Psychology of Music
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The effect of music listening on work performance
Teresa Lesiuk
Psychology of Music 2005 33: 173
DOI: 10.1177/0305735605050650
The online version of this article can be found at:
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173

A RT I C L E

The effect of music listening on
work performance

Psychology of Music
Psychology of Music
Copyright © Society for Education, Musicand Psychology Researchvol (): ‒ [-() :; ‒]
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TERESA LESIUKU N I V E R S I T Y O F W I N D S O R , C A N A DAThis study measured the effect of music listening on state positive
affect, work quality and time-on-task of computer information systems
developers. Effects of music on work performance, in this case, software design,
may be explained by increases in state positive affect. Data from 56 (male = 41,
female = 15) developers were obtained from four different Canadian software
companies. Data were collected in the participants’ actual work environments
over five weeks. Results indicated that state positive affect and quality-of-workwere lowest with no music, while time-on-task was longest when music wasremoved. Narrative responses revealed the value of music listening for positivemood change and enhanced perception on design while working. Evidence isprovided of the presence of a learning curve in the use of music for positive moodalteration. Overall, the study contributes to the development of a model that
aspires to elucidate music and workplace interactions; as well, it has implications
for organizational practice.

A B S T R AC T

K E Y W O R D S : affect, computer software design, creativity, mood, music psychology,

productivity, work stress

Productivity, or ‘quality-of-work,’ in computer information systems design
may be measured by creativity of the software design. Creativity, the ability to
produce work that is both novel and appropriate (Sternberg and Lubart,1999), is adversely affected by stress and certain moods. Mild positive feelings
have been found to influence positively the way cognitive material is organized, thus influencing creativity. A beneficial effect of music on task performance may be explained by increases in state positive affect (Isen, 1999;
Schellenberg, 2001; Thompson et al., 2001). When music evokes a pleasantmood and an increased arousal state, participants perform better on nonmusical tasks. Thus, there is support for an increase in creative problem

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Psychology of Music 33(2)solving and task performance by workers, in this case, computer systems
developers.

Computer software developers
The designing of computer systems is part of a larger industry known as
Information Technology (IT), an industry that is competitive, continuallychanging, and one that constantly requires creative approaches in software
design. The trend to ‘downsize’ or ‘rightsize’ is reported to lead to stress forcomputer developers and detrimental consequences for their design work
(Glass, 1997; Wastell and Newman, 1993). Fujigaki (1993) found stress present
throughout almost all software work phases of systems development, eventaking on different forms in two different phases. During the requirementsdefinition and design stage (involving planning and designing a softwaresystem) stress responses were in the form of high anxiety and depression,
while during the coding phase, the stress took on the form of irritability andfalling morale. Further, Fujigaki maintains that designing of systems isparticularly vulnerable to stress-caused errors. He found that 42 percent of
all design faults were directly attributable to programmer stress explainingthat ‘deep thinking, such as searching for solutions in a huge problem space... is required in the design phase ... and is easily affected by stress, whichcauses imperfect investigation’ (p. 34).

Music and affect
Past music and affect theories underlie the basic affective responses of individuals who partake in music listening. Present-day theories build on the pasttheories, and eventually help construct a fuller picture of music listening
within a work context. Fiske (1996) eloquently dismantles the common
notion that ‘music is a language of the emotions.’ Shared cultural knowledgeof emotions suggests that music communicates knowledge across groups oflisteners, but cultural knowledge does not take account of an individual’s
personal experiences. In his critique of several pertinent music and affecttheories (i.e., Kivy, 1990; Langer, 1967; Meyer, 1956), Fiske concludes
that mood states following music listening are the result of an individual’s
unique past experiences. He clarifies the difference between aesthetic
response and listener mood state responses to music. That is, mood statesoccur as a result of individuals projecting their many past experiences andbeliefs (with the inherent emotional content) onto their experience of thetonal–rhythmic events presented in music. Conversely, an aesthetic responseis the outcome of the listener’s relative understanding of tonal–rhythmic
pattern interrelationships.
A number of studies suggest that music experiences produce felt peakexperiences (Goldstein, 1980; Panksepp, 1995). Sloboda (1991) examined

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Lesiuk: The effect of music listening on work performance‘peak’ experiences to music listening, including measurable emotional
responses such as thrills, shivers, laughter, lump in the throat, and tears.
Reported peak experiences that coincided with specific musical structures
were perceived by the researcher as metaphors. For example, the physicalresponse of tears evoked by endings in the music may be understood as a typeof metaphor of either loss or relief. These types of metaphors provide acathartic outlet, allowing listeners to impose their own ‘emotion-stories’ onmusic experiences, and are psychologically beneficial for motivation and selfimage. Of importance to the present music and work study, Sloboda reported
that repeated listening to a piece of music did not diminish strong emotional
responses.
Music may also serve as an anxiolytic treatment, that is, an anxiety preventative or anxiety reducing measure. Knight and Rickard (2001) exploredthe effect of sedative music on participants’ subjective and physiologicalstress levels following a cognitive stressor involving preparation of an oralpresentation. Significant increases in physiological stress were reported for
those who prepared the task without music, while the presence of music
suppressed significant increases in subjective anxiety, systolic blood pressure,and heart rate.
Further, individuals value their own or ‘preferred music’ when considering the music that best relaxes them. Following a comparison of five different
types of music Stratton and Zalanowski (1984) reported a significant correlation between degree of relaxation and preference for music. Davis andThaut (1989) reinforced the importance of preference in a study measuringphysiological and psychological subjective responses to preferred, relaxing
music. Varying types of music chosen by the subjects were effective in reducing anxiety. The researchers concluded that preference, familiarity or
past experiences with the music may have an overriding effect on positive
behaviour change than the type of music. Additionally, significant withinsubject results were reported for the physiological measures, and theresearchers concluded that idiosyncratic responses occur in a habitualmanner to music preference.
Of relevance to the computer software development community, Lesiuk(2000) reported decreased levels of state anxiety when music was used prior
to and throughout a computer programming task. Seventy-two students in
an introductory computer programming course were assigned to a groupwith either (1) no music, (2) music prior to, or (3) music prior to and during atask requiring students to locate syntax in logic errors. Results indicated astatistically significant effect of the music listening on state anxiety levels.
Most importantly, the group with music prior to and during the task experienced the least amount of anxiety. While there was no statistically significant
difference between groups for the programming task, the group with themost music achieved the highest mean scores in both tasks.
Everyday emotional responses to music are not without a social context

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Psychology of Music 33(2)(DeNora, 2001; Hargreaves and North, 1999). Sloboda and O’Neill (2001)
investigated everyday emotional responses to music by employing a method
known as the experience sampling method (ESM) in which ‘real-world’
events are studied as they unfold in everyday life. Participants carried pagers
during their waking hours and were paged randomly once within every twohours daily for a period of one week. When paged, participants recorded theircurrent activity in a response booklet with the purpose of capturing everyday
emotional experiences associated with music. The study reports that 44
percent of all pagings (or ‘episodes’) involved music. An analysis of recorded
emotional responses revealed that music experiences increased positivity,
alertness, and focus on the present. The researchers concluded that, in general,music made the participants ‘feel better.’Music and work studies
A number of earlier studies has shown the beneficial effects of music listening on work productivity (Fox, 1971; Kirkpatrick, 1943; Wokoun, 1969).
Presenting extraneous environmental stimuli by way of stereo headsets(music and radio) is one way in which work performance has shown
improvement (Oldham, 1995). An analysis of listening habits such as music
type selection, duration of listening, and shifting from one music type toanother revealed little relation to outcomes such as employees’ moods andproductivity, job satisfaction and turnover intentions. The mood state ofrelaxation best explained the relationship between the use of the stereos andproductivity, and environmental interference most effectively explained therelation between the stereo use and organizational satisfaction. Familiarity
with the music and discontinuous presentation were suggested as mosteffective in improving performance, turnover intentions, organizational satisfaction, and mood states.
Individual differences must be accounted for when investigating the effects
of music listening on employee anxiety and work tasks. Individual differences
was an important factor in a pre-test post-test control group study carried out
with 33 air traffic controllers from the Federal Aviation Association inLongmont, Colorado (Lesiuk, 1992). All controllers completed trait anxiety
and extroversion/introversion measurements, as well as a stress diagnostic
inventory prior to the formal study. Results showed that whether the group
sat in silence or listened to music, their stress levels reduced significantly.
However, an interaction effect was exhibited for individuals with high trait
anxiety and introversion, who did not demonstrate reduction in anxiety.
Further, in a measure of the controllers’ perceived activity of air traffic, thisparticular personality-trait combination perceived a higher level of air traffic
activity than other employee personalities.
It is possible that the more individuals listen to or perform music the more
likely it is that they will experience increasingly strong emotional responses to

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Lesiuk: The effect of music listening on work performancemusic. Lehmann (1997) calls this emotional sensitivity to music the trainingmediation hypothesis. He investigated an alternative hypothesis to the emotional sensitivity response to music listening, that of affect-transfer in which
emotionally sensitive people respond strongly to music. Lehmann (1997)
rated musicians and non-musicians according to their emotional sensitivity
responses to habitual and situational listening. He found that there were no
reliable differences between the two groups in regards to the measurement
for affect intensity to everyday life events. While the affect-transfer hypothesis
was not confirmed, the training-mediation hypothesis was supported revealing that the more knowledge one has of music the more the emotional
responsiveness.

Positive affect and task performance
Mild positive feelings can influence the way cognitive material is organized,
thus influencing creativity (Ashby et al., 1999; Estrada et al., 1997; Isen,
1999; Schellenberg, 2001; Thaut, 1989). Participants who experienced apositive mood as a result of music-film mood inducement demonstrated
better creative problem solving than participants who had a neutral or
depressed mood (Isen et al., 1987). The study provides evidence that there isgreater integration or perception of relationships among stimuli with people
who are feeling mild positive affect (i.e. feeling happy).
Researchers (Thompson et al., 2001) challenged interpretations of acurrent controversial phenomenon known as the ‘Mozart effect.’ Lately, as aresult of a study showing small increases in spatial reasoning (Rauscher etal., 1993), some popular public thinking has been that ‘Mozart makes you
smarter’ and has even led to government funding to provide classical musiccompact discs to parents of newborns. Thompson et al. (2001) replicated thestudy with additional measures of mood and arousal, based on previous
findings that very high or low levels of arousal or anxiety inhibited cognitive
performance and on other findings that positive mood leads to improved
problem-solving tasks. The findings revealed that the ‘Mozart effect’ wasindeed replicated as participants who listened to the Mozart sonata performed significantly better on the spatial-reasoning task than those listening
to an Albinoni piece. However, the ‘effect’ was not attributed to Mozart assuch, but to differences in arousal and mood. That is, the participants listening to Mozart scored significantly higher on positive mood and arousal
measures, lower on a negative affect measure, and higher on an enjoyment
measure than those listening to the Albinoni selection and those who sat insilence. The researchers concluded that enjoyable stimuli induce positive
affect and heightened arousal and, that in turn leads to moderate improvements in task performance.

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Psychology of Music 33(2)AimThis study investigated the effect of music listening on the mood and task
performance of computer information systems developers. Most music andworkplace studies are applied studies with little theoretical prediction aboutoutcomes of organizational behaviour as a result of music experiences. In
fact, most music–work descriptions in current music psychology focus on the
Muzak effect, presenting a limited view of the interactions that occur
between workers and music listening. Further, the need for testing a music
and work theory persists despite the challenge of being unobtrusive in work
environments. This study focuses on a particular work culture, that of computer information systems development, investigating the role of music andaffect within this emotionally dynamic context. While several perspectives ofmusic and affect are relevant in the music–worker relationship, increased shortterm positive mood through music listening is emphasized in this article.

MethodPARTICIPANTS

The participants in this current study were computer information systems
developers (male = 41, female = 15) from four different companies located in
two Canadian cities. The 56 participants were 19 to 55 years of age(M = 32.8 years) with work experience ranging from 6 months to 20 years(M = 6.24 years). There was a wide range of formal musical education
experience from 0 to 15 years (M = 1.95 years). There was also a wide rangeof reported daily music listening time, from no time spent to nearly the entire
day (M = 1.72 hours, SD = 2.11 hours).
RESEARCH DESIGN

The study is a quasi-experimental field study utilizing an interrupted timeseries with removed treatment design. While attempting to capture thenatural interactions that occurred between the developers and music listening in the workplace, the researcher had to be sensitive so as to minimize
interrupting the participants’ assignments and work environment. A background (demographics) questionnaire and a trait affect questionnaire wereadministered a few days prior to the five-week study. Three dependent variables consisting of State Positive Affect, Quality-of-Work and Time-on-Task
were measured twice a week, two days apart, with days alternated throughout the five weeks. State Positive Affect is defined as the extent to which anindividual displays enthusiasm for life (Watson and Tellegen, 1985). A daily
music log was also administered for each day of weeks 2, 3, and 5 for participants to record their state mood prior to and following listening, the amountof time spent listening, and their music selections. Figure 1 illustrates thedesign and administration of the dependent variables.

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Lesiuk: The effect of music listening on work performance

Baseline

Week 2

Week 3

Week 4

Week 5Baseline
M W

Music onT
TH
Music log

Music onW
FMusic log

Music off
M
W

Music onT
TH
Music log

F I G U R E 1 Study design and administration of State Positive Affect, Quality-of-Work, Time-

on-Task and music log.

For the first week of the study (the baseline week), participants were
instructed to do what they normally did in respect to music listening. For
example, if they usually listened to music at work, they would continue to doso. State Positive Affect, Quality-of-Work and Time-on-Task were measured
twice in that first week, once on Monday afternoon and once on Wednesday
afternoon.
At the beginning of the second week the music library of 65 CDs was set
up in the vicinity of the developers. Style categories of music in the library
included Alternative, Classical, Country, Heavy Metal, Light Jazz, New Age,
Pop, and a category labeled Other (e.g. Folk, Spirituals). Developers could
listen to music of their own choosing either from their own collections orfrom the music library. They could listen to the music within their office cubicle via their personal stereo or with headsets on their computer. Participants
were simply instructed to ‘Listen to the music when you want to, as you wantto.’ The same directions were given for week 3.
At the beginning of week 4 the music library was removed. Participants
were given the statement ‘This fourth week please do not listen to music’, andasked to confirm by their signature that they had not listened to music eachday. At the beginning of the fifth week the music library was reinstated alongwith the questionnaires.
MEASURES

Trait positive and negative affectivity were measured by a 25-item true/false
questionnaire developed by Watson and Tellegen (1985). The trait measure
reveals stable and consistent differences in individuals’ general affective level.
The measure consists of 14 negative affect (NA) items and 11 positive affect(PA) items scored by the number of ‘true’ responses. For example, the statement ‘I always seem to have something pleasant to look forward to’ if marked
as true, would be awarded one point towards PA, reflecting the extent towhich a person feels enthusiastic, active, and alert. Watson, Clark andTellegen (1988) report that the Positive and Negative Affect Schedule (PANAS)
scales, upon which this questionnaire is based, are highly reliable (coefficients ranging from .84 to .90). Of note, positive and negative affect are notopposite ends of a continuum; they repeatedly emerge as distinct factors that

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Psychology of Music 33(2)are independent of each other (George and Brief, 1992). Although this measure comprises both negative and positive affect responses, this article reports
the results of the trait positive affect.
State mood, an individual’s experience of short-term emotional fluctuation, either positive or negative, was measured by the Job Affect Scale (JAS)(Brief et al., 1988). Both State Positive Affect and NA were scored on a Likert
scale (1 = extremely slightly to 7 = extremely strongly). The JAS, originally a
20-item questionnaire, was reduced to 12 items (Oldham, 1995) for the purpose of decreasing the amount of time spent on answering questionnaires
during work time. The State Positive Affect items consisted of positively
scoring strong, excited, and elated and reverse scoring of sleepy, drowsy, andsluggish. The highest possible score is 42 and the lowest possible score is 6.
The higher the score the more intense the affect. This article reports the state
positive affect responses.
A researcher-designed self-assessment questionnaire was created in consultation with several systems developers in order to obtain the best possible
measurement of quality of computer information systems work (seeAppendix). The Quality-of-Work Questionnaire items consisted of responding
on a Likert scale (1 = not at all, 2 = somewhat, 3 = moderately, to 4 = verymuch so) to questions assessing life-cycle task performance. Life-cycle tasks,
also referred to as a systems development life-cycle, involve five phases that
help organize the creation of a computer information system. The five phases
include systems analysis, design, development, implementation and maintenance. Participants self-assessed their work quality by responding to items
such as: Was the solution creative? Was the solution effective? Did you experience
fewer mental blocks than usual? Items were positively scored, summed, andthen divided by 5 to obtain a mean. The higher the score the higher the work
quality (scores could range from 5–20).
A researcher-designed Time-on-Task question (inserted in the Quality-ofWork Questionnaire) required all participants to indicate if they completed
their life-cycle task in less time (scored as 0), the same time (scored as 5), or
needed more time (scored as 10) than their ‘intended’ time for the task.

Results
TRAIT POSITIVE AFFECTIVITY

Responses from the Trait Positive Affectivity measure are relatively high considering the highest possible score is 11 (M = 7.27, SD = 2.71). No working
group norms for comparison purposes were available for this measure. Thesefindings suggest that the developers, on average, are a relatively positive
group of participants.
MUSIC LISTENING TIME

Concerning daily average music listening time and trait positive affect, theDownloaded from pom.sagepub.com at UNIV OF MIAMI on August 3, 2012

Lesiuk: The effect of music listening on work performance
TA B L E 1 State Positive Affect response to music listening by week

M

SD

p

Music 1
Pre
Post

26.33
27.02

4.48
4.33

.024

Music 2
Pre
Post

26.84
27.68

3.95
4.80

.006

Music 3
Pre
Post

25.32
27.40

4.71
4.61

.000

higher the trait positive affect, the more time spent listening to music (r =.328, p < .05). However, this finding may be interpreted as the more timespent listening to music, the greater the increase in trait positive affect. There
was also a statistically significant negative correlation of age and music
listening (p < .01), indicating that the older the developers, the less time spentmusic listening. Analysis of each individual company reinforced this finding,as the mean ages, from companies with youngest to oldest, had respectively
less amounts of time spent listening to music.
STATE POSITIVE AFFECT

Table 1 reports the combined companies’ State Positive Affect prior to andfollowing music listening for each music listening week.
Table 1 reveals that intra-week, pre to post State Positive Affect increasedin each of the three music listening weeks (p < .05). The greatest increase of
2.08 points in State Positive Affect occurred in week 5 (music 3), the week
following the ‘no-music’ listening week. These results show that developers
benefit in increased State Positive Affect when listening to music.
STATE POSITIVE AFFECT

Figure 2 illustrates the combined companies’ descriptive statistics for State
Positive Affect week 1 through week 5.
State Positive Affect is highest in week 3 (M = 27.58) and lowest in the ‘nomusic’ week, week 4 (M = 25.50). An analysis of variance for trend finds astatistically significant cubic trend F(55, 1) = 3.66, p < .05. A post-hoc
analysis, Wilcoxon Matched-pairs Signed-rank test, indicates a statisticallysignificant difference between baseline and week 3 (music) (p = .040) andbetween week 3 (music) and week 4 (no music) (p = .004). It appears thatmusic listening is not only beneficial for improving mood from pre to post
listening sessions, but also over longer time frames. State Positive AffectDownloaded from pom.sagepub.com at UNIV OF MIAMI on August 3, 2012181

Psychology of Music 33(2)

30.0

28.0

Mean

182

26.0

24.0

22.0
baseline pa

week 2 pa

week 3 pa

week 4 pa

week 5 pa

F I G U R E 2 State Positive Affect across five weeks. This line graph represents averaged weekly

State Positive Affect, initially measured twice per week in the afternoons, across five weeks.

increased steadily to the third week of the study. Further evidence of thebenefit on mood is observed when the developers’ moods drop as a result of
the removal of music. Whether or not the music is a pleasant and/or arousing stimulus, it is apparently missed, and once music listening is reinstated,developers’ responses suggest they once again benefit. The incremental
increase in positive affect from baseline, to week 2 and then again to week 3
suggests that music listening has a positive effect on all music listeners. Why
the increase in positive affect over time, bearing in mind the developers
retained their normal music listening habits in the baseline week? Perhaps
those that were usually non-listeners take time to benefit from the music
listening.
QUALITY- OF - WORK

Figure 3 reports the combined companies’ descriptive statistics for Quality-ofWork week 1 through week 5.
Figure 3 demonstrates that Quality-of-Work is lowest in week 4, the ‘nomusic’ listening week. Quality-of-Work scores drop during the ‘no music’listening week (M = 11.15), followed by an increase in Quality-of-Work whenthe music is reinstated in week 5 (M = 11.74). An analysis of variance fortrends found a statistically significant quadratic trend F(55, 1) = 6.32, p <.05. The finding of a quadratic trend, as opposed to a cubic trend, as illustrated
by the State Positive Affect responses, may be explained by the following.
There was a subtle perturbation in responses between the baseline to weeks 2
and 3. Music listening was present in the first three weeks of the study with

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Lesiuk: The effect of music listening on work performance
15.0

14.0

Mean

13.0

12.0

11.0

10.0

9.0
baseline QofW week 2 QofW week 3 QofW week 4 QofW week 5 QofWF I G U R E 3 Quality-of-Work across five weeks. This line graph represents averaged weekly

Quality-of-Work, initially measured twice per week in the afternoon, across five weeks.

perhaps more ‘permission’ to listen in weeks 2 and 3 (as the music library
was set up in the companies during those weeks). The effect of the music
listening between baseline and the music listening weeks was somewhat
weak as many of the developers listened to music during the baseline week.
However, the forced condition of ‘no music’ during week 4, as expected,
showed decreased scores with a return to higher levels once the music wasreinstated in week 5. A post-hoc analysis indicates a statistically significant
difference between the baseline week and week 4 (no music) (p = .020).
Evidence that a music listening effect may be present is seen when scores
return to near baseline level with the reinstated music, although this effect isrealized in evidence of very small numerical changes.
TIME - ON - TASK

Figure 4 reports the combined companies’ descriptive statistics for Time-onTask week 1 through week 5.
The developers spent more time on their tasks than they intended in week
4, the ‘no music’ listening week (M = 6.61). An analysis of variance finds astatistically significant quadratic trend F(55,1) = 7.77, p < .05. There appear
to be substantial effects of music listening on Time-on-Task. A Wilcoxon
Matched-pairs Signed-rank test indicates a statistically significant effectbetween the baseline week and week 4 (no music) (p = .007); between week 2(music) and week 5 (music resumed) (p = .041); between week 3 (music) andweek 4 (no music) (p = .048); and between week 4 (no music) and week 5(music resumed) (p = .000).
When music listening is removed in week 4, the Time-on-Task increases

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Psychology of Music 33(2)

8.0

7.0

6.0

Mean

184

5.0

4.0

3.0
baseline TonT

week 2 TonT

week 3 TonT

week 4 TonT

week 5 TonTF I G U R E 4 Time-on-Task across five weeks. This line graph represents averaged weekly Time-

on-Task, initially measured twice per week in the afternoon, across five weeks.

significantly as shown when compared to the baseline week, week 3 (music),
and week 5 (music). A difference appears between week 2 (music) and week 5(music) as Time-on-Task is slightly higher during week 2 and lowest whenmusic is reinstated in week 5. The improvement in Time-on-Task when themusic is present (weeks 2, 3, and 5) is likely a result of several responses tothe music condition. That is, in part or in whole, their state mood is betterwith the music than without it, and lastly, they may be pacing themselves
throughout the workday as a result of the music listening. Their best ‘timed’
performance occurred in week 5 (music reinstated). This was perhaps a resultof returning to preferred working and music conditions.

Discussion
STATE POSITIVE AFFECTState Positive Affect increased from pre to post listening in each of the threemusic listening weeks (p > .01). Additionally, the findings revealed that State
Positive Affect measured over the five weeks increased incrementally frombaseline to the first and second weeks of the music listening period. It then fell
when the music was removed in week 4 (no music), but rose again when themusic listening was reinstated in week 5. These State Positive Affect responses,
both from pre and post music listening, and from the longer-term measurements, are consistent with research literature suggesting that mood isimproved by music listening whether as a result of aesthetic experience (e.g.
Fiske, 1996), peak experience (e.g. Sloboda, 1991), or simply part of everyday life (e.g. DeNora, 2001).

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Lesiuk: The effect of music listening on work performance

The general hypothesis proposed in this article is that music listening insome work environments evokes positive affect or mild positive feelings,
which in turn increase performance on tasks requiring creative output.
There is, however, need for further examination of this postulation. For
example, does music listening reliably bring about mild positive feelingresponses? There is evidence, outside the findings reported here, that this is
the case most of the time. That is, Sloboda and O’Neill (2001) reported thateveryday music listening rarely resulted in less positivity (i.e. only 13% of thetime). Further, people choosing music to ‘wallow in’ for the purpose of evoking a sad emotional state, may actually be practising enjoying ‘bad’ emotions(Apter, 2001). In other words, sadness is experienced as an enjoyable form
rather than a serious unpleasant form. Therefore, even when mild positive
affect is not the aim of the individual, it is the result.
Upon separate analysis of State Positive Affect of each participating company, one company’s responses to music listening were unequalled. Thisparticular company’s participants, considered at the outset a ‘low music
listening’ group, did not show an increase in positive affect until the third
week of music listening (week 5 in the study design). This result was unexpected. Does it take time for those unaccustomed to listening to music at work
to benefit from the effects of music on mood? Do individuals need time to discover how music affects them and how they can best use that effect in theirworkplace? This seems to be the case for one novice listener who stated afterthe ‘no music’ listening week: ‘I plan to continue listening to music at work. I
never listened to music before or felt the need. This was a positive experience.’QUALITY- OF - WORK AND TIME - ON - TASK

Evidence that a music listening effect on the quality of work is present is seenin the significant quadratic trend (p < .05). Very small changes in Quality-ofWork scores were reported across the five weeks with the lowest Quality-ofWork score occurring during the ‘no music’ week, followed by an increase inperformance with the resumed music in week 5. Only very small differencesin Quality-of-Work scores would be expected in the software design industry
as these employees are usually very competent designers demonstrating ahigh level of performance (company director, personal communication,2001). Despite the small changes in work performance scores, the findings
support the suggestion that state positive affect increases one’s ability torelate and integrate divergent material (Estrada et al.,1997; Isen, 1999; Isenet al., 1987; Thompson et al., 2001). This applies to the computer softwaredevelopers’ need for integrating divergent materials when designing anddeveloping programs for computer information systems. A comment fromone participant, ‘It [music listening] helped in relaxation, getting around[mental] blocks by changing thoughts instead of getting “tunnel visioned,”’reflects the influence of music listening to alter mood and its role in altering
perception of materials; the resulting effect, both resilient and dependable, ofDownloaded from pom.sagepub.com at UNIV OF MIAMI on August 3, 2012

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Psychology of Music 33(2)State Positive Affect on cognition and social behaviours has implications fororganizational settings.
Additionally, listening to music reduced time on systems life-cycle tasks(p < .05). All pair-wise comparisons involving the use of music listening
showed that when music was removed there was more time spent on tasks
than intended. Individuals are more energized and more alert through perceiving and physically responding to music listening. This synchrony withthe music assists in pacing their work tasks, and further, in pacing their workday. The finding harkens back to earlier studies investigating the use of musicin tedious work tasks. Wokoun (1969), for example, found that vigilance was
improved through music listening by creating and administrating a musicprogram designed to increase tempo at peak fatigue times. Even earlier
accounts dating as far back as World War I recorded the effect of music on
pacing during work tasks (Glossop, 1961).
THE AMOUNT OF TIME ONE LISTENS TO MUSIC

Contrary to previous findings that the amount of music listening made nodifference to mood (Oldham, 1995), this study found a modest but statisticallysignificant positive correlation (r = .328, p < .05) between music listening
time and trait positive affect. This result suggests that the stable trait of positive affect is aligned with music listening and may be interpreted two ways.
That is, first, that individuals with a positive affect disposition tend to listen tomusic while they work, or secondly, that individuals are positively affected
over time by music listening. What does this music listening group ‘know’that the habitual non-listeners do not ‘know’? Possibly, these individuals may
be proficient at recognizing their current mood state and the projected, necessary mood state for the context or task they have to tackle (DeNora, 2001).
During an informal conversation, a project manager, also an avid music
listener, disclosed his music choices for several different development tasks
and the certainty of his choices to enhance his work for those tasks. Such
participants appear to be knowledgeable music users. Comments of the developers reveal the thinking process involved with practising such knowledge.
Examples of comments are, ‘It [music] put me in a better frame of mind – ittook me to a place I like to be,’ and ‘Effectively altered your surrounding tosuit your mood at the time. If you are tired, you could listen to more upbeat
music to wake you up, etc.’ IT personnel may find music a reliable cathartic
outlet and their responses to particular musical works may even prove to bereliable. Of relevance to the software development industry, employees may
experience the same emotional response to a piece of music over repeatedlistening.
INDIVIDUAL DIFFERENCES

Subgroups of individuals, as a result of personality disposition, may require
mood enhancing experiences. As was found in Lesiuk’s (1992) study with air

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Lesiuk: The effect of music listening on work performancetraffic controllers, controllers who were extroverted, with either high or low
trait anxiety, experienced anxiety reduction in conditions of both sitting insilence and listening to music. However, those controllers who were introvertedwith high trait anxiety did not experience anxiety reduction in either condition. As shown in this study, music listening over time was not only beneficial
to increasing state positive mood but also trait positive disposition. Many
developers habitually listened to music so much so that in the recruiting stage
some chose not to participate because of having to go without music listening for the required one week. Perhaps the developers’ chronic music listening habits provided a buffer to otherwise anxiety provoking work stressors.
Lehmann’s (1997) finding that individuals with greater music training
had greater emotional responses, is reflected in this study’s finding of asignificant positive correlation found between amount of music listening andtrait positive affect. Developers who listened to greater amounts of music,
considered more knowledgeable than those who usually did not listen,derived greater emotional benefit.
Lastly, a significant correlation was found between trait positive affect andtrait-curiosity (r = .482, p = .01). This is important as curiosity is essential fordevelopers who are regularly required to be creative in designing computer
software systems. Music listening may provide an opportunity for developers
to align themselves with their optimum creativity.
CHALLENGES OF THE STUDY

No company was chosen as a control for this study. While a control company
was considered in the planning of the design, it became evident that eachcompany was very individual. The companies had their own work ‘culture’and therefore a control group would serve only as a comparison to anotherwork ‘culture.’
There was an absence of a guided listening framework for the developers.
The developers could listen to music as they chose during the music listening
weeks, but did so without any suggestions or guidelines as to what to listen towhen working. For example, if developers were feeling sluggish and in need ofmore energy, faster tempi music selections could have been suggested.
However, in keeping with the exploratory nature of the field study, thisconsideration will have to be implemented in a future study.
Demand characteristics may have been present. Due to the reliance on
self-reports some developers may have intentionally guessed at what the
researcher wanted to find. However, it is more likely that the demand characteristics were actually minimized and possibly negated as a result of theresearcher’s data collection approach. She very openly stated she was interested in the effect of music listening, whatever it was, and that she was reallyinterested in hearing about the work environment and their work life. With
this approach the participants seemed to easily report thoughts and feelings
about listening to music, about work stresses and even about the study itself.

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Psychology of Music 33(2)Further evidence that the demand characteristics were minimized appearedin the differing responses across variables and across companies. The companies seemed to report responses that in the end described their work culture.
Lastly, there was not a random assignment of companies for this study.
Perhaps the company directors’ approval of investigating music listening in
the workplace corresponded with the companies’ work ethos. In other words,
the permission to listen to music while working provided a ripe context for the
beneficial subjective music listening experiences.
THE PROMOTION OF MUSIC LISTENING IN THE WORKPLACE

When music listening in the work environment is encouraged by project
directors and the workers are amenable to music listening, then certainly
music listening has a positive effect. It was evident from observations of twoof the four companies that the project managers influenced, or at least supported the developers’ use of music listening. One project director used music
avidly to aid him in his programming and designing tasks. Another project
director had stacks of music compact discs and a headset at his desk. He also
spoke highly of the use of music while programming and was very interested
in the outcome of this study. It follows that when music is removed in such
music listening work cultures there is a psychological withdrawal of animportant stimulus. Developers experience the negative effects of the removalof a stimulus they have come to depend on. The negative effects of removing
the music are found in lowered state positive mood responses, slightly lowered
quality-of-work, and more time spent on tasks than originally intended.
Furthermore, there are age-related differences in music listening whileworking. Inferential results revealed a statistically significant negative correlation of age and music listening (p < .05), meaning the older the individual
the less time spent music listening. Despite the individual work culture andresulting preference for music listening, an important question remains.
Should music listening be encouraged in the workplace, even for groups thatusually do not listen to music? Perhaps the strongest evidence to an affirmative response is found in the responses of the third company (called Company
C). Company C’s baseline reflected a non-music listening group. It was also
one that was experiencing a substantial amount of stress from the challenges
of a merger. The group’s initial pre to post listening state mood responses tomusic actually showed a decrease in positive affect. That particular type of
response would not be helpful in any work environment. However, by the last
week of the study (the third week of music listening), the developers showed
an increase in positive affect. Again, as mentioned earlier, there may be alearning curve of music listening use. The researcher’s response to the question, Should music listening be encouraged in the workplace?, is that over time,music listening based on workers’ choice to listen ‘when they want as theywant,’ is beneficial for state positive affect, quality-of-work, and time spent on
a task.

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Lesiuk: The effect of music listening on work performance

Appendix: Quality-of-Work Questionnaire
Assessment of Quality of Work
Complete the following questions in terms of the work you have done recently.
A. Task (check one):____planning____requirements gathering/analysis____design/development (including coding)____testing____implementation____maintenance____other? (___________)

1. In solving the problem, was the solution creative?
1
Not at all2
Somewhat

3
Moderately

4
Very much so

2. In solving the problem, was the solution effective?
1
Not at all2
Somewhat

3
Moderately

4
Very much so

3. Considering your ‘intended’ time on task, did you complete the task in___the same time____in less time____needed more time?

4. Did you experience fewer ‘blocks’ than usual in the (planning/development/testing)process?
1
Not at all2
Somewhat

3
Moderately

4
Very much so

5. Did you use a new approach in your (planning/development/testing) practices?
1
Not at all2
Somewhat

3
Moderately

4
Very much so

6. Are you pleased with the (planning/development/testing) you just completed?
1
Not at all2
Somewhat

3
Moderately

4
Very much so

AC K N OW L E D G E M E N T S

This research was supported in part by the Rosavear Fund, University of Western
Ontario, London, Ontario, Canada. I would like to thank the company participants fortheir feedback.

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is Assistant Professor of Music Therapy, University of Windsor,
Ontario, Canada. Her current course teachings include Theories of Music Therapy,
Improvisation in Music Therapy, Psychology of Music and supervising clinical fieldplacements. She completed her PhD at the University of Western Ontario and iscontinuing to investigate the role of music in high stress occupations (e.g. air traffic
control and computer information systems).
Address: School of Music, University of Windsor, Windsor, Ontario, Canada, N9B 3P4.
[email: tlesiuk@uwindsor.ca]

TERESA LESIUK

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