Rule growth in heterarchical organizations: The case of Holacracy

Bureaucratic organizations dominated the last century because they enabled managers and employees to perform tasks with unprecedented efficiency. These structures seem to reach their limits in the “digital era” (e.g., Clegg (2012)). Consequently, organizations in a wide range of industries consider switching to a less-hierarchical organizational design, as these seem to deal better with the fast-changing environment (Bernstein et al., 2016). To compensate for a missing predefined organizational structure, heterarchical organizations, i.e. organizations without a formal hierarchy, make sophisticated use of digital tools for coordination and communication (Fosbrook, 2016). Holacracy (Bernstein et al., 2016) is one specific manifestation of a heterarchical organizational design (Fosbrook, 2016). Holacratic organizations consist of self-managed circles (i.e. 1 teams) with employees holding different roles in one or more circles. Because of the absence of formal hierarchy, holacratic organizations (Bernstein et al., 2016) heavily rely on formal rules to remain organized. Even with increasing size, these organizations continuously change and adapt to emerging situations by creating, modifying, and dropping roles and circles. This observation stands in contrast to literature on bureaucratic growth that assumes that growth leads to inertia (Schulz, 1998). We set out to address this contradiction and investigate the research question: ”How does change unfold in holacratic organizations?” We use the case example of Springest, an organization that adopted Holacracy and experienced rapid growth over the last two years. Springest’s organizational structure is changing constantly (see figure 1), as authority is distributed through the organization and organizational members have the authority to change roles and circles within a given frame of rules. We approach the aforementioned research question from an empirical angle. We extracted system data of Springest and analyzed this information by using network analysis. Additionally, we carried out a statistical analysis to test for the significance of patterns we observed in the dynamic network. A preliminary result of our analysis is a dynamic network visualization that demonstrates how roles are created, changed, and extinct. While our preliminary findings contradict the negative density-dependence of rule birth, our analysis and subsequent interpretation remain to be completed.