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In This Chapter
-
Building Micro-Foundations for Talent
Management
- Introduction
- Defining Talent Management
- Micro-Foundations: A Brief Overview
- Multiple Levels, Overlooked Individual Heterogeneity and Unanswered Questions
- Choosing the Point of Differentiation: Strategic Positions or Stars
- Building a Differentiated Talent Architecture for organizational performance
- Towards a Multi-Level Understanding of Talent Management
- Implications
- Conclusions
- References
Abstract
Talent management (TM) has captured the attention of senior organizational leaders, human resource professionals, and academic scholars since entering the mainstream when McKinsey declared the War for Talent in the mid-1990s (Cappelli & Keller, 2017; Collings, Cascio, & Mellahi, 2017). While initial interest in the topic was largely restricted to a practitioner audience, since about 2009, we have seen a significant body of academic work on the topic emerge (Gallardo-Gallardo et al., 2013; Gallardo-Gallardo et al., 2020; McDonnell et al., 2017; Meyers et al., 2020). However, this literature is highly diverse, ranging from discussions of the conceptual boundaries of TM, to TM practices and the intended outcomes or effects of TM (Thunnissen et al., 2013: 1744). The literature has, however, failed to develop consensus around the meaning and definitions of TM (Cappelli & Keller, 2017; Collings & Mellahi, 2009). The link between TM and performance has also yet to be established (Collings et al., 2019), and indeed, there is little consensus on dominant TM practices. In many ways, the field is still operating with generic notions of “attract, develop, and retain” and lacks specificity.
Building Micro-Foundations for Talent Management
Introduction
Talent management (TM) has captured the attention of senior organizational leaders, human resource professionals, and academic scholars since entering the mainstream when McKinsey declared the War for Talent in the mid-1990s (Cappelli & Keller, 2017; Collings, Cascio, & Mellahi, 2017). While initial interest in the topic was largely restricted to a practitioner audience, since about 2009, we have seen a significant body of academic work on the topic emerge (Gallardo-Gallardo et al., 2013; Gallardo-Gallardo et al., 2020; McDonnell et al., 2017; Meyers et al., 2020). However, this literature is highly diverse, ranging from discussions of the conceptual boundaries of TM, to TM practices and the intended outcomes or effects of TM (Thunnissen et al., 2013: 1744). The literature has, however, failed to develop consensus around the meaning and definitions of TM (Cappelli & Keller, 2017; Collings & Mellahi, 2009). The link between TM and performance has also yet to be established (Collings et al., 2019), and indeed, there is little consensus on dominant TM practices. In many ways, the field is still operating with generic notions of “attract, develop, and retain” and lacks specificity.
Theoretically the foundations of the literature on TM are also diverse. For example, Gallardo-Gallardo et al. (2013) identified four dominant theoretical frameworks informing TM research – the resource-based view, international human resource management, employee assessment, and the institutional lens. They criticized the field for applying multiple frameworks in single projects, claiming that such combinations may create “an inconsistent ‘story’ and often also a severe mismatch between theory and data” (2013: 276). A more recent review by McDonnell et al. (2017) concluded that there are two primary streams of the literature that dominate TM research: “the management of high performers and high potentials, and the identification of strategic positions and TM systems” (2017: 86). The authors also lament the disjoined nature of the field and call for greater clarity around the conceptual boundaries of TM. Achieving such clarity is central to the exposition of the relevant underlying mechanisms of TM, which are currently only partially understood.
This chapter aims to begin to fill in this gap. We provide a mapping of conceptual boundaries using a micro-foundational approach (Felin & Foss, 2005). We then use this mapping as a diagnostic tool to help us identify some of the key but under-researched issues that future research in this important field must explore. We begin by defining TM and reviewing the basic logic behind the arguments of micro-foundations. Then we illustrate that the key state-of-the-art discussions in TM are currently happening at a number of different levels. We finally argue that these discussions will benefit from explicating underlying mechanisms at the individual level.
Defining Talent Management
Given the lack of agreement over
defining talent management, it is important to be clear on how the construct
is defined in the context of the current chapter. We adopt Collings and Mellahi's (2009)
definition, which was identified by Gallardo-Gallardo et al. (2013) as the most cited definition of
TM. They define TM as: … activities and processes that involve the systematic identification
of key positions which differentially contribute to the organization's
sustainable competitive advantage, the development of a talent pool of
high potential and high performing incumbents to fill these roles, and
the development of a differentiated human resource architecture to
facilitate filling these positions with competent incumbents and to
ensure their continued commitment to the organisation. (2009: 304)
This definition broadens the TM agenda beyond a sole focus on leadership talent and highlights the importance of key positions that have the potential to disproportionately contribute to competitive advantage. As we outline below, key positions thus become the locus of differentiation from a strategic perspective. They are defined by their centrality to organizational strategy and by the potential for significant difference in output between an average and top performer in the position (quality pivotal) or by the potential differential in output when the number of people in the role increase (quantity pivotal) (Becker & Huselid, 2006; Cascio & Boudreau, 2016; Collings & Mellahi, 2009; Minbaeva & Collings, 2013).
The focus on talent pools calls for a move from vacancy led recruitment toward “recruiting ahead of the curve” (Sparrow, 2007). This reflects an emphasis on “flow” or “process” notions of human capital, as opposed to the more traditional “static” or “stock” perspective on human capital (Burton-Jones & Spender, 2011; Collings et al., 2017). Peter Cappelli likens this approach to managing talent through a supply chain. This approach aims to minimize talent risk in the supply chain. Such risks can be qualitative, meaning the organization does not have the types of knowledge, skills, abilities, and other characteristics (KSAOs) required to deliver on its strategy, or quantitative, when the firm does not have the requisite number of people available to deliver on its strategy. The challenge for organizations is to systematically identify future business needs in terms of knowledge, skills, and capabilities that will be required in the future but are not currently available in house and recruit on this basis (Collings et al., 2019). Stahl et al.'s (2012) study of global TM confirmed that the high performing organizations they studied followed a talent pool strategy – recruiting the best people and then finding positions for them.
Finally, the differentiated HR architecture is intended to increase organizational performance through maximizing the work motivation, organizational commitment, and extra- role behavior of those in the talent pool (Collings & Mellahi, 2009). Extant research provides some support for this position. Marescaux et al. (2013) demonstrated that employees who perceived they had received more favorable treatment in the workplace displayed higher levels of affective commitment. Similarly, Gelens et al. (2014) found that being designated as “talent” was perceived as a signal of organizational support, which, in turn, triggered affective commitment. However, the evidence is not conclusive on the positive impact of differentiating HR, and there is ample opportunity for further study here (Collings, 2017; Meyers et al., 2017).
Micro-Foundations: A Brief Overview
Micro-foundations have been an important theme in management research and can be seen as an instance of “reductionism” (Foss, 2010). Advocates of the micro-foundations movement in management argue that micro-foundations must be provided as building management theories for three critical reasons:
- Alternative explanations: There are likely to be many alternative lower-level explanations of macro-level phenomenon that cannot be rejected with macro-analysis alone. Even if a large sample can be constructed on the basis of macro units of analysis, a problem of alternative explanations may persist.
- Managerial interventions/practices: The ultimate goal of managerial interventions is to modify individual behavior. However, not every organizational practice gets implemented as intended, and not everything that is implemented affects individual behaviors to a similar degree. The differences between intended and implemented managerial practices are caused by a range of organizational antecedents that would cause variations in the implementation across organizational units. There are also variations in individuals' reactions to the actual/implemented practices, which may explain differences in perceived practices. Hence, a single-level perspective can never be adequate for research on managerial interventions.
- Fundamental causes and predictability: Fundamental to micro-foundations perspective is the recognition that micro-level phenomena are embedded in macro contexts and that “macro phenomena often emerge through the interaction and dynamics of lower-level elements” (Kozlowski & Klein, 2000).
All three arguments speak directly to the need for building micro-foundations in TM research. However, the fundamental question for TM research is “where this deep structure is located, as there may be several analytical levels below a given aggregate phenomenon” (Foss, 2010: 13; original italics). We argue that for TM, given its focus on KSAOs, individuals should be considered as ultimate “micro”, and individual heterogeneity must be treated as a source of explanations for variance observed at organizational level.
Yet, until now, the TM field has taken a more collective level (aggregate) approach, reasoning in terms of “talent pools”, “talent management systems”, and “talent architecture”, which are posited to somehow directly influence firm performance. However, the link between aggregate “talent” and performance at the organizational level is merely a correlation, and one that has yet to be verified empirically. Building a causal link between aggregate “talent” and performance will require acknowledgement of the fact that “the system's behavior is in fact resultant of the actions of its component parts” and hence “knowledge of how the actions of these parts combine to produce systematic behavior can be expected to give greater predictability than statistical relations of surface characteristics of the system (Coleman, 1990: 3). Because little attention has been paid to heterogeneity between individuals, nor indeed to heterogeneity within individual performance (Minbashian, 2017), it is reasonable to characterize the treatment of the linkages between TM practices and organizational performance as a black box. We argue that exposition of this black box is the most pressing challenge facing TM field. Finally, we know very little about the link between investments in TM and corporate performance (Collings, 2014; Collings et al., 2017). This is largely because we lack the understanding of the dynamic processes of aggregation from individual talent contribution to collective, organizational performance.
Overall, we argue that drilling for micro-foundations in TM will allow researchers to discover “novel aggregate consequences of explicitly micro-foundational assumptions” (Foss, 2010: 29). Because individuals are treated as a homogenous group, there are a number of questions that remain unanswered. “To fully explicate organizational analysing … one must fundamentally begin with and understand the individuals that compose the whole, specifically their underlying nature, choices, abilities, propensities, heterogeneity, purposes, expectations and motivation” (Felin & Foss, 2005: 441). This will also create a much more fine-grained understanding of how organizations are better able to cope with challenges associated with human-capital-based competitive advantage (Coff & Kryscynski, 2011).
Multiple Levels, Overlooked Individual Heterogeneity and Unanswered Questions
Currently, central discussions in TM are happening at a number of different levels. This may be a consequence of the diversity of theoretical frameworks applied in the area to date. Yet, in all of these discussions, heterogeneity of individuals is often assumed but not articulated.
We focus on two key ongoing conversations, which will benefit significantly from explicitly articulating micro-foundations:
- Choosing the point of differentiation for TM
- Building a differentiated architecture for organizational performance.
Choosing the Point of Differentiation: Strategic Positions or Stars
The notion of workforce differentiation is fundamental to TM. This focus emerged as a result of the squeeze on resources after the economic crisis of the 1970s when firms found it increasingly challenging to sustain an inclusive approach (Cappelli & Keller, 2017), combined with an increasing realization that investing equally in all employees led to unnecessarily high costs for firms (Becker & Huselid, 2006). Workforce differentiation has its roots in the strategy literature and emphasizes the importance of choices about where to invest in human capital (Collings, 2017). It recognizes that firms can create value through differences in the design and management of workforce strategy (Becker et al., 2009) and calls for a greater investment in employees expected to deliver greater value or return of investment for the firm (Collings, 2017; Gallardo-Gallardo et al., 2013; Huselid & Becker, 2011).
However, a key question for firms is choosing a point of differentiation for their TM systems. The individual star was identified as the nexus of differentiation in earlier approaches to TM (Collings, 2017). This work takes a bottom-up focus in theory development, and foregrounds the perspective that employees contribute to the firm's strategic objective simply because of their individual value and uniqueness (Becker & Huselid, 2006). This perspective was central to the McKinsey perspective emphasized in the War for Talent, and was given further credence by high profile advocates such as Jack Welch at General Electric. It recognizes a relatively small group of employees as generating the greatest value for the organization. Employees are thus classified into two broad groups: a small group of stars or A-players “with talent” and a larger group of B- and C-players who are considered average or below average performers (Meyers & von Woerkom, 2014).
This view is also reinforced by recent literature on stars that explains that star performance is better captured by a power distribution than a normal distribution, owing to the disproportionate out that these exceptional performers deliver (Aguinis & O'Boyle, 2014). A central notion to differentiating at the level of the individual employee is that talent is relatively stable and reflected in intelligence and other individual differences (DeLong & Vijayaraghavan, 2003; Meyers & von Woerkom, 2014; Pfeffer, 2001), or graduating from a top school (Gladwell, 2002). Hence, recruitment and selection are prioritized in the context of attracting top talent given the stable and innate view of talent underpinning the perspective (Vaiman et al., 2012). Performance management was also viewed as a key enabler of such differentiation as tools like forced distribution emerged as central in identifying talent and forcing managers to differentiate in identifying those employees (see Collings, 2017, for a more elaborate discussion).
More recently the locus of differentiation in TM has shifted to the job level and the importance of strategic or pivotal jobs as the driver of competitive advantage is central to this perspective (Boudreau & Ramstad, 2007; Becker et al., 2009; Collings & Mellahi, 2009). This shifts the focus to a top-down perspective in theorizing. As Becker and Huselid note “When employees are able to contribute to a firm's strategic objectives they have (strategic) value” and that “…not all strategic processes will be highly dependent on human capital” (2006: 904). In line with more recent shifts in the strategic HR literature, this reflects an increased focus on the practices that impact human capital rather than the human capital itself (Delery & Roumpi, 2017; Wright & McMahan, 1992). This approach recognizes that human capital is of little economic value unless it is deployed in the implementation of the organization's strategic intent (Becker & Huselid, 2006; Bowman & Hird, 2014) and that the organizational capabilities that harness this human capital are as central as the human capital itself in this context (Linden & Teece, 2014).
Theoretically dynamic capabilities are identified as the fulcrum of workforce differentiation (Collings, 2017; Collings et al., 2018). From this perspective, competitive advantage is found from the unique way in which a firm can execute business processes in implementing its strategy. Such capabilities are reflective of the unique history, assets, and capabilities that any firm possesses (Bowman & Hird, 2014) and generally built as opposed to bought (Rumelt, 1984). While the production and sale of a relatively defined and stable portfolio of goods and services can be achieved through stable capabilities or ordinary capabilities, in more fast-paced or evolving contexts more dynamic capabilities are called for (Linden & Teece, 2014). This is reflective of more recent theorizing on human capital that acknowledges the more dynamic business environment that firms are faced with and recognizes that static conceptualisations of human capital requirements are no longer effective (Cascio & Aguinis, 2008; Cappelli, 2008; Lepak et al., 2011). This perspective also considers the potential impact of the future value of human capital beyond its present value (Lepak et al., 2011).
Dynamic capabilities are reflective of the firm's capacity to integrate, build, and reconfigure internal and external resources in adapting and responding to the evolving business environment (Linden & Teece, 2014). Routines are identified as key in reconfiguring intangible assets, such as human and social capital, in ways that facilitate the renewability, augmentation, and creative responses to dynamic and unpredictable business conditions and have been applied to recent conceptualizations of TM (Collings, 2014; Collings et al., 2018; Teece et al., 1997). This reflects the value of organizational routines – repetitive, recognizable patterns of interdependent actions involving various actors through which work is accomplished in organizations – in creating stability and boosting efficiencies and guiding organizational activity (Feldman & Pentland, 2003).
In a recent example of this perspective, Collings et al. (2018) identify pivotal positions as the starting point in any consideration of TM (see also Cascio & Boudreau, 2016). Their approach very clearly reflects the job as the locus of differentiation, and they adopt a top-down approach to theorizing consistent with this logic.
The discussion around the point of differentiation could benefit greatly from strengthening the focus around individual heterogeneity. First, when arguing for the choice of critical/pivotal positions, the literature often fails to recognize the importance of the fit of the individual to the chosen strategic position, given the individual's KSAOs. O'Boyle and Kroska (2017) frame this question in the context of the great person theory, arguing that it is premised on the notion that some individuals innately display particular traits and abilities that make them destined for greatness regardless of context. Using the example of Julius Caesar, they question if he were born in 19th-century London, would he have risen to a similar level of prominence as he did in late-republican Rome two millennia previously? This view is reflective of earlier contributions to the TM literature were premised on loading the organization with star performers.
More recently this approach has been challenging. Recent research points to the potential benefits of stars to teams as boundary spanners providing early access to critical new knowledge (see Kehoe, Rosikiewicz, & Tzabbar, 2017) but equally the potentially destructive effect of star overload in firms (Groysberg, Polzer, & Elfenbein, 2011). For example, Aguinis, O'Boyle, Gonzalez-Mulé, and Joo (2016) point to the impact of environmental factors that can serve as conductors in enhancing emergence and star performance versus insulators that can minimize and impede stars (see also O'Boyle & Kroska, 2017). Yet, much of the literature continues to treat stars as a homogenous group and hence implying the additive effect overlooking issues of complementarity and possible synergies between stars.
Building a Differentiated Talent Architecture for organizational performance
As noted above, a central premise of differentiating the talent architecture is to increase organizational performance through maximizing the work motivation, organizational commitment, and extra-role behavior of those in the talent pool (Collings & Mellahi, 2009). The underlying objective is to invest disproportionately in positions that offer the potential for above average impact (Boudreau & Ramstad, 2007). This is consistent with recent strategic HRM literature that acknowledges that better management of the core workforce will likely have the greatest impact on value creation and sustainable competitive advantage (Delery & Shaw, 2001; Lepak & Snell, 1999; Schmidtt, Pohler, & Willness, 2017). This means that organizations should make informed decisions around the optimal level of talent required in key positions and other roles (Huselid & Becker, 2011) and the appropriate levels of investment in individuals in those roles (Collings, 2017).
A key tension that emerges in discussions of a differentiated HR architecture is that while heterogeneity in aspects of the employment experience has the potential to motivate those in the talent pool, it also raises the risk of perceptions of inequality or injustice for those outside the talent pool (De Boeck et al., 2018; Meyers et al., 2017). De Boeck et al.'s (2018) recent comprehensive review of the literature concerning how those who were and were not designated as talent reacted to the designation is a useful contribution in this regard. It is important to state that their findings should be considered with the caveat that there is little empirical research showing a direct relationship between workforce differentiation or strategic TM more broadly and organizational performance outcomes (Collings, 2017; Meyers et al., 2017). Indeed, the extant research has tended to focus on proxy measures of performance at the individual level, as opposed to organizational-level outcomes. A key example is affective commitment. Theoretically affective commitment has been proposed as a key bridge between TM and organizational performance (Collings & Mellahi, 2009; Gelens et al., 2014).
De Boeck et al.'s (2018) analysis showed that TM practices generally correlated with positive affective (job satisfaction and organizational commitment) and behavioral (task performance and reduced turnover intentions) outcomes, with smaller positive effects on cognitions (psychological contract fulfilment and beliefs in knowledge, skills, and abilities). An interesting finding from qualitative studies they reviewed was that talent status was simultaneously associated with more negative affective reactions such as stress and insecurity. However, when they compared the evidence on the differential impact between those designated as talent versus those not designated, the former was also correlated with higher reported levels of work effort and stronger intentions to remain with the organization.
The situation with regard to differences between the groups in terms of attitudinal and cognitive reactions was less clear. This was largely traced to imbalance between the perceived employer and employee obligations. Those designated as talent often expected to receive more from their employers than they were prepared to give in return and also reported higher levels of psychological contract breech. This suggests the designation of talent translates into a more demanding attitude towards their employer from the perspective of the talented employees (Meyers et al., 2017). However, we need far more research that considers both positive and negative impacts of bundles of TM practices and of the differentiated HR architecture has on these outcomes.
By bringing the individual level of analysis, we should be able to better understand how individual employees deliver performance outcomes that are linked to the strategic intent of the organization. Based on a micro-perspective, this literature positions individual knowledge, skills, abilities, and other characteristics (KSAOs) as positively related to performance regardless of context. Hence, the value is additive and a more-is-better approach is appropriate (c.f. Ployhart & Moliterno, 2011). Equally, as is evidenced through the human capital resources literature individual-level human capital is not necessarily isomorphic with firm-level human capital. This literature highlights that, while individual human capital may facilitate individual performance, this will not necessarily translate to firm-level performance (Ployhart & Moliterno, 2011). This points to the value of micro-foundations in developing a multi-level understanding of TM to which we now turn.
Towards a Multi-Level Understanding of Talent Management
As we illustrate above, TM is discussed at multiple levels. We have also argued that regardless of the level, to advance the discussion, it must be rooted in the individual behaviors, since individuals are embedded in various contexts.
Figure 3.1 illustrates the causal relations of embeddedness and simplifies causal mechanisms underlying TM with “arrows” linking various “nodes” located at multiple levels of analysis. This visualization should only be viewed as a map for future research, rather than a complete theoretical model. The model is further specified in the below guidelines for research and practice.
Figure 3.1 Road map
Guideline 1 (arrows 1, 2, and 3): Future research and practice in talent management should be more explicit about the choice of the point of differentiation, its origins, and its consequences.
Level: from macro (organizational) towards meso (working group), disaggregation.
Towards this, the starting point should be in business strategy and the guiding question is “What kind of organizational capabilities are needed to deliver our business strategy?” As a Chief Human Resource Officer (CHRO) explains: “Business strategy defines the capabilities needed to win. Those capabilities drive the definition of talent and the decisions about how it is deployed organizationally. All other processes support the deployment of talent to build capabilities” (Alziari, 2017: 379).
Depending on the point of differentiation, the differentiated talent architecture needs to be built. It should include TM practices aimed at developing individuals (e.g., stars or talents) and/or identifying critical/pivotal positions and ensuring they are resourced appropriately. A useful consideration is how core (HR practices that have equal value in all strategic business processes) and differentiated HR architectures (TM practices designed based on the point of differentiation) would interact in the organization. A central consideration is that employees perceived the system to be fair. For example, Bjorkman et al.'s (2013) research on perceptions of talent status and impacts on key individual level outcomes found that the perceptions of fairness of the system by which individuals were designated as talent were central in explaining their reaction to their designation. This is key, as individual employees may withhold effort if they perceive that the firm has not dealt fairly with them (Minbaeva, 2013; Wright & McMahan, 1992).
Guideline 2 (arrows 4 and 5): Future research and practice in talent management should account for variance in individual perceptions of differentiated HR architecture, resulting in variance in AMO and subsequently individual behavioral responses to talent management.
Level: micro-level, embedded individual.
To stimulate multi-level thinking in future studies, we suggest that TM practices included in the differentiated HR architecture could be further specified as intended, implemented, and perceived HR practices. Wright and Nishii (2007) define intended HR practices as those that are “tied directly to the business strategy or determined by some other extraneous influences” (p. 11), and distinguish such practices from implemented (those that “are actually implemented”) and perceived (practices that are “perceived and interpreted subjectively by each employee”) HR practices (Wright & Nishii, 2007: 11).
Fundamental to the understanding of the relationship between employee performance and organizational outcomes is the ability (individual KSAOs), motivation (drivers of individual behavior), and opportunity (conditions that enable or constrain task performance beyond an individual's direct control) (AMO) framework (Blumberg & Pringle, 1982). Generally expressed as Performance = f(A × M × O), the factors interact in a multiplicative fashion. All three elements must be present for high performance; a low value on any dimension results in markedly lower performance outcomes (Blumberg & Pringle, 1982; Kim et al., 2015). Kim et al. (2015) liken this to a virtuous cycle, with each factor supporting the other two. If any of the three are weak, however, a vicious cycle may emerge, reducing value in the organization.
Guideline 3 (arrows 6 and 7): Future research and practice in talent management should develop the measures for the effectiveness of talent management at the work-group level, which when aggregated could explain the variance in performance in between differentiated talent and core group. It should be possible to observe a disproportionate contribution of the differentiated talent to business performance.
Level: aggregation from meso (working group) to macro (organizational).
With this guideline, we advocate a more nuanced understanding of how talent performance unfolds in the firm. We believe that a more expansive measure of “return on talent” should be developed at a working group level. The measure should capture a group-level shared response to the implemented differentiated HR architecture, treating some employees differently from others (based on the choice of the point for differentiation). Although individuals form the perceptions, a social information processing perspective suggests that such work-related perceptions are “filtered through the collective sense-making efforts of the group of employees with whom an individual most often works and interacts” (Kehoe & Wright, 2013: 370; see also Bowen & Ostroff, 2004).
Furthermore, when forming the judgments, employees who have no experience or cannot recall personal responses to differentiation are likely to rely on the experience of co-workers to whom they are socially close (Kehoe & Wright, 2013). To evaluate differentiation effectiveness, the analysis needs to be undertaken at the organizational level where the performance of the differentiated group needs to be compared to a non-differentiated one. For example, a growing body of literature on strategic TM calls for the consideration of pivotal positions as a key point of departure for building differentiated talent-management systems. After the pivotal positions are identified, the organization can investigate whether there is high variability in performance among the people who occupy them. This requires creating metrics able to capture performance variability. For example, Nathan Myhrvold, former Chief Technology Officer at Microsoft, says “the top software developers are more productive than average software developers not by a factor of 10X or 100X or even 1,000X but 10,000X” (Becker, Huselid, & Beatty, 2009: 61).
Equally, while this may be a relatively small proportion of overall employees (10–20% of employees), based on insights from the literature on “stars”, we argue that these employees are likely to contribute disproportionately to unit performance. By definition, star employees display disproportionately high and prolonged performance relative to peers (Aguinis & O'Boyle, 2014; Call et al., 2015; O'Boyle & Kroska, 2017). The disproportionate impact of star employees is reflected in recent research that points to a power distribution in star performance where a smaller percentage of employees contribute a disproportionate amount of value, the so-called 80:20 rule (O'Boyle & Kroska, 2017). The value that stars generate is also considered to be multiplicative as opposed to additive in contributing to higher-level outcomes (O'Boyle & Kroska, 2017). The value of non-stars or so called “B-players” is of course recognized in enabling stars and in performing well in less pivotal roles (Groysberg & Lee, 2008). However, as noted above, individual capability and performance may not be fully isomorphic with firm-level outcomes, and the organizations TM system should be designed to maximize the contribution of star employees (Collings et al., 2018; Ployhart & Moliterno, 2011).
Implications
Implications for Research
Individual talent is simultaneously embedded in several contexts, such as the specific context of his/her team, organization, and the external business-network context. However, current research on TM often fails to recognize this multiple embeddedness of talent. Individual interactions with various contexts in which this individual is embedded may also be an important source for variance observed at supra-individual level (in addition to the variance created by individual heterogeneity). We argue that future research should recognize the nested nature of talent as it has implications for talent identification, talent mobility, and performance implications of TM programs.
Future research should also explicate the causal mechanisms behind the assumed aggregation from individual behavioral responses to the expected “return on talent” at the organizational level. Aggregation from “micro” to “macro” assumes complex interdependencies between action of individual and that of others in the same context. Explaining such interdependencies has proved to be a “main intellectual hurdle both for empirical research and for theory that treats macro-level relation via methodological individualism” (Coleman, 1986: 1323).
Theoretically, the HR process literature might represent a useful lens through which to explore these questions in the context of TM (Ehrnrooth & Björkman, 2012; Nishii, Lepak, & Schneider, 2008). This perspective can provide important insights into how individuals perceive TM practices and how this can impact on the effectiveness of these practices in achieving individual and unit level performance. An alternative theory which could significantly aid in exploring the micro-foundations perceptive is the human capital resources perspective (Ployhart & Moliterno, 2011; Ployhart, Nyberg, Reilly, & Maltarich, 2014; Weller, Hymer, Nyberg, & Ebert, 2018). Theoretically human capital resources provide a useful means of theorizing how individual human capital can be valuable for unit level outcomes.
Implications for Practice
A micro-foundations preceptive also has significant implications for practice. Key among those is a recognition that designing a TM system that appears to meet an organization's strategic requirements is a necessary but not sufficient condition to improve organizational effectiveness. This is because how that system and the practices underlying it are implemented will have a significant impact on their effectiveness. Equally significant is how these practices are perceived by the employees that are the target of the interventions. All three of these steps must be aligned for practices to have the desired effect. This brings the line managers as implementers of such practices to the fore as key stakeholders (Alfes, Truss, Soane, Rees, & Gatenby, 2013; Ehrnrooth & Björkman, 2012).
Conclusions
Despite over two decades of discussion of talent management, our understanding of the link between TM and organizational outcomes such as performance is very limited. In this chapter, we argue that this limited understanding is explained in part by the unclear conceptual and intellectual boundaries of the area. One key limitation in this regard is a poor understanding of the mechanisms by which TM links to organizational outcomes. Our argument is that a micro-foundations perspective provides a key building block in explicating the linkages between TM and these organizational outcomes. This is premised on our understanding of individuals as the key source of variance in organizational level outcomes. Our hope is that the chapter will motivate further research from this important prescriptive.
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