A Commentary on Wolf and Stock-Homburg (2025): Robot-Managers and Emergent Human Capital Resources

Abstract

This commentary examines Wolf and Stock-Homburg’s (2025) timely study on robotic management through the lens of human capital resource (HCR) theory. This perspective holds that human capital is more than an individual trait; it is a collective, unit-level resource that emerges through organizational and social processes. HCR theory thus shifts the focus beyond the dyadic dynamics of individual-level acceptance of robot-managers. In particular, I explore three specific dimensions of robotic management: (1) how robot-managers may influence the behavioral, cognitive, and affective processes that drive HCR emergence; (2) how robot-managers may shape the relational dynamics crucial for HCR emergence; and (3) how substituting entry-level managers with robots may disrupt the firm’s managerial HCR developmental pipeline. These insights reframe the challenge of deploying robot-managers in subtle but important ways by bringing into sharp focus the collective-level implications of their use in organizations. Ultimately, effective and relational robotic management is not simply about short-term employee acceptance: robot-managers will reshape how HCRs emerge and create value in the (coming) age of intelligent machines.

Introduction

Scholars define human capital as “a unit-level resource that is created from the emergence of individuals’ knowledge, skills, abilities, or other characteristics (KSAOs)” (Ployhart & Moliterno, 2011, p. 128). That is, human capital is not just an individual trait; it is a resource that emerges at the unit-level ¹ through organizational and social processes that combine individual “capacities that directly influence unit-relevant outcomes” (Ployhart et al., 2014, p. 380). Emergent human capital resources (HCRs) were what I thought about when I read Wolf and Stock-Homburg (2025) … I also couldn’t help thinking of K-2SO, the droid from Rogue One: A Star Wars Story. Early in the film, one of the main characters resists K-2SO’s input on her team’s mission. Only later, as they work together, does she come to accept the droid as a trusted partner. In the Stars Wars universe, where intelligent machines usually serve as little more than tools, K-2SO stands out as a robot whose human-like interactions make it an essential, valued, and accepted collaborator with a high-performing HCR. While I doubt that Wolf and Stock-Homburg had K-2SO in mind when designing their experiment on employee acceptance of robotic managers, their research provokes important questions about the emergence of HCRs in organizations where robots meaningfully contribute to the work of human teams.

This commentary views Wolf and Stock-Homburg’s (2025) findings through the lens of HCR theory (Nyberg & Moliterno, 2019). Taking an HCR perspective on this timely and engaging study focuses attention on more than the dyadic dynamics of individual-level acceptance, and brings into high resolution the collective-level dynamics that will inevitably arise when organizations deploy robotic managers. This shift reframes the challenge of robotic management in a subtle and important way. It is not just about winning individual acceptance; it is about the impact robots will have on the emergent HCRs they manage.

I first consider how robot-managers will influence the behavioral, cognitive, and affective dynamics that drive the HCR emergence process (Ployhart & Moliterno, 2011). I then draw on Ray et al.’s (2023) insights into the role of social capital in HCR emergence to explore the relational dynamics that may come into play when HCRs engage with robot-managers. Finally, I turn to the development of managerial human capital (Helfat & Martin, 2015), asking how replacing entry-level managers with robots might disrupt the experiential learning path through which firm-specific managerial HCRs accumulate. Taken together, I argue that the question of deploying “useful” and “relational” robot-managers extends far beyond employees’ short-term acceptance: robot-managers will reshape how HCRs emerge and create value in the (coming) age of intelligent machines.

From Individual Acceptance to HCR Emergence

Wolf and Stock-Homburg (2025) focuses on individual employees’ acceptance of a robot-manager. To the extent that robot-managers engage in the collective work of the unit, they will necessarily play an instrumental role in the emergence of the unit’s HCR. It is important, therefore, to note that emergence is a function of both the unit’s task complexity and its “emergence-enabling states:” the shared behavioral, cognitive, and affective processes that attend the creation of HCR (Ployhart & Moliterno, 2011). In other words, even if each employee in a unit possesses strong individual capacities, a valuable HCR will not necessarily emerge unless employees, working in their specific task environment, effectively coordinate their actions, share mental models, and maintain trust and cohesion. This raises an intriguing question: what are the implications of a robot-manager participating in these dynamics and interacting with the emergent HCR?

The answer to this question is not immediately obvious. Intensive synchronization, mutual adjustment, and interdependent action enable the emergence of value-creating HCRs in complex task environments. If the robot-manager provides clear instructions, consistent feedback, and unbiased task coordination, it could facilitate emergence enabling coordination and communication, resulting in an HCR where individual employees have aligned expectations about “how we work together” under the robot’s guidance. Insofar as Wolf and Stock-Homburg’s (2025) participants perceived the robot as useful and reliable, this suggests a team climate where members collectively trust and execute the robot’s task directives. This would support HCR emergence.

On the other hand, Wolf and Stock-Homburg’s (2025) finding that over-performance by the robot-managers limits acceptance hints at behavioral and cognitive dynamics that might impede HCR emergence. If the robot is too capable, team members might become passive or disengaged, undermining the very interdependence needed to execute complex tasks. For example, a human manager might encourage discussion to troubleshoot an issue that arises during the unit’s work. A highly useful robot-manager might instead algorithmically assign elements of a solution to different employees, efficiently resolving the problem but bypassing the opportunity for individuals to learn consensus building. Thus, over time, reliance on the robot’s instructions may impede the development of shared mental models, resulting in a unit-level HCR that functions well under routine conditions but lacks resilience or creative synergy when faced with novel problems. In essence, the robot’s usefulness to individuals might result in the classic trade-off between efficient top-down coordination and organic team learning.

The extent of individual-level acceptance itself may also impact the emergent HCR. If employees converge on accepting the robot, cooperation may flow easily. If most unit members embrace the robot-manager while one or two harbor reservations, the group may experience a fractured leadership climate. This would place a significant drag on the HCR, since the shared perception of leadership is an important cognitive emergence enabling state. And if perceptions of the robot-manager diverge dramatically, conflicts or coordination breakdowns may occur. Wolf and Stock-Homburg’s (2025) experiment was not designed to explore these collective-level dynamics. This creates a significant opportunity for future research: in real organizations, acceptance likely will not be all-or-nothing among the individual employees who make-up the unit HCR.

Social Capital and Emergent HCRs

A striking aspect of Wolf and Stock-Homburg’s (2025) findings is the relatively limited payoff of making a robot-manager more human-like in its social interactions. From the perspective of HCR theory, this prompts a closer look at the social fabric of teams led by robot-managers. Ray et al. (2023) argues that social capital—the network of shared relationships and shared norms that facilitate coordination—is a crucial dimension of HCR emergence. These researchers emphasize three relevant dimensions of social capital: structural (patterns of interaction), cognitive (shared understandings), and relational (trust and interpersonal bonds). Focusing on these relational dynamics shines a light on the social dimensions of a robot-manager’s leadership: do robot-managers complement the formation of HCR-enabling social capital?

First, consider relational social capital. Effective human managers typically build trust, provide mentorship, and show concern: behaviors that strengthen employees’ interpersonal bonds. While robots might mimic certain human social behaviors, they likely will lack genuine emotion and empathy. Interestingly, Wolf and Stock-Homburg (2025) finds that the relational dimension of employees’ attitudes have a degressive association with acceptance, while Ray and colleagues (2023) propose that relational capital has a curvilinear relationship with HCR emergence. Thus, while a robot-manager’s ability to generate only a moderate level of relational social capital might avoid the pitfalls of overly cozy teams, it may not inspire the loyalty and commitment that human leaders can evoke in the HCR.

Of course, HCRs that include a robot-manager might develop alternative forms of relational social capital. Team members could bond with each other around the robot, jointly figuring out how to handle its quirks, or even lightheartedly commiserating about their robot boss’ odd habits. Paradoxically, this may engender a relational complementarity: weak robot-human ties could become a shared reference point that strengthens human-human ties, thereby fostering unit relational social capital. Conversely, if some team members strongly embrace the robot-manager and others distrust it, the robot could become a source of division, hampering the HCR’s relational social capital. This again underscores the need to view acceptance as a collective phenomenon.

The structural dimension of social capital (the pattern and frequency of interactions) is also important. On one hand, if the robot acts as a central hub, systematically collecting input from each member and redistributing information, it will reduce the need for direct human-to-human communication. As a result, the density of interactions among humans in the unit will decrease and the HCR’s structural social capital will suffer. This may be a concern, since dense connections that generate more shared knowledge enable the emergence of HCRs. On the other hand, it may be possible to design robot-managers that encourage more interaction among team members. In Wolf and Stock-Homburg (2025), participants made decisions individually, so structural effects did not surface. In real deployments, organizations should monitor whether the robot is replacing human communication or reinforcing it.

Finally, the cognitive dimension of social capital will also influence HCR emergence under a robot-manager. A robot may excel at disseminating information, thereby enhancing the unit’s shared knowledge base. Indeed, one advantage of robot-managers may be this consistency: they can ensure everyone receives the same information and follows the same protocols, strengthening the social capital that is based on shared mental models. However, shared cognition is not limited to tasks: it also includes the unit’s values. HCRs often coalesce around a leader who articulates the vision that imbues the unit’s work with meaning, and Wolf and Stock-Homburg’s (2025) findings suggest that this may be a challenge for robot-managers.

The challenge and opportunity for organizations, then, is to design human-robot interactions such that the robot-managers’ capabilities and the HCR’s social capital reinforce each other. This could mean pairing robot-managers with human mentors or team coaches who supply emotional support in the HCR, encourage peer leadership in areas where the robot is deficient, and program the robot to act in ways that prompt more human-to-human interaction. By intentionally managing this complementarity, firms might be able to encourage the creation of emergence-enabling social capital in HCRs that include a robot-manager.

Managerial HCR Development

Beyond its impact on the dynamics of emergence, the introduction of robotic lower-level managers raises questions about the development of the organization’s managerial HCRs over time (Helfat & Martin, 2015). Wolf and Stock-Homburg motivate their study by noting the shortage of skilled workers, particularly at lower management levels, and suggest robot-managers as one solution. This recommendation may be a double-edged sword: while robots might close a near-term talent gap, relying on them will likely affect the pipeline of the firm’s managerial HCR in the long-term. Put simply, today’s choice to employ robot-managers affects the development of tomorrow’s managerial HCRs.

Lower-level managerial roles (e.g., team supervisors) serve as a crucial training ground for future leaders. Lower-level managers rely mostly on generic skills, as opposed to the more firm-specific skills of higher executives (Castanias & Helfat, 1991, 2001). Through learning by doing, managers develop firm-specific capacities—tacit knowledge of the organization’s processes, culture, and networks—that cannot be gained from external education alone. Since managers develop these valuable capacities through immersive experience within a particular company, it behooves the firm to have a developmental path for future leaders. Replacing lower-level managers with robots would effectively narrow this pipeline, removing the primary avenue by which firms traditionally cultivate managerial HCRs. In essence, using robot lower-level managers may create a soft spot in the firm’s leadership development efforts and undermine the long-run accumulation of managerial HCRs.

On the other hand, the use of robot-managers may create opportunities for upskilling managers and/or facilitate hybrid skill development. Working with a robot-manager could push employees to develop new capacities such as digital collaboration skills, data-driven decision-making, and adaptability to AI systems. In this way, the HCR could become adept at interpreting AI-based output, providing feedback to improve its algorithms, or performing the human-only aspects of teamwork: mentoring, interfacing with clients, etc. Over time, this could result in pipeline of managerial HCRs who have a valuable, firm-specific, and hybrid mix of technical domain expertise, interpersonal team skills, and the ability to work effectively with AI. Thus, rather than simply removing a developmental opportunity, robot-managers could redirect employee HCR capacities, potentially aligning with the increased demand for advanced cognitive and social skills.

Finally, it is worth contemplating the broader organizational implications of robot-managers on the firm’s ability to attract and retain managerial HCRs. If lower-level employees rarely interact with human managers, this may weaken the organization’s culture of mentorship and apprenticeship, impact engagement and retention of early career professionals, and dissuade candidates in the external labor market. On the other hand, a robot-manager who is perceived as making unbiased decisions could increase some employees sense of inclusion and foster meritocratic development. Of course, this is predicated on the organization’s ability to train unbiased robot-managers. In sum, introducing robot-managers at the lower-level of an organization is not just a one-time structural change. It sets in motion attendant mechanisms that will alter the skills, interactions, and learning trajectories of the organization’s managerial HCRs.

Conclusion

Hollywood’s K-2SO was an incredibly effective complement to a high performing HCR. While Wolf and Stock-Homburg’s (2025) robot-managers may not have the same competencies, their scholarship hints at a world with this kind of robot-human interaction. As part of that vision, it is important to remember that an organization’s HCR does not comprise a collection of dyadic robot-human relationships. Rather, is a complex and emergent resource that is characterized by organizational dynamics shaping how human beings—and, perhaps, robots—work together to complete organizational tasks.

Integrating HCR theory enhances Wolf and Stock-Homburg’s (2025) insights by situating their findings in a broader multi-level framework connecting micro-level employee acceptance to macro-level HCR emergence. This encourages a holistic perspective on organizations as social systems where new technology must mesh with the dynamics of HCRs emergence to create value. Scholars and practitioners should therefore evaluate the introduction of robot-managers not just on immediate efficiency or novelty, but on their capacity to enable and enhance the collective knowledge and skills of the organization’s HCR. In the (coming) age of robot-managers, HCR theory offers a compass to navigate “one size does not fit all” scenarios in the evolving landscape of human-robot teams.