Longitudinal Relationships Between Planned Happenstance Skills and Life Adjustment and the Moderating Role of Career Barriers

Abstract

The purpose of the present study was to examine the longitudinal relationships between planned happenstance skills (PHS) and life adjustment and to examine whether this relationship was moderated by the degree of career barriers. The participants were 307 Korean college students going through a school-to-work transition. The results showed that PHS preceded and were positively associated with life adjustment. Additionally, the association between PHS and life adjustment differed by the levels of career barriers; individuals with greater PHS were more likely to adjust well in life even when they perceived high career barriers. The findings suggest empirical support for planned happenstance theory. Implications regarding career counseling interventions for college students in a school-to-work transition are discussed.

Keywords

planned happenstance skills life adjustment career barriers autoregressive cross-lagged model

Life and career cannot be separated, and career is an important part of life adjustment (Krumboltz, 2009). Krumboltz (2009) further asserted that career counselors should help their clients not only to be satisfied with their own career but also to be able to handle their own lives for their adjustment. Psychological adjustment is often considered as a fundamental goal of psychological counseling (Society of Counseling Psychology, 2006). Its importance is also valued in the current career development field, as work is usually a significant part of one’s life (Ash, 1999). In sum, career is a vital factor in life because they have an impact on individuals’ overall well-being (Krumboltz, 2009), which has also been supported by previous research showing a close relation between career satisfaction and life adjustment (Bretz & Judge, 1994; Heyman, Sayers, & Bellack, 1994; Ilardi, Leone, Kasser, & Ryan, 1993).

Careers are important in life, and individuals do not simply make a career decision due to frequently nonlinear characteristics of careers. The world of work has become much more complex and dynamic than before, and careers are not any more linear or predictable. While traditional career theories and practice have emphasized individuals’ decision-making of careers, more recent career development theories have integrated the environmental changes and started to include concepts, such as nonlinearity, meaning-making, and randomness (Pryor & Bright, 2003; Savickas, 2013).

In the perspective of new career theories, life adjustment and life satisfaction can be increased by taking advantage of chance events. Especially, individuals have been more required to adjust to changes that occurred in school-to-work transition. Krumboltz and Worthington (1999) addressed that learning is important in school-to-work transition because learning activities would improve individuals’ abilities to generate satisfying lives for themselves. In addition, Krumboltz, Foley, and Cotter (2013) emphasized that individuals in school-to-work transition utilize planned happenstance skills (PHS) more than those who are not in career transitions, and utilizing PHS eventually leads to life satisfaction.

Mitchell, Levin, and Krumboltz (1999) developed the planned happenstance theory and postulated five PHS. They mentioned that it is important for individuals to search for and utilize happenstance events in their career development. They also stated that individuals with these five skills would utilize happenstance efficiently. Specifically, curiosity refers to “exploring new learning opportunities,” persistence refers to “exerting efforts despite setbacks,” flexibility refers to “changing attitudes and circumstances,” optimism refers to the capability to “view[ing] new opportunities as possible and attainable,” and risk-taking refers to “taking actions in the face of uncertain outcomes” (Mitchell, Levin, & Krumboltz, 1999, p. 77).

A couple of studies examined the effects of planned happenstance skills on career-related variables such as occupational identity statuses and career decision self-efficacy. Specifically, Ahn and his colleagues (2015) found that the five components of PHS were closely associated with Korean high school students’ vocational identity statuses in that those who reported greater PHS were more likely to be in an achievement status. Other study showed the longitudinal effects of PHS on an increase in career decision self-efficacy and a decrease in dysfunctional career thoughts (B. Kim et al., 2015). These studies illustrated that PHS are helpful for university students in unfolding their career pathways.

Theoretically, Krumboltz and Worthington (1999) suggested that flexible and adaptable learning experiences would lead to achieve more life satisfaction. However, an empirical evidence of the relationship between PHS and life adjustment has not been examined. Rather, previous studies examined the association between life adjustment and career-related variables such as career decision-making, career confidence, and career planning (Creed, Muller, & Patton, 2003; Strauser, Lustig, & Çiftçi, 2008). Accordingly, it is needed to check whether PHS indeed affect adjustment as the happenstance learning theory addressed. B. Kim, Jang, et al. (2014) reported that those who were actively engaged in their careers were more likely to use PHS and had more certainty in their career decisions. These results imply that adjustment precedes career-related behavioral variables. Nonetheless, it is still unclear whether PHS affect adjustment or vice versa.

The relation between PHS and life adjustment is not linear, so it is not simple to examine the association. It may be more specifically explained by individuals’ perception of career-related barriers. Krumboltz (2009) asserted that overcoming career-related barriers, such as dysfunctional beliefs, would eventually enhance individual’s life satisfaction. Career-related barriers are usually led by clients’ negative perspectives (London, 1997; Swanson & Woitke, 1997). According to Swanson and Woitke (1997), it is crucial in a counseling session to understand the clients’ barriers related to career in order to help the clients consider a wide range of available career options. Many previous researchers classified career barriers into internal conflicts, such as dysfunctional beliefs and external frustration (Crites, 1969; O’Leary, 1974; Swanson & Tokar, 1991a).

Especially, career barriers are considered as moderators in the career development process (Swanson & Woitke, 1997; A. M. Weiner, 2000). B. Weiner (1985) and A. M. Weiner (2000) postulated perceived barriers as a moderator in his attribution theory. Swanson and Tokar (1991b) expanded perceived barriers as career barriers because perceived barriers play a critical role in one’s career path. Especially, among individuals who perceive higher career barriers, it can be assumed that those who have better PHS are likely to adjust better, whereas those who have poorer PHS are likely to adjust poorer. This can be possible because those who have better PHS are likely to perceive career barriers as opportunities, not uncontrollable obstacles. From this perspective, career barriers are important factors to evaluate the learning process in individuals’ lives.

According to Krumboltz (1998), career counselors should accomplish goals that their counseling session helps clients to deal with concerns and to adjust their lives. In line with this perspective, Mitchell and colleagues (1999) emphasized how to generate benefits from chance events in order to adjust an environment. In other words, the planned happenstance theory postulates the existence of chance event between PHS and life adjustment. In short, based on the chance events, PHS could function and then contribute to increased life adjustment. In the present study, career barriers were considered as chance events, so it was hypothesized as a moderator in the research model.

Purpose of the Current Study

Consequently, the purpose of the current study is to explore the longitudinal relations between PHS and adjustment, taking into consideration the level of career barriers. The hypotheses of the study are the following:
Hypothesis 1: PHS would precede and be positively associated with life adjustment.

Hypothesis 2: The precedence of PHS on life adjustment would be significant only on a high-level group of career barriers.

As the theory contends (Krumboltz, 2009; Krumboltz & Worthington, 1999), PHS would precede adjustment, and these skills would affect participants’ adjustment positively. In addition, because career barriers were used as a moderator in previous research on experiencing career success and career-related variables such as career indecision (Swanson & Woitke, 1997; A. M. Weiner, 2000), it would be meaningful to examine if career barriers also moderate the relationship between PHS and adjustment.

Method

Participants

Undergraduate students in their seventh semester were recruited via an e-mail offering US$10 for participation in a yearlong longitudinal study. Participants attended different types of universities located in Seoul and other regions in South Korea. Data were collected via web-based surveys over three waves during the time of school-to-work transition: 6 months before graduation (Time 1), around graduation (Time 2), and 6 months after graduation (Time 3). Participants generated their own eight-digit identification numbers at first, and the three data sets were merged using these numbers. When the data were merged, participants received an e-mail to verify the eight-digit numbers at each time point. Participants were included in the present study only if they voluntarily participated in at least two waves. This procedure yielded a number of 307 participants in total (n = 307 at Time 1, n = 307 at Time 2, and n = 258 at Time 3). In the present study, the male participants were 167 (54.5%) at Time 1, 165 (53.7%) at Time 2, and 138 (53.5%) at Time 3. The female participants were 140 (45.6%) at Time 1, 142 (46.3%) at Time 2, and 120 (46.5%) at Time 3. Participants ranged in age from 24 to 33 (M = 27.08, SD = 1.72).

Measures

Planned happenstance career skills

Planned happenstance career skills were measured with the Planned Happenstance Career Inventory (PHCI; B. Kim, Jung, et al., 2014), which was created based on the planned happenstance theory (Mitchell et al., 1999). The PHCI included the following five subscales: curiosity, persistence, flexibility, optimism, and risk-taking. The congruent and discriminant validity of the PHCI were confirmed using Korean samples by the relationship with closely associated measures, such as career stress, career decision self-efficacy, and career preparation behavior (B. Kim, Jung, et al., 2014). Previous research revealed that PHS were positively correlated with career decision self-efficacy and career preparation behavior and were negatively correlated with career stress (B. Kim, Jung, et al., 2014). Participants responded to 25 items on a 5-point Likert-type scale (1 = I do not agree at all to 5 = I strongly agree), including “I am deeply interested in new activities that could help my career decision (curiosity),” “I will persistently try my best even if I encounter unexpected difficulties (persistence),” “I believe my career path can be changed anytime (flexibility),” “I believe my future is filled with opportunities (optimism),” and “I will challenge myself even if there is no guarantee of success in the field (risk-taking).” All items were averaged. Higher scores indicated a greater likelihood of using the five skills. Cronbach’s αs at each wave were .91 (Time 1), .90 (Time 2), and .92 (Time 3).

Life Adjustment

Life adjustment was measured with the Kim’s Life Adjustment Scale (S. Kim, 2014), which consisted of selected items from the following four scales: the Korean version of the Psychological Well-Being Scale, the Ryff Scale of Psychological Well-Being, the Satisfaction with Life Scale, and the Student Adaptation to College Questionnaire Scale. Participants responded to 5 items on a 5-point Likert-type scale (1 = I do not agree at all to 5 = I strongly agree), including “I am satisfied with my current life,” “I feel overwhelmed because of the duties I need to perform every day (reverse-coded),” “I invest my time and money for self-improvement and recharging my batteries,” “I maintain good relationships with others,” and “I think I am healthy these days.” Using the sample of Korean college students, S. Kim (2014) performed the factor analysis for confirming the items. The results indicated that the model fits of one-factor model were much better than those of the two-factor model. In the one-factor model, Kim reported that the standardized factor loadings of adaptation were higher, ranging from .49 to .70. Cronbach’s αs at each wave were .70 (Time 1), .66 (Time 2), and .67 (Time 3).

Career barriers

Career barriers were measured with the Korean version of the Career Barriers Inventory (KCBI; E. Kim, 2001), which was adapted and validated from the Career Barriers Inventory (CBI; Swanson & Tokar, 1991b). The KCBI included the following nine subscales: difficulty in interpersonal relationships, a lack of self-awareness, financial problems, external conflict, a lack of information, issues of age, physical inferiority, a lack of interest, and future-related anxiety. We chose the selected items considering the happenstance learning theory (Krumboltz, 2009), which emphasizes chance events and learning process for career in uncertainty. Generally, interactions with events and other people regarding one’s life have effects on the learning process (Woo & Reeves, 2007). After considering the suggestions of Krumboltz (2009) and Woo and Reeves (2007), 9 items of the following subscales that were related to the learning process for one’s career were chosen: difficulty in interpersonal relationship, future related anxiety, and external conflict. The items were scored on a four-point Likert-type scale (1 = I do not agree at all to 4 = I strongly agree), and 2 of the 9 items were coded reversely. Higher scores indicated greater difficulty with facing correlated career problems. Each subscale of the KCBI developed by E. Kim (2001) showed good internal consistency (Cronbach’s α = .71–.81). Previous research showed that career barriers were negatively correlated with career decision self-efficacy and were positively correlated with future-related anxiety using Korean sample (Cha, Kim, & Kang, 2015). Moreover, previous research showed the good concurrent validity of career barriers with Korean samples using the shortened version of the scale (Jang, 2013). To test the validity of the 9-item version used in the present study, we conducted a one-factor confirmatory factor analysis (CFA). The model fit was acceptable (Tucker–Lewis index [TLI] = .96, CFA = .98, root mean square error of approximation [RMSEA] = .06). Cronbach’s α at Time 1 was .79 in the current study.

Results

SPSS 18.0 was used to run descriptive statistics and a correlation analysis, and Amos 18.0 was used to run a multigroup autoregressive cross-lagged model. The full information maximum likelihood method was used for missing data because it is a vital method to handle the incomplete data (Schafer & Olsen, 1998). Due to the incomplete data, standardized root mean square residual could be not calculated for the tested models. In order to confirm whether the research model and tested models were statistically acceptable or not, χ², CFI, TLI, and RMSEA were used.

Two groups, above (n = 167) and below (n = 140), were generated using the median of the KCBI. To assure that the two groups were significantly different in terms of planned happenstance career skills and life adjustment, t-test was conducted at Time 1. As a result, the differences in planned happenstance career skills (t = 7.04, p < .001) and life adjustment (t = 6.87, p < .001) were statistically significant. In the current study, the KCBI (E. Kim, 2001) was used in order to measure career barriers, and we used a median split as Slaney and Brown (1983) did. To test the moderation effect of career barriers, the multigroup analysis was conducted based on the autoregressive cross-lagged model. The fit indices of an unconstrained model were compared to constrained models in order to confirm the significance of the moderation effect (Steenkamp & Baumgartner, 1998).

As presented in Table 1, the means and standard deviations were relatively stable across the three waves. Furthermore, the results of cross-sectional and longitudinal correlations indicated that PHS and life adjustment were positively related to each other over time (from Time 1 to Time 3, p < .001). Meanwhile, career barriers showed a negative relation to PHS and life adjustment across all time points (from Time 1 to Time 3, p < .001).

Table 1.
Mean, Standard Deviation, and the Cross-sectional and Longitudinal Correlations.

Time 1 (N = 307) Time 2 (N = 307) Time 3 (N = 253)

PHS ADJ CB PHS ADJ PHS ADJ

Time1 PHS

ADJ .46*

CB −.38* −.46*

Time2 PHS .66* .33* −.29*

ADJ .35* .53* −.39* .44*

Time3 PHS .63* .39* −.36* .66* .42*

ADJ .28* .53* −.35* .32* .57* .43***

M 3.79 3.50 2.22 3.80 3.48 3.71 3.42

SD .48 .67 .56 .48 .67 .48 .63

Note. PHS = planned happenstance skills; ADJ = adjustment; CB = career barrier.

p < .001.

Next, we performed a multigroup analysis based on the levels of career barrier variable to examine the longitudinal relationships between PHS and life adjustment using the autoregressive cross-lagged model. Autoregressive cross-lagged analysis (ACLA) is a useful modeling for determining the temporal precedence between the variables (Bast & Reitsma, 1997; Curran & Bollen, 2001). ACLA can statistically test the temporal precedence between the variables across time based on the condition which a researcher constrains measurement errors. In order to use ACLA, a researcher should test configural invariance, path invariance, and error covariance invariance (Steenkamp & Baumgartner, 1998).

The configural invariance indicated that participants identically perceive the conceptions of measured variables across groups. Then, the path invariance test was conducted to test whether the regression coefficients of each latent variable were identical or not. Finally, the researcher confirmed that the association between variables was not accidently happened but was significantly tested by constraining covariance between errors. All invariance tests were conducted to each variable. Therefore, a total of seven invariance models were tested in this study. The most suitable model was selected after the invariance tests were determined, and the multigroup autoregressive cross-lagged model was conducted. According to the steps suggested by Steenkamp and Baumgartner (1998), in order to confirm the prerequisites for ACLA before the hypotheses were tested, the models (Model 1 to Model 7) were as follows.

Model 1, the baseline model, was not constrained on any variable, path, and error. Model 2 was with partial measurement invariance constraint on measuring variable of latent variable (PHS) each time. Model 2 was constrained for testing partial measurement invariance because the partial measurement invariance model showed a better fit than the full measurement invariance model. Flexibility and persistence, which are the dimensions of the PHCI, were free for the partial measurement invariance model in each time point. Because life adjustment was a measurement variable, the measurement invariance test was not conducted for life adjustment. Model 3 was tested autoregressive path coefficient (paths from PHS T1 to PHS T3) invariance. Model 4 was an autoregressive path coefficient (paths from ADJ T1 to ADJ T3) invariance model. Model 5 was a cross-lagged path coefficient (paths from PHS to ADJ) invariance model. The Model 5 was for testing the hypothesis of the precedence of PH to life adjustment. Model 6 was a cross-lagged path coefficient (paths from ADJ to PHS) invariance model. Model 7 was an error covariance (from ADJ to PHS) invariance model, which was correlated between errors of ADJ and PHS on each time. These seven models were the steps for confirming whether the hypothesized model was statistically reasonable or not. After comparing the time invariance model (Models 2–7) to the baseline model (Model 1) which was not constrained, we discovered that the error covariance invariance (from ADJ to PH) model (Model 7) was the most suitable model in terms of the fit. Thus, Model 7 was selected as the final model. The difference in χ² values between Model 1 and Model 7 was not statistically significant, and the difference in the general goodness of fit (CFI, TLI, and RMSEA) of Model 1 and Model 7 did not exist. This indicates that Model 7 fits the data best. The results are summarized in Table 2.

Table 2.
Summary of Model Fit Statistics in Time Invariance Tests.

Model χ ² df TLI CFI RMSEA

Model 1 347.97 112 .87 .92 .08

Model 2 352.52* 114 .88 92 .08

Model 3 352.52* 115 .88 .92 .08

Model 4 352.68* 116 .88 .92 .08

Model 5 352.92* 117 .88 .92 .08

Model 6 354.28* 118 .88 .92 .08

Model 7 355.24* 119 .88 .92 .08

Note. Model 1 = baseline model; Model 2 = model with partial measurement invariance constraint on measuring variable of latent variable (planned happenstance skills) each time; Model 3 = autoregressive path coefficient (from PHS T1 to PHS T3) invariance model; Model 4 = autoregressive path coefficient (from ADJ T1 to ADJ T3) invariance model; Model 5 = cross-lagged path coefficient (from PHS to ADJ) invariance model; Model 6 = cross-lagged path coefficient (from ADJ to PHS) invariance model; Model 7 = error covariance (from ADJ to PHS) invariance model; PHS = planned happenstance skills; ADJ = adjustment; TLI = Tucker–Lewis index; CFI = comparative fit index; RMSEA = root mean square error of approximation.

p < .001.

Configural invariance verification for multigroup analysis was also conducted to test whether the same model was suitable for both the above-median and below-median groups. Table 3 showed all of the models we used to verify measurement invariance, structural invariance, and error covariance invariance between the groups. Each of the seven models was sequentially compared to find the best fitting model. Comparing the group invariance model (s 2–7) to the baseline model (Model 1), the difference in χ² values between Model 1 and Model 4 was not statistically significant. However, the difference in χ² values between Model 4 and Model 5 was statistically significant. Therefore, the cross-lagged paths from PHS to ADJ were significantly different between the 2 groups. The results are summarized in Table 3 and Figure 1.

Table 3.
Summary of Model Fit Statistics in Group Invariance Tests.

Model χ ² df Δχ² Δdf TLI CFI RMSEA

Model 1 436.94 238 .89 .92 .05

Model 2 437.65* 239 0.71 1 .89 .92 .05

Model 3 438.07* 240 0.42 1 .89 .92 .05

Model 4 438.11* 241 0.04 1 .89 .92 .05

Model 5 443.77* 242 5.66 1 .89 .92 .05

Model 6 444.85* 243 1.08 1 .89 .92 .05

Model 7 446.79* 244 1.94 1 .89 .92 .05

Note. Model 1 = baseline model; Model 2 = model with partial measurement invariance constraint on measuring variable of latent variable(planned happenstance skills) each time; Model 3 = autoregressive path coefficient (from PHS T1 to PHS T3) invariance model; Model 4 = autoregressive path coefficient (from ADJ T1 to ADJ T3) invariance model; Model 5 = cross-lagged path coefficient (from PHS to ADJ) invariance model; Model 6 = cross-lagged path coefficient (from ADJ to PHS) invariance model; Model 7 = error covariance (from ADJ to PH) invariance model; PHS = planned happenstance skills; ADJ = adjustment; TLI = Tucker–Lewis index; CFI = comparative fit index; RMSEA = root mean square error of approximation.

p < .001.

Figure 1.
Path coefficients from the multigroup autoregressive cross-lagged model. Above median of Career Barriers (Bold)/below median of Career Barriers. ADJ = Life adjustment (observed variables); PH = planned happenstance career skills (latent variables). p < .001. **p < .01. ns = not significant.

The direction of paths between planned happenstance and adjustment was also examined using the autoregressive cross-lagged model. As Figure 1 shows, planned happenstance and life adjustment at a given time point (t time) were significantly related to their own values at previous time points (t − 1 time) regardless of the two groups indicating a certain degree of stability: from ADJ T1 to ADJ T2 = .40/.40 (above/below), from ADJ T2 to ADJ T3 = .43/.42, from PHS T1 to PHS T2 = .66/.63, and from PHS T2 to PHS T3 = .64/.72 (p < .001). Meanwhile, the cross-lagged path coefficient from PHS to life adjustment only in the high career barriers group was significant: from PHS T1 to ADJ T2 = .21, from PHS T2 to ADJ T3 = .22 (p < .01). In contrast, cross-lagged path coefficients from life adjustment to PHS in both the high- and the low-level career barrier groups were not significant. In conclusion, this result indicated that PHS had a statistically significant effect on life adjustment across time, particularly among individuals who perceived high levels of career barriers.

Discussion

First, we found an indication that PHS significantly predicted life adjustment across Time 1 to Time 3 but only in the high career barriers group. In theory, PHS are utilized when one tries to change a chance event into a significant career opportunity. Therefore, PHS trigger positive behavioral changes and can contribute to productive adaptation to a new environment (Krumboltz et al., 2013; Mitchell et al., 1999). Through this process, PHS may proceed toward career satisfaction and life satisfaction. According to previous research (Bretz & Judge, 1994; Heyman et al., 1994; Ilardi et al., 1993), there is a close relationship between career satisfaction and life adjustment. Taking into consideration the previous research and the findings of this study, the results suggest that PHS precede life adjustment and play an important role in improving individuals’ life adjustment.

However, this relation was only evident among those who perceived high career barriers. Previous researchers mentioned that career barriers may have a moderation effect on the process of setting career goals (Bowman, 1988; Slaney, 1980; Slaney & Brown, 1983). Using multigroup analysis, the current study found that career barriers exert moderation effects in the relationship between PHS and life adjustment. In other words, planned happenstance skills are the crucial components for increasing life adjustment, particularly when individuals confront many career barriers. This finding could be explained by existing literature such as those on the happenstance learning theory. Krumboltz (2009) stated that PHS, which stimulate the learning process for careers, help individuals with high levels of career barriers to perceive risk events into opportunities. Therefore, this finding showed that increasing PHS would be helpful for individuals facing career barriers during the school-to-work transition in order to encourage them to adapt to the changing environment.

Moreover, the result of the current study also supported the theory and even extended the theory by showing that planned happenstance skills not only predicted career-related variables but also preceded psychological variables such as psychological adjustment based on the empirical evidence. As mentioned previously, the theory emphasizes that PHS allow individuals to advance individuals’ lives, such as life adjustment (Krumboltz, 2009; Krumboltz & Worthington, 1999). Krumboltz (2009) emphasized that career and life should not be detached in career research because career and life cannot be separated. Other researchers also stressed that career and life are intertwined, and thus, career is an extent form of one’s life (Lent, 2013; Offet-Gartner, 2003). The current study discovered the intertwined relation between planned happenstance skills and adjustment.

Finally, the findings of the current study can be useful for career counselors who help clients adjust to life as well as careers. Moreover, the findings that planned happenstance had differential effects on life adjustment by levels of career barriers provide insights for clients who have negative perceptions regarding their career paths. As noted by Swanson et al. (1996), perceived career barriers are more likely to function as burdens than actual barriers. Our measure of career barriers can be considered as perceived career barriers rather than actual barriers. In this respect, developing intervention programs focused on enhancing planned happenstance skills could be one way to facilitate the progress of counseling.

Despite these implications, this study has several limitations. First, we selected only 9 items in the KCBI to measure career barriers (E. Kim, 2001), which may have affected the results of the study. Although we only measured 9 items due to brevity reasons, we intentionally chose the items based on the suggestions of previous researchers (Krumboltz, 2009; Woo & Reeves, 2007) and gained fairly acceptable validity of the scale, which gave us further confidence regarding the findings. Second, self-reported questionnaires were collected, so the measurements could overestimate or underestimate participants’ current states. Third, we solely relied on self-reported data. Future researchers should consider using multiple assessment methods to measure the constructs. For example, we measured individuals’ perceived barriers, but one can take into account including relatively objective barriers, such as financial constraints. Fourth, we divided the group using the median value of career barriers because there was no standardized cutoff score for the CBI. Further studies should have another statistical consideration in order to examine the possible relationships among associated variables, such as a causality by using experimental designs. Fifth, although we took advantage of longitudinal design, the associations reported here are based on correlations, and we cannot infer direct causation. Conducting experimental research to examine the role of PHS as a predictor of life adjustment is desired to test strict cause–effect relationships between variables. Furthermore, future studies should examine various mediators and moderators between PHS and life adjustment. By examining such relations, career counselors can obtain practical information from the results. It would enable career counselors to suggest evidence-based interventions or preventions based on client’s particular situations. In addition, previous studies have focused on examining the effect and usefulness of PHS on individuals’ career development. Based on the previous studies, further studies should explore the predictors of planned happenstance skills. It will allow career counselors and researchers to know how to develop PHS of clients.

		Time 1 (N = 307)	Time 2 (N = 307)	Time 3 (N = 253)
Time1	PHS
ADJ	.46***
CB	−.38***	−.46***
Time2	PHS	.66***	.33***	−.29***
ADJ	.35***	.53***	−.39***	.44***
Time3	PHS	.63***	.39***	−.36***	.66***	.42***
ADJ	.28***	.53***	−.35***	.32***	.57***	.43***
M		3.79	3.50	2.22	3.80	3.48	3.71	3.42
SD		.48	.67	.56	.48	.67	.48	.63

Model	χ ²	df	TLI	CFI	RMSEA
Model 1	347.97***	112	.87	.92	.08
Model 2	352.52***	114	.88	92	.08
Model 3	352.52***	115	.88	.92	.08
Model 4	352.68***	116	.88	.92	.08
Model 5	352.92***	117	.88	.92	.08
Model 6	354.28***	118	.88	.92	.08
Model 7	355.24***	119	.88	.92	.08

Model	χ ²	df	Δχ²	Δdf	TLI	CFI	RMSEA
Model 1	436.94***	238			.89	.92	.05
Model 2	437.65***	239	0.71	1	.89	.92	.05
Model 3	438.07***	240	0.42	1	.89	.92	.05
Model 4	438.11***	241	0.04	1	.89	.92	.05
Model 5	443.77***	242	5.66	1	.89	.92	.05
Model 6	444.85***	243	1.08	1	.89	.92	.05
Model 7	446.79***	244	1.94	1	.89	.92	.05

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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2014S1A5B8060944).

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