Work Placement Supervisor Support and Students’ Proactive Career Behaviors: The Moderating Role of Proactivity

Abstract

Drawing upon social cognitive career theory model of career self-management, we examined the relationship between work placement supervisor support (WPSS) and students’ proactive career behaviors (PCB), mediating role of work placement learning self-efficacy and the moderating effect of proactivity in the indirect relationships. Data were collected from 275 university undergraduate students undertaking placement learning in 129 firms. The regression analysis revealed that WPSS associated positively with all the constructs of PCB namely: career planning, proactive skills development, career consultation, and career network building, as well as work placement learning self-efficacy. Also, work placement learning self-efficacy mediated the relationship between WPSS and the constructs of PCB except career consultation. Proactivity moderated the relationship between work placement learning self-efficacy and career planning and career network building, and the indirect effects of WPSS on career planning and career network building via work placement learning self-efficacy were significant at low, average, and high levels.

Keywords

work placement learning faculty-based supervisor support proactive career behaviors work placement learning self-efficacy

Introduction

Quality support from faculty or university-based supervisors assigned to students during work placement learning is essential for enhancing students’ career interests and actions (Okolie et al., 2021b). During work placement learning, the faculty-based supervisors play important roles such as tutoring students on career or work-related tasks, providing helpful emotional and informational support toward solving difficult learning tasks and providing helpful feedback to students (Okolie et al., 2021a; Smith et al., 2007; Yaghi & Bates, 2020). In this study, we draw upon Zhang et al. (2021) to conceptualize teacher career-related support as work placement supervisor support (WPSS) and define it as the extent to which students undertaking placements learning perceives that their faculty-based supervisors have values and care for their work-related skills development, career development goals, future career plans, and helping with answers to their questions about gaining work experiences. As Metheny et al. (2008) have explained, effective support for students in learning environments can be in form of care which should be easily accessible and positive in communicating students’ high expectations for educational and career development. Previous research (Brooks & Youngson, 2016) has explained that the quality of placements may not always be satisfactory as students may face several difficulties in learning new skills such as inability to cope with challenging tasks, adapting to new learning environments, the mismatch between students’ expectations and the nature of the work, and lack of career-related information that may enhance their career development. This necessitates the need for quality WPSS for students during work placement learning.

We argue that faculty-based placement supervisors may support students in areas such as provision of career-related information and guidance, skills assessment, resume preparation, job search skills development, job interview preparation, guidance on filling out job applications, and up-to-date information about job skills demand of employers. For example, Zhang et al. (2021) identified three important career-related support that teachers, instructors or training supervisors can provide for students to enhance their career-related behaviors in any learning environment namely: enhancement of self-exploration, informational support, and emotional support. Zhang et al. (2021) argued that these forms of support can help students to identify their weaknesses, strengths, interests in planning their careers, and their ability to fit into the workplace. However, a major priority in these debates concerns the need to facilitate students’ proactive career behaviors (PCB) during work placement learning.

Drawing upon Strauss et al.’s (2012) conceptualization of PCB as self-directed career activities that one engages to proactively manage their future careers, students who perceived quality WPSS may likely develop PCB in four ways namely; career planning, career consultation, proactive skills development and career network building. Strauss et al. (2012) argued that individuals who can manage the four PCB may likely explore more career options, set career goals, build career interests, develop skills required by employers, develop self-efficacy, and confidence in their abilities, gain more work experiences that may enhance their future employability. Previous studies (Brown et al., 2006; Seibert et al., 2001) have found that PCB can influence one’s level of career outcomes such as career satisfaction, job search and job preparation. As the social cognitive career theory (SCCT) model of career self-management (CSM) (Lent & Brown, 2013) postulates, self-efficacy is a core determinant of an individual’s career behavior in any learning environment. Self-efficacy is defined as the extent to which individuals believe in their ability to perform certain career-related tasks in an experienced way (Lent & Brown, 2013). Linked to work placement learning context, when students’ self-efficacy is high, they may likely experience increased motivation, perseverance, and likelihood of accepting positive thoughts about their abilities. Thus, we draw upon the CSM model to operationalize self-efficacy as work placement learning self-efficacy, to help explain the underlying psychological mechanism through which the relationship between WPSS and student PCB may exist. Also, the CSM model postulates that person variable (e.g., personality or proactivity) may influence students’ formation of self-efficacy in their learning environment, which may strengthen their tendencies to engage in career-related behaviors (Lim et al., 2016). This may be because, proactive individuals may be more likely to ‘‘scan for opportunities, show initiative, take action, and persevere until they reach closure by bringing about change’’ (Bateman & Crant, 1993, p. 105).

Despite the need to understand factors that motivate students’ engagement in PCB (Clements & Kamau, 2018; Zhang et al., 2021), little is known about how support from faculty-based supervisors might influence higher education students’ PCB during work placement learning; the underlying psychological mechanism through which the relationships may exist, and the CSM model’s person factor (proactivity) that may strengthen the relationships. The present study fills this theoretical and empirical gap by looking at the mediating role of work placement learning self-efficacy and the moderating effect of proactivity in the relationship between WPSS and student PCB. Our study contributes to the CSM model by validating the assumptions and demonstrating how WPSS may influence higher education students’ PCB during work placement learning.

Theoretical and Hypotheses Development

SCCT-CSM model (Lent & Brown, 2013) recognizes support as an important factor that influences individuals’ career interests and actions. The CSM model postulates that quality support in one’s learning environment (contextual variable) and person variable (e.g., personality and gender) may shape learning experiences, and influence the individual’s ability to translate career interests into actions by increasing their career-related self-efficacy and outcome expectations. Previous studies (Lent & Brown, 2013; Swanson & Gore, 2000) found evidence of a positive effect of contextual support on career development processes in young people. Drawing upon the CSM model (Lent & Brown, 2013; Lim et al., 2016), we operationalize contextual variable as WPSS and operationalize person variable as proactivity. Also, we focus on a core variable of CSM model—self-efficacy given its centrality in influencing the development of individuals’ career interests (e.g., activity preference) and career goals (e.g., intention to engage in a career activity) (Sawitri & Creed, 2021). Self-efficacy is defined as a “perceived ability to manage specific tasks necessary for career preparation, entry, adjustment, or change across diverse occupational paths” (Lent & Brown, 2013, p. 561). Linked to work placement learning context, students who perceive quality support from their supervisors may form higher self-efficacy in work placement learning tasks and perform more career-related tasks leading to PCB. Building upon the CSM model (Lent & Brown, 2013), we operationalize self-efficacy as work placement learning self-efficacy and define it as students’ belief that they can perform various placement learning tasks successfully. Also, going by the CSM model’s postulations that contextual support may influence individuals’ engagement in self-managed career behaviors (Lent & Brown, 2013; Lim et al., 2016), we operationalize the self-managed career behaviors as PCB and draw upon Strauss et al.’s (2012) conceptualization to examine the effect of WPSS on the constructs of PCB (i.e., career planning, career consultation, proactive skills development, and career network building). We argue that examining the effect of WPSS on the individual constituent of PCB may provide a better insight into the relationship between WPSS and PCB. SCCT posits that “for individuals to develop an interest in certain areas for which they have talent, their environments must expose them to the type of direct, vicarious and persuasive experiences that can give rise to robust efficacy beliefs and positive outcome expectations” (Lent et al., 2002, p. 752). Going by this, we might expect that students who perceive quality WPSS may be more likely to form higher work placement learning self-efficacy, which may, in turn, increase their PCB. Also, we expect that such effects may be strengthened by the students’ level of proactivity. Drawing upon CSM Model (Lent & Brown, 2013) and previous findings (Okolie et al., 2021b; Sawitri & Creed, 2021; Swanson & Gore, 2000), we hypothesize that:

Hypothesis 1

WPSS is positively associated with (a) career planning, (b) career consultation, (c) proactive skills development, and (d) career network building.

WPSS, work placement learning self-efficacy, and PCB

CSM Model (Lent & Brown, 2013) identified support as an important contextual factor to individuals’ development of career-related behaviors, given that students may learn to develop their own career-related plans when they perceive quality support from their placement learning supervisors. Drawing upon CSM Model assumptions, students who perceived enhancement of self-exploration, informational, and emotional support during work placement learning may likely form higher self-efficacy, which may, in turn, influence their higher engagement in PCB leading to achieving their future career goals (e.g., Lim et al., 2016; Zhang et al., 2021). Therefore, WPSS is important for students’ undertaking work placement learning given its centrality in helping students to overcome barriers to gaining work experiences and engagement in PCB. We focus on WPSS because supervisors are considered the most relevant and influential persons to students undertaking work placement learning (Inceoglu et al., 2019; Okolie et al., 2021). Previous studies have emphasized that contextual factors need to be further explored to gain more understanding of their contributions to academic and career actions such as PCB (Kantamneni et al., 2016; Strauss et al., 2012).

Building on CSM model and Strauss et al. (2012), we conceptualize PCB as students’ proactive engagement in activities that may enable them to explore career opportunities and be ready to face challenges associated with their future career goals. We build on Hirschi et al.’s (2014, p. 577) definition of PCB as “the degree to which somebody is proactively developing his or her career as expressed by diverse career behaviors.” From the work placement learning perspective, examining the proactiveness of students undertaking work placement learning is important as it may help students to explore their placement learning environments, gain more work-related experiences to enhance their future careers and employability and adapt to challenges associated with moving from school to workplace learning environment. Specifically, students who engage more in PCB may be more likely to engage in future career planning, proactive skills development, career consultations, and career network building (Strauss et al., 2012). Thus, building upon the CSM Model, we would expect that students who perceived higher support from their work placement faculty-based supervisors may be able to develop higher work placement learning self-efficacy which would, in turn, increase their engagement in PCB. Thus, we hypothesize that:

Hypothesis 2

WPSS is positively associated with work placement learning self-efficacy.

Hypothesis 3

Work placement learning self-efficacy is positively associated with (a) career planning, (b) career consultation, (c) proactive skills development, and (d) career network building.

Hypothesis 4

Work placement learning self-efficacy would mediate the relationship between WPSS and PCB constructs: (a) career planning, (b) career consultation, (c) proactive skills development, and (d) career network building.

Moderating Role of Proactivity

The CSM Model takes into consideration of person variable such as personality or proactivity that may affect one’s career-related behaviors. In the CSM model, Lent and Brown (2013) identified person factors as potential moderators of the relationship between career goals and some proactive career-related behaviors such as planning. However, proactivity in the context of the present study refers to students' tendencies to recognize career-related opportunities during work placement learning, key into the recognized opportunities and influence their external environment (Bateman & Crant, 1993). Highly proactive students may likely search for career opportunities during work placement learning, explore the opportunities and engage in learning relevant skills that may lead them to achieve their career goals. Such students may likely persist in accomplishing their career goals despite the barriers (Bateman & Crant, 1993). We expect that highly proactive students may be more likely to be future-oriented and focus more on seeking out opportunities to enhance their future careers and employability (Altura et al., 2020).

Also, we expect that highly proactive students may be more likely to adopt in their work placement learning environment than their colleagues who may not be able to cope with challenges associated with learning in the workplace. For example, Parker et al. (2010) postulated that individuals with higher proactivity may be likely to engage more in goal orientation given that they motivate themselves from their desire to build a better future. Therefore, proactivity is characterized as an antecedent to PCB and considered as a moderator in the relationship between WPSS and PCB in the present study. Previous studies (Seibert et al., 1999; Zacher, 2013) have shown that individuals with higher proactivity expressed more career-related behaviors such as job search intensity, career success and adaptability and that proactivity moderated the relationship between contextual variables and career-related behavior (Zacher & Bock, 2014). Thus, we consider proactivity as a crucial resource for students undertaking work placement learning, particularly, when work placement learning self-efficacy was low (i.e., proactivity would influence the effect of work placement learning self-efficacy on PCB). Thus, proactivity was considered a moderator and included as a person variable in our model. Therefore, proactivity may make up for the low development of work placement learning self-efficacy regarding engaging in PCB during work placement learning. Building upon the CSM model and previous findings, we hypothesize that:

Hypothesis 5

Proactivity would moderate between work placement learning self-efficacy and (a) career planning, (b) proactive skills development, (c) career consultations, and (d) career network building, and the relationships would be stronger when proactivity is higher.

Hypothesis 6

Proactivity would moderate the indirect effect of work placement learning self-efficacy on (a) career planning, (b) proactive skills development, (c) career consultations, and (d) career network building, and the relationships would be stronger when proactivity is higher.

Overall, this study builds on the CSM model to examined the relationship between the contextual variable (WPSS) and career actions (PCB), and the mediating role of self-efficacy (work placement learning self-efficacy) and the moderating role of the person factor (proactivity) in the indirect relationships. Thus, we tested a moderated mediation model (Figure 1).

Figure 1.

Results of the Structural Model. *** p < .001; * p < .05. All standardized regression weights are reported.

Method

Sample and Procedures

In this study, students undertaking work placement learning were recruited through the help of 129 Nigerian organizations/firms that accepted them for placements. A structured questionnaire which contains items of the variables; WPSS, work placement learning self-efficacy, proactivity, and PCB constructs were completed face-to-face by participants (Creswell, 2015). The firms ranged from production, mining, marketing and sales, construction, services, information technology, and network providers. Data were collected from the same participants at two different time points with an interval of 3 weeks, and participants responded to the questionnaire containing items of WPSS, work placement learning self-efficacy, proactivity, and PCB throughout the two time points. Permissions for the study were gotten from the organizations selected for this study. Each participant signed a consent form; assured of anonymity and confidentiality, and was informed that participation was voluntary. At time-lag one, 333 participants willingly completed the questionnaire and handed them to the researchers. We coded their responses with 4-digit numbers formulated to help us match their responses in the second time point. At time-lag 2 (3 weeks interval), the same questionnaire used in time-lag 1 was shared with the same participants to completed the surveys and handed over to the researchers. Collecting data from the same participants at different time points was necessary, in that it reduces the problem of common method bias associated with self-report measures (Podsakoff et al., 2003). Previous studies (e.g., Khan et al., 2020) have collected data at different time points to avert the problems associated with common method bias. After matching the responses using the 5-digit codes, 58 copies of the questionnaire with inconsistent responses during time-lag 1 and time-lag 2 were removed. Thus, the time-lagged sample was 275 (82.58% matched responses) used for the final dataset. Among these participants were 124 (45.09%) males and 151 (54.91%) females between the age range of 20 and 25 years old.

Measures

Work Placement Learning Self-efficacy

This was measured by adapting the 7-item “Self-efficacy in Skills Upgrading” scale (Lim & Chan, 2003). Responses ranged from (1 = Strongly disagree to 5 = Strongly agree). Sample item includes: “I have the capability to handle the demands of the work placement learning tasks.” The original scale had a Cronbach’s α = 0.85. Chukwuedo and Ogbuanya (2018) reported reliability of the original scale as .88, .94, and .95, respectively. In the present study, the one-factor model showed a good data fit: χ² = 19.32; df = 14; χ²/df = 1.38; CFI = 0.97; TLI = 0.95; GFI = 0.96; SRMR = 0.03, RMSEA = 0.04, with the following reliability and validity values: CR = 0.88, AVE = 0.72, DV = 0.85, MSV = 0.45, and Cronbach’s α = 0.89 (Hu & Bentler, 1999).

Proactivity

This variable was measured using 10-item shortened Proactive Personality Scale (Bateman & Crant, 1993). Responses ranged from (1 = Strongly disagree to 6 = Strongly agree). A sample item includes “If I see something I don’t like, I fix it.” Previous research (Major et al., 2012) have reported a Cronbach’s alpha value of > .90. Also, Seibert et al. (2001) reported that the scale had a Cronbach’s alpha = 0.85. Also, prior study (Sawitri & Creed, 2021) had reported a high reliability of the scale, Cronbach’s alpha = 0.83. In the present study, four items were gradually removed during CFA for loading below the standardized regression weight o 0.70 (Hair et al., 2010). The remaining six items (one-factor model) showed a good data fit: χ² = 11.97; df = 9; χ²/df = 1.33; CFI = 0.98; TLI = 0.96; GFI = 0.96; SRMR = 0.02, RMSEA = 0.05, with no reliability and validity concerns: CR = 0.89, AVE = 0.74, DV = 0.88, MSV = 0.33, and Cronbach’s α = 0.90 (Hu & Bentler, 1999).

Proactive Career Behaviors

This variable was measured using the 13-item “Proactive Career Behaviors” scale (Strauss et al., 2012). The scale consists of four subscales; career planning (4 items) with sample item “I am thinking ahead to the next few years and plan what I need to do for my career,” proactive skills development (3-items) with sample item “I develop knowledge and skill in tasks critical to my future work life,” career consultation (3-items) with same item “I make my supervisor aware of my work aspirations and goals” and career network building (3-items) with sample item “I am building a network of contacts or friendships to provide me with help or advice that will further my work chances.” Responses ranged from (1 = Strongly disagree to 5 strongly agree). A previous study (Shama, 2013) had used the 13-item scale in their study of future work selves and work-related outcomes, and reported a Cronbach’s alpha of .88. However, in the present study, the four-factor model showed a good fit: χ² = 105.53; df = 61; χ²/df = 1.73; CFI = 0.96; TLI = 0.95; GFI = 0.94; SRMR = 0.04, RMSEA = 0.06. The validity and reliability values for each of the PCB constructs; career planning: CR = 0.82, AVE = 0.59, DV = 0.77, MSV = 0.44, and Cronbach’s α = 0.83. Proactive skills development: CR = 0.95, AVE = 0.85, DV = 0.92, MSV = 0.01, and Cronbach’s α = 0.95. Career consultation: CR = 0.93, AVE = 0.82, DV = 0.91, MSV = 0.01 and Cronbach’s α = 0.93. Network building: CR = 0.87, AVE = 0.68, DV = 0.83, MSV = 0.01 and Cronbach’s α = 0.86 (Hu & Bentler, 1999).

Work Placement Supervisor Support

This variable was measured by adapting the 16-item “Career-related Teacher Support Scale” (Zhang et al., 2021). The responses ranged from (1 = Never to 5 = Always). The three subscales include: “Enhancement of self-exploration” (6 items, α = 0.90), “Informational support” (5 items, α = 0.92), and “Emotional support” (5 items, α = 0.91). A sample item includes “My work placement supervisors give me a lot of confidence for my career development.” In the present study, we conducted a confirmatory factor analysis for the scale and used the 16 items as one-factor model. However, keeping the focus on the contents, 5 items were gradually dropped due to their low standardized regression weights below 0.70 (Hair et al., 2010) and the remaining 11 items showed a good data fit: χ² = 33.11; df = 22; χ²/df = 1.52; CFI = 0.98; TLI = 0.96; GFI = 0.96; SRMR = 0.03, RMSEA = 0.05 with the following validity and reliability values: composite reliability (CR) = 0.95, average variance extracted (AVE) = 0.86, discriminant validity (DV) = 0.93, maximum shared variance (MSV) = 0.23, and Cronbach’s alpha (α) = 0.96 (Hu & Bentler, 1999).

Control Variables

We controlled for the age and gender of the students to learn whether they might have a positive influence on student PCB.

Results

Preliminary Analysis

There were no missing data from the participants’ responses; thus, no technique was employed to analyze missing data in this study. Table 1 shows the results of bivariate correlations among the study variables. WPSS positively correlated with career planning (r = 0.48, p < .01), proactive skills development (r = 0.34, p < .01), career consultation (r = 0.27, p < .01), career network building (r = 0.47, p < .01), and work placement learning self-efficacy (r = 0.46, p < .01), respectively. Also, work placement learning self-efficacy positively correlated with career planning (r = 0.58, p < .01), proactive skills development (r = 0.40, p < .01), career consultation (r = 0.14, p < .01), and career network building (r = 0.60, p < .01), indicating possible mediation analysis proposed in this study. The analysis showed no evidence of positive correlation between proactivity and WPSS (r = −0.04, p = .72) indicating that the moderator (proactivity) was an auxiliary variable which clarifies causal relationship and does not correlate with independent variable (Wu & Zumbo, 2007). Although, there is a positive correlation between proactivity and proactive skills development (r = 0.12, p < .05).

Table 1.

Mean, Standard Deviation, and Bivariate Correlations among Variables.

		Mean	SD	1	2	3	4	5	6	7	8
1	Age	1.54	0.45	1
2	Sex	1.67	0.47	−0.10	1
3	Work placement supervisor support	15.27	4.34	−0.03	−0.01	1
4	Proactivity	10.83	4.19	−0.11	0.03	−0.09	1
5	Work placement learning self-efficacy	13.06	3.14	−0.05	−0.04	0.46**	−0.06	1
6	Career planning	10.99	2.77	0.03	−0.17**	0.48**	−0.09	0.58**	1
7	Proactive skills development	11.22	4.02	0.09	−0.11	0.34**	0.12*	0.40**	0.54**	1
8	Career consultation	11.67	3.24	0.15*	0.04	0.27**	0.07	0.14*	0.13*	0.08	1
9	Career network building	11.96	3.15	−0.06	−0.10	0.47**	−0.08	0.60**	0.58**	0.30**	−0.02

*p < .05; ** p < .01 (2-tailed).

Testing Unmoderated Direct and Indirect Effects

To test the hypotheses, we followed the “kX variables PROCESS commands” procedures (Hayes, 2017, p. 144), selected “Model four” in the options menu and applied 5000 resample bootstrapping to compute the direct and indirect effects (including covariates) concurrently (Figure 1). The regression analysis showed that WPSS positively associated with career planning (β = 0.48, t₍₂₇₃₎ = 9.25, p < .001), proactive skills development (β = 0.34, t₍₂₇₃₎ = 6.04, p < .001), career consultation (β = 0.27, t₍₂₇₃₎ = 4.65, p < .001), and career network building (β = 0.47, t₍₂₇₃₎ = 9.27, p < .001). Hence, hypothesis 1a, 1b, 1c, and 1d were accepted. The analysis showed that WPSS positively associated with work placement learning self-efficacy (β = 0.46, t₍₂₇₃₎ = 9.25, p < .001), indicating that hypothesis 2 was accepted. Also, work placement learning self-efficacy positively associated with career planning (β = 0.44, t₍₂₇₂₎ = 8.15, p < .001), proactive skills development (β = 0.31, t₍₂₇₂₎ = 4.87, p < .001) career network building (β = 0.51, t₍₂₇₂₎ = 10.92, p < .001), and not for career consultation (β = 0.08, t₍₂₇₂₎ = 0.13, p = .79). Hence only hypotheses 3a, 3c, and 3d were accepted.

As shown in Table 2, the analysis revealed that work placement learning self-efficacy mediated the relationship between WPSS and career plaining (β = 0.14, CI₉₅: 0.08, 0.19), proactive skills development (β = 0.12, CI₉₅: 0.07, 0.22) and career network building (β = 0.17, CI₉₅: 0.09, 0.25), and not for career consultation (β = 0.01, CI₉₅: −0.07, 0.09). Thus, only hypotheses 4a, 4c, and 4d were accepted. Regarding the control variables, the analysis revealed that sex of the students was not positively associated with career planning (β = -0.17, p = .73), career consultation (β = 0.05, p = .31), proactive skills development (β = -0.13, p = .29), and career network building (β = -0.09, p = .91). However, age was associated with career planning (β = 0.13, p = .01) and career consultation (β = 0.15, p = .03) whereas age was not positively associated with proactive skills development (β = 0.10, p = .17) and career network building (β = -0.05, p = .09). This result implies that as students’ age increases, their interests in planning their future careers and make career consultations increases.

Table 2.

Results Showing Indirect Effects

Indirect Effects	Estimates	Errors	95% Confidence Intervals
			Lower Limit	Upper Limit
WPSS → Work placement learning self-efficacy → Career planning	0.14***	0.01	0.08	0.19
WPSS → Work placement learning self-efficacy → Proactive skills development	0.12***	0.01	0.07	0.22
WPSS → Work placement learning self-efficacy career consultation	0.01	0.01	−0.07	0.09
WPSS → Work placement learning self-efficacy → Career network building	0.17***	0.01	0.09	0.25

***p < .001; All individual standardized regsion weights are reported.

Testing the Moderating Effects of Proactivity and Moderated Mediation Model

To test the hypotheses 5 and 6, we followed the procedures (Hayes, 2017; Preacher et al., 2007), which focuses on the moderated mediation analysis (Figure 1). In the options menu, “Model 14” was selected, and we applied 5000 bootstraps samples at 95% bias-corrected confidence intervals to run the analysis. Proactivity was added in the “W” options menu to analyze the moderated mediation model. As shown in Figure 1, the interaction effect of proactivity x work placement learning self-efficacy on career planning was statistically significant (b = 0.02, t₍₂₇₀₎ = 2.17, p = .02) with a statistically significant model fit summary (F_{(4, 270)} = 45.08, p < .001, R² = 0.40) and statistically significant test of highest order unconditional interaction (F_{(1, 270)} = 4.72, p = .02, ΔR² = 0.01). Also, the test of X by M interaction was statistically significant (F_{(1, 269)} = 8.17, p = .005). Thus, hypothesis 5a was accepted. The interaction effect of proactivity x work placement learning self-efficacy on proactive skills development was not statistically significant (b = 0.00, t₍₂₇₀₎ = 0.15, p = .89), though with a statistically significant model fit summary (F_{(4, 270)} = 16.11, p < .001, R² = 0.19) and a non-statistically significant test of highest order unconditional interaction (F_{(1, 270)} = 0.02, p = .89, ΔR² = 0.00). Hence, hypothesis 5b was rejected. The interaction effect of proactivity x work placement learning self-efficacy on career consultation was not statistically significant (b = 0.01, t₍₂₇₀₎ = 0.12, p = .71) with a non-statistically significant model fit summary (F_{(4, 270)} = 1.33, p = .26, R² = 0.01) and a non-significant test of highest order unconditional interaction (F_{(1, 270)} = 0.01, p = .71, ΔR² = 0.00). Thus, hypothesis 5c was rejected. The interaction effect of proactivity x work placement learning self-efficacy on career network building was statistically significant (b = 0.03, t₍₂₇₀₎ = 3.22, p< .001) with a statistically significant model fit summary (F_{(4, 270)} = 52.21 p < .001, R² = 0.44) and statistically significant test of highest order unconditional interaction (F_{(1, 270)} = 10.34, p < .001, ΔR² = 0.02). Also, the test of X by M interaction was statistically significant (F_{(1, 269)} = 11.31, p < .001). Thus, hypothesis 5c was accepted (also, see Table 2).

The analysis shows the simple slopes and visualization of the conditional direct effects of WPSS at three points (-1SD, Mean, +1SD) along the moderator variable (i.e., proactivity) (Hayes, 2017), as we focus on the statistically significant interaction effects. To the career planning (Figure 2), the simple slope showed that at -1SD on proactivity, the effect of work placement learning self-efficacy on career planning was positive (b_slope = 0.30, p < .001), at the Mean (b_slope = 0.38, p < .001), and at +1SD (b_slope = 0.47, p < .001). Also, to career network building (Figure 3), the simple slopes showed that at -1SD on proactivity (b_slope = 0.33, p < .001), at the Mean (b_slope = 0.47 p < .001) and at +1SD (b_slope = 0.59, p < .001) (see Table 2). The analysis also shows (Table 2) that the conditional indirect effect of WPSS on career planning via work placement learning self-efficacy was positive at -1SD (β = 0.11, CI₉₅: 0.06, 0.11), at the Mean (β = 0.14, CI₉₅: 0.08, 0.19) and at +1SD (β = 0.16, CI₉₅: 0.10, 0.23). Also, the indirect effects of WPSS on career network building via work placement learning self-efficacy was positive at-1SD (β = 0.12, CI₉₅: 0.05, 0.21), at the Mean (β = 0.16, CI₉₅: 0.09, 0.24) and at +1SD (β = 0.21, CI₉₅: 0.14, 0.29) (Table 3).

Figure 2.

Proactivity x work placement learning self-efficacy on career planning.

Figure 3.

Proactivity x work placement learning self-efficacy on career network building.

Table 3.

Conditional Direct and Conditional Indirect Effects

Moderated Mediation Results	Estimates	Standard Errors	t-Value	95% Confidence Intervals
				Lower Limit	Upper Limit
Conditional effects of the moderator (proactivity x work placement learning self-efficacy) on career planning
Low (−1 SD)	0.30***	0.05	4.71	0.17	0.43
Average (Mean)	0.38***	0.05	7.98	0.29	0.48
High (+1 SD)	0.47***	0.04	7.93	0.35	0.58
Conditional effects of the moderator (proactivity x work placement learning self-efficacy) on career network building
Low (−1 SD)	0.33***	0.07	4.72	0.19	0.47
Average (Mean)	0.47***	0.05	8.82	0.36	0.57
High (+1 SD)	0.59***	0.06	9.29	0.47	0.73
Conditional indirect effects
WPSS → Work placement learning self-efficacy → Career planning
Low (−1 SD)	0.11***	0.03	—	0.06	0.16
Average (Mean)	0.14***	0.03	—	0.08	0.19
High (+1 SD)	0.16***	0.02	—	0.10	0.23
WPSS → work placement learning self-efficacy → Career network building
Low (−1 SD)	0.12***	0.04	—	0.05	0.21
Average (Mean)	0.16***	0.03	—	0.09	0.24
High (+1 SD)	0.21***	0.04	—	0.14	0.29

***p < .001.

The results of the Omnibus tests of conditional indirect effect showed a statistically significant index of moderated mediation (Hayes, 2017; Preacher et al., 2007). At CI₉₅ bootstraps, the effect of proactivity x work placement learning self-efficacy on career planning was positive and significant (b_Index = 0.01, CI₉₅: 0.00, 0.02) and career network building (b_Index = 0.01, CI₉₅: 0.00, 0.03), indicating that the indirect effects were conditional on the level of proactivity of the students in their career planning and career network building behaviors. Thus, hypotheses 6a and 5d were accepted.

Discussion

This study relied on SCCT-CSM to examine how contextual factor (operationalized as WPSS) influenced the constructs of PCB among undergraduate students undertaking work placement learning in industries/organizations. Also, it examined how self-efficacy (operationalized as work placement learning self-efficacy) mediated the relationships and how the person factor (operationalized as proactivity) moderated the relationships. The study is a CSM model driven moderated mediation model of WPSS, work placement learning self-efficacy, PCB, and proactivity. The strength of our study lies in relying on the CSM model to test all the hypotheses and the robust analysis conducted to test the moderated mediation model. The findings make important contributions to PCB literature given the scarce research in the interactions among the CSM model’s person and core variables (work placement learning self-efficacy and proactivity) in the relationships. The findings showed that WPSS significantly influenced the constructs of PCB namely; career planning, career consultation, proactive skills development, and career network building (hypotheses 1a–1d). These results may be expected given the widely positive reports about social support and its implications for students’ career behaviors (Caspersen & Smeby, 2020; Okolie et al., 2021b; Zhang et al., 2021); however, these results serve as baseline hypotheses, which the confirmations in the present study are essential. These findings imply that quality WPSS is important for students during work placement learning given its centrality in promoting students’ career development outcomes including PCB. Based on the CSM model, the findings could also be interpreted that as students perceive higher support from their placement supervisors, the more they are likely to increase their engagement in PCB. Thus, WPSS in form of verbal encouragement, career-related informational support, tasks learning support, enhancing students’ self-exploration, and emotional and practical support are important in facilitating students’ PCB during work placement learning.

The study found that WPSS positively influenced the students’ work placement learning self-efficacy (hypothesis 2) and that work placement learning self-efficacy positively influenced student career planning, proactive skills development and career network building (hypotheses 3a, 3c, and 3d). Drawing upon the CSM model, these findings imply that the support the students perceived during work placement learning from their supervisors facilitated their formation of self-efficacy in learning the career or work-related skills which further positively increased their engagement PCB behaviours; career planning, proactive skills development, and career network building in this population. These findings are consistent with CSM model that contextual support can influence individuals’ formation of self-efficacy belief which may increase their career-related behaviours such as PCB (e.g., Lent & Brown, 2013). However, we do not find any evidence of the positive influence of work placement learning self-efficacy on career consultation in this population (hypothesis 3b). This may be as a result of students’ perception of their ability to engage in career consultations during work placement learning in this population.

The study revealed that work placement learning self-efficacy mediated the relationship between WPSS and three constructs of PCB namely; career planning, proactive skills development and career network building (hypotheses 4a, 4c, and 4d) except career consultation (hypothesis 4b). These findings suggest that higher perceived support from students’ placement supervisors may lead to developing a higher sense of work placement learning self-efficacy, which may, in turn, result in increasing engagement in career planning, proactive skills development, and career network building. Therefore, WPSS may not be the only factor that increases students’ PCB in this population, rather, higher work placement learning self-efficacy that students developed as a result of perceived increasing WPSS. The study revealed that proactivity interacted with work placement learning self-efficacy to strengthen the effects of work placement learning self-efficacy on career planning and career network building (Hypotheses 5a and 5d). The results showed that the interaction effect of proactivity was significant and positive at low, average and high (−1 SD, Mean and +1 SD) on work placement learning self-efficacy. This finding agrees with the CSM model and shows that proactive as a person factor is important, and higher proactive students may be more likely to consistently lookout for new career-related opportunities to explore to improve their chances of getting employed after graduation. Thus, proactivity may compensate for students with low work placement learning self-efficacy, but further contribute to increases students’ engagement in PCB, particularly, career planning and career network building during placements. Regarding the conditional indirect effect of WPSS on career planning and career network building via work placement learning self-efficacy as moderated by proactivity (Hypotheses 6a and 6d), the results showed that the indirect effect was strengthened at low, average and high (−1 SD, Mean and +1 SD). Linked to the CSM model, person factor such as proactivity can strengthen or increase individuals’ ability to translate their interests into actions (i.e., PCB) (Lent & Brown, 2013; Lim et al., 2016). These results indicate that work placement learning self-efficacy is an important factor that can influence students’ engagement in PCB such as career planning and career network building at various levels of proactivity.

Implications, Limitations and Future Research Study

Our study contributes to existing literature and progresses CSM model research on contextual support, person factor, self-efficacy, and PCB in work placement learning context through these results. The findings support the suitability of CSM model to WPSS and PCB, revealing that university students undertaking placements in industries can develop work placement learning self-efficacy towards engaging in PCB. Also, the findings have demonstrated the importance of CSM model’s person factor (i.e., proactivity) in strengthening students’ engagement in career planning and career network building as important PCB in this population. The findings have important implications for higher education institutions (sending and home institutions) and industries (the host institution) in the work placement learning programme. The findings suggest the need for continuous professional development of work placement supervisors to ensure that students assigned to them receive quality support in form of emotional support, informational support, career guidance, instrumental support, appraisal support, and tasks learning feedback to facilitate their PCB leading to successful careers (Okolie et al., 2021b; Strauss et al., 2012; Zhang et al., 2021). Also, the findings highlight the importance of focusing on improving students’ proactivity to help those with low work placement learning self-efficacy, and in turn, strengthen their engagement in PCB behaviours namely: career planning and career network building (Sawitri & Creed, 2021; Strauss et al., 2012). Work placement faculty-based supervisors and higher education institution administrators should consider the students’ level of proactivity when designing the work placement learning activities to increase student engagement in PCB. Students should be encouraged to exercise proactivity such as making their own career-related decisions, getting involved in exploring various placement career or work-related skills development opportunities that can help them to be more proactive in engaging in PCB. Also, there is a need for other interventions to improve students’ proactivity in gaining work experiences during work placement learning programme as this may help to assist students who may perceive low WPSS and whose level of work placement learning self-efficacy may be low.

The study acknowledges some limitations. First, the respondents had not completed the work placement learning programme which may likely affect their responses. We recommend that future research may consider comparing perceptions of students who had completed placement learning programmes with those currently undertaking placements. Although we adopted the time-lag data collection approach to help reduce the problem of bias associated with self-report measures, we acknowledge that the approach may not be adequate to establish causality. Therefore, future studies may consider adopting other measures such as ethnographic and experimental research approaches which allow the establishment of cause and effects. These limitations may create more future research opportunities to progress research on WPSS and PCB in work placement learning context.

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.

ORCID iD

Ugochukwu C. Okolies

Author Biographies

Ugochukwu Chinonso Okolie is a lecturer in the Department of Vocational and Technical Education, Alex-Ekwueme Federal University, Ndufu-Alike, Ikwo, Nigeria. His research interests include work placement learning, higher education research, and TVET development in developing world contexts. Dr Okolie has published extensively in internationally reputable journals such as Industry and Higher Education, Education + Training, Higher Education Research & Development, Innovations in Education & Teaching International and the Journal of Vocational Education & Training. He has two edited TVET books published by IGI-Global, USA. Okolie has presented research papers during major United Nations University, African Development Bank and other conferences in Africa and Asia. The majority his works can be found at

Sunday Mlanga holds a PhD (Management Accounting) Babcock University, Nigeria. He is a member of the Association of National Accountants of Nigeria, Fellow of Chartered Institute of Taxation of Nigeria, Member of Nigeria Institute of Management (Chartered) and Associate member Chartered Institute of Stockbrokers, Nigeria. His current research projects include application of information and communication technology on management accounting deliverables of Nigerian Quoted manufacturing Companies. Dr. Mlanga is a senior academic and the Deputy Dean, Faculty of Management Sciences, Alex Ekwueme Federal University Ndufu Alike, Nigeria. His hobby includes reading, listening to music, and watch educating TV programmes. Orcid: 0000-0003-2770-5206. Email: sundaymlanga@yahoo.com

Hyginus Emeka Nwosu holds a PhD and MBA in Business Administration from Nnamdi Azikiwe University, Nigeria. Also, Dr Nwosu holds B.Sc and B.Ed in both Business Administration and Education Administration, respectively, from Enugu State University, Nigeria. He is a Senior lecturer with the Alex Ekwueme Federal University Abakaliki, Ebonyi State, Nigeria. His research interests include entrepreneurship, strategic management, career and organizational development, administration, organizational behavior, and human resources management. Dr Nwosu is a member of many academic associations including Academy of International Business (USA) and Institute of Corporate Administration of Nigeria (FCAI). He has published over twenty (20) scholarly articles in peer-review Journals and five (5) books. His hobby includes reading, writing and football. Email: hyman56@yahoo.com

Kelechi Mezieobi is a lecturer in the Department of Arts and Humanities Education, Faculty of Education, Alex Ekwueme Federal University Ndufu-Alike, Ikwo, Ebonyi State, Nigeria. He previously taught education management and social studies at the Imo State University Owerri Nigeria. Kelechi has published widely (nationally and internationally) in areas of social studies, education management and general education. His research interests include social studies education, inclusive education, educational management, climate change and environment management safety practices. He belongs to several academic and social associations including Alvana 2002 Alumni Association where he served as president for several years. Email: kcmez2016@gmail.com

Cornelius Ofobuisi Okorie is a senior lecturer in the Department of Political Science, Faculty of Social Sciences, Alex Ekwueme Federal University Ndufu-Alike, Ebonyi Stste, Nigeria. Dr Okorie teaches undergraduate and postgraduate courses in Political Science and Public Administration. He formerly taught at the Ebonyi State College of Education Ikwo and National Teachers’ Institute Kaduna, respectively. He served as the Editor In-Chief of the EBSCOEI Journals of politics and administration, published widely in many reputable journals, and has won research grant from the Tertiary Education Trust Fund. His current research interest is on human resources management. He is a member of the Nigerian Political Science Association (NPSA) and loves reading, listening to music and football. Email: ocorneloko@gmail.com

Okechukwu S. Abonyi, holds a BSc Degree in Biology Education, Masters and Doctor of Philosophy in Measurement and Evaluation of the University of Nigeria Nsukka. He became a Professor of Measurement & Evaluation in 2006 and was appointed the Dean of the Faculty of Education from 2006 – 2009. Prof Abonyi is a Fellow of the Science Teachers Association of Nigeria (STAN) and a former Editor-in-Chief of STAN Journal. He has successfully supervised 18 doctoral degree students. Email: okeyabonyi@yahoo.com

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