The Happy-Productive Worker Thesis Revisited

Abstract

Despite extensive research on the subject spanning over 70 years, uncertainty still remains as to whether happier workers are in fact more productive. This study combined longitudinal prospective and experience sampling methods to examine the relationship between happiness and self-reported productivity among Directors employed in the public and private sectors. Analyses at a trait level suggested happy people were more productive. Similarly, at the state level of analysis, people were more productive when they were happier. Among the happiness indicators examined (job satisfaction, quality of work life, life satisfaction, positive affect, and negative affect) positive affect was most strongly, but not exclusively, tied to productivity at both the state and trait levels. Discussion focuses on reconciling a long history of mixed findings regarding the happy-productive worker thesis.

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RESEARCH PAPER
The Happy-Productive Worker Thesis Revisited
John M. Zelenski ÆSteven A. Murphy ÆDavid A. Jenkins
Published online: 28 February 2008
Springer Science+Business Media B.V. 2008
Abstract Despite extensive research on the subject spanning over 70 years, uncertainty
still remains as to whether happier workers are in fact more productive. This study
combined longitudinal prospective and experience sampling methods to examine the
relationship between happiness and self-reported productivity among Directors employed
in the public and private sectors. Analyses at a trait level suggested happy people were
more productive. Similarly, at the state level of analysis, people were more productive
when they were happier. Among the happiness indicators examined (job satisfaction,
quality of work life, life satisfaction, positive affect, and negative affect) positive affect
was most strongly, but not exclusively, tied to productivity at both the state and trait levels.
Discussion focuses on reconciling a long history of mixed findings regarding the happy-
productive worker thesis.
Keywords Productivity Positive affect Negative affect Job satisfaction 
Life satisfaction Quality of work life Happiness Emotions Personality 
Experience sampling
1 Introduction
Since the 1930s there has been a great deal of interest in the relationship between employee
well-being and productivity. Hersey (1932) reported a positive relationship between daily
emotions and performance, whereas Kornhauser and Sharp (1932) reported that worker
J. M. Zelenski (&)
Department of Psychology, Carleton University, 1125 Colonel By Drive,
Ottawa, ON, Canada K1S 5B6
e-mail: john_zelenski@carleton.ca
S. A. Murphy
Sprott School of Business, Carleton University, Ottawa, ON, Canada
D. A. Jenkins
Public Works and Government Services Canada, Ottawa, ON, Canada
123
J Happiness Stud (2008) 9:521–537
DOI 10.1007/s10902-008-9087-4

attitudes (more cognitive assessments of happiness) were altogether unrelated to efficiency.
Knowing whether or not happiness in the workplace promotes productivity has important
implications for management and strategies for workplace improvements. Despite con-
siderable research on the subject, uncertainty remains today as to whether happier workers
are indeed more productive (see Wright and Cropanzano 2004). Researchers have sug-
gested that inconsistent findings linking happiness and productivity may be due to
inconsistent measurement. Most often in studies of happiness and productivity, happiness
has been operationalized as job satisfaction (e.g., Brief and Weiss 2002). However, job
satisfaction may not be an effective proxy for happiness.
The term ‘happiness’ lacks scientific precision, and some researchers refer to subjective
well-being (SWB) instead. SWB is also an inclusive term comprised of multiple, empiri-
cally distinct constructs. For example, emotional experience (often operationalized as the
independent dimensions of positive affect and negative affect) is correlated with, yet
distinct from, more cognitive appraisals of subjective well-being such as life satisfaction
(at a global level) or various domain satisfactions, such as job satisfaction (see Diener
2005; Kim-Prieto et al. 2005). We retain the term happiness for its historical and com-
monsense value in discussing the happy-productive worker literature, and view it as a
broad term akin to SWB. However, it is important to recognize that happiness has meant
different things to different researchers, and more specific statements (i.e., about a par-
ticular indicator) necessarily qualify broader statements about happiness. Our empirical
approach recognizes the many facets of happiness (or SWB) by employing multiple
happiness indicators and comparing them.
There is also growing recognition that research on the happiness-productivity link must
distinguish between state and trait levels of analysis (Cote
´1999; Fisher 2003; Wright et al.
2004). That is, it is different to ask, ‘‘Do fluctuations in happiness covary with (or cause)
short-term differences in productivity?’’ (a state approach), than, to ask, ‘‘Are happy people
more productive over long periods of time?’’ (a trait approach). These two questions may
have different answers, and may suggest different processes linking happiness and pro-
ductivity. In this study we employ longitudinal prospective and experience sampling
approaches with multiple happiness indicators to examine both state and trait associations
between happiness and self-rated productivity among Canadian middle managers (i.e.,
Directors).
1.1 Rationale for the Happy/Productive Worker Hypothesis
Theory Y management suggests that happier people will be more productive, and many
empirical findings are consistent with this idea. For example, Bolger and Schilling (1991)
found that employees who were more prone to negative emotions were more likely to use
contentious interpersonal tactics and thus provoke negative reactions from co-workers.
According to Cropanzano and Wright (2001), less happy employees are more sensitive to
threats, more defensive around co-workers, and more pessimistic. Conversely, happier
employees are sensitive to opportunities, more helpful to co-workers, and more confident.
Truly miserable employees, those who are depressed, are likely to display little energy or
motivation, and, thus, accomplish little.
Fredrickson (1998,2001) suggests that positive emotions function to ‘broaden and
build’ skills and social bonds. For example, individuals in positive mood states are more
cooperative, more helpful, and less aggressive (Isen and Baron 1991), likely improving
productivity in social or collaborative work contexts. In addition, positive emotions may
522 J. M. Zelenski et al.
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lead to better performance in more complex jobs by enhancing creative problem solving
(Estrada et al. 1997; Madjar et al. 2002). Beyond their immediate effects (i.e., on creativity
or social facilitation), positive emotions may also aid in building resources for future
performance. That is, positive emotions likely foster new skill acquisition and the building
of social capital that may be utilized at a later time (Fredrickson 2001). This suggests that
trait measures of happiness (particularly positive emotions) could predict long-term pro-
ductivity, even if happy states were unrelated (or even negatively related) to short-term
productivity.
Although positive emotions likely foster productivity under many conditions, this effect
is probably not ubiquitous. That is, just as pleasant emotions bias cognition and behavior in
some ways (e.g., fostering creativity and sociability), unpleasant emotions bias cognition
and behavior in other ways that may be useful under some circumstances. For example,
negative moods seem to bias people’s attention towards details rather than global meaning
(Gasper and Clore 2002), improving task performance when a detailed level of analysis is
required. Complicating things further, the valence of moods may interact with people’s
motivations or instructions. For example, Martin et al. (1993) showed that positive moods
predicted persistence when people were told to work until they felt like stopping, whereas
negative moods predicted persistence when people were told to work until they could do no
more. Even these interactive effects may further depend on the person’s accountability
(Sanna et al. 1996).
In sum, particular sets of emotions, motivations, personalities, tasks, etc. will combine
in very complex ways to predict performance. However, across the various tasks typically
required of employees, happiness will, on balance, likely benefit overall productivity. In
this study we investigate whether the relationship depends on the particular happiness
indicator or the timeframe (state vs. trait) in question.
1.2 Productivity and Job Satisfaction
Weiss and Cropanzano (1996) referred to the search for a relationship between job sat-
isfaction and job performance as the ‘Holy Grail’ of organizational behavior research, and
the happy-productive worker hypothesis has been extensively studied (e.g., Judge et al.
2001; Ledford 1999; Staw and Barsade 1993). The common theme running through these
studies is the belief that employees who are happier or more satisfied with their job will
also be better performers on those jobs. Despite the emotional flavor of lay conceptions of
‘happiness,’ job satisfaction scales do not typically focus on emotions, instead asking
employees to rate their satisfaction with their pay, working conditions, job as a whole, etc.
(e.g., Brayfield and Rothe 1951; Quinn 1979). Fisher (2000,2003) suggests that this
measurement decision contributes to weak or inconsistent findings. For example, even
among meta-analyses, findings are somewhat inconsistent. In an early meta-analysis,
Vroom (1964) reported a median correlation between job satisfaction and performance of
0.14. More recently Iaffaldano and Muchinsky (1985) reviewed 74 studies and reported a
mean corrected correlation between the two variables of 0.17. In contrast, Petty et al.
(1984) reported a mean correlation of 0.31 from their meta-analysis, only 1 year earlier.
Judge et al. (2001) estimated an underlying correlation between the two variables of about
0.30. Although all these analyses suggest at least some positive relationship between job
satisfaction and productivity, there remains disagreement on its magnitude.
Wright and Cropanzano (2004) argue that the relationship between happiness and
productivity would be stronger if happiness were operationalized more broadly than job
Happy-Productive Workers 523
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satisfaction. They state, ‘‘recent research has consistently demonstrated that high levels
of…well-being can boost performance on the job’’ (p. 341). In a review of research into
the happy/productive worker thesis, Cropanzano and Wright (2001) argue that in such
studies, happiness has been inconsistently operationalized as the presence of positive
affect, the absence of negative affect, lack of emotional exhaustion, and as job satisfaction.
They suggest that although some of these constructs are meaningfully associated with
performance, others may not be as central.
1.3 Productivity and other Measures of Happiness
Recent reviews and theorizing have suggested that affect, especially positive affect, will be
particularly important to productivity (Cote
´1999; Lucas and Diener 2003; Lyubomirsky
et al. 2005), but findings are nonetheless mixed. For example, Staw et al. (1994) offer
compelling evidence for an association between affective experiences at work and pro-
ductivity. The authors employed a longitudinal design and found that reports of affect and
depression predicted both pay and performance evaluations some 18 months later. In
addition, Fisher (2003) found that affect predicted job performance better than job satis-
faction did. However, some studies have failed to find an association between affect and
performance (e.g., Wright and Staw 1999) and others have disagreed over whether positive
affect (e.g., Staw and Barsade 1993) or negative affect (e.g., Wright et al. 2004)isa
stronger predictor of performance.
In addition, some studies suggest that state, rather than dispositional, measures of affect
are more strongly related to performance. Wright and Cropanzano (1998) and Wright and
Staw (1999), utilizing dispositional measures of affect, found no association between either
positive or negative affect and performance. George (1991) reported that recent experience
of positive affect at work (i.e., state affect) predicted supervisor ratings of customer ser-
vice, whereas trait affect failed to predict customer service (a proxy measure of
productivity). Similarly, Fisher (2003) found that productivity was more strongly related to
some happiness indicators at the state, rather than trait, level of analysis, but that trait
positive affect was still a good predictor.
Emotional exhaustion, the converse of vitality and a component of burnout, is another
(un)happiness indicator one would expect to negatively relate to performance; however,
even here findings are unclear. Both Wright and Cropanzano (1998) and Wright and
Bonnet (1997a) did find that emotional exhaustion was negatively related to performance,
but Wright and Bonnet (1997b) failed to find an association between the two.
1.4 The Present Study
Although the bulk of research suggests some association between happiness and produc-
tivity, the details of this relationship remain unclear. Central issues include the many ways
happiness has been operationalized, and whether state and/or trait happiness predicts
productivity. To address these questions we conducted a study utilizing multiple happiness
indicators, assessed as both states and traits.
We selected happiness measures that would cover both cognitive and emotional
approaches to assessment, with an eye towards measures commonly used in past research.
Job satisfaction (Quinn 1979) was a clear choice as the majority of research on the happy-
productive worker thesis has used it. Job satisfaction is a primarily cognitive approach (i.e.,
based on judgements of conditions) to domain specific items (i.e., satisfaction with pay,
524 J. M. Zelenski et al.
123

support, opportunities, etc.). Our second measure, life satisfaction (Diener et al. 1985), is
also a cognitive assessment, but much more general in that it encompasses all aspects of
life, and does not assess specific domains. That is, it is assumed that respondents consider
domains and weights idiosyncratically to make judgments about the conditions of their
lives in general. Therefore, life satisfaction provides the broadest test of the happy-pro-
ductive worker hypothesis as the happiness may be completely unrelated to work. Drawing
on the logic behind the life satisfaction measure (i.e., asking generally to allow for indi-
vidual variation in what comprises important aspects and relative weights), we also
included our own single-item measure of work-life quality (i.e., ‘‘Overall how would you
rate the quality of your work life?’’).
Although modestly correlated with cognitive assessments of happiness, measures of
affect are conceptually and empirically distinct (Lucas et al. 1996). We assessed trait
positive and negative affect in two similar ways. The commonly used PANAS question-
naire (Watson et al. 1988) includes two dimensions of activated (high arousal) pleasant and
unpleasant emotions termed positive affect and negative affect, and was used to assess trait
emotion. In the experience sampling portion of our study, we asked participants to rate a
similar (though not identical) set of emotion adjectives. In sum, the present study examines
which of these happiness components, if any, are associated with productivity. Addition-
ally, if multiple happiness indicators predict productivity, do they do so independently, or
because they share common features?
Most definitions of productivity draw on a manufacturing model of inputs and outputs
(Cleveland 1999; Vora 1992), but inputs and outputs are extremely difficult to measure in
organizational research on individuals, particularly when objective indicators (e.g., units
produced) are not applicable or accrue only after long periods of time (e.g., publications).
In the field of organizational behavior, work performance has been a variable of great
interest as the goals and objectives of organizations are measured in terms of performance
outputs. Performance includes all of the actions that are relevant to the achievement of
organizational goals and can be measured in terms of each individual’s productivity
(Campbell et al. 1993). In other words, individual productivity is the degree to which an
employee executes his or her role with reference to certain specified standards set by the
organization.
Unsurprisingly, organization researchers have acknowledged some measurement diffi-
culty with productivity (Dobni 2004; Hodgkinson 1999). Yet despite the dearth of clear,
objective productivity indicators, individuals have a good sense of their own productivity,
and may be in the best position to report on it. Self-report measures of productivity have
been utilized in management research for decades (Landy and Farr 1983). Landy and Farr
suggested that self-report measures can be an effective way of tapping into employee
perceptions. Self-reported productivity can be in the form of a general question (as was the
case in our study), or broken into various facets of the work environment. In order to
effectively tap into the latter, researchers would require a fairly intimate understanding of
the job-specific characteristics of respondents, along with a fairly homogenous group of
respondents for that particular measure. Self-reported productivity raises questions about
biased responding, but has provided results similar to other indicators (Butler et al. 2007).
This generally suggests its validity, but self-reported productivity is most reliable when
recall bias is minimized. For instance, Stewart et al. (2001) showed that a 4-week recall
period was associated with significant recall bias compared with a 1-week period. (Our
study uses a 3-day period.)
Still, some researchers have tried to examine more objective measures of productivity
(e.g., Burton et al. 1999) that employ computer-based tracking systems to monitor and
Happy-Productive Workers 525
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measure employee productivity. These systems track productivity, typically independent of
the subject, and thereby preventing a biased response. It should be noted that objective
measures of productivity are not without their own inherent difficulties. Most modern,
dynamic workplaces are not amenable to simplistic output measures of quantitative per-
formance as the sole indicator of how well a person is performing their job (e.g., how does
one objectively assess the performance of a manager that has to try to calm a volatile
situation involving his/her direct reports?). Second, work productivity includes both quality
and quantity and the quality aspect has proven difficult to quantify in many tasks. Third,
there is the issue of whether the objectives measures should be absolute (e.g., calls taken at
a call center) or comparative (calls taken relative to the shift average that day). In weighing
the pro’s and con’s of subjective and objective measures, Prasad et al. (2004, p. 241)
concluded that ‘‘There is no way of indicating a-priori which is a better index of pro-
ductivity for a given job.’’ We have followed other productivity researchers (e.g., Eaton
2003; Fisher 2003; Loeppke et al. 2003) in using the self-report method in this study.
To examine both state and trait happiness as predictors of productivity, we used an
experience sampling method (ESM) combined with a prospective baseline assessment of
dispositions. That is, happiness indicators were first assessed with standard trait instruc-
tions, providing a set of prospective trait indicators. Over the next 8 weeks, participants
rated their happiness and productivity bi-weekly. This allows us to calculate both average
levels (i.e., between-subjects or trait level of analysis) as well as individual variation over
time (i.e., within-subject or state level of analysis). ESM makes it possible to examine both
the question: ‘‘Do people who tend to be happier also tend to be more productive?’’
(between-subjects), as well as the question: ‘‘Are people more productive on days when
they are happier?’’ (within-subject). Finally, combining the baseline measures of happiness
with ESM data allow us to predict average productivity prospectively over 8 weeks.
The literature on the happy-productive worker thesis is rife with inconsistent findings,
but overall, it suggests a general positive relationship between the two constructs. As a
result, we predict that most happiness indicators will correlate with self-reports of pro-
ductivity at all three levels of analysis (prospectively, between-subjects, and within-
subject). However, recent findings and theorizing suggest a few more refined hypotheses:
1. Positive affect has the strongest theoretical links with performance (see Lyubomirsky
et al. 2005), and captures the emotional flavor of happiness better than satisfaction
measures (Fisher 2000). Therefore, we predict that positive affect will be the best
predictor of productivity. Because positive affect can have both short-term and long-
term benefits (Fredrickson 1998), we predict that it will be the best predictor at both
the state and trait levels of analysis.
2. Because job satisfaction measures dictate aspects of work life that may or may not
actually be important to individual employees, we predict that our single item QWL
measure (i.e., that allows participants to consider work life quality as they wish) will
predict productivity better than more standard measures of job satisfaction (at both
state and trait levels).
3. Day-to-day variations in life satisfaction will encompass much more than changes in
work life. As a result, we expect that life satisfaction will only predict productivity at
the trait level (i.e., prospectively and between-subjects, not within-subject). In other
words, we predict that generally happy people (assessed with SWLS) will be more
productive, but global satisfaction will not covary with short-term productivity.
526 J. M. Zelenski et al.
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2 Method
2.1 Participants and Procedure
Participants were 75 Directors employed in the private sector and the Canadian federal
government drawn from data involving a larger study of 143 (response rate of 57% from
the larger study). Given that the sample consisted exclusively of Directors, participants had
similar levels of authority and numbers of subordinates. That is, participants typically
reported to a junior VP (or public sector equivalent), and had both budgetary and human
resources management responsibilities with an average of nine direct subordinates. Par-
ticipants had a mean age of 43.51 (SD =8.07) and 48 (64%) were male. Fifty-five (73%)
were married or in common law relationships and 38 (49%) had children. Participants who
completed this study (n=75) were compared to non-completers in the full sample
(n=68) on baseline measures (trait happiness indicators) and demographic variables, and
only one difference approached statistical significance; completers were marginally
significantly older (p=.09; all other p’s [.3).
In order to evaluate the relationship between various facets of happiness and
productivity, an experience-sampling methodology (ESM) was employed. Participants
were instructed to complete a short self-report questionnaire online every Monday and
Thursday for 8 weeks and to consider only the three previous days in formulating
their responses. Each questionnaire assessed emotional experience at work, QWL, job
satisfaction, life satisfaction, and productivity. Prior to the experience sampling phase
of the study, all participants completed a battery of baseline measures online,
including dispositional measures of emotional experience, job satisfaction, and life
satisfaction.
Analyses involved only participants who completed the ESM questionnaire at least
five times (n=75) in order to preserve the integrity of the experience-sampling
method. These participants provided an average of 10.19 (SD =3.63), and a total of
715 reports. Scores for missing reports were not imputed. Between-subjects analyses
were performed on data after calculating a mean score per variable, per participant
from the reports provided over the 8 weeks. Aggregating over 2 months provides scores
akin to a dispositional assessment (Epstein 1983). Within-subject analyses were con-
ducted by examining relationships between day-to-day reports of happiness and
productivity. More specifically, each rating was converted to a z-score where the
‘population’ of scores includes only other occasions where the same participant made
the same rating. As a result, these z-scores control for mean-level individual differ-
ences, and reflect variation due to differences in time/situations (Scollon et al. 2003;
Zelenski and Larsen 2000). In other words, within-subject correlations reflect covari-
ation in 3-day timeframes independent of dispositional contributions.
2.2 Measures
In the ESM phase, emotional experience at work was assessed by asking participants to
indicate the extent to which they experienced six positive emotions (elated, enthusiastic,
excited, happy, joyous, and lively) and nine negative emotions (angry, annoyed, anx-
ious, disgusted, distressed, embarrassed, jittery, nervous, and sad). A six-point scale
ranging from none to extremely was provided. Scores for the positive and negative
Happy-Productive Workers 527
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emotions were averaged to obtain a single figure for positive affect and one for neg-
ative affect.
1
The baseline survey assessed average (dispositional) emotional experience with the
PANAS (Watson et al. 1988). The PANAS measures the two dimensions of positive affect
and negative affect by asking participants to rate the extent to which they experience 20
emotions on average using a five-point scale ranging from not at all to extremely.
Quality of Work Life (QWL) was assessed by a single item that read: ‘‘Overall how
would you rate the quality of your work life?’’ accompanied by a five-point scale ranging
from poor to excellent (ESM phase only).
Job satisfaction was assessed by the Job Satisfaction Scale (Quinn 1979). Using a five-
point scale ranging from very dissatisfied to very satisfied, participants were asked to
indicate their satisfaction with nine items including ‘‘Your job in general’’ and ‘‘The tasks
you perform on the job’’. Job satisfaction was assessed in both the baseline and ESM
phases (with instructions altered to include only a 3-day time frame in the ESM phase).
Life satisfaction was assessed via the Satisfaction With Life Scale (SWLS, Diener et al.
1985) that presents participants with five statements including ‘‘The conditions of my life
are excellent’’ asking them to indicate, using a six-point scale, their agreement with each
item. Life satisfaction was assessed in both the baseline and ESM phases (with instructions
altered to include only a 3-day time frame in the ESM phase).
Productivity at work was assessed by asking ‘‘How productive were you in your work
role?’’ A five-point scale ranging from not very productive to exceptionally productive,
with a mid-point of average productivity, was provided (ESM phase only).
3 Results
In the ESM phase, participants reported being somewhat satisfied with their jobs and their
lives as a whole. Typically participants rated their quality of work-life as ‘good’. Partic-
ipants reported experiencing, on average, a very low level of negative affect at work and a
somewhat low level of positive emotion, although the variance was considerable (see
Table 1). As one might expect, participants reported average levels of productivity.
Table 1 Means, variance and internal consistency
Construct Mean Standard deviation Internal consistency
Positive affect 2.03 0.88 0.95
Negative affect 0.76 0.49 0.90
Job satisfaction 3.39 0.63 0.88
Quality of work-life 3.63 0.62 N/A
Life satisfaction 3.46 0.77 0.94
Productivity 3.32 0.45 N/A
Note: Descriptive statistics have been adjusted to a five-point scale
1
We included the terms ‘happy’ and ‘sad’ in these scales because they increase the face validity of
assessing the happy-productive worker thesis. This choice is not entirely consistent with strict definitions of
positive affect and negative affect, e.g., as operationalized in the PANAS. Because of this discrepancy, we
also calculated emotion scales that omitted ‘happy’ and ‘sad’. These scales correlate .99 to .97 (across PA
and NA, between- and within-subject) with their equivalents reported here, and the variation in scoring has
virtually no impact on further analyses (correlations or regressions).
528 J. M. Zelenski et al.
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Between-subjects correlations among the various constructs appear in Table 2, within-
subject correlations in Table 3. Not all correlations were statistically significant, but all
were positive, except those involving negative affect, as expected. Finally, it is worth
noting that although most of the happiness indicators were significantly intercorrelated, the
relationships were not so strong so as to suggest complete redundancy (r’s from -.05 to
.78).
Roughly consistent with predictions, between-subjects positive affect and QWL dem-
onstrated moderate correlations with productivity (r=.35, .32, respectively, p\.01),
whereas job satisfaction and life satisfaction demonstrated slightly weaker but statistically
significant correlations with productivity (r=.22, .24, respectively, p\.05). Contrary to
expectations, negative affect was not significantly associated with productivity. In short,
across most indicators, happy people tended to be more productive than unhappy people.
Within-subject positive affect and QWL demonstrated moderate correlations with
productivity (r=.36, .35, respectively, p\.01), whereas job satisfaction demonstrated a
weaker but statistically significant correlation with productivity (r=.19, p\.01). Daily
variations in life satisfaction and negative affect were not significantly associated with
productivity (Table 3). Therefore, productivity covaried with happiness on a day-to-day
basis across many of the happiness indicators. That is, people were more productive on
days when they were happier compared to days when they were less happy.
Although the various happiness indicators are conceptually distinct, they were also
intercorrelated with one another. To determine whether or not different components of
happiness independently predict productivity, we conducted two linear regression analyses
where happiness indicators (i.e., 2-month average scores for between-subjects and z-scores
for within-subject equations) were entered simultaneously to predict productivity
Table 2 Correlation matrix of happiness and productivity (between-subjects)
Variables 1 2 3 4 5 6
1 Positive affect 1
2 Negative affect -.04 1
3 Job satisfaction .27* -.13 1
4 Quality of work-life .38** -.18 .78** 1
5 Life satisfaction .42** -.19 .51** .51** 1
6 Productivity .36** -.05 .22* .32** .25* 1
* Significant at p\.05
** Significant at p\.01
Table 3 Correlation matrix of happiness and productivity (within-subject)
Variables 1 2 3 4 5 6
1 Positive affect 1
2 Negative affect .07 1
3 Job satisfaction .25* -.16* 1
4 Quality of work-life .25* -.30* .38* 1
5 Life satisfaction .18* -.20* .34* .23* 1
6 Productivity .36* -.04 .19* .35* .07 1
* Significant at p\.01
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(between-subjects or within-subject scores as above). Table 4reports the results of the
between-subjects regression. Together, the happiness indicators explained 17% of the
variance in productivity, but positive affect was the only significant predictor in this
equation (b=.27). Although not statistically significant, QWL’s predictive power was of
similar magnitude (b=.24). Nonetheless, although most bivariate correlations were sig-
nificant, happiness indicators were largely redundant at the between-subjects level, with
positive affect capturing the majority of the variance.
Table 5reports the results of the within-subject regression. Together, the happiness
indicators explained 23% of the variance in productivity. Both positive affect and QWL
independently predicted productivity (b=.32, .29 respectively), but other indicators were
not statistically significant. Although the bivariate correlation between job satisfaction and
productivity was significant, job satisfaction did not account for any significant increase in
the variance explained by positive affect and QWL at the within-subject level. Similar to
the between-subjects regression, positive affect was the best predictor of productivity.
The between-subjects and within-subject analyses focus on different time frames
(trait vs. state), but both sets of analyses are based on measures of happiness and
productivity collected concurrently. To see if happiness could predict productivity
prospectively, we correlated baseline assessments of happiness with average produc-
tivity assessed during the following 2 months (i.e., during the ESM phase). These
correlations are reported in Table 6. Positive affect and life satisfaction were both
significant predictors of productivity (r’s =.33 and .27, respectively). Although job
satisfaction and negative affect were not significantly related to productivity, the cor-
relations were in the predicted directions.
Finally, to examine the relative predictive power of baseline measures, we ran a linear
regression where all happiness indicators were entered simultaneously and used to predict
average productivity (i.e., the same productivity score as the between-subjects analyses,
see Table 7). Together, the happiness indicators explained 15% of the variance in
Table 4 Regression equation
predicting productivity
(between-subjects)
Predictor Beta TpRR
2
Positive affect .27 2.16 .03
Negative affect .00 .01 .99
Job satisfaction -.05 -.30 .77
QWL .24 1.27 .21
Life satisfaction .04 .26 .79
.41 .17
Table 5 Regression equation
predicting productivity
(within-subject)
Predictor Beta TpRR
2
Positive affect .32 7.59 .01
Negative affect .04 .85 .40
Job satisfaction .02 .32 .75
QWL .29 6.67 .01
Life satisfaction -.02 -.53 .59
.48 .23
530 J. M. Zelenski et al.
123

productivity. Similar to the between-subjects and within-subject analyses, positive affect
again emerged as the best (and only significant) predictor of productivity.
In sum, happier people were indeed more productive, and this was especially true when
happiness was conceptualized as the frequent experience of positive emotions.
4 Discussion
This investigation revisited the happy-productive worker thesis with special attention to
three factors. The state-trait distinction, alternative conceptualizations of happiness, and
causal direction were explored using an experience sampling method, multiple happiness
indicators, and a prospective design. In its general form, the happy-productive thesis
received support. However, the extent of that support depended on what is meant by
‘happiness’. For example, positive affect showed a robust relationship with productivity,
but negative affect showed no significant relationship. In addition, productivity was related
to happiness across methodological contexts. That is, happiness (especially positive affect)
was associated with productivity at both the state and trait levels of analysis. Moreover,
trait measures of happiness collected before the experience sampling phase predicted
average productivity, suggesting that happiness may cause higher productivity (though our
results do not rule out a reciprocal relationship or a third variable explanation).
Based on recent speculation and research, we hypothesized that positive affect would be
an especially important contributor to productivity (Lucas and Diener 2003; Lyubomirsky
et al. 2005). Our findings clearly support this idea, as have those of Staw and Barsade
(1993), Harter et al. (2003), and Fisher (2003). Notably, the relationship between positive
affect and productivity was consistent across three methodological contexts (prospective,
between-subjects, and within-subject) suggesting that people who typically experience
more positive affect are more productive, and that people have their greatest productivity
Table 6 Correlation matrix of productivity and happiness (baseline)
Variables 1 2 3 4 5
1 Positive affect 1
2 Negative affect -.35** 1
3 Job satisfaction -.09 .10** 1
4 Life satisfaction .52** -.38** .06 1
5 Productivity .33** -.19 .14 .27* 1
*p\.05
** p\.01
Table 7 Regression equation
predicting productivity
(baseline)
Predictor Beta TpRR
2
Positive affect .28 2.08 .04
Negative affect -.07 -.58 .56
Job satisfaction .17 1.51 .14
Life satisfaction .09 .65 .52
.39 .15
Happy-Productive Workers 531
123

while experiencing positive moods. Our multi-method approach allows us to argue against
some alternative explanations. For example, it would be possible for positive affect to
facilitate later productivity even if it hindered current output. That is, when in positive
moods, people may socialize more or take more creative or risky approaches to tasks
diminishing immediate payoff, but facilitating long-term performance. Although a rea-
sonable hypothesis, our data suggest that the payoff of positive affect comes quickly as
productivity covaried with positive affect at the within-subject level. In other words,
positive affect seemed to facilitate fairly immediate productivity.
Because we used a 3-day window for ESM reports, it is possible that productivity and
positive affect were not experienced simultaneously, but instead, at different times during
the 3-day periods. Although possible, an alternative hypothesis would have to account for
the reliable within-subject covariation. Therefore, the most reasonable alternative is the
reverse causal hypothesis. That is, rather than positive affect causing productivity, high
productivity may cause positive affect. It is difficult for our data to rule out this explanation
at the state level of analysis, but future research could help resolve the issue with exper-
imental mood manipulations (at the risk of low generalizabiltiy) or with more frequent
experience sampling (e.g., hourly) and lagged correlations to investigate whether positive
affect precedes productivity or vice versa. Even without this data, a reciprocal relationship
seems most plausible (c.f., Cote
´1999). Unless the work holds no value for a person,
productivity will likely elevate mood. In addition, the experimentally demonstrated effects
of positive mood states, such as creativity, sociability, and coping resources (Fredrickson
1998), are very plausibly linked with increased productivity. However, this may depend on
the particular demands of the job (Lucas and Diener 2003).
Although the causal direction remains uncertain at the state level of analysis, our data
suggest that trait happiness may indeed cause increased productivity when considering the
baseline measures. That is, the causal claim is strengthened by the fact that happiness
indicators (positive affect and life satisfaction) predicted workers’ productivity over the
following 2 months. It is possible that a third variable (e.g., past productivity) caused trait
happiness to increase to high levels and also facilitated future productivity, but this seems
unlikely without better evidence for productivity (or another plausible third variable)
having long-term influences on trait happiness. Given the relative stability of trait happi-
ness indicators, it is unsurprising that other research has shown happiness predicting
productivity further into the future (Staw et al. 1994), and had we measured productivity
longer, we may have observed a continued relationship.
Positive affect consistently predicted productivity, but this was not true of all happiness
indicators. For example, contrary to the findings of Wright et al. (2004) and Cote
´(1999),
negative affect was unrelated to productivity across all three methodological approaches
(i.e., between-subjects, within-subject, and prospectively). Our results are more consistent
with the findings of Wright and Cropanzano (1998), Wright and Staw (1999), and Fisher
(2003). When interpreting this null result, it is important to keep in mind that our par-
ticipants reported experiencing very low levels of negative affect, which precludes the
conclusion that high levels of negative affect do not interfere with productivity. Presum-
ably, some employees can experience low levels of anxiety, nervousness, etc. without it
impacting their ability to perform. However, strong negative emotions, particularly when
manifest as stress-related disorders such as depression, may be more likely to cause low
productivity.
We observed bivariate correlations between job-satisfaction and productivity roughly
equivalent to meta-analytic estimates drawn from a large literature, that is, in range of
0.14–0.30 (Vroom 1964; Judge et al. 2001). Although job satisfaction was a significant
532 J. M. Zelenski et al.
123

predictor at both between-subjects and within-subject levels of analysis, it was not among
the strongest predictors, and job satisfaction did not predict productivity significantly when
other happiness indicators were taken into account.
It is interesting to contrast the job-satisfaction results with those of our other cognitive
judgment of work-related happiness, the single-item QWL assessment. We hypothesized
that the QWL measure would predict productivity better than job satisfaction, and results
supported this prediction. That is, the QWL bivariate correlations with productivity were
stronger than those of job-satisfaction, and QWL was a better predictor than job-satis-
faction when considered simultaneously in regression analyses at both between-subjects
and within-subject levels. (Unfortunately, we did not include a prospective measure of
QWL.) Although the job-satisfaction measure was longer (and thus presumably more
reliable), the QWL measure may do a better job of capturing the aspects of work-life that
are most important to people. That is, the QWL measure allows individuals to consider
anything they like in arriving at their estimates of quality. In contrast, the job satisfaction
measure prescribed the domains under consideration (e.g., pay and opportunities for
advancement). It is possible that individuals’ QWL judgments included evaluations of their
emotional experience (c.f., positive affect was the best predictor of happiness). However,
this cannot fully account for QWL’s better prediction because QWL remained a significant
predictor even when controlling for affect. Therefore, it seems likely that the job satis-
faction measure misses aspects of work life potentially important to productivity.
We expected that happiness indicators specific to work-life would be related to changes
in productivity, but a variable as broad as life satisfaction, encompassing so many other
areas of life, might not be significantly associated with short-term variations in produc-
tivity. Results were consistent with this hypothesis. Life satisfaction did predict average
levels of productivity between-subjects and prospectively, consistent with the idea that
happy people are more productive people. At the same time, day-to-day variation in life
satisfaction was not related to changes in productivity as evidenced by a lack of correlation
within-subject. Instead more proximal changes in work-related happiness indicators
(emotions, job satisfaction, QWL) predicted productivity, indicating some degree of
domain specificity when considering state variations. Finally, life satisfaction was not an
independent predictor of productivity in regression analyses, suggesting that the positive
affect that contributes to life satisfaction judgments explains the higher productivity they
predict.
Taken together, our results suggest that happiness, particularly positive affect, may
contribute to high productivity. Such a conclusion begs the question, should organizations
invest in increasing the happiness of their employees? The trait-level findings suggest that
happy people are more productive people. To the extent that (un)happiness is a disposition
that resists change, organizational efforts to increase happiness may be unlikely to payoff
in productivity gains. However, we also found that short-term variations in happiness
predicted short-term variations in productivity. That is, even after statistically removing
stable individual differences in happiness, people were more productive when they were in
good moods. Therefore, organizations do stand to benefit by creating work environments
that promote better moods, even for dispositionally unhappy members. (It may also be
possible to increase happiness long-term with fairly minimal interventions, see Seligman
et al. 2005). Our results also suggest that such efforts be directed at increasing the expe-
rience of positive emotions, as they were consistently the strongest predictors of
productivity. For example, a pay increase could increase job satisfaction (pay is typically
an item on job satisfaction assessments), but if this pay increase does not also lead to
positive affect, the impact on productivity may be minimal.
Happy-Productive Workers 533
123

A limitation of the present study is that productivity was self-rated with a single item.
Others have discussed the advantages of single item measures in organizational research,
particularly when constructs are difficult to define objectively but are well understood by
informants (e.g., Eaton 2003; Nagy 2002; Rossiter and Bergkvist 2007). For example,
adding near synonyms may increase reliability from a classic psychometric perspective,
but additional items may actually dilute content validity. Indeed, the presence of statisti-
cally significant predicted relationships lends some support to the measure’s reliability and
validity.
The self-report format raises the possibility that happiness and self-reported produc-
tivity co-varied because of a social desireablity effect with some participants rating all
aspects of their lives favorably regardless of actual experience. However, this seems
unlikely for two reasons. First, it would be difficult for such an effect to explain the within-
subject (day-to-day) covariation between positive affect and productivity. In addition, we
found that the associations between happiness and productivity depended on the particular
happiness indicator. That is, social desireablity would have created significant correlations
between productivity and all measures of happiness, but we did not observe this. Although
a 360-degree assessment would have been ideal, we believe that self-rated productivity is
among the best single indicators for our sample. Because our participants were all
Directors, it would be difficult to obtain a purely objective measure (e.g., number of
widgets produced). Supervisor or peer ratings of productivity could provide useful infor-
mation, but such ratings are susceptible to halo effects where likeable people are rated as
more productive. Moreover, individuals are likely in the best position to report on their
own short-term variations in productivity, as assessed by our experience sampling method
(see Fisher 2003). Mastering an important skill or developing a powerful strategy might
indicate exceptional productivity, but be completely unobservable on the day it occurs.
Finally, we assessed productivity in 3-day windows and created actual averages for trait
level analyses (i.e., rather than mental averages typical of non-ESM questionnaires).
Robinson and Clore (2002) have demonstrated that short-term reporting windows solicit
episodic knowledge (i.e., actual occurrences) rather than general beliefs (semantic
knowledge) less tied to recent objective (or even ‘subjective’) reality. As a result, the ESM
method may further contribute to the validity of our productivity measure because par-
ticipants are able to accurately assess their productivity over short periods of time.
In sum, there are good reasons to think that a single item self-report measure of pro-
ductivity is valid. Nonetheless, our results and conclusions must be interpreted with the
caveat that this particular measure has not been validated against other indicators of
productivity, and future studies would benefit from using additional productivity
indicators.
Although our findings were quite clear and consistent across methodological contexts,
this stands in contrast to a literature full of mixed findings. Cropanzano and Wright (2001)
have highlighted the fact that some of this inconsistency could be due to differences in the
happiness indicators employed by different researchers, and Fisher (2003) suggests that
within-subject variation may be stronger than the more typically studied between-subjects
variation. We addressed these issues by including five indicators that assessed multiple
conceptualizations of happiness at both state and trait levels of analysis. Most of these
indicators were associated with happiness at both levels, but the strength of the relation-
ships did differ by indicator. Therefore, it is possible that some inconsistency in the
literature is due to different assessment tools (combined with low power in the case of null
results). However, this explanation is also clearly limited. As one of many possible
examples of direct contradiction, we found that positive affect consistently predicted
534 J. M. Zelenski et al.
123

productivity, but that negative affect was consistently unrelated to productivity. This
pattern is at odds with a recent study showing the exact opposite pattern, (i.e., negative
affect, but not positive affect, predicting productivity, Wright et al. 2004). Moreover,
where Fisher (2003) found stronger within-subject relationships with most happiness
indicators, we did not see much difference across levels of analysis. Therefore, future
research will need to examine additional potential moderators of the happiness-produc-
tivity link (e.g., type of work, see Lucas and Diener 2003 for informed speculation on job
characteristics as moderators). It follows that our findings must be taken with a strong
caveat; we assessed Canadian Directors, and caution must be exercised before applying
these results to other populations and occupations.
5 Summary and Conclusion
This investigation included a number of unique strengths, specifically multiple measures of
happiness, repeated measures (experience sampling), and prospective happiness measures
allowing for analyses at both state and trait levels of analysis. These strengths allowed us to
address a number of important distinctions emerging in work on the happy-productive
worker thesis. Our results suggest that happiness may indeed foster productivity. Findings
were consistent at both the trait level of analysis (happy people are productive people) and
at the state level of analysis (people are more productive when in happy moods). However,
the extent of support for the happy-productive worker thesis may depend on what is meant
by ‘happiness’. Of the many ways to conceptualize happiness, positive affect was con-
sistently most strongly linked with productivity. Although there are strong theoretical
reasons and an emerging literature suggesting positive affectivity’s importance in pro-
ductivity (Lyubomirsky et al. 2005), the relationship is unlikely to be ubiquitous across
occupations and tasks.
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