Employer networks for human capital: a scoping review on their drivers, dynamics and outcomes Open Access

Organizations face important challenges, including the ageing of the workforce (Spulber, 2019; Grenčiková et al., 2022), the pace of disruptive technology (e.g. artificial intelligence) (Goos, 2018; Acemoglu et al., 2022) and the need to be highly flexible in a competitive and globalized world (Rubery et al., 2016; Engbersen et al., 2020). The ramifications are that many jobs are becoming redundant and skills are obsolete (Acemoglu et al., 2022). At the same time, it has become more difficult for organizations to attract, develop and retain talent. Consequently, companies are confronted with a disbalance between demand and supply for labour (Goos, 2018; Engbersen et al., 2020; Acemoglu et al., 2022). These issues are common concerns for many organizations and are often difficult or complex to resolve within organizational boundaries (Bouwen and Taillieu, 2004; Martin-Rios, 2012; Curşeu and Schruijer, 2020). Therefore, a new type of interorganizational network has emerged, i.e.“Employer networks”, in which organizations collaborate to deal with these common human capital challenges (Bakker et al., 2018; Courchesne et al., 2024).

Today, various Employer networks exist differing in structure, the industries involved, and their stage of development. Examples of employer networks can be found in the Netherlands, e.g. Facta non Verba (Bakker et al., 2018; Courchesne et al., 2025), as well as in the UK, i.e. Pathways to Work (Lindsay et al., 2008) or local Employer networks in the US, where firms collaborate by sharing resources such as HR expertise or personnel (Bills et al., 2021). The commonality of these employer networks is their aim to manage human capital issues, particularly fostering job mobility and sustainable employability among the organizations' workforces (Bakker et al., 2018; Courchesne et al., 2024). Whereas in many networks, the focus is on the employee, some networks centre on entrepreneurs and higher management, broadening their expertise and knowledge on key issues for leaders (e.g. Europe's big transitions), including energy transition, infrastructure and mobility, education and labour market challenges (Von der Leyen, 2024).

Although networks are a common phenomenon in organizational science, there is no all-encompassing definition in the literature. Provan and Kenis (2007) define the term network narrowly as a group of three or more legally autonomous organizations that work together to achieve their own goals and a collective or common goal. This term was initially coined to refer to networks such as strategic alliances or partnerships that share a common goal, often related to the business, such as outsourcing activities or innovating to introduce a new market proposition (Bergenholtz and Waldstrøm, 2011). The current study will, however, focus on this new emerging type of employer network, primarily aimed at resolving human capital issues, specifically the development and mobility of human capital (Courchesne et al., 2024). For that purpose, these employer networks develop and organize shared HRM practices (e.g. opportunities for personal or career development or job matching) for employees, professionals, entrepreneurs and/or relevant external target groups with distance to the labour market (e.g. unemployed, people with disabilities, school leavers) (Courchesne et al., 2024).

The notion of Employer networks fits with recent developments in specific niches in the literature, including developments in (1) the emerging field of sustainable human resource management (Ehnert et al., 2016; Aust et al., 2019) and (2) the area of Entrepreneurial Ecosystems Human Resources Management (EE-HRM) (Roundy and Burke-Smalley, 2021). What these developments have in common is the emphasis on the creation of cross-organizational or meta-organizational HRM practices to either address collective human capital issues or nurture human capital in support of ecosystem development (Jabbour and Santos, 2008; Kramar, 2014; Roundy and Burke-Smalley, 2021). As empirical research on this type of employer network is still developing (Courchesne et al., 2024), this scoping review takes stock of state-of-the-art developments regarding this phenomenon and aims to contribute to further research.

This study, therefore, inventories the available empirical studies in which employer networks are studied either (1) regarding their outcomes (e.g. sustainable employability or mobility of the workforce), (2) the drivers of their emergence, or (3) the factors or mechanisms that explain how (constructive) collaborative relationships in these networks are shaped. We include qualitative and quantitative peer reviewed scientific journal articles, irrespective of the level of analysis (employee, organizational, network, or broader (e.g. regional) and the outcome considered. However, practice exceeds empirical research on employer networks focussing on human capital issues (Bakker et al., 2018). Moreover, the application of strict inclusion and exclusion criteria regarding topic and sources strengthens the expectation in advance that relatively few articles will be included in the analysis of this scoping review. By synthesizing prior empirical research, this scoping review addresses the following research question: What are the drivers and outcomes of employer networks, and what factors and mechanisms explain the collaboration processes within them? Therefore, this study aims to map all empirical studies on these topics, provide insight into what we know about the drivers and outcomes of these employer networks, and identify common threads regarding the underlying mechanisms derived from the theoretical frameworks adopted. In addition, this scoping review identifies important gaps in the literature for a better understanding of employer networks and underpins implications for future empirical studies.

Several theories in the literature have been used to understand why employers collaborate in networks to address common issues and how these collaborative efforts develop over time. Table 1 overviews relevant theoretical frameworks and their key explanatory constructs. These were identified prior to the systematic search conducted in this scoping review, and were based on the authors' initial literature screening and their knowledge of the field.

The theory of Collaborative Governance (CGR) (Emerson et al., 2011) is commonly applied to understand the achievement of common goals in public or public-private partnerships characterized by a more informal, flat structure in which decision-making is based on consensus-making (Ansell and Gash, 2007; Emerson et al., 2011). The theory has aided understanding of the drivers (e.g. uncertainty, interdependence, leadership, consequential incentives), dynamics (e.g. trust, reciprocity) and outcomes (e.g. organizational learning and adaptation) involved in the collaboration process between organizations.

Multi-Party Collaboration (MPC) can aid in explaining how multiple actors - public and private (e.g. organizations, NGOs, scientists, civilians) - collaborate on resolving complex and wicked issues, i.e. climate change, energy transitions and urban development (Bouwen and Taillieu, 2004; Curşeu and Schruijer, 2018). Although MPC has not been used to understand collaborative efforts linked to human capital issues thus far, the underlying tenet is that solving a complex problem requires cooperation between parties by creating a common goal. Central to the collaborative dynamic are collective learning processes.

Institutional Theory (IT) (DiMaggio and Powell, 1983) posits that organizations are shaped less by efficiency considerations than by pressures for legitimacy arising from their institutional environment. Through (e.g. coercive, mimetic, normative) isomorphic processes, organizations in the same institutional context tend to become increasingly similar over time. As a result, formal structures and practices often reflect socially constructed rules and expectations rather than purely technical or economic rationality. A basic concept in IT is Institutional Fields (IF), which are socially constructed spaces between organizations with a common meaning and understanding, sharing rules and resources (DiMaggio and Powell, 1983). The interaction between three types of mechanisms, cultural-cognitive (e.g. common goals and understanding), regulatory (e.g. resources and rules) and normative (e.g. common beliefs, values and norms), provides for collaborative dynamics, which can lead to the creation and adaptation of new rules and procedures within the field (Courchesne et al., 2024). Whereas the former two theories centre more on the processes of collaboration, this theory enhances the development of a collaborative structure by creating common values, rules, and procedures.

The Public Network Management (PNM) theory (Agranoff and McGuire, 2001) primarily focuses on public organizations participating in networks led by other public organizations. This theory also emphasizes the creation of a common goal, network dynamics, and collaboration, yet its main focus is on “managing” the network, addressing topics such as power and leadership.

Network Dynamics (ND) stresses how organizational networks emerge, evolve, and change over time (Ahuja et al., 2012). Whereas other network theories highlight more strongly the drivers or outcomes of network participation, this theory focuses on the dynamics of network collaboration, which are regarded essential to evolving as a network or changing its structure or network architecture.

In his Collective Action Theory (CAT), Olson (1965) highlights that rational parties do not spontaneously cooperate for the collective good. The free-rider effect refers to the situation in which parties want to benefit from cooperation without making any effort. Group size is relevant, as it is more difficult for a party to hide as a free rider in a small group. This phenomenon can be conceived as the counterpart to the mechanism of reciprocity central in some of the aforementioned theories.

Putnam et al. (1993) argue in their Social Capital Theory (SCT) that strong social networks, mutual trust, norms of reciprocity, and successful cooperation between individuals and larger entities, such as groups, are the key to a stable, healthy, and well-functioning society and organizational life.

Porter's (1990) Cluster Theory (CT) explains why certain countries or regions are successful in specific industries and how geographical concentrations of companies (e.g. Silicon Valley) stimulate economic growth, innovation, and new businesses. This concentration of companies with variation in knowledge, talent, and competition creates a self-reinforcing innovation system focused on economic growth and higher productivity.

In summary, examining the key constructs in Table 2 linked to interorganizational network theories reveals a few similarities, although there are clear differences between the theoretical approaches. One commonality is that these theories concern process elements. However, the emphasis on which processes are geared to differs: realization of a network structure (ND), a playing field where new rules and procedures exist (IT, IF), or collective learning towards the achievement of a common goal (CGR, MPC, PNM). Also, all approaches account for the influence of a relational component between participants, for example, in terms of collaborative dynamics including aspects like reciprocity, trust, accountability, interdependence, or mutual understanding. Similarly, each theory acknowledges the importance of dynamics in a collaborative process for understanding how a network is developing and, at the same time, how outcomes (benefits) are achieved. Although various theories emphasize the informal nature of networks or the equality between parties (CGR, MPC, IF, CT), others also stress the importance of structure, governance, and leadership (CGR, PNM, CAT, SCT, IT). Some factors, like leadership, appear key, as models portray them as both drivers of network collaborations and collaborative dynamics (CGR).

This review explores how these frameworks could be useful for understanding key subareas.

This scoping review takes stock of the recent developments on this topic and aims to contribute to further research. This study chose a scoping review because it lends itself particularly well in cases where the available literature on a topic has not been comprehensively reviewed or little research has been done (Peters et al., 2015; Munn et al., 2018).

The article search or screening process is illustrated in Figure 1. In this process, the PRISMA 2020 guidelines and checklist were followed (Page et al., 2021), meeting the requirements for transparency, consistency, and reproducibility of the selection process, as well as the completeness of the information found. Strict adherence to these guidelines ensures that the steps taken to search for articles are assessable and, in the end, increases the reliability of the results found. In line with these requirements, the following steps are described in detail.

First, keywords were identified based on our central research question encompassing the phenomenon (employer networks) and relevant presumed proximal (i.e. process-related) (i.e. organizational learning) or more goal-directed or distal outcomes (i.e. (sustainable) employability). A comprehensive list of multiple synonyms was compiled for these two key domains as search terms in relevant scholarly databases ( Appendix 1). The list was completed and finally approved by all four authors.

The second step involved searching for potentially suitable articles by initiating searches in relevant databases using the comprehensive list of search terms. EBSCOhost was used as the gateway to multiple databases. The databases in this scoping review are Academic Search Elite, Business Source Premier, APA PsycInfo, APA PsycArticles and Psychology and Behavioural Sciences Collection. The possible use of Google Scholar as a source was considered but not applied due to limitations in replicability, as indicated by Gusenbauer and Haddaway (2019).

Boolean logic (i.e. AND or OR) was used to express relationships among the different search terms. Keywords (and their synonyms) within the domains were combined with OR, and the domains were then combined with AND. After applying the search strategy, duplicates were automatically removed, resulting in 500 articles. The first author scanned the abstracts of all articles to assess whether each met or could meet the inclusion criteria, resulting in 42 selected articles. This list of 42 articles was presented to the second author for assessment. The inclusion or exclusion criteria were meticulously applied to the list of 42 articles by the first and second authors, and all articles were discussed. This initial selection step yielded 7 eligible articles, based on consensus between the two authors. In addition, references in the relevant selected studies were also checked (i.e. snowball method) to ensure no relevant publications were missed (Cloostermans et al., 2014). This additional selection step was also assessed jointly by both authors, leading to the addition of 7 articles and a final selection of 14 eligible articles. Articles were reviewed until March 2025.

The selection of only 14 articles from this extensive search corresponded to an important extent with the authors' expectations. Practice exceeds empirical research in this area (Bakker et al., 2018). The specific demarcation of employer networks, with a focus on addressing human capital issues, and the strict application of the inclusion and exclusion criteria implied that 14 relevant articles were found.

Based on earlier research on inter-organizational networks (Provan and Kenis, 2007), the novelty of the type of networks under focus and the aim of our study, eight criteria for inclusion were formulated: (1) a network of organizations with less formal or semi-formal structured relationships (i.e. no subcontracting, strategic alliance, joint venture), (2) consists of at least two parties, (3) either across or from the same industry (private and/or public), (4) have a common goal to foster an aspect of human capital of its workforce in general or its members (staffing issues, employability, mobility and learning and development) by organizing shared HRM activities, (5) irrespective of the level (employee, organization, network), (6) produces unique qualitative or quantitative empirical data (reviews, theoretical papers and grey literature are excluded), (7) in peer-reviewed scientific journals. No restriction was placed on the year of publication, (8) only articles written in English were selected.

The following section begins with a description of the selected papers' generic and methodological issues.

General descriptions, methodological and network characteristics of the selected papers are provided in Table 2.

Concerning the year of publication, one article (no. 1) was published before the year 2000, and the remaining fourteen articles were all published in the range from 2008 to 2025. Four studies have been conducted in the Netherlands (nos. 5,6,12,14), four in the United States (nos. 2,3,11,13), and three in the United Kingdom (nos. 1,4,7). The remaining three studies were conducted in Guatemala (no. 8), Italy/Germany (no. 9), and Sweden (study no. 10).

Eight papers (nos. 1,2,7–12) adopted a qualitative research design (e.g. semi-structured interviews, observations, document analysis). Four papers (nos. 3–4,6,13) employed a quantitative research method in the form of a survey, and two papers (nos. 5.14) deployed a mixed-methods approach by combining a structured survey and semi-structured interviews.

Regarding the level of analysis, two papers (nos. 4.13) were conducted at the organizational level. One paper (no. 2) simultaneously studied the organizational and network levels. Two papers (nos. 1.5) simultaneously studied both the network and individual levels. One paper (no. 14) conducted an individual-level analysis. In the remaining papers (nos. 3.6–13), the network-level of analysis was chosen.

The employer networks studied in the different papers range in size from 6 (no. 10) to 324 organizations (no. 3). Eight studies did not specify the number of participating organizations (nos. 1,2,7–9.11–12.14).

Concerning the parties involved, 9 of 15 articles involve collaboration between public and private organizations (nos. 1,3,5–8.12–14). The remaining five studies involve collaboration between private organizations (nos. 2,4,9–11). Three of these studies (nos. 2,4,9) involve cross-sectoral collaborations between organizations. The networks in the two remaining studies centre on organizations representing the automotive (no. 10) and accounting (no. 11) industries.

The employer networks' primary target groups and aims differ across the various studies. In four studies, the networks have a shared focus on the fostering of the employability of target groups with constrained labour participation (e.g. welfare recipients (no. 5) and unemployed (no. 13). Two studies (nos. 12.14) extend these target groups to all working-age people. The main goal of these networks, in addition to human capital development, is to guide this broader target group to (continued) labour participation or support employees in their work transition to a new job by providing job seekers and employees with various shared HRM practices (e.g. training programs, job coaches, matching events (nos. 1,3,5,12–14). One study (no. 8) extends the beneficiaries' development of a local community to a higher level of prosperity by strengthening their learning capacity.

Seven studies are specifically geared toward fostering the development of human capital among network stakeholders (e.g. employers and entrepreneurs). Skills enhancement for these key stakeholders occurs through the orchestration of learning activities, access to external HR expertise, development of joint HR practices, and sharing best practices as well as knowledge and expertise (nos. 2,4,6,7,9–11).

Also, the analytical position, i.e. the empirical perspective taken on interorganizational networks across the studies, differs. Based on our analyses, two broad categories can be distinguished. The first category of papers studied employer networks as determinants of outcomes at the individual, organizational or network level. These studies focus on specific target groups presumed to be the beneficiaries of the networks (e.g. employment of job seekers, youth with disabilities, or the unemployed). The outcomes studied can be regarded as outputs produced by the network (Lindsay et al., 2008; Carter et al., 2009; Van Gestel et al., 2019; Courchesne et al., 2025).

The second category of papers concentrates on the development and growth, either qualitatively (e.g. collective learning) (Leung et al., 2019) or quantitatively (e.g. size) (Schmidt and Kochan, 1977). Central in studies are (1) the collaborative processes between stakeholders and how these foster, for example, learning and value creation (Wu et al., 2014; Leung et al., 2019; Koster, 2021; Pennec and Raufflet, 2016) or collective knowledge sharing between members (Wulf and Butel, 2017; Yström et al., 2019) and (2) the factors or mechanisms enhancing network growth and development (Marchington et al., 2011; Wulf and Butel, 2017; Koster, 2021; Bills et al., 2021; Courchesne et al., 2024).

This classification of studies aligns with Emerson et al.'s (2011) CGR Model, which distinguishes between the “outputs” of collaborative actions (e.g. solving human capital issues) and the factors and processes that steer collaborative actions to produce those “outputs”.

Although the articles show considerable variation in research methods, we seek to identify and provide insight into what is known about the drivers and outcomes of these employer networks, and to identify common threads regarding the underlying mechanisms derived from the theoretical frameworks adopted. In presenting our findings, we follow the sequence: first drivers, then factors or mechanisms, and finally outcomes.

Several studies have empirically identified contextual factors that determine the emergence or initial development of network collaboration as the primary drivers of stakeholders' cooperation. In some studies, the triggers for participating in networks involve improving the organization's market position or performance (Leung et al., 2019; Bills et al., 2021). Drivers identified in these studies include developing competitive advantages and technological innovations, exploring international opportunities, and accessing necessary resources. These are, in turn, considered to be connected to human capital issues, as in the study by Leung et al. (2019), in which the motivation for network cooperation is to improve the entrepreneur's performance by fostering the development of the entrepreneur's human capital from novice to expert. Yet, in other studies, the human capital issue is explicitly at the centre. For instance, in the study by Courchesne et al. (2024), the driver of network collaboration lies in finding solutions to a specific human capital problem: resolving labour shortages in a strained labour market. The tight labour market, which is a shared problem, acts as a pressurizing factor for employers to collaborate on job mobility and sustainable employability (Courchesne et al., 2024).

The following category of studies focuses primarily on the important underlying factors or mechanisms that determine the development of (continuous and/or successful) collaboration in interorganizational networks. Collaboration is generally defined as interactions between network members aimed at a collective goal or benefit. Collaboration, as the central objective, is a precondition for network success (and hence outcomes) (Courchesne et al., 2025), influenced by external contextual factors and internal factors related to the network's structure or the relationships between participants. Courchesne et al. (2024) presented, based on their empirical findings, a classification of these factors or mechanisms into structural and interpersonal factors. Bills et al. (2021) used the term relational factors to refer to interpersonal factors or mechanisms. In what follows, these distinctions are adapted accordingly to structure our findings.

Concerning network collaboration, various structural factors such as network form or structure, standardized (HRM) procedures and rules, contractual agreements, and roles and expectations of key stakeholders, were empirically studied (Marchington et al., 2011; Bills et al., 2021; Courchesne et al., 2024). The studies emphasize first of all, the flat structure of these networks as an important factor for successful collaboration. A flat network structure is a success factor, characterized by a low stakeholder hierarchy with informal mutual rules. Although previous studies identified informal rules as a success factor (Courchesne et al., 2024), Marchington et al. (2011) highlight the importance of formalization. The networks they studied, aiming at creating new goods or services, standardized procedures and rules at the employee-network interface (i.e. HRM), promoted the integration of participants. Second, roles and expectations (Courchesne et al., 2024) refer to the specific roles of participants in a network, such as the network coordinator or the steering committee, who are responsible for managing and leading an employer network. A steering committee, of which the network coordinator is often part, is complemented by (HR) representatives of participating organizations. Such a steering committee sets both the network goals and a future agenda for collaboration.

Another structural factor studied is some form of “contractual” agreement (Bills et al., 2021), including core licence agreements and detailed guidelines for network members (i.e. organizations). Standardization enhances cooperation and integration, contributing to the continuity or growth of the network, and strengthening its legitimacy and longevity (Leung et al., 2019).

Some studies also focused on interpersonal factors related to the “values” underlying the collaboration. Successful network collaboration requires transparency, respect, trust and reciprocity (Bills et al., 2021; Courchesne et al., 2024). These studies demonstrate that interpersonal factors enhance interaction among network members and, therefore, contribute to effective collaboration. Interpersonal factors also contributed to the integration of new ideas (Pennec and Raufflet, 2016) and to the sharing of knowledge (Wulf and Butel, 2017; Yström et al., 2019). The mechanism of trust promotes not only collaboration across member firms but also concerns the well-being of participants (Bills et al., 2021). In the study of Courchesne et al. (2025), workers' trust in the network strengthened the impact of participation in network activities on workers' sustainable employability. Koster's (2021) study examines the relationship between collaborative community mechanisms, including trust, and organizational learning practices. This study demonstrates that trust enhances stakeholders' use and implementation of organizational learning practices. The willingness of network participants to engage with each other, creating reciprocity, in achieving their ambitions is essential (Wulf and Butel, 2017; Ystöm et al., 2019; Bills et al., 2021; Courchesne et al., 2024, 2025).

Although not all studies are explicit on this issue, there is evidence for (reciprocal) relations between structural and interpersonal factors. The study by Courchesne et al. (2024) emphasizes that the network structure is designed to facilitate interpersonal factors. A flat structure, combined with few (informal) rules, strengthens collaboration based on trust and openness. Also, Bills et al. (2021) distinguished between transactional (i.e. network structure) and relational (trust, reciprocity) factors. The network structure (i.e. rules, procedures, governance, information systems) provides a starting point for encouraging collaboration between members, as a foundation for inter-personal factors (e.g. trust and reciprocity). Also, networks composed of small groups of participants foster greater trust and openness in sharing valuable knowledge (Wulf and Butel, 2017). Hence, structural and interpersonal factors or mechanisms are regarded as complementary and mutually reinforcing (Bills et al., 2021; Courchesne et al., 2024).

A few studies also explicitly address these connections between structural and interpersonal factors within the tension between competition and cooperation (the coopetitive paradox) in the collaboration dynamic (Bills et al., 2021), which particularly applies when employers operate in the same sector as their competitors while participating in intersectoral partnerships (Leung et al., 2019; Bills et al., 2021). Some studies suggest avoiding undesirable competitive effects by excluding competitors from the same region in the network (Leung et al., 2019); others illustrate how structural and interpersonal factors can prevent or resolve tensions and conflicts among network members. Bills et al. (2021) demonstrate that structural factors (i.e. governance and information systems), especially during initial interactions between members, are important in preventing conflicts and tensions. Moreover, interpersonal factors (i.e. trust, transparency, reciprocity) facilitate interrelationships and cooperation (Leung et al., 2019; Bills et al., 2021).

Considering studies on specific target groups (e.g. unemployed, welfare recipients, youth with disabilities, employees), the outcomes studied include subjective outcomes (e.g. stakeholders' experiences of network cooperation, sustainable employability) as well as more objective outcomes (e.g. employment impact) for these groups. Also, the key factors supposedly determining these outcomes vary strongly. Several conclusions can be drawn. First, stakeholders (e.g. employers, employees, target groups) generally view network approaches positively. Employers perceive a network approach as contributing to the purpose of the network (Carter et al., 2009), target groups as providing significant employment impact (e.g. job opportunities) (Lindsay et al., 2008; Van Gestel et al., 2019), and employees as fulfilling their personal needs (Courchesne et al., 2025). Second, these studies report favourable employment impacts, including increases in job matches, trial placements, and sustainable employability (Lindsay et al., 2008; Carter et al., 2009; Courchesne et al., 2025). Third, several factors were found to impact the occurrence of outcomes. When employers become dissatisfied with the services provided by a public partner organization, for example, the Public Employment Service (PES) (Lindsay et al., 2008; Van Gestel et al., 2019), they change from active partners to passive clients during follow-up activities. In addition, the study of Carter et al. (2009) emphasizes another factor: involvement. This study demonstrates that employers reduced their participation in activities when they were insufficiently involved in the organization of activities, in this case, specifically those organized by the schools (i.e. common partner activities, promoting program information on websites, and guest speakers), aiming to employ young people with disabilities.

Besides these two factors, the specific role of the public organization in the network is also an important factor in network success (Lindsay et al., 2008; Van Gestel et al., 2019). Lindsay et al. (2008) examined the participation of a large national provider, Public Employment Services (PES), in two pilot programs, both designed as a network collaboration, Pathways to Work (PTW) and Working Neighbourhoods (WN). In the PTW program, the role of the PES is dominant, leading, and strategic; in the WN program, the role of the PES is less dominant, leaving more room for collaboration between the PES and specialist providers (e.g. training bureau). The advantage of a PES as a strategic national provider is that it provides capacity, expertise and highly skilled professionals. However, collaboration between the PES on more equal terms with specialist providers leads to greater employment impacts (Lindsay et al., 2008). In addition to knowledge and expertise, a strong, supportive entity (e.g. lead organization (s) or network administrative organization) is crucial to a network's effectiveness (Courchesne et al., 2024).

Although drivers, factors or mechanisms, and outcomes are described separately in this section, they are closely interlinked. Drivers for organizations to participate in employer networks, such as labour shortages (Leung et al., 2019; Bills et al., 2021), are translated into network goals and achieved as outcomes through the networks' collaboration. Structural and interpersonal factors and mechanisms primarily influence how and to what extent network collaboration outcomes are achieved.

This scoping review focuses on employer networks, specifically networks in which different stakeholders work together to address human capital issues concerning complex and shared issues in the areas (e.g. attracting and retaining skilled personnel, development of human capital) that organizations find difficult to solve alone. Research on this phenomenon is emerging. This scoping review aims to identify what is currently known about the drivers and outcomes of such employer networks, as well as the factors or mechanisms that influence the collaboration process within them. Although there is diversity in scope and approach, several conclusions can be drawn concerning the drivers, outcomes, mechanisms, or factors underlying the collaboration processes of these employer networks. We have bundled these into five central topics for discussion. These topics are the basis for recommendations for future research.

This review reveals various reasons for organizations to participate in a network, referred to as drivers or motivators. In general, drivers arise from the broader system context (i.e. strained labour market, economic competition), as also anticipated by the theories of CGR (Emerson et al., 2011) and IT (DiMaggio and Powell, 1983). Courchesne et al. (2024) defined drivers for employer networks as contextual factors that enable organizations to collaborate within a network. Overlooking our selection of studies, drivers are diverse, varying in their immediate coupling to human capital issues, such as strengthening market position or performance, developing innovations, and solving competitive challenges or labour shortages (Leung et al., 2019; Bills et al., 2021; Courchesne et al., 2024). However, a business incentive can be clearly identified across all drivers. Several theories offer perspectives on the sharing of such incentives in a network context. Emerson et al. (2011) indicate that when a business incentive aligns with a common interest, it is the primary driver of organizational network collaboration. A common goal (e.g. fostering the employability of employees or target groups) aligns the members of a network collaboration. Also, in line with IT (DiMaggio and Powell, 1983), the pressures generated by human capital challenges may trigger processes of institutional isomorphism, driving organizations to join inter-organizational networks. In this setting, new rules and practices are developed. In MPC theory, establishing a common goal is a prerequisite for cooperation on complex wicked problems. In line with these theoretical perspectives, this review also identified that the nurturing of a common goal - regarded in the literature as an interpersonal factor - contributes to both network collaboration and the achievement of network outcomes (Marchington et al., 2011; Koster, 2021; Courchesne et al., 2024). Hence, this assumes that the driver (initial incentive) for organizations to participate in a network collaboration warrants translation into a shared understanding of a common goal, which needs to be established and reinforced at the relational level between organizations. However, the studies do not explicitly investigate how the initial driver for network collaboration remains paramount throughout the subsequent process. Over time, the collaboration process will benefit significantly from ongoing (re)alignment between the network's ambitions and its members' individual goals. Therefore, further research is warranted to understand better the relationship between the initial drivers of network participants and how these are reshaped and enacted in the ongoing process of network collaboration. A longitudinal qualitative research method (e.g. a case study with semi-structured interviews or focus groups over time) with active network participants may provide this.

To our surprise, leadership is often implicitly addressed in the reviewed studies. However, a few articles focus on single aspects of leadership related to network collaboration. Bills et al. (2021) describe leadership as “governing the network” and explicitly refer to preventing or managing conflicts between network members. The issue of managing conflicts is linked to CT (Porter, 1990), specifically to the imbalance between collaboration and competition (coopetition), and is inherent to CAT (Olson, 1965) when there is asymmetric self-interest between actors.

Beyond mitigating conflicts, Courchesne et al. (2024) associated leadership with the pioneering role during network formation or during the phase of sustaining the network by facilitating interaction. In their study, this role is carried out by a network coordinator or a steering committee, which is often (but not exclusively) composed of members of the participating organizations. This governance mode aligns with Provan and Kenis (2007), who distinguish between governance by internal network participants (i.e. the lead organization or a group of member organizations) and governance by an external network administrative organization in the person of a network facilitator.

This dual function of leadership (i.e. pioneering and sustaining) aligns with the theory of CGR (Emerson et al., 2011), distinguishing the role of leadership twofold: as a driver to participate in an employer network (i.e. pioneering), and a key factor which enhances the “capacity for joint action” during collaboration. Clearly, leadership is a critical factor, and as suggested by models of EE-HRM (Roundy et al., 2021), it will be important for any ecosystem that the human capital of such critical stakeholders in the ecosystem (i.e. entrepreneurs, linking pins) is continuously developed by enacting HRM practices (i.e. talent acquisition, learning and development, performance management).

In this sense, leadership is conceptualized as the role of a network member as being “supportive”, ensuring adequate resources and having the power to manage the network (Agranoff and McGuire, 2001; Emerson et al., 2011). However, several studies in this review have pointed to the risks of how leadership (and power) are enacted in the context of employer networks. Lindsay et al. (2008) and Van Gestel et al. (2019) suggest that when the leading role of a PES as a strategic government organization becomes overly dominant, it creates an imbalance in autonomy, leading network partners (employers) to become dissatisfied with the network and transform into passive recipients. As the theory of PNM emphasizes, regarding power and leadership in managing the network (Agranoff and McGuire, 2001), a more equal distribution of power among network members is important. Given the flat structure and large informal ties between parties, the networks central in our study, power differences are likely to be best kept within narrow boundaries.

Although the studies do not present a unified view of leadership's role in network collaboration, leadership affects both network formation and collaboration among its members. Therefore, a suggestion for further research is to examine effective leadership roles and styles tied to key persons in the network (e.g. as network coordinator) as well as how leadership (and power) is distributed and shaped between parties (e.g. steering committee, employers, PES) in employer networks, also over time. The role of leadership (i.e. initiator, sponsor, mediator, supportive) aligns with CGR, which is identified as a crucial element depending on the context or situation of the collaboration (Emerson et al., 2011). Also, ND (Ahuja et al., 2012) may provide a useful categorization of phases in network development in which particular modes of leadership may be deemed more or less important. Further research may want to adopt a contingency perspective in their empirical efforts. A qualitative research method (e.g. case study with semi-structured interviews or focus groups) with the network's organizational representatives, such as entrepreneurs, HR managers or network coordinators is a relevant approach). Finally, future research may also focus on the replenishment and development of the human capital of the parties involved in the network's governance and leadership. EE-HRM may provide a useful lens for that purpose.

Courchesne et al. (2025) studied network outcomes tied to the network's goals on an individual level, including sustainable employability and human capital development. In several other studies, job mobility or the sustainable employability of target groups (e.g. unemployed, welfare recipients) at the network level is investigated (Lindsay et al., 2008; Carter et al., 2009; Van Gestel et al., 2019). Although these levels are relevant, no studies thus far have explicitly focused on the organization level, e.g. the sustainable employability of the entire organizational workforce as an outcome. Yet, this is important because the longevity and success of networks also depend on reconciling network ambition with the goals and pursuits of member organizations (Vangen and Huxham, 2012; Arnold and Kolleck, 2025).

In addition, whereas research thusfar identified to some extent the implications of collaborative processes at the network level, theoretical frameworks such as CGR, MPC, ND and IT also foresee implications on collaborative processes and outcomes at an organizational level, such as organizational learning and adaptability (Bouwen and Taillieu, 2004; Emerson et al., 2011; Ahuja et al., 2012; DiMaggio and Powell, 1983). Research on the sustainable employability of an organization's workforce may focus on the organization's capacity to learn and adapt. Benefits in human capital (skills, knowledge, connections) gained through networked collaboration can be transferred to the organization. An organization's learning and adaptive capacity determines the extent to which this knowledge can be incorporated (Gibb et al., 2017; Fredrich et al., 2019), thereby affecting the sustainable employability of its workforce. Research on this level is warranted, as network members not only embrace collective ambitions for the greater good but also focus on their individual ambitions and will translate what they absorbed from the network into their organization. Therefore, the impact of members' network participation on organizational outcomes, such as organizational learning and adaptive capacity, can be studied, ideally in longitudinal designs. Both qualitative and quantitative methods can be relevant, depending on the specific research question that is chosen to address this topic.

Studies in this review reveal the influence of structural elements (i.e. network form, network size, rules, procedures) on network collaboration and success. Several studies emphasize the informal nature of (employer) networks, characterized by a flat structure, minimal hierarchy, and informal rules (Koster, 2021; Courchesne et al., 2024). As an employer network develops, it may adopt a foundation or association form, which automatically entails greater agreement, which parallels the theory of Institutional Fields (DiMaggio and Powell, 1983), where collaborative dynamics lead to the creation and adaptation of new rules and procedures within a new playing field. A collaborative structure is primarily based on the development of common values, rules and procedures.

In contrast, Marchington et al. (2011) and Bills et al. (2021) emphasize the importance of formal structures (i.e. standard procedures and systems, contractual agreements). This difference may be explained by differences in network subtypes or in the contexts in which networks operate. Bills et al. (2021) described networks primarily consisting of competing firms in the same industry (i.e. accountancy). The networks in Marchington's study are public-private and cross-sector (i.e. healthcare, IT, event sector), and may warrant integration through formalization. However, despite possible differences in network type or context, views vary regarding the degree of structuring and formalization. Even though employer networks may differ in type (i.e. public-private, cross-sector) or the context in which they operate, it is unclear exactly how and which structural factors (i.e. form, rules, procedures) contribute to successful collaboration. Also, it is unexplored whether and how structural factors are interrelated and to what degree employer networks benefit from structuring. Despite the informal nature of employer networks, a degree of structuring will be necessary for their functioning. Therefore, it is essential to investigate the influence of structural factors on network collaboration and network success, whether their impacts differ, and whether this depends on boundary conditions. Again, longitudinal designs are most helpful for determining the (longer-term) impact of network structure on network collaboration.

This review (e.g. as indicated by Courchesne et al., 2024) pointed in line with SCT, CGR, MPC, PNM, the relevance of interpersonal mechanisms including trust, respect, transparency, reciprocity, and common goal (Emerson et al., 2011; Bouwen and Taillieu, 2004; Agranoff and McGuire, 2001). Network structure is the starting point for encouraging trust, transparency and reciprocity (Bills et al., 2021). Wulf and Butel (2017) indicate that a smaller network size (i.e. the number of participants) positively affects the level of trust and openness within the network. This insight aligns with CAT (Olson, 1965), which suggests that rational parties do not simply cooperate spontaneously toward a collective goal due to the free-rider effect. Group size is important because smaller groups mitigate this free-rider effect. This phenomenon can be conceived as the counterpart of the mechanism of reciprocity.

Yet, Courchesne et al. (2024) emphasized that network structure is designed to facilitate interpersonal factors and that maintaining informal rules and a flat structure benefits from trust and openness. Hence, structural and interpersonal factors or mechanisms are reciprocal and mutually reinforcing (Bills et al., 2021; Courchesne et al., 2024), which aligns with the theory of CGR, where mechanisms or factors, referred to as collaborative dynamics, work interactively together, fostering collaborative actions focused on a shared purpose or common goal (Emerson et al., 2011). However, it remains unclear how structural and interpersonal factors mutually reinforce or interact or whether a specific combination of factors may significantly impact network collaboration and network outcomes. Therefore, to gain better insight, further research is needed on how the (combination of) structural and interpersonal factors mutually reinforce each other, on what this implies for network collaboration and achievement of outcomes. A diverse combination of qualitative and quantitative research methods can be helpful, applied sequentially or simultaneously, to provide a better understanding of what (combination of) factors improve network collaboration and performance.

The selection of articles for this scoping review followed a careful process. Despite all due care, some restrictions may affect the outcome of the selection process. A comprehensive list of keywords guided the pre-selection of articles. Although all authors reviewed the list of keywords, alternative keywords are conceivable, such as specific forms geared to the labour market, such as Active Labour Market Programs (ALMPs). Recently, there has been considerable attention in the broader literature to this form of collaborative governance and employer engagement in interventions aimed at sustainable employability for vulnerable groups (Valizade et al., 2022; Ingold et al., 2023). Yet, we did not search for those specifically, as ALMPs in general do not meet the description of employer networks, as it often misses the collaborative informal relationships between parties (Van Gestel et al., 2019), and the co-organizing of shared HRM activities. The studies on such policies that met these criteria were included.

We relied on five databases for our search because the scientific domain is represented in them. The first and second authors assessed the rough list of potentially suitable articles using 8 inclusion criteria. As the number of articles was limited, the authors chose to approach selection from an open dialogue perspective rather than compute interrater reliabilities. Only English-written articles were chosen, implying that articles written in other languages were not included. As employer networks are more common in some regions and are an emerging phenomenon, it is possible that papers in national scientific journals were missed. Although transparency in coding and reviewing the literature was highly prioritized, some subjectivity in interpretations cannot be ruled out.

In the contemporary labour market, employer networks increasingly collaborate to address human capital issues. This scoping review takes stock of this emerging phenomenon. Earlier empirical research on this topic is relatively scarce and diverse. This review synthesized prior research on three key areas of interest: the drivers, dynamics, and outcomes of employer networks. Specifically, this review identifies points of connection across areas, connects these to theories in the field and provides five concrete and meaningful recommendations for further research.