Social Cognitive Predictors of Academic Interests and Goals in South Korean Engineering Students

Abstract

The purpose of this study was to investigate the applicability of social cognitive career theory (SCCT) in a cross-cultural setting by examining the relationships between the social cognitive variables of South Korean engineering students and their engineering interests and major choice goals across university type and gender. Participants (N = 660) completed measures of academic self-efficacy, coping self-efficacy, outcome expectations, engineering interests, contextual supports and barriers, and major choice goals. The results of the study revealed that the SCCT interest and major choice model offered an adequate overall fit to the full sample. The findings also indicated that the SCCT interest and choice model provided an acceptable fit to the data across university type and gender. The implications of the findings on practice for counseling South Korean engineering college students are discussed.

Keywords

social cognitive career theory career counseling cross-cultural application South Korean engineering students

Advancements in science and technology have been and will continue to be the engine for economic growth and national security. Maintaining these gains requires an adequate and well-educated workforce of scientists and engineers. However, the number of engineering students has been declining for the past two decades in the United States (National Science Board, 2004) as well as in other countries. South Korea is no exception. Even though engineering graduates have greatly contributed to the rapid economic growth in South Korea, the number of college students choosing an engineering major has been decreasing since the year 2000 (Korea National Center for Education Statistics & Information, 2009). Furthermore, 61.8% of students who dropped out of college were engineering majors in 2009 (Lee, 2012). For these reasons, counseling psychologists and career counselors in South Korea face the challenge of identifying what considerations determine whether individuals continue with an engineering career once they are in the field. In the present study, we attempt to examine factors influencing South Korean engineering students’ interests and major choice goals.

Social Cognitive Constructs and Their Mediational Relations to Engineering Interests and Major Choice Goals

Social cognitive career theory (SCCT; Lent, Brown, & Hackett, 1994) is a theoretical model that makes specific statements about variables (i.e., self-efficacy, outcome expectations, and perceived contextual supports and barriers) that can have associations with engineering students’ interests and major choice goals (see Figure 1). Numerous studies conducted in the United States have suggested that self-efficacy (Lent et al., 2001, 2005; Navarro, Flores, & Worthington, 2007) and outcome expectations (Lent et al., 2001, 2005; Navarro et al., 2007; Smith, 2002) predict students’ interests, choice of major, and academic persistence. In particular, self-efficacy has been proven to be a predictor of later outcome expectations, interests, and goals. For example, self-efficacy assessed twice during the two-semester period was an excellent predictor of engineering students’ outcome expectations, interests, and goals (Lent et al., 2008). In SCCT, interests are assumed to be related to the development of goals for further activity exposure (Lent et al., 1994). In fact, engineering interests have been positively associated with major choice goals (e.g., intentions to select or remain in an engineering major; Lent et al., 2003, 2005; Schaefers, Epperson, & Nauta, 1997) and have been shown to mediate the relationship between self-efficacy and major choice goals (Bonitz, Larson, & Armstrong, 2010; Lent et al., 2005). In addition, contextual supports were positively linked to self-efficacy and goals, whereas contextual barriers were negatively linked to self-efficacy and goals (Lent et al., 2001, 2005). Contextual supports and barriers have been related to major choice goals indirectly via self-efficacy (Lent et al., 2001). Further, freshman students’ goals to pursue science, technology, engineering, and math (STEM) coursework or majors were predictive of their subsequent choice behavior (i.e., entry into an engineering major 3 years later, engineering major persistence over the course of three academic semesters; Lapan, Shaughnessy, & Boggs, 1996; Lent et al., 2003).

Figure 1.

Standardized parameter estimates from the path analysis of the combined sample (N = 660). SE1 = academic self-efficacy; SE2 = coping self-efficacy; OE1 = self-evaluation outcome expectation; OE2 = physical and social outcome expectations; IN1-7 = 7 items on interests; MG1-4 = 4 items on major choice goals; CS1 = social support; CS2 = financial support; CB1 = social barriers; CB2 = instrumental barriers; CB3 = gender barriers. *p < .05. **p < .01. ***p < .001.

Importance of Considering Cultural Context of Career Choice

Although substantial research conducted in Western contexts has investigated social cognitive variables relating to students’ persistence in an engineering major, relatively few empirical studies with East Asian samples have been published in major journals in the field of vocational psychology. Recently, researchers have highlighted the need to include more diverse populations, including international populations, in studies in journals of vocational psychology (e.g., Chaichanasakul et al., 2011a, 2011b; Flores & Heppner, 2002). This position has been posited due to the major criticism that existing career research was conducted based on European American cultural values and thus failed to adequately address the intricacies of diverse populations (Chaichanasakul et al., 2011a, 2011b; Flores & Heppner, 2002). In particular, Leong, Hardin, and Gupta (2010) noted that people in collectivist cultures approach career issues from an interdependent perspective, which means that career success for these individuals will be closely tied to getting along with others and being able to fit into the larger community. For example, Asian students are more likely to choose majors and occupations that match the expectations of their parents, regardless of the students’ own personal inclinations to work in different fields (Inman & Yeh, 2007). Therefore, it is reasonable to expect that for Asian students, contextual variables including parents’ attitudes and preferences may have particularly strong associations with other social cognitive variables (e.g., self-efficacy, outcome expectations, and major choice goals).

A few studies to date have explicitly applied SCCT to South Korean engineering students. M. Kim (2008), for example, tested an SCCT model in which self-efficacy, outcome expectations, and contextual supports and barriers influence engineering interests and found that the model fit the data well. However, her study did not include major choice goals as a research variable, with several paths posited in the SCCT major choice models being untested. Y. E. Kim (2009) tested SCCT interest and choice models containing all of the social cognitive variables. Her study also has limitations, however, due to the fact that natural science majors were included in the sample. Lent, Lopez, Sheu, and Lopez (2011) suggested that it is important to examine whether an SCCT model applies to a specific discipline in the STEM fields because such fields differ in important aspects, such as their major tasks, proportion of female employees, and labor market demands. Thus, in this study, we sought to extend prior research on SCCT by examining the theory’s potential for appreciating the interests and major career goals of South Korean engineering students.

This study examined the role of gender in the relationships between social cognitive variables and major choice goals. In South Korea, women are generally underrepresented in both engineering majors and the engineering professions. According to Statistics Korea (2012), compared to 94.6% of men, only 5.3% of women in the engineering field have worked more than 10 years, and few women tend to advance into the ranks of senior engineering management. Women in engineering majors therefore have few or no female role models or mentors. This female-unfriendly climate discourages women from opting engineering majors (Min & Lee, 2005). Although greater attention has been paid recently to the career development of engineering students, there is a need to focus specifically on the career considerations of women as well as to understand the factors influencing their career decisions. However, no study to date has examined the utility of SCCT interest and choice models across gender in South Korea. The present study tested the structural invariance of the hypothesized model across gender.

We also examined whether the relationships among the SCCT variables would differ on the basis of university type. Nora (2001) emphasized that formal and informal academic interactions with faculty, involvement in learning communities, social experiences, campus climate, and mentoring relationships with faculty, peers, and advising staff determine subsequent goals, institutional commitment, and persistence in college. The present study compared a small polytechnic university with a large private university. In South Korea, it is mandatory for high school students who wish to enter college or university to take a national college entrance exam. Achievement levels on the exam range from 1 to 9, with Level 1 being the highest; those who are admitted to top-ranked large private universities achieve Levels 1–2, while those admitted to small polytechnic universities usually achieve Levels 4–5 (Korea Institute for Curriculum and Evaluation, 2012). Because large private universities offer various kinds of support to improve students’ competitiveness, students at such universities tend to report strong perceptions of organizational effectiveness (e.g., the quality of educational courses, financial support, and opportunities for communicating with faculty; K. Kim, 1995). Additionally, Y. C. Kim (2012) found that students whose parents are of high socioeconomic status have a much better chance of getting admitted to large private universities. Therefore, we assumed that large private universities would have more positive environmental features (e.g., diverse curricula, more support programs for engineering students, more opportunities for faculty–student mentoring, and parent’s financial supports) that, from an SCCT perspective, would be likely to foster academic progress and career aspirations. No study to date has examined the utility of SCCT interest and choice models across university type in South Korea. The present study tested the structural invariance of the hypothesized model across university type. Examinations of differential path effect sizes across gender and university type may suggest fruitful targets for interventions and may provide useful data regarding the extent to which SCCT can be generalized to South Korean engineering students.

Overview of the Present Study

On the basis of the SCCT’s interest and choice models and the findings of recent studies, we formulated several sets of predictions as illustrated in the path diagram in Figure 1. As in prior research on SCCT, we hypothesized that self-efficacy would be positively related to outcome expectations (Hypothesis 1; M. Kim, 2008; Y. E. Kim, 2009; Lent et al., 2001, 2003, 2005, 2011) and that self-efficacy and outcome expectations were expected to be positively related to interests (Hypotheses 2a and 2b; Lent et al., 2001, 2005). In addition, based on Lent et al. (2005), we anticipated that self-efficacy, outcome expectations, and interests would be directly related to major choice goals (Hypotheses 3a, 3b, and 3c). Congruent with previous research on SCCT, the results were expected to show direct links between contextual variables and self-efficacy (Hypotheses 4a and 4b; M. Kim, 2008; Y. E. Kim, 2009; Lent et al., 2003, 2005, 2011) and between contextual variables and major choice goals (Hypotheses 4c and 4d; Lent et al., 2005, 2011). Specifically, we hypothesized that contextual supports would be positively related to self-efficacy and goals, whereas contextual barriers would be negatively related to self-efficacy and goals. Considering previous findings (M. Kim, 2008; Y. E. Kim, 2009), we hypothesized that contextual supports would be positively related to outcome expectations and that contextual barriers would be negatively related to outcome expectations (Hypotheses 5a and 5b). Beyond these direct relations, it was expected that interests would mediate the link of self-efficacy with major choice goals (Hypothesis 6a; Lent et al., 1994, 2005; Navarro, Flores, & Worthington, 2007). As in prior research on SCCT (Gainor & Lent, 1998), it was expected that interests would mediate the links of self-efficacy and outcome expectations with major choice goals (Hypothesis 6b). Finally, consistent with prior research on SCCT (Lent et al., 2001), we hypothesized that self-efficacy would mediate the links of contextual supports and barriers with major choice goals (Hypotheses 7a and 7b).

Method

Participants and Procedure

Study participants consisted of 660 (141 women and 519 men) college students of engineering major courses at two South Korean universities. Of the participants, 477 (100 women and 377 men) were students at a 4-year private university. They were primarily second-year (44.0%) and third-year (56.0%) students, with a mean age of 21.35 years (standard deviation [SD] = 1.96). The remaining 183 (41 women and 142 men) were students from a public polytechnic university. They were also primarily second-year (82.0%) and third-year (18.0%) students, with a mean age of 22.02 years (SD = 2.74). The survey was conducted at the beginning of 2-hr general mathematics and introductory physics classes. All participants received instrument packets, including consent forms. The instrument packets consisted of 78 items and took approximately 10–15 min to complete. Participants completed the questionnaires willingly, and all responses were kept completely confidential.

Instruments

For the current study, all of the measures were translated from English to Korean in a three-step process. In the first step, the first author, who holds a bachelor’s degree in English language and literature, and the second author, who received a doctorate in counseling psychology in the United States, discussed the measures and translated them from English into Korean. Second, a back translation from Korean to English was conducted by a bilingual student (a psychology major) who was unfamiliar with both the original versions of the measures and the purpose of this study. Third, a native English speaker with a master’s degree in counseling compared the original items with the back-translated items to evaluate the semantic equivalence and accuracy. To test the cross-cultural validity of the measures, factor structures of all of the measures except for the Major Choice Goals Scale (MCGS), which consisted simply of 4 items, were examined by means of two sorts of factor analyses: (a) an exploratory factor analysis using SPSS18.0 program to initially explore the factor structure of the scales and then (b) a confirmatory factor analysis with maximum likelihood estimation using Mplus Version 5.1 (Muthén & Muthén, 2006). We used four indices to assess the measurement model’s goodness of fit: (a) the comparative fit index (CFI) ≥ .90, (b) the Tucker–Lewis index (TLI) ≥ .90, (c) the standardized root mean square residual (SRMR) ≤ .05, and (d) the root mean square error of approximation (RMSEA) ≤ .08 (Hu & Bentler, 1999; Kline, 2005). We expected the measures to meet these cutoffs for most of the indices. The Akaike information criterion (AIC) was used to compare models, with the model with the lower AIC being more desirable because less information is lost (Burnham & Andersen, 2002).

Self-efficacy

Bandura (1997) asserted that academic behavior is best predicted by measures tapping multifaceted aspects of self-efficacy, such as perceived ability to achieve academic milestones and to cope with perceived academic barriers. Therefore, in the present study, engineering students’ self-efficacy was operationally defined as the students’ degree of confidence in performing engineering-related tasks as well as their perceived ability to cope with barriers or problems that they may encounter. The Academic Milestones Scale (AMS; Lent, Brown, & Larkin, 1986) was used to measure the ability of participants to successfully perform a variety of academic tasks required for success in an engineering course (e.g., “How much confidence do you have in your ability to excel in your engineering major over the semester?”). The AMS is a 5-item measure that uses a 10-point scale ranging from 0 (no confidence) to 9 (complete confidence). In the present study, one factor with an Eigenvalue greater than 1 was extracted, accounting for 76.16% of the total variance. A confirmatory factor analysis showed that the one-factor model appeared to provide an acceptable fit to the data, χ²(4, N = 660) = 46.18, p < .001, CFI = .99, SRMR = .02, RMSEA = .126, 90% confidence interval (CI) [.095, .160]. The original version of this scale showed adequate internal consistency (α = .89) as well as theory-consistent relationships with the measures of academic performance, persistence, and perceived career options (Lent et al., 1986). In terms of validity, AMS scores correlated positively with social support, barriers coping efficacy, outcome expectations, interests, and goals in the present study. For the current sample, Cronbach’s α for the AMS was .94.

The Barriers Coping Efficacy Scale (BCES; Lent et al., 2003) was used to measure participants’ confidence in their ability to cope with a variety of barriers or problems that engineering students may encounter (e.g., “I am able to cope with a lack of support from others”). The BCES is a 7-item measure that uses a 10-point scale ranging from 0 (strongly disagree) to 9 (strongly agree). Lent et al. (2003) found that the scale was related to the measures of task self-efficacy, choice, barriers, and support in theory-consistent ways. In the present study, one factor with an Eigenvalue greater than 1 was extracted, accounting for 46.27% of the total variance. A confirmatory factor analysis showed that the single-factor model appeared to provide an adequate fit to the data, χ²(13, N = 660) = 84.22, p < .001, CFI = .96, SRMR = .03, RMSEA = .091, 90% CI [.073, .110]. In terms of validity, BCES scores in the present study were correlated positively with social support, academic coping efficacy, outcome expectations, interests, and goals. Lent et al. (2003) reported that the BCES yielded an internal consistency of .89. The internal consistency estimate for the current sample was .87.

Engineering outcome expectations

The Engineering Outcome Expectations Scale (EOES; Lent & Brown, 2006) was used to measure participants’ expectations related to the consequences of obtaining an engineering degree in college (e.g., “An engineering degree will allow me to earn an attractive salary”). The EOES is a 10-item measure that uses a 10-point scale ranging from 0 (strongly disagree) to 9 (strongly agree). In the present study, two factors with Eigenvalues greater than 1 were extracted, accounting for 60.70% of the total variance. The factors were labeled as intrinsic outcome expectations (e.g., self-satisfaction) and extrinsic outcome expectations (e.g., approval of significant others). We dropped 2 items (Items 6 and 8) with cross loadings with a difference of less than .15 from an item’s highest factor loading. The two-factor model showed an acceptable fit to the data, χ²(18, N = 660) = 140.80, p < .001, CFI = .97, SRMR = .05, RMSEA = .102, 90% CI [.086, .118], AIC = 302.43. In the present study, an alternative one-factor model was also considered. A confirmatory factor analysis showed that the one-factor solution provided a poorer fit to the data, χ²(19, N = 660) = 943.409, p < .001, CFI = .79, SRMR = .07, RMSEA = .252, 90% CI [.238, .266], AIC = 623.11. Thus, we selected the two-factor model. Previously, the EOES was found to yield an adequate internal consistency estimate (α = .91) and was shown to be related to the measures of task and coping efficacy, interests, and major choice goals (Lent et al., 2003). In the present study, internal consistency estimates for the two subscales were both .90, and the α coefficient for the total scale score was .94. Also, as expected, EOES scores were correlated positively with social support, self-efficacy, technical interests, and goals in the present study.

Technical interests

The Technical Interests Scale (TIS; Lent et al., 2003) was used to measure participants’ level of interest in seven engineering-related activities (e.g., solving complicated technical problems and reading articles or books about engineering issues). Responses were obtained on a 5-point scale ranging from 1 (very low interest) to 5 (very high interest). In the present study, one factor with an Eigenvalue greater than 1 was extracted, accounting for 47.56% of the total variance. A confirmatory factor analysis showed that the one-factor model appeared to provide an adequate fit to the data, χ²(13, N = 660) = 62.39, p < .001, CFI = .97, SRMR = .03, RMSEA = .076, 90% CI [.058, .095]. In terms of validity, consistent with SCCT, TIS scores correlated positively with social support, self-efficacy, outcome expectations, and goals in the present study. The original version of this scale produced an adequate level of internal consistency (α = .83; Lent et al., 2003). The internal consistency estimate for the current sample was .85.

Contextual supports and barriers

In this study, contextual supports and barriers were defined as proximal contextual variables—in particular, environmental support (facilitative influences) and barriers (obstacles)—that people anticipate will accompany their pursuit of their goals (Lent & Brown, 2006). Contextual supports and barriers were measured using the Contextual Supports and Barriers Scale (Lent et al., 2001) that consists of 35 items. Study participants were asked to indicate on a 5-point Likert-type scale (1 = strongly disagree, 5 = strongly agree), if they were to pursue an engineering major, how likely they would be to obtain 15 types of support (e.g., “If I were to pursue an engineering major, I would get encouragement from my friends for pursuing this major”) and to encounter 20 barriers (e.g., “If I were to pursue an engineering major, I would feel pressure from my parents or other important people to change my major to some other field”). An exploratory factor analysis produced two subscales for the support items (Social and Financial) and three subscales for the barriers items (Social, Instrumental, and Gender; Lent et al., 2003). A confirmatory factor analysis showed that the two-factor model for the support items provided an acceptable fit to the data, χ²(89, N = 660) = 483.09, p < .001, CFI = .88, SRMR = .06, RMSEA = .082, 90% CI [.075, .089], AIC = 492.42. An alternative one-factor model was also considered. The one-factor model provided a poorer fit to the data, χ²(90, N = 660) = 711.29, p < .001, CFI = .82, SRMR = .07, RMSEA = .10, 90% CI [.095, .109], AIC = 729.69. Thus, we selected the two-factor model. Also, as expected, social support, which reflects perceived social and financial support, was correlated positively with self-efficacy, outcome expectations, interests, and goals. The present sample produced Cronbach’s α coefficients of .86 (Social Support), .74 (Financial Support), and .87 (total contextual supports). A confirmatory factor analysis showed that the three-factor model for the barriers items provided an acceptable fit to the data, χ²(149, N = 660) = 747.86, p < .001, CFI = .85, SRMR = .07, RMSEA = .078, 90% CI [.073, .084], AIC = 552.39. An alternative one-factor model was also considered. The one-factor model provided a poorer fit to the data, χ²(170, N = 660) = 1,495.56, p < .001, CFI = .69, SRMR = .08, RMSEA = .11, 90% CI [.104, .114], AIC = 1,064.94. Thus, we selected the three-factor model. As expected, social barriers scores were correlated negatively with social support and goals in the present study. Cronbach’s αs for the current sample were .71 (Social Barriers), .73 (Instrumental Barriers), .81 (Gender Barriers), and .85 (total contextual barriers).

Major choice goals

Goals were defined as the intention to persist with an engineering major (e.g., “I plan to remain enrolled in an engineering major over the next semester”). Goals were measured using the MCGS (Lent et al., 2003). The MCGS is a 4-item measure that is scored on a 5-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree). Consistent with SCCT, major choice goals were correlated positively in the present study with support, self-efficacy, outcome expectations, and interests. Lent et al. (2005) reported an α coefficient of .93, and the present sample produced an α of .78.

Results

Preliminary Analyses

Scale means, SDs, and correlations among the measured variables for the full sample are reported in Table 1. The social cognitive variables were generally correlated with one another, as expected. To examine whether gender and university type differences existed in the social cognitive variables, we performed a 2 (university type) × 2 (gender) multivariate analysis of variance on the set of predictor and criterion variables (see Table 2). Box’s test of equality of covariance, Box’s M = 73.54, F(69, 81119) = 1.13, p = .22, and Levene’s test for equality of variances were not significant, suggesting that the assumptions of homogeneity of covariance and variance were met. There were significant differences by the function of university type, F(6, 651) = 6.27, η² = .06, and gender, F(6, 651) = 5.84, η² = .05; the University Type × Gender interaction was, however, nonsignificant, F(6, 651) = .98, η² = .01. Subsequent univariate analyses of variance showed that students at the private university reported significantly higher self-efficacy, outcome expectations, goals, and contextual supports than did students at the polytechnic university. In addition, men reported significantly higher self-efficacy and interests than did women, whereas women reported greater contextual barriers than did men.

Table 1.

Descriptive Statistics and Bivariate Correlations.

Variable	1	2	3	4	5	6	7	8	9	10	11
Contextual supports
1. Social supports	—
2. Financial supports	.50**	—
Contextual barriers
3. Gender barriers	.01	.10**	—
4. Financial barriers	−.10*	−.30**	.29**	—
5. Social barriers	−.14**	−.01	.51**	.60**	—
Self-efficacy
6. Academic self-efficacy	.52**	.22**	−.11**	−.05	−.10**	—
7. Coping self-efficacy	.50**	.32**	−.07	−.07	−.08*	.58**	—
Outcome expectations
8. Intrinsic outcome expectations	.58**	.23**	−.06	−.07	−.18*	.51**	.47**	—
9. Extrinsic outcome expectations	.52**	.28**	.05	−.08	−.05	.48**	.44**	.67**	—
10. Interests	.39**	.17**	−.01	.07	.01	.39**	.39**	.39**	.26**	—
11. Goals	.45**	.19**	−.16**	−.12**	−.24**	.45**	.27**	.39**	.30**	.29**	—
M	3.21	2.81	1.95	2.88	2.41	5.78	4.69	5.79	5.03	2.95	3.87
SD	0.68	0.95	0.96	0.70	0.70	1.85	1.50	1.72	1.82	0.77	0.75
Range	1–5	1–5	1–5	1–5	1–5	0–9	0–9	0–9	0–9	1–5	1–5

Note. N = 660.

*p < .05. **p < .01.

Table 2.

Social Cognitive Variables by University Type and Gender.

Variable	The large private university (n = 477)		The public polytechnic university (n = 183)		F (1, 658)	η²	Women (n = 141)		Men (n = 519)		F (1, 658)
Variable	M	SD	M	SD	F (1, 658)	η²	M	SD	M	SD	F (1, 658)	η²
Contextual supports	3.22	0.03	2.91	0.05	30.13***	0.04	3.05	0.66	3.15	0.66	2.75	0.00
Contextual barriers	2.41	0.03	2.42	0.05	0.02	0.00	2.63	0.64	2.36	0.62	20.67***	0.03
Self-efficacy	5.44	0.07	4.70	0.11	33.52***	0.05	4.90	1.54	5.33	1.47	8.88**	0.01
Outcome expectations	5.64	0.07	5.16	0.12	11.95**	0.02	5.32	1.58	5.55	1.62	2.21	0.00
Interests	2.99	0.04	2.85	0.06	4.32*	0.01	2.79	0.72	2.99	0.78	8.90**	0.01
Goals	3.93	0.03	3.73	0.06	9.55**	0.01	3.83	0.80	3.89	0.74	0.61	0.00

Note. M = mean; SD = standard deviation.

*p < .05. **p < .01. ***p < .001.

Test of the Hypothesized Model for the Combined Sample

Testing the measurement model

Model fit was tested with four indices: the CFI, the TLI, the SRMR, and the RMSEA. First, the measurement model was evaluated through a confirmatory factor analysis. Results indicated that the measurement model provided an acceptable fit to the data (CFI = .95, TLI = .93, SRMR = .04, RMSEA = .06, 90% CI [.05, .07]). Significant factor loadings (p < .01) indicated that all of the latent factors were adequately operationalized. This measurement model was therefore used to test the structural model.

Testing the hypothesized structural model

The hypothesized structural model was evaluated for the combined sample. This model provided an acceptable fit to the data (CFI = .96, TLI = .95, SRMR = .04, RMSEA = .06, 90% CI [.05, .07]). Consistent with Hypotheses 1, 2a, and 3a, self-efficacy was positively related to outcome expectations (β = .58, p < .001), interests (β = .52, p < .01), and major choice goals (β = .33, p < .05; see Figure 1). Consistent with Hypotheses 4a, 5a, and 4c, contextual supports were positively related to self-efficacy (β = .66, p < .001), outcome expectations (β = .26, p < .01), and major choice goals (β = .18, p < .05). In addition, consistent with Hypothesis 4d, contextual barriers were negatively related to major choice goals (β = −.21, p < .001). However, Hypotheses 2b, 3b, 3c, 4b, and 5b were not supported: Outcome expectations were not positively related to interests; outcome expectations were not positively related to major choice goals; interests were not directly related to major choice goals; contextual barriers were not negatively related to self-efficacy; and contextual barriers were not negatively related to outcome expectations. Collectively, the predictors accounted for 37% of the variance in major choice goals.

Testing the significance of indirect effects

Shrout and Bolger’s (2002) bootstrap procedure was used to estimate the significance of the indirect effects. We instructed Mplus to first create 5,000 bootstrap samples from the original data set (N = 660) by random sampling with replacement and then, when analyzing the structural model, to generate indirect effects and bias-corrected CIs around the indirect effects. Indirect effects are deemed to be significant if the 95% CI does not include zero. Consistent with Hypothesis 7a, self-efficacy mediated the link of contextual supports to major choice goals. However, Hypotheses 6a, 6b, and 7b were not supported: Interests did not mediate the link of self-efficacy with major choice goals; interests did not mediate the links of self-efficacy and outcome expectations with major choice goals; and self-efficacy did not mediate the link of contextual barriers with major choice goals.

Exploration of Group Differences

Multiple-group analysis by university type

We performed a multiple-group analysis to examine possible differences in structural paths as a function of university type. Two multiple-group models were tested. In the first model, the values of the hypothesized structural paths were allowed to vary for the two universities. In the second (i.e., invariant) model, the values of the structural paths were constrained to be equal for the two groups. If the invariant model did not differ in fit from the model in which the structural paths were allowed to vary, then the structural path coefficients would be similar between groups. Alternatively, if the two models did differ in fit, then one or more structural paths would be different between groups. In each model, factor loadings were held invariant to ensure that the constructs were being measured similarly between groups.

A test of the model in which the structural paths were allowed to vary indicated that the model provided an acceptable fit to the data (CFI = .95, TLI = .94, SRMR = .06, RMSEA = .045, 90% CI [.039, .051]). Testing of the model in which the structural paths were constrained to be equal also suggested an acceptable fit to the data (CFI = .94, TLI = .93, SRMR = .06, RMSEA = .047, 90% CI [.041, .052]). The difference in fit between these two models was significant, χ ² _difference(20, N = 660) = 61.72, p < .001, suggesting structural path differences between the two universities. To determine which pairs of structural path coefficients differed significantly from one another, we compared the model in which all structural paths were constrained to be equal against a series of alternative models in which one set of structural paths at a time was allowed to vary. These tests indicated that one structural path varied between the two universities: contextual supports to major choice goals, χ ² _difference(1, N = 660) = 3.68, p < .05. The relationship between contextual supports and major choice goals was significant only for the private university.

Multiple-group analysis by gender

Next, we performed a multiple-group analysis to examine possible differences in structural paths as a function of gender. A test of the model in which the structural paths were allowed to vary across gender indicated that the model provided an acceptable fit to the data (CFI = .94, TLI = .92, SRMR = .06, RMSEA = .050, 90% CI [.045, .056]). A test of the model in which the structural paths were constrained to be equal also suggested an adequate fit to the data (CFI = .94, TLI = .93, SRMR = .06, RMSEA = .050, 90% CI [.045, .056]). The difference in fit between these two models was not significant, χ² _difference(20, N = 660) = 25.9, p > .05, suggesting that none of the structural path coefficients differed by gender.

Discussion

In this study, we attempted to ascertain whether the core tenets of SCCT can be extended to the circumstances of South Korean engineering students. Our results showed that the hypothesized structural model based on SCCT offered an adequate fit to the data, which suggests that the SCCT interest and choice model provide a useful theoretical framework for explaining the interests and choice goals of South Korean engineering students. This finding is consistent with those from previous studies (M. Kim, 2008; Y. E. Kim, 2009; Lent et al., 2005, 2011) in which SCCT interest and choice models offered an adequate overall fit. Specifically, in the present study, self-efficacy was positively related to outcome expectations, engineering interests, and choice goals, suggesting that South Korean engineering students with higher levels of engineering self-efficacy are likely to display positive outcome expectations, exhibit a stronger level of engineering interests, and persist as engineering majors.

It is noteworthy that the direct paths from outcome expectations to interests and choice goals were not statistically significant. These results are inconsistent with the SCCT interest and choice model (Lent et al., 1994) as well as with previous findings that showed that outcome expectations were related to interests (Lent et al., 2001, 2005; Quimby, Wolfson, & Seyala, 2007) and major choice goals (Lent et al., 2001; Quimby et al., 2007). However, the current findings are consistent with those of other studies (Lent et al., 2008, 2011; Smith, 2002), in which outcome expectations did not produce significant paths to either interests or choice goals for engineering students. One possible explanation for the observed discrepancy is that our sample was mostly composed of second- and third-year students who had already made the decision to remain engineering majors, and outcome expectations might no longer influence the interest and goals of those who have chosen their major. This conjecture appears to be supported by previous findings. In some studies involving second- and third-year students, outcome expectations did not influence interest (Lent et al., 2011; Smith, 2002) and major choice goals (Lent et al., 2005, 2011; Smith, 2002).

Contrary to our hypothesis, the results indicated a lack of association between interests and major choice goals. This finding is not consistent with SCCT or with previous results (Lent et al., 2001, 2003, 2005) that showed that interest was strongly predictive of major choice goals. However, our results are consistent with those of Flores and O’Brien (2002) who showed that career interests did not influence career goals in Mexican American women. Leong and Chou (1994) and Markus and Kitayama (1991) posited that with regard to career decisions, Asians and Asian Americans place greater emphasis on interdependence, obligation to the in-group, and consideration of significant others, in comparison to their personal interests. Also, as with outcome expectations, for upper level students who have already decided upon their major, their interest may not influence their major choice goals. In some SCCT studies with upper level students (Lent et al., 2011; Smith, 2002), interest was not related to major choice goals, whereas for first-year students, interest was directly related to major persistence goals (Lent et al., 2005).

As hypothesized, contextual supports was positively related to self-efficacy, outcome expectations, and major choice goals. Our results indicate that South Korean engineering students who perceive support from their parents, faculty, and friends are likely to maintain strong levels of self-efficacy, sustain positive outcome expectations, and persist as engineering majors. These findings are consistent with those of previous studies (Lent et al., 2001, 2003, 2005). In addition to the direct paths to outcome expectations and interests, contextual supports indirectly affected outcome expectations through self-efficacy and interests through self-efficacy and outcome expectations. These findings are consistent with those of previous studies of South Korean college students (M. Kim, 2008; Y. E. Kim, 2009). In contrast, we found only partial support for the linkages between contextual barriers and other social cognitive variables. Only the direct path from contextual barriers to major choice goals was significant. These results are inconsistent with those from previous studies (Lent et al., 2001, 2003, 2005, 2011) in which contextual barriers were negatively related to self-efficacy, outcome expectations, and major choice goals. This anomaly might be explained by the fact that unlike the previous studies, which comprised mainly first-year students, the current sample was composed entirely of second- and third-year students. Unlike those of first-year students, the self-efficacy and outcome expectations of second- and third-year students who have already decided upon an engineering major may not be affected by contextual barriers.

In the present study, the SCCT’s major pathways were upheld for both universities. However, one difference was found in the path from contextual supports to major choice goals; the path coefficient was significant only for those in a large private university. As mentioned earlier, because private universities are keenly interested in student achievement and because students at such universities are of relatively higher social status, the students are well supported by their school and parents. These continual, contextual sources of support have a positive influence on their goals. On the other hand, students in polytechnic universities have a relatively higher perception of contextual barriers to their ongoing education compared to students in private universities. Therefore, contextual barriers have a more critical influence than contextual supports on their goals. Although some social cognitive variables (i.e., self-efficacy, interests, and contextual barriers) differed as a function of gender, none of the structural path coefficients was variant across gender. These findings suggest that the predictive utility of the social cognitive variables may not be moderated by gender for South Korean engineering students and that the SCCT variables may help explain the engineering-related interests and major choice goals of South Korean women as well as men.

Limitations and Future Research Directions

Several limitations of this study are particularly noteworthy. First, most participants were upper level students who were enrolled at either a large private university or a small public polytechnic university. Thus, the results of this study should not be generalized to all South Korean engineering students. More studies are needed to assess the applicability of SCCT models to other South Korean engineering students (e.g., first-year students, 2-year college students, graduate students, and vocational training institute students). Second, despite the fact that we tested the SCCT models based on theoretical grounds, the associations among the constructs are still correlational. Therefore, as is the case with all studies that rely on SEM analyses, there may be alternative models that would fit the data. For example, bidirectional relationships between self-efficacy and interests have been found (Nauta & Epperson, 2003). Therefore, longitudinal studies that assess causality are necessary, as this could not be done in cross-sectional studies. Third, although the measures used in this study showed high levels of internal consistency and cross-cultural validity, they did not undergo rigorous validation processes. Therefore, further studies are needed to validate the SCCT-related measures with South Korean engineering students. Although the SCCT variables were found to be important facets of South Korean engineering students’ major persistence levels, other factors might also be considered. For instance, other contextual variables (e.g., existence of university counseling services and vocational guidance programs), which are not included in SCCT models, could also be investigated.

Implications and Recommendations for Career Counseling

The current findings suggest practical implications that should be considered by career counselors of South Korean engineering students. First, counselors may need to focus on enhancing students’ self-efficacy, as this construct is positively related to outcome expectations, interest levels, and major persistence goals. To heighten self-efficacy, counselors may need to provide interventions that are based on the four sources of efficacy: mastery experiences, vicarious experiences, social persuasion, and physiological reaction (Byars & Hackett, 1998). Interventions that target mastery experiences may include problem-solving experiences involving proximal goals. Proximal goals, defined as goals close at hand, can be achieved more quickly and result in greater performance that can raise self-efficacy more rapidly than distal goals (Schunk, 2001). For example, counselors can help engineering students develop a structured series of tasks to be completed in 2–3 weeks to improve their performance in applied mathematics, while checking their progress in counseling sessions. Using the principle of vicarious experiences, the counselor can enhance the student’s self-efficacy by connecting him or her with graduate students in engineering who are interested in mentoring programs, allowing the student to share difficulties and listen to graduate students’ similar difficulties and solutions. Counselors may also act as cheerleaders for engineering students, applying the principle of social persuasion by providing active verbal supports as well as genuine, appropriate, and realistic feedback, so that students can learn to try on new career roles, explore more options, and overcome both internal and external career barriers (Sullivan & Mahalik, 2000). Finally, to reduce students’ anxiety that is likely to make them doubt their ability to succeed, counselors may teach them anxiety-management strategies, including breathing and self-talk exercises as well as relaxation techniques.

In this study, contextual supports (i.e., perceived career supports from family, faculty members, classmates, and role models) have particularly strong associations with engineering self-efficacy. Previous studies have shown that Asian American students place a great deal of importance in maintaining harmonious relationships with other members of their group and are sensitive to feedback from group members. In particular, receiving positive feedback from group members may strongly influence the enhancement of self-efficacy (Markus & Kitayama, 1991). In South Korea, individualistic values are slowly increasing but collectivism is still dominant (Seo, 2010). Therefore, counselors in South Korea could hold regular seminars for parents and faculty members on the topic of self-efficacy for engineering students to provide them with a better understanding of what students really need in terms of practical resources and support. Moreover, given that engineering students tend to experience many hardships while pursuing their major, receiving support from family and faculty members could enhance students’ coping efficacy.

Finally, counseling interventions should be tailored to the type of university. For students attending private universities, more contextual supports may be effective in helping them persist in engineering majors. For those attending polytechnic universities, however, lowering contextual barriers (e.g., lack of relational support and financial difficulties) may be a more effective means to encourage persistence in achieving engineering goals. For example, it would be beneficial to talk with such students about contextual barriers, their thoughts on whether and how such barriers will affect their personal experiences in relation to persisting in an engineering major, and how to overcome barriers and find support systems in their immediate environment.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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