I'm fascinated by how organizations learn to be more intelligent via and along with their human constituents, and perhaps more importantly, conditions where they fail to do so. My dissertation topic revolves around the process of intra-organizational knowledge aggregation. In particular, I study how organizational learning is shaped by (1) organizational structure & (2) the shift to remote work, (3) employee turnover, and (4) power and coalitional dynamics among organizational actors.
The emergent nature of this multi-agent problem calls for the use of computational simulation as my main research method. To get raw materials for inspiring and testing models, I'm enthusiastic about machine learning approaches.
Remote work (RW) is likely to become a permanent feature of organizational life. But despite numerous benefits to individual workers, RW can also decrease the ability of an organization to process information and thus diminish performance. We argue that such a tradeoff may be avoidable: Adjusting organizational structure can reduce the negative impact of RW in most organizations. Using a computational model, we show that complexity and turbulence in the organizational environment, in conjunction with organizational structure, play a crucial role in determining the impact of RW on organizational outcomes. Our analysis reveals that while some organizations may be relatively unaffected by RW, others may need to adapt their organizational structures to maintain performance. We identify factors to consider in RW implementation and propose avenues for further research to help better understand this paradigmatic shift.
Hiring employees from high-performing rivals is a common means of acquiring knowledge and enhancing capabilities. But there is mixed evidence as to whether firms actually benefit from hires with knowledge that is very distant from their own, suggesting limits to this channel. With the help of a computational model, we explore these limits by examining how the knowledge distance of a new hire moderates the hiring firm’s ability to absorb knowledge. Specifically, we show that knowledge complexity has a critical impact on the benefits of hiring an employee with distant knowledge and that the level of refinement of the hiring firm’s knowledge shapes benefits from hiring. We thus reconcile prior conflicting findings and provide further nuance to our understanding of this important phenomenon and the key mechanisms.
The discussions of organizational politics and processes of organizational adaptation have developed as largely independent streams of work. We suggest that organizational politics—in particular the power dynamics of the dominant coalition—can be a driver for both patterns of “continuity and change” within organizations. The inertial force is two-fold. The dominant coalition can influence corporate strategies to conform to its interest and, further, even organizational units that were not initially part of the dominant coalition will tend to adapt their policies to these corporate strategies. However, the dynamics within the organization’s political structure, and in particular the reconfiguration of its dominant coalition, may facilitate the process of organizational change and adaptation. A political process only requires the shift in the relative power of one coalition over another to change the dominant coalition and the overall direction of an organization, whereas a well-intentioned apolitical strategist shifts strategy only when there is a shift in the perceived interest of the mass of the organization. We find that despite the inertial forces of organizational power, under settings of moderate goal conflict within the organization, a political process leads to a more dynamic and adaptive organization than one that is apolitical, i.e., guided by an understanding of the common interest rather than the contestation of the individual interests of its subunits.