait NA contributes to the prediction of turnover intentions beyond the effect of trait PA,
while trait PA does not add uniquely beyond the effect of trait NA. However, this
analysis is restricted by the lack of studies examining the impact of positive moods, and
only analyzes data based on trait affect. The potential for positive mood states to
contribute beyond negative mood states remains an open question for further
investigation.
Helping Behaviors
Helping behaviors have long been linked to positive affective states in the social
psychology literature as a review of experimental studies by Carlson, Charlin and Miller
(1988) demonstrate. Research into workplace helpfulness, an aspect of contextual
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performance (Borman & Motowidlo, 1993), has generally shown a strong relation with
PA and weak or non-existent relations with NA (Brief & Weiss, 2002). Trait PA has
been shown to relate strongly to a variety of workplace helping behaviors including:
altruistic organizational citizenship (e.g., helping coworkers with work related problems;
George, 1991; Fisher, 2002; Kelley & Hoffman, 1997; Lee & Allen, 2002), customer-
directed service behavior (i.e., help provided with the customers best interests in mind;
Kelley & Hoffman, 1997), customer service (Fisher, 2002; George, 1991), organization
focused helping (Lee & Allen, 2002), and service quality (Kelley & Hoffman, 1997).
Much less research exists examining trait NA and helping behavior. What research thatdoes exit shows a non-significant relationship between NA and helping behaviors (Fisher,
2002; Lee & Allen, 2001).
Results of two studies examining the relationship between positive mood and
coworker focused helping behaviors supported the existence of a positive relationship as
evidenced by sizable positive correlations of r = .48 (Fisher, 2002), and r = .24 (George,
1991). In both studies negative moods correlated non-significantly with helping
behaviors. Further examination of the relationship between positive and negative
emotions and helping behaviors was conducted by Lee and Allen (2001) through use of
Watson and Clarks (1994) PANAS-X measure. Lee and Allen (2001) examined the
correlations of three discrete positive emotions (attentiveness, joviality, self-assurance)
and four discrete negative emotions (fear, hostility, sadness, guilt) with organizational
citizenship behaviors focused on helping either the organization in general or coworkers.
Results were generally consistent with the overall PA and NA correlations, but did
demonstrate some variability. Overall PA correlated .18 with coworker helping and .24
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with organizational support while the specific emotion correlations ranged from .12 to .19
and .15 to .25 respectively. Similar patterns between broad and specific constructs were
found for NA, where overall NA correlated -.02 with coworker helping and -.05 with
organizational support while specific negative emotion correlations ranged from -.08 to
.10 and -.08 to -.01 respectively. Noteworthy is the positive correlation of the specific
emotion of fear with coworker helping ( r = .10) while all other negative emotions and
NA were negatively related to coworker helping. Lee and Allen (2001) cite this
distinctiveness as evidence warranting further examination of the usefulness of discrete
emotions in the prediction of workplace helping behaviors.
Job Performance
Few outcomes are considered more important to Industrial Organizational
psychologists than job performance. Often, widespread respect for a new (or reemerging)
area of study does not develop until a number of studies reveal a consistent relationship
with critical workplace outcomes such as job performance (e.g., personality, emotional
intelligence). Until such relationships are established, skeptics may simply dismiss
research in these areas as less important and of marginal value to organizations. Affect in
the workplace has recently reemerged as a hot topic for applied psychologists and
management researchers after a period when emotions were often considered unwanted
influences which deflected us from the path of objectivity (Muchinsky, 2000, p. 802)
and thus inappropriate for study in organizational settings. Moreover, Muchinsky (2000)
calls for the recognition of affect as an important construct in personnel selection and job
performance, a call echoed by others (Brief & Weiss, 2002).
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Evidence regarding the trait NA job performance relationship is also supportive with
correlations including -.12 (Wright & Cropanzano, 1998), -.26 (Cropanzano et al., 1993),
and -.33 (Van Yperen, 2003). Taken as a whole, there is ample evidence to expect a
relatively stable relationship, across job contexts, between trait affect and job
performance.
The research exploring the relationship between job performance and mood states
is not as clear as research examining the relationship between trait affect and job
performance. The primary reason for this ambiguity is the paucity of workplace studies
examining moods and job performance, as well as the diversity of methodologies andlimitations of each study. Findings from three studies are germane to understanding the
targeted relationship. Totterdell (2000) examined the role of happy mood in professional
cricket players in relation to self ratings of performance and two objective indicators of
sports performance (batting and bowling average). Results indicated that all three aspects
of performance were related to positive mood ( r = .50, .36, .26, for self rated, batting, and
bowling average respectively). In a more traditional workplace setting (supervisors and
social services workers), Wright, Cropanzano and Meyer (2004) conducted two studies to
examine the relationship between mood and past year performance, and mood and
current performance. Positive mood did not correlate significantly with performance in
either study ( r = -.03 and .08 respectively), while negative mood did ( r = -.26 and -.31
respectively). While encouraging, these findings must be considered with caution as the
sample size for each study was moderate ( N = 45 and 72 respectively).
In an interesting study of affect and job performance, George (1991) examined
both trait PA and positive mood in a sample of 221 sales employees. Findings indicated
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behaviors (e.g., stealing), Lee and Allen (2002) reported substantial variability among
correlations between affect and counterproductive work behaviors ( r = -.14 for PA and -
.09, -.11, and -.17 for self-assurance, attentiveness, and joviality respectively) and general
NA and discrete negative emotions ( r = .14 for NA and .05, .07, .09, and .27 for guilt,
fear, sadness, and hostility respectively). While counterproductive behaviors are different
from job performance, it is reasonable to generalize the finding that discrete emotions
may have a unique pattern of relationships with specific work-related outcomes distinct
from general affect.
Summary
Review of the preceding sections provides a strong foundation for understanding
the role of affect in the workplace. While extensive research has examined aspects of
trait affect and mood in relation to stress, job attitudes, withdrawal behaviors, and
helping, less research has focused on understanding the effect of moods on supervisor
rated job performance in traditional work settings. As such, the current study seeks to
enhance our knowledge by examining the relationship between daily mood states and
supervisor ratings of daily job performance.
Measurement
Scientific measurement of mood, emotions and trait affect can be traced back
almost half a century (Zuckerman & Lubin, 1965) and most commonly uses some form
of adjective checklist (e.g., PANAS, Watson, Clark & Tellegen, 1988). Exceptions do
exist, such as Russell, Weiss and Mendelsohns (1989) Affect Grid (a single item grid
designed to simultaneously measure the two axes of valence and arousal comprising the
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circumplex structure of emotion). The overwhelming majority of research examining the
role of affect in the workplace (and affect in general) adopts an adjective checklist
measurement approach, and as the literature reveals scant criticism of this approach, there
is little reason to adopt an alternative measurement approach.
Another measurement issue arises out of some researchers unfortunate decisions
to assume equivalence between measures of PA and NA with the big five (Digman,
1990) personality traits of extroversion and neuroticism, respectively (George, 1996;
Rusting & Larsen, 1997). While these construct pairs do correlate significantly, it would
be a critical mistake to call them equivalent (Judge & Larsen, 2001) and, use theminterchangeably as some researchers have done (Schaubroeck, et al., 1992). Two
compelling reasons support treating PA and NA independently of the big five.
First, both personality and affect constructs emerge from distinct theoretical
backgrounds (i.e., personality theory versus emotion theory) and while there is overlap in
their manifestation (e.g., individuals high in NA are likely to display behaviors similar to
those displayed by individuals high in neuroticism), clear conceptual and definitional
distinctions warrant their continued separation. Second, clear empirical evidence
suggests that these measures are not interchangeable. For example, research by Ilies and
Judge (2002) reported a correlation of .40 between extroversion and PA, and a correlation
of .25 between neuroticism and NA. Assuming perfect reliability, these relationships
leave 84% and 94% of the variance unaccounted for hardly comprehensive overlap.
Thoresen et al. (2003) provide additional empirical evidence of the importance of
separating PA and NA from extraversion and neuroticism in their meta-analysis of affect
and job perceptions by comparing results of their meta-analysis including personality
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traits as measures of PA and NA with results from personality traits alone. Their findings
indicate that, by adding pure measures of PA and NA, meta-analytic correlations
improved noticeably for both PA (e.g., PA with job satisfaction related .34, while
extraversion related .22) and NA (e.g., NA with turnover intentions related .28, while
neuroticism related .12). This evidence is consistent with recent calls by Brief and Weiss
(2002), Weiss and Cropanzano (1996), and Lord and Kanfer (2002) for researchers to be
precise in their definitions and measurement of affect. Moreover, it supports the use of
measures specifically constructed to assess affect and not similar but distinct personality
traits in future research efforts.
Limitations and Current Research
After review of the current literature examining the role of affect in the
workplace, a number of key limitations are identified that future researchers need to
address. The three limitations of particular concern are: (1) the widespread misuse of the
labels such as mood, trait affect, and emotion, (Larsen, Diener & Lucas, 2002); (2)
the relative lack of studies examining both state and trait affect (George, 1991); and (3) a
lack of longitudinal field studies (Fisher, 2002). By addressing these concerns, the
current study aims to advance current understanding in several key ways: (1) explore the
impact of trait affect and mood in relation to the important outcome of job performance;
(2) adopt an experience sampling methodology to examine both within-person and
between-person relationships; and (3) build on our current knowledge regarding the
impact of non-work life events on workplace mood and job performance. Results from
the current study are expected to provide valuable insight into the nature of how daily
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and (3) temporal differences (i.e., AET focuses on proximal emotional episodes while the
offered model adopts a more distal timeframe by focusing on daily moods).
Reasons for the deviations from the original AET model are based on both insight
from recent empirical tests of AET and practical considerations. An example of an
empirical finding unsupportive of AET is research challenging AETs exclusion of trait
affect as a direct antecedent of workplace behavior and attitudes (Fisher, 2002; George,
1991; Totterdell, 2000). In fact, given the number of studies (see previous review)
consistently reporting significant relationships between trait affect and workplace
behavior and job attitudes, Weiss and Cropanzanos (1996) claim to the contrary issomewhat unexpected. Not surprising, however, is Weisss (2002) recent
acknowledgement that trait affect does play a role in the prediction of some workplace
behaviors and attitudes.
Differences based on practical considerations exist largely because of the
difficulty in measuring the multitude of workplace events, affective reactions to them,
and the subsequent impact of affect on behavior and attitudes. While AET offers a rich
framework built on a deep understanding of the role of affect in the workplace, muting
this temporal specificity to examine the impact of daily moods (as opposed to immediate
emotions) offers a more practical measurement strategy from the criterion perspective
(i.e., measuring employees job performance in the last 15 minutes versus measuring job
performance over the course of a day). Additionally, by focusing on antecedent events
occurring prior to an employees arrival to work, practical implications may be drawn to
assist a company in improving performance should a link be established between daily
moods and performance. For example, finding that negative life events (e.g., financial,
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family) lead to negative daily moods, which in turn, result in lowered job performance
could provide justification (or incentive) for a company to offer flexible benefits aimed at
reducing work family conflict (Eby, Casper, Lockwood, Bordeaux & Brinley, 2005). For
these reasons, the modified AET model is adopted in preference to the original AET
model.
Two final notes must be made regarding the current revised AET model. First,
while the model provides a process by which work outcomes influence subsequent mood
states and non-work life events (Figure 3), the current study offers only a partial
assessment of the full model. Because the focus of the current study is on understandingthe role of affect in relation to daily job performance, examining the impact of
workplace-generated moods on non-work life is tangential to current aims and thus is
excluded from further consideration here. However, Judge and Ilies (2004) examined the
effect of workplace-generated moods on subsequent moods and emotions experienced at
home and found support for the relationship. Second, because trait affect was measured
by taking the average of individuals daily mood states, the relationship between trait
affect and mood does not reflect a true relation, but rather a statistical artifact, and as such
will not be reported or discussed. As such, given the preceding discussion and n light of
the modified AET model offered above, a set of hypothesized relationships is offered
below.
Hypotheses
Hypothesis 1 : Across individuals, positive life events will have a positive effect on positive daily mood.
Hypothesis 2 : Across individuals, negative life events will have a positive effect onnegative daily mood.
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Hypothesis 3 : Across individuals, positive life events will have a positive effect on thediscrete affective states of (3a) confidence and (3b) happiness.
Hypothesis 4 : Across individuals, negative life events will have a positive effect on the
discrete affective states of (4a) anger, (4b) fatigue, (4c) sadn