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Background:
Systematic Review

The Role of Social Capital in Employability Models: A Systematic Review and Suggestions for Future Research

by
Matejka Letnar
1,* and
Klemen Širok
1,2
1
Faculty of Management, University of Primorska, 6000 Koper, Slovenia
2
Faculty of Health Sciences, University of Primorska, 6310 Izola, Slovenia
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(5), 1782; https://doi.org/10.3390/su17051782
Submission received: 22 January 2025 / Revised: 15 February 2025 / Accepted: 18 February 2025 / Published: 20 February 2025
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Abstract

:
This article provides a systematic review of the role of social capital in employability models. Although social capital is recognized as a key resource in employment and society, its role in academic research on employability is frequently neglected. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this systematic review reveals an underrepresentation of social capital within employability models, as empirical studies do not attribute the same significance to it as observed in everyday life. The analysis found social capital was identified as a determinant in only 16 out of 47 empirical employability models. In less than half of these models, social capital is included as an independent variable, while, in the remaining models, it is incorporated within another explanatory factor. Notably, only in four models are all three dimensions of social capital (structural, cognitive, relational) included. This raises questions about the validity of existing employability models, emphasizes the necessity of social capital inclusion, and calls for future empirical research. Fostering social capital in employability is pivotal for the economic and social sustainability of aging societies, as it mitigates labor shortages, ensures fiscal stability, supports innovation, and enhances social sustainability through inclusivity and intergenerational equity.

Graphical Abstract

1. Introduction

Access to quality employment continues to present challenges for specific segments of the population, making it crucial for social inclusion, reducing inequalities, and the EU’s sustainable development [1,2]. Both the professional and popular literature in the field of recruitment and human resource management, as well as the prevailing public notion, consider social ties and personal contacts as important, if not the key factor, in obtaining employment. Social capital is the advantage derived from an individual’s position in the social structure, which, due to connectivity through relationships and networks of different strengths, provides both opportunities and limitations. Social capital as a source of information about the routes to employers is like invisible “capital”, accessible only with contact through mutual interactions [3]. Specific career self-management behaviors, such as building and using contacts, play a critical role in the effective use of social capital, ultimately enhancing employability outcomes [4]. Additionally, because expanding one’s social capital can create career networks that foster personal development and provide career support, social capital is likely to increase an individual’s capacity to create, identify, and realize career opportunities throughout an entire career [5,6]. With constant long-term investment, social capital becomes an asset providing, in particular, expanded access to information relevant to finding and obtaining employment, and thus also to a more successful move to employment [7]. Realizing the increasingly important role of social capital, public employment services often promote and provide networking as part of employment services in their career centers [2,8] to facilitate the transition into employment. For a more efficient transition of youth into the labor market, higher education institutions offer students either specialized courses [9] or networking opportunities to “open doors for career opportunities” [10,11,12]. Employment websites and job portals [13] portray networking as an effective method of job searching, accompanied by practical information about the acquisition of networking skills. Utilizing social contact channels reduces employers’ costs in finding suitable employees and increases productivity, as employees hired through these channels also tend to stay longer in their respective companies [14].
We can conclude that social capital is increasingly important in both theory and practice, particularly in the context of job and talent acquisition. At the individual level, the integration of sustainable approaches into employment policies is crucial for ensuring long-term employment stability and advancement, supporting sustainable employment and career development [15,16]. At the macro level, enhancing the employability of an aging workforce fosters economic sustainability by mitigating labor shortages, ensuring fiscal stability, and supporting innovation, while advancing social sustainability through inclusivity, intergenerational equity, and the promotion of well-being [17,18].
In employability models, social capital, contrary to expectations, has only limited significance, although it seems to play an important role in the lives of those who rely on employment for their well-being. Different employability models incorporate social capital factors to varying degrees, or may not consider them at all. The authors of ref. [19], in their systematic review of the interrelationships among and within the conceptual dimensions of employability, call for attention to the social capital dimension, as its neglect potentially limits the understanding and development of crucial social networks for professional and personal advancement. An initial scoping review found only one systematic review of the conceptualization of employability that highlights the role of social capital, social connections, and relationships in individual employability [20]. However, this study did not systematically assess the inclusion of social capital in employability models nor examine its impact on their validity. To address this gap, our review evaluates how social capital is incorporated across various employability models and explores the potential consequences for model validity, providing a more comprehensive understanding of social capital’s role in employability. While social capital is widely acknowledged as a critical factor in employment transitions and career development, its role in employability models remains inconsistent and, in some cases, overlooked. This review systematically examines its inclusion across employability models to assess its contribution to their validity and predictive power.

1.1. Transition to Employment and Employability

Employability is a multidimensional concept, the definition of which has evolved over time. The general agreement among both policymakers and researchers is that it is important for individuals to be employable, that is, to safeguard their chance to find and maintain a job [21]. This implies that numerous established employability models exist within the theoretical framework, encompassing various predictors/factors, determinants, and outcomes of employability. Employability, at its simplest level, is the (perceived) ability to attain sustainable employment appropriate to one’s qualification level, or understood as the probability of obtaining a job or the “ability to be employed” [22]. Some definitions of employability do not incorporate the concept/notion of employment [23,24,25]. Hence, employability is becoming increasingly understood also as the potential for dynamism, adaptation, and responsiveness in the professional field. In such a dynamic environment, only changes are constant, as “being employable” is not equivalent to “having a job” [26]. The development of individuals’ employability is viewed as a crucial step toward improving access to employment [27]. Thus, in practice, employment is understood as a realized transition from the status of no employment (jobless) to employment [28].

1.2. The Role of Social Capital in Employability

Regardless of which employability perspective one focuses on, it cannot be ignored that social capital plays an important, positive role in employability. In recent decades, the concept of social capital has become theoretically and empirically widely used and ‘popular’ across different scientific disciplines. The sociological approach (refs. [3,29,30]) understands social capital as sources, knowledge, and information that have a reliable meaning within the network of relations that individuals maintain with other people. The economic approach understands social capital as benefits and balances within social networks [31], highlighting the type (character) and the extent of the involvement in various informal networks and formal social organizations [32]. Social capital refers to the strength and quality of one’s social and professional networks [33]. As the individual finds employment through a network of contacts, social capital becomes integrated into the interpersonal dimension/aspect of employability [34]. Sources of social capital are only accessible through social interactions. The exchange of information and its access within relational networks represents the value of social capital, providing advantages in job search [35] and influencing the stability of employment from both economic and broader societal perspectives throughout one’s career [36,37,38]. Concurrently, while seeking information, individuals also spread information and expand their network of contacts within relational networks, facilitating access to various career domains. Thus, in the domain of employability and career progression through professional networks and trust-based relationships, Nahapiet and Ghoshal define social capital, on page 243, as “the sum of the actual and potential resources embedded within, available through, and derived from the network of relationships possessed by an individual or social unit” [39].
Social capital can be classified into different categories: structural, relational, and cognitive [39]. Structural social capital includes the existence of linkages between people, their configuration, including density, connectivity, and hierarchy, and the degree to which they are appropriable by the actor for purposes other than those for which they were created, such as network ties, roles, rules, precedents, and procedures [7]. Relational social capital refers to the characteristics and qualities of personal relationships formed over time, focusing on behavioral connections among individuals rather than just structure. Trust is crucial in these relationships, as it builds the quality of these connections based on reciprocity, reliability, and expectations [39] when individuals face difficulties or stress in the workplace, as it provides support and facilitates problem-solving. Finally, cognitive social capital has been described as the resources providing shared representations, values, attitudes, beliefs, interpretations, and systems of meaning among parties. In these relationships, people receive approval and support from the community, which is especially important during times of job seeking or unemployment [39,40].
Social capital as a potential factor and determinant in employability has quite a few distinctive features. It requires constant investment due to expectations of future “uncertain” returns, it is suitable for various uses, such as information or advice, and can compensate for other shortcomings. It requires constant maintenance and is devalued by “non-use”, although, on the contrary, constant use does not wear it out. It cannot be owned by an individual, as it exists in relationships between people [7].

2. Materials and Methods

2.1. Identification of Studies

The systematic review was based on a literature search in the bibliographic databases Sage Journals, Emerald, JSTOR, Science Direct, Taylor & Francis, Scopus, Web of Science, and Wiley, the search was completed in August 2023. These databases were selected due to their strong coverage of social sciences, labor market research, and employability models. They provide access to high-quality, peer-reviewed empirical studies in employment, human resource management, and labor economics, ensuring methodological rigor and relevance for our research scope. The search strategy used in the databases applied a combination of terms and keywords, including employability AND concept* OR model OR determinant* OR dimension* OR variable* AND job search* OR job seek* OR transition* OR career*.

2.2. Inclusion and Exclusion Criteria

Articles were considered for inclusion based on the following specific criteria. (1) The article discusses the concept of employability, job search, transition to work, and career. Besides employability, we also included career and job search models because gathering information, even while employed, can contribute to future job opportunities and facilitate transitions to new employment. However, among the four different types of employability conceptualizations—supply and demand-side, input- and outcome-based, competence-based, and individual and organizational perspectives—we excluded articles that focused solely on perceived employability, which reflects perceived opportunities during potential job changes rather than actual employability. (2) Inclusion required a model encompassing employability from an individual’s perspective. (3) This article needed to explore the relationship between social capital or its associated determinants as expressed within the employability model. (4) This article had to be written in English, and (5) it is necessary for the employability model to be validated (supported) by empirical research. Conceptual models without empirical research are excluded from the analysis selection but are still discussed.
The quality of studies was assessed based on reported (if reported) validity and reliability data. Since there is no uniformly established and generally accepted standard regarding what constitutes a sufficient level of reliability and validity in measurement, we relied on widely accepted standards in the field [41,42]. To assess instrument reliability, Cronbach’s alpha or the test–retest coefficient (Pearson’s correlation) were used, with minimal requirements of 0.60 or higher for internal consistency and test–retest reliability. Discriminant validity was evaluated based on the instrument’s ability to distinguish between related but different concepts, with adequacy criteria defined as a correlation of 0.50 or less. Convergent validity refers to the extent to which an instrument’s scores are similar to those of another instrument intended to measure the same concept and was considered acceptable at 0.50 or higher. Since content validity refers to the extent to which the instrument comprehensively captures the concept being measured according to its conceptual definition, content validity was deemed ’adequate’ if multiple or all dimensions of social capital were included. Criterion validity requires the availability of external criteria that can be used as a basis for evaluating the instrument’s test scores [43]. Where construct validity was reported through factor analysis and/or structural equation modeling (SEM), the guidelines from ref. [41] were used. Construct validity is increasingly recognized as a unifying concept that includes both content and criterion-related validity. It assesses how well a measurement instrument represents the theoretical construct it aims to measure. While no single metric or model can definitively establish construct validity, it is supported by statistical evidence, such as correlations between the instrument’s scores and those of other related measures, and factor loadings that indicate whether items align with the intended construct. Factor loadings above 0.40 are generally considered adequate, with values above 0.70 being excellent. The explained variance in exploratory factor analysis (EFA) should ideally be at least 50–60%. A good model fit typically shows RMSEA values below 0.06 and CFI values above 0.90. Factor loadings in Confirmatory Factor Analysis (CFA) should ideally be greater than 0.50, with loadings above 0.70 being strong, indicating that the items significantly contribute to the construct. Additionally, the Average Variance Extracted (AVE) should exceed 0.50 to indicate adequate convergent validity, while discriminant validity is supported when the AVE for each construct is greater than the shared variance with other constructs. Concurrent validity, which is closely related to convergent validity, is also used to demonstrate construct validity. Some researchers consider various types of validity, including convergent, criterion, and concurrent validity, under the term construct validity. Since these types of validity are interrelated, if convergent validity is not measured, the reported criterion or concurrent validity is taken into account.

2.3. Selection

The exclusion criteria were applied according to the PRISMA flow diagram [44], as presented in Figure 1 and Supplementary Materials. Based on the abstract, 476 (38.8%) out of 1226 articles were discarded because there were 365 (30%) duplicates, 75 (6.1%) were removed for other reasons (e.g., book chapters), and 36 (2.9%) were not published in English. Of the remaining 750 abstracts, 167 (22.2%) were discarded because they did not describe employability in the employment context. In cases of doubt regarding potential eligibility, a decision was made through consensus between both authors to ensure the accuracy of the extracted data. In total, 583 articles were retrieved for a full review. Of these, 536 (91.9%) were excluded: 42 (7.2%) were conceptual theoretical models, 55 (9.4%) described policy concept models, 102 (17.5%) described programs and evaluations, 29 (5%) were overview models, 146 (25%) were models that did not directly address employability in the employment context, and 162 (27.8%) were empirical studies of employability without a model. Of the remaining 47 employability models, 31 (66%) did not include social capital as a variable or item, while 16 (34%) met our inclusion criteria and were selected for a full review, representing the articles assessed for relevance. The entire process, which followed PRISMA guidelines Ref. [44], is illustrated in Figure 1, showing the systematic selection and exclusion of studies.

2.4. Data Extraction and Analysis

The data extraction process was driven by the research aim. The focus was on whether social capital or any of its dimensions is present in employability models. Next, the empirical findings related to social capital in employability models, its integration, and statistical results were examined. The operationalization of both social capital and employability was also examined. Furthermore, we identified the purpose and name of each employment model and its publication year and summarized the author’s theoretical perspective on employability and interpretation of social capital. Additionally, we compared the type of empirical research and sample size. In cases of doubt, the classification and interpretation of data were discussed until an agreement was reached between the authors.

3. Results

The results are presented in Table 1, which includes information on the author(s) and publication year, types of employability definitions, operationalization of social capital, reliability, model fit, validity, and the identified relationship between social capital and employability.

3.1. Definitions of Employability and Social Capital

The definitions of employability adopted by the authors included in the analyses do not have the same denotation and are semantically different. Theoretical frameworks on employability are not used unambiguously; rather, they often intertwine. From the theoretical underpinnings, we observe that most of the authors’ definitions can simultaneously fall into different categories (supply and demand; input- and outcome-based; competence-based; individual and structural (organizational). The analysis clearly indicates that authors do not use distinct and unambiguous definitions but rather resorted to interweaving different research perspectives and definitions.
None of the authors adopted a unified definition of social capital; instead, they developed their own operational definitions through the interpretations of various theoretical approaches. The authors’ operational definition is often very narrow. Although no single definition explicitly captures all dimensions of social capital, studies indicate a wide range of its impacts, from networking in the corporate environment to supporting and adding value to individuals’ lives.

3.2. Operationalization of Included Dimensions of Social Capital

Social capital as a variable in the models was not clearly conceptualized and operationalized (measured). It is explicitly identified as an independent variable in four models: as social capital in two, social relationships in one, and networking competence in one. Social capital is combined with other constructs in four models: with networking in one, with social support and networking in one, with human capital in one, and as a part of an independent variable of employability in one. Social capital also appears as a subdimension of employability in five models: as corporate sense in three, as part of deployment in one, and as employability capital in one. Additionally, a two-factor model of social capital is used in one study, it is considered a factor of resource-based employability in another, and included in personal and professional development in a third.
The analysis distinctly demonstrates a prevailing inclusion of the structural dimension of social capital which is included in all models (see Table 1). In the model Ref. [53], family employability support is mentioned and highlighted, which, in its content, represents the structural dimension. Therefore, this model is also considered to include the structural dimension, although not explicitly emphasized. In 13 articles (81.2%), there is an incorporation of the structural and cognitive dimensions, whereas, in six (37.5%), the structural and relational dimensions are intertwined. In one model, Ref. [46], only the structural dimension is included, while the other two dimensions are not covered.

3.3. Data Collection and Research Design

The analysis revealed a noticeable diversity in samples. They vary from the lowest, which includes N = 167 business consultants [46], to the largest, which comprises 7881 graduates [47]. The predominant sampling types used were availability and purposive sampling. Research participants encompass diverse groups, most often including students and graduates [47,57,58,59], job seekers, and the unemployed [49,50,54]. Other research participants include individuals involved in the work environment as employees, supervisors, employability research experts, knowledge workers, business consultants, and assistant professors. The study by Ref. [58] incorporates results for the purpose of the employability concept as part of a larger research project. Three studies [48,52,60] included multiple samples of participants. Five studies collected data at different time points across varying time intervals/periods [49,51,53,54,59].

3.4. Social Capital in Employability Models: Empirical Findings

The empirical results regarding the role of social capital suggest varying associations with diverse dimensions of employability and other factors closely related to employability. In both direct and indirect associations, social capital significantly correlates with various aspects of employability. This includes promotions (0.19, p < 0.10), gross income (0.20, p < 0.10), and subjective hierarchical success (0.35, p < 0.01) as reported by Ref. [48].
Ref. [60] found social capital to be directly correlated, among other variables, with job satisfaction (0.336, p < 0.001), interpersonal success (r = 0.291, p < 0.001), financial success (r = −0.166, p < 0.001), and hierarchical success (r = 0.323, p < 0.001). Ref. [47] highlighted social capital’s direct relationship with hierarchical level in current job (ß = 0.07, p < 0.008), vertical match as required education level (ß = 0.08, p < 0.008), and job satisfaction (ß = 0.18, p < 0.008).
Indirectly, Ref. [49] showed that reemployment interventions contributed to the development of employability dimensions, such as human capital (β = 0.08, p < 0.05) and career identity (β = 0.09, p < 0.05), with the exception of social capital. Ref. [45] highlighted that networking mediates the relationship between knowledge and employability, with a significant indirect association of 0.644 (p < 0.021). In terms of regression coefficients and variance, Ref. [45] reported that the overall employability score positively correlated with all four dimensions: human capital and professional development (r = 0.79, p < 0.001), social capital and networking (r = 0.76, p < 0.001), career identity and self-management (r = 0.81, p < 0.001), and environmental monitoring (r = 0.79, p < 0.001).
Ref. [47] found that social capital was related to four job quality indicators: hierarchical level, vertical and horizontal match, and job satisfaction. Employability indicators explained 19% of the variance in hierarchical level, ΔR2 = 0.19, ΔF(71, 5492) = 19.3, p < 0.008, with social capital (β = 0.07, p < 0.008). The variance in vertical match explained was 24%, ΔR2 = 0.24, ΔF(71, 5492) = 25.0, p < 0.008, with social capital (β = 0.08, p < 0.008). For horizontal match, the variance explained was 32%, ΔR2 = 0.32, ΔF(71, 5492) = 37.8, p < 0.008, with social capital (β = 0.04, p < 0.008). In job satisfaction, employability indicators accounted for 23% of the variance, ΔR2 = 0.23, ΔF(71, 5491) = 23.8, p < 0.008, with social capital (β = 0.18, p < 0.008).
Additionally, Ref. [58] emphasized, in a qualitative study, that connections and networks as social capital motivated career exploration, decisions to work abroad, career advancement, and gaining advantages in professional and academic communities.
In longitudinal studies, the role of social capital significantly predicted reemployment and career success. Ref. [49] found that social capital (0.08, p < 0.05) significantly impacted reemployment. Ref. [54] highlighted that networking (0.65) and social support (0.31) contribute to employability, confirmed through a six-month follow-up. Ref. [51] demonstrated that employability at T1 predicts subjective career success at T2 (β = 0.19, p < 0.001).
However, social capital may not always have a significant impact, as indicated by studies showing that other factors also play a critical role in shaping employment opportunities and career success [46,49,57]. These findings underscore the substantial role of social capital and networking in various aspects of career development and job quality.

3.5. Assessment of Empirical Model Robustness

Models with validated and conceptualized employability differ from other studies by offering more structured and theoretically supported approaches to measuring and understanding employability. For example, Ref. [48] developed a multidimensional model of employability, including competencies such as anticipation, adaptability, and corporate sense. Ref. [60] validated a shorter form of this instrument, demonstrating that these competencies are strong predictors of job performance and satisfaction. Ref. [55] conceptually defined “employability capital” and emphasized the role of social capital in various dimensions of employability. Ref. [53] confirmed a comprehensive causal model of employability that includes personal traits and career achievements. These models differ from other studies, such as those by [49,50], which focus on specific aspects of employability, such as interventions for the unemployed or the role of informal social networks, but do not offer a comprehensive theoretical framework. Ref. [50] employs a combination of survey methods but lacks clear confirmation of internal validity and reliability. Ref. [49] demonstrates good internal validity through extensive questionnaires and statistical analyses, though the impact of social capital is less pronounced.
The analysis of the reliability of social capital dimensions reveals varying levels of internal consistency. The scale for the overall concept of “social capital” in studies by Refs. [47,52] exhibited different levels of reliability; the first study reported Cronbach’s α = 0.72 (principal axis factor analysis uncovered a one-dimensional solution with 79% of explained variance, whereby the item factor loadings were greater than 0.75), while the second study achieved Cronbach’s α = 0.81. Regarding the reliability of social capital as a subdimension, “corporate sense” demonstrated good internal consistency in studies by Refs. [46,60], with Cronbach’s α values ranging from 0.72 to 0.83 and 0.80, respectively. In contrast, the dimension “social relations” in the study by Ref. [57] showed lower reliability with Cronbach’s α = 0.69, possibly due to a limited number of items or other factors affecting response consistency. The dimension “networking” discussed by Ref. [59] showed good internal consistency with Cronbach’s α = 0.81. The dimension “family support” in the study by Ref. [53] exhibited very good internal consistency (α = 0.89). Overall, the highest reliability values were observed for “corporate sense” in supervisor ratings (α = 0.93) and “family support” (α = 0.89). Conversely, the lowest reliability values were observed for “social relations” (α = 0.69), suggesting the need for improvements in measuring this dimension or expanding the number of items to increase reliability.
Overall, the reliability analysis of employability scales showed a very high internal consistency and reliable measurement of employability. Ref. [60] achieved high Cronbach’s α values exceeding 0.90 for all subscales in their short-form five-dimensional employability instrument. The 28-item employability scale in the study by Ref. [52] demonstrated a Cronbach’s α of 0.92. Ref. [51] also assessed the reliability of the entire employability scale using McDonald’s omega, which in a three-wave cross-lagged study was 0.94 at T1 and T2 and 0.95 at T3, showing the stability of measurements over time. Ref. [46] demonstrated the content validity with the Rasch measurement model by stating acceptable fit statistics and positive item-measure correlations for content validity, the generalizability of the measure (high-reliability estimates of 0.92 and 0.98 for respondents and items), and external validity. In assessing the unidimensionality of Ref. [48] instrument (using Rasch principal contrasts analysis), 40% of the variance was explained.
Various types of validation measures for employability models have been reported. Predictive and content validation were the most commonly used (four studies each), followed by convergent and discriminant validation (three studies each) and construct, concurrent, and external validation (two studies each). Validations such as face, internal, divergent, criterion, factorial, precision, and initial validation were mentioned only once. Two studies reported six types of validation, while three studies did not provide data on validation [50,54,56].
Among the 11 studies, the model by Ref. [60] demonstrated the highest validity; it developed and validated a 22-item short employability model that showed a good fit in both the test sample (RMSEA = 0.048, CFI = 0.937, TLI = 0.927, SRMR = 0.052) and the re-test sample (RMSEA = 0.049, CFI = 0.920, TLI = 0.906, SRMR = 0.055). Ref. [49] used logistic regression, which showed significant predictive power for re-employment (χ2(14) = 79.27, Δχ2(1) = 7.51). Ref. [52] validated their model with multiple criteria, which demonstrated high validity (Cronbach’s α ≥ 0.81, significant factor loadings). Ref. [56] confirmed the validity of the academic employability model (CFI = 0.969, TLI = 0.954, RMSEA = 0.068). Based on the analysis of the 11 reported fit model data points, the model by Ref. [49] has the lowest validity. While their model demonstrated significant predictive power for reemployment (χ2(14) = 79.27, Δχ2(1) = 7.51), it is not as robust as other models in terms of data fit and the criteria used.

4. Discussion

The results of the systematic review of the role of social capital in employability models lead to several conclusions.
Despite its recognized theoretical significance and relevance in the field of employment and everyday life, this systematic review clearly indicates that social capital, as a crucial factor, has been notably overlooked in employability models. This is evident from the PRISMA selection process, which yielded only 16 empirical employability models that included social capital out of 47 identified employability models. Although we also found 42 conceptual employability models, only 23 of them incorporated social capital. Moreover, a close examination of academic journals confirms the lack of a comprehensive review article that systematically explores the role of social capital in employability models. Among the 16 empirical employability models with incorporated social capital, only four, representing less than half, include social capital as a standalone latent variable, while the others are integrated as part of various model factors or subdimensions, such as personal and professional development, corporate sense, networking, human capital, deployment, and social support. Even when incorporated, social capital is only partially represented in employability models, typically as one or at most two dimensions, despite its inherently three-dimensional nature. Across all models analyzed, the structural dimension, represented by professional and personal networks, is the most frequently emphasized, while the cognitive dimension appears in 15 models as part of support and in six models as the relational dimension derived from social relationships. Only four models encompass all three dimensions.
The chronological review highlights the ongoing ambiguity in precisely operationalizing social capital, though recent studies tend to address this deficiency. Regarding the temporal perspective and how social capital has been conceptualized, there has been a noticeable increase in the quality of recent models from the past few years. They only incorporate networking as a credible pathway to employment opportunities, though it is just one dimension of social capital. Only the latest models, such as those by Refs. [45,52,53,59], incorporate all three dimensions of social capital. It is also evident that, in employability models, there is an increasing trend, particularly in recent studies, of highlighting the importance of networking competence for both career management and continuous employment opportunities through connections made by individuals. The qualitative aspect of social capital, based on theory and encompassing relational and cognitive dimensions, although crucial for leveraging social networks in individual employability and career success, is still too often missing. The heuristic model proposed by Ref. [24] is the most commonly used framework for the operationalization of social capital. This model has served as a foundation for Refs. [47,49,54,56]. However, our analysis indicates that the same heuristic model leads to different empirical results due to variations in samples, units of analysis, research purposes, and model variables.
The reliability and validity measures of the models are generally good. However, our analysis highlights validity issues stemming from flaws in the conceptualization–operationalization process, although increasing the inclusion of social capital in employability models, mostly by incorporating only one dimension of social capital: networking. The inclusion of only one dimension raises concerns about the face and the content validity of these models. This issue may also be related to the initial selection of operational definitions of social capital. We can also conclude that the operationalization of social capital with a smaller number of items coincides with lower quality measurement, specifically, the poorer explanatory power of the models and reduced internal validity.
It is often overlooked that structural networks represent the external manifestation of the number of connections, rather than the quality of relationships within them, as a source that determines or indicates a more successful transition or sustainable employability. Given the complex interconnections among these dimensions, it is unlikely that a single dimension of social capital (such as structural) exists in isolation from other dimensions. This is especially pertinent for older workers, whose advantage lies not just in the breadth of their professional networks but also in the depth of relational quality and cognitive capital accumulated over years. Understanding the factors influencing the employability of older workers and generational differences is essential for organizations to shape effective human resource policies [61]. For older workers, structural networks like LinkedIn may have limited relevance compared to the relational and cognitive dimensions cultivated over decades of professional experience, personal connections, and knowledge, which remain valuable assets in the changing labor market [61], while, for students, it is crucial to focus on improving interpersonal communication, actively participate in student organizations, and develop their skills in various areas [62]. Our review indicates that social capital is often mistakenly addressed with unclear terminology. This might explain the finding that in some models, despite the observed high Cronbach’s α for social capital, in the final model, social capital has not been found to be a significant explanatory variable [57]. As noted by Ref. [49], limitations regarding relationship quality were already observed during the validation of the Perceived Social Competence Social Capital Scale questionnaire [63], used for analyzing social capital in studies, particularly in terms of neglecting the relational dimension of social capital.

The Limitations

This systematic review has certain limitations. It is confined to empirical research articles focusing on employability models that incorporate elements of social capital. In our theoretical framework, we attentively considered the research context within which authors define both employability and social capital. We did not incorporate theoretical models. Additionally, labor market interventions and/or evaluation studies focusing on their effects were excluded, as were studies centered on academic programs and the assessment of intended or anticipated employment. Research on various types of post-retirement employment was also not included. Furthermore, certain conceptualizations of employability were excluded in the initial review phase, such as employability perception, career management, job search, employment skills, and career success. A significant limitation is that we did not compare the strength of models with and without the inclusion of social capital. Our review was restricted to studies that (a) are available in the databases Sage Journals, Emerald, JSTOR, Science Direct, Taylor & Francis, Scopus, WoS and Wiley, (b) have unrestricted access, (c) are published in professional or scientific journals, (d) are written in English, and (e) recognized the operationalization of social capital and at least one of its dimensions as key components within the employability model.
Our selection criteria—particularly the restriction to studies published in English and indexed in specific databases—may have influenced the composition of our sample. Given that academic publishing is often dominated by English-language research, it remains possible that studies published in other languages or focused on different economic contexts could present alternative perspectives on the role of social capital in employability models. To minimize potential selection bias, we validated our search strategy by cross-checking results against a predefined set of key studies to ensure that our queries retrieved the most relevant literature. Additionally, we employed the ascendancy approach, manually reviewing the reference lists of included studies to identify additional relevant sources that may not have been retrieved through database searches alone. Furthermore, this review intentionally excluded grey literature sources in favor of formally published, peer-reviewed empirical research. While this ensures methodological rigor and reliability, we acknowledge that some insights from alternative sources—such as policy reports, working papers, and industry studies—may provide additional perspectives on the role of social capital in employability.

5. Conclusions

By synthesizing existing research, this study provides novel insights into the role of social capital in employability models and sheds light on two key aspects. First, the critical question regarding the relationship between employment and social capital remains unanswered. While there is a growing tendency to include social capital in employability models, it is difficult to definitively state that its inclusion will uniformly improve the explanatory power and validity of these models. Secondly, neglecting social capital represents a potential validity problem in employability research and improper or non-exhaustive operationalization of social capital is also questionable. It is evident that, for the accumulation and effective use of social capital, not only the quantity of established connections is crucial, but, more importantly, their quality.
Based on the review findings, several important conclusions are drawn for future employability research. More exhaustive empirical research on the role of social capital in certain employability contexts and populations is needed. Here, despite the diverse theoretical approaches and the operationalization of social capital in employability models, greater attention should be given to the operationalization of social capital, as we highlight the absence of a definitive “gold standard” for operationalizing or measuring social capital. As individuals adapt to the ever-changing landscape, the interaction of seeking and disseminating information leads them to tap into the growing significance of social networks and connections. Incorporating diverse levels of connections and types of social capital in employability models opens up the opportunity to observe traceability, dynamics, and the influence on (and from) the individual across different employment scenarios, leading to corresponding measures aimed at sustaining more consistent employability. Furthermore, a key area for future research is the need for meta-analyses of employability models that systematically assess the role of social capital in predicting employment outcomes. Such analyses should compare models that integrate social capital with those that do not, evaluating its impact on model validity, labor market transitions, and career sustainability across different populations. By synthesizing findings across diverse studies, meta-analytical approaches can help determine whether social capital enhances predictive accuracy and contributes to more robust employability models.
In light of the aging population and the urgent need to sustain economic growth and public finances, it is crucial to fully harness the multidimensional nature of social capital. This is particularly important for policies designed to enhance labor market participation among older workers. Such efforts align with EU development indicator 3.12 (unemployment rate and long-term unemployment), which underscores the importance of activating unemployed individuals and integrating them into the labor market [64]. To enhance labor market participation among older workers, employers and policymakers can leverage social capital to improve recruitment, workforce development, and long-term employability. The most effective approach is to integrate mentorship incentives, supporting two-way knowledge-sharing programs and corporate mentorship tax benefits, which have already shown success in employer-driven workforce strategies [65]. Public employment services can expand their role by offering career coaching for mid-to-late careers, lifelong learning grants, and job-shadowing programs, reflecting national labor market policies aimed at workforce retention. Similar initiatives are already in place through the Senior Community Service Employment Program (SCSEP) in the United States [66]. Additionally, industry-specific senior talent networks and reverse career fairs should be implemented to provide direct employer connections, ensuring experienced professionals remain visible in the job market. While university career centers have long supported student employability, similar models should be adapted for older job seekers, leveraging structured peer networking and alumni engagement programs [67]
While this systematic review provides a comprehensive synthesis, we acknowledge a key limitation regarding the geographic distribution of analyzed studies. The majority of the reviewed research originates from developed economies. Although studies from Vietnam [57] and those involving participants from multiple countries [55] contribute some geographical diversity, research on employability models in emerging markets remains underrepresented in the current literature. The absence of studies from certain economic contexts highlights a crucial gap in the field, suggesting that future research should broaden the geographic scope by exploring how social capital influences employability in diverse labor market conditions, particularly in low- and middle-income countries. Additionally, expanding future research to include grey literature—such as policy reports, working papers, and industry studies—could provide valuable insights into how social capital influences employability across different economic and institutional contexts.”

Supplementary Materials

The Supplementary Materials have been updated and are available at the following link: https://osf.io/xpr3n (accessed on 2 February 2025) [68].

Author Contributions

Conceptualization, M.L and K.Š.; methodology, M.L and K.Š.; validation, M.L. and K.Š.; formal analysis, M.L and K.Š.; investigation, M.L.; data curation, M.L.; writing—original draft preparation, M.L and K.Š.; writing—review and editing, M.L. and K.Š.; supervision, K.Š.; funding acquisition, K.Š. All authors have read and agreed to the published version of the manuscript.

Funding

The author acknowledges financial support from the Slovenian Research and Innovation Agency (ARIS—Javna agencija za znanstveno raziskovalno in inovacijsko dejavnost RS), SiZDRAV (Sinergija med zdravjem, delom in izobraževanjem <https://cris.cobiss.net/ecris/si/sl/project/21789, accessed on 2 February 2025>—Synergy between Health, Work and Education), Grant number: P5-0454.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Sustainability 17 01782 g001
Figure 1. PRISMA flow diagram of study search and selection.
Figure 1. PRISMA flow diagram of study search and selection.
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Table 1. Comparative analysis of articles across selected fields.
Table 1. Comparative analysis of articles across selected fields.
Author (Publication Year)Type(s) of EMPL DefinitionOperationalization of SC; Dimension of SCReliabilityModel FitValidityIdentified Relationship Between SC and EMPL
M. Fyczyńska, C. Ciecierski (2020) [45]Competence based; IndividualIndependent variable, as networking competence;
(S/C/R)		N. R.+ConstructNetworking competence is a strong predictor of EMPL among knowledge workers with the impact on EMPL (β = 0.85 ***). Mediatory relationships are found between knowledge work and networking competence (β = 0.79 ***) and between networking competence and EMPL (β = 0.81 ***), both of which are positive and significant. Networking competence mediates the relationship between knowledge work and EMPL, with a one standard deviation increase in knowledge work translating to EMPL by a = 0.792 standard deviation, of which a = 0.644 standard deviation is due to indirect impacts of networking competence.
D.E. Froenlich, et al. (2018)
[46]		Competence based; IndividualAs corporate sense, a subdimension of EMPL;
(S/−/−)		alpha+Content
External		Corporate sense items cover the entire spectrum of likelihood of endorsement, and with the average EMPL of respondents (1 logit) above the average item endorsement likelihood (0 logits), these items are generally likely to be endorsed.
V. González-Romá, et al. (2018) [47]Individual; InputSC as independent model variable; (S/C/−)alpha++Construct
Discriminant		Six EMPL indicators showed a statistically significant relationship with employment status, including SC (β = 0.08, Wald (1) = 21.49 *). SC was related to four job quality indicators: hierarchical level in current job (β = 0.07 *), vertical match (required education level vs. attained education level) (β = 0.08 *) and horizontal match (field of study vs. job field) (β = 0.04 *), and job satisfaction (β = 0.18 *). Variance explained by EMPL indicators was 19% for hierarchical level (ΔR2 = 0.19, ΔF = 19.3 *), 24% for vertical match (ΔR2 = 0.24, ΔF = 25.0 *), 32% for horizontal match (ΔR2 = 0.32, ΔF = 37.8 *), and 23% for job satisfaction (ΔR2 = 0.23, ΔF = 23.8 *).
C.M.V. Heijde, B. Van Der Heijden (2006)
[48]		Competence based; IndividualAs corporate sense, a subdimension of EMPL;
(S/C/−)		alpha++Predictive
Convergent Divergent
Discriminant
Criterion
Content		Corporate sense appears to be a significant predictor for the number of promotions in the entire career (β = 0. 19 **), gross income (β = 0.20 **), and subjective hierarchical success (β = 0.35 ***), strongly indicating the positive impact of the selected SC dimensions on both objective and subjective career success.
J. Koen, et al. (2013) [49]Individual; SupplyIndependent variable, SC;
(S/C/−)		alpha++Predictive
External		SC and human capital showed no significant relationship with job search intensity as the variance in job search intensity was primarily explained by the more cognitive–affective (adaptability) and motivational (career identity) dimensions. Finding reemployment depended largely on long-term unemployed people’s EMPL and slightly on their job search activities, as it was observed that SC (β = 1.51 **) and human capital (β = 1.38 **)—but again also career identity (β = 1.29 **)– predicted reemployment success.
C. Lindsay, et al. (2003) [50]Supply; Competence basedSC as a part of deployment as subdimension of EMPL;
(S/−/R)		N. R.N. R.N. R.Personal contacts notified 42% of recent job seekers about vacancy, compared to 33% of the long-term unemployed. For those unemployed less than 6 months, personal or community-based contacts were the primary method for 28% (versus 9% of long-term unemployed). Approximately 8% of job seekers prioritized direct approaches to employers. Word of mouth and personal recommendations were key for selecting candidates in various organizations, despite newspaper advertising.
A. Lo Presti, A. De Rosa (2023)
[51]		Input; Competence basedSC and networking as factors of resource-based EMPL;
(S/C/−)		McDonald’s omega++InitialA three-wave cross-lagged study examined the relationships between resource-based EMPL and work-related well-being. As for direct and reversed causation, EMPL at T1 predicted both objective career success (β = 0.12 *) and subjective career success at T2 (β = 0.19 ***). Additionally, objective career success at T1 positively predicted EMPL at T2 (β = 0.14 *). Regarding indirect effects, EMPL at T1 showed an indirect effect on emotional exhaustion at T3 (β = −0.04 *) through subjective career success at T2.
A. Lo Presti, et al. (2019) [52]Input; SupplySC and networking as subdimensions of EMPL;
(S/C/R)		alpha++Concurrent PredictiveHuman capital and SC were deemed the most crucial factors, followed by personal career aspects and the ability to navigate the labor market. The overall EMPL score positively correlated with all four of its dimensions: human capital and professional development (r = 0.79 ***), SC and networking (r = 0.76 ***), career identity and self-management (r = 0.81 ***), and environmental monitoring (r = 0.79 ***). Additionally, SC and networking positively correlated with career identity and self-management (r = 0.46 ***), and environmental monitoring (r = 0.48 ***). Moreover, subjective career success positively correlated with all four EMPL dimensions, with SC and networking (r = 0.37, p < 0.001).
A. Lo Presti, et al. (2020) [53]Input; Output; Competence based; IndividualSC as a part of independent variable of EMPL;
(S/C/R)		alpha++Not explicitly mentionedEMPL positively correlated, among other factors, with family EMPL support (r = 0.10 *), EMPL culture (r = 0.12 **), subjective (r = 0.38 ***), and objective career success (r = 0.30 ***).
S. McArdle, et al. (2007) [54]Supply; InputSC as social support (observed variable) and networking as latent variable; (S/C/−)N. R.T1: ++
T2: +		N. R.In a longitudinal study, T1 (baseline six-month period), the regression showed that networking (β = 0.65), and social support (β = 0.31) contributed to EMPL. At T2 (six months after the initial survey), the social support (β = 0.31) contributed to re-employment, while the relation of networking was not significant.
E. Peeters, et al. (2019) [55]Supply; Competence basedSC as subdimension of EMPL capital;
(S/C/−)		N. R.+Face validityThe development and validation of the conceptual framework of EMPL capital narrowed it down to four dimensions: job-related attitudes, job-related expertise, career-related EMPL capital, and development-related EMPL capital. A special emphasis is placed on SC, which is crucial in all dimensions for enhancing employment opportunities through professional networks. Distinguishing SC from other EMPL factors is challenging, highlighting the need for more precise measurements in each dimension.
M.C. Saffie-Robertson, J. Fiset (2020)
[56]		Input; Supply; IndividualSC and human capital as subdimensions of EMPL;
(S/−/R)		N. R.N. R.N. R.Online survey with open ended questions found that SC and human capital were deemed to be important components in the successful navigation of the academic job market. Especially, informal networks allowed students access to openings that they otherwise would have missed and to feel more confident when negotiating. For 39% of respondents, SC and human capital are key to succeeding in the academic job market through network development and publications.
P.V.M. Thang, W. Wongsurawat (2016) [57]Input; OutputIndependent variables as social relationship;
(S/C/−)		alpha++Discriminant
Convergent (satisfied)		Social relationships (measured through connections with parents and relatives, relationships with friends, and connections with teachers) showed no significant effects on both EMPL and job search duration.
L. Thi Tran, et al. (2021) [58]Supply; IndividualInside 2 variables: personal and professional development; (S/C/−)alphaN. R.InternalA qualitative study shows that connections and networks as SC motivated career exploration abroad, decisions to work abroad, career advancement and opportunities, and gaining advantage in professional and academic communities.
M. Tomlinson, et al. (2022) [59]InputTwo-factor model of SC;
(S/C/R)		alpha+Concurrent
Face
Content		The significance of improved knowledge of the job market field on Self Perceived EMPL (r = 0.38 ***), including employer contacts and organizational gatekeepers, is notable. The facilitating role of social networks on Self-Perceived EMPL (r = 0.44 ***) stands out, especially in brokering better job market insight, such as opportunity awareness.
B. Van der Heijden, et al. (2018) [60]Competence based; InputAs corporate sense, a subdimension of EMPL
(S/C/−)		alpha++Construct
Factorial
Content
Precision
Predictive
Convergent		The corporate sense is important for EMPL, with its predictive value, among others, correlating well with job satisfaction (r = 0.336 ***), interpersonal success (r = 0.291 ***), financial success (r = −0.166 **), and hierarchical success (r = 0.323 ***). This underscores the importance of investing in networking skills, active participation in diverse working groups, and sharing responsibilities, expertise, and successes—manifesting corporate sense.
Note. SC: Social capital; EMPL: Employability; Dimensions of SC: (S—Structural SC, C—Cognitive SC, R—Relational SC, − not mentioned); CFA: confirmatory factor analysis; SEM: structural equation modeling; alpha: Coefficient Cronbach’s alpha (α); N. R.: Not reported; T1, T2, T3: time point in longitudinal point design; Statistical significance: * p < 0.05, ** p < 0.01, and *** p < 0.001. Fit model: + acceptable; ++ good.
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Letnar, M.; Širok, K. The Role of Social Capital in Employability Models: A Systematic Review and Suggestions for Future Research. Sustainability 2025, 17, 1782. https://doi.org/10.3390/su17051782

AMA Style

Letnar M, Širok K. The Role of Social Capital in Employability Models: A Systematic Review and Suggestions for Future Research. Sustainability. 2025; 17(5):1782. https://doi.org/10.3390/su17051782

Chicago/Turabian Style

Letnar, Matejka, and Klemen Širok. 2025. "The Role of Social Capital in Employability Models: A Systematic Review and Suggestions for Future Research" Sustainability 17, no. 5: 1782. https://doi.org/10.3390/su17051782

APA Style

Letnar, M., & Širok, K. (2025). The Role of Social Capital in Employability Models: A Systematic Review and Suggestions for Future Research. Sustainability, 17(5), 1782. https://doi.org/10.3390/su17051782

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