The Application of Social Cognitive Career Theory to Sport and Leisure Career Choices

10.1177/0894845305279164Journal of Career Development / December 2005Cunningham et al. / Social Cognitive Career Theory

The Application of SocialCognitive Career Theoryto Sport and LeisureCareer ChoicesGeorge B. CunninghamTexas A&M University

Jennifer BrueningUniversity of Connecticut

Melanie L. SartoreMichael SagasTexas A&M University

Janet S. FinkThe Ohio State University

Social cognitive career theory is used to investigate student intentions to enterthe sport and leisure industry. Data are gathered from 197 undergraduate stu-dents from four universities located across the United States. Path analysis sup-ports the general model, as self-efficacy and outcomes expectations hold posi-tive associations with vocational interests, which in turn are positively related tochoice goals. Self-efficacy is also positively associated with outcomes expecta-tions. Additionally, through comparison of competing models, support is gar-nered for the effects of supports (i.e., human and social capital) and barriers (i.e.,discrimination and lack of advancement opportunities) to be manifested on self-efficacy. Theoretical and practical implications are discussed.

Keywords: self-efficacy; outcome expectations; career intentions

122

Journal of Career DevelopmentVolume 32 Number 2

December 2005 122-138© 2005 Curators of theUniversity of Missouri

10.1177/0894845305279164http://jcd.sagepub.com

hosted athttp://online.sagepub.com

Authors’ Note: George B. Cunningham, Ph.D., Laboratory for Diversity in Sport, Departmentof Health and Kinesiology, Texas A&M University, TAMU 4243, College Station, TX 77843-4243; e-mail: [email protected].

Understanding and predicting individual behavior has been a topic of inter-est for psychologists and sociologists alike. This is also true when it

comes to understanding student behavior with respect to professional andcareer choices. The interest has led to the development and refinement of vari-ous theoretical frameworks aimed at understanding such behavior (e.g., Hol-land, 1985; Super, 1990). To be sure, interest in understanding vocational andcareer choices is evidenced by the research that has taken place in a variety offields, including accounting (Coleman, Kreuze, & Langsam, 2004), recreationtherapy (Jin & Austin, 2000), and sport (Cunningham, Sagas, Dixon, Kent, &Turner, 2005). Indeed, gaining an understanding of factors that influencecareer choice is useful for career counselors, professors, and students alike.

One of the more recent theories used to understand career choice is socialcognitive career theory (SCCT; Lent, Brown, & Hackett, 1994). This theoryrepresents an extension of Bandura’s (1986) social cognitive theory and isaimed at explaining the manner by which “people (a) develop basic academicand career interests, (b) make and revise their educational and vocationalplans, and (c) achieve performances of varying quality in their chosen aca-demic and career pursuits” (Lent et al., 2003, p. 458). In short, SCCT focuseson three cognitive-person constructs—self-efficacy, outcome expectations,and choice goals—and how these constructs interact with environmental fac-tors to predict the choices people make concerning their academic and voca-tional choices. Each of these constructs, and their relationship with oneanother and environmental factors, is presented below.

Self-efficacy refers to “people’s judgments of their capabilities to organizeand execute courses of action required to attain designated types of perfor-mances” (Bandura, 1986, p. 391). Research has reliably demonstrated thathigh-efficacy beliefs are related to various outcomes, including behavioralchoices (e.g., exercise; Hagger, Chatzisarantis, & Biddle, 2002), leadershipstyles (Sullivan & Kent, 2003), ambition for managerial positions (Cunning-ham, Sagas, & Ashley, 2003; Van Vianen, 1999), managerial performance(Robertson & Sadri, 1993), and turnover intentions (Cunningham et al.,2003). In addition, although some have considered the effects of a “general”self-efficacy (e.g., Saks & Ashforth, 2000), SCCT predictions are concernedwith task-specific self-efficacy (Lent et al., 1994). Thus, rather than generalefficacy expectations predicting future attitudes and behaviors, Lent et al.(1994) suggest that efficacy expectations related to a specific activity shouldresult in subsequent outcomes, such as vocational interests, choice goals, andbehaviors (see also Bandura, 1986).

Outcome expectations are concerned with the perceived consequences ofcarrying out specific behaviors, such that one considers the outcome, B, of per-

Cunningham et al. / Social Cognitive Career Theory 123

forming behavior A (Lent et al., 1994). Lent et al.’s (1994) theory is consistentwith previous theorizing from Vroom (1964), as, in the latter’s model, behav-ior is viewed as largely dependent on whether one perceives the behaviorresulting in particular outcomes as well as the value he or she places on saidoutcomes. Thus, persons are more likely to choose a particular academic orvocational path if he or she envisions favorable outcomes resulting from thatbehavior.

Finally, choice goals are conceptualized as “the intention to engage in a par-ticular action or series of actions” (Lent et al., 1994, p. 94). It is expected thatchoice goals are the most proximal antecedent of actual behavior, with consid-erable theoretical (e.g., Ajzen, 1991) and empirical support (Griffeth, Hom, &Gaertner, 2000; Hagger et al., 2002) for this tenet. In addition, vocationalinterests, or interests in a particular career, are thought to serve as direct ante-cedents of choice goals (Lent et al., 1994).

According to SCCT, both self-efficacy and positive outcome expectationsare thought to positively relate to vocational interests (Lent et al., 1994). Thus,if persons believe they have the abilities to perform tasks within a given joband perceive positive outcomes associated with obtaining that job, then theyare more likely to have an interest in that particular vocation. In addition, voca-tional interests are thought to give rise to choice goals (i.e., behavioral inten-tions), which in turn are thought to result in behavior (Lent et al., 1994). Forexample, an interest in entering the sport and leisure industry is likely to resultin intentions to enter the industry and, ultimately, actually entering the field.There is considerable research to support these basic tenets, including studieswith interscholastic (Lopez, Lent, Brown, & Gore, 1997) and university stu-dents (Lent et al., 2001) and studies with ethnically diverse participants(Gainor & Lent, 1998).

In addition to these cognitive-person variables, SCCT also holds that envi-ronmental factors, such as barriers and supports (i.e., facilitators), influencevocational and academic choices. Specifically, Lent et al. (1994) proposed thatcontextual factors would have a direct bearing on both choice goals and subse-quent behavior. Such theorizing recognizes that self-efficacy estimates andoutcome expectations do not exist in a vacuum. Rather, such expectations areinfluenced by personal experiences (e.g., parental support) and environmentalfactors (e.g., discrimination).

Two perspectives have developed concerning the influence of environmen-tal factors. According to Lent et al. (2001), contextual influences should affectchoice goals and behaviors such that

depending on the nature of the environmental supports and barriers they per-ceive in relation to particular choice options, people will be more or less likely

124 Journal of Career Development / December 2005

(a) to transform their career interests into corresponding career goals and (b) toimplement these goals. (Lent et al., 2001, p. 475)

This perspective has been labeled the direct paths model (Lent et al., 2003) andholds that “environmental factors relate to choice goals and actions directly”(Lent et al., 2003, p. 459). Empirical tests of the direct paths model haveyielded limited support (Lent et al., 2001; Lent et al., 2003).

An alternative to the direct paths model is the mediated paths model. Thisperspective draws from general social cognitive theory (Bandura, 1999, 2000)and supposes that the effects of environmental influences are indirect in thatthey influence choice goals and behavior through self-efficacy. Thus, fromthis perspective, supports and barriers are thought to directly influence self-efficacy, which in turn influences subsequent interests, choice goals, andactions. Recent research has supported the mediated paths model in that sup-ports (i.e., factors thought to facilitate a certain behavior) and barriers (i.e.,factors thought to impede a certain behavior) have been found to affect choicegoals and actions indirectly through self-efficacy (Lent et al., 2001; Lent et al.,2003). Consequently, this model was tested in the current examination.

Finally, previous research has examined barriers and supports from a gen-eral perspective as opposed to examining specific supports or specific barriers.Examination of specific environmental influences provides a more “fine-grained” analysis and has been encouraged in the literature (Lent et al., 2003).For that matter, although the basic cognitive-person elements of SCCT havereceived considerable support, examination of contextual influences haslagged behind considerably (see also Flores & O’Brien, 2002; Lent et al.,2001; Lent et al., 2003). As a result, there have been continued calls for greaterexamination of environmental factors within the SCCT paradigm as well asstudies in various contexts (Lent, Brown, & Hackett, 2000; Lent et al., 2001;Lent et al., 2003).

Given the need for further inquiry, this study used SCCT to examine studentintentions to enter the sport and leisure industry. Based on SCCT (Lent et al.,1994) and the empirical support for the mediated paths model (Bandura,1999), several hypotheses were advanced. Specifically, it was expected thatbarriers (Hypothesis 1) and supports (Hypothesis 2) would be significantlyassociated with self-efficacy. We further predicted that self-efficacy (Hypoth-esis 3) and outcome expectations (Hypothesis 4) would be positively related tointerest in entering the sport and leisure industry and that interest would hold apositive association with intent to enter the sport and leisure industry (Hypoth-esis 5). Finally, we hypothesized that self-efficacy would hold a positive asso-ciation with outcome expectations (Hypothesis 6). The methods and partici-


pants incorporated to test these hypotheses and answer the research questionsare described below.

Method

Participants

The sample consisted of 197 students enrolled in sport and leisure coursesat four universities (selected based on researcher affiliation) located through-out the United States. Participants were drawn from a convenience sample ofstudents enrolled in sport management and leisure management courses. Thedistribution of participants was relatively similar across the four institutions(n = 73, n = 41, n = 34, and n = 49), and preliminary analyses revealed thatresponses to questionnaire items did not vary by university. All persons pro-vided informed consent, and involvement in the study was voluntary. Partici-pant age ranged from 19 to 39, with a mean age of 21.70 (SD = 2.39). Previousexperience in the sport and leisure industry ranged from 0 to 16 years, with amean of 2.83 (SD = 3.24). The sample consisted of mostly men (55.1%, n =108), and Caucasians made up 83% of the sample (n = 164). The remainder ofthe sample was distributed as follows: 9.7% African Americans (n = 19), 3.1%Hispanics (n = 6), 1.5% Asians (n = 3), and 2% listed as Other (n = 4). Most ofthe participants were either seniors (49.2%, n = 95) or juniors (39.9%, n = 77),and all participants had majors. Note that some numbers do not total the over-all sample (n = 197) due to missing data.

Measures

Items were either adapted from existing scales or constructed specificallyfor the study. To assess the psychometric properties of the measures, a pilotstudy was conducted with 53 students from two universities. Results demon-strated general support for the reliability of the measures. However, additionalanalyses revealed that reliability could be improved by targeting specificitems; thus, changes were made to those constructs. For example, discrimina-tion items were changed from targeting specific social groups (e.g., “womenoftentimes face discrimination” or “ethnic minorities oftentimes face discrim-ination”) to reflect discrimination based on social categories in general (e.g.,“it is possible that I will be treated differently within the sport/leisure industrybecause of my demographics [e.g., age, sex, race]”). Such changes improvedthe internal consistency of the measure. Other changes made to the question-naire were similar in nature. In addition, pilot study participants providedopen-ended responses concerning barriers, supports, and outcome expecta-



tions that were coded and used to inform the construction of the finalquestionnaire items.

We then distributed the questionnaire to a panel of experts (n = 3) who hadeither conducted research incorporating SCCT or had previous research expe-rience with psychological foundations of work. The panel suggested severalchanges, which were also implemented. Based on these steps, the final ques-tionnaire contained demographic information (as listed above) and measuresto assess self-efficacy, outcome expectations, vocational interest, choicegoals, supports, and barriers. All items were measured using a 7-point Likert-type scale that ranged from 1 (strongly disagree) to 7 (strongly agree). Itemswere randomly ordered on the questionnaire. To ensure clarity as to what wasmeant by the sport and leisure industry, directions on the questionnaire read asfollows: “Jobs in the sport and leisure industry include, but are not limited to,those in athletics, recreational sport, professional sport, national sport organi-zations, sport tourism, and the like.” Each measure is described in greaterdetail below.

Self-efficacy. Self-efficacy to enter the sport and leisure industry was mea-sured with six items adapted from Van Vianen (1999): “I expect I can performwell in a job in the sport/leisure industry”; “I have self-assurance that I couldearn a position within the sport/leisure industry”; “because of my capabilities,I expect I can earn a position within the sport/leisure industry”; “I am capableof learning the skills needed for a job in the sport/leisure industry”; “I am con-fident I could successfully work within the sport and leisure industry”; and“the work I would do in the sport/leisure industry would be very difficult forme” (reverse scored). The internal consistency estimate for the measure (α =.82) was high.

Outcome expectations. Based on responses from the pilot study, two facetsof outcome expectations were developed for the study: satisfaction with sport/leisure and power. These facets of outcome expectations are consistent withBandura’s (1986) physical (i.e., power) and self-evaluative (i.e., satisfaction)classes of outcome expectations. Three items were developed for each facet.Lent et al. (1994) described outcome expectations as “if I do this, what willhappen” (p. 83) scenarios. Therefore, items were preceded by the phrase“entering the sport/leisure industry would mean . . .” The three items used tomeasure satisfaction with sport/leisure were “the opportunity to continue to bearound sport and leisure activities,” “satisfaction from being in the sport/leisure environment,” and “many benefits associated with sport and leisure.”The three items in the power facet included “a good salary,” “power in my job,”and “the ability to hold a position of authority.” The reliability estimates for


satisfaction with sport and leisure (α = .84) and power (α = .74) were bothwithin the acceptable ranges.

Vocational interest. Interest in the sport management profession was mea-sured with four items developed for the study: “Entering the sport/leisureindustry following graduation is something that interests me,” “working in thesport/leisure industry does not really interest me” (reverse scored), “workingin the sport/leisure industry following graduation would be an interestingoption for me,” and “I have no interest working in the sport/leisure industryonce I graduate” (reverse scored). The measure demonstrated high internalconsistency (α = .86).

Choice goals. Intentions, or choice goals, were measured using four itemsdeveloped for the study: “I intend to enter the sport/leisure industry followinggraduation,” “I will try to enter the sport/leisure industry following gradua-tion,” “entering the sport/leisure industry following graduation is something Iplan to do,” and “I intend to work somewhere other than the sport/leisureindustry following graduation” (reverse scored). Measuring choice goals inthis manner is consistent with previous literature, where intentions wereoperationalized as the extent to which one intended to, tried to, and planned toconduct a certain behavior (Hagger, Chatzisarantis, & Biddle, 2001). Theinternal consistency estimate (α = .91) for the measure was high.

Contextual barriers and supports. Based on the pilot study and a review ofthe literature, two barriers—discrimination and advancement opportunities—and two supports—human capital and social capital—were included in thestudy. Discrimination was measured with three items: “It is possible I will betreated differently within the sport/leisure industry because of my demo-graphics (e.g., age, sex, race),” “I anticipate facing discrimination in the sport/leisure industry based on my demographics (e.g., race, sex, age),” and “I do notforesee being treated differently in the sport/leisure industry based on mydemographic characteristics (e.g., age, sex, race)” (reverse scored). Advance-ment opportunities were measured with four items adapted from Wallace(2001), which were preceded by the phrase “within the context of the sport/leisure industry, I feel as if I would. . . .” The four items were “be promotedquickly” (reverse scored), “have a hard time advancing in the profession,”“have several opportunities for career advancement” (reverse scored), and“have few chances to ‘get ahead.’” The first support, human capital, is con-cerned with the education, training, and experience one has that contribute tosuccess in the industry (Nordhaug, 1993; see also Cunningham & Sagas,2002). Three items were used to measure human capital: “I have sufficient pre-

vious experience to enter the sport/leisure industry,” “I have the training toenter the sport/leisure industry,” and “my educational background has pre-pared me for a job in the sport and leisure industry.” Finally, social capital isconcerned with the contacts one has that contribute to success in and access toinformation about an industry (c.f. Seibert, Kraimer, & Liden, 2001). The fouritems used to measure this construct were “I feel as if I have sufficient contactsto help me in entering the sport/leisure industry,” “I have a large enough net-work of contacts to make entering the sport/leisure industry possible,” “I donot have the contacts to help me earn a job in the sport/leisure industry”(reverse scored), and “I feel as if I know enough people in the field to obtain aposition within the sport/leisure industry.” The reliability estimates for dis-crimination, advancement opportunities, human capital, and support capitalwere all high (α = .90, .75, .77, and .94, respectively).

Data Analysis

Means, standard deviations, and bivariate correlations were calculated forall variables. Prior to testing the hypotheses, a series of confirmatory factoranalysis (CFA) procedures were conducted to assess the factor validityevidence of the measures. The first CFA included items from the cognitive-person constructs (i.e., self-efficacy, vocational interests, choice goals, andoutcome expectations), and accordingly, a four-factor model was specified. Inthe second CFA, items from the barriers and supports were included, and afour-factor model was specified. The separate CFA procedures were necessary(as opposed to a single model) because of parameter-to-participant require-ments. Models that were a close fit to the data would provide factor validityevidence of the constructs used in the study. The hypotheses were then testedthrough observed variable path analysis. This procedure was preferred to fullstructural equation modeling because of parameter-to-participant criteria.Model fit was tested using three fit indices: root mean square error of approxi-mation (RMSEA), comparative fit index (CFI), and parsimonious normed fitindex (PNFI). The CFA procedures and path analysis were run using Analysisof Moment Structures 5.0.

Results

Factor Validity

The first CFA, which included items from the cognitive-person constructs,provided a good fit to the data, χ2 (n = 197, 160) = 362.23, p < .001; RMSEA(90% confidence interval [CI]: .069, .091) = .08; CFA = .92; PNFI = .66. The


second CFA, which included items from the barriers and supports, also pro-vided a general good fit to the data, χ2(n = 197, 71) = 144.48, p < .001; RMSEA(90% CI: .056, .090) = .07; CFA = .95; PNFI = .62. Both of these results pointto the factor validity evidence of the study constructs.

Hypothesis Testing

Preliminary analyses revealed skewness and kurtosis issues with some ofthe variables; therefore, the data were transformed by squaring each variable.Descriptive statistics are presented in Table 1. In general support of the SCCTmodel, (a) self-efficacy and the two outcome expectations were positivelyrelated to one another, (b) self-efficacy and the two outcome expectations werepositively related to both choice goals and vocational interests, and (c) voca-tional interests were positively associated with choice goals. Furthermore, thebarriers held negative correlations with choice goals, vocational interests, andself-efficacy, whereas the supports held positive associations with thesevariables.

It is also worth noting that the correlation between choice goals and voca-tional interests was particularly high (r = .79). Given the high correlation, wefit an additional cognitive-person CFA, with vocational interests and choicegoals specified as a single construct. Relative to the original cognitive-personCFA (described earlier), this model fit the data less well, χ2(n = 197, 164) =462.34, p < .001; RMSEA (90% CI: .086, .107) = .09; CFA = .88; PNFI = .65.Furthermore, the chi-square difference test revealed that the first model, wherechoice goals and vocational interests are seen as separate constructs, was a sig-nificantly better fit than the alternative model, ∆χ2(4) = 100.11, p < .001. Giventhis support, we modeled the two constructs as distinct from one another.

Path analyses were used to test the hypotheses and the competing models ofthe effects of contextual factors. This model fit was a close fit to the data,χ2(20) = 31.96, p < .05; RMSEA (90% CI: .01, .09) = .055; CFI = .98; PNFI =.42. Thus, this model received strong support (see Figure 1). Hypothesis 1 pre-dicted that barriers would be significantly related to self-efficacy. This hypoth-esis received full support, as both expectations of discrimination (β = –.15, p <.05) and truncated advancement opportunities (β = –.28, p < .05) held signifi-cant, negative associations with self-efficacy. In partial support of Hypothesis2, human capital was also significantly associated with self-efficacy (β = .43,p < .05), but social capital was not (β = .10, ns).

Hypotheses 3 and 4 predicted that self-efficacy and outcome expectationswould hold significant, positive associations with vocational interests, respec-tively. Hypothesis 3 received support, as self-efficacy was positively associ-ated with vocational interests (β = .26, p < .05). With respect to outcome


131

Tabl

e 1

Mea

ns, S

tand

ard

Dev

iati

ons,

and

Biv

aria

te C

orre

lati

ons

Var

iabl

e1

23

45

67

89

1. C

hoic

e go

als

.—2.

Voc

atio

nal i

nter

est

.79*

**.—

3. O

utco

me

expe

ctat

ion—

satis

fact

ion

.58*

**.6

4***

.—4.

Out

com

e ex

pect

atio

n—po

wer

.20*

*.1

9*.2

2.—

5. S

elf-

effi

cacy

.48*

**.5

7***

.67*

**.1

4*.—

6. B

arri

er—

disc

rim

inat

ion

–.06

–.10

–.15

*–.

07–.

30**

*.—

7. B

arri

er—

adva

ncem

ent o

ppor

tuni

ty–.

19**

–.21

**–.

34**

*–.

31**

*–.

51**

*.3

4***

.—8.

Sup

port

—so

cial

cap

ital

.22*

*.2

1**

.28*

**.1

7*.3

5***

–.03

–.25

***

.—9.

Sup

port

—hu

man

cap

ital

.34*

**.3

8***

.43*

**.2

0**

.57*

**–.

07–.

34**

*.5

0***

.—

M5.

826.

276.

144.

496.

043.

153.

044.

955.

44SD

1.24

0.94

0.89

1.00

0.74

1.61

1.02

1.33

1.02

Not

e:M

eans

and

stan

dard

devi

atio

nsar

eth

ose

ofth

eno

ntra

nsfo

rmed

data

.*p

< .0

5. *

*p<

.01.

***

p<

.001

.

expectations, outcome expectation and satisfaction were strongly associatedwith vocational interests (β = .47, p < .05), whereas outcome expectation andpower were not (β = .02, ns); thus, Hypothesis 4 received partial support.

Hypothesis 5 predicted that vocational interests would be positively relatedto choice goals. This hypothesis was supported, as the two constructs held ahigh, positive association (β = .79, p < .05). Finally, Hypothesis 6 was alsosupported, as self-efficacy held a significant positive association with bothoutcome expectation and power (β = .16, p < .05) and outcome expectationsand satisfaction (β = .62, p < .05).

Additional Analyses

In addition to these analyses, we also explored the possibility that sex andethnicity influenced the aforementioned relationships. Analysis of varianceprocedures showed that women rated discrimination and lack of advancementopportunities higher than did men. Furthermore, ethnic minorities rated dis-crimination higher than did Caucasians. These findings suggest that themodel fit might be improved by considering sex and ethnicity as “personinput” variables.

To examine the influence of sex and ethnicity, an additional path analysiswas computed, with (a) sex specified as affecting both discrimination andadvancement opportunities and (b) ethnicity specified as affecting discrimina-


Barrier: Discrimination

Barrier: Advancement Opportunities

Support: Human Capital

Support: Social Capital

Self-Efficacy 47%

Outcome Expectations:

Power 2%

Outcome Expectations: Satisfaction

39%

Vocational Interests

44%

Choice Goals 63%

-.15* -.28* .43* .10

.79* .26*

.16* .62*

.02 .46*

Figure 1Illustrative Summary of Mediated Paths Model

*p < .05.


tion, both of which are consistent with the previous analysis of variance find-ings. Results indicated that the alternative model has a poor fit to the data,χ2(42) = 153.73, p < .001; RMSEA (90% CI: .10, .13) = .12; CFI = .83; PNFI =.50. Furthermore, the alternative model was a significantly poorer fit to thedata than was the hypothesized model, ∆χ2(22) = 121.77, p < .001. Thus,although there were mean differences in some variables based on personaldemographics, inclusion of sex and ethnicity into the path model did notimprove the fit of the model.

Discussion

This study extended the extant research by incorporating SCCT and consid-ering psychological and contextual factors thought to influence career choice.Results demonstrated general support for the model in terms of the effects ofcognitive-person variables. Specifically, as predicted, (a) self-efficacy wasrelated to both outcome expectations, vocational interests, and choice goals;(b) outcome expectations and satisfaction were related to vocational interests;and (c) vocational interests held a positive association with choice goals.

Several points related to these findings are worthy of further elaboration.First, based on our pilot work, we developed a multifaceted measure of out-come expectations, which consisted of outcomes related to (a) satisfaction and(b) power in the field. This multifaceted approach to measuring outcomeexpectations differs from some previous literature (e.g., Lent et al., 2001; Lentet al., 2003), where outcomes expectations were assessed with a general mea-sure. However, implementing a multifaceted measure is beneficial from boththeoretical and practical standpoints. From a theoretical perspective, our twomeasures are consistent with Bandura’s (1986) physical (i.e., power) and self-evaluative (i.e., satisfaction) classes of outcome expectations. Thus, there issupport from general social cognitive theory for considering multiple dimen-sions of the construct. From a practical perspective, it is useful to understandwhich facet is more salient in predicting various outcomes. Our results indi-cated that expected satisfaction with the sport and leisure industry was themore prominent driver of subsequent choice goals, whereas expected power(i.e., holding a position of authority) was not related to such outcomes. Thesefindings suggest that affective responses to the field (i.e., satisfaction) aremore critical in formulating vocational decisions than possible position orinfluence in the field.

Second, results indicate that vocational interests and choice goals, althoughsharing considerable variance, are distinct concepts. That the two did hold ahigh correlation with one another is consistent with previous research and the-

oretical predictions (Lent et al., 1994; Lent et al., 2001; Lent et al., 2003).However, our CFAs demonstrated the distinctiveness of the constructs. Thus,from a theoretical perspective, one can have interest in a given behavior but,for whatever reason, not intend to conduct that behavior. Alternatively, from amore practical consideration, students may profess an interest in the sport andleisure industry (as might be evidenced by their choice of academic major) butstill remain lost between that interest and subsequent choice goals. Thus,counselors and professors should continue to cultivate intentions (i.e., choicegoals) among students even when interests (i.e., vocational interests) areexpressed.

In addition to testing the relationships between the cognitive-person vari-ables, we were also interested in examining the influence of contextual vari-ables. Consistent with previous research related to academic choice (Lentet al., 2001; Lent et al., 2003), support was found for the mediated paths model(Bandura, 1999, 2000), whereby the effects of contextual influences were seenon self-efficacy as opposed to influencing vocational interests and choicegoals directly. Thus, barriers or supports to entering a given profession areinternalized such that these effects are seen on one’s efficacy expectations,which in turn influence vocational interest and choice goals. Therefore, theeffects of environmental determinants on interests and goals are indirectthrough self-efficacy.

As with the outcome expectations, we found support for multidimensionalcontextual factors. Specifically, discrimination and outcome expectationswere perceived to impede one’s attitudes and volition toward the sport and lei-sure industry. Indeed, these findings are consistent with literature from anarray of disciplines (i.e., leisure, sport management, organizational psychol-ogy), which has suggested that both discrimination (Cunningham, 2004;Cunningham & Sagas, 2002; Shinew & Arnold, 1998) and a lack of advance-ment opportunities (Shinew & Arnold, 1998; Wallace, 2001) possibly encum-ber various persons’ careers. If these barriers influence career choice evenprior to entering the sport and leisure industry, hindrances within the industryare invariably robust. Furthermore, it is possible that the sport and leisureindustry is losing young, qualified professionals even before they have achance to enter the profession because of these impediments to entering theindustry. Thus, clearly, strategies should be implemented to alleviate thesebarriers within specific organizations and the industry as a whole (for possibleremedies, see Fink, Pastore, & Riemer, 2001).

In addition, support was also found for a multifaceted perspective of sup-ports. Specifically, both social capital and human capital were thought to aid inone entering the sport and leisure industry. These findings are consistent with


previous research that suggests that investments in training, experience, oreducation (i.e., human capital) or industry contacts (i.e., social capital) con-tribute to the ease with which one might enter or advance in a given field(Cunningham & Sagas, 2002; Nordhaug, 1993; Seibert et al., 2001). Bothhuman and social capital held significant bivariate correlations with self-efficacy; however, when considered in multivariate analyses, only human cap-ital held a significant relationship with self-efficacy. These findings suggestthat students drew confidence from their human capital investments and con-sidered such investments to be more important than network ties when it cameto entrance into the profession. Therefore, the old adage “it is not what youknow, but who you know” does not appear to hold true in this sample.

Contributions, Limitations, and Future Directions

This study makes several contributions to the extant literature. As previ-ously noted, although cognitive-person aspects of SCCT had been thoroughlyresearched (see, e.g., Gainor & Lent, 1998; Lopez et al., 1997), contextualinfluences on academic and vocational choice had received little attention(Lent et al., 2000); thus, researchers have encouraged additional inquiry in thisarea as well as further tests of the competing models (Lent et al., 2000). Fur-thermore, Lent et al. (2003) encouraged additional research concerning themultidimensional nature of supports and barriers. This study responded tosuch calls and demonstrated the utility of (a) adopting the mediated pathsmodel and (b) considering the multifaceted nature of barriers, supports, andoutcome expectations.

This study is also limited in several respects. First, data were collected onthe same questionnaire, thereby opening the possibility of common methodvariance (see Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). Of course, thatmany of the items were negatively worded (and subsequently reverse scored)reduces this threat to a certain degree. Second, SCCT predicts the temporalordering of variables illustrated in Figure 1 (Lent et al., 1994); however, thecross-sectional nature of our study precludes definitive assumptions of causal-ity. Longitudinal designs would alleviate the two aforementioned limitations.

Additionally, this study only measured choice goals (i.e., intentions) andnot actual behavior. Thus, although we know if students intended to enter thesport and leisure industry, we are unsure if they actually did so. It should benoted, however, that theoretical contributions (Ajzen, 1991; Lent et al., 1994)and empirical (Hagger et al., 2001, 2002; Lent et al., 1994) research stronglysupport the intentions to behavior linkage. Thus, we have reasonable confi-dence that participants who intended to enter the sport and leisure professionactually did so.


In addition to those listed above, there are other avenues for future inquiry.For example, how do students formulate their perceptions of discriminationand truncated advancement opportunities? Furthermore, what steps can betaken to make the field more accessible to all persons? Fink et al.’s (2001)research suggests that diversity management strategies are predictive ofrecruitment and attraction of talented workers, including persons from diversebackgrounds. Future research along these lines may prove beneficial. In arelated way, do cognitive-person or contextual factors vary depending on thespecific sector of the sport and leisure industry one wishes to enter? These arequestions that, although largely exploratory, could provide additional under-standing of why persons do or do not enter the sport and leisure industry.

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George B. Cunningham (Ph.D., The Ohio State University) is an assistant professor of sportmanagement and the director of the Laboratory for Diversity in Sport in the Department ofHealth and Kinesiology at Texas A&M University. His research interests are in the areas ofdiversity, group processes, and employee attitudes.

Jennifer Bruening earned her Ph.D. from The Ohio State University and is an assistant profes-sor of sport management at the University of Connecticut. Her research interests are primarily inthe areas of diversity issues in sport and the career development of students and student-athletes.

Melanie L. Sartore is a sport management doctoral student at Texas A&M University. Herresearch is in the area of discrimination, diversity, and employee attitudes.

Michael Sagas (Ed.D., Texas A&M University) is an assistant professor of sport managementand the director of the Laboratory for the Study of Intercollegiate Athletics in the Department ofHealth and Kinesiology at Texas A&M University. His research focuses on the under-representation of women and ethnic minorities in coaching and leadership positions in sport.

Janet S. Fink (Ph.D., The Ohio State University) is an associate professor in the School of Phys-ical Activity and Educational Services at The Ohio State University, where she researches atti-tudes toward sport, diversity, and the marketing of sport.


The Application of Social Cognitive Career Theory to Sport and Leisure Career Choices

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