research NO PLAGERISM

Self-expression through sport participation: exploring participant desired self-image Jerred Junqi Wanga, Daniel L. Wann b, Zhenqiu (Laura) Luc and James J. Zhangd

aDepartment of Sport Management, Wellness & Physical Education, University of West Georgia, Carrollton, GA, USA; bDepartment of Psychology, Murray State University, Murray, KY, USA; cDepartment of Educational Psychology, University of Georgia, Athens, GA, USA; dDepartment of Kinesiology, University of Georgia, Athens, GA, USA

ABSTRACT Research questions: The current study is aimed at providing preliminary answers to two research questions: (1) What salient self-images do people pursue for self-expression in the context of sport participation? (2) To what extent does participant desired self-image (PDSI) influence consumer behavior? Research methods: In Phase 1 of the study, a comprehensive review of literature, two focus groups, and an open-ended survey (N = 113) were conducted to generate the initial pool of self- images. In Phase 2, an exploratory factor analysis using online survey data (N = 370) was conducted to explore the underlying factor structure of PDSI. In Phase 3, a confirmatory factor analysis and a structural equation modeling analysis using online survey data (N = 483) were conducted to validate the proposed PDSI scale and test the influence of PDSI on consumer behavior. Results and findings: A PDSI measurement scale was developed and validated, resulting in 19 desired self-images under three dimensions: inner self-merit, lifestyle pursuance, and social self- presentation. Findings of the structural relationship model revealed that PDSI influenced personal involvement, money expenditure, and time expenditure. Implications: This study preliminarily unearthed salient items in PDSI, highlighted the symbolic nature of sport activities, and demonstrated the importance of PSDI in sport participation. These findings provided implications for practitioners to accommodate PDSI through long- term and integrated marketing efforts and shed a light on studies in branding, community sport, and public health.

ARTICLE HISTORY Received 5 December 2016 Accepted 21 February 2018

KEYWORDS Participant desired self- image; self-expression; self- branding; sport participation

Consumption is driven not only by the functional and emotional values that a product offers but also by the symbolic meanings derived from that product (Holt & Cameron, 2010; Levy, 1959). These non-functional symbolic meanings serve as salient and struc- tured language to express consumers’ self-image in contemporary society (Baudrillard, 1998; O’Cass & McEwen, 2004; Wattanasuwan, 2005). As noted by Swann (1983), effective symbols need to possess three features: being noticeable by others, being able to evoke certain specifiable reactions from others, and being able to be controlled by individuals.

© 2018 European Association for Sport Management

CONTACT Jerred Junqi Wang jwang@westga.edu Department of Sport Management, Wellness & Physical Edu- cation, University of West Georgia, 1601 Maple Street, Coliseum 2033, Carrollton, GA 30118, USA

EUROPEAN SPORT MANAGEMENT QUARTERLY 2018, VOL. 18, NO. 5, 583–606 https://doi.org/10.1080/16184742.2018.1446994

Given that regular sport participation displays these features, it increasingly serves as one of the effective tools for self-expression in various social occasions (Kirkcaldy, Shephard, & Siefen, 2002; Scheerder, Vanreusel, & Taks, 2005; Slutzky & Simpkins, 2009). The explora- tion of participant desired self-image (PDSI) could shed some light on utilizing symbolic meanings to promote sport participation and on understanding its interrelatedness and co-functions with branding, social development, and public health.

Sport marketing studies have begun to explore the symbolic consumption of sport pro- ducts, including but not limited to various forms of participatory activities, sporting goods, spectator sports, and media programming. Two research streams have emerged in the past decade. One has focused on the match-up effect of self-product symbolic congruity (e.g. Kang, 2002; Kwak & Kang, 2009; Sirgy, Lee, Johar, & Tidwell, 2008), finding that high con- gruity between self and product image promotes consumer behavioral outcomes (Birdwell, 1968; Sirgy, 1982, 1986). The other has focused on the brand meanings of sport products, such as brand personality of sport events (e.g. Lee & Cho, 2012), brand personality of sport teams (e.g. Braunstein & Ross, 2010; Heere, 2010; Ross, 2008), and athlete brand image (e.g. Arai, Ko, & Kaplanidou, 2013; Braunstein & Zhang, 2005; Carlson & Donavan, 2013). Both research streams have highlighted the key role of sport symbolic consumption; however, they have either focused on the match-up effect in the abstract image (e.g. ideal/ actual self-image) or overlooked the actual desires of sport participants. As a result, the self-images that are highly desired in sport participation remain unknown. Given that identifying effective product components serves as a preliminary and fundamental step to further improve production and delivery (Zhang, 2015), the current study sought to develop a measurement scale to identify salient desired self-images in sport participation and empirically assess the influence of PDSI on consumer behavior.

Review of literature

Symbolic consumption of sport participation

Symbolic value of sport participation As an antecedent of consumer purchasing behavior, product value constitutes the foun- dation of all marketing activities (Holbrook, 1994). Despite being somewhat diverse, scho- larly perspectives on product value have largely fallen into three areas: functional (utilitarian) value, symbolic value, and hedonic (experiential) value. As summarized in Table 1, functional value refers to the objective and instrumental usefulness of product attributes (e.g. durability, quantity, and sturdiness) that can solve practical and task- related problems (Bhat & Reddy, 1998; Mathews, Ambroise, & Brignier, 2009; Park, Jaworski, & Maclnnis, 1986; Smith & Colgate, 2007). Scholars have drawn the conceptual line between hedonic and symbolic value in two primary ways. One research stream con- ceptualizes symbolic value as a component of hedonic value because both are intangible and subject to personal interpretation (e.g. Hirschman & Holbrook, 1982). Another research stream, consisting primarily of branding studies, further separates symbolic value from the hedonic category and considers it an independent dimension of product value (e.g. Keller, 1993; Mathews et al., 2009; Park et al., 1986; Smith & Colgate, 2007). Specifically, hedonic value emphasizes intrinsic product attributes that have multi-sensory and affective benefits (e.g. pleasure, emotion, and feeling stimulation), whereas symbolic

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value involves extrinsic social meanings that people attach to products to fulfill their per- sonal and social needs (e.g. self-expression and outer-directed self-esteem) (Keller, 1993; Park et al., 1986; Smith & Colgate, 2007).

Generally, effective symbols possess at least three characteristics: being noticeable by others, being able to evoke certain specifiable reactions from others, and being able to be controlled by individuals (Swann, 1983). Nowadays, sport participation continues to garner tremendous consumer attention, as evidenced by all-pervasive sport products and increasingly health-conscious sport participants around the world. For example, 41% of European Union citizens (European Commission, 2014), 56% of the US

Table 1. Summarized literature on understanding the dimension of product value. Author Dimensions Conceptualization

Bhat and Reddy (1998) Functional value Related to specific and practical consumption problems

Symbolic value Related to self-image and social identification Hirschman and Holbrook (1982) Utilitarian value Tangible benefits of goods and services

Hedonic value Multi-sensory, fantasy, and emotive aspects of one’s experience with products

Keller (1993) Functional benefits Intrinsic advantages of product or service consumption; usually correspond to product- related attributes

Experiential benefits Feelings associated with using a product or service; usually correspond to product- related attributes;

Symbolic benefits Related to underlying needs for social approval or personal expression and outer-directed self-esteem; extrinsic advantages of product or service consumption; usually correspond to non-product-related attributes

Levy (1959) Functional value What products can do Symbolic value What products mean

Mathews, Ambroise, and Brignier (2009) Utilitarian value Instrumental (functional, task-related) and related to cognitive evaluation; linked to the notion of product performance and usefulness

Hedonic value Subjective and emotional; related more to fun and entertainment than to task completion

Symbolic value Less product-related than hedonic benefits; includes self-expression, social approval, and self-esteem

Park, Jaworski, and Maclnnis (1986) Functional needs Needs for products that solve consumption- related issues

Symbolic needs Desires for products that fulfill internally generated needs for self-enhancement, role position, group membership, or ego identification

Experiential needs Desires for products that provide sensory pleasure, variety, and/or cognitive stimulation

Smith and Colgate (2007) Functional/ instrumental value

The extent to which a product (good or service) has desired characteristics, is useful, or performs a desired function

Experiential/hedonic value

The extent to which a product creates appropriate experiences, feelings, and emotions for the customer

Symbolic/expressive value

The extent to which customers attach or associate psychological meaning to a product

Cost/sacrifice value The extent to which customers minimize the costs and other sacrifices involved in the purchase, ownership, and use of a product

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population above the age of 6 (Physical Activity Council, 2016), and 60% of Australians above the age of 15 (Australian Bureau of Statistics, 2015) regularly participate in sport activities. Oftentimes, sport activities communicate positive, neutral, or even negative symbolic meanings that are shaped by activities’ characteristics, mass media, and mar- keting activities. Through sport participation, consumers build associations with par- ticular sport activities, often transferring the meaning of those objects to themselves (Gwinner & Eaton, 1999). The tie strength in those symbolic associations could be par- tially controlled by consumer willingness, such as invested money and exposure inten- sity. This positive construability at the micro level could reduce the risks of using symbolic products in self-expression (e.g. unpredictable time, meanings, strengths, and costs) and allow participants to be more accurate in building and maintaining con- nections with sport activities. Thus, it is reasonable to speculate that participation in sport activities generates symbolic value and could be utilized to express self-image (or self-concept), namely ‘the totality of the individual’s thoughts and feelings having reference to himself as an object’ (Rosenberg, 1979, p. 7).

According to Baudrillard (1998), consumer needs and wants in commercialized society have three features: (1) being unlimited due to a limitless promotion generated by the urban concentration, (2) being ongoing caused by the continuity of social com- petition, and (3) being systematic as the response to the entire cultural system. As a manifestation of consumer needs and wants, sport symbolic consumption for self- expression therefore is a longstanding and systematic process that involves both passive and active components. At the macro level, mass media and dominant brands code the commonly-shared symbolic meanings of sport activities (e.g. conspicu- ousness and valence), foster consumer demand for various symbolic meanings, and influence the way individuals consume these sport activities (Baudrillard, 1998; Lee, 1990). On this basis, the pursuit of self-expression through sport participation is increasingly controlled by the market, which leads to the reliance on market forces to navigate the system of objects. At the micro level, consumers show the initiative to build, maintain, or dissociate their connections with sport activities based on self- characteristics (e.g. genetic predispositions, learning history, personal goals, and inter- ests) and specific social environment (Cialdini et al., 1976; Elliott, 1999; Escalas & Bettman, 2003; Fournier, 1998; Hofmann, Strack, & Deutsch, 2008; Hogg, 1998). To achieve the desired state, consumers would constantly scan the social environment to identify potential target objects (Grubb & Grathwohl, 1967), strategically and integra- tively utilize the meanings of these objects (Baudrillard, 1998; Thompson & Loveland, 2015), and continuously self-examine their own consumption practices (Piacentini & Mailer, 2004). Therefore, sport symbolic consumption for self-expression is not discrete or isolated behavior. Rather, through the initiation, continuation, and advancement of an agenda for individuals to be ‘well’ or ‘healthy’, people are likely to consume an ongoing stream of sport activities and related products to achieve their desired self-images.

Antecedents of sport symbolic consumption

Self-verification tendency People tend to gravitate toward familiar, stable, and predictable things to reduce uncer- tainty and maintain a knowable and reliable social environment (Swann, 1983, 1990).

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Most often, people adopt one of two common self-verification strategies. One is to find opportunities to create a self-confirmatory social environment using signs and symbols, selective interaction, and interpersonal prompts. In the context of sport participation, individuals can confirm their self-image by using the social image of sport activities and interacting with other participants. The other strategy is to look for more self-confirma- tory evidence than actually exists, primarily through intrapersonal channels such as selec- tive attention, selective encoding and retrieval, and selective interoperation (Swann, 1983, 1990). For instance, people can selectively participate in certain sport activities or selec- tively construct the meaning of sport participation. Through both of an actual social environment and a subjective intrapersonal mentality, individuals construct social images to confirm their self-concept in everyday life (Lecky, 1945).

Self-enhancement tendency Self-enhancement refers to one’s motivation to increase feelings of personal worth, gain social approval, and maximize positive feedback from others (Epstein, 1983; Escalas & Bettman, 2003; Schlenker, 1980). According to the self-concept enhancement tactician (SCENT) model (Sedikides & Strube, 1997), self-enhancement strategies usually fall on the spectrum between candid and tactical. Candid self-enhancement refers to overt expressions of self-superiority; in contrast, tactical self-enhancement consists of subtler expressions of self-love, taking into consideration long-term repercussions and situational, social, and societal constraints. Compared with candid self-enhancement, which is likely to lead to negative consequences such as unfavorable impressions, mockery, or social exclusion, tactical self-enhancement is more acceptable and persistent in socialization (Sedikides, Gaertner, & Toguchi, 2003). Given that various sport activities carry plentiful symbols that are consistent with contemporary social norms, sport participation is likely to be an ideal channel for tactical self-enhancement. For example, in the social context of North America, playing golf can be an option for an up-scale image that elevates one’s social status; boxing can indirectly satisfy one’s desire for an image of toughness; and jogging can provide an image of healthy lifestyle.

Social environment According to the social learning theory (Bandura, 1977, 1989), cognitive development is influenced by social agents who disseminate norms, attitudes, motivations, and beha- viors to the learner (e.g. parents, significant others, peers, and mass media). Specifically, human beliefs, desires, and behavior are developed and modified by social agents through modeling and reinforcement (Bandura, 1977, 1989; Moschis & Churchill, 1978). The process of modeling requires vicarious learning. This capability allows humans to use deliberate or inadvertent observation, rather than direct involvement or participation, to understand and manage environment stimuli. Through this observa- tional learning and modeling of heightened behavior that is portrayed symbolically through various social agents, individuals garner tremendous multiplicative power for acquiring new knowledge (Bandura, 1989). Reinforcement refers to the ways in which behavior is either rewarded or punished. External reinforcement primarily comes from social agents, while intrinsic reinforcement occurs through one’s internal value system (e.g. pride, satisfaction, and sense of accomplishment) (Bandura, 1977). Both reinforcement mechanisms guide individuals to learn which self-images are

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acceptable or unacceptable in their social environment, further encouraging or discoura- ging them to acquire such self-images. Mechanisms of both modeling and reinforce- ment underscore the importance of social influences (e.g. media, brands, and value system) in shaping people’s ongoing symbolic consumption and highlight the systematic nature of building and maintaining self-image in daily consumption.

Desired self-images in sport participation

In sport marketing, studies about symbolic consumption have followed one of two primary streams. The first stream is grounded in self-congruity research (Birdwell, 1968; Sirgy, 1982, 1986), primarily examining the match-up effect of consumer self-image and product/brand user-image on purchase behavior. According to these studies, the higher the congruity between consumer self-image and typical user-image of a given product, the greater purchase intention a consumer will have. In the past decade, scholars have examined this theoretical proposition in multiple sport settings. Using two experimental studies, Kang (2002) assessed the validity of the self-congruity effect in ski participation. Findings showed that when the user-image of skiing matched actual self-image (i.e. the characteristics that someone believes he or she actually possesses) and ideal self-image (i.e. characteristics that someone ideally would like to possess), consumers were likely to have a higher purchase intention. Kwak and Kang (2009) further examined the effect of self-congruity on the purchase of sport- related products. The results indicated that the match-up between self-image (both actual and ideal) and typical user-image of sport products increased perceived quality and purchase intention. In addition, Sirgy et al. (2008) showed that the congruity between self-image and event image increased consumer loyalty toward brands sponsoring a sport event.

This research stream has illustrated the importance of overall self-image in sport con- sumption; however, existing studies have hardly investigated the specific self-images that consumers pursue through sport participation. For example, the finding that ideal self- event congruity played a significant role in promoting event participation has not helped practitioners understand ‘what area needs improvement and how such improve- ment can be made’ to frame the congruity (Zhang, 2015, p. 4). To capitalize on the match-up effect of symbolic meanings between consumers and products, marketers need first to figure out one party’s symbolic desires and then frame the other party to help build construct fit. Considering the foundational role of consumers in the market- place, the desired self-images of sport participants should be identified first to maximize the positive effect of self-product congruity.

The other research stream has emerged from brand image/personality studies exploring the symbolic meanings of sport products (e.g. events, teams, and athletes). By portraying brands and products as having non-functional human characteristics, marketers can enhance the favorability of brand image (Phau & Lau, 2001), increase levels of trust and loyalty (Fournier, 1998), and provide a basis for product differentiation (Aaker, 1996). Considering these favorable impacts, multiple sport marketing scholars have made attempts to explore the underlying symbols of sport products. Given the various sources of influence and the different valences of sport brand traits (Lee & Cho, 2009), general symbolic scales, such as the brand personality scale of Aaker (1997), do not seam- lessly fit sport settings (Braunstein & Ross, 2010; Heere, 2010; Lee & Cho, 2009; Ross, 2008). Therefore, various symbolic scales for sport products have been developed,

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including sport event personality scales (e.g. Lee & Cho, 2012), sport team personality scales (e.g. Braunstein & Ross, 2010; Heere, 2010; Ross, 2008), and athletic image scales (e.g. Arai et al., 2013; Braunstein & Zhang, 2005; Carlson & Donavan, 2013).

Candidate image, personality, and underlying factor structures in the aforementioned studies have laid a solid foundation for investigating the symbolic meanings of sport brands; however, this research stream has largely overlooked the active role in acquiring symbolic meanings and rarely explored the characteristics that consumers desire. Conse- quently, identified characteristics of sport products might not always match what consu- mers actually seek. For example, an individual may understand the nature of boxing but may not be a fan of the sport. This individual could accurately characterize boxing as having a high level of aggressiveness and physicality but would not seek out those traits for his or her self-image. Even a fan of boxing might not appreciate all of the images associ- ated with boxing. The logic of these examples also applies to other sport-related product categories, such as sport events, athletes, and equipment.

In brief, the strengths and limitations of both research streams discussed above signify the importance of exploring desired self-images in sport participation. A sound measure- ment scale of PDSI would provide marketers with references for determining what sport symbolic meanings are favored by sport participants. This information would be valuable for marketers to increase sport participants’ consumption intention and enhance the effec- tiveness and efficiency of marketing promotions.

PDSI and consumer behavior

According to the model of motivation process (Schiffman & Kanuk, 2004), unfulfilled needs, wants, and desires in their continuum arouse psychological tension, which is an unpleasant psychosocial state or feeling. This psychological tension drives consumers to seek outside stimuli, and consumption behavior is likely to follow if they perceive that a product stimulus is likely to satisfy their unfulfilled desires through reinforced provision. Based on this motivation process, Funk (2008) further proposed the sport and event con- sumer motivation process, modified to accommodate sport settings (e.g. separating the needs domain from the wants domain). Two overarching prerequisites of positive consu- mer behavior were acknowledged in both models: (a) consumers have unfulfilled needs, wants, and desires, and (b) stimuli can satisfy these needs, wants, and desires. That is, posi- tive consumer behavior is likely to occur when individuals perceive that sport participation might fulfill their symbolic desires.

Due to the rich symbolism of participatory sport products, including leisure and phys- ical activities (Dimanche & Samdahl, 1994; Kang, 2002; Kirkcaldy et al., 2002), sport events (Funk, Toohey, & Bruun, 2007; Kaplanidou & Gibson, 2012), and derivative sport equipment and apparel (Kwak & Kang, 2009), a sufficient supply of symbolic mean- ings in sport participation, at least at an aggregate level, is likely to exist. In this way, one of the two prerequisites of consumption has been confirmed. Therefore, a high level of PDSI would lead to positive consumer behavior.

Among the key constructs for assessing consumer behavior, personal involvement and actual consumption are two commonly used measures. Personal involvement is an indi- vidual’s perceived relevance of a marketing stimulus, a perception driven by inherent needs, values, and interests (Mittal, 1995; Zaichkowsky, 1985), including both enduring

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(long-term and stable) and situational (temporary and changeable) involvement (Havitz & Mannell, 2005; Houston & Rothschild, 1978). As this study was focused on people’s long- term sport participation, personal involvement here would fall toward the end of enduring tendency. Personal involvement has been widely adopted to examine symbolic consump- tion in various industries, such as music (e.g. Larsen, Lawson, & Todd, 2010), fashion (Auty & Elliott, 1998; Banister & Hogg, 2004), leisure (Kyle, Graefe, Manning, & Bacon, 2003; Schouten & McAlexander, 1995), and tourism (e.g. Ekinci, Sirakaya-Turk, & Preciado, 2013; Gross & Brown, 2006). Personal involvement in sport represents the perceived interest and importance of sport to an individual (Shank & Beasley, 1998). Building upon the personal involvement inventory (PII) of Zaichkowsky (1985), Shank and Beasley (1998) developed a widely-adopted sport involvement inventory that includes eight semantic differential items to accommodate the sport consumption setting (e.g. Ko, Kim, Claussen, & Kim, 2008; Koernig & Boyd, 2009; McGehee, Yoon, & Cárdenas, 2003). The majority of marketing practices aim to promote purchase behavior. Among the various measures of sport consumption (e.g. money, time, word-of-mouth, and TV view- ership), money and time expenditure are directly measurable, are tied to sport partici- pation, and have been widely used in previous studies (e.g. Lera-López & Rapún- Gárate, 2007; Taks, Renson, & Vanreusel, 1994). Based on the above discussion, the fol- lowing three hypotheses were proposed and tested in this study:

H1: PDSI would positively impact personal involvement in sport participation.

H2: PDSI would positively impact money expenditure in sport participation.

H3: PDSI would positively impact time expenditure in sport participation.

In addition, the hierarchy of effects model by Lavidge and Steiner (1961) suggests that con- sumer responses evoked by marketing stimulation move hierarchically through four stages: cognitive, affective, conative, and behavioral. As implied by its definition, personal involvement is a subjective and attitudinal construct in the domain of affection and therefore, is supposed to take place prior to actual consumption behavior. This relationship has been supported by a number of empirical studies in sport marketing (e.g. Ko et al., 2008; Koernig & Boyd, 2009; McGehee et al., 2003). Accordingly, the following hypotheses were proposed and tested:

H4: Personal involvement would positively impact money expenditure in sport participation.

H5: Personal involvement would positively impact time expenditure in sport participation.

Together, all five hypotheses constitute a relationship model (Figure 1) for examining the nomological validity of the proposed PDSI measurement tool and discovering how and to what extent PDSI influences sport consumer behavior.

Method and results

Phase 1: generating the initial pool of desired self-images

Method Toexploresalientdesiredself-imagesinsportparticipation,adata-drivenapproachwasused based on two considerations. First, although the topic of self-image has been covered in the general literature, no systematic study was found to identify salient desired self-images of

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sport participants and the factor structure of these self-images. It might be unfeasible to con- ceptualize a priori schema about PDSI dimensionalities through the literature review. Second, a data-driven approach has been widely adopted when exploring the salient brand characteristics of sport products, such as sport team personality (e.g. Braunstein & Ross, 2010; Heere, 2010), sport event personality (e.g. Lee & Cho, 2012), athlete images (e.g. Braunstein & Zhang, 2005), and destination images (e.g. Hosany, Ekinci, & Uysal, 2006; Kaplan, Yurt, Guneri, & Kurtulus, 2010). As the pursuance of desired self-images through sport participation could be considered a way of self-branding (i.e. individuals develop and promote the characteristics they most desire for themselves), starting the ana- lyses with data-driven approach fits the current research context and reasoning process. Third, the data-driven approach (an inductive reasoning process) provides an open- minded perspective as a starting point to comprehensively explore the statistical dimension- ality of PDSI in the context of sport participation. The findings could serve as an empirical foundation to support future investigations by using a deductive approach.

Phase 1 generated candidate items of PDSI via a qualitative approach. Two screening criteria were set to choose appropriate self-images in sport participation: (a) each charac- teristic should be derived from the context of sport, and (b) the description of each charac- teristic should express an individual’s self-image. Overall, Phase 1 consisted of four steps. First, a comprehensive review of literature was conducted in the areas of human person- ality, brand image/personality, athlete brand image, event personality, and motives in sport consumption. Second, two focus groups of four people were organized to assess whether PDSI existed in sport participation. Research respondents included students at undergraduate and graduate levels and faculty members who were affiliated with a large public university in the United States and also professionals who were sport consumers. Specifically, each focus group was conducted according to the following procedure:

Figure 1. Proposed relationship model. Notes: PDSI = Participant desired self-image, INV = Personal involvement, Money = Monetary expenditure, Time = Time expenditure.

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(1) the primary investigator introduced the purpose and topic of the focus group study; (2) the primary investigator explained the concept of PDSI and used two specific examples (i.e. participation in golf and purchase of newly released running shoes) to help respon- dents understand its meaning; (3) respondents were asked whether they had pursued desired self-images in reality; and (4) if they had, they were asked to share these experi- ences and provide their perceptions of relevant images. Third, to supplement the pool of desired self-images generated from the literature review, an open-ended PDSI survey was conducted. Respondents were asked to list in written form at least 10 desired self- images meeting the two criteria mentioned above. Fourth, all generated self-images were sent to a panel of four experts (i.e. two professors, one practitioner, and one doctoral student in sport marketing) for a content validity test, in which all self-image items were evaluated based on the above-mentioned screening criteria to reduce inaccuracy and redundancy of expressions.

Results The qualitative research process produced 82 self-images. Respondents in the two focus groups helped confirm the presence of desired self-images in sport participation, either by succinctly expressing their desires or by recalling actual experiences. Additionally, a total of 113 respondents in the United States filled out the open-ended survey over a span of one week through Amazon Mechanical Turk, which yielded another list of 107 self-images. Altogether, 189 candidate self-images were shown to the panel of experts for a test of content validity. Based on their feedback, 83 self-images were dropped due to overlap with other images, unclear expressions, or irrelevance. The final pool contained 106 self-images without redundancy. The wording of the items was also improved based on the feedback provided by the panel members.

Phase 2: initial exploratory factor analysis

Method An online questionnaire designed using Qualtrics included three sections preliminary PDSI items, sport participation, and socio-demographics. For the PDSI section, the follow- ing question was presented: ‘to what extent do you desire to obtain the following images from participating in the sport you indicated?’ Respondents were asked to rate the 106 candidate self-images on a 5-point Likert scale (1 = ‘absolutely no desire for this image’ to 5 = ‘extremely high desire for this image’). To avoid the order effect in filling out ques- tionnaires, the 106 self-images were set to be listed in a random order. In the current study, self-images with a mean value in the top 40% range of the 5-point scale (i.e. larger than 3.4) were considered highly desired and were further analyzed in subsequent data analysis. To measure sport participation, respondents were asked to identify one sport activity in which they had most frequently participated (at least once per month). For sample description purpose, respondents were asked to provide demographic information about gender, age, ethnicity, household income, and education level.

A total of 413 online questionnaires were collected in three weeks through Amazon Mechanical Turk, which has been widely adopted as an effective data collection method in behavioral and psychological research (Buhrmester, Kwang, & Gosling, 2011; Chandler, Mueller, & Paolacci, 2014; Paolacci & Chandler, 2014). Excluding 43 questionnaires due to

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a severe rate of missing items (i.e. over 30%) or failure in the attention check questions, valid data from 370 respondents in the United States were included in subsequent ana- lyses. Table 2 presents the socio-demographic information of the respondents and Table 3 lists major sport activities that survey respondents had most frequently partici- pated. Descriptive statistics for socio-demographics and desired self-images were calcu- lated using Statistical Package for the Social Sciences (SPSS) 19.0. An exploratory factor analysis (EFA) was conducted using Mplus 7.0. To determine the number of extracted factors, both parallel analysis and traditional approach (i.e. using eigenvalue, explained variance, and scree plot) were considered (Hair, Black, Babin, & Anderson, 2010; Hayton, Allen, & Scarpello, 2004). The following criteria were used to determine the final list of items: (a) each item had a factor loading equal to or greater than .50 without severe cross-loading (i.e. the difference between two loading values is less than .40), (b) each factor was interpretable, (c) each loaded item on a factor was interpretable, and (d) each factor had at least three items.

Results As shown in Table 4, 49 out of 106 candidate self-images met the criterion of being highly desired (i.e. a mean value larger than 3.4) and were, therefore, analyzed in the EFA. The significant Bartlett Test of Sphericity (p < .01) and the Kaiser–Meyer–Olkin statistic of .978 indicated the appropriateness of conducting EFA with the current data.

In the parallel analysis, three factors’ eigenvalues from sample correlation matrix were larger than their eigenvalues obtained from the parallel analysis (see Table 4), suggesting that these three factors should be retained. However, given that the retained items should have a factor loading equal to or greater than .50 and without severe cross-loading, the

Table 2. Socio-demographic information of respondents in Phase 2 (N = 370). Variable Category N %

Gender Male 226 61.1 Female 144 38.9

Age 18–25 73 19.7 26–35 150 40.5 36–45 71 19.2 46–55 55 14.9 56 and above 21 5.7

Ethnicity African American 24 6.5 American Indian 3 0.8 Asian 29 7.8 Caucasian 282 76.2 Hispanic 25 6.8 Other 7 1.9

Household income Below $20,000 45 12.2 $20,000–39,999 86 23.2 $40,000–59,999 65 17.6 $60,000–79,999 64 17.3 $80,000–99,999 55 14.9 $100,000–149,999 41 11.1 $150,000–199,999 9 2.4 Above $200,000 5 1.4

Education In high school now 2 0.5 High school graduate 62 16.8 In college now 47 12.7 College graduate 186 50.3 Advanced degree 66 17.8 Other 7 1.9

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EFA with parallel analysis produced only one latent factor. The measurement literature indi- cates that a parallel analysis may at times lead to under-factoring if there is (a) a high corre- lation between factors, (b) a large number of variables, or (c) a primary factor with a large eigenvalue (Hayton et al., 2004; Mulaik, 2009; Turner, 1998). All of these three situations occurred in the current situation. Thus, the traditional approach was deemed more appropriate and was thus adopted, which produced four latent factors, with a total of 63.52% variance explained. The extracted factor matrix was further rotated using the Geomin rotation tech- nique. Following the aforementioned criteria of retaining items, the fourth factor was removed due to severe double loading of its items. Three factors, with a total of 21 items were retained (Table 4), explained 60.77% of the total variance. The factors were labeled as inner self-merit (11 items), lifestyle pursuance (3 items), and social self-presentation (7 items). Although these three PDSI dimensions all fall under the overarching domain of socialization, they are clearly distinct. The dimension of inner self-merit emphasizes funda- mental human needs (e.g. mental health and sound personality) that are not necessarily involved in interpersonal relationships. In contrast, items under the dimension of social self-expression are more advanced and rely heavily on the existence of interpersonal relation- ships to be meaningful. Differing from the first two dimensions, which are strongly tied to tra- ditional sport images, lifestyle pursuance contains items that are more relevant to recreation, leisure, and health, highlighting the well-being of sport participants.

Phase 3: confirmatory factor analysis and structural equation modeling

Method A new online questionnaire was designed using Qualtrics. In addition to the retained PDSI items, sport participation items, and socio-demographic items, the questionnaire included

Table 3. Major sport activities that survey respondents had most frequently participated.

Sport activities

Survey respondents in Phase 2 (N = 370)

Survey respondents in Phase 3 (N = 483)

N % N %

Badminton 4 1.1 5 1.0 Baseball 14 3.8 12 2.5 Basketball 83 22.4 101 20.9 Bowling 8 2.2 5 1.0 Boxing 4 1.1 3 0.6 Cycling 8 2.2 9 1.9 Football 22 5.9 29 6.0 Frisbee 2 0.5 3 0.6 Golf 23 6.2 22 4.6 Hiking 2 0.5 5 1.0 Hockey 7 1.9 5 1.0 Kickball 2 0.5 5 1.0 Martial arts 2 0.5 4 0.8 Racquetball 3 0.8 2 0.4 Running/Jogging 35 9.5 47 9.7 Soccer 27 7.3 55 11.4 Softball 25 6.8 26 5.4 Swimming 7 1.9 8 1.7 Tennis 26 7.0 47 9.7 Volleyball 20 5.4 28 5.8 Walking 4 1.1 3 0.6 Weight lifting 12 3.2 8 1.7 Yoga 2 0.5 5 1.0

594 J. J. WANG ET AL.

items to measure sport consumption behavior (e.g. personal involvement, money expen- diture, and time expenditure). The sport involvement inventory used in previous sport marketing studies (e.g. Ko et al., 2008; Koernig & Boyd, 2009; McGehee et al., 2003; Shank & Beasley, 1998) was modified to include seven of the original eight semantic differential items (i.e. boring–exciting, uninteresting–interesting, worthless–valuable,

Table 4. Results of exploratory factor analysis in phase 2 (N = 370). Desired self-image Mean SD Factor 1 ISM Factor 2 LP Factor 3 SSP

Enjoying life 4.07 1.03 .090 .759* −.017 Being athletic 4.04 1.04 .123 .438* .004 Being health-conscious 3.99 1.04 .192* .693* .047 Being physical fit 3.99 .99 .005 .298* .472* Being confident 3.92 1.01 .735* .016 −.033 Being determined 3.88 1.07 .515* .058 .159 Being dedicated 3.85 1.06 .640* .154* −.029 Being optimistic 3.85 1.11 .698* −.066 .110 Being enthusiastic 3.84 1.12 .762* .135* −.041 Being self-motivated 3.84 1.07 .766* .081 −.066 Being skilled 3.83 1.08 .313* −.034 .255 Being capable 3.82 1.07 .580* .018 .149 Being hardworking 3.81 1.07 .676* .108* −.020 Being competitive 3.81 1.17 .060 .046 .380* Having work-life balance 3.81 1.10 −.024 .729* .309* Being happy 3.79 1.11 .556* .047 .121 Being cheerful 3.79 1.10 .273* .051 .475* Being mentally strong 3.77 1.10 .660* .027 .089 Pursuing fair play 3.75 1.14 .018 −.029 .628* Having a strong sense of teamwork 3.74 1.25 −.224* .010 .759* Being self-disciplined 3.70 1.13 .244* .082 .516* Being perseverant 3.68 1.11 .798* −.030 −.015 Being well-rounded 3.67 1.13 .503* .083 .254* Having good leadership 3.66 1.08 .083 .073 .623* Being respectable 3.66 1.16 .422* −.010 .376* Being successful 3.65 1.18 .443* .065 .238* Being passionate 3.65 1.15 .797* .054 −.118 Being in shape 3.64 1.18 .002 .419* .232* Being friendly 3.63 1.17 .030 −.002 .839* Being reliable 3.62 1.16 .556* −.161* .420* Being quick-thinking 3.62 1.14 .283* −.012 .445* Being down-to-earth 3.62 1.12 .474* .077 .225* Being willing to learn 3.61 1.17 .479* .003 .380* Being open to experience 3.60 1.14 .346* .094 .381* Being sociable 3.59 1.14 −.090 .054 .793* Being responsible 3.58 1.16 .585* .047 .252* Being goal-driven 3.56 1.18 .109 .297* .429* Being self-sufficient 3.53 1.18 .595* .027 .276* Being tough 3.52 1.20 .360* .118* .045 Being focused 3.52 1.18 .370* −.017 .495* Seeking excitement 3.51 1.22 .133 .190* .392* Being supportive 3.50 1.18 .207 −.077 .676* Being prepared 3.49 1.17 .556* −.105* .369* Having high integrity 3.48 1.24 .476* −.062 .437* Being versatile 3.48 1.19 .368* .016 .436* Being spirited 3.48 1.17 .220* .117* .489* Being fun-loving 3.47 1.20 .253* −.015 .353* Having a good work ethic 3.46 1.16 .141 .041 .729* Having a sound mind 3.43 1.23 .761* −.122* .093 Eigenvalues from sample correlation matrix 26.08 2.05 1.64 Eigenvalues from parallel analysis 1.78 1.70 1.64

Notes: ISM = Dimension of inner self-merit, LP = Dimension of lifestyle pursuance, SSP = Dimension of social self-presen- tation.

*p < .05.

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unappealing–appealing, useless–useful, irrelevant–relevant, and important–unimportant). Each item was phrased on a 7-point Likert scale. Respondents were asked to estimate their annual money expenditure and weekly time expenditure participating in their chosen sport. Money and time expenditure were evenly anchored across 2000 dollars and 20 hours, respectively, on 10-point scales, with one additional option to capture levels in excess of the maximum.

The online questionnaire was available through Amazon Mechanical Turk for four weeks. Excluding problematic questionnaires with a severe rate of missing values and failure in the attention check questions (two personal involvement items were reserve coded), the data of 483 respondents in the United States were considered valid. Table 5 pre- sented the socio-demographic information of these respondents, who were each rewarded $0.71 through theAmazon Mechanical Turk system. Major sport activities that respondents had most frequently participated are incorporated into the earlier Table 3.

Confirmatory factor analysis (CFA) was first conducted for the PDSI items, and struc- tural equation modeling (SEM) was applied to assess the influence of PDSI on consumer behavior. To examine goodness-of-fit, the following indices were employed: chi-square (χ2), normed chi-square (χ2/df), root mean square error of approximation (RMSEA), 90% confidence interval (CI) of RMSEA, possibility of close fit (PCLOSE), comparative fit index (CFI), Tucker–Lewis index (TLI), and standardized root means square residual (SRMR) (Browne & Cudeck, 1993; Hair et al., 2010; Hu & Bentler, 1999; Kline, 2005). To examine scale reliability, Cronbach’s alpha (α), construct reliability (CR), and averaged variance extracted (AVE) were calculated. To assess construct validity, convergent validity and discriminant validity were examined. The former was assessed using factor loadings,

Table 5. Socio-demographic information of respondents in phase 3 (N = 483). Variable Category N %

Gender Male 252 52.2 Female 231 47.8

Age 18–25 106 21.9 26–35 206 42.7 36–45 96 19.9 46–55 53 11.0 56 and above 22 4.6

Ethnicity African American 52 10.8 American Indian 9 1.9 Asian 36 7.5 Caucasian 338 70.0 Hispanic 41 8.5 Other 7 1.4

Household income Below $20,000 55 11.4 $20,000–39,999 128 26.5 $40,000–59,999 92 19.0 $60,000–79,999 97 20.1 $80,000–99,999 56 11.6 $100,000–149,999 43 8.9 $150,000–199,999 6 1.2 Above $200,000 6 1.2

Education In high school now 1 .2 High school graduate 78 16.1 In college now 66 13.7 College graduate 252 52.2 Advanced degree 79 16.4 Other 7 1.4

596 J. J. WANG ET AL.

and the latter was evaluated by using correlations among latent factors (Kline, 2005) and the Fornell and Larcker testing (1981). In the case where model comparison was involved, the indices of Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used. Descriptive statistics about respondents’ socio-demographics and desired self-images were calculated using SPSS 19.0. CFA and SEM were conducted using Mplus 7.0 with the estimator of MLR (i.e., maximum likelihood estimation with robust standard errors).

Results of CFA Goodness of fit indicesof CFA were acceptable: χ2 = 587.308, p < .001; χ2/df = 587.308/186 = 3.158, RMSEA = .067 (90% CI = .061–.073), PCLOSE < .001, CFI = .918, TLI = .907, AIC = 24402.878, BIC = 24678.759, and SRMR = .056. However, two items had factor loadings well below the suggested criterion of .70 (Hair et al., 2010): ‘image of having a sound mind’ (λ = .519) under inner self-merit and ‘image of having a good work ethic’ (λ = .512) under social self-presentation. Also, the mean values of these two items were 3.30 and 3.22, respect- ively, below the cutoff criterion of 3.4. Therefore, CFA was conducted again without these two items, producing better fit indices: χ2 = 448.127, p < .001; χ2/df = 448.127/149 = 3.008, RMSEA = .064 (90% CI = .058–.071), PCLOSE < .001, CFI = .932, TLI = .922, AIC = 21445.434, BIC = 21696.235, and SRMR = .049. Compared with the initial CFA model, the subsequent one had a smaller chi-square value (Δχ2 = 139.181, Δdf = 37, p < .001) and superior fit indices. In terms of reliability, the Cronbach’s alpha, CR, and AVE values (Table 6) of the revised PDSI model exceeded the suggested criteria: .70 for Cronbach’s alpha, .70 for CR, and .50 for AVE (Hair et al., 2010; Kline, 2005). As shown in Table 6, except for one item (.691), factor loadings of the PDSI items were above the suggested value of .70 (Hair et al., 2010), indicating good convergent validity. The inter-factor corre- lations were .803 (between inner self-merit and lifestyle pursuance), .784 (between inner self-merit and social self-presentation), and .756 (between lifestyle pursuance and social self-presentation), all below and then superior to the criterion of .85 (Kline, 2005). As the results of the Fornell-Larcker testing, square root of AVEinner self-merit and AVElifestyle pursuance were larger than the correlation between inner self-merit and lifestyle pursuance; however, the square root of AVEsocial self-presentation was less than correlations between inner self-merit and social self-presentation and between lifestyle pursuance and social self-presentation. Further, the SEM models were reanalyzed with constraining three correlations to 1, respect- ively. All of three SEM models produced fit indices that are inferior to the original ones. Results of inter-factor correlations and the Fornell-Larcker testing together have showed an acceptable level of discriminant validity in the measurement model.

Results of SEM SEM with a second-order PDSI model was conducted to disclose the role of overall PDSI in sport consumer behavior and to preliminarily examine the nomological validity of the proposed scale (i.e. how well the target construct relates to other theoretically related con- structs) (Churchill, 1995). The fit indices of the initial SEM with a second-order PDSI model were acceptable: χ2 = 1071.896, p < .001; χ2/df = 1071.896/343 = 3.125, RMSEA = .066 (90% CI = .062–.071), PCLOSE < .001, CFI = .893, TLI = .882, and SRMR = .051. Given the superior fit indices of the PDSI model in CFA and the observability of money and time expenditure, the relatively inferior model fit of SEM was likely due to

EUROPEAN SPORT MANAGEMENT QUARTERLY 597

the measure of personal involvement. This assumption was confirmed by the results of CFA for the measurement model of personal involvement (7 items): χ2 = 215.364, p < .001; χ2/df = 215.364/14 = 15.383, RMSEA = .173, CFI = .775, TLI = .663, and SRMR = .083. Based on the modification indices provided by Mplus, two problematic items were dropped from the measurement model, largely improving the fit indices: χ2 = 27.081, p < .001; χ2/df = 27.081/5 = 5.4162, RMSEA = 0.096, CFA = .955, TLI = .911, and SRMR = .037.

With the 5-item personal involvement scale, the model fit indices of SEM were much better: χ2 = 715.351, p < .001; χ2/df = 715.351/292 = 2.450, RMSEA = .055 (90% CI = .050–.060), PCLOSE = .060, CFI = .933, TLI = .925, and SRMR = .043. As shown in Figure 2, all five hypotheses were confirmed. In terms of direct effects, PDSI positively influenced personal involvement (β = .407, p < .01), money expenditure (β = .162, p < .01), and time expenditure (β = .159, p < .01). Personal involvement positively influ- enced money expenditure (β = .164, p < .01) and time expenditure (β = .147, p < .01). In terms of indirect effects, PDSI positively influenced money expenditure (β = .067, p < .01) and time expenditure (β = .060, p < .01) through personal involvement, signifying that personal involvement partially mediated the relationships between PDSI and two indices of actual consumption (Sobel, 1982). The standardized total effects of PDSI on personal involvement, money expenditure, and time expenditure were .407 (p < .01), .229 (p < .01), and .219 (p < .01), respectively, providing initial evidence about the nomo- logical validity of the proposed PDSI scale.

Table 6. Mean value, standard deviation (SD), factor loadings (λ), Cronbach’s Alpha (α), construct reliability (CR), average variance extracted (AVE) for the proposed PDSI scale in CFA (N = 483). Factor/Item Mean SD λ α CR AVE

Inner self-merit .957 .957 .693 Being happy 3.79 1.14 .851 Being dedicated 3.88 1.11 .863 Being confident 3.91 1.12 .863 Being self-motivated 4.01 1.07 .819 Being enthusiastic 3.93 1.07 .792 Being hardworking 3.96 1.09 .841 Being passionate 3.88 1.13 .838 Being mentally strong 3.85 1.15 .838 Being perseverant 3.89 1.10 .863 Being optimistic 3.82 1.07 .747

Lifestyle pursuance .851 .854 .661 Enjoying life 4.16 1.02 .815 Having work-life balance 3.77 1.16 .780 Being health-conscious 3.93 1.06 .842

Social self-presentation .876 .876 .541 Pursuing fair play 3.68 1.21 .730 Having a strong sense of teamwork 3.71 1.29 .728 Being friendly 3.72 1.17 .756 Being sociable 3.64 1.20 .743 Having good leadership 3.56 1.19 .752 Being supportive 3.71 1.14 .691

Personal involvement .862 .866 .566 Worthless-valuable 6.13 .96 .699 Unappealing-appealing 6.17 1.17 .638 Useless-useful 5.97 1.07 .833 Irrelevant-relevant 5.95 1.04 .808 Important-unimportant 5.90 1.19 .766

598 J. J. WANG ET AL.

Discussion

Through a combination of inductive and deductive investigations, Phases 1 and 2 gener- ated a preliminary pool of 49 desired self-images. These self-images were drawn from mul- tiple symbolic sources (e.g. sport event personality, team personality, athlete brand image, human personality, brand image, and lifestyle, and even some unexplored symbolic desires), suggesting that although PDSI overlaps existing symbolic constructs, it is still distinct.

The PDSI scale identified and tested in Phases 2 and 3 contains 19 self-images in three general dimensions: inner self-merit, lifestyle pursuance, and social self-presentation. Although 19 self-images spread across different dimensions, all of these self-images are positive in nature, reflecting people’s self-enhancement tendency in symbolic consump- tion of sport participation. These 19 self-images are also closely tied to people’s symbolic desires in mentality or socialization. For most sport participants, symbolic meanings of these self-images help construct a stable inner or social self-concept, serving the purpose of self-verification. The identified PDSI structure can further be explained by the attribution theory (Heider, 1958), which suggests that people have an innate tendency to understand the causes of an event or behavior. Socialization in everyday life, including success or failure in certain activities, drives individuals to explore, subjectively or objec- tively, potential explanations for these outcomes, effectively activating their tendencies of self-enhancement and/or self-verification (Kelley & Michela, 1980; Weiner, 1985). The identified dimensionality of PDSI indicated that inner self-merit, lifestyle pursuance, and social self-presentation factors were three major themes related to people’s attribution pattern in self-enhancement and self-verification.

Figure 2. Results of SEM with second-order PDSI model. Note: *p < .05, **p < .01, ISM = Dimension of inner self-merit, LP = Dimension of lifestyle pursuance, SSP = Dimension of social self-presentation, PDSI = Participant desired self-image, INV = Personal involvement, Money = Monetary expendi- ture, Time = Time expenditure.

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Meanwhile, as suggested by the social learning theory, one’s cognition about self-image grows out of socialization and is heavily impacted by the ideas and values that constitute our culture and specify how that culture should be perceived (Bandura, 1977, 1989; Baudrillard, 1998; Grubb & Grathwohl, 1967). Therefore, at the macro level, desired self-images in sport participation are shaped by social agents and value systems in which consumers are situated. In the current study, the identified 19 self-images were favored across different geographic locations, genders, and ethnic groups, and were encouraged by various social agents (e.g. mass media, opinion leaders, and religious resources), especially the inner self-merit and social self-presentation items. In contrast, lifestyle pursuance, though not so popular until recent decades, had the highest mean value, signifying its sal- ience and attractiveness in PDSI. This finding echoes previous studies that have identified the need for flexible and individualized lifestyles among sport participants (e.g. Horne, 2011; Lera-López & Rapún-Gárate, 2007; Wheaton, 2004).

The desired self-images identified in the PDSI scale confirmed the symbolic value of sport activities, especially serving as sophisticated social language to express participants’ needs and wants in inner self-presentation, lifestyle pursuance, and social self-presen- tation. More importantly, the factor structure in the scale has provided a basic framework to further explore participants’ abstract needs, wants, and desires in sport symbolic con- sumption. In today’s sport industry, the supply parties (e.g. participatory event organizers and service providers) are still prominent and influential in creating sport demand at the macro level; even so, the demand party (i.e. sport participants) with more advanced media technologies and richer consumption choices plays an increasingly active and salient role at the micro level of marketing practice. The unremitting spectrum of consumer needs, wants, and desires associated with participating in sport activities deserve to be frequently analyzed, monitored, and integrated into service provisions and promotional activities.

Phase 3 also identified positive relationships between overall PDSI and other key con- structs in consumer behavior, preliminarily confirming the nomological validity of the proposed PDSI scale. Additionally, PDSI was tied more to personal involvement (β = .407, p < .01) than to actual money (β = .162, p < .01) and time (β = .159, p < .01) expen- diture. According to the hierarchy of effects model of Lavidge and Steiner (1961), consu- mer responses evoked by marketing stimulation move hierarchically through four stages: cognition, affection, conation, and behavior. Therefore, compared with actual consump- tion, PDSI was more likely to impact personal involvement in the domain of affection. These relationships have highlighted the importance of PDSI in influencing people’s sport participation. As indicated by Baudrillard (1998), symbolic exchange escalates with the development of urban concentration that comes with the exponential growth of social communications. The PDSI therefore would play an increasingly important role in driving individuals’ sport consumption.

Given the on-going and systematic nature of symbolic consumption for self-expression (Baudrillard, 1998), sport participants would have the potentials to continually consume sport activities and even extend their consumptions to other related products (e.g. sporting goods, athlete/team fanship, social media, and sport tourism) to achieve self-images in inner self-merit, social self-presentation, and/or lifestyle pursuance. The PDSI dimensions, salient self-images, and structural relationships identified in this study have offered a pre- liminary understanding about the nature of salient desired self-images in sport partici- pation and would certainly shed a light on the co-functions of PDSI with branding

600 J. J. WANG ET AL.

(e.g. strengthening self-brand congruity, see Chaplin & Roedder, 2005), social develop- ment (e.g. elevating community cohesion, see Cohen, 2001), and public health (e.g. increasing subjective health and well-being, see Downward, Dawson, & Mills, 2016).

Implications

The current study took initial steps to unearth what salient self-images sport participants pursue to express themselves, deepening the understanding of consumers’ abstract needs and wants in sport symbolic consumption and advancing related motivation studies. The resolved PDSI scale concisely captured the conceptual structure of these self-images and identified three general dimensions in the context of sport participation, providing a mea- surable solution for assessing salient PDSI and a basic framework to further explore sym- bolic consumption for self-expression in different sport and social contexts. Findings of this study helped confirm the salient symbolism of sport participation and reiterate the importance of PDSI in sport participation; likely, these have also shed a light on consu- mers’ information processing in other related consumption contexts such as tourism, recreation, and social media.

Although constructing symbolic meanings is an abstract, complicated, and even mythological endeavor (Holt, 2004; Holt & Cameron, 2010), previous studies have high- lighted a few indirect approaches in management innovations such as launching a cultural studio (see Holt & Cameron, 2010) and marketing efforts such as product designs and re- designs (Chuang & Ma, 2001; Crilly, Moultrie, & Clarkson, 2004), endorsement and spon- sorship selections (Escalas & Bettman, 2009; Gwinner & Eaton, 1999), and advertising per- suasion (Meenaghan, 1995). All of these can be reasonably put into practical perspectives. In terms of product design, organizers of participatory events could arrange/adjust activi- ties or services to accommodate PDSI in social self-presentation; designers of sporting goods (e.g. apparel, accessories, and fitness equipment) may consider integrating the elements or functions that enable consumers to pursue or exhibit healthy lifestyle images. In terms of advertising persuasion, as the match-up between self-image and typical user-image can trigger positive consumer behavior (Birdwell, 1968; Sirgy, 1982, 1986), the identified self-images could be highlighted in shaping the image of typical product users in order to frame self-product congruity in advertising. Given that people will form schemas of various objects, including their desired self-images, they tend to process stimuli that are consistent with those stored schemas (Fiske & Pavelchak, 1986; Stoltman, 1991). To connect with target consumers in endorsement and sponsorship activities, the desired self-images could serve as criteria for selecting appropriate sponsees and endorsers who carry similar symbolic images.

Limitations and future study

The limitations of the current study are noteworthy. First, the measurement of sport con- sumption, including money and time expenditure, relied on respondents’ recall, which might lead to some degree of inaccuracy. Future studies should develop a more accurate measure of actual consumption. Second, this study focused on the concept of self- expression in sport participation at an aggregated level, leaving the PDSI in different sport contexts unexamined. Future studies should explore detailed and possible

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differential PDSI in various participatory sports (e.g. individual sports, team sports, main- stream sports, and emerging sports). Third, survey participants of the current study were limited to North America, reducing the generalizability of research findings to other regions. As suggested by the social learning theory, participants desired self-image and meanings of sport activities are subject to the influence of social agents and value systems; therefore, future studies are needed to explore sport symbolic consumption and PDSI in various socio-demographic contexts.

Disclosure statement

No potential conflict of interest was reported by the author.

ORCID

Daniel L. Wann http://orcid.org/0000-0003-2440-5523

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