The young population is most affected by the difficulties in the Spanish labor market. The problem of youth unemployment in Spain is clearly structural because: (1) The outdated education system does not offer effective responses to employers’ requirements; (2) There is a strong polarization between highly educated and less educated young people, which means that there is a relatively large group of people without secondary education; (3) During the economic crisis thousands of jobs in the construction sector or related sectors disappeared. Most jobs were occupied by lower educated youngsters; (4) The Spanish labor regulation has induced labor market duality. This has led young people to occupy precarious employment (Bacaria et al., 2015). The result is that youth unemployment rate is very sensitive to the economic cycle. That is, youth unemployment stays relatively high in periods of economic expansion but rises quickly during recession (Hernández et al., 2012; Gómez and Arévalo, 2013).

The implication is that young people’s access to the Spanish labor market is an arduous task. In this study, we seek ways to improve the unemployed youngsters’ job search process. Although previous research has advanced job search models, the role played by psychological capital and perceived employability in this population group and in economically adverse contexts is unexplored.

Grounded in Ajzen’s (2012) Theory of Planned Behavior, Van Hooft (2016) introduces the Theory of Planned Job Search Behavior as the result of integrating both motivational and self-regulatory perspectives on job search. This theoretical framework integrates the main mechanisms that are important in the job search process. On the one hand, as job search is a difficult and complex task, having and maintaining motivation is essential for securing continuous job search activities. On the other hand, as job search is not only difficult but also unpleasant, job seekers need self-regulation for ensuring task persistence and performance. The job search literature (Wanberg et al., 1999; Kanfer et al., 2001; Manroop and Richardson, 2015) has demonstrated the added value of including contextual-level and global-level motivators in predicting job search intention and behavior. In response, Van Hooft (2016) proposes Vallerand’s Hierarchical Model of Motivation as a useful frame to justify the extension of the Theory of Planned Job Search Behavior for analyzing which resources can help people to be engaged in job search. Based on both approaches, we have built an integrated conceptual model (Figure 1). In what follows, we describe the components of the model and how they are related.

Job search intention constitutes a central aspect in the Theory of Planned Job Search Behavior. Following Van Hooft (2016, p. 6), this component reflects the strength of an individual’s motivation to engage in job search. Job search behaviors are performed according to the intentions previously formed (Van Hooft and Noordzij, 2009; Van Hooft, 2016).

In order to understand how job search intentions and ultimately job search behaviors are shaped, Van Hooft (2016) proposes Vallerand’s motivational resources classification. This classification distinguishes between three hierarchical levels which vary in generality, stability and proximity to behavior, as follows: a) global-level motivating factors are generalized constructs that are stable over situations and are applicable to all life domains; b) contextual-level motivating factors concern specific life domains (e.g., employment); and, c) situational-level motivating factors refer to specific behaviors (e.g., job search). Vallerand recognizes a top-down direction from higher to lower levels. More specifically, motivating factors at proximal levels (e.g., from global level-motivating factors to contextual-level motivating factors) are more strongly related than motivational factors at a distal level (e.g., from global-level motivating factors to situational-level motivating factors) (Vallerand, 1997).

We propose psychological capital as a global-level motivating factor because it is not tied to a specific domain. It <psychological capital> is understood as a second-order construct composed of four components (optimism, resilience, hope and self-efficacy) and is defined as a state of positive psychological development characterized by having the confidence to face challenges and difficult tasks (self-efficacy); make positive attributions about present and future triumphs (optimism); visualize goals and persevere in pursuing goals, as well as redirect the objectives when necessary to achieve success (hope); and recover and even emerge stronger from adversity (resilience) (Luthans et al., 2007). In concert, those components relate to a tendency to hold cognitions and positive appraisals of one’s own ability (Luthans et al., 2007; Harms et al., 2018). Several studies have demonstrated that psychological capital has particular resonance in job search (Chen and Lim, 2012; Afua et al., 2016; Lim et al., 2016; Georgiou and Nikolaou, 2018). For example, Chen and Lim (2012) showed a positive association between psychological capital, perceived employability, and active job search. Lim et al. (2016) proposed psychological capital as a useful resource for reducing job search fatigue. Finally, Georgiou and Nikolaou (2018) argued that psychological capital helped individuals to persist in job seeking, which is linked to more job interviews, job offers, and reemployment. Psychological capital helps individuals to appraise job search in a more positive way, so that they find it easier to cope with stress, difficulties, and obstacles during their job search (Caska, 1998; Wanberg et al., 2005; Van Hooft, 2016).

We propose perceived employability, understood as the perceived ability to obtain sustainable employment appropriate to one’s qualification level (Rothwell et al., 2008), as a contextual-level motivating factor. It is tied to the employment domain with specific relevance to job search (Koen et al., 2013; Vanhercke et al., 2015; Lo Presti and Pluviano, 2016; Yizhong et al., 2017). For example, Koen et al. (2013) showed that employability improves not only the job search process but also long-term unemployed reemployment opportunities. Both Vanhercke et al. (2015) and Lo Presti and Pluviano (2016) suggested an association between perceived employability and well-being, and those job seekers with a better health status are more likely to carry out a successful job search (Crossley and Highhouse, 2005) and find a quality job (McKee-Ryan et al., 2005; Paul and Moser, 2009; Lim et al., 2016). Finally, Yizhong et al. (2017) argued that higher levels of perceived employability lead to higher levels of job search self-efficacy and job search intention in university graduates.

We advance job search attitude, subjective norm and perceived behavioral control over job search as situational-level motivating factors. Job search attitude is defined as the individual’s (positive or negative) evaluation of the job search process and job attainment (Van Hooft et al., 2004). According to the Theory of Planned Job Search Behavior, those job seekers with more positive attitudes toward job search will invest more time in looking for a job as they anticipate better results (Van Hooft, 2016).

Subjective norm is understood as the social pressure that an individual perceives when looking for a job. The Theory of Planned Job Search Behavior suggests that those unemployed who perceive a greater social pressure to look for a job from their immediate surroundings will dedicate more time to their job search process.

Perceived control over job search refers to people’s belief they can perform effectively when looking for a job. As a result, the more positive features people have of getting a job, and the stronger they value these features, the more likely they are to perceive job search as useful (Van Hooft, 2016). According to the Theory of Planned Job Search Behavior, these three variables are the most proximal antecedents of job search intention. Job search intention then is seen as the antecedent of job search behavior (Van Hooft, 2016).

Following Vallerand’s model, global-level motivating factors are expected to relate to contextual-level motivating factors and situational-level factors. This leads to the following set of hypotheses. First, we expect that psychological capital relates to perceived employability. This aligns with previous research that underlines the importance of personal resources and dispositions in fostering perceived employability (Berntson, 2008; Chiesa et al., 2018). Earlier studies support psychological capital as an antecedent of perceived employability (Chen and Lim, 2012; Afua et al., 2016; Chiesa et al., 2018). Accordingly, we hypothesize that:

Second, we hypothesize that perceived employability, as a contextual-level motivating factor, relates to job search attitude and perceived control over job search (situational-level motivating factors). Previous studies have suggested that perceived employability can help to understand how people appraise job search (Blau et al., 2013; Vanhercke et al., 2015), and this appraisal is captured in their attitude vis-à-vis job search. Perceived employability has been shown as a predictor of self-efficacy in several studies (Berntson, 2008; De Cuyper and De Witte, 2010; Vanhercke et al., 2015; De Battisti et al., 2016), and self-efficacy is often used as an operationalization of control over job search (Van Hooft et al., 2004). Note that we have not hypothesized any relation between perceived employability and subjective norm because there is little empirical basis for doing so (e.g., see Yizhong et al., 2017). The idea that perceived employability and subjective norm are unrelated is plausible: perceived employability does not need to be related to how significant others youngsters (e.g., parents, partner, friends, …) think about their job search.

Third, we expect that psychological capital relates to job search attitude and job search control through perceived employability. This aligns with the idea that global-level motivating factors are distal to situational-level factors, and the relationship between them is indirect through contextual-level factors. This results in the following hypothesis:

Finally, the Theory of Planned Job Search Behavior argues that job search attitude, subjective norm and perceived control over job search are directly related to job search intention and mediators of the relationship between other variables and job search intention (Van Hooft, 2016). In line with this argument, on the one hand, job search attitude and perceived control over job search will mediate the relationship between perceived employability and job search intention. On the other hand, as we have not hypothesized any relationship between perceived employability and subjective norm, we expect a direct association between subjective norm and job search intention. According with Van Hooft (2016) the more social pressure from important other job seekers perceive, more time they are likely to spend on their job search, as this could positively affect their relationship with significant others. In addition, job search intention will relate to job search behaviors, since changes in intentions will be followed by changes in behavior (Ajzen, 2012). Accordingly, we hypothesize that:

Data were collected in Spain in collaboration with the public employment service (Employment and Training Service of the Region of Murcia, SEFCARM) between January and June 2016. Labor counselors invited unemployed youngsters job seekers aged between 16 and 29 to enroll in our study during their counseling trajectory. This study was carried out in accordance with the recommendations of ethics committee of Universidad de Murcia. All subjects gave written informed consent in accordance with the Declaration of Helsinki.

The sample included 568 young unemployed people with an average age of 23.4 years (SD = 3.79, range = 16–29 years). The large majority of the respondents were Spanish (91.1%). About half of the respondents (52.4%; N = 300) were men. Most respondents were single (81.8%; N = 469). The others (17.5%; N = 100) were married or lived together. In terms of education, 33.5% (N = 192) had university studies, 27.2% (N = 156) completed secondary education, 19.9% (N = 114) completed primary education and 18.8% (N = 108) followed Basic Vocational Training. Most respondents were looking for a job after one or several previous jobs (46.2%; N = 265), followed by those who were studying and looking for work at the same time (37.9%; N = 217) and those who were looking for their first job (14.8%; N = 85). Lastly, the average time in unemployment was 8.47 months approximately (SD = 2.24).

Data were obtained using instruments previously validated in Spanish and similar populations.

Job Search. It includes five subscales based on previous research carried out by different authors and validated in Spanish by Fernández-Valera (2018). Participants had to rate the items on a scale ranging from 1 (nothing) to 5 (a lot).

(A) Job search behavior. We used the scale proposed by Van Hooft and Noordzij (2009). This scale is composed of eight items. Participants were asked how much time they have devoted to each of the job search activities in the last 4 months. Job search activities included, for example, contacting job placement agencies or looking for a job on the Internet. An example item is: “How much time did you spend on the following job search activities in the last 4 months: Looking for jobs on the internet” (Item 8). The coefficient α for this scale was 0.80.

(B) Job search intention. We used the same eight items from the previous scale, yet adapted to reflect intention. An example item is: “How much time do you intend to spend talking to friends or family about possible job opportunities in the next 4 months” (Item 2). The coefficient α for this scale was 0.85.

(C) Job search attitude. Van Hooft et al. (2004) distinguish between instrumental and affective job search attitude. We used only instrumental job search attitudes as this seems most relevant in a context of crisis and following first analyses (Fernández-Valera, 2018). The three items for instrumental job search attitude are based on the work of Vinokur and Caplan (1987). They concern the degree to which job seekers consider job search sensible, prudent and useful taking into account the next 4 months. An example of an item would be: “It is wise for me to search for a (new) job in the next 4 months.” The coefficient α for this scale was 0.65.

(D) Subjective norm. Based on the work of Vinokur and Caplan (1987), subjective norm was evaluated using two items. The participants were asked to indicate how much they perceive their environment thinks they should look for a job in the next 4 months. For example: “My significant other thinks that I should search for a (new) job in the next 4 months.” The coefficient α for this scale was 0.90.

(E) Perceived control over job search. This refers to the perception of control over both internal and external resources that may influence job search. We included perceived control over internal resources as this seems most relevant in a context of economic crisis, which found support in preliminary analyses reported by Authors (Fernández-Valera, 2018). Perceived control over internal resources included eight items based on the previous work of Ellis and Taylor (1983) and Van Ryn and Vinokur (1992). An example of an item is: “I trust that I can offer a good impression in job interviews.” The coefficient α for this scale was 0.64.

Psychological Capital. The OREA questionnaire developed by Meseguer-de Pedro et al. (2017) was used to measure psychological capital. It consists of 12 items, three items for each dimension of psychological capital. The items of OREA questionnaire are rated on a four-point scale, that is, going from 1 (strongly disagree) to 4 (strongly agree). Sample items per dimension are: “In difficult times I usually expect the best” (Optimism); “I usually reach my goals even if there are obstacles” (Resilience); “I think my life is worth it” (Hope); “I am confident about I could effectively handle unexpected events” (Self-efficacy). The coefficient α for this scale was 0.82.

Perceived employability. Perceived employability was measured using three items from the Employment Outlook Scale of Career Exploration Survey (Stumpf et al., 1983), validated in Spanish by Ripoll et al. (1994). Responses were made using a five-point scale that varies from 1 (strongly disagree) to 5 (strongly agree). A sample item is: “It is possible for me to find a job in a company of my choice.” The coefficient α for this scale was 0.81.

Control variable. As there were diverse groups in our sample (e.g., those who were looking for a first job, those who had worked before and those who was looking for work and studying at the same time) who might have different perceptions about labor market access, we controlled for current situation (0 = First job; 1 = Previous job; 2 = Studying and looking for a job). We test hypotheses with and without control variables, with virtually similar results. We report results without control variables following the principle of parsimony (Vandekerckhove et al., 2015).

We first inspected descriptive statistics using the Statistical Package for the Social Sciences Version 22.0. Table 1 shows the means, standard deviations, and correlations of variables included in this study.

We then used Mplus 7 to test the hypotheses (Asparouhov and Muthén, 2012) applying a maximum likelihood estimation. Models were evaluated using the Comparative Fit Index (CFI), Tucker Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), Standardized Root Mean Square Residual (SRMR), and Akaike Information Criterion (AIC) fit indices, with conventional standards.

We first performed a confirmatory factor analyses for all the variables simultaneously. The seven-factor measurement model in which psychological capital is a second order factor provided an adequate fit to the data [χ²/(df) = 2.32, p < 0.01, CFI = 0.91, TLI = 0.90, RMSEA = 0.05, SRMR = 0.07; AIC = 43714.02]. This model provided a better fit compared to a model in which psychological capital is a single first-order factor, χ²/(df) = 2.11, p < 0.01, CFI = 0.89, TLI = 0.87, RMSEA = 0.04, SRMR = 0.07; AIC = 491123.68.

We then used structural equation modeling with latent variables to test our hypotheses: We modeled paths as shown in Figure 1, however goodness of fit indices of were not satisfactory [χ²/(df) = 3.34, p < 0.01, CFI = 0.84, TLI = 0.82, RMSEA = 0.07, SRMR = 0.06]. We inspected the modification indices and added the following correlations: following the Theory of Planned Job Search Behavior (Van Hooft, 2016) we added correlations between the antecedents of job search intention, namely, (1) job search attitude and subjective norm; (2) job search attitude and perceived control over job search; (3) subjective norm and perceived control over job search. Furthermore, we also added correlations between items of job search behavior and job search intention, as those items were largely similar (see Appendix Table A1): in particular, we connected (4) items 5 of job search behavior and 7 of job search intention; (5) items 1 of job search behavior and 8 of job search intention; (6) items 6 of job search behavior and 9 of job search intention; (7) items 7 of job search behavior and 10 of job search intention. The hypothesized model provided a reasonably good fit to the data [χ²/(df) = 1.70, p < 0.01, CFI = 0.95, TLI = 0.94, RMSEA = 0.04, SRMR = 0.04] (see Figure 2).

With respect to Hypotheses 3 and 4 concerning mediation, we applied the bias-corrected (BC) bootstrapping confidence interval (CI) analyses with 1000 bootstrap sample in Mplus (Preacher and Hayes, 2008). Using BC bootstrapping CI analyses, we construct 95 per cent confidence intervals of our tests.

The results of this study have implications for young job seekers and employment services. The International Labour Organization (ILO) (2015) highlights that persistently high youth unemployment rates have long-term consequences, for example skills erosion, increase of social exclusion and a higher level of poverty. Furthermore, the experience of unemployment at the beginning of professional life leaves scars, for example in terms of future salary (Bell and Blanchflower, 2011), job and personal satisfaction (Morsy and Jaumotte, 2012) and long-term unemployment (Arulampalam et al., 2001). We demonstrated that psychological capital may facilitate job search as a way to overcome unemployment.

Regarding implications for theory, the results show partial support for the extension of the Theory of Planned Job Search Behavior through Vallerand’s Hierarchical Model of Motivation. Perhaps this is because the specific population group (unemployed youngsters) and the context (Spanish economy) we have analyzed. Regarding practical implications, both public and private employment services could design intervention programs aimed at increasing unemployed’s psychological capital. Inspiration can be found in the study by Georgiou and Nikolaou (2018) who showed that psychological capital can be developed among greeks job seekers following Luthans et al.’s (2007) PCI (Psychological Capital Intervention) training model.

As regards the proposed model, to avoid the need of adding correlations between very similar wording items, further research is needed to probe its strength with different job search intention and job search behavior measures. For example, previous research (Song et al., 2006; Wanberg et al., 2010; Yizhong et al., 2017) have applied Vinokur and Caplan (1987) 2 item scale for job search intention and Wanberg et al. (1999) 12 item scale for job search behavior. Further investigation could extrapolate the proposed model to other populations and contexts. For example, in NEET (Not in Education, Employment or Training) population, unemployed people over 45 years or long-term unemployed people, also in favorable economic contexts. Besides, we have not hypothesized any relation between perceived employability and subjective norm, even when the social network is considered one of the components of some employability approaches (Lo Presti et al., 2019). Future studies may put more emphasis on subjective norms as previous research has reported an association between the fact of receiving helpful and supportive messages from their close environment and job search intensity in Millenials (Holmstrom et al., 2013; Smith, 2019). Finally, according to Smith (2019) a common millennial generation job search strategies are characterized by both the use of interpersonal networking and the use of online channels. As a result, further research would also benefit from focusing on variables related to social networks (for example, regarding our proposed model, social capital as a global motivating factor and job contact network as a situational motivating factor) and the analysis of the influence of technology on the traditional job search landscape.

Our study has some limitations. First, we used self-ratings. Though commonly used in job search research, this may induce common method variance, which could lead to inflated relationships (Spector, 2006).

Second, we measured job search behaviors in terms of intensity and frequency. While this is commonly done, job search behaviors are likely more complex and multidimensional. Future research may want to focus upon job search methods, job search quality and job seekers’ procrastination during job search (Van Hooft et al., 2012).

Third, our study was cross-sectional. This means that we cannot make causal conclusions. Yet, our hypotheses are in line with earlier work and grounded in theory.

Fourth, in the Structural Equation Model we have added correlations between items from the job search behavior and intention scales. This was done owing to very similar wording across the ítems, yet admittedly a cause for concern.