Exploring Proactive Career Behavior Questionnaire Psychometrics Properties: An Adaptation to Spanish Distance Undergraduate Students

Abstract

Proactivity about a professional career predicts the employability of students in the job market. Having a scale will facilitate research and intervention using proactive behaviors. The objective of the present study is to adapt the Proactive Career Behavior questionnaire in Spanish distance university students. To that end, the questionnaire is administered to 934 undergraduates. The factorial structure, validity and reliability of the questionnaire are analyzed, as well as the invariance of the scale as a function of gender. The current study demonstrates that the Spanish questionnaire was found to be a reliable and valid measure of proactive career behavior of distance undergraduates. The same number of items and the tetra-factorial structure of the original are maintained. The original structure shows to be invariant across gender. The Spanish version of the questionnaire has adequate psychometric properties. Reliability is high and validity is right. The hypothesized relationships between constructs are supported. The Spanish adaptation shows reliable and valid outcomes for both genders, with resilience, personal initiative, and future orientation correlating with students’ proactive career behaviors.

Plain language summary

Understanding career proactivity in Spanish distance learning students

Being proactive about one’s career can help students become more employable. To better understand and support these proactive behaviors, it’s useful to have a reliable tool or questionnaire. This study aims to adapt an existing Proactive Career Behavior Questionnaire for Spanish-speaking students who are studying remotely. We administered the questionnaire to 934 distance-learning university students in Spain. These students are primarily adult learners, a group that is often underrepresented in research. We analyzed the questionnaire’s structure, validity, reliability, and whether the results were consistent across different genders. Our study found that the Spanish version of the questionnaire is both reliable and valid for measuring proactive career behaviors in distance-learning students. The adapted questionnaire kept the same number of items and structure as the original version and worked equally well for both male and female students. The results showed high reliability and proper validity, confirming our expectations about the relationships between the different aspects measured by the questionnaire. The Spanish adaptation of the Proactive Career Behavior Questionnaire is a reliable and valid tool for assessing proactive career behaviors in adult learners who study online. This tool can help educators and researchers understand and support the career development of these students, particularly in terms of their resilience, personal initiative, and future orientation. By focusing on this underrepresented group, we can better address their unique needs and challenges.

Keywords

professional development career planning student survey occupational aspiration career proactivity

Introduction

A university education is considered a pre-professional setting; hence students learn the skills they need for future success, including employability. It is common for students to plan their careers, and those decisions impact their professional development, particularly in distance education undergraduates. These students, who are mainly female and older than those in face-to-face education, are motivated by self-development and increased career opportunities (Carlsen et al., 2016). Due to the paradigm of lifelong learning, more adult learners nowadays choose distance higher education (Eurostat, 2024). However, these students are underrepresented as they constitute a non-normative sample (see Wong, 2018) which means there are no measurement tools adapted to this population. Distance education is more flexible, reduces geographical barriers, and allows for improved job opportunities (Schneller & Holmberg, 2014). Nonetheless, student support encompasses a wide range of activities to facilitate academic, organizational or social needs, from their progress through their chosen course of study to the period in which they develop further career pathways after graduating (Carlsen et al., 2016). Accordingly, focusing on the factors that contribute to employability is essential for this kind of student. Professors and vocational guidance units may take an active role in helping to clarify, promote, or encourage adult learners to attain greater autonomy in their careers. However, the measurement questionnaires not always are adapted to this type of student.

Current careers are characterized by being nonlinear. In terms of how students can actively maintain their own career paths and break from a passive role in response to their surroundings, proactivity plays a main role (Peng et al., 2021). Proactivity about a professional career can be defined as a series of planning behaviors, skill development, information seeking, and establishment of networks aimed at career building (Sonnentang, 2016). Proactive behaviors by students during college are crucial to forming professional networks and dealing with barriers to success (Valls et al., 2020). It has been observed that proactive coping strategies, such as persistence, are tied to better managing academic demands and challenges by cushioning their impact on variables like stress and academic performance (O’Connor et al., 2017; Salmela-Aro & Read, 2017). Proactivity about a professional career implies that the student starts, changes, or perceives situations that favor taking useful actions instead of passively responding to an imposed change. Besides, some studies show that student proactivity moderates the effects of the support received during internships and predicts academic success (Okolie et al., 2023). Studying this construct in students would give us a more precise understanding of employability, which should be considered above and beyond the ability to get a job in a formal organization and keep it (Fugate et al., 2004).

On a theoretical level, proactivity is considered a means of reducing discrepancies between the current situation and a reference value (i.e., professional aspiration). Control theory proposes that human behavior aims to create and maintain conformity with desired outcomes. This generates feedback loops, which are the basis of self-regulated behavior (Carver & Scheier, 2011). For example, someone formulates a current sense of their career, and compares it to a reference value: the ideal situation. The discrepancy between their sense of the current situation and the reference value spurs behavior to close the gap (Carver & Scheier, 2011). Besides that, Deci and Ryan’s (2000) self-determination theory offers a valid framework for explaining proactive career behaviors. People undertake behaviors to satisfy motivations and values important to their self-concept. They seek, therefore, to be consistent with their career self, taking actions oriented toward goal attainment. Satisfying the basic psychological needs of autonomy, competence, and relatedness builds up the resources needed to be resilient (Vansteenkiste & Ryan, 2013). Strauss et al. (2012) operationalizes proactive career behavior along four dimensions. The first factor is called career planning. It evaluates aspects of commitment and the establishment of timelines and goals along one’s career path. People who score high on this tend to set timelines and think often about their next goals. In undergraduates, we have seen that planning career goals predicts commitment to one’s studies (Clements & Kamau, 2018). The second factor is proactive behavior. This measures the self-initiated actions taken to perfect skills that might be required in the future, either through experience or participation in goal-directed activities. High scores on this factor indicate that the respondent frequently participates in various activities, even when those skills are not necessary in the immediate future but allow them to gain experience. The third factor is termed seeking advice. This implies evaluating behaviors geared toward obtaining information from mentors that is needed for career development. People with high scores tend to frequently seek counsel from, or initiate conversations with, professors or experts who can advise them. The fourth and final factor is called professional networks, and gauges how much initiative one takes to make connections, contacts, and friendships that may be of support. People who score high on this last factor tend to stay in touch with former colleagues or make new contacts who could help, support, or advise on career matters.

Proactive career behavior is related to other concepts, such as future orientation, resilience, and personal initiative. The first one, future orientation, refers to setting goals in a future plan and it predicts academic commitment, proactive behavior, or life satisfaction (Praskova & Johnston, 2021; Rudolph et al., 2017). This construct encompasses motivational, affective, and cognitive components. Motivationally, it reflects individuals’ goals, aspirations, and long-term intentions. On the affective level, it involves emotions and evaluative thoughts, such as optimism about the future. Cognitively, it includes abilities like strategic planning, decision-making, and envisioning upcoming opportunities (Cabras & Mondo, 2018). These elements make future orientation particularly relevant for graduates as they prepare to make important decisions about their professional paths (Chua et al., 2015). Following Sonnentang (2016), proactivity includes future-oriented behavior whose objective is to bring about change. One focuses on the long term to consider future needs and engages in behaviors that are not necessarily explicitly required of their role (Mensmann & Frese, 2016). From the perspective of person-environment interactions, the student chooses situations in which to participate, create, and evaluate their surroundings, and in turn forms interpersonal connections to achieve their goal. Studies of career life have observed a component that is socio-interpersonal as well as skill-based and motivational (Hogan et al., 2013). An example would be behaviors aimed at networking, seeking feedback from mentors, and goal setting. Therefore, students’ future orientation will positively be associated with proactive career behavior.

Regarding resilience, it refers to a set of behaviors geared toward overcoming obstacles and achieving success in the face of risk (Russell et al., 2020). It is commonly understood as the ability to adapt effectively to adversity and maintain well-being despite challenges and has more recently been recognized as a core element of positive psychological capital (Fletcher & Sarkar, 2013; Luthans et al., 2015). It is defined as the absence of persistent problems and a positive adaptation facing adversity, more recently has been included in the positive psychological capital (Fletcher & Sarkar, 2013; Luthans et al., 2015). This personal resource predicts adaptation in engineering women in their studies and the employability of university students (Baluku et al., 2021; Khilji & Pumroy, 2019; Santilli et al., 2020). As noted by Bateman (2016), proactive behavior can serve as a strategic and goal-oriented response to changing circumstances. Students may adopt such strategies to reshape existing conditions and steer their academic or professional paths toward a preferred future. This adaptive process often involves facing uncertainty and accepting the potential loss of valuable resources. Consistent with Hobfoll’s (2011) conservation of resources theory, resilience acts as a buffer by helping individuals manage these risks while enhancing the likelihood of positive outcomes. Therefore, resilience—considered a key component of psychological capital—is expected to show a positive association with proactive career behavior.

Lastly, personal initiative and proactive career behavior, both are proactivity constructs (Mensmann & Frese, 2016). Nevertheless, personal initiative is linked to the active completion of work tasks within an organization, unlike proactive career behavior, which involves person-environment fit. Frese et al. (1996, p.38) defined personal initiative as a constellation of behaviors in individuals with the following attributes: to be consistent with the organization’s mission, to have long-term focus, to be goal-directed and action-oriented, to persist in the face of barriers and setbacks, and to be self-starting and proactive. Besides, personal initiative involves taking responsibility and wanting to take the reins at work, while proactive professional behavior refers to forward-thinking actions to advance one’s career goals (Frese et al., 1996). In students, personal initiative has a demonstrated relationship between passion and commitment to studies (Bernabé et al., 2016). Although, both constructs involve self-initiate behavior, personal initiative is related to work tasks, and proactivity career behavior is related to behaviors necessary for professional development (Mensmann & Frese, 2016; Sonnentang, 2016). Hence, it is expected they are strongly related given that both are proactivity constructs, but in a theoretical level proactive career behavior is a proactivity specific domain.

In the present study, we aim to adapt the Proactive Career Behavior Questionnaire for a sample of Spanish distance education undergraduates. First, we propose to confirm the multifactorial structure of the construct. Second, we aim to analyze the validity of the questionnaire by examining its relationship with future orientation, resilience, and personal initiative in this specific population. This study will help bridge the gap in measurement tools tailored for distance education students, providing a more accurate understanding of employability and career development in this growing demographic.

Method

Design and Participants

The participants were 934 Spanish undergraduate students of psychology from distance university. The sample was comprised of 75.3% women and 24.7% men. Their average age was 36 years (SD = 10.25). Their distribution across classes was as follows: first years (10.2%), second years (12.7%), third years (20.1%), and fourth years (57%).

Measures

Sociodemographic Characteristics

An ad hoc questionnaire was used to collect demographic information, including participants’ gender and age. It also gathered data on their specific degree program and academic year.

Proactive Career Behavior

The Proactive Career Behavior Questionnaire (PCBQ) was developed by Strauss et al. (2012), measures respondents’ level of proactivity about a professional career. The original questionnaire consists of 13 items distributed across four factors: career planning (4 items, e.g., “I am planning what I want to do in the next few years of my career”); proactive behavior (3 items, e.g., “I develop skills which may not be needed so much now, but in future positions”); seeking advice (3 items, e.g., “I seek advice from my professor/s about additional training or experience I need in order to improve my future work prospects”); professional network (3 items, e.g., “I am building a network of colleagues I can call on for support”). A 5-point Likert-type response scale was used. The validity and reliability test of the questionnaire for the assessed dimensions showed the following results: career planning (α = .91); proactive behavior (α = .77); seeking advice (α = .93), and professional network (α = .89).

Personal Initiative

A version of Frese et al.’s scale was utilized, adapted for the Spanish population by Lisbona and Frese (2012). The scale is made up of six items. Here is a sample item: “Whenever there is a chance to get actively involved, I take it.” A 5-point Likert-type response scale was used. The reliability test of the questionnaire for the assessed dimension was conducted (α = .82).

Resilience

It was assessed using the Connor-Davidson’s Resilience Scale (Connor & Davidson, 2003), and its use has been validated in Spanish populations (Soler et al., 2016), Its ten items measure capacity for resilience. One example of an item is “I am not easily discouraged by failure.” A 5-point Likert-type response scale was used. The reliability test of the questionnaire for the assessed dimension was conducted (α = .89).

Future Orientation

We used the future scale of the Zimbardo Temporal Perspective Inventory (Zimbardo & Boyd, 1999), adapted by Díaz-Morales (2006). The scale assesses the tendency to plan and fulfill distant-future goals. It consists of 10 items (sample item: “I complete projects on time by making steady progress”). A 5-point Likert-type response scale was used. The reliability test of the questionnaire for the assessed dimension was conducted (α = .70).

Procedure

The first step was for the research team to translate the items into Spanish. They were then reviewed using an inter-rater procedure, to evaluate content relevance. Later, items were adapted for students by including “professor” as the direct object. Participants were invited to take part by the research team to participate, providing them with the link through variousonline platforms. Snowball sampling was used. Data collection was carried out using Qualtrics^®. Prior to administering the questionnaire, participants were informed of the confidentiality and anonymity of their responses following an informed consent procedure, and we asked for their agreement to continue with the study. This study was approved by the University Ethics Committee and conforms to the ethical guidelines of the Declaration of Helsinki.

Statistical Analysis

Descriptive statistical analyses are applied to questionnaire scores, along with factor analysis, and tests of reliability and validity. First, we analyzed skewness, kurtosis of scores, and Mardia’s coefficient (1970) for the data set. Second, scores are randomly sorted into two samples for factor analysis and test construct validity. Sequentially, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) are carried out in keeping with current recommendations (Bandalos & Finney, 2010; Izquierdo et al., 2014; Lloret-Segura et al., 2014). In the first sample (n₁ = 459), the factors resulting from EFA are analyzed. Then, in the second sample, the solution obtained and the one expected are examined using CFA (n₂ = 466). In EFA as well as CFA, the matrix of correlations resulting from data collection is utilized, along with maximum likelihood estimation since the data are normally distributed (Costello & Osborne, 2005). Orthogonal rotation was applied—varimax (Rennie, 1997). EFA applies the parallel test (Horn, 1965) to estimate the number of factors in the rotated solution. Various indices are used to study the goodness of fit to the data. An item is considered to define a factor well if its factor loading is .50 or above (Hair et al., 2006). Globally, the chi-squared statistic (χ²) is considered to evaluate a model’s goodness of fit to the data. Another goodness of fit index is CFI. Values over .90 indicate acceptable goodness of fit (Hu & Bentler, 1999). Regarding the RMSEA index, a value <.05 is considered good, and values >.05 and <.10 are considered to have moderate goodness of fit, while for the SRMR index, values <.08 would be excellent (Hu & Bentler, 1999). Finally, after factor analysis, we look at the reliability and convergent validity of the PCBQ’s measures with the composite reliability coefficient (CRC), and average variance extracted (AVE). For CRC, values over .70 are acceptable, and for AVE, values over .50 are considered representative of the latent variable (Bagozzi & Yi, 1988; Hair et al., 2006; Nunnally & Bernstein, 1978). Our analysis of discriminant validity takes Forner-Larcker’s criterion (1981) into account. To explore the PCBQ’s measurement invariance as a function of gender, configural equivalence was tested through multigroup CFA (Byrne, 2008; Chen, 2007). The variations on the indices CFI (ΔCFI) and RMSEA (ΔRMSEA) are employed. Strong invariance is considered to apply when ΔCFI ≤.01 and ΔRMSEA ≤.015 (Cheung & Rensvold, 2002).

For the descriptive analysis, the correlation matrix and convergent variables analysis are conducted using the SPSS 21 statistical package. Factor analysis of the PCBQ is carried out in Factor 10.10.02 (Ferrando & Lorenzo-Seva, 2017), and AMOS is utilized to examine the final causal model and analysis of invariance.

Results

Preliminary Analysis

Table 1 displays descriptive statistics for the PCBQ’s 13 items. Mardia’s coefficient (χ² = 257,398, p < .000) indicated an acceptable multivariate distribution (Mardia, 1970) for the whole sample. Refer to Table 1 for measures of central tendency and item variability in the two samples. In both cases, the multivariate distribution was acceptable (n₁: χ² = 245,652, p < .000; n₂: χ² = 263,324, p < .000).

Table 1.

Descriptive Analysis of Questionnaire Items.

N	Item	Mean	SD	Skewness	Kurtosis
934	PB01	3.98	1.05	−0.99	0.48
	PB02	3.93	0.99	−1.04	0.78
	PB03	3.93	0.99	−0.91	0.44
	PB04	4.04	0.95	−1.09	1.01
	PB05	3.69	1.04	−0.65	−0.10
	PB06	4.00	0.94	−1.03	0.92
	PB07	3.83	0.89	−0.81	0.69
	PB08	2.63	1.31	0.27	−1.11
	PB09	2.50	1.29	0.41	−0.96
	PB10	2.37	1.28	0.50	−1.05
	PB11	3.18	1.28	−0.28	−1.10
	PB12	2.96	1.27	−0.11	−0.96
	PB13	3.41	1.24	−0.57	−0.68
n ₁	Item	Mean	SD	Skewness	Kurtosis
459	PB01	3.87	1.07	−1.04	0.61
	PB02	3.92	1.01	−1.02	0.69
	PB03	3.90	1.00	−0.85	0.40
	PB04	4.01	0.97	−1.16	1.29
	PB05	3.68	1.07	−0.71	−0.03
	PB06	3.98	0.94	−1.03	0.98
	PB07	3.81	0.89	−0.92	1.14
	PB08	2.72	1.34	0.18	−1.16
	PB09	2.58	1.31	0.34	−1.04
	PB10	2.43	1.30	0.46	−0.95
	PB11	3.19	1.28	−0.32	−1.02
	PB12	2.92	1.27	−0.08	−1.08
	PB13	3.42	1.25	−0.59	−0.63
n ₂	Item	Mean	SD	Skewness	Kurtosis
465	PB01	3.88	1.03	−1.03	0.66
	PB02	3.94	0.97	−1.71	1.37
	PB03	3.96	0.99	−1.08	0.99
	PB04	4.05	0.93	−1.15	1.34
	PB05	3.69	1.01	−0.68	0.14
	PB06	4.02	0.95	−1.18	1.50
	PB07	3.85	0.89	0.89	1.02
	PB08	2.54	1.28	0.34	−1.01
	PB09	2.42	1.25	0.46	−0.85
	PB10	2.32	1.25	0.51	−0.90
	PB11	3.16	1.29	−0.27	−1.04
	PB12	2.99	1.27	−0.16	−1.07
	PB13	3.40	1.24	−0.57	−0.66

Construct Validity

In order to carry out exploratory factory analysis (n₁ = 459), the correlation matrix from the sample was confirmed to be acceptable (χ² (78) = 33,967.1; p = .000; KMO = .844). Four factors were extracted that together explain 78% of variance in this construct. The parallel test (Horn, 1965) suggested extracting four components that account for the most variance (I > 1.11). Results showed a well-defined distribution, with factor loadings above .50 (Hair et al., 2006) on the latent variables, reproducing the original structure of the PCBQ (Table 2). The solution obtained had an optimal number of items per factor, by sample size, for Factor 1 (h² = .82 – .46); Factor 2 (h² = .63 – .55); Factor 3 (h² = .98 – .77), and Factor 4 (h² = .75 – .68) based on Lloret-Segura et al.’s (2014) guidelines. Items were distributed as a function of their content into a tetra-factorial structure with the following dimensions: Career Planning (CP), Proactive Behavior (PB), Seeking Advice (SA), and Professional Networks (PN). The model’s goodness of fit indices were satisfactory (RMSEA = .062; CFI = .99; SRMR = .013).

Table 2.

Distribution of PCBQ (Strauss et al., 2012) Items by Factor (n₁ = 459).

Item	CP	PB	SA	PN
1. I am planning what I want to do in the next few years of my career. (Planifico lo que quiero hacer en los próximos años de mi carrera.)	.849
2. I am thinking ahead to the next few years and plan what I need to do for my career. (Pienso en los próximos años y planeo lo que tengo que hacer para mi carrera)	.872
3. I engage in career path planning. (Estoy comprometido en la planificación de la carrera)	.681
4. I have recently begun to think more about what I would like to accomplish in my studies during the next year. (Recientemente he empezado a pensar más en lo que me gustaría lograr en mis estudios durante el próximo año)	.581
5. I develop skills which may not be needed so much now, but in future positions. (Desarrollo habilidades que no se necesitan tanto ahora, pero si en puestos futuros)		.606
6. I gain experience in a variety of areas to increase my knowledge and skills. (Gano experiencia en diferentes áreas para aumentar mis conocimientos y habilidades)		.591
7. I develop knowledge and skills in tasks critical to my future work life. (Desarrollo el conocimiento y la habilidad sobre tareas críticas de mi futura vida profesional)		.638
8. I seek advice from my professor/s about additional training or experience I need in order to improve my future work prospects. (Busco el consejo de mi (s) profesor (es) sobre la formación adicional que necesito para mejorar mis perspectivas para mi futuro trabajo)			.910
9. I initiate talks with my professor about training or work assignments I need to develop skills that will help my future work chances. (Tengo conversaciones con mi profesor sobre formación o tareas de trabajo que necesito para desarrollar habilidades que ayudarán a mis oportunidades de trabajo futuro.)			.989
10. I make my professor aware of my work aspirations and goals. (Hago que mi profesor sepa de mis aspiraciones y metas de trabajo)			.868
11. I am building a network of contacts or friendships with colleagues to obtain information about how to do my work or to determine what is expected of me. (Estoy construyendo una red de contactos o amistades con colegas para obtener información sobre cómo hacer mi trabajo o para determinar lo que se espera de mí)				.769
12. I am building a network of contacts or friendships to provide me with help or advice that will further my work chances. (Estoy construyendo una red de contactos o amistades para proporcionarme ayuda o consejo que fomente mis oportunidades de trabajo)				.722
13. I am building a network of colleagues I can call on for support. (Estoy construyendo una red de colegas a los que puedo pedir ayuda)				.842

Note. List of items adapted to Spanish student version.

Following the guidelines in Izquierdo et al. (2014), our second step was to reproduce the underlying structure from before, now in the second sample using CFA (n₂ = 465). The model’s goodness of fit indices was again satisfactory (χ²(32) = 37,120; RMSEA = .036; CFI = .997; GFI = .999). This confirmed the original tetra-factorial structure of the PCBQ. The items’ factor loadings were acceptable on the dimensions career planning (λ = .92 – .63), proactive behavior (λ = .84 – .70), seeking advice (λ = .96 – .86), and professional networks (λ = .87 – .82) as per Hair et al. (2006) (Figure 1).

Figure 1.

Factor model of the Spanish version of PCBQ (n₂ = 465).

Convergent Validity and Reliability of Measures

Convergent validity was determined through the statistical significance of the factor loadings of the indicators of each latent construct (Table 3). To evaluate the reliability of the measures, the composite reliability coefficient (CRC) was calculated, which is more appropriate than Cronbach’s alpha, since it does not depend on the number of attributes associated with each concept Hair et al. (2006). The composite reliability coefficient (CRC) was over .70. Meanwhile the value of AVE, which indicates representativeness of the latent variable, was over .50 for most items. Both reliability tests cleared the proposed cut-offs for acceptability (Hair et al., 2006; Nunnally & Bernstein, 1978). Regarding configural invariance as a function of gender, the PCBQ’s factorial structure exhibited measurement invariance (χ²(9) = 8,630; p = .472; CFI = .978; RMSEA = .038; ΔCFI = .000; ΔRMSEA = −.001).

Table 3.

Factor Loadings and Reliability Coefficients (N = 934).

Latent variable	Item	λ	CRC	AVE
Career planning	PC01	.86	.91	.80
	PC 02	.89
	PC 03	.74
	PC 04	.57
Proactive behavior	PC 05	.65	.74	.48
	PC 06	.69
	PC 07	.74
Seeking advice	PC 08	.86	.91	.77
	PC 09	.94
	PC 10	.82
Professional networks	PC 11	.82	.85	.65
	PC 12	.78
	PC 13	.82
Resilience	RE01	.75	.77	.53
	RE02	.74
	RE03	.70
Personal initiative	IN01	.73	.76	.52
	IN02	.67
	IN03	.74
Future orientation	FO01	.71	.73	.48
	FO02	.72
	FO03	.63

Discriminant and Concurrent Validity

To evaluate the presence of discriminant validity between constructs the square root of the AVE must be higher than the correlation between constructs (Fornell & Larcker, 1981). Table 4 shows the correlations between three constructs and, on the diagonal, the square root of the AVE. Let us now shift attention to the resilience, personal initiative, and future orientation items. Each of their factor loadings was over .50 and correctly defined their respective variables, since they had the highest factor loadings (Kim & Mueller, 1978). As such, they can be considered to measure the degree of differentiation from the constructs that the questionnaire measures. Table 4 presents the matrix of correlations among constructs. All intercorrelations among variables in the questionnaire’s structural model were statistically significant. All the constructs were closely related, but we observed sufficient discriminant validity, based on the criterion of Fornell and Larcker (1981). In other words, the √AVE values of each construct were higher than their correlations with other constructs.

Table 4.

Correlation Matrix and Discriminant Validity.

Variables	M	SD	(1)	(2)	(3)	(4)	(5)	(6)	(7)
1. Career planning	3.91	0.91	.89
2. Proactive behavior	3.83	0.80	.46	.69
3. Seeking advice	2.50	1.22	.24	.32	.88
4. Professional networks	3.18	1.13	.22	.31	.44	.81
5. Resilience	3.74	0.86	.18	.25	.12	.15	.73
6. Personal initiative	3.80	0.71	.22	.25	.18	.19	.46	.72
7. Future orientation	4.03	0.77	.27	.26	.14	.13	.28	.38	.69

Note. All values are significant p < .005 and √AVE values appear on the diagonal.

The correlations obtained between the PCBQ dimensions and proactivity outcomes (Table 4) provide first support for the concurrent validity of the PCBQ. For the same purpose, SEM was carried out. Goodness of fit indices were acceptable for the proposed model: χ² (52) = 143,310; CFI = .97; GFI = .95; RMSEA = .04. In terms of the relationship among variables, the PCBQ significantly predicted outcomes on resilience (71%), personal initiative (82%), and future orientation (69%) in the analyzed sample.

Discussion

This study’s primary objective was to adapt a measurement instrument for proactivity about a professional career, for use in Spanish distance students. Regarding factor structure, the present study reproduced this construct’s distribution into four factors: career planning, proactive skill development, career consultation, and network building. All four showed acceptable levels of reliability, and the model’s overall structure was satisfactory. Therefore, the existence of four factors is confirmed according to Strauss et al. (2012) in distance undergraduate students. This structure also appears to be invariant depending on gender, so the adaptation of the measuring instrument can be used for both genders in this type of student. Results confirm the relationship between the career proactivity components as has been observed in recent graduate students, where the formulation of career objectives, planning, and professional networks are linked (De Vos et al., 2010). Behaviors indicative of proactive career development in distance students include exploring diverse career avenues, strategizing for goal attainment, actively participating in professional seminars, soliciting advice from peers, and enrolling in courses to enhance the requisite skills for their future endeavors.

In terms of validity and reliability of measures, the PCBQ shows adequate representativeness of the latent variable when the questionnaire is applied to this population. Besides, all reliability tests exceed the adequacy criterion. Regarding concurrent validity, a statistically significant relation was observed with the constructs we had expected based on the body of theory. Proactive career behavior was related to future orientation, resilience and personal initiative. Furthermore, the discriminant validity was also satisfactory. Proactive professional behavior is differentiated from the other constructs although they maintain a relationship between them as expected. Lastly, PCBQ predicts students’ future orientation, resilience, and personal initiative.

Regarding future orientation, results are in line with previous studies suggesting that proactive strategies predict these self-regulatory behaviors in students (Cabras & Mondo, 2018; Öztemel & Yıldız-Akyol, 2021; Praskova & Johnston, 2021; Sohl & Moyer, 2009). As can be seen, in general terms, future orientation is linked to the performance of proactive behaviors related to their professional career in the sample of students. Therefore, future orientation is an important resource to foster meaningful work (Fay et al., 2023). Specifically, it is possible that variables linked to future expectations or expected identities can explain this relationship more precisely. Specifically, the role of the future work self, in terms of the work identity to which one aspires, may be a more precise predictor variable to intervene to improve the proactivity behaviors carried out by distance students (Chishima & Wilson, 2021; Guan et al., 2017; Taber & Blankemeyer, 2015).

Consistent with prior research, proactive strategies have been identified as predictors of levels of resilience (Merrell et al., 2010). Resilience, recognized as a crucial asset, is linked to the proactive behavior of students (Pérez-López et al., 2016). This personal resource also has a positive impact on the proactive career behaviors of distance learners (Tutar et al., 2019). It is feasible that students’ resilience is intertwined with proactive components, including emotional and cognitive abilities, which produce various effects, fostering development in undergraduate students, encouraging initiative, and improving academic performance (de la Fuente et al., 2022; Gómez- Molinero et al., 2018; Reeve et al., 2020). Hence, within the domain of distance undergraduate education, the correlation between students’ resilience and their proactive approach in effectively addressing challenges, adapting to, and understanding the learning environment is crucial for their academic success (Simons et al., 2018).

In line with Mensmann and Frese’s investigations (2016), proactive career behavior predicted personal initiative, so it may be a key predictor in the job sphere. Personal initiative is closely linked to proactive career behavior within the framework of the social cognitive paradigm, which establishes relationships among self-efficacy, outcome expectations, proactive personality traits, and supervisor support for achieving successful and sustainable career outcomes (Lent et al., 2022). It functions to enrich the significance of work by establishing a link between individuals and the future. This connection is notably reinforced in professions that confront an uncertain and unpredictable future (Fay et al., 2023; Mustafa et al., 2023). The demonstration of personal initiative generates a positive influence on the perceived congruence between demands and capabilities over a prolonged period. Individuals exhibiting proactive career behaviors typically encounter an increased alignment between their competencies and the demands of their job positions (Sylva et al., 2019). As observed, distance students who dedicate time to their careers exhibit behaviors of planning, goal setting, and organization in other areas of life. These self-regulatory behaviors are certainly indispensable for learning and life-long career building.

Implications

It was observed that undergraduates positively rated advice about professional development from their instructor (Martínez et al., 2019). Furthermore, in terms of professional orientation, offering students more autonomy about their careers promotes self-regulatory processes in their career trajectory, pursuing goals, making contacts, and enjoying personal career building (Sánchez-García & Suárez-Ortega, 2018). Similarly, positive effects have been observed from future-work-self instruction, which is formulated by the student and orients their professional career (Kao et al., 2022). It is therefore necessary to understand how proactivity may influence these aspects of employability.

Empirical implications of other variables that may predict proactive behaviors would lead these authors to examine them in relation to levels of commitment and well-being. That is because commitment has been seen as an important predictor of academic performance and well-being (Northey et al., 2018). With an eye to theoretical implications, deeper exploration is needed into theoretical explanations that may predict this type of behavior on an individual level, such as personality variables, passion for the activity, or social comparison. That would contribute to improving a given career orientation and expand our understanding of student variables behind proactivity in career building. In a context such as the present, where people increasingly want greater control over their careers, proactivity about a professional career helps improve the choice of career path, future job fit, and lifelong learning.

Limitations and Future Research

The constraints of this study encompass utilizing self-report assessments and adopting a cross-sectional design. While these methods are commonly employed in analyzing such variables, there is a potential for participant response bias, as noted by Podsakoff et al. (2003). Concerning the sample makeup, although it mirrors the gender distribution of distance-learning students, biases in students’ perceptions of specific university programs due to gender stereotypes are evident (Barberá et al., 2008). Thus, broadening the representation of university majors in the sample and adopting a gender-aware approach would be advantageous. Finally, it is necessary to study the predictive validity of the instrument, although to do so it is necessary to initially have a tool with adequate construct and concurrent validity adapted to the study population.

Conclusions

To conclude, this questionnaire points the way to studying how university professors and career consultants should help distance learners develop ambitious career goals. The Spanish adaptation of Proactive Career Behavior in distance undergraduates shows reliable and valid outcomes for both genders, with resilience, personal initiative, and future orientation correlating with students’ proactive behaviors. Many times, these students are underrepresented as they are a non-normative sample. Having a tool to measure proactive career behavior allows us to study planning, motivation to improve skills or how it builds a professional network in adult learners. In order to study variables that can predict these behaviors and be able to intervene in them, by providing better services from career guidance services.

Footnotes

ORCID iDs

Miguel Bernabé

Richard Merhi

Ana Lisbona

Francisco Palací

Ethical Considerations

All participants gave their informed consent to be involved before they participated in this study. Informed consent included the publication of anonymized responses. All procedures performed were by the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standard. All procedures performed in this study were by ethical standards and the procedures were approved by the ethics committee of Universidad Nacional de Educación a Distancia.

Funding

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

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.

Data Availability Statement

The datasets generated and analyzed during the current study are available in the following DOI:10.17632/2yw2z64sck.1

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