Affect and Job Performance The Effect of Daily Mood States on Employees' Overall and Contextual Performance

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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

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Affect and Job Performance The Effect of Daily Mood States on Employees' Overall and Contextual Performance