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A Grounded Theory of Young Tennis Players’ Use of Music to Manipulate Emotional State

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The main objectives of this study were (a) to elucidate young tennis players' use of music to manipulate emotional states, and (b) to present a model grounded in present data to illustrate this phenomenon and to stimulate further research. Anecdotal evidence suggests that music listening is used regularly by elite athletes as a preperformance strategy, but only limited empirical evidence corroborates such use. Young tennis players (N = 14) were selected purposively for interview and diary data collection. Results indicated that participants consciously selected music to elicit various emotional states; frequently reported consequences of music listening included improved mood, increased arousal, and visual and auditory imagery. The choice of music tracks and the impact of music listening were mediated by a number of factors, including extramusical associations, inspirational lyrics, music properties, and desired emotional state. Implications for the future investigation of preperformance music are discussed.
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Grounded Theory of Music Use 1
Running head: Grounded Theory of Music Use
A Grounded Theory of Young Tennis Players’ Use of Music to
Manipulate Emotional State
Daniel T. Bishop, Costas I. Karageorghis, & Georgios Loizou
Brunel University, West London, UK
Final submission: 4 May 2007
Correspondence to: Daniel T. Bishop, School of Sport & Education, Brunel University, West
London, Uxbridge, Middlesex UB8 3PH, England, UK. Tel: 00 44 (0)1895-266 500, Fax: 00 44
(0)1895-269 769. Email: daniel.bishop@brunel.ac.uk.
Coauthors’ mailing addresses:
Costas I. Karageorghis, School of Sport & Education, Brunel University, West London,
Uxbridge, Middlesex UB8 3PH, England, UK.
Georgios Loizou, School of Sport & Education, Brunel University, West London, Uxbridge,
Middlesex UB8 3PH, England, UK.
Grounded Theory of Music Use 2
Abstract 1
The main objectives of this study were (a) to elucidate young tennis players’ use of music to 2
manipulate emotional states, and (b) to present a model grounded in present data to illustrate this 3
phenomenon and to stimulate further research. Anecdotal evidence suggests that music listening 4
is used regularly by elite athletes as a pre-performance strategy, but only limited empirical 5
evidence corroborates such use (e.g., Gluch, 1993). Young tennis players (N = 14) were selected 6
purposively for interview and diary data collection. Results indicated that participants 7
consciously selected music to elicit various emotional states; and frequently-reported 8
consequences of music listening included improved mood, increased arousal, and visual and 9
auditory imagery. The choice of music tracks and the impact of music listening were mediated 10
by a number of factors, including extra-musical associations, inspirational lyrics, music 11
properties, and desired emotional state. Implications for the future investigation of pre-12
performance music are discussed. 13
Key Words: emotion, response, qualitative, pre-performance routine. 14
Grounded Theory of Music Use 3
A Grounded Theory of Young Tennis Players’ Use of Music to Manipulate Emotional State 1
Music listening as a pre-performance strategy in sport has received limited attention in 2
sport psychology research (e.g., Gluch, 1993) despite evidence for the capacity of music to elicit 3
strong emotions (Gabrielsson, 2001), and for the prevalence of music listening as a mood-4
regulation strategy in adolescents (Saarikallio & Erkkilä, 2007). This is possibly due to the 5
idiosyncratic nature of musical preferences and responses to music, which limit the 6
generalizability of findings. However, the idiosyncratic nature of emotional responses to music 7
provides a potential avenue for manipulating athletes’ pre-competitive emotions. 8
Regular changeover periods offer tennis players 90 s of introspection, which may lead to 9
sharp degradation in performance when under pressure (choking; Baumeister, 1984). However, 10
changeovers also afford players an opportunity to positively influence their emotional state; and 11
listening to music can achieve this end (Scherer, 2004; R. E. Thayer, Newman, & McClain, 12
1994). Although listening to music is not permitted during play in some major events such as The 13
Wimbledon Championships (Lawn Tennis Association [LTA], personal communication, October 14
10, 2005), the International Tennis Federation Junior Circuit Regulations 2007 make no such 15
stipulations at the time of writing. 16
Evidence suggests that listening to music during physical activity can reduce perceived 17
exertion (Copeland & Franks, 1991) and enhance affective state (Boutcher & Trenske, 1990); and 18
these effects may be mediated by an attentional shift from internal (somatic) cues to external 19
(music) cues (Szabo, Small, & Leigh, 1999). Musical accompaniment can also improve 20
performance in predominantly aerobic (Atkinson, Wilson, & Eubank, 2004) and anaerobic 21
(Simpson & Karageorghis, 2006) events. Karageorghis, Drew, and Terry (1996) examined the 22
effect of stimulative music (tempo 134 bpm) relative to sedative music (90 bpm) and white noise, 23
when played immediately prior to execution of a grip strength task. They found that grip strength 24
Grounded Theory of Music Use 4
was greater after listening to the stimulative music; the extent of this effect may be moderated by 1
aspects of the athlete’s personality (Crust & Clough, 2006). Psychophysiological indices of 2
performance also exhibit some relationship with music properties: Heart rate during treadmill 3
walking displays a moderate correlation with preferred music tempo (Karageorghis, Jones, & 4
Low, 2006); and fast tempo music reduces the latency of neural responses to visual stimuli when 5
contrasted with the same music played at a slower tempo – reflecting a faster evaluation of the 6
stimulus (Amezcua, Guevara, & Ramos-Loyo, 2005). 7
To address the problematic issue of selecting appropriate music for sport and exercise 8
contexts, Karageorghis, Terry, and Lane (1999) developed the Brunel Music Rating Inventory 9
(BMRI), which stemmed from a conceptual framework wherein four factors are deemed to 10
contribute to what Karageorghis et al. termed the motivational qualities of music: Rhythm 11
response, musicality (both music factors), cultural impact, and association (both personal 12
factors). The BMRI was later redesigned to form the BMRI-2 (Karageorghis, Priest, Terry, 13
Chatzisarantis, & Lane, 2006), an instrument tailored to exercise environments. However, due to 14
the complex nature of precompetitive emotions (Hanin, 2000), assessing the motivational 15
qualities of a track may not capture the efficacy of music listening as a pre-performance strategy. 16
Also, the study of music in sport has focused primarily on the potential role of music as an 17
ergogenic aid when used to accompany performance (e.g., Atkinson et al., 2004), despite the 18
ability of music to elicit enduring autonomic and endocrine responses (Scherer, 2004). 19
Emotions are an integral part of human existence, mediating almost every facet of our 20
behavior. They comprise physiological (Ekman, Levenson, & Friesen, 1983), facial expression 21
(Russell & Bullock, 1985), behavioral (Frijda, 1986), and affective (Russell, 1980) components. 22
Damasio (2000) highlighted the regulatory role of emotions, stating that they lead “…in one way 23
or another to the creation of circumstances advantageous to the organism…their role is to assist 24
Grounded Theory of Music Use 5
the organism in maintaining life” (p. 51). Although contemporary sport is not a matter of life and 1
death, it has evolved to become a substitute for such situations. Optimal emotional patterns are 2
necessary for sporting success (Hanin, 2000), and there is evidence that music can elicit 3
potentially performance-facilitating neurophysiological (Menon & Levitin, 2005) and affective 4
(Gabrielsson, 2001) states. 5
Scherer and Zentner (2001) identified three central routes by which emotions are elicited 6
via music listening. The first of these is the memory route, wherein music acts as a powerful 7
trigger to recollection of an emotive event. The empathy route necessitates the listener’s ability to 8
identify the emotions being expressed by the performer, which Scherer and Zentner speculated to 9
be most viable when listening to a highly admired performer, or when the music is played in an 10
emotional manner. The final route is appraisal, wherein the perceiver evaluates the personal 11
significance of an event for his or her wellbeing, according to a number of criteria such as 12
intrinsic pleasantness. Appraisal of pleasantness is a core feature of emotional responses (Russell, 13
1980), and determines our orientation toward/away from environmental stimuli, including music 14
(Zentner & Kagan, 1996). Scherer and Zentner also noted two peripheral routes to musically-15
induced emotion: Proprioceptive feedback, which they describe as coupling of internal rhythms 16
to external drivers (cf. rhythm response; Karageorghis et al., 1999); and facilitating the 17
expression of pre-existing emotions, which refers to the loosening of emotional control typically 18
exhibited in social contexts (Ekman, 1972). 19
Scherer (2004) noted that music may elicit both utilitarian and aesthetic emotions, 20
defining the former as “high intensity emergency reactions, often involving a synchronization of 21
many organismic subsystems…driven by the appraisals in the central nervous system” (p. 241). 22
Utilitarian emotions parallel primary emotions, which may arise through the triggering of innate 23
dispositional representations – patterns of neural activity primed to respond to key stimulus 24
Grounded Theory of Music Use 6
features (Damasio, 1994). Primary emotions have important functionality in allowing us to adapt 1
to highly consequential life events; the feeling of fear is a manifestation of the fight-flight 2
response to a looming predator, for example. The apparently survival-oriented functionality of 3
utilitarian emotions contrasts with that of the rather weaker aesthetic emotions (cf. secondary 4
emotions; Damasio, 1994), which may only elicit symptoms such as goose bumps or moist eyes. 5
According to Scherer, aesthetic emotions are appraisals of visual or auditory stimuli in terms of 6
their artistic qualities, which is somewhat removed from the transactional self-referenced 7
appraisal involved in utilitarian emotions (cf. Lazarus, 1991). 8
Sloboda and Juslin (2001) delineated two broad sources of emotional responses to music. 9
Intrinsic sources are structural characteristics of the music stimulus (e.g., tempo), which primarily 10
mediate emotional intensity to promote action tendencies (Frijda, 1986) via well-established 11
subcortical mechanisms (see Panksepp, 1998). Extrinsic sources are psychological in nature and 12
may be iconic (i.e., derived from resemblance between the overall musical structure and some 13
other emotive agent) or associative – arbitrarily formed through associative learning, oftentimes 14
via single-trial conditioning. Sloboda and Juslin proposed that extrinsic sources are stronger 15
determinants of the content of musically-induced emotions (e.g., positive or negative). 16
The circumplex model of affect (Russell, 1980) offers a template for assessing both the 17
content and intensity of emotions experienced in sport. The circumplex comprises two bipolar 18
perpendicular dimensions – activation (arousal) and valence (pleasantness) – which bisect to 19
subdivide the circumplex, yielding four quadrants. Proponents of the circumplex contend that 20
every experienced emotion can be located at some point within one of the four quadrants. For 21
example, excitement lies in the extremities of the quadrant bordered by the upper halves of the 22
activation and valence continua (highly arousing – highly pleasant). Russell, Weiss, and 23
Mendelsohn (1989) subsequently developed the Affect Grid, which they concluded to be a 24
Grounded Theory of Music Use 7
reliable and valid measure of both arousal and pleasure. This single-item measure has since been 1
successfully employed as a measure of in-task affect during simulated driving performance 2
(Edmonds, Mann, Tenenbaum, & Janelle, 2006). 3
North and Hargreaves (1997) used a two-item measure to investigate musically-induced 4
emotions, and concluded that ratings of the emotions expressed by a musical excerpt can be 5
reliably predicted by (a) the extent to which listeners like the piece, and (b) the extent to which 6
they are aroused by it, an assertion which has since received empirical support (Ritossa & 7
Rickard, 2004). Arousal is a concept central to many theories and models of emotion (e.g., J. F. 8
Thayer & Faith, 2001), and arousal regulation strategies have been posited as important 9
moderators of emotional control in sport (Jones, 2003). Liking for musical stimuli is related to 10
the pleasure derived (Ritossa & Rickard, 2004), and happiness – a key component of the feeling 11
of pleasure – has attracted attention in sport emotion research (Jones, Lane, Bray, Uphill, & 12
Catlin, 2005). Therefore, circumplex-based measures such as the Affect Grid (Russell et al., 13
1989) present informative yet expedient tools for measuring emotional responses to music 14
listening in sport. 15
Summary 16
To date, considerable research has been conducted on the ideal emotional state for 17
performance (e.g., Edmonds et al., 2006), emotional responses to music (e.g., Scherer, 2004), and 18
the psychophysical effects of music in sport and exercise (e.g., Karageorghis et al., 1996). 19
However, music listening as a pre-performance strategy to elicit facilitative emotions in sport 20
remains largely under-researched. Although habitual music use by athletes has been reported 21
(Gluch, 1993), there is still little understanding of the processes by which pre-performance music 22
is selected, or of its intended or actual affective consequences. 23
Grounded Theory of Music Use 8
Therefore, the main objective of this study was to examine the use of music to manipulate 1
emotional states by young tennis players who indicated the use of music listening as a pre-2
performance strategy, to gain a better understanding of their emotional responses to music and 3
the factors that mediate these responses. Research on musically-induced emotions (e.g., Scherer, 4
2004) and music listening in sport and exercise (e.g., Karageorghis et al., 1999) was used in 5
conjunction with pilot data to develop a suitable interview schedule (Patton, 2002). Participants 6
recorded their emotional responses to music heard during interview using a variant of Russell et 7
al.’s (1989) Affect Grid, and discussed their reasons for selection. Some participants agreed to 8
complete a two-week diary detailing their daily music listening, together with their reasons for 9
listening, and emotional responses, to music. All emergent concepts were incorporated into a 10
model which will provide a template to guide (a) athletes’ music selections and (b) future 11
research efforts which seek to identify causal relationships between emotional responses to pre-12
performance music and performance itself. 13
Method 14
Pilot Interviews 15
Following institutional ethics approval, seven unstructured pilot interviews were 16
conducted with a convenience sample of two women and five men (mean age = 26.1 years, SD = 17
4.7 years), in order to develop a suitable interview schedule, and to inform the first author’s 18
interviewing style for the main study. Interviewees participated in a range of sports (rowing, 19
basketball, marathon running, hockey, weightlifting, tennis, and soccer), and represented a range 20
of competencies (recreational to international). Participants were asked generic questions relating 21
to their music use, such as “What do you listen to as part of your pre-performance preparation?”, 22
“How do you decide to listen to that choice of music?”, and “How does it make you feel?” 23
Grounded Theory of Music Use 9
The pilot study revealed a potential methodological problem: The difficulty inherent in 1
eliciting information from participants about why they would select any given music track. Very 2
little, if any, conscious thought was given to the underlying reasons for selection of a given track 3
or artist; the reason was largely reduced to a simple, “because I like it”. This necessitated the 4
development of suitable elaboration probes (Patton, 2002), such as “What do you like about it?” 5
Pilot study participants also found difficulty in remembering and naming tracks; therefore, 6
participants selected to take part in the main study were asked to complete a pre-interview 7
questionnaire, and were requested to bring five of their pre-performance music tracks to 8
interview. 9
Participants 10
An international junior tennis center in southwest London, England, UK, catering to 11
young tennis players from a wide variety of sociocultural backgrounds, was chosen as a suitable 12
site for data collection. It was anticipated that this site would expedite the selection process, 13
facilitate cross-case comparisons, and allow subsequent refinement and extension of ideas 14
emanating from the data (Dey, 2003). 15
An initial questionnaire was administered to 67 players at the center; LTA rating was used 16
as an index of respondents’ current ability. Forty-seven players returned the questionnaire and the 17
data collated were used to purposively select players for interview according to a number of 18
informativeness-related criteria: (a) Those participants who had provided questionnaire responses 19
which described their music listening habits in the greatest detail; (b) those with an LTA rating of 20
5.1 and above, as they occupy the top 10% of all British players; (c) and those who had indicated 21
music listening as part of their performance preparation routine. 22
Fourteen participants – seven women and seven men (mean age = 18.4 yrs, SD = 1.97 yrs) 23
who satisfied the inclusion criteria, and had at least 5 years’ competitive tennis experience (mean 24
Grounded Theory of Music Use 10
= 7.4 years, SD = 2.6 years), were recruited. The ethnicities represented comprised White 1
UK/Irish (n = 10), White European (n = 2), Afro Caribbean (n = 1), and White US (n = 1). 2
Interview Guide 3
Respondents’ answers to the initial questionnaire and a literature search highlighted a 4
number of sensitizing concepts on which to build a loose interview guide (example questions in 5
parentheses): Music properties (“Are there any particular segments of this track you like?”); 6
extra-musical associations (“Does it make you think of anything?”); sociocultural variables 7
(“How do you think this music is perceived by your peers?”); music-related imagery (“Does this 8
music conjure up any images?”), and listening habits (“Where are your top three places for 9
listening to music?”). It also became apparent during the first interview that the guide should be 10
shorter and less specific; therefore it was amended accordingly
1
. 11
Interview Materials 12
Interviews were recorded using a digital voice recorder (Olympus
VN-480PC; Olympus
13
Corporation, Tokyo) and were transferred onto a laptop computer via a software interface 14
(Olympus Digital Wave Player v. 2.0.0; Olympus
Corporation, Tokyo) for ease of transcription. 15
In vivo notes were taken, to capture nonverbal information and any key concepts that emerged. 16
The note-taking process also assisted the first author in pacing the interview appropriately 17
(Patton, 2002). 18
Procedure 19
Pre-interview music questionnaire. Players selected at the preliminary stage were invited 20
to take part in a 1 hr interview about their music listening habits, and their use of music in the 21
context of tennis. Each participant was given a paper catalogue containing 2,024 music tracks 22
from the first author’s personal music collection. These tracks were grouped by genre (e.g., 23
alternative, R’n’B, classical), then alphabetized; all catalogue entries were held in electronic 24
Grounded Theory of Music Use 11
music format. To ensure that performance-related music was discussed at interview, the front 1
sheet of the catalogue requested that participants list five emotional states they deemed crucial for 2
success in tennis, and to specify music tracks which made them either feel or think about each 3
state. Participants were asked to bring along any music tracks not included in the catalogue to the 4
interview. All selected tracks were played during the interview to stimulate discussion. 5
Interviews. Participants read an information sheet and provided written informed consent. 6
Interviews took place in a quiet room, lasted 37-84.5 min (M = 52.4 min), and were digitally 7
recorded. Music tracks were played from audio software via a stereo receiver (Pioneer SX-8
205RDS; Pioneer Corporation, Tokyo), which outputted through two 50 W speakers (Pioneer 9
CS-767; Pioneer Corporation, Tokyo) placed 2.2 m apart and equidistant (1.2 m) from the 10
participant. Sound intensity was measured using a digital sound meter
2
(AZ 8928; AZ Instrument 11
Corporation, Taichung City, Taiwan) mounted on a tripod at the participant’s head height. Prior 12
to discussion of each selected track, the researcher requested that participants adjust the intensity 13
of the music to one which they would typically apply to engender the named emotional states. 14
They were also requested to rate each track not only for liking and arousal potential (cf. North & 15
Hargreaves, 1997), but also for familiarity and popularity with peers, on 11-point bipolar scales; 16
this was done while listening at a standardized intensity of 55 dBA. It was expected that the 17
combination of qualitative and quantitative data would give a “powerful mix” (Miles & 18
Huberman, 1994, p. 42). 19
Diary. Ten of the 14 participants agreed to complete a 2-week, page-a-day diary. 20
Participants were informed that seven completed pages would be sufficient, but anything more 21
would be helpful; and that they would receive three text messages on their mobile phone per day: 22
In the morning, at lunchtime, and in the evening. The morning and lunchtime messages served as 23
prompts for the participant to recall and note any music heard up until that point; it was expected 24
Grounded Theory of Music Use 12
that this preconscious prompting would facilitate subsequent recall of a behavior that tends to 1
proceed in a relatively habitual – and therefore unmemorable – fashion. The evening message 2
served as a prompt to complete that day’s page. 3
Each page required the completion of a brief summary of daily activities carried out while 4
listening to music, and details of a memorable music listening episode on each daily page. It was 5
decided that, although very few music listening episodes would relate directly to music listening 6
immediately prior to performance, to have such a narrow focus would provide too little data. 7
Given that music may be an effective moderator of pre-performance mood (Gluch, 1993), that 8
athletes exhibit symptoms of competition-related emotions up to 1 week prior to competing 9
(Hanton, Thomas, & Maynard, 2004), and that all participants were engaged in at least one 10
competitive event during the diary completion period, all music listening episodes were 11
considered relatable to the aims of the study. Participants were asked to log as many episodes as 12
they could recall, and rated any music heard during diary completion for both liking and arousal 13
potential. 14
Observations. Rapley (2003) asserted that “No form of interview study, however devious 15
or informal, can stand as an adequate substitute for observation data” (p. 29); nonetheless, 16
observable phenomena of music listening are scarce. During playback of each track the first 17
author recorded changes in participants’ facial expressions and behavior (e.g., smiling, 18
piloerection, and increased liveliness); in vivo notes on the interview schedule indicated the 19
precise nature and timing of these changes. 20
Data Analysis 21
Grounded theory (Glaser & Strauss, 1967) was chosen as the appropriate method for data 22
collection and analysis, because it is a technique for developing theory from actual data. In 23
accordance with the tenets of grounded theory, interviews were semi-structured so as to allow for 24
Grounded Theory of Music Use 13
the emergence of novel information offering new directions. While emergent theory was 1
grounded in the raw data, there was inevitable interplay between the first author and the data, 2
which could have led to the identification of themes concordant with the researcher’s beliefs, to 3
the exclusion of others. Therefore, a number of precautions were taken to minimize the impact of 4
such biases, most important of which was triangulation (Miles & Huberman, 1994). 5
Triangulation was achieved by utilizing diverse data sources (e.g., gender, age, ethnicity, 6
and musical preferences); multiple data types (e.g., written notes, interview transcripts, diary 7
notes); an array of methods (interview, diary, observation); and recruitment of other researchers 8
who did not participate in data collection to assist in data analysis. All raw interview and diary 9
data were given to a peer debriefer who possessed knowledge of both psychomusicology and 10
music listening in sport, to either corroborate or refute the first author’s interpretations of the raw 11
data, and to suggest alternative explanations. Inter-rater agreement for interview data was 93%, 12
inter-rater agreement for diary data was 95%; this procedure could not be performed for 13
observation data. Other procedures were conducted with regard to trustworthiness criteria in 14
qualitative research (Lincoln & Guba, 1985): The first author was engaged with the data for a 15
prolonged period, persistently observed the participants, identified negative instances, kept a 16
reflexive journal, and gave copies of interview transcripts to participants in order to confirm their 17
representativeness. 18
Coding procedures. Interview data were analyzed immediately and compared with 19
existing data (constant comparison, Glaser & Strauss, 1967), in order to establish whether or not 20
there was anything worth pursuing (C. F. Seale, personal communication, January 11, 2005): 21
Recurrent themes (e.g., the presence of memorable life episodes in relation to selected music) 22
emerged after a visual inspection of the first three interview transcripts; this prompted the 23
Grounded Theory of Music Use 14
continuance of data collection, regardless of diary data contribution (the first completed diary 1
was only returned 2.5 weeks post-interview). 2
Pre-interview music questionnaire data. Participants listed a total of 70 emotional states 3
which they deemed crucial to their success in a tennis match. Identical responses across 4
participants enabled immediate reduction into 42 raw data themes (Table 1), which the first and 5
third authors independently clustered into 16 and 19 first-order themes, respectively. On 6
discussion, it was mutually decided that 18 first-order themes best represented the raw data 7
obtained (inter-rater agreement = 100%). The first and third authors independently grouped these 8
themes into five and seven dimensions, and subsequently agreed that five general dimensions 9
ultimately provided the most parsimonious representation of the data. 10
Interview and diary data. All interview and diary data were fully coded and analyzed 11
inductively using QSR NVivo (v. 2.0): Complete Word file transcriptions were imported into 12
NVivo, where free nodes (cf. open coding, Strauss & Corbin, 1998) were used to categorize 13
chunks of text. Free nodes were created (N = 1,087), which were then grouped into 57 trees (cf. 14
axial coding, Strauss & Corbin, 1998). All tree concepts were subjected to a visual inspection, 15
and combined where appropriate in order to develop central categories. The decision to include a 16
concept as a central category was made according to frequency of occurrence: Central categories 17
were chiefly included due to either (a) their occurrence across all participants and all data 18
sources, or (b) their frequent occurrence across data sources only. When the development of 19
categories reached theoretical saturation (Strauss & Corbin, 1998) data collection ceased. In 20
accordance with Strauss and Corbin’s (1998) guidelines for selective coding, all central 21
categories were incorporated into the model in Figure 1, so that the findings could be presented as 22
a set of interrelated concepts. 23
Grounded Theory of Music Use 15
Results 1
The initial questionnaire, pre-interview music questionnaire, interview, and diary 2
collectively yielded qualitative and quantitative data pertaining to the how, what, when, where, 3
and why of participants’ music listening. Figure 1 depicts an integration of all emergent concepts 4
into a process model; these concepts are elucidated below. 5
General Overview of Participants’ Listening Habits 6
Initial questionnaire data indicated that participants enjoy listening to indie, light rock, 7
rap, R’n’B, garage, rock, easy listening, dance, old skool, love songs, hip-hop, techno, and 8
alternative.
3
The overlap between many of these categories (e.g., old skool and hip-hop) may 9
reflect the growing implicit perception of music as a homogenous entity. Data also indicated that 10
participants used music to psych-up; to feel more positive, motivated, and confident; and to 11
dissociate from external stressors. 12
Interview and diary data indicated that participants listened to music daily, for two hours 13
or more, on average. Data from the initial questionnaire, interviews and diary pages indicated that 14
participants were predominantly traveling, preparing for tennis (including in the locker room), in 15
their bedrooms, or working out in the gym when listening to music. All participants listened to 16
commercial radio stations and watched music video channels (e.g., MTV) daily when at home. 17
Music Selection 18
Central to the present data, and the model in Figure 1, is that participants purposely 19
selected music to attain a desired emotional state. This was initially borne out in the initial 20
questionnaire, and participants’ responses to the pre-interview music questionnaire indicated that 21
they deliberately selected music to elicit five broad categories of emotional states: Appropriate 22
mental focus, confident, positive emotional state, psyched-up, and relaxed (see Table 1). This too 23
was corroborated – by interview data: 24
Grounded Theory of Music Use 16
I listen to this a lot before I go to matches and before matches, together with the last track, 1
being the last song I’ve heard, when I get out of the car, to give me that feel-good factor. 2
(Participant 14) 3
Sometimes, when…I’m playing bad, I might bring out my iPod, like at a changeover, and 4
maybe listen to this song, to give me a confidence boost....Or if I’m [annoyed], I’ll listen 5
to a relaxing song that will make me chill out. It helps a lot. (Participant 9) 6
This use of music had been developed by some participants to such an extent that they 7
used a medley of tracks in an attempt to optimize their emotional state: 8
The thing is [tracks] 1, 2, 4 , and 5 are needed to feel [confident]. So I was trying to find a 9
song that makes me feel [confident], but there isn’t one song that does that. But listening 10
to all of these four songs makes me feel these four things, which makes me feel confident. 11
I haven’t found a [single] song yet which makes me feel confident. 12
(Participant 4) 13
The five categories of reported emotional outcomes of music listening were integrated 14
into the final stage of the model in Figure 1. 15
Determinants of Emotive Music 16
Interview data indicated that five factors repeatedly occurred across and within 17
participants, to determine the likeability and arousal potential of participants’ pool of emotive 18
music: Extra-musical associations, peer and family influences, the involvement of the music in 19
film soundtracks and music videos, acoustical properties, and identification with artist or lyrics. 20
Grounded Theory of Music Use 17
Extra-musical associations. Extra-musical associations with significant persons, places, 1
or past events were a prominent feature in many (49/70) of the tracks selected by participants. 2
These associations were often formed as a result of single-trial conditioning: 3
Can you hear it? The football? He says the bit, “…they’ve taken the lead in the European 4
Championship final”, and whatever. It just reminds me of Euro 2004 [soccer 5
championships], Greece winning it.…it like, brings back happy memories, helps me 6
forget about everything else. (Participant 10) 7
There was also evidence of participants specifically associating tracks with good past 8
performance: 9
I’m choosing Another Day because that reminds me of this girl called [Lauren]…. I 10
wanted to use a word that means something to me; I couldn’t use fight…. I wanted 11
something that’s going to be mental arousal on different levels…. So that’s why I use 12
John Secada, because it reminds me…of good tennis. (Participant 8). 13
If I listen to this song before a match, and I play really well…if I hear it again, then I’ll 14
think of stuff in the match, how well I did, if I’m just like in my room. (Participant 11) 15
Participants could also provide specific details of associated significant places, events or 16
others associated with the memorable music tracks which they described on diary pages (Table 17
3). 18
Peer and family influences. Some participants indicated that their peers or family 19
members introduced them to a music track: 20
Grounded Theory of Music Use 18
I never used to like Matchbox 20, I used to hate music like that, then [a friend] introduced 1
me to one track of theirs, and I thought I’d give it a shot, and now I like listening to it…. 2
It’s weird how things change. (Participant 9) 3
All tracks were rated as relatively popular with peers, with the exception of those selected 4
to elicit a positive emotional state (see Table 2): 5
Oh, again, no, they won’t like this. Then again, that’s something that I pride myself on…I 6
don’t really care what other people think…if it’s cheesy, I don’t care. (Participant 8) 7
Film soundtracks and music videos. Film soundtracks were highly prevalent in the 8
musical selections provided by participants. This extended to some now clichéd tracks from the 9
Rocky film series, four of which were on general release before 12 participants had been born: 10
I love those movies because my dad said, when I was about 13, you really should watch 11
Rocky. He put it on one morning, and I just loved it…. I’m not a massive fan, but you can 12
watch it any time, it just gets you so pumped up, and the song just sticks, and it…just gets 13
you….pumped-up for anything. (Participant 4) 14
[because] it reminds me of…how I’ve been preparing…I’ve got to the match and I’ve 15
prepared myself, and…in the Rocky films, this is the music when he was training for his 16
fights, this is the music he was listening to. And when I’m doing my training, I listen to 17
this as well. (Participant 6) 18
Other participants selected tracks from films without sporting connotations: 19
Grounded Theory of Music Use 19
I heard it in Johnny 5...the Short Circuit film...I saw it quite recently, [because] I haven’t 1
seen it since I was really little, and we always really liked it. I saw it quite recently…at 2
the end it’s really good… (Participant 3) 3
All participants watched music video channels, and the videos accompanying music 4
tracks were often easily recalled: 5
…she’s in this house, like Alice in Wonderland, and I just think of that every time I hear 6
this song, I don’t know why. It’s just something that sticks in my mind. (Participant 5) 7
…she’s got black wings on. She’s just walking around singing it, and he’s dragging his 8
guitar. But apart from that, it’s just like a deserted caravan park… (Participant 3) 9
Acoustical properties. The properties of the tracks were cited as factors influencing 10
participants’ selection of music: 11
…it’s a really soothing tune. It doesn’t, like, work me up, and it doesn’t like, go over the 12
top; it’s just nice and level. (Participant 11) 13
It just makes me relaxed. It’s not very loud, it’s not heavy beats, heavy bass, it’s just very 14
like, relaxed. (Participant 9) 15
And also I like this bit; it’s in the middle of the song, and it changes, and she holds the 16
same note for a long time, and again, it like builds up and then it’s back to the words 17
again that make me feel that I can accomplish things. (Participant 12) 18
Identification with artist or lyrics. There was evidence that participants listened closely to 19
the lyrics of their selections and empathized with the artists (cf. Scherer & Zentner, 2001); 20
further, that this formed part of the decision-making process when selecting tracks: 21
Grounded Theory of Music Use 20
This is, again…the lyrics of this song, I think, make a lot of girls feel confident. She’s 1
basically talking about, “Boyfriend, there you go, and don’t come back!” She’s really 2
standing up for what she thinks. (Participant 11) 3
Um, there’s like, “I can climb a mountain high”…. It’s just like, I can do anything, I can 4
achieve anything. It makes me think of that when I listen to it. (Participant 12) 5
Emotional Responses to Music 6
All personal music tracks discussed in interview – without exception – were rated as 7
highly liked, highly arousing, and highly familiar (see Table 2); this was irrespective of the 8
intended emotional outcome. Participants’ mean ratings for liking and arousal were located in the 9
upper-right quadrant of Russell et al.’s (1989) Affect Grid. Tracks chosen for psyching-up were 10
played at the highest intensities and exhibited faster tempi than tracks selected for other 11
emotional outcomes; tracks selected in order to relax were lower, on average (see Table 2). 12
Three broad categories of emotional responses to music listening – intended or reported – 13
emerged as primary themes in diary data (see Table 3). Participants indicated that they used 14
music in order to psych-up, relax and/or to promote an attentional focus shift; feeling more 15
psyched-up or relaxed and improved or maintained mood were frequently reported outcomes. All 16
participants reported imagery in response to music heard during interview and exhibited overt 17
physical reactions. 18
Imagery. Participants used vivid description of visual images when listening to music 19
during interview, and sometimes appeared to reminisce: 20
I can actually picture one of the goals I scored when this is playing…..I just can’t think of 21
anything bringing back such a strong memory as this song….it’s so vivid, especially if I 22
Grounded Theory of Music Use 21
close my eyes, especially when I’m listening to this song. I remember the goal I scored, 1
the pitch we were playing on, I remember everything…(Participant 8) 2
Participants also described auditory imagery, which can be described as “hearing in the 3
mind’s ear” (A. P. Moran, personal communication, September 5, 2005): Participants remarked 4
that they sing along to various tracks, even in the absence of the physical stimulus; this extended 5
to hearing the song in their mind’s ear while on court: 6
Yeah, I sing on court….It would be California, or a specific one. It would be specific 7
phrases in my head. (Participant 2) 8
Physical reactions. Given that emotional responses have behavioral (Frijda, 1986) and 9
facial expression (Russell & Bullock, 1985) components, participants’ reactions to music tracks 10
were noted and subsequently incorporated into the model (see Figure 1). The most frequently 11
observed reactions were smiling (in response to 58 out of 70 tracks), increased motor behavior 12
(24/70), and piloerection (7/70); however, these responses did not differ as a function of the 13
intended emotional outcome of listening to each music selection. 14
Discussion 15
The main objectives of this study were (a) to examine the use of music to manipulate 16
emotional states by young tennis players who indicated the use of music listening as a pre-17
performance strategy, in order to gain a better understanding of their emotional responses to 18
music and the factors that mediate these responses; and (b) to put forward a model grounded in 19
present data which will provide a template to guide not only athletes’ music selections, but also 20
future research efforts which seek to identify causal relationships between emotional responses to 21
pre-performance music and performance. Qualitative and quantitative interview and diary data 22
Grounded Theory of Music Use 22
were combined to generate a grounded theory of this phenomenon. Grounded theory (Glaser & 1
Strauss, 1967) was considered the best method, because such an approach is most likely to 2
enhance our understanding and to guide subsequent action (Strauss & Corbin, 1998). 3
Central to the present data is the fact that all participants selected music to manipulate 4
their emotional state. Music selections were highly idiosyncratic: Considerable interindividual 5
differences existed in the genre and acoustical properties of tracks selected to achieve identical 6
emotional states. For example, Participant 1 selected Still D.R.E. by Snoop Dogg and Dr. Dre 7
(Gangsta Rap; 92 bpm) in order to feel confident, while Participant 3 selected Holding Out For A 8
Hero by Bonnie Tyler (Power Ballad; 144 bpm) to achieve the same end. Some participants used 9
a medley of tracks in an attempt to attain an ideal emotional state, consistent with the notion that 10
idiosyncratic emotional profiles are necessary for successful sporting performance (Edmonds et 11
al., 2006). Given that (a) research has shown that emotional profiles fluctuate considerably in the 12
week leading up to competition (Hanton et al., 2004), (b) music listening pervaded daily diary 13
entries, and (c) improved/maintained mood was an oft-cited consequence of music listening, 14
stricter control of music heard in the lead-up to competition may be an important strategy for 15
regulating young athletes’ pre-performance mood; a notion supported by work examining non-16
athletic populations (Saarikallio & Erkkilä, 2007; R.E. Thayer, Newman, & McClain, 1994). 17
The determinants of participants’ emotive music in the present model can be delineated 18
according to Sloboda and Juslin’s (2001) classification of extrinsic and intrinsic sources of 19
emotion in music. Four of the five determinants can be classified as extrinsic factors; acoustical 20
properties are intrinsic. Extrinsic sources of emotion were mentioned more frequently, and with 21
greater description, than intrinsic sources. Participants’ music selections were also highly 22
idiosyncratic (typified by a broad range of artists and genres represented), which may reflect the 23
inescapably unique combination of peer and family influences on their cultural exposure to music 24
Grounded Theory of Music Use 23
(cf. North & Hargreaves, 1995). Thus, extrinsic sources appear to be stronger determinants of 1
emotional content than are the acoustical properties, as per Sloboda and Juslin’s (2001) assertion. 2
Music also offers young people the opportunity to create a strong, albeit temporary, unique self-3
identity (Larson, 1995). Participants were able to draw such an identity from the artist’s 4
performance of the selected tracks (cf. empathy route; Scherer & Zentner, 2001), which might 5
have occurred independently of peer/familial influences. 6
While some data were evidently determinants of, or responses to, music listening, other 7
recurrent data concepts were not so clearly delineated. These concepts were combined with 8
extant research (e.g., Scherer & Zentner, 2001) to develop a set of theoretical mediatory factors 9
for inclusion in the model (demarcated by dashed lines). They were included so that process – an 10
essential part of Strauss and Corbin’s (1998) approach to theory building – could be incorporated 11
into the model. Diary data indicated three potential situational mediators of liking and arousal 12
potential. Participants had a desired emotional state to attain, largely to psych-up, relax, or 13
dissociate, which suggests that improvement of emotional state was an important regulatory goal 14
(cf. Saarikallio & Erkkilä, 2007). Their present emotional state was quite often negative, 15
indicating that music more frequently served to enhance mood (cf. R.E. Thayer, Newman, & 16
McClain, 1994). Further, all data sources uncovered a diverse array of listening environments, 17
including in a car, on a train, in the locker-room, on-court, and in the gym. Given the interaction 18
between affective auditory and visual stimuli (Baumgartner, Lutz, Schmidt, & Jäncke, 2006), the 19
impact of music might have been diminished or enhanced by such environmental factors. Thus, 20
practitioners choosing to prescribe music listening as a pre-performance strategy should take the 21
athlete’s immediate environment into account when doing so. 22
Three determinants of emotive music were considered modifiable, in that the athlete is 23
able to base their instantaneous selection upon these factors, in order to manipulate the content 24
Grounded Theory of Music Use 24
and intensity of the experienced emotions. They were extra-musical associations, acoustical 1
properties, and inspirational lyrics. Because (a) all participants watched music video channels 2
and listened to commercial radio stations, and (b) evidence exists for a positive relationship 3
between exposure and music preferences (North & Hargreaves, 1995; Witvliet & Vrana, 2007), 4
exposure was also included as a potentially influential mediator. This refers not only to the 5
frequency and volume of exposure to music tracks, but also to the pairing of tracks with a 6
strongly emotive event in the past; this potent memory route for emotion induction may 7
supersede other more deliberative (cognitive) mechanisms (Scherer & Zentner, 2001). It is 8
feasible to promote such extra-musical associations through the creation of personal music 9
videos: A music track which has been paired with a motivational and technically exemplary 10
video may elicit a (learned) dispositional representation (Damasio, 1994) of a facilitative 11
emotional state via Scherer and Zentner’s memory route. 12
Once a music track has been selected, there is potential to modify some physical attributes 13
of the listening situation at the delivery stage, to mediate the experienced emotional intensity. 14
Pre-competitive emotions may persist and fluctuate over the course of one week (Hanton et al., 15
2004); therefore, listening-performance onset delay was included in the model. According to 16
Sloboda and Juslin (2001), the intrinsic music property tempo is one of the most potent 17
determinants of emotional response. Contemporary technology affords the music consumer the 18
opportunity to manipulate such modifiable music properties; and the mode of delivery (e.g., iPod 19
via headphones) considerably affects the fidelity of the reproduction of the original sound 20
(contextual features; Scherer & Zentner, 2001). Also, the global proliferation of MP3 players 21
means that music consumers are afforded a unique privatized auditory environment, regardless of 22
location. This portability has important implications for an athlete’s perception of, and affective 23
response to, his or her environment – external (Baumgartner et al., 2006) or internal (Copeland & 24
Grounded Theory of Music Use 25
Franks, 1991). Hence, modifiable music properties and mode of delivery were important 1
additions to a contemporary model of music listening in sport. 2
Because modern MP3-playing technology features such as time scaling enable the user to 3
alter the tempo of a track without affecting its pitch, the same track can satisfy a greater number 4
of pre-performance needs. Athletes can moderate both the intensity and content of experienced 5
precompetitive emotions by manipulating the intrinsic (e.g., tempo) and extrinsic (e.g., extra-6
musical associations) properties of their music selections according to the demands of the sport or 7
subcomponent of that sport. J. F. Thayer and Faith (2001) noted that “Valence represents the 8
evaluative outcome necessary to initiate an approach or withdrawal response, and arousal reflects 9
the investment in the directional tendency” (p. 456). A loud, fast, and highly disliked track may 10
be appraised as not only very unpleasant, but also as potentially harmful, resulting in 11
(paradoxically) adaptive reorienting motor responses (cf. Zentner & Kagan, 1996). 12
Players unanimously rated all selected tracks as highly liked (M = 10.1) and highly 13
arousing (M = 9.3), regardless of intended emotional outcome, including tracks purportedly 14
selected for relaxation. This finding is comparable to that of Saarikallio and Erkkilä (2007), 15
whose participants used music predominantly to strengthen positive feelings, to move away from 16
negative feelings, and to increase emotional intensity (cf. R. E. Thayer, Newman, & McClain, 17
1994). However, the present quantitative data may also reflect the nature of a sport that is 18
intensely competitive, and requires frequent and intense bursts of energy; relaxed in this context 19
may refer to playing style, as opposed to complete psychophysical relaxation: 20
So, in my mind, I feel like I’ve got to be calm and relaxed, but I’ve still got to have 21
energy, and, like run down balls and things like that. So it reflects how I want to be in a 22
tennis match. (Participant 12) 23
Grounded Theory of Music Use 26
The tempo of tracks associated with relaxation was also noticeably lower than for tracks 1
selected for other emotional outcomes. Conversely, tracks associated with psyched-up were 2
played at a higher intensity (M = 102.3 dBA) and exhibited faster tempi (M = 120.9 bpm) than all 3
other tracks, consistent with Scherer and Zentner’s (2001) suggestion that proprioceptive 4
feedback prompts the individual to couple internal rhythms with such external drivers. This is 5
also supported by research into the relationship between exercise heart rate and preferred music 6
tempo (Karageorghis, Jones, et al., 2006). However, these quantitative data contrast with the high 7
subjective arousal values provided for all tracks used for relaxation. This appears to reinforce the 8
notion that strong emotional experiences with music are influenced by situational, music, and 9
personal factors, and may also reflect Saarikallio and Erkkilä’s (2007) unification of relaxation 10
and getting energy goals of music listening into an ultimate regulatory strategy of revival. 11
Visual imagery is an important strategy by which athletes can regulate their emotions 12
(Jones, 2003), and it emerged as a theme in interview data. However, the present participants also 13
reported auditory imagery (e.g., singing to oneself) as a consequence of music listening. Auditory 14
imagery obeys the same neural principles as visual imagery: Association cortex reconstructs the 15
original percept, such that it is possible to “have a song on the brain” in the absence of the 16
physical stimulus, especially when the track is familiar (Kraemer, Macrae, Green, & Kelley, 17
2005). Therefore, singing the lyrics of a familiar music track to oneself may be another powerful 18
means for achieving a performance-facilitating emotional state; prior combination of the familiar 19
track with an emotive video may enhance this effect further (cf. Baumgartner et al., 2006). 20
At all stages of data collection, participants reported an attentional focus shift through 21
music listening. Diary data (Table 3) suggests that this was primarily dissociative, in line with 22
past research (Szabo et al., 1999); however, exercise intensity was not a limiting factor, as the 23
majority of listening episodes occurred in a non-exercising state. Therefore, singing may not only 24
Grounded Theory of Music Use 27
enable athletes to dissociate from stressors in the competitive environment, but could also 1
function as part of a pre-performance routine to promote automaticity; this active attentional 2
manipulation would render over-deliberation difficult, thereby safeguarding against choking 3
(Baumeister, 1984). Neurophysiology researchers are increasingly identifying the neural 4
correlates of emotional responses to music (e.g., Menon & Levitin, 2005), and wider availability 5
of neural mapping technology means that we can now more readily investigate the relation 6
between pre-performance music and activation of brain areas involved in self-referential 7
processes, for example; this could extend to investigation of these regions when attending to 8
visual stimuli immediately post-music listening. 9
Valence is a fundamental component of emotional life (e.g., Russell, 1980) and happiness 10
– a component of the feeling of pleasure – is a key emotional construct in contemporary sport 11
emotion research (Jones et al., 2005). The recurrence of pleasure and displeasure throughout all 12
stages of data collection corroborated the presence of positive or negative valence in all recorded 13
emotional responses; and diary responses indicated improved or maintained mood as a 14
consequence of music listening, consistent with past research (R. E. Thayer, Newman, & 15
McClain, 1994). Pleasure was also very evident in participants’ facial expressions, but these data 16
were somewhat limited, which is unfortunate when considering the universality and predictive 17
value of facial expressions of emotions (Russell & Bullock, 1985). This was due largely to the 18
absence of electromyographic (EMG) measures. Witvliet and Vrana (2007) found that 19
participants’ zygomatic (smiling) EMG activity was greater for high-arousal positive music than 20
for all other music, and corrugator (frowning) activity was decreased with increased exposure 21
to/familiarity with all music. Participants in the present study only selected highly familiar music; 22
therefore the emotions being expressed – facially or otherwise – were predisposed to be highly 23
positive. 24
Grounded Theory of Music Use 28
Another limitation of the present study was the fact that only 2 of the 14 participants had 1
sufficient knowledge of musical structure to articulate some of the properties of the music they 2
had selected. As a consequence, it was difficult to accurately discriminate the role of different 3
intrinsic sources in determining participants’ emotional responses to their music selections. The 4
use of an instrument such as the BMRI-2 (Karageorghis, Priest et al., 2006) may circumvent this. 5
Participants rated all music selections as highly liked and highly arousing. Therefore, correlating 6
BMRI-2 item scores with Affect Grid (Russell et al., 1989) ratings, for example, may enable us to 7
more accurately identify the relative contributions of extrinsic and intrinsic sources of emotion in 8
athletes’ responses to highly liked, highly arousing music. 9
Conclusion 10
In summary, one of the most notable and recurrent themes throughout all stages of data 11
collection was the active use of music as an emotional regulation strategy, corroborating past 12
findings (Gluch, 1993). A host of environmental and contextual factors appear to influence an 13
individual’s music preferences, culminating in a highly individualized portfolio of music tracks. 14
Although components within the first stage of the model will serve to sensitize the practitioner or 15
athlete to the selection of appropriate music, there is low potential here for intervention. 16
Conversely, the factors at the music selection and delivery stages are easily manipulable, and 17
represent a potentially fruitful avenue for future investigation. For example, increasing the tempo 18
and/or intensity of a musical excerpt may increase the magnitude of an affective response and 19
concomitant action tendencies (Frijda, 1986) such as increased motor behavior. This component 20
of the fight-flight response not only relates to Damasio’s (2000) life-preservation role for 21
emotions, but could also mean the difference between sporting success and failure. 22
Grounded Theory of Music Use 29
References 1
Amezcua, C., Guevara, M. A., & Ramos-Loyo, J. (2005). Effects of musical tempi on visual 2
attention ERPs. International Journal of Neuroscience, 115, 193-206. 3
Atkinson, G., Wilson, D., & Eubank, M. (2004). Effects of music on work-rate distribution 4
during a cycling time trial. International Journal of Sports Medicine, 25, 611-615. 5
Baumeister, R. F. (1984). Choking under pressure: Self-consciousness and paradoxical effects of 6
incentives on skillful performance. Journal of Personality and Social Psychology, 46, 7
610-620. 8
Baumgartner, T., Lutz, K., Schmidt, C. F., & Jäncke, L. (2006). The emotional power of music: 9
How music enhances the feeling of affective pictures. Brain Research, 1075, 151-164. 10
Boutcher, S. H., & Trenske, M. (1990). The effects of sensory deprivation and music on 11
perceived exertion and affect during exercise. Journal of Sport & Exercise Psychology, 12
12, 167-176. 13
Copeland, B. L., & Franks, B. D. (1991). Effects of types and intensities of background music on 14
treadmill endurance. The Journal of Sports Medicine and Physical Fitness, 31, 100-103. 15
Crust, L., & Clough, P. J. (2006). The influence of rhythm and personality in the endurance 16
response to motivational asynchonrous music. Journal of Sports Sciences, 24, 187-195. 17
Damasio, A. (1994). Descartes' error: Emotion, reason, and the human brain. London: Penguin. 18
Damasio, A. (2000). The feeling of what happens: body, emotion and the making of 19
consciousness. London: Vintage. 20
Dey, I. (2003). Grounded theory. In C. Seale, G. Gobo, J. F. Gubrium & D. Silverman (Eds.), 21
Qualitative research practice (pp. 80-93). Thousand Oaks, CA: Sage. 22
Grounded Theory of Music Use 30
Edmonds, W. A., Mann, D. T. Y., Tenenbaum, G., & Janelle, C. M. (2006). Analysis of affect-1
related performance zones: An idiographic method using physiological and introspective 2
data. The Sport Psychologist, 20, 40-57. 3
Ekman, P. (1972). Universals and cultural differences in facial expressions of emotion. Paper 4
presented at the Nebraska Symposium on Motivation, Lincoln, Nebraska. 5
Ekman, P., Levenson, R. W., & Friesen, W. V. (1983). Autonomic nervous system activity 6
distinguishes among emotions. Science, 221, 1208-1210. 7
Frijda, N. H. (1986). The emotions. New York: Cambridge University Press. 8
Gabrielsson, A. (2001). Emotions in strong experiences with music. In P. Juslin & J. A. Sloboda 9
(Eds.), Music and emotion: Theory and research (pp. 431-449). Oxford, UK: Oxford 10
University Press. 11
Glaser, B., & Strauss, A. (1967). The discovery of grounded theory. Chicago: Aldine. 12
Gluch, P. D. (1993). The use of music in preparing for sport performance. Contemporary 13
Thought, 2, 33-53. 14
Hanin, Y. L. (2000). Emotions in sport. Champaign, IL: Human Kinetics. 15
Hanton, S., Thomas, O., & Maynard, I. (2004). Competitive anxiety responses in the week 16
leading up to competition: the role of intensity, direction and frequency dimensions. 17
Psychology of Sport and Exercise, 5, 169-181. 18
Jones, M. V. (2003). Controlling emotions in sport. The Sport Psychologist, 17, 471-486. 19
Jones, M. V., Lane, A. M., Bray, S. R., Uphill, M., & Catlin, J. (2005). Development and 20
validation of the Sport Emotion Questionnaire. Journal of Sport & Exercise Psychology, 21
27, 407. 22
Grounded Theory of Music Use 31
Karageorghis, C. I., Drew, K. M., & Terry, P. C. (1996). Effects of pretest stimulative and 1
sedative music on grip strength. Perceptual and Motor Skills, 83, 1347-1352. 2
Karageorghis, C. I., Jones, L., & Low, D. C. (2006). Relationship between exercise heart rate and 3
music tempo preference. Research Quarterly for Exercise and Sport, 26, 240-250. 4
Karageorghis, C. I., Priest, D. L., Terry, P. C., Chatzisarantis, N. L. D., & Lane, A. M. (2006). 5
Redesign and initial validation of an instrument to assess the motivational qualities of 6
music in exercise: The Brunel Music Rating Inventory-2. Journal of Sports Sciences, 24, 7
899-909. 8
Karageorghis, C. I., Terry, P. C., & Lane, A. M. (1999). Development and initial validation of an 9
instrument to assess the motivational qualities of music in exercise and sport: The Brunel 10
Music Rating Inventory. Journal of Sports Sciences, 17, 713-724. 11
Kraemer, D. J. M., Macrae, C. N., Green, A. E., & Kelley, W. M. (2005). Musical imagery: 12
sound of silence activates auditory cortex. Nature, 434, 158. 13
Larson, R. (1995). Secrets in the bedroom: Adolescents' private use of media. Journal of Youth 14
and Adolescence, 24, 535-550. 15
Lazarus, R. S. (1991). Emotion and adaptation. New York: Oxford University Press. 16
Panksepp, J. (1998). Affective neuroscience: The foundations of human and animal emotions. 17
New York: Oxford University Press. 18
Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic enquiry. Beverley Hills, CA: Sage. 19
Menon, V., & Levitin, D. J. (2005). The rewards of music listening: Response and physiological 20
connectivity of the mesolimbic system. NeuroImage, 28, 175-184. 21
Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook 22
(2nd ed.). Thousand Oaks, CA: Sage Publications Ltd. 23
Grounded Theory of Music Use 32
North, A. C., & Hargreaves, D. J. (1995). Subjective complexity, familiarity, and liking for 1
popular music. Psychomusicology, 14, 77-93. 2
North, A. C., & Hargreaves, D. J. (1997). Liking, arousal potential, and the emotions expressed 3
by music. Scandinavian Journal of Psychology, 38, 45-53. 4
Patton, M. Q. (2002). Qualitative research and evaluation methods (3rd ed.). Thousand Oaks, 5
CA: Sage. 6
QSR. (2003). NVivo (Version 2.0) [Computer software]. Doncaster, Victoria, Australia: QSR 7
International Pty Ltd. 8
Rapley, T. (2003). Interviews. In C. Seale, G. Gobo, J. F. Gubrium & D. Silverman (Eds.), 9
Qualitative research practice (pp. 15-33). Thousand Oaks, CA: Sage. 10
Ritossa, D. A., & Rickard, N. S. (2004). The relative utility of 'pleasantness' and 'liking' 11
dimensions in predicting the emotions expressed by music. Psychology of Music, 32, 5-12
22. 13
Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social 14
Psychology, 39, 1161-1178. 15
Russell, J. A., & Bullock, M. (1985). Multidimensional scaling of emotional facial expressions : 16
Similarity from preschoolers to adults. Journal of Personality and Social Psychology, 48, 17
1290-1298. 18
Russell, J. A., Weiss, A., & Mendelsohn, G. A. (1989). Affect grid: A single-item scale of 19
pleasure and arousal. Journal of Personality and Social Psychology, 57, 493-502. 20
Saarikallio, S., & Erkkilä, J. (2007). The role of music in adolescents' mood regulation. 21
Psychology of Music, 35, 88-109. 22
Grounded Theory of Music Use 33
Scherer, K. S. (2004). Which emotions can be induced by music? What are the underlying 1
mechanisms? And how can we measure them? Journal of New Music Research, 33, 239-2
251. 3
Scherer, K. R., & Zentner, M. R. (2001). Emotional effects of music: Production rules. In P. 4
Juslin & J. A. Sloboda (Eds.), Music and emotion: theory and research (pp. 361-392). 5
Oxford, UK: Oxford University Press. 6
Simpson, S. D., & Karageorghis, C. I. (2006). The effects of synchronous music on 400-m sprint 7
performance. Journal of Sports Sciences, 24, 1095-1102. 8
Sloboda, J. A., & Juslin, P. N. (2001). Psychological perspectives on music and emotion. In P. N. 9
Juslin & J. A. Sloboda (Eds.), Music and emotion: Theory and research (pp. 71-104). 10
New York: Oxford University Press. 11
Strauss, A., & Corbin, J. (1998). Basics of qualitative research: Techniques and procedures for 12
developing grounded theory. London: Sage. 13
Szabo, A., Small, A., & Leigh, M. (1999). The effects of slow- and fast-rhythm classical music 14
on progressive cycling to physical exhaustion. Journal of Sports Medicine and Physical 15
Fitness, 39, 220-225. 16
Thayer, J. F., & Faith, M. (2001). A dynamical systems interpretation of a dimensional model of 17
emotion. Scandinavian Journal of Psychology, 42, 121-133. 18
Thayer, R. E., Newman, J. R., & McClain, T. M. (1994). Self-regulation of mood : Strategies for 19
changing a bad mood, raising energy, and reducing tension. Journal of Personality and 20
Social Psychology, 67, 910-925. 21
Grounded Theory of Music Use 34
Witvliet, C. V. O., & Vrana, S. R. (2007). Play it again Sam: Repeated exposure to emotionally 1
evocative music polarises liking and smiling responses, and influences other affective 2
reports, facial EMG, and heart rate. Cognition and Emotion, 21, 3-25. 3
Zentner, M. R., & Kagan, J. (1996). Perception of music by infants. Nature, 383, 29. 4
Grounded Theory of Music Use 35
Footnotes 1
1. The final version is available on request from the author responsible for correspondence. 2
2. This device was calibrated using a Brüel and Kjær (Nærum, Denmark) Sound Level 3
Calibrator, type 4231. 4
3. A full listing of genres is available on request from the author responsible for correspondence. 5
6
Grounded Theory of Music Use 36
Table 1 1
Intended Emotional Outcomes of Music Listening 2
3
Raw Data Theme
s
(k
= 42)
First Order Themes (
k
=
18)
General dimensions
(
k
= 5)
Able to focus
Concentration
Ability to focus
Clear mind
Clear mind under pressure
Clea
r mind
Clearly focused
Focused
Zoned in
Ke
yed in
Focused
Mentally in control
Mentally prepared
Prepared
Mentally prepared
Appropriate mental focus
Belief and confidence
Self
-
belief
Confident
Fearless, courageous
Conf
ident
Remembr
ance of previous good play
Past performance success
Confident
Feeling fresh
Feeling fresh
General happiness
Feeling happy
Positive
Positive attitude
Positive/happy
Positivity
Tough (mentally)
Mentally tough
Positive emotional state
Energiz
ed
Excited/eager
Fire it up
Energized
Motivated
Wanting the satisfaction of winning
Determined
Motivated (ready for anything)
Never give up
Prepared for a fight
Willing to fight to end
Up for it
Driven to win
Psyched
-
up
Psyched-
up
Pumped
-
up
Pumped
Pumped
-
up
Psyched
-
up
Calm
Calm thinking
Calmness
Calm
Loose/"no worries"
No worries
Relaxed
Relaxed/chi
lled/breathe
Relaxed
Relaxed
Grounded Theory of Music Use 37
Table 2 1
Quantitative Data for Participants’ Personal Music Selections 2
Emotional
state
(no. of
responses)
Selected
intensity/
dbA
Tempo/
bpm
Liking
1
Arousal
potential
2
Familiarity
3
Popularity
with peers
4
Appropriate
mental
focus
(n = 15)
Mean: 93.8 101.3 10.2 8.7 9.5 7.7
Median: 88.4 96.0 10.0 9.0 10.0 8.0
Mode: N/A 96.0 10.0 10.0 11.0 10.0
Confident
(n = 16)
Mean: 98.8 113.0 9.6 9.0 9.2 7.1
Median: 104.6 111.0 10.0 9.0 10.0 7.0
Mode: 108.9 96.0 10.0 9.0 10.0 6.0
Positive
emotional
state
(n = 7)
Mean: 93.1 114.0 10.3 9.7 9.3 5.3
Median: 84.6 126.0 10.0 10.0 10.0 5.0
Mode: N/A N/A 11.0 10.0 10.0 3.0
Psyched-up
(n = 21)
Mean: 102.3 120.9 10.2 10.5 8.9 6.6
Median: 108.7 132.0 11.0 11.0 11.0 6.0
Mode: 108.9 90.0 11.0 11.0 11.0 9.0
Relaxed
(n = 11)
Mean: 87.9 87.9 10.6 9.2 9.9 6.9
Median: 88.6 84.0 11.0 10.0 10.0 8.0
Mode: N/A 72.0 11.0 10.0 11.0 9.0
3
1. 1 = -5 = not at all liked; 11 = +5 = highly liked 4
2. 1 = -5 = not at all energizing; 11 = +5 = highly energizing 5
3. 1 = -5 = not at all familiar; 11 = +5 = highly familiar 6
4. 1 = -5 = not at all popular; 11 = +5 = highly popular 7
Grounded Theory of Music Use 38
Table 3 1
Diary Data: Situational Mediators and Emotional Outcomes of Music Listening 2
Item Illustrative Quotations First Order Theme General Outcome
“Because it gives me a lot of energy and it gets me going.”
“I use it to get me fire up (I had match practice this day).”
To get pumped or psyched-up (n = 12) Psyched-up (n = 12)
“Just to relax me, unwind myself and chill out, clear my mind.”
“Because I just wanted to listen to some music that I find relaxes m
e.”
To relax or calm down (n = 11)
Relaxed (n = 11)
“Just as background music, no particular preference, just let the music play.”
“While I’m writing this it just gives me something else to do while I’m writing do
wn.”
To accompany another activity (n = 8)
“I was super super bored.”
“Had listened to the rest of the CDs, and fancied a bit of a change. It was a bit faster…”
To alleviate boredom (n = 7)
Why did you choose to listen to this
particular track/artist/type of music?
“Just to take my mind off the traffic, the pressure of the tournament, draw, etc.”
As a distraction (n = 1)
Attentional focus shift
(n = 16)
“Good mood, [because] I was very relaxed and it was such a nice day.”
“Good mood, positive and confident.”
Good mood (n = 18)
“Ok.”
“I was in an OK mood, but as usual music made working out much easier!”
Okay (n = 7)
Positive mood
(n = 25)
“I felt a bit tired and quiet.”
“A little tired. I’m not good at waiting around, so I was
a little bored.”
Tired (n = 18)
“Bad mood.”
“I felt…a bit annoyed because I had to pull out of [a tournament].”
Bad mood (n = 4)
“Slightly down.”
“Slightly depressed and very tired.”
Depressed (n = 3)
“I was nervous before listening to the music.”
Nervous (n = 1)
How did you feel BEFORE hearing
it?
“…just a bit bored.”
Bored (n = 1)
Negative mood
(n = 27)
“…I became happy, laughing.”
“The music as usual made me feel a lot better.”
Good mood (n = 22)
“None really.”
“No change.”
No change (n = 2)
Improved or maintained
mood (n = 24)
“Feeling good, Pumped up!”
“…the music made me forget the t
iredness a bit and pumped me up.”
Pumped / psyched-up (n = 18) Psyched-up (n = 18)
“This music by [artist] always helps me to relax and unwind.”
What effect did the music have on
your mood/behavior, if any?
“It just relaxed me, and I don’t think I thought about anything while listening to it.”
Calm / relaxed (n = 8)
Relaxed (n = 8)
“Being home in East London.”
“Usually in my dad’s car on the way to a tennis match.”
Places (n = 10)
“I now associate it with that rugby match.”
“Dancing in a cl
ub.”
Events (n = 6)
Places or events
(n = 16)
“The Aussie guys in my house, who were singing very funnily to it.”
“A friend I went to school with, because he wrote it for me.”
Friends (n = 13)
“My sister. It reminds me of when we go to our favorite club in Liverpool.”
Family (n = 2)
Significant others
(n = 15)
“Nothing I can think of.”
What do you associate with this
music?
“None.”
Nothing (n = 11)
Nothing (n = 11)
3
Grounded Theory of Music Use 39
Figure 1. A model of young tennis players’ use of music to manipulate emotional state.1
Grounded Theory of Music Use 40
Emotional responses
Pool of
emotive music
Determinants of
emotive music
Emotional intensity
mediators (delivery)
Listening-performance
onset delay
2
Mode of delivery
2
e.g., MP3 player, car
audio system
Modifiable music
properties
1
e.g., tempo, pitch,
intensity
Pleasure
1,2,3,4
Altered arousal
1,2,3
e.g., psych-up, relax
Attentional focus shift
1,2,3
e.g., dissociation
Confidence
1,3
Imagery
1,2
e.g., visual, auditory
Improved or maintained
mood
2
Physical reactions
4
e.g., heightened or
depressed motor activity
Situational
mediators
Desired emotional state
1,2,3
e.g., psyched-up,
confident
Environmental factors
2,3
e.g., traveling to competition
or working out in the gym
Present emotional state
2
e.g., good mood, nervous
Extra-musical associations
1
e.g., past performances,
iconic film
Acoustical properties
1
e.g., rhythm, melody,
harmony
Inspirational lyrics
1
e.g., “I can climb a
mountain high”
Exposure
2
e.g., radio, music TV
Modifiable emotional
content and intensity
mediators (selection)
Extra-musical
associations
1,2
Peer and family
influences
1
Film soundtracks and
music videos
1
Acoustical properties
1
Identification
with artist or lyrics
1
Music
selection
Listening to
liked and
subjectively
arousing
music
1
1. Interview data. 2
2. Diary data. 3
3. Questionnaire data. 4
4.
Interview observation data.
5
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