Deploying to Develop Unit Human Capital Resources

Unit human capital resources (HCRs) represent capacities for action and are positively associated with unit performance when members are deployed based on their ability to perform unit-relevant tasks (Ployhart, Nyberg, Reilly, & Maltarich, 2014). Research (Wolfson & Mathieu, 2018, 2021) and practitioner prescriptions (Avila & Delfino, 2017) have focused on deploying HCRs to maximize current performance outcomes. Such an emphasis risks deemphasizing other vital considerations, such as developing unit HCRs for sustaining unit performance over time (Ployhart, Weekley, & Ramsey, 2009). Indeed, Wolfson and Mathieu (2021: 453) highlight this point in their call for scholars to move beyond performance and consider the “additional costs and benefits associated with HCR deployments.”

The emergence and development of unit HCRs is conceptualized as “the transformation and amplification of individual human capital into a unit-level human capital resource” (Eckardt & Jiang, 2019). Mathieu and Luciano (2019: 172) noted that “(1) emergence is a multilevel phenomenon that can be a product of bottom-up, top-down, and/or level aligned forces; (2) different types of aggregate constructs will develop toward different structures or forms; and (3) emergence occurs over time.” A unit HCR thus originates in the knowledge, skills, abilities, and other characteristics (KSAOs ¹ ) of individuals and, through the operation of process mechanisms driving individuals’ interactions in context, emerges and evolves as a distinct unit-level construct (Ployhart & Moliterno, 2011). This conceptualization suggests that units can develop their HCR by improving individuals’ unit-relevant KSAOs, relationships among members facilitating unit HCR emergence, or both.

This article aims to position unit deployment choices as a locus of unit HCR emergence, explain why they are a powerful tool for unit HCR development, and specify when units should use deployments for unit HCR development. Research on HCR development has tended to focus on how individuals’ talents and group capacities can be enhanced through formal training and development interventions (see Ployhart & Hale, 2014). The $87.6 billion that organizations spent on formal training in 2018 (Training Industry Report, 2018) demonstrates a clear belief in the effect of formal training on HCR development. However, HCR scholars increasingly acknowledge the potential of informal learning as “it accounts for the majority of learning within organizations and may help to develop strategic skill sets, perhaps better than formal instructor-based training” (Noe & Tews, 2012: 102). Indeed, scholars have developed a strong case that individual building blocks of HCRs—job-relevant knowledge and skills—are primarily learned on-the-job rather than in formal training (Noe, Clarke, & Klein, 2014; Noe & Tews, 2012). Thus, it is promising for HCR scholars to expand beyond formal training as the primary antecedent to unit HCR growth and consider other potential HCR development sources such as informal, on-the-job learning.

Research also strongly suggests that relational bonds are critical to developing unit-level HCRs and are likely to emerge from on-the-job interactions (Crocker, 2019; Ray, Nyberg, & Maltarich, in press; Wolfson & Mathieu, 2021). Thus, unit HCRs are not just a product of members’ KSAOs, but are also the extent to which members can coordinate to meet task demands. In their seminal work on the subject, Ployhart and Moliterno (2011: 135) identify emergence enabling states (“how unit members act, think, and feel”) and the complexity of the unit's task environment (“the degree to which the unit's tasks require interdependence and coordination among members”) as unit-level emergence enabling mechanisms (Eckardt & Jiang, 2019). However, we suggest that these mechanisms and their effects on unit HCR emergence emanate from unit deployment choices.

An HCR deployment represents a unit-level configuration at a point in time reflecting the formal or informal allocation of existing unit members to unit-relevant tasks. We first explain how and why deployments represent a locus of HCR development through their transformation and amplification of unit HCR and the development of unit emergence enabling states. We then propose that using deployments for development can generate four distinct types of unit HCR portfolios and articulate how units can cultivate each via deployments. We spend the remainder of the article identifying the conditions under which each type of portfolio is most valuable.

We seek to contribute to the unit HCR literature in three ways. First, we advance unit HCR emergence process theorizing by identifying and defining HCR deployments as a locus point of unit HCR emergence and specify the affected individual and unit-level developmental mechanisms. In so doing, we highlight an overlooked but powerful tool available to units to develop HCRs. Second, we take a portfolio view of unit HCRs (Nyberg, Moliterno, Hale, & Lepak, 2014) to articulate how deployments provide different types of flexibility to the unit, altering the diversity of deployment options available. Finally, we provide practical guidance on when unit HCR portfolios will likely generate the most significant returns for sustained unit performance and how units can use developmental deployments to cultivate each portfolio.

Deployments and Unit HCR Development

Scholars have made recent advancements in extending traditional economic notions of human capital (Becker, 1962) by specifying how human capital can provide firms with valuable resources that contribute to performance parity and competitive advantage (Ployhart et al., 2014). Unit HCR is touted as particularly important as it can generate value beyond the sum of its individual members’ contributions while also being particularly difficult for competitors to imitate (Ployhart & Moliterno, 2011; Ployhart et al., 2014). Unit HCR consists of “individual or unit-level capacities based on individual KSAOs that are accessible for unit-relevant purposes” (Ployhart et al., 2014: 376).

HCR scholars typically define units as organizational collections of employees, including groups such as teams, departments, business units, or organizations (e.g., Nyberg et al., 2014; Ployhart & Moliterno, 2011). Mathieu and Chen (2011: 623) submitted that, rather than relying on formal designations, modern “organizational entities [units] can be identified in terms of their distinguishable roles, patterns of expected behavior and norms, forms of social structure, and higher-order goals to which all lower-level units subscribe.” For our purposes, we focus on units that are typically larger than a single team yet smaller than entire organizations—or what Arrow, McGrath, and Berdahl (2000: 34) described as:

open and complex systems that interact with the smaller systems (i.e., the members) embedded within them and the larger systems (e.g., organizations) within which they are embedded. [Units] have fuzzy boundaries that both distinguish them from and connect them to their members and their embedding contexts.

Thus, following Arrow et al. (2000) and Ployhart and Moliterno (2011), we consider units as small, complex organizational systems.

Units can thus be considered as a portfolio of HCRs reflecting two primary asset types (Nyberg et al., 2014). First and most straightforward, the unit may access individual members’ HCRs, rooted in their individual KSAOs, for unit-relevant tasks (Nyberg et al., 2014; Ployhart & Hale, 2014). Second, units may also access unit-level HCRs that develop through a dynamic emergence process involving members’ task-based interactions and the resultant social capital in the presence of emergence enabling states (Ployhart & Moliterno, 2011; Ray et al., in press).

However, it is important to note two key points when interpreting these advancements. First, HCR scholars are intentional with their use of the term capacities. Conceptualizing HCRs as capacities indicates that HCRs represent potential action (Ployhart et al., 2014), thereby highlighting that the value of HCRs is contingent to some degree on how units utilize their HCRs. This conceptualization is consistent with the broader resource-based view where scholars have evidenced the importance of how firms use their resources in connecting firm resource access to firm performance outcomes (D’Oria, Crook, Ketchen, Jr., Sirmon, & Wright, 2021). This relationship has empirical support at the unit-level too. For instance, Crocker and Eckardt (2014) analyzed data from Major League Baseball teams and found that managerial skill in appropriately deploying players offset the effects of low HCR quality. Beck, Schmidt, and Natali (2019) found that units configuring member deployments according to situation criticality improved distal performance. Finally, Wolfson and Mathieu (2018, 2021) found that units realized more value from HCRs when they aligned members’ HCRs with task demands, with further benefits when members had more accumulated shared team-task experience (STTE).

Second, due to the dynamic nature of HCRs and unit task environments, scholars acknowledge the importance of continuing to develop, modify, and extend unit-level HCRs (e.g., Kim & Ployhart, 2014; Ployhart et al., 2009). The literature on HCR development has primarily focused on formal human resource (HR) practices (e.g., selection systems and formal training programs) as the mechanism for HCR development (Boon, Eckardt, Lepak, & Boselie, 2018). In reviewing the literature, we found that 67% of articles exclusively invoke HR practices as the mechanism for development, versus only 26% that considered alternative means (e.g., knowledge management systems, MOOCs, international assignments). ² Previous research demonstrates the value of HR practices for HCR development. For example, evidence shows that firm selection and training investments have important but distinct effects on preparing their HCRs to aid recovery from a crisis (Kim & Ployhart, 2014). A study of 45 banking units in Ghana demonstrated a positive connection between high-performance work practices and collective human capital enhancement (Aryee, Walumbwa, Seidu, & Otaye, 2016). Finally, in their meta-analysis of 116 studies, Jiang, Lepak, Hu, and Baer (2012) found a strong positive relationship between high-performance work systems and organizations’ human capital. The accumulated evidence presents a clear case for a positive relationship between HR practices and HCR development.

Yet, other evidence suggests an increasing importance of non-formal modes of development in organizations (Noe et al., 2014). Indeed, Beier, Torres, and Gilberto (2017: 179) note that “workers report that less than 10 percent of their personal development comprises formal training and development activity, making unstructured learning at work (e.g., on-the-job training, peer-learning) the most common method for worker continuous learning and development.” Informal on-the-job learning represents possibly the most potent mechanism for HCR development. It is estimated to account for up to 75% of learning within organizations, much of which is tacit knowledge gleaned from experience (Noe et al., 2014; Noe & Tews, 2012). HCR development is also influenced by members’ interactions that enhance their collective capacities, which are vital to effective joint functioning (Ray et al., in press; Wolfson & Mathieu, 2021). For example, more frequent member task-based interaction is essential for the development of shared behavioral capacities, such as implicit coordination (Rico, Sánchez-Manzanares, Gil, & Gibson, 2008), and cognitive capacities, such as transactive memory systems (TMSs) and shared mental models (Mohammed, Ferzandi, & Hamilton, 2010; Ren & Argote, 2011). Finally, member interaction can also influence unit states, such as cohesion, through orchestrating interactions between more agreeable and conscientious members (Van Vianen & De Dreu, 2001). Indeed, the degree to which unit members effectively share and develop new knowledge through interaction amplifies the value of member learning gains that can help units reduce costs, enhance efficiency, bolster innovation, and boost revenue (Noe et al., 2014).

Deployments represent one of the most potent tools in units’ arsenals to encourage and direct these non-formal modes of development. We are not the first to suggest that deployments are essential for development. Hatch and Dyer (2004) found that deployments with greater member learning opportunities were positively related to learning-by-doing performance in semiconductor manufacturing plants. In studying how law firms leverage partner expertise and practice diversification, Kor and Leblebici (2005) found that pairing more new attorneys with an expert partner resulted in the strongest performance outcomes, but selecting new members was more helpful when diversifying into a new service or geographic market. Bell, Brown, and Weiss (2018) offer a theoretical framework linking team composition decisions to building HCR flexibility in the organization by emphasizing member adaptive attributes or developing intraorganizational networks that allow for easier reconfiguration. Based on our review, there is no work to date (1) detailing how deployments generate multilevel development, (2) how these decisions link to changes in the unit HCR portfolio, or (3) specifying when certain forms of development are more valuable. Given the importance of non-formal development in organizations and the impact of deployments on such development, we think a comprehensive, multilevel framework detailing how units can use deployments for strategic development is warranted and necessary.

We present a theoretical framework for leveraging developmental deployments in Figure 1. In the first half of the manuscript, we define unit HCR deployments and articulate three ways in which they fuel development. We first draw on experiential and informal learning theorizing to articulate how deployments generate within member HCR development (Proposition 1). We then leverage the social capital literature to explain how deployments influence the development of social capital resources between members (SCR; Proposition 2). Insights from the literature on teams guide our identification of unit emergent states as the third point of impact for developmental deployments (Proposition 3). We then transition to articulating when and why such development may be valuable. We introduce a typology of four unit HCR portfolios differing in their HCR and SCR developmental needs and specify how units can use deployments to obtain desired development (Propositions 4 and 5). Finally, we outline when such development may be most effective under two unit task conditions: task dynamism and complexity (Propositions 6 to 9).

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Figure 1

Theoretical Framework for Leveraging Developmental Deployments to Build Unit HCR Portfolios

Defining Unit HCR Deployment

We define a HCR deployment as a unit-level configuration describing the formal or informal allocation of existing unit members to unit-relevant tasks at a point in time. As a configural phenomenon, deployments characterize properties of the unit that are products of dynamic unit contexts, inputs, processes, and outcomes (Mathieu & Luciano, 2019). We include existing unit members in our definition to exclude hiring activities from being part of the HCR deployment definition. Hiring represents a more permanent arrangement governed by contractual relationships compared to deployments that are more fluid allocations of some or all existing unit members to tasks. Accordingly, we limit our definition to existing unit members while acknowledging that hiring practices feed unit deployment options. By unit-relevant tasks, we refer to unit members’ activities to achieve unit goals (Arrow et al., 2000). Unit-relevant tasks are more granular than unit job roles, which are an aggregation of the unit-relevant tasks that an individual is hired to complete.

Our distinction that HCR deployments are a unit-level construct can be better understood through a discussion of the individual-level concept of job assignments and how they differ from unit-level deployment. We use the term job assignment to describe the task responsibilities of individual workers. Each unit member has a portfolio of assignments at any given time. These assignments are usually a subset of the possible work tasks that would be considered the typical tasks associated with the job role. For example, a retail worker might have job assignments related to being a cashier and stocking shelves and can also be designated as part of a quality improvement team as another dimension of her job assignments. In contrast, HCR deployments are a unit-level construct that captures the overall configuration of unit members’ assignment portfolios. These configurations are time-bound and change only with significant scope adjustments. For instance, the day-to-day scheduling of task coverage for particular times and places is too granular to be thought of as resulting in new deployments. In other words, deployments do not change with every minor adjustment, but member or task changes to a configuration instantiate a new deployment.

Our definition specifies HCR deployment as a unit-level configuration at a particular time, which helps distinguish the resulting unit property from the process of allocating unit members to tasks. At its core, the process of an HCR deployment decision, which can be both formal and informal, involves recognizing a unit-relevant task demand (e.g., a specific surgery, new client opportunity, an enemy incursion) and deciding which member(s) are responsible for which tasks(s)—and thereby can span from a single member to an entire unit. Factors determining a unit assignment decision in response to task demands can vary widely based on the focal context, unit leaders, and other unit task demands. For example, consider a surgical unit in a hospital. One element of the process could be task criticality which affords a subpar environment for on-the-job learning so that units may emphasize maximally aligning members’ skills with task demands (Luciano, Bartels, D’Innocenzo, Maynard, & Mathieu, 2018; Tannenbaum, Beard, McNall, & Salas, 2009). Further, processes could differ by role. Surgeons could be deployed based on skill fit, anesthesiologists based on patient loads, and nurses based on personality fit with the surgeon or availability. While the primary motive studied to date is maximizing unit task performance, other evidence suggests motives such as development (e.g., job shadowing; Myers, 2018) and preservation (e.g., workload management; Guthier, Dormann, & Voelkle, 2020) may drive deployment processes. Thus, while processes are likely idiosyncratic to units and tasks, every unit exhibits an HCR deployment configuration.

Deployments—A Locus of Unit HCR Emergence

Unit HCRs are developed and sustained through the unit HCR emergence process (Ployhart & Moliterno, 2011). The unit HCR emergence process involves transforming and amplifying individual HCRs into distinct and valuable unit-level HCRs (Eckardt & Jiang, 2019; Ployhart & Moliterno, 2011). Transformation involves “changes to the individual stock of KSAOs that occurs via knowledge transfer and learning from unit-member interactions” (Eckardt & Jiang, 2019: 79). When discussing transformation, we focus on change in an individual's unit-relevant KSAOs. Whereas formal learning programs can serve to transform employees’ KSAOs, Wolfson, Tannenbaum, Mathieu, and Maynard (2018: 16-17) submitted that three types of informal field-based learning behaviors can also yield improvements in individual HCRs: “(1) experimentation/new experiences (e.g., seeking new assignments, doing a task differently); (2) feedback/reflection (e.g., actively seeking feedback and advice; debriefing); and (3) vicarious learning behaviors (e.g., intentionally observing others and talking with them about their work).” For example, a new hire observing an established team member handle a client helps develop product knowledge and client service skills specific to that unit's client base (Myers, 2018).

Amplification refers “to the creation of contextual factors that enhance the performance generated by a particular stock of individual KSAOs” (Eckardt & Jiang, 2019: 79). Such amplification is often conceptualized as HCR complementarities, defined as the “synergistic relationships between two or more HCRs,” in which the resultant combined value is greater than the sum of the individual parts (Ployhart & Cragun, 2017: 135). When discussing amplification, our focus is on the relationships between individuals and salient contextual features that alter the value of their HCR in combination. For example, TMSs represent a collaborative division of labor for learning (i.e., specialization), storing, and leveraging (i.e., coordinating) members’ unit-relevant knowledge (Ren & Argote, 2011). TMSs enhance the value of members’ KSAOs by creating contextual mechanisms to focus and coordinate individuals’ contributions optimally.

Transformation and amplification change the nature of unit HCRs both in terms of members’ HCRs available to the unit (e.g., through member KSAO improvement), the complementarities between members (e.g., implicit coordination; Rico et al., 2008), and the enhancement of contextual factors influencing such development (e.g., psychological safety; Edmondson, 1999). These processes are emergent, in that they both build individuals’ KSAOs and develop mechanisms to combine their contributions for unit purposes over time.

Deployments play a crucial role in determining the nature of member interactions which fuel the transformation and amplification processes underpinning unit HCR emergence (Eckardt & Jiang, 2019). Indeed, bottom-up emergent phenomena, such as unit HCRs, are defined according to four core concepts: (1) Phenomena originate at the individual level (e.g., KSAOs of members) and manifest in different forms at the unit-level (e.g., shared vs. patterned relationships between members); (2) emergence takes time to unfold; (3) they derive from member interactions and the rules influencing such interactions; and (4) contextual features constrain or enable such interactions (Kozlowski, 2019). Deployment choices provide clear direction and boundaries over members’ task-based interactions (Component 3). As these decisions are repeated (Component 2), they influence the transformation of individual KSAOs (Component 1) and between member relationships (Component 1) and the context of such interactions (Component 4). Accordingly, deployments represent a critical influence on unit HCR development.

Such choices are prevalent for various units, such as when consulting partners determine staffing for a recently approved project (Hitt, Bierman, Shimizu, & Kochhar, 2001). Surgeons selecting their team for the next operation are, in effect, providing a mechanism influencing members’ interactions (Luciano et al., 2018). Managers of sporting teams exert similar influence when choosing which players to play at a particular moment in a match (Beck et al., 2019). Managerial choices about who to deploy to resolve production and design issues for semiconductor manufacturers define boundaries for member interaction (Hatch & Dyer, 2004). Retail store managers deciding shift schedules, department assignments, and coordination coverage make similar choices (Ployhart et al., 2009). In short, unit deployment choices occur in various industries and offer a potentially potent means to develop unit HCR.

Transforming Within Member HCR

The first way that deployments influence unit HCR development is the transformation of individuals’ KSAOs (Eckardt & Jiang, 2019). Individual development results in member HCR development if the transformation improves their KSAOs that are both accessible and relevant to unit performance (Ployhart et al., 2014). Although KSAOs have been positioned as relatively stable (Ployhart et al., 2014), the components differ in their malleability. Individual knowledge and skills tend to be tied to a specific context, are malleable, and can change in the short term (Cerasoli et al., 2018; Landy & Conte, 2012; Ployhart, 2021). Abilities (e.g., intelligence, cognitive ability) and other characteristics (e.g., personality, integrity, etc.) are more enduring characteristics that “tend to be fairly stable through adulthood” (Ployhart, 2021). Thus, while these more stable characteristics are not likely developmental targets, they “influence the development of more proximal and malleable KSAOs, such as knowledge and skill” (Ployhart, 2021: 1780). Accordingly, individual HCR development primarily results from changes in individual knowledge and skill levels rather than abilities or other characteristics. Detailing the specific complexities beyond these general distinctions is outside the intended scope of this paper, but we want to acknowledge the nuances involved in the KSAO transformation process. However, for the purposes of this paper, we simply refer to development through individual KSAO transformation as within member HCR development.

Deployments generate within member HCR development by influencing the nature of members’ on-the-job experiences. Kolb (2014: 49), discussing experiential learning theory, defines learning as “the process whereby knowledge is created through the transformation of experience” where an individual's concrete experiences undergird opportunities for reflection, conceptualization, and experimentation. More specific to workplace learning, Tannenbaum et al. (2009) drew upon experiential learning theory in advancing their dynamic model of informal learning. Their framework centers upon an individual's actions and experiences as the grist of effective informal learning, coupled with an intent to learn and opportunities for feedback and reflection. This framework suggests that deployments of members motivated to learn from tasks, coupled with mechanisms for feedback and reflection, are ripe for transforming member HCR via on-the-job experiences. Meta-analytic evidence shows that informal learning is associated with significant individual knowledge and skill gains (Cerasoli et al., 2018).

Myers’s (2018) concept of covicarious learning phenomena provides an example of a typical developmental deployment in an organization. In describing covicarious learning, Myers (2018) outlines a situation in which an individual was transferred into a technology division of a consulting company. Upon arriving in the new unit, the individual shadowed an experienced unit member through recurring task experiences. The assignment aimed to transform the newcomer's unit-relevant knowledge and skills by providing specific on-the-job experiences and interactions with a trusted and experienced team member. Although this deployment’s developmental value partially hinges on newcomer characteristics (e.g., motivation to learn), expert characteristics (e.g., willing and able to provide feedback), and context (e.g., sufficient time for reflection); none of these factors matter absent the choice to deploy the newcomer to this learning opportunity.

Facilitating informal learning via deployments is especially valuable for within member HCR development. For instance, Noe et al. (2014: 254) submitted, “informal learning may be equally important to or even more important than other forms of learning for the development of human capital resources.” Learning through on-the-job experiences reflects organizationally valued skills accrued via task engagement (Wolfson et al., 2018). Ployhart (2021) would describe such learning as highly proximal to the behaviors affecting member performance. Such high-proximity learning is “more contextually embedded and thus more strongly related to performance” (Ployhart, 2021: 1781). The knowledge and skill development arising from these experiences are accessible (as they have been observed) and relevant (high proximity to performance) to the unit. Unit relevance and accessibility are crucial as these definitional elements separate individual KSAOs and human capital from HCRs (Ployhart et al., 2014). The above suggests that one way deployments influence unit HCR development is by orchestrating on-the-job experiences directing what members learn and guiding within member HCR development:

Proposition 1: One mechanism through which deployments influence unit HCR development is transforming within member HCRs.

Amplifying Unit HCR Through SCR

A second mechanism by which deployments influence unit HCR development is influencing member SCR development. SCRs are capacities embedded in the relationships between unit members that enable them to better utilize others’ HCRs in conjunction with their own (Wolfson & Mathieu, 2021). They reflect a unit-level resource whose foundations reside in unit member dyads that serve as the building block for more complex collective manifestations (e.g., triads, larger subgroups; see Park, Grosser, Roebuck, & Mathieu, 2020a). Regarding their importance to unit HCR development, Wolfson and Mathieu (2021: 438) suggest SCRs are “an essential component for extracting value from HCRs” and “become exceedingly important and bolster how HCR complementarities lead to improved performance.” Development of these valued resources stems from members’ interactions and significantly affects the emergence of HCR complementarities (Ray et al., in press).

Theory emphasizes the importance of interaction as an essential structural antecedent to the development of social capital, generally viewed as the resources embedded within relationships between individuals (Adler & Kwon, 2002; Lin, Cook, & Burt, 2001; Nahapiet & Ghoshal, 1998). This same thread of research provides support for the importance of individuals’ interactions affecting the quality and shape of an individual's (or unit's) network representing their social capital (Adler & Kwon, 2002; Burt & Soda, 2021; Lin et al., 2001; Nahapiet & Ghoshal, 1998; Tsai & Ghoshal, 1998). Similarly, Raffiee and Byun (2020: 40) highlight the importance of interaction, writing, “social capital resources are developed over the course of repeated interactions between parties.” Taken together, social capital theorizing, applied to human capital and more broadly, suggests that interactions shape the quantity and quality of relationships that people experience (Burt & Soda, 2021).

Deployments play a pivotal role in developing between member SCRs as they determine the range and extent of task-based interactions between members. What distinguishes interactions resulting from deployments is that they are grounded in unit-relevant tasks, unlike other forms of interaction such as onboarding activities, mentoring programs, and water cooler conversations. Indeed, HCR scholars have recently advanced frameworks taking co-evolutionary perspectives positioning the interaction of human and social capital as essential to unit HCR emergence (Crocker, 2019; Ray et al., in press). Deployments providing STTE between unit members aid in the development of shared capacities between them that are unit-relevant, such as entrained rhythms, which allows the unit to realize performance benefits (i.e., synergies, process gains) beyond what mere familiarity provides (Harrison, Mohammed, McGrath, Florey, & Vanderstoep, 2003; Luciano et al., 2018; Wolfson & Mathieu, 2021). We can infer the importance of deployments in shaping SCRs within the unit as they determine which member groupings accumulate STTEs over time.

Although important, deployments do not guarantee SCR development and purposeful experimentation is still needed (Ployhart & Cragun, 2017; Ray et al., in press). Much of the difficulty in developing such complementarities arises from the relational idiosyncrasies involved in developing between member SCRs (Brymer & Hitt, 2019; Ray et al., in press). However, the difficulty of developing SCRs also provides the social complexity and path dependencies that make them difficult to imitate and valuable (Dierickx & Cool, 1989; Ployhart & Cragun, 2017). In other words, building advantageous SCRs such as TMSs or shared mental models (Mohammed, Rico, & Alipour, 2021) afford competitive advantages because they are embedded in the unique organizational context and not easily imitable (Ployhart & Chen, 2019; Ployhart & Moliterno, 2011). A good example of the importance of relationships to complementarities are the empirical findings demonstrating that star knowledge workers’ performance significantly declines when moving to a firm without their team members, but no such decline is evident when stars brought team members with them to the new firm (Groysberg & Lee, 2009; Groysberg, Lee, & Nanda, 2008).

While these studies show the positive impacts of relationships in building complementarities, accumulating STTE between members need not be positive (Brymer & Hitt, 2019). For instance, Luciano et al. (2018) found that deployed surgical teams with high levels of STTE performed well when facing novel, complex tasks, but became complacent, failing to regulate one another during routine, simple procedures. Pairing the wrong personalities together can also generate negative relations. Ferguson and Peterson (2015) found that relationship conflict between members can ensue when deployments contain individuals with varying levels of propensity to trust, which could result in suboptimal workarounds and performance decrements (Park, Mathieu, & Grosser, 2020b).

These findings reinforce the difficulty and the value of using deployments to direct STTE between members to cultivate SCRs that can spur HCR complementarity emergence. Over time, how units choose to deploy members together develops and shapes the web of SCRs between them. In so doing, deployments influence unit HCR development through orchestrating member interactions to develop SCRs that amplify the value of HCR through identifying and developing complementarities among unit members. Thus, to the extent that deployments promote advantageous SCRs such as TMS, shared mental models, and information exchange, units can reap benefits in terms of amplifying the value of their HCRs:

Proposition 2: One mechanism through which deployments influence unit HCR development is amplifying the value of HCRs through developing between member SCRs.

Enhancing Development Through Emergence Enabling States

Unit emergence enabling states (EESs) represent the third point of impact. EESs facilitate the HCR emergence process from the individual to unit level. An EES “binds unit members together and allows their interactions through the task environment to amplify and transform KSAOs into a unique, unit-level human capital construct” (Ployhart & Moliterno, 2011: 137). Thus, it is important to consider how unit EESs emerge and develop over time, given their critical influence over transforming and amplifying lower-level HCRs and SCRs.

The development and sustainment of unit EESs arise from unit members’ interactions over time. Kozlowski and Klein (2000) refer to this as bottom-up emergence, wherein individuals’ interactions give rise to collective phenomena. As members’ interactions give rise to unit EESs, unit EESs enable or constrain members’ interactions and experiences, influencing subsequent unit EESs (Waller, Okhuysen, & Saghafian, 2016). Figure 1 depicts this mutual influence process with the circular arrows between unit EESs and within unit HCR and SCR development. Deployments guide member interactions that influence unit EES formation and sustainment, subsequently supporting or constraining HCR and SCR development in later deployments. For instance, units that develop a psychologically safe climate will enhance member and unit development (Edmondson, 1999). How members develop, grow, and change together, paired with their subsequent deployment interactions, will reinforce or alter the unit environment in which they operate. This cyclical process continues throughout the life of the unit.

For example, evidence consistently shows that team member familiarity relates positively to effective and efficient communication (Marlow, Lacerenza, Paoletti, Burke, & Salas, 2018). Deployments of the same unit members can improve member familiarity and facilitate their communication capacity. Similarly, repeated deployments that afford the accumulation of STTE have been shown to relate to the development of members’ shared mental models (Mohammed et al., 2010), which are the foundation of implicit coordination (Rico et al., 2008). Repeated deployment experiences provide conditions for the subconscious sharing of tacit knowledge (Hadjimichael & Tsoukas, 2019). Finally, deployments accounting for the diversity of “other characteristics” among members can impact the various affective states of the unit. For instance, deployments of members that vary in their propensity to trust tend to harm the trust that develops between deployed members, generating relationship conflict and lower performance (Ferguson & Peterson, 2015). Given the importance of deployments in determining members’ interactions, how units choose to deploy members should influence unit EESs development.

Like SCRs, there are few clear pathways to a reliable deployment strategy to obtain specific unit EESs as they emerge from the confluence of several concurrent variables and processes. However, to the extent that previous deployment strategies help to build positive ESSs, such as cooperative climates, psychological safety, or collective efficacy (Rapp, Maynard, Domingo, & Klock, 2021), unit members will be motivated to develop their individual HCRs and integrate them with others’ for unit purposes. Therein lies the advantage for those units that discover and cultivate unit EESs that enhance member HCR and SCR development: they are challenging to replicate and thus are likely to contribute to sustained advantage (Ployhart et al., 2014). Consistent with insights from HCR complementarities (e.g., Ployhart & Cragun, 2017), through purposeful experimentation, units can work to cultivate unit environments that encourage within member HCR and between member SCR development:

Proposition 3: One mechanism through which deployments influence unit HCR development is by amplifying the development of HCRs and SCRs through triggering emergence processes affecting EESs.

Deploying to Develop a Unit HCR Portfolio

Scholars have proposed that conceptualizing unit HCR as a portfolio of “individual human assets” facilitates understanding their dynamic complexities (Nyberg et al., 2014). Our first three propositions detail how deployments influence such change in unit HCR, expanding beyond individual assets (i.e., HCRs) to include the relational assets between individuals (i.e., SCRs) and unit-level states and processes. As a result, we conceptualize the unit HCR portfolio as consisting of individuals’ HCRs, SCRs between members, and processes and states at the unit-level. We adopt this portfolio perspective because it allows us to consider the guiding principles that direct leaders’ developmental efforts. In other words, not all development is useful for all units. Instead, development needs to be tied into the unit's desired array of HCRs and SCRs. These desires likely derive from the unit's strategic purpose(s) as part of the strategy implementation process (Gupta & Govindarajan, 1984), which drives unit goals, incentives, and HCR and SCR needs for strategy implementation.

We offer four representative unit HCR portfolios to illustrate how different deployments influence distinct development. We depict these four portfolios in the center of Figure 1 as (1) Fluid Generalists, (2) Stable Generalists, (3) Fluid Specialists, and (4) Stable Specialists. Each portfolio differs in the desired depth or breadth of unit HCR and SCR. Developing HCR depth reflects a desire for unit members to individually possess a narrower and deeper set of knowledge in a specific task domain relevant to the unit (e.g., Ching, Forti, & Rawley, 2021; Teodoridis, Bikard, & Vakili, 2019). HCR breadth reflects a desire for unit members to have knowledge and skills across a relatively wider range of task domains relevant to the unit (Ching et al., 2021; Teodoridis et al., 2019). SCR depth reflects greater STTE between narrower subsets of unit members, whereas SCR breadth reflects less STTE shared between a broader range of unit members (Luciano et al., 2018; Wolfson & Mathieu, 2021). We first detail how units can use deployments to develop a unit HCR portfolio and when each may be most valuable.

Unit Resource Flexibility

Unit members differ in their intrapersonal functional diversity, or the degree they “are narrow functional specialists with experience in a limited range of functions, or broad generalists whose work experiences span a range of functional domains” (Bunderson & Sutcliffe, 2002: 880). We refer to these member qualities as their HCR depth and breadth. As members increase in HCR depth relative to breadth, they become more specialized in their functional task areas. As HCR breadth increases relative to depth, members become more generalist, familiar with a broader range of functional task areas.

Units cultivating members with greater HCR depth provide the advantage of leveraging members’ HCRs for high returns when deployed to their functional task area. Yet, having more specialized members requires a high degree of division of labor to leverage their specialization (Fahrenkopf, Guo, & Argote, 2020). However, members become substantially less effective when deployed beyond their specific domain of expertise (Wolfson & Mathieu, 2018, 2021). Units that cultivate members with greater HCR breadth provide the unit with greater flexibility in deploying members effectively and lessen the pressure presented by the alignment challenge. However, they will be less effective than a specialist member when deployed to the specialist's domain (Postrel, 2002). Cultivation of members’ HCR depth and breadth exists on a continuum such that units developing access to only specialists or generalists represent the opposite ends and are less likely. A range of potential mixtures of specialist and generalist configurations are possible within the unit (Postrel, 2002). For example, Hall et al. (2018) describe how having a “broker” who can effectively coordinate the activities of disciplinary specialists can enhance the effectiveness of “science teams” that are notoriously difficult to manage. The mix of members’ HCR breadth and depth affects unit resource flexibility.

Unit resource flexibility is “the extent to which a resource can be applied to a larger range of alternative uses, [which reduces] the costs of switching the resource from one use to another, and [reduces] the time required to switch the resource” (Bell et al., 2018: 454). Unit resource flexibility thus reflects the degree to which unit members, in aggregate, can be deployed to a broader or narrower range of unit-relevant tasks. Units with high resource flexibility contain members with greater HCR breadth, allowing the unit to deploy members to a broader range of tasks more effectively. Units with low resource flexibility contain members with greater HCR depth as units can deploy members to a narrower range of tasks with greater effectiveness.

Our conceptualization of unit resource flexibility, which emphasizes functional diversity within unit members, differs from a similar concept of unit resource heterogeneity. Unit resource heterogeneity, which represents the breadth of functional task areas covered across unit members (Bunderson & Sutcliffe, 2002), depicts the maximal functional task coverage a unit could address with at least one member. Resource heterogeneity depicts a range of functional task areas the unit could address, while resource flexibility depicts the ease with which units can deploy members to various task scenarios.

In Figure 2, we provide examples of units to illustrate differences in unit resource flexibility. The upper half of the graph depicts units that cultivate greater HCR depth through member specialization. For example, a top management team represents a specialized unit wherein each member has a clear domain of expertise for which they are responsible (Boone & Hendriks, 2009). Surgical units have similar specialization wherein unit members have clear roles (i.e., surgeon, scrub nurse, etc.), and deploying members to different roles would not be feasible and could be potentially dangerous (Vashdi, Bamberger, & Erez, 2013). While specialization is often necessary, it limits the task range of unit member deployments, lowering unit resource flexibility. The lower half of the graph depicts units where members lean towards being generalists, enhancing such flexibility. For example, educational units have instructors with focal expertise who can shift to teaching other subjects as the unit requires (National Center for Education Statistics, 1996). Similarly, despite traditional roles, some basketball teams are becoming increasingly fluid in their role structure to emphasize “switchability” that allows members to shift and exchange roles dynamically (Fenichel, 2021). These examples highlight that unit resource flexibility decreases as units move from the bottom to the top of Figure 2 as member specialization requires a higher division of labor (Fahrenkopf et al., 2020).

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Figure 2

Illustrative Real-World Examples of Unit Portfolio Types

Individual assignments and the resulting unit-level deployments represent a powerful tool in shaping the resource flexibility of the unit HCR portfolio through influencing task exposure. Specialists accrue their domain-specific expertise through repeated learning experiences in the focal domain, while generalists experience a wider range of experiences that yield knowledge spread across multiple domains (Ching et al., 2021; Teodoridis et al., 2019). Assignments emphasizing repeated task exposure for a member build domain-specific expertise. Such a strategy enhances member specialization and the unit's HCR depth while decreasing resource flexibility as it becomes increasingly costly to assign that member elsewhere. Assignments providing varied task exposure build members’ expertise across multiple domains, increasing unit resource flexibility as they can assign members to a broader range of tasks. However, doing so comes at the cost of less specialized expertise in any given area:

Proposition 4a: Deployments providing members repeated task exposure increase member HCR depth which is negatively related to unit resource flexibility. Proposition 4b: Deployments providing members varied task exposure increase member HCR breadth which is positively related to unit resource flexibility.

Unit Coordination Flexibility

Greater unit SCR depth reflects richer affective, behavioral, and cognitive connections between narrower subsets of members. In contrast, greater unit SCR breadth indicates shallower relational connections between a broader subset of unit members. We also view SCR depth and breadth as existing on a continuum such that cultivating access to only subgroups with total closure (i.e., no connections with other unit members beyond the focal group) or no closure (i.e., little connection with a focal group) represent the opposite ends and are less likely in practice than situations of moderate closure (Oh, Labianca, & Chung, 2006). While this simplifies the likely configural and multiplex nature of SCRs (Park et al., 2020a), these terms are sufficient to advance our discussion here. The various configurations of between-member connections impacting unit SCR breadth/depth affect unit coordination flexibility.

Unit coordination flexibility is “the ability to resynthesize, reconfigure, or redeploy resources quickly and effectively” (Bell et al., 2018: 454). It enhances the unit's ability to co-deploy members more effectively. Enhancing unit HCR coordination flexibility relates to increasing the number of SCR bonds (affective, behavioral, or cognitive) between members. If the same members are deployed together over time, they will likely forge valuable relational capacities such as improved coordination (Rico et al., 2008) and shared mental models (Mohammed et al., 2010). However, this can undermine the ability of other members to interact with them as effectively, often limiting the flexibility of future deployments to said subset of members. Such limitations will be particularly insidious if members’ availability is constrained by scheduling challenges or high turnover (Hausknecht & Holwerda, 2013).

Returning to Figure 2, units on the left-hand side of the figure illustrate those units higher in coordination flexibility. Those on the right represent lower levels of coordination flexibility. The ethnographic study from Bechky and Okhuysen (2011) provides an illustrative contrast between film production crews (left-hand side) and SWAT teams (right-hand side). They gathered information on several aspects of operations concerning how such units respond to organizational surprises. Of relevance here were the distinctions between membership fluidity on a given deployment. Film production crews organized as different project teams wherein members frequently interacted with one another for the first time in most projects. The fluidity of these interactions reflects a loose integration between individuals, allowing the unit to more fluidly reorganize members to suit project needs (Burt & Soda, 2021). By contrast, the SWAT team exhibited a stable membership where 12 out of 17 members had been part of the team for at least 5 years. These more stable interaction patterns reflect the tight integration associated with more traditional team types with relatively deep SCR bonds that limit coordination flexibility as member change is more disruptive (Burt & Soda, 2021).

Deployments are a powerful tool for shaping unit coordination flexibility through directing member interaction. Units desiring to develop a portfolio of stable and deep connections between members can do so through deployments emphasizing repeated STTE exposure to enhance the entrained rhythms between members (Harrison et al., 2003). Doing so will result in stronger SCR between a narrower subset of members but limit unit coordination flexibility. Deployments providing varied STTE exposure between members expand coordination flexibility as more SCR links between members are established. Although this provides the unit with a broader array of deployment options, it also limits the quality of SCRs that can develop between a specific subset of members:

Proposition 5a: Deployments providing members repeated STTE exposure deepen between member SCRs which are negatively related to unit coordination flexibility. Proposition 5b: Deployments providing members varied STTE exposure broaden between member SCRs which are positively related to unit coordination flexibility.

When Resource and Coordination Flexibility Are Most Valuable

At this point, a natural question for managers is: When should we use deployments for development? While each deployment choice contains a significant vector of potential influences, a central consideration should be the characteristics of the unit task environment as it informs which unit HCR portfolio type may achieve a better fit with unit task demands over time. Task characteristics play a central role in determining which members’ skills and relationships are relevant and how to effectively deploy them to realize unit performance (Ployhart et al., 2014; Wolfson & Mathieu, 2018, 2021). In this final section, we identify two characteristics—task dynamism and complexity—and explain their impact on the value of using deployments to develop unit resource and coordination flexibility to sustain unit performance.

We define unit task dynamism as the “regularity in and amount of change occurring in the [task] environment” (Sirmon, Hitt, & Ireland, 2007: 275). Unit task environments low in dynamism experience relatively stable and predictable patterns of tasks (e.g., routinized manufacturing units, fast food service establishments, data entry units), and increases in dynamism suggest environments where task demands are “highly fluid and marked by change and uncertainty” (e.g., restaurants offering weekly rotating menus, consulting units, early childhood education providers; Ployhart and Moliterno [2011: 137]).

Units facing greater uncertainty in their task demands will experience greater challenges in aligning members’ HCRs with task demands relative to units in more stable environments as the potential range of HCR needs is broader with greater temporal variation. Units can lessen this challenge by increasing the flexibility of their resources (i.e., unit members) by cultivating members with greater HCR breadth. Doing so eases the alignment challenge as units can deploy members flexibly across a broader range of tasks. However, more stability in the unit task environment lessens the alignment challenge as units can better anticipate HCR needs for unit task demands. Accordingly, cultivating members with greater HCR breadth generates a lower yield as their deployment flexibility is less valuable. Units can experience greater benefit from developing members’ HCR depth in specific functional task areas as units can reliably deploy them to those areas with greater effectiveness.

Unit task complexity is defined as “the degree to which the unit's tasks require interdependence and coordination among members” (Ployhart & Moliterno, 2011: 135). Task environments exhibiting low levels of complexity suggest pooled work arrangements wherein members work asynchronously, have little need for communication or knowledge of other unit members, and unit output represents a summation of individual efforts (Ployhart & Moliterno, 2011; Van De Ven, Delbecq, & Koenig, 1976). Increases in complexity reflect a greater need for synchronized coordination, knowledge of other members, and more intensive workflow structures (Ployhart & Moliterno, 2011). Ployhart and Moliterno (2011: 137) provide examples across the spectrum ranging from a low complexity task of a tug-of-war competition “where every member must only pull in the same direction” to highly complex task environments such as “an emergency room medical team providing trauma care on a patient.”

Units facing greater complexity in the coordination demands between members place a premium on deploying members with established shared connections to enable them to respond effectively as a group to task demands. Units can better meet this challenge not by enhancing coordination flexibility but rather by cultivating member pairings and teams with greater SCR depth. While limiting coordination flexibility, this allows units to deploy these member groupings to tasks requiring more substantial coordination efforts more effectively as they have the SCR depth necessary to meet these coordination demands. Conversely, groupings with weaker SCR bonds will experience difficulty navigating these complex task environments (Luciano et al., 2018). However, as task coordination demands lessen, it behooves units to increase their coordination flexibility by developing members’ SCR breadth. The lower coordination demands require relatively less SCR between members to coordinate sufficiently well. Thus, widening the scope of SCR between unit members provides the unit access to a broader range of effective member combinations to meet task demands.

Together, these task features create the four unit task environments shown in Table 1. Units operating in different environments should receive differential returns from deploying to increase the unit resource and coordination flexibility in their portfolio (Bell et al., 2018). For instance, units in task environments low in dynamism and complexity, in the lower left of Table 1, are less likely to obtain long-term benefits from enhancing unit resource flexibility, given the higher predictability of future task requirements. The ability to predict future task requirements limits the value of cultivating resource flexibility, as units can identify member-task alignments more easily. Conversely, given the lower coordination demands of this environment, units benefit from cultivating unit coordination flexibility. Greater coordination flexibility increases the range of member combinations that units can deploy, reducing availability issues. Units in this environment should seek to cultivate unit members’ HCR depth and SCR breadth. Accordingly, units should design deployments to emphasize repeated task exposure to focal expertise domains while varying member exposure to create flexibility and familiarity between a broader range of unit members:

Proposition 6: In task environments low in dynamism and complexity, deployments developing low unit resource flexibility and high unit coordination flexibility will be positively related to unit performance change.

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Table 1

Value of Using Deployments for Development Under Conditions of Task Dynamism and Complexity

		Task Dynamism
		Low	High
Task Complexity
	High	Unit Task Environment: Stable and predictable future task requirements. Increasingly intense coordination requirements. Unit HCR Portfolio Match: Stable Specialists. Resource flexibility needs are low due to stable task demands. Coordination depth is critical as stronger SCRs are needed to meet coordination demands. Developmental Deployment Strategy: Deployments should emphasize repeated task- and teaming-interactions to develop rich pools of HCR member specialization and thick SCR bonds between members to capitalize on predictable task demands while meeting intense coordination requirements.	Unit Task Environment: Frequent changes in knowledge and skill task requirements. Increasingly intense coordination requirements. Unit HCR Portfolio Match: Stable Generalists. Resource flexibility needs are high to provide unit deployment options to address a broader range of tasks. Coordination flexibility is less valuable as stronger SCRs are needed to meet coordination demands. Developmental Deployment Strategy: Deployments emphasizing varied task exposure enhances resource flexibility, allowing units to deploy members to a broader range of tasks. Deployments emphasizing repeated member exposure deepen SCR bonds between members to meet intense coordination requirements.
	Low	Unit Task Environment: Stable and predictable future task requirements. Simple to non-existent coordination requirements. Unit HCR Portfolio Match: Fluid Specialists Resource flexibility needs are low due to stable task demands. Coordination flexibility is valuable as tasks require simpler forms of coordination that can be more easily developed across a wider range of unit members. Developmental Deployment Strategy: Deployments emphasizing repeated task exposure develop member specialization while varying member exposure develops coordination flexibility to expand options for deploying specialists together.	Unit Task Environment: Frequent changes in knowledge and skill task requirements. Simple to non-existent coordination requirements. Unit HCR Portfolio Match: Fluid Generalists. Resource flexibility needs are high to provide unit deployment options to address a broader range of tasks. Coordination flexibility is valuable as tasks require simpler forms of coordination that can be developed across a wider range of unit members. Developmental Deployment Strategy: Deployments emphasizing varied task and member exposure enhances resource and coordination flexibility, allowing units to deploy members to multiple task types with a wide range of members more effectively.

Environments high in either dynamism or complexity, but not both, differ in the potential returns from developing unit resource and coordination flexibility. Units in highly dynamic task environments with low complexity, shown in the lower right of Table 1, should seek to cultivate a fluid generalists unit HCR portfolio. They benefit from cultivating unit resource flexibility by expanding members’ HCR breadth, allowing the unit to deploy the member to a wider variety of task types more effectively. Given the increased regularity of task changes in these environments, units will need to cultivate sufficient types and amounts of members’ skills to provide a diverse array of deployment options to better fit future task demands (Wolfson & Mathieu, 2021). Further, given the more straightforward coordination demands, relatively low SCRs between members will suffice. These conditions incentivize units to cultivate resource and coordination flexibility through varied task and member exposure. The result will increase members’ potential alignments and generate more robust SCR connections to expand deployment options in this dynamic task environment:

Proposition 7: In task environments high in dynamism and low in complexity, deployments developing high unit resource and coordination flexibility will be positively related to unit performance change.

Units in more complex but less dynamic task environments, shown in the top left of Table 1, should seek to cultivate a stable specialists unit HCR portfolio. They benefit from emphasizing HCR and SCR depth at the expense of flexibility. Cultivating coordination flexibility through deployments is less beneficial for highly complex task environments as members need deeper SCR bonds to meet coordination demands effectively. This environment requires deploying for repeated exposure to the same members and tasks as much as possible. Varying deployment membership to pursue flexibility would only be advisable once the core team has developed sound entrained rhythms on the focal task (Luciano et al., 2018). Similarly, with relatively stable task demands, emphasis should be placed on developing members’ HCR depth due to the predictable task structure. Varying task exposure is only advisable once a member has saturated their domain expertise (Postrel, 2002):

Proposition 8: In task environments low in dynamism and high in complexity, deployments developing low unit resource and coordination flexibility will be positively related to unit performance change.

Finally, we suggest that units embedded in complex task environments with high dynamism, shown in the top right of Table 1, should seek to build a stable generalists unit HCR portfolio emphasizing greater resource flexibility while foregoing coordination flexibility. Units in such environments are likely to experience greater returns by cultivating uniquely flexible members. They will need to develop a sufficiently diverse aggregation of members’ HCRs while generating enough substantive depth between member SCRs so that units can deploy individuals effectively together in complex task situations. In these complex environments, exploring and cultivating HCR complementarities—which often take the form of different team-based structures within the unit (Ployhart & Chen, 2019)—are increasingly needed and thus most worth the investment (Ployhart & Cragun, 2017). Using deployments to identify and nurture SCR bonds between members, who also possess the HCR malleability to handle various task demands, is particularly valuable:

Proposition 9: In task environments high in dynamism and complexity, deployments developing high unit resource flexibility and low unit coordination flexibility will be positively related to unit performance change.

Discussion

This article articulates how deployments influence unit HCR development and specifies conditions under which they can be most valuable. We identify and define deployments as pivotal in providing opportunities to develop within member HCRs, between member SCRs, and unit EESs influencing unit HCR development. We then specify that such development informs the cultivation of four distinct unit HCR portfolios that differ in the HCR and SCR depth, influencing unit resource and coordination flexibility. We conclude by specifying two unit task environment conditions—dynamism and complexity—that yield distinctly different returns for using deployments to cultivate particular unit HCR portfolios. We suggest that using deployments to develop HCR depth (breadth) is valuable when task dynamism is low (high), whereas their use to develop SCR depth (breadth) is valuable when task complexity is high (low). Next, we discuss our framework's theoretical and practical implications and promising future research directions.

Theoretical and Practical Implications

This work contributes to the unit HCR literature in three ways. First, building upon the extant unit HCR emergence literature (Ployhart & Moliterno, 2011), we leverage informal learning, social capital, and emergence process literatures to explain why deployments influence HCR and SCR development. The confluence of these literatures emphasizes interaction and that unit deployment decisions are a locus point of unit member interaction. We specify three influences of deployments on unit HCR development: (1) transformation of individuals’ HCR; (2) change in SCRs that amplify the value of HCRs; and (3) development of unit EESs.

Second, we identify four distinct unit HCR portfolios in which unit-level characteristics, specifically resource and coordination flexibility, are cultivated through using deployments for development. We then specify how units can use deployments emphasizing repetition or variation with unit tasks and members to pursue one of four general unit HCR portfolio forms. These contributions are significant because they acknowledge different unit developmental needs, specify what those are, and provide practical guidance on how units can use deployments to cultivate each distinct portfolio.

Finally, we identify two conditions in the unit task environment—dynamism and complexity—which provide managers with practical guidance on when and for what purpose developmental deployments are most beneficial. Units in dynamic task environments will experience the greatest returns from using deployments to develop HCR breadth to cultivate greater resource flexibility. Expanding members’ unit-relevant skills enhances the range of effective potential deployment options in subsequent deployments. In contrast, managers in complex task environments will experience the greatest returns when using deployments to cultivate SCR depth to provide members time to generate strong SCR bonds necessary to meet environmental demands. Orienting deployments developmental impact to the unit task environment adds specificity and moves beyond specifying how or what is being developed to consider when such development is most valuable for a unit.

Future Research Directions

We envision several future research directions that would extend our insights. First and foremost, these propositions need to be empirically examined using longitudinal research designs and analyses (Ployhart & Vandenberg, 2010), ideally measuring the actual processes generating these gains rather than just construct fluctuation (Kozlowski, 2019). We echo other scholars’ calls to improve our measures of HCRs (Nyberg et al., 2014) to facilitate such endeavors.

Second, future work should empirically explore what conditions are indicative of higher probabilities of successful developmental deployments. For instance, the extant literature suggests that characteristics of the: (1) learners (e.g., motivation to learn, promotion focus, zest); (2) experts (e.g., expertise quality, willingness to provide feedback); (3) task (e.g., just beyond learner capacities, tolerance for performance deviation); and (4) unit (e.g., psychological safety, feedback processes, HCR needs), may all influence the quality of a developmental opportunity (e.g., Cerasoli et al., 2018; Colquitt, LePine, & Noe, 2000; Edmondson, 1999; Noe, Tews, & Marand, 2013; Tannenbaum et al., 2009; Wolfson et al., 2018). A worthwhile endeavor would be synthesizing and empirically assessing such factors to provide scholarly and practical guidance regarding the conditions enabling developmental deployment effectiveness.

Third, another fruitful avenue for future research is to consider whether altering the unit's task strategies for different circumstances, thereby changing the value of HCRs through how units use them, can augment using deployments for development. Unpacking the deployment–task strategy relationship holds promise in understanding the relationship between managerial deployment choice and task complexity, as conceptualized by Grant (1996).

Fourth, our theorizing does not consider the relevant top-down effects influencing managerial deployment choices and the relationship to the unit HCR emergence process. Future research must understand relevant top-down effects, as they are similarly critical to the emergence process as bottom-up effects and influence process mechanisms (Mathieu & Luciano, 2019). For instance, how does the formal HRM system influence unit choices, or how does the munificence of external labor markets impact the value of using deployments for development? How can firms design training and development systems that complement informal employee experiences? How might these processes differ in highly unionized or nonprofit contexts? In short, while we have highlighted a pair of unit task characteristics here, several other influences remain ripe for scholarly exploration.

Finally, more needs to be known about unit managers’ processes in deploying unit personnel. For instance, managers likely need to balance performance and developmental concerns when composing deployments (Smith & Lewis, 2011), and future research should endeavor to understand that process better. Relatedly, developing unit HCR is contingent on accurately assessing which capabilities are necessary for long-term unit performance and marshaling the capacity to coordinate across unit levels to effectively transform unit HCR over time. These are examples of the future research needed to better understand the intricacies of the managerial role in deploying to develop unit HCR.

Conclusion

We highlight the powerful potential of using deployments to develop unit HCR. Deployments reflect a key locus point of unit HCR development and have a substantial effect on transforming and amplifying unit HCR over time. Units using deployments to cultivate a particular unit HCR portfolio can create long-term value by enriching future deployment options, but such value is contingent on the task environment. Through this work, we advance scholarly understanding and hope to spark interest in the relationship between HCR deployments, unit HCR development, and sustaining unit performance.