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Article

The Impact of Perceived Barriers to Career Advancement: A Study with a Sample of Italian University Students

by
Mara Marini
*,
Stefano Livi
,
Laura Prislei
,
Chiara Parisse
,
Alessandra Cecalupo
,
Federica Scarci
and
Guido Benvenuto
Department of Social and Developmental Psychology, Sapienza University of Rome, 00185 Rome, Italy
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(11), 8874; https://doi.org/10.3390/su15118874
Submission received: 28 March 2023 / Revised: 20 May 2023 / Accepted: 24 May 2023 / Published: 31 May 2023
(This article belongs to the Special Issue Individual and Collective Factors in Achieving Sustainable Development Goals)
Browse Figure
Versions Notes

Abstract

:
The goals established by the 2030 Agenda for Sustainable Development, and the new needs that globalization has created, have prompted researchers to examine young people’s educational and professional experiences in the context of economic and social disadvantages, considering agency capacities and psychological factors. Following these research interests, this study aimed to evaluate Italian university students’ perceptions of socioeconomic barriers to achieving professional goals and the effects of these perceived barriers on some indicators of adaptation. The study included 401 Italian university students (M = 24.18, SD = 3.33). The results demonstrated that economic constraints can be a psychological barrier to career advancement by negatively influencing the perception of being able to actively choose one’s professional future and by diminishing the psychological resources required to adapt to complex environments. In turn, this can indirectly affect the perceived gap between a person’s career goals and their progress toward achieving them, thereby impeding their adaptation processes. The implications of the research are discussed.

1. Introduction

Despite international progress in reducing economic and social disparities, inequalities—defined by income, gender, education level, and ethnicity—continue to be an obstacle to equal opportunities in our communities [1,2]. As the OECD has documented [3], there are different types and levels of inequality in member states and these multidimensional socioeconomic disparities hinder the growth and development of our societies. At the individual level, economic and social inequalities stand in the way of opportunities for those at the lower end of the social spectrum to achieve their full potential and success. This is what the OECD refers to as the “broken social elevator”, which describes the effects of inequality on social mobility [3]: «Family and communities in many countries seem to be trapped on the bottom rungs of the social ladder, particularly since the early 1980s. This means that children born into the bottom of the income distribution have less chance to move up and improve their occupational status and earnings than their parents and previous generations» (p. 3). Similar to other Southern European OECD nations, Italy has relatively low indicators of upward social mobility in education, employment, and income. Indeed, in Italy, individual socioeconomic status is strongly correlated with family socioeconomic status [3]. Therefore, those born into disadvantaged families have limited opportunities to participate in intergenerational mobility processes (a phenomenon which the OECD refers to as the concept of the “sticky floor”). Instead, those at the top of the social hierarchy tend to stay there and take advantage of the educational, employment, and financial opportunities that come with their social status (the “sticky ceiling”).
For socioeconomically disadvantaged children and adolescents, lifelong education is a tool for promoting social mobility and preventing the intergenerational transmission of disadvantage [3,4]. We are witnessing an unprecedented increase in young people’s participation in tertiary education systems, which is driven by the extreme precariousness of contemporary social and economic systems, and the purely human desire to satisfy the need for self-realization by improving one’s living conditions [5]. Notwithstanding, social disparities continue to persist [3]. Access and success in tertiary education are still heavily influenced by students and the family’s socioeconomic status, especially in Italy [3,6]. In addition, the transition from school to work appears challenging for students from low socioeconomic backgrounds [4,6,7].
Considering these issues, the 2030 Agenda for Sustainable Development [1] has established several objectives to be attained by 2030. Specifically, the fourth goal requires that each nation provides all children, adolescents, and adults, especially the most vulnerable, with an inclusive and high-quality education based on their needs. Moreover, Objective 10 aims to ensure, by 2030, the reduction of economic and income disparities and the social, economic, and political inclusion of the poorest population. Meeting these goals is essential for building a sustainable future and a world where everyone can thrive and reach their full potential. To achieve the goals of the 2030 Agenda, a deeper comprehension of the educational experiences of vulnerable students and the factors and mechanisms underlying the aforementioned inequalities is required. In addition to structural variables, which necessitate targeted economic and social policies, special consideration must be given to psychological variables that interact with them and allow for multilevel interventions.
Social scientists have developed theories and models to comprehend and explain the educational and professional experiences of young people, especially in the context of economic and social disadvantages. Beginning with Tinto’s model of student retention [8], subsequent theoretical approaches have increasingly focused on the factors that shape the academic experiences of “non-traditional” students, emphasizing the role of family influences, such as socioeconomic and cultural resources, in their academic adaptation. These models have spawned an abundance of research, and we now have a deeper understanding of the educational experiences and professional development profiles of young people. However, the role of individual agency capacities and psychosocial factors in educational and professional paths has been overlooked.
Specifically, Career Construction Theory (CCT) [9], Social Cognitive Career Theory (SCCT) [10], and Psychology of Working Theory (PWT) [11] have identified some self-authorship capacities to achieve success in academic and work contexts despite socioeconomic background constraints. The focus of these theoretical models has been on examining the dynamic interaction between individual characteristics and the environment, as well as the effects of this interaction on people’s beliefs about themselves and the world. These theoretical frameworks have introduced some psychosocial adjustment indicators (e.g., career adaptability) [12] that are crucial for fostering career development and have sparked interest in the experiences of “non-traditional” college students. Nevertheless, more research involving secondary and university students is necessary, particularly in the Italian context [13,14,15,16,17,18,19].
The purpose of this research was to provide a deeper comprehension of the academic and professional adaptation processes of college students. Specifically, the goal of this study was to identify the psychological processes that influence students’ ability to achieve their professional goals. In the theoretical section, this contribution offers an overview of the most recent professional psychology and counseling models as applied to college populations. Moreover, it emphasizes the role of perceived career barriers in comprehending the outcomes associated with socioeconomic constraints. Indeed, based on the theoretical models presented, we hypothesized that how students perceive socioeconomic disadvantages can affect their psychological health and career advancement. This has all been achieved by integrating the knowledge gained thus far as a result of the fruitful work of SCCT, CCT, and PWT researchers. At an empirical level, the research hypotheses and statistical models tested have thus been guided by these theoretical models and their integrations.

2. Theoretical Background

Social Cognitive Career Theory is one of the most well-known vocational psychology theories. It resulted from integrating multiple theories and research areas. SCCT, developed by Lent and colleagues in the early 1990s, provides a comprehensive theoretical framework for studying and predicting educational and career behavior [10]. Deeply influenced by Bandura’s studies [20,21], SCCT emphasizes the interaction between sociocognitive variables (such as self-efficacy, expectations, and goals) and personal and environmental factors (such as gender, ethnicity, supports, and barriers) in determining the educational and career paths of adolescents and adults. This theory is distinguished by its emphasis on the factors underlying educational and career interests and values, as well as its investigation of the conditions that facilitate or impede the exercise of individual agency. Indeed, most SCCT-based research has focused on populations that, due to various disadvantages, have lower educational and career success chances in specific contexts, e.g., [22,23,24,25,26,27,28].
Recently, SCCT authors have acknowledged that, «Like most other theories of career development, the focus has, metaphorically speaking, been more on the destination than on the journey, that is, on where people end up, occupation wise, rather than on how they get there or how they manage new challenges once they arrive» [29] (p. 557). Consequently, SCCT researchers are increasingly investigating how individuals adapt to their environments to achieve academic and professional success. The focus then shifts to the individual’s agentic capabilities and adaptive career behaviors [29,30]. Specifically, in the most recent SCCT model (the Social Cognitive Model of Career Self-Management) [29,30], the concept of adaptability proposed by the Career Construction Theory [9,12,31] is referenced. In CCT, career adaptability is defined as, «a psychosocial construct that denotes an individual’s resources for coping with current and anticipated tasks, transitions, traumas in their occupational roles» [32] (p. 662), and thus refers to that set of personal resources that helps people deal with professional tasks, present or future, and produce flexible, and thus adaptable, responses in their interactions with the living environment, especially when it is unstable and unpredictable [32]. According to these perspectives, career adaptability is essential for assisting individuals with life planning and career advancement [32,33,34,35]. In recent years, empirical research has focused extensively on the career adaptability of adults and college students. In the working adult population, recent conceptualizations of the CCT—such as the Career Construction Adaptation Model [32,35,36]—have demonstrated that career adaptability is associated with various dimensions of work, e.g., [37,38,39,40,41,42,43], and personal wellbeing, e.g., [40,44,45,46]. For university students, career adaptability is associated with adaptive responses that facilitate the achievement of career goals. Numerous studies have demonstrated that this psychosocial resource can assist young people in coping with developmental tasks, professional transition phases, and a wide variety of unpredictable and uncertain work situations [47]. Since the adaptation of the Career Adapt-Abilities Scale to the Italian context [32,48], national research on this topic has significantly increased. However, there are currently few studies on the career adaptability of Italian adolescents and young adults [46,47,48]. Studies based on the Life Design Paradigm for career counseling, developed by Savickas et al. [35], have shown significant associations between career adaptability and future orientation [43,49,50,51,52,53,54,55], courage [56,57], and life satisfaction [43,49,52,53,55,56,58]. In two studies, the association between career adaptability and relationships with significant others at school and in the family has also been established [58,59].
Despite a growing interest in the predictors, outcomes, and mechanisms associated with career adaptability, the role of contextual variables in the development of this valuable resource has been deeply neglected, especially in the Italian context. According to our knowledge, there are no published studies addressing these issues. This is likely because CCT identifies personality traits, which characterize the individual’s adaptive readiness as the most significant predictor of career adaptability [36]. However, in his Psychology of Working Framework (PWF) [60], Blustein emphasized the need to consider the environment’s constraints and opportunities when examining educational and career trajectories. Beginning with a severe critique of the prevalent professional theories of the time, according to which the individual’s profession is the direct result of a free and conscious choice process, the author argued and empirically demonstrated that most people face various constraints, limitations, and barriers in choosing their occupation, often as a consequence of socioeconomic factors [60]. PWF researchers have recently developed a theoretical model—the Psychology of Working Theory (PWT)—that is integrated into a SCCT model—the Social Cognitive Model of Career Self-Management [29]—to validate the hypotheses proposed in the original PWF framework [11]. Recent versions of the PWT present economic and marginalization constraints as distinct but overlapping factors linked to social affiliations that can directly and indirectly—via career adaptability [32] and work volition [61]—affect a person’s professional life [11]. Work volition, which Duffy and colleagues define as, «the perceived ability to make occupational choices despite constraints» [62] (p. 401), is a central construct within the PWT: «Work volition is a perception, which is hypothesized to develop—in part—from real structural, environmental, and personal barriers and constraints that are prevalent for individuals who experience greater levels of economic constraint and/or marginalization» [11] (p. 135). It has been developed to comprehend the experience of individuals who, due to various factors, including social affiliations, have limited occupational alternatives and are unable to choose a career. Considering the opportunities or barriers these individuals encounter, work volition refers to the perceived ability to make professional decisions in accordance with one’s skills or interests. Although the PWT model is relatively new and there are few studies demonstrating the direction and strength of the hypothesized relationships between the variables considered, previous research has shown that background variables, such as social status, ethnicity, physical disability, sexual orientation, and gender, can represent obstacles and translate into real or perceived barriers to achieving desired professional goals [61]. In adult populations, the PWT draws attention to an individual’s ability to obtain decent work [11]. According to research on this topic, people with greater economic resources and less marginalization experience are more likely to attain decent work because they are more adaptable in the workplace and perceive themselves as capable of deciding and choosing their careers [63,64,65]. PWT constructs are also applied to the study of adolescent and young adult career development. Indeed, after developing and validating an appropriate measure of work volition for the university population [62], researchers have found a positive correlation between the work volition of undergraduates and adaptive outcomes. In particular, students who believe they can choose their future career are generally more satisfied with their personal and academic lives, more committed to achieving their educational and professional goals, tend to perceive their academic and career dimensions more positively, and perceive fewer career barriers, e.g., [62,66,67,68,69]. Although work volition and career adaptability have been conceptualized as mediators of the direct relationship between economic constraints, marginalization experiences, and decent work, the relationship between these psychosocial constructs and other model variables is still unclear. The first two published studies aimed at elucidating the relationships between work volition and career adaptability, and their effects on life satisfaction and academic satisfaction, considered various career adaptability dimensions as predictors of work volition [66,67]. However, the first longitudinal study to test PWT hypotheses revealed that work volition is a mediator between economic constraints and career adaptability [70]. With a few exceptions [71,72,73,74,75], subsequent studies have examined the relationships among the predictors of decent work—or future decent work perceptions when the sample consisted of students [76,77]—considering work volition and career adaptability as distinct or related constructs [72,78,79,80,81,82,83,84], or testing the hypothesis that, within the broader model, work volition is a predictor of career adaptability [63,64,65,74,76,77,85,86,87]. In summary, published works on the topic have supported the PWT model and confirmed a direct and indirect relationship, mediated by work volition, between economic constraints and marginalization experiences and decent work. However, the function of career adaptability as a mediator remains ambiguous [63,64,65,72,78,79,81,84]. Consequently, several authors, especially in recent studies and based on limited empirical evidence, propose reconsidering the role of career adaptability within the PWT model [78,86].
In conclusion, SCCT, CCT, and PWT are the most prevalent theories in the literature for understanding professional career development. However, few studies have applied these models to college student populations to consider the influence of contextual variables on their psychosocial adjustment resources. This research was conducted in part to address these shortcomings. Its purpose was to integrate and implement these theoretical models in the Italian context.

3. The Present Study

Based on the presented theoretical framework, our study aimed to determine the influence of socioeconomic status-related contextual constraints on young students’ psychological and professional adaptation [11]. Limited research has been conducted in this area, particularly in the Italian context.
The hypothesized relationships between the investigated variables are depicted in Figure 1.
As summarized in Table 1, the model was constructed to incorporate the theoretical perspectives presented in the introductory section of this manuscript.
Following the assumptions of the examined theories (PWT, SCCT, and CCT), we developed 12 hypotheses to evaluate the contribution of economic constraints, perceived career barriers, and self-authorship capabilities, as well as their relationship, to the psychological and professional adaptation of the study’s participants (see Table 1).
In our conceptual and statistical model, career goals discrepancy—the perceived gap between desired career goals and progress toward their achievement [88] as regarded as a dependent variable and an indicator of the professional adaptation achieved. Indeed, in line with the considered theoretical models, the degree to which individuals achieved their educational and career goals served as an adaptation response [89] and an outcome of career development [88]. Several studies [66,67,68,69] have shown that students who perceive a sense of volition in career planning report high life and academic satisfaction and are more committed to achieving their goals. Therefore, the concept of career discrepancy appears appropriate for elucidating adaptive outcomes resulting from the interaction of the variables taken under consideration in our study. This construct was derived from significant motivational theories applied to career development—such as Control Theory [90] and Goal-Setting Theory [91]—and it is defined as a predictor of professional success [92].
Consistent with previous research, e.g., [70,76], and the presumptions of PWT [11], subjective social status was used as an indicator of socioeconomic constraint and was regarded as a crucial variable in defining the psychological and professional adaptation of adolescents and young adults. Compared to objective social status indicators, this construct reflects the individual’s perception of their socioeconomic advantage, e.g., [93]. Numerous studies have shown that subjective social status plays an essential role in professional development trajectories, e.g., [94], by influencing a sense of control, agency, and confidence in one’s ability to attain desired educational and professional goals, e.g., [95]. Based on the work of PWT scholars, subjective social status has emerged as a valid predictor of self-authorship capabilities; it is an essential variable in defining adolescent psychological and professional adjustment. However, studies conducted in the context of SCCT have demonstrated that the outcomes associated with background variables, such as economic constraints, are determined by an individual’s subjective interpretations (positive or negative). According to SCCT, the perceived barriers accompanying individual educational and professional development trajectories significantly impact adaptive behavior [96]. Recent conceptualizations of SCCT [97] have identified perceived barriers (and the presence of diversified support sources) as the most significant predictors of individual beliefs (self-efficacy and expectations). Similarly, the PWT asserts that actual obstacles, such as economic constraints or marginalization, can restrict human agency and produce psychological barriers [11]. However, in studies that tested PWT hypotheses, the effects of actual economic constraints on perceived career barriers have never been considered. This is a limitation of the literature, given that numerous studies examining the relationship between work volition and perceived barriers have demonstrated that these constructs are related but distinct and that perceived barriers can serve as predictors of work volition [61,62,98]. Expanding research in this field would enhance our comprehension of the psychological mechanisms that underlie social affiliations and professional advancement.
Consequently, when applying the PWT model to our research, perceived career barriers were introduced as a variable capable of explaining the relationship between economic constraints and the indicators of psychological and professional adjustments. We therefore hypothesized that:
Hypothesis 1.
Economic constraints positively predict perceived career barriers.
Hypothesis 2.
Perceived career barriers negatively predict work volition.
Hypothesis 3.
Perceived career barriers negatively predict career adaptability.
Hypothesis 4.
Perceived career barriers positively predict career goals discrepancy.
We also hypothesized that there would be no direct relationship between economic constraints and self-authorship capabilities (work volition and career adaptability) and between economic constraints and career goals discrepancy. Specifically, we hypothesized that perceived career barriers would entirely explain the relationship between economic constraints and work volition, as well as the association between economic constraints and career adaptability. Likewise, we hypothesized that perceptions of career barriers would entirely explain the relationship between economic constraints and perceived career discrepancy:
Hypothesis 5.
Economic constraints are associated with work volition through perceived career barriers.
Hypothesis 6.
Economic constraints are associated with career adaptability through perceived career barriers.
Hypothesis 7.
Economic constraints are associated with career goals discrepancy through perceived career barriers.
In addition, consistent with the PWT, we hypothesized that work volition and career adaptability are distinct but related constructs and that they predict adaptive responses. Notably, the following hypotheses were developed:
Hypothesis 8.
Work volition and career adaptability are positively associated.
Hypothesis 9.
Work volition negatively predicts career goals discrepancy.
Hypothesis 10.
Career adaptability negatively predicts career goals discrepancy.
Based on the integration of the theoretical models considered in this study and the models tested, beginning with the PWT and SCCT, we also postulated two sequential indirect effects. In particular, we hypothesized that:
Hypothesis 11.
Economic constraints are associated with career goals discrepancy through perceived career barriers and work volition.
Hypothesis 12.
Economic constraints are associated with career goals discrepancy through perceived career barriers and career adaptability.

4. Methods

4.1. Participants and Procedure

Participants were recruited through the crowdsourcing platform Prolific. Only Italian university students participated in the research. The online questionnaire, accessible through a link to Qualtrics, was completely anonymous and participation was entirely voluntary. The data were collected in June 2021. This study included 401 students aged 19 to 39 (M = 24.18, SD = 3.33). Only completed questionnaires (401 out of 421) were regarded as valid. Regarding gender, 51.6% identified as female, 46.1% as male, and 2.2% as non-binary. Regarding university career, 16% of students were in their first year, 23.2% were in their second year, 27.9% were in their third year, 14% were in their fourth year, and 19% were in their fifth year. At the time of the investigation, 89.8% of students reported being financially dependent on their parents.

4.2. Measures

Ph.D. students translated all the scales that had not been previously validated in the Italian context. A bilingual Ph.D. student translated the items from English to Italian and then from Italian to English. Before proceeding with the study, two Ph.D. candidates compared translations to determine if there was consensus on each item. As a result of this procedure, the content of several items was modified to increase their readability.
Subjective social status. As is common practice in PWT investigations, the MacArthur Scale of Subjective Social Status [99] was utilized to assess economic constraints [11,64,70,73,76,77,100]. The instrument asked participants to rate themselves on a fictitious social standing scale. The following statement was then made: “Please consider this scale to depict everyone’s position in our society. On the tenth rung of the ladder, you will find those who hold high social positions, have copious financial resources, have attained high levels of education, and hold the most prestigious professional positions. At the bottom of the ladder (position 1), individuals occupy positions that are inferior to those described previously; they have fewer economic resources, have obtained low levels of education, and hold less prestigious employment positions or no employment. Choose the position on the scale that most accurately reflects your social standing, taking your family of origin into account.” Therefore, participants were asked to rank their family’s perceived social status on a scale spanning from 1 (perceived low social status) to 10 (perceived high social status). Thus, the original instrument was modified. However, since the majority of participants (89.8%) reported being financially dependent on their parents, we deemed this modification appropriate. For the purposes of this investigation, the scale scores were inverted so that high scores indicated economic constraints.
Work Volition and Perceived Career Barriers. Work volition and perceived career barriers were evaluated using the Work Volition Scale—Student Version (WVS-SV), developed by Duffy and colleagues [62]. Participants responded on a Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). Utilizing Jamovi 2.2.5 [101], we conducted an exploratory factor analysis (EFA) and a confirmatory factor analysis to assess the adequacy of the measure’s Italian translation. The total sample was divided into two subsamples. EFA (sample size = 187), utilizing the principal axis factoring method and an oblique rotation (oblimin), revealed the presence of a two-factor structure (Factor 1: volition; Factor 2: constraints; explained variance = 57%) with adequate factorial saturations ranging from 0.46 to 0.85 (KMO = 0.87; Bartlett’s test = p < 0.001) [102,103]. Compared to the original version, seven ambiguous items (with double saturations, saturations below 0.32, or that did not saturate on any factor) were removed [103]. Specifically, the EFA recommended removing two items from the volition factor. This was appropriate because, in the Italian adaptation, the first item was formulated as a reverse item, denoting the absence of work volition. Similarly, the second item referred to the lack of volition due to the perception of a specific barrier—discrimination—and, unlike the other items, did not refer to the perception of general volition. Regarding the second factor (constraints), the EFA suggested eliminating five items (corresponding to items 8, 10, 15, 16, and 17 of the original scale; cfr. original measures: [62]). This made sense after analyzing the items’ contents. Indeed, four items described difficulties in career advancement that did not refer to specific obstacles (example of items: “The only thing that matters in choosing a job is to make ends meet”, “When looking for work, I will take whatever I can get”). Furthermore, regarding the volition factor, one item described discrimination-related barriers, unlike the other items; it was, therefore, excessively specific. Consistent with previous research on the Work Volition Scale, these findings confirmed the factor structure in an adult population [61]. However, confirmatory factor analysis (CFA) (sample size = 217) did not confirm the factor structure that emerged during exploratory analysis, as indicated by inadequate fit indices (CFI = 0.93; TLI = 0.93; RMSEA = 0.10 [95% CI: 0.08, 0.12], SRMR = 0.06) [104,105]. Modification indices suggested removing the saturation of item 13 (constraints factor: “I worry that my life circumstances will prevent me from achieving my long-term career goals”) from the volition factor, which had been set to 0; they also indicated the presence of high residual correlations between items 13 and 14. Examining the content, item 13 appeared to refer to general perceptions of barriers to professional development, influenced by nonspecific life circumstances. The remaining four items, on the other hand, considered economic and familial conditions as a significant impediment to professional development. As a result, we decided to eliminate item 13. Following this decision, the model fit indices appeared adequate (CFI = 0.97; TLI = 0.96; RMSEA = 0.07 (CI 95% = 0.04; 0.09); SRMR = 0.05) [104,105]. The final scale consisted of five items for the dimension of volition (“Once I enter the work world, I will easily find a new job if I want”, “I will be able to choose jobs that I want”, “I will learn how to find my own way in the world of work”, “I feel total control over my future job choices”, and “I will be able to do the kind of work I want, despite external barriers”) and four items for the dimension of constraints (“In order to provide for my family, I will have to take jobs I do not enjoy”, “Due to my financial situation, once I get a job I couldn’t change jobs even if I wanted to”, “I feel that my family situation limits the types of jobs I might pursue”, “Due to my financial situation, I will need to take any job I can find”). The correlations between factors were consistent with PWT theoretical assumptions, according to which, work volition and perceived barriers, despite being negatively correlated, were distinct constructs (r = −0.37). Considering the total sample, both dimensions demonstrated high levels of internal consistency (volition factor: α = 0.88; constraints factor: α = 0.80). As indicated in the presentation section, work volition and perceived career barriers were investigated in relation to one another and in connection with the other examined constructs. The objective was to shed light on the psychological mechanisms underlying self-authorship capabilities and to assess the influence of perceived social status and students’ perceptions of barriers on the individual adaptation indicators.
Career Adaptability. Career adaptability was assessed using the Career Adapt-Abilities Scale—Italian Form [46]. Based on theoretical assumptions [32], this instrument has four dimensions: concern, control, curiosity, and confidence (24 items: 6 items for each dimension). Concern is expressed regarding a person’s ability to plan and attain professional goals. Control refers to a person’s perception that they can act autonomously, consciously, and responsibly to achieve their career objectives. Curiosity is the capacity to investigate one’s surroundings and be receptive to new information, opportunities, and various workplace alternatives. Lastly, confidence reflects the extent to which individuals believe they can effectively manage professional challenges and obstacles [32]. Participants responded to each item on a Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Although a great deal of empirical research has demonstrated the existence of these four dimensions, which are inextricably linked, they are frequently considered in aggregate [89]. The scale’s internal consistency was excellent (α = 0.93).
Career Goal–Performance Discrepancy. We used the Career Goal Discrepancy Scale [88], which measures the perceived gap between the career goals set by an individual (and thus their future selves) and the progress made toward achieving them (the current self). The scale consists of 12 items that measure 4 discrepancy domains: achievement (discrepancy between results achieved and results to be achieved in order to meet one’s career goals), effort (perceived discrepancy between the effort made and the effort required to achieve the career goal), standard–related discrepancies (perception that the career goal is too high), and ability (perception of not having the ability to achieve the desired goal). Since the purpose of our study was to examine the influence of subjective social status on university students’ perceptions of their ability to achieve their career goals, we decided to use only the three-item ability discrepancy subscale (α = 0.85), which seemed most appropriate for measuring these perceptions (the items were: “I thought I had the ability to get the career I want, but now I am not so sure”, “I know the career I want, but don’t think I have what it takes to reach it”, and “I am not sure I am capable of meeting the requirements for the career I really want”). This decision is consistent with previous research [11,95], which demonstrated that socioeconomic status could influence career development by affecting individuals’ confidence in their skills and competencies. Participants responded using a Likert-type format (from 1 = strongly disagree to 5 = strongly agree), with higher scores indicating greater discrepancy.

5. Data Analysis and Results

Jamovi (version 2.2.5) [101] was used to compute descriptive analyses and correlations. We ran the structural equation model (SEM) using R Studio [106]. All variables showed acceptable levels of skewness and kurtosis [103], indicating the presence of a normal distribution. All prerequisite assumptions for conducting a structural equation model were met [107]. Due to our interest in the presence of economic constraints, we utilized reverse scores for subjective social status.
Primarily, we evaluated the presence of gender differences in the examined variables. The one-way analyses of variance (ANOVAs) revealed that there were no statistically significant differences between economic constraints (F(2398) = 1.129, p = 0.32), perceived career barriers (F(2398) = 0.025, p = 0.98), work volition (F(2398) = 1.980, p = 0.14), career adaptability (F(2398) = 0.264, p = 0.77), and career goals discrepancy (F(2398) = 1.164, p = 0.31) based on gender.
Correlation analyses (Table 2) revealed that economic constraints were positively correlated with perceived career barriers (r = 0.35, p < 0.001) and career goals discrepancy (r = 0.35, p = 0.003), and negatively correlated with work volition (r = −0.14, p = 0.004). This indicated that as economic constraints increased, so did the perceived barriers to achieving desired career goals, and the perceived gap between set career goals and actual progress made. In addition, consistent with studies conducted by PWT researchers, the results suggested that students with low socioeconomic status perceived they had less volition in their career decision-making processes. There were no statistically significant associations between economic constraints and career adaptability (r = 0.07, p = −0.17). These findings validated the ambiguous role of career adaptability in the PWT model; this variable did not appear to be associated with social status indicators. Concerning perceived career barriers, they showed negative correlations with work volition (r = −0.42, p < 0.001) and career adaptability (r = −0.28, p < 0.001), as well as positive correlations with career goals discrepancy (r = 0.38, p < 0.001). This indicated that students’ perceptions of their ability to choose their professional future, and whether they possessed the skills to regulate themselves and their behavior, decreased when students perceived they had economic barriers to career development. Simultaneously, the perceived disparity between career goals and actual progress appeared to grow as perceived barriers increased. In addition, career adaptability was positively associated with work volition (r = 0.54, p < 0.001) and negatively associated with career goals discrepancy (r = −0.43, p < 0.001). Finally, work volition was negatively associated with career goals discrepancy (r = −0.42, p < 0.001). Therefore, career adaptability and work volition appeared to be closely associated psychosocial constructs. Moreover, as perceptions of one’s ability to regulate oneself and one’s professional behavior increased, the perceived gap between prospective career goals and actual progress decreased.
We used structural equation models (SEM) with maximum likelihood estimation to test our hypotheses (see Table 1). In SEM, both the measurement model and structural model are considered. The items were regarded as observed variables. The constructs (economic constraints, perceived career barriers, work volition, career adaptability, career goals discrepancy) were then calculated as latent variables based on the responses to the items. For latent variables with more than five observed items, we created parcels [107,108]. Consequently, four item parcels for career adaptability were developed based on its four subdimensions (concern, control, curiosity, confidence). The remaining factors did not require parceling. To assess the degree of fit between the hypothesized model and the empirical data, the following indices were employed: incremental or comparative indices (Tucker–Lewis Index or TLI, Comparative Fit Index or CFI), approximation indices (Root Mean Square Error of Approximation or RMSEA), and sample indexes of fit (Standardized Root Mean Residual or SRMR). TLI > 0.90, CFI > 0.95, RMSEA ≤ 0.06, and SRMR ≤ 0.08 were considered indicators of model adequacy [105]. To assess the statistical significance of the indirect effects, we employed 1000 bootstrap samples, and 95% confidence intervals were constructed.

5.1. The Measurement Model

Prior to evaluating the structural model, the measurement model’s fit was assessed. This step was useful for determining if the observed variables accurately represented the latent constructs. Specifically, one item was utilized for subjective social status, four items were utilized for perceived career barriers, five items were utilized for work volition, four item parcels were utilized for career adaptability, and three items were utilized for career goals discrepancy. According to the results, the model’s fit indices (χ2 (98) = 176.631, p < 0.001; CFI = 0.98; TLI = 0.97; RMSEA = 0.05, 90% CI [0.03, 0.05], p-value RMSEA = 0.79; SRMR = 0.04) were satisfactory. All factor loadings were significant, with values greater than 0.40 and between 0.59 and 0.91. Since the independent and dependent variables were collected from the same source at a single time, we considered the possibility of common method bias (CMB). To detect CMB, we followed the post-hoc statistical procedures of Williams and McGonagle (2016) [109] and applied the Comprehensive Analysis Strategy for investigating common method variance with self-report measures using latent variables. First, we conducted a preliminary measurement model analysis to evaluate the psychometric properties of the latent variables and the observed indicators. The results revealed that factor correlations were adequate and no greater than 0.80 [110]. The reliability indices of each indicator, the composite reliability indices, and the Average Extracted Variance were also adequate and, respectively, greater than or equal to 0.30, 0.60, and 0.50 [111]. We then tested and compared a single latent factor measurement model with a four-factor measurement model. The single latent factor measurement model showed inadequate fit indices (χ2 (104) = 1413.28, p < 0.001; CFI = 0.61; TLI = 0.56; RMSEA = 0.18, 90% CI [0.17, 0.18], p-value RMSEA = 0.00; SRMR = 0.12) and the Chi-squared difference test indicated retaining the model with four factors (Δ χ2 (6) = 1236.7, p < 0.001). Additionally, following the indications of Williams and McGonagle [109], we used the unmeasured latent method construct (ULMC) approach to evaluate if the common method variance affected the observed relationship between the measurement model’s indicators (see Table 3). First of all, we created a Baseline Model by adding a latent method variable to the measurement model, i.e., an orthogonal first-order factor measured with all the observed indicators. The Baseline Model was used to test the effects of common method variance and create nested models to be compared using the Chi-square difference test. In the Baseline Model, we utilized all the observed indicators, without using item parcels, to further control for the effect of method bias on the relationships between the observed indicators. In the first phase of analysis, all method factor loadings (MFLs) were set to 0 in the Baseline Model. This model was then compared to a nested “unrestricted” model (MethodU Model; see [109] for the model labels), in which MFLs were freely estimated. The Chi-square difference test revealed that the Baseline Model showed a worse fit than the MethodU Model (Δ χ2 (36) = 144.48, p < 0.001), indicating the presence of method bias. As a result, we compared the MethodU Model with a nested “intermediate” model (MethodI Model), in which the MFLs were fixed to be equal for observed indicators of the same substantive factor but were allowed to vary between substantive factors. Using the Chi-square difference test, we compared models to test the hypothesis that MFLs within the substantial latent variables were equivalent. The results suggested rejecting the MethodI Model (Δ χ2 (29) = 99.155, p < 0.001). Consequently, there were unequal method effects within the substantial latent variables. The presence of a bias in the relationships between the substantive latent variables was then assessed by comparing the MethodU Model to the nested MethodR Model, which included “restrictions” on the substantive factor correlations. Specifically, in the MethodR Model, the correlations between the substantive factor were set to the values arising in the MethodU Model, where the latent method variable was present, and MFLs were freely estimated. This procedure enabled us to determine whether the latent method variable affected the relationship between the substantive factors. The Chi-square difference test between model fits did not reveal statistically significant differences (Δ χ2 (21) = 4.9016, p = 0.999), indicating that the common method variance did not influence the relationships between the substantive factors.
We then quantified the common method variance in substantive indicators and latent variables [109,112]. The results revealed that the latent method variable accounted for 15% of the variance in the substantive indicators. Consequently, we decomposed the total reliability of each substantive indicator and calculated the substantive and method reliability for all substantive latent variables. The results indicated that all substantive latent variables had adequate substantive reliability (ranging from 0.80 to 0.94). Conversely, method reliabilities were lower (ranging from 0.08 to 0.29). In light of these findings, we concluded that did not need to include method variable in tested model, as the common method variance, despite being present, did not influence the correlations among the substantive factors.

5.2. The Structural Model

The structural model was used to test the proposed hypotheses. First, we evaluated the model’s fit considering all direct and indirect paths. The model has adequate fit indices: χ2 (110) = 196.839, p < 0.001; CFI = 0.98; TLI = 0.97; RMSEA = 0.04, 90% CI [0.03, 0.05], p-value RMSEA = 0.82; SRMR = 0.04 (see Model 1 in Table 4). In particular, economic constraints significantly and positively predicted only perceived career barriers (β = 0.38, p < 0.001), and perceived career barriers significantly and negatively predicted work volition (β = −0.54, p < 0.001) and career adaptability (β = −0.34, p < 0.001); moreover, perceived career barriers significantly and positively predicted career goals discrepancy (β = 0.21, p < 0.01), work volition significantly and negatively predicted career goals discrepancy (β = −0.23, p < 0.01), and career adaptability significantly and negatively predicted career goals discrepancy (β = −0.29, p < 0.001). Therefore, all the hypotheses regarding the direct effects (see Table 1) were confirmed (Hypothesis 1, Hypothesis 2, Hypothesis 3, Hypothesis 4, Hypothesis 9, Hypothesis 10). Additionally, the significant and positive correlation between work volition and career adaptability (β = 0.52, p < 0.001) also supported Hypothesis 8. Regarding the indirect effects, the findings confirmed the existence of an association between economic constraints and work volition via perceived career barriers (β = −0.21; 95% CI [−0.17, −0.07]; p < 0.001), and also between economic constraints and career adaptability via perceived career barriers (β = −0.13; 95% CI [−0.08, 0.02]; p < 0.001). These results supported our hypotheses (Hypothesis 5 and Hypothesis 6) and demonstrated the importance of perceived socioeconomic barriers, as well as actual socioeconomic barriers, for influencing students’ psychosocial resources and their perceptions of their ability to choose and decide about their career paths. The absence of a direct relationship between economic constraints and work volition (β = 0.05, p = 0.35), career adaptability (β = 0.06, p = 0.37), and career goals discrepancy (β = 0.02, p = 0.49) further suggest the essential role of perceived barriers in the psychosocial and professional adjustment of young people. Considering our last three hypotheses regarding indirect effects, the results confirmed the indirect effect of economic constraints on career goals discrepancy via perceived career barriers (Hypothesis 7: β = 0.08; 95% CI [0.01, 0.09]; p = 0.01). In addition, the two hypothesized sequential indirect effects (Hypotheses 11 and 12) were also confirmed. Specifically, economic constraints had an indirect effect on career goals discrepancy via perceived career barriers and work volition (β = 0.05; 95% CI [0.01, 0.06]; p = 0.03), and similarly, economic constraints had an indirect effect on career goals discrepancy via perceived career barriers and career adaptability (β = 0.04; 95% CI [0.01, 0.05]; p < 0.001). The model accounted for 14.7% of the variance in perceived career barriers, 27.6% of the variance in work volition, 10.6% of the variance in career adaptability, and 36.2% of the variance in career goals discrepancy.
To determine the importance of perceived career barriers in explaining the variables examined, as well as the role of actual socioeconomic barriers, we tested a model nested with the previous one in which the direct path between economic constraints and perceived career barriers was set to 0 (see Model 2 in Table 4). The Chi-square difference between the two models was statistically significant (Δχ2 = 53.98, df = 1, p < 0.001). This result indicated that the constraints imposed on the model made it worse (χ2 (111) = 196.839, p < 0.001; CFI = 0.96; TLI = 0.95; RMSEA = 0.06, 90% CI [0.05, 0.07], p-value RMSEA = 0.14; SRMR = 0.07). As a result, these findings suggested that perceived socioeconomic barriers play a significant role in determining career development.

6. Discussion

In the past decade, interdisciplinary research has demonstrated that socioeconomic disparities have a significant influence on students’ educational and professional development, e.g., [113,114,115,116]. Specifically, several international studies have demonstrated the existence of a “socioeconomic achievement gap” [117], i.e., an educational achievement disparity between students of varying socioeconomic status. This suggests that economically disadvantaged children and adolescents worldwide do not have the same educational opportunities as their more privileged peers, and that school systems are failing to fulfill their role as “great equalizers” [118]. The national literature has only recently begun to address social inequality in higher education [119,120], and the data are alarming. Despite the absence of formal barriers to entry, participation in the Italian university system is marked by significant socioeconomic disparities [121,122,123]. Nevertheless, as highlighted in the introduction to this manuscript, participation in higher education is growing [5]. Considering the current economic crisis, education appears to be the only way to improve one’s living conditions. Consequently, alongside mainstream students (such as 19-year-olds from middle-class socioeconomic and cultural backgrounds), classrooms are welcoming so-called “non-traditional” students [124] who frequently have multiple educational and social needs (such as first-generation students, members of ethnic minority groups, and economically disadvantaged people).
To address the issue of educational and professional career development, numerous researchers in the field of vocational psychology have developed integrated theoretical models. In the introductory section of this paper, we presented the most recent theories addressing the issue of career development for disadvantaged groups: Career Construction Theory [32], Social Cognitive Career Theory [10], and Psychology of Working Theory [11]. To comprehend the processes by which people form interests, make decisions, and achieve educational and career success, these approaches describe the dynamic interaction between individuals and their environment. Focusing on the constraints and resources that characterize the educational/professional paths of people living in economic hardship and marginalization, these theories identify some authorship capabilities that, strongly influenced by structural variables (such as economic constraints), can be developed or strengthened through targeted interventions.
According to PWT, we investigated the impact of economic constraints on career adaptation among a sample of Italian university students. Specifically, we examined the relationship between economic constraints and perceived career barriers in professional development trajectories, as proposed by SCCT. Indeed, in recent models, SCCT researchers have demonstrated that barriers to professional development can impede the achievement of professional goals by diminishing the individual’s self-efficacy and expectations of outcomes [30]. To the best of our knowledge, this is the first study to consider perceived socioeconomic barriers as a variable that influences the relationship between economic constraints and other psychosocial variables, such as work volition and career adaptability. Our results appear to validate the utility of this integration. Indeed, when perceived socioeconomic barriers are not included as a predictor in the proposed and tested model, economic constraints appear to have no association with work volition, career adaptability, or perceived discrepancies in achieving career goals. In contrast, perceived career barriers appear to explain the indirect effect of economic constraints on the various indicators of psychological and professional adaptation considered (work volition, career adaptability, and career goals discrepancy). These findings are consistent with the PWT theoretical model, which posits that socioeconomic status is an objective barrier to young people’s and adults’ educational and professional careers, which can manifest as a psychological obstacle [11]. Nevertheless, most studies involving undergraduate students and based on PWT have measured work volition (using the Work Volition Scale—Student Version [62]) ignoring its multidimensionality. Work volition has thus far been viewed solely in its volitional sense; that is, as the perceived capacity to make career-related decisions despite obstacles. Actual perceived socioeconomic barriers have not been measured and entered as distinct variables in the models tested, and therefore have not been considered as predictors of career development trajectories [63,65,66,68,71,72,73,74,75,76,77,78,79,80,81,82,83,84,87,100]. Nevertheless, numerous studies examining the relationship between work volition and perceived barriers have demonstrated that these constructs are related but distinct, and that perceived barriers can predict work volition [61]. This represents a gap in the academic literature. Indeed, although incorporating the role of perceived socioeconomic barriers into the PWT model reduces its simplicity and parsimony, it can increase the theory’s explanatory power. Moreover, contemplating the role of socioeconomic barriers provides a psychological dimension that, despite being influenced by objective socioeconomic constraints, is modifiable and thus can be enhanced through targeted interventions.
Ultimately, our research has shown that economic constraints can be a psychological barrier to career development, harming not only students’ perceptions that they can actively choose their future profession, but also the psychological resources they require to adapt to complex environments. This, in turn, can have an indirect impact on the perceived gap between a person’s career goals and their progress toward achieving them, impeding adaptation processes, and negatively affecting the individual’s wellbeing. Regarding this, applying the PWT model and integrating it with the CCT and SCCT, we utilized perceptions of one’s ability to attain desired professional goals—career goals discrepancy—as an adaptive response [92]. As stated in earlier sections of this paper, this concept appears appropriate for analyzing career development during the college experience, where students construct their professional identities. Indeed, according to the SCCT, educational and occupational goals are the most powerful predictors of actual behavior [29,30,97]. Recent SCCT models [97] have identified perceived barriers (and supports) to career development as the most significant predictors of individual beliefs, and their indirect effect on educational and career goals is emphasized, e.g., [125,126,127]. Moreover, according to CCT [9,32,35], adaptation responses represent individuals’ behaviors and beliefs to address career development (35, 39). In this context, career goals (future and desired) are professional states that direct a person’s behavior. Consequently, evaluating the disparity between career goals and the progress toward attaining them can be a valuable indicator for identifying potential obstacles that may arise during career planning. In contrast to previous studies that have applied the PWT to college students [67,70,71,74,75,77,87,100], career goals discrepancy was utilized here as an alternative to future perceptions of decent work. Although PWT uses the concept of decent work (work that provides security, adequate rest time, fair remuneration, and health coverage, and corresponds to the individual’s values) to measure outcomes in working adult populations, we believe that focusing on perceptions of being able to obtain a qualitatively rewarding job in the future may be limiting when examining the adaptation processes of university students. Indeed, in addition to the rarity of studies utilizing this construct [76,77], the characteristics of so-called future decent work appear to be subject to the labor market trend and the political–economic structure of various countries, and, therefore, may not be a reliable indicator of adaptive outcomes based on the individual’s characteristics. These assertions, however, are strictly hypothetical. Additional national and international research is necessary to explore the perspectives of college students and young adults regarding their perceptions about the decent work.
In conclusion, in terms of the theoretical implications, our study investigated the psychological and professional outcomes of socioeconomic constraints, focusing more on perceived career barriers. This could aid researchers and practitioners in gaining a deeper understanding of the college experience of all students, especially those from low-income families. This proposal is supported by the theoretical approaches presented. The results of our study indicate that integrating the SCCT, CCT, and PWT models could foster our understanding of the relationship between economic constraints and professional goals. Thus, our study validates the usefulness of employing an integrated theoretical framework when investigating the career development trajectories of college students. Indeed, the SCCT, CCT, and PWT overlap in several thematic areas, particularly regarding the psychological and educational–professional adaptation processes of students from disadvantaged groups.
On a practical level, our research offers guidance on how to assist individuals, especially young college students, with developing their careers. In a context such as the current one, in which employment prospects are becoming less defined and predictable, professional interventions should assist young people in developing the psychosocial resources necessary to successfully navigate career development [35]. In this context, our research emphasizes the importance of considering the relationships between individual psychosocial resources and structural factors, such as socioeconomic status, when studying the career trajectories of young people, giving voice to their perceptions of barriers to career advancement. Such perceptions may be intervention targets; therefore, they should be investigated during guidance and counseling activities.

7. Limitations and Future Directions

Current research is restricted by several general limitations. Firstly, the study employed a cross-sectional design. This precludes any causal inferences regarding the hypothesized relationships between the variables under consideration. In future research, either a longitudinal design or experimental manipulations should be employed. Secondly, quantitative tools were used exclusively to collect the data. Future research should include methods such as interviews or focus groups, which are designed to elicit the unique experiences of young people. In addition, given the significance of socioeconomic inequality in determining career paths, future research should examine case studies in greater detail. This would enable us to expand the assumptions of the theoretical models currently used in the study of career development and better comprehend the experiences of students from low-income families. Thirdly, none of the measures used in this study have been validated in the Italian context, except for the career adaptability scale. It would be advantageous to design studies with the goal of improving the psychometric properties of the variables used in this investigation. Additionally, this would enhance career development research and the application of the presented theoretical models in an Italian context. Fourthly, a singular item was used to quantify economic constraints. Even though PWT researchers frequently employ the MacArthur Scale of Subjective Social Status to assess economic constraints, the psychometric properties of the measures used to test PWT-based models have received growing attention in more recent studies [85]. In addition to subjective social status, future research may benefit from collecting objective socioeconomic status indicators. Furthermore, more attention should be focused on the presence of economic constraints across the lifespan. Fifthly, the sample comprised only Italian students. Even though this contributes to the enrichment of national studies, the results should be replicated in samples of various nationalities, especially considering the proposed integration of the examined theories. Sixthly, in the tested model, only perceived obstacles to career advancement were considered. This is a limitation because research conducted within the context of SCCT demonstrates that both supports and barriers have strong relationships with self-efficacy, expectations and goals [125]. Future research should include these dimensions in the model to improve the theoretical integrations between the SCCT, CCT, and PWT.
Despite these limitations, which compromise the generalizability of the results, our study has some advantages. Firstly, Italian college students were included in the sample. The career development literature in Italy still requires improvement, despite a growing interest among researchers. In addition, as suggested by some authors, e.g., [77], our study has the advantage of not being limited to applying the models to specific populations (such as groups of students in economic and social hardship), but rather it involved a heterogeneous population. This allowed for greater comprehension and application of the theoretical models, as well as allowing for future comparative studies that can define the weight of variables considered and their relationships within groups with distinct characteristics. Finally, we proposed incorporating the most prominent professional psychology and counseling models, which we hope will contribute to the advancement of future research.

8. Conclusions

Although some objective variables (such as socioeconomic status) can directly influence educational and professional paths, the effects of contextual variables are partially determined by the individual’s subjective interpretations (positive or negative), as demonstrated by our research. Therefore, the perceived barriers accompanying the individuals’ educational and professional development trajectories can have a significant impact on the adaptation and wellbeing processes [91]. In addition to enhancing the predictive power of models used to study career development, the awareness of these phenomena enables the identification of psychological factors upon which to implement targeted interventions.
The goals of Agenda 2030 [1] guided this research indirectly. In addition to the instrumental utility that awareness of these issues provides, through which, for instance, we could design interventions that promote adaptability, support, career goal attainment, and consequently, wellbeing, this study reveals the interdependence of certain social realities that often appear to be distinct from one another. Even though the 2030 Agenda separates the goals related to quality education from those related to reducing inequality, the study conducted demonstrates that these are strongly interconnected; therefore, we must always look at the social complexity in which we are immersed in an interconnected way.

Author Contributions

Conceptualization, M.M., S.L., L.P., C.P., A.C. and F.S.; Methodology, M.M., L.P. and C.P.; Formal analysis, M.M., L.P. and C.P.; Investigation, M.M., A.C. and F.S.; Writing—original draft, M.M., L.P. and C.P.; Writing—review & editing, M.M., S.L., L.P., C.P., A.C. and F.S.; Visualization, M.M., L.P. and C.P.; Supervision, S.L. and G.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

All procedures performed in the studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent Statement

Informed consent was obtained from all the subjects involved in the study.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 15 08874 g001 550
Figure 1. The theoretical model.
Figure 1. The theoretical model.
Sustainability 15 08874 g001
Table
Table 1. Hypotheses, research questions, theoretical and statistical models, and results.
Table 1. Hypotheses, research questions, theoretical and statistical models, and results.
Research QuestionsHypothesesTheoretical ModelStatistical ModelResults
RQ1: Can economic constraints act as a psychological barrier to young people’s adjustment?H1: Economic constraints positively predict perceived career barriers.PWTSEM
(direct association)		Confirmed
RQ2: Can perceived barriers to one’s professional future have an influence on students’ psychosocial resources?H2: Perceived career barriers negatively predict work volition.SCCT, PWTSEM
(direct association)		Confirmed
H3: Perceived career barriers negatively predict career adaptability.SCCT, PWT,
CCT		SEM
(direct association)		Confirmed
RQ3: Can perceived barriers to one’s professional future have an influence on students’ career adaptation?H4: Perceived career barriers positively predict career goals discrepancy.SCCT, PWTSEM
(direct association)		Confirmed
RQ4: What are the psychological mechanisms through which economic constraints can influence students’ psychosocial resources?H5: Economic constraints are associated with work volition through perceived career barriers.SCCT, PWTSEM
(indirect association)		Confirmed
H6: Economic constraints are associated with career adaptability through perceived career barriers.SCCT, PWT,
CCT		SEM
(indirect association)		Confirmed
RQ5: What are the psychological mechanisms through which economic constraints can influence students’ career adaptation?H7: Economic constraints are associated with career goals discrepancy through perceived career barriers.SCCT, PWTSEM
(indirect association)		Confirmed
RQ6: How are work volition and career adaptability related to one another?H8: Work volition and career adaptability are positively associated.PWTSEM
(bidirectional association)		Confirmed
RQ7: Can students’ psychosocial resources influence their career adaptation?H9: Work volition negatively predicts career goals discrepancy.PWTSEM
(direct association)		Confirmed
H10: Career adaptability negatively predicts perceived career discrepancy.PWT,
CCT		SEM
(direct association)		Confirmed
RQ8: What are the psychological mechanisms through which economic constraints can influence students’ career adaptation? H11: Economic constraints are associated with career goals discrepancy through perceived career barriers and work volition.SCCT, PWTSEM
(indirect association)		Confirmed
H12: Economic constraints are associated with career goals discrepancy through perceived career barriers and career adaptability.SCCT, PWT,
CCT		SEM
(indirect association)		Confirmed
Notes: RQ (Research Question), H (Hypothesis), CCT (Career Construction Theory), PWT (Psychology of Working Theory), SCCT (Social Cognitive Career Theory), SEM (structural equation model).
Table
Table 2. Correlations among the variables with descriptive statistics.
Table 2. Correlations among the variables with descriptive statistics.
MSD12345
1. Economic Constraints5.221.56-
2. Perceived Career Barriers3.401.340.353 **-
3. Work Volition4.051.21−0.145 **−0.422 **-
4. Career Adaptability3.710.65−0.068−0.280 **0.535 **-
5. Career Goals Discrepancy2.861.130.150 **0.376 **−0.422 **−0.428 **-
Note. ** p < 0.001.
Table
Table 3. Unmeasured latent method construct (ULMC) approach to test common method variance (CMV): model fit values.
Table 3. Unmeasured latent method construct (ULMC) approach to test common method variance (CMV): model fit values.
Modelχ2dfCFITLIRMSEA (CI)SRMR
Baseline Model1231.1825730.9250.9170.044 (0.048; 0.048; p-value = 0.994)0.047
MethodU Model1050.3415370.9440.9340.039 (0.035; 0.044; p-value = 1)0.039
MethodlI Model1169.2985660.9320.9240.042 (0.038; 0.046; p-value = 0.999)0.050
MethodR Model1056.3355580.9460.9390.038 (0.033; 0.042; p-value = 1)0.050
Table
Table 4. Regression paths in the structural models.
Table 4. Regression paths in the structural models.
Model 1
PredictorCriterionEstimateSEStand.p-Value
Economic Constraints Perceived Career Barriers0.2430.0370.3830.000
Economic ConstraintsWork Volition0.0290.0290.0520.318
Economic ConstraintsCareer Adaptability0.0220.0220.0590.293
Economic ConstraintsCareer Goals Discrepancy0.0210.0300.0340.488
Perceived Career BarriersWork Volition−0.4760.068−0.5430.000
Perceived Career BarriersCareer Adaptability−0.2010.040−0.3430.000
Perceived Career BarriersCareer Goals Discrepancy0.2080.0640.2130.001
Work VolitionCareer Goals Discrepancy−0.2550.080−0.2300.002
Career AdaptabilityCareer Goals Discrepancy−0.4810.107−0.2900.000
Indirect effects
EC→WV→CGD −0.0070.008−0.0120.337
EC→CA→CGD −0.0110.010−0.0170.304
EC→PCB→CGD 0.0500.0170.0820.002
EC→PCB→WV −0.1160.021−0.2080.000
EC→PCB→CA −0.0490.011−0.1310.000
EC→PCB→WV→CGD 0.0290.0100.0480.004
EC→PCB→CA→CGD 0.0240.0070.0380.001
Model 2
PredictorCriterionEstimateSEStand.p-Value
Economic Constraints Work Volition0.0100.0260.0190.690
Economic ConstraintsCareer Adaptability0.0140.0190.0380.458
Economic ConstraintsCareer Goals Discrepancy0.0290.0270.2930.047
Perceived Career BarriersWork Volition−0.4600.063−0.5280.000
Perceived Career BarriersCareer Adaptability−0.1940.036−0.3330.000
Perceived Career BarriersCareer Goals Discrepancy0.2020.0590.2100.000
Work VolitionCareer Goals Discrepancy−0.2550.080−0.2320.001
Career AdaptabilityCareer Goals Discrepancy−0.4810.107−0.2920.000
Indirect effects
EC→WV→CGD −0.0030.007−0.0040.692
EC→CA→CGD −0.0070.009−0.0110.464
Note. Economic Constraints (EC); Perceived Career Barriers (PCB); Work Volition (WV); Career Adaptability (CA); Career Goals Discrepancy (CGD). In each model, the covariance between work volition and career adaptability was considered.
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Marini, M.; Livi, S.; Prislei, L.; Parisse, C.; Cecalupo, A.; Scarci, F.; Benvenuto, G. The Impact of Perceived Barriers to Career Advancement: A Study with a Sample of Italian University Students. Sustainability 2023, 15, 8874. https://doi.org/10.3390/su15118874

AMA Style

Marini M, Livi S, Prislei L, Parisse C, Cecalupo A, Scarci F, Benvenuto G. The Impact of Perceived Barriers to Career Advancement: A Study with a Sample of Italian University Students. Sustainability. 2023; 15(11):8874. https://doi.org/10.3390/su15118874

Chicago/Turabian Style

Marini, Mara, Stefano Livi, Laura Prislei, Chiara Parisse, Alessandra Cecalupo, Federica Scarci, and Guido Benvenuto. 2023. "The Impact of Perceived Barriers to Career Advancement: A Study with a Sample of Italian University Students" Sustainability 15, no. 11: 8874. https://doi.org/10.3390/su15118874

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