Decoding cultural conflicts

As pioneers of the Carnegie Perspective recognized, conflicts in organizations can exist even when incentives of all parties are aligned. These can often be traced to differences in cognitions such as beliefs and values, which are foundational components of any given culture. This paper refines the operationalization of cultural clashes by identifying differences in beliefs about causality ("which actions cause which outcomes") and morality (in the broad sense of "what is evaluated as desirable") as two fundamental sources of conflict. In our first study, we demonstrate empirically that participants recognize and distinguish between these two sources of conflict. In our second study, we test the hypotheses that while misalignments in either causal or moral codes increase observers' perceptions of relationship conflict, negative affect, likelihood of avoidance, and lower perceived likelihood of conflict resolution, the effects are stronger for misalignments in moral codes than misalignments in causal codes and strongest when both causal and moral codes are misaligned. We test these arguments using vignette-based experimental studies. Our findings support our hypotheses. This research has significant implications for the understanding of conflict dynamics within and beyond organizational contexts. By recognizing the pivotal role of cultural differences in shaping conflicts, organizations and decision-makers can better anticipate, manage, and potentially preempt such conflicts.

The Dual Challenge of Search and Coordination for Organizational Adaptation: How Structures of Influence Matter

Organizations increasingly need to adapt to challenges in which search and coordination cannot be decoupled. In response, many have experimented with “agile” and “flat” designs that dismantle traditional forms of hierarchy to harness the distributed knowledge of specialized individuals. Despite the popularity of such practices, there is considerable variation in their implementation as well as conceptual ambiguity about the underlying premise. Does effective rapid experimentation necessarily imply the repudiation of hierarchical structures of influence? We use computational models of multiagent reinforcement learning to study the effectiveness of coordinated search in groups that vary in how they influence each other’s beliefs. We compare the behavior of flat and hierarchical teams with a baseline structure without any influence on beliefs (a “crowd”) when all three are placed in the same task environments. We find that influence on beliefs—whether it is hierarchical or not—makes it less likely that agents stabilize prematurely around their own experiences. However, flat teams can engage in excessive exploration, finding it difficult to converge on good alternatives, whereas hierarchical influence on beliefs reduces simultaneous uncoordinated exploration, introducing a degree of rapid exploitation. As a result, teams that need to achieve agility (i.e., rapid satisfactory results) in environments that require coordinated search may benefit from a hierarchical structure of influence—even when the apex actor has no superior knowledge, foresight, or capacity to control subordinates’ actions.

Formal Hierarchy As a Source of Upward Status Disagreement? A Theoretical Perspective

Formal hierarchies may be presumed to reduce uncertainty about the status ordering of employees as they imply a consistent global ranking. However, formal hierarchies in organizations are not merely linear, but are characterized by branching and nesting (i.e., they comprise subunits within the organization and subunits within other subunits), which creates a local ranking of individuals within each subunit. This can create tension between global and local formal ranks as status cues. Moreover, individuals may also draw on informal status cues that are inconsistent with formal ranks. Consequently, organizational members may experience upward status disagreement (USD), whereby each assumes they have higher status than the other. We offer a theoretical model that identifies important conditions under which cues arising from the structure of the formal hierarchy—either on their own or in conjunction with informal status cues—can be a source of USD. We also explore when USD can result in status conflict and identify moderators of this relationship. Our research has implications for how the frequency of USD can be mitigated as organizational structures become more complex and the workforce becomes increasingly diverse.

Adaptation or Persistence? Emergence and Revision of Organization Designs in New Ventures

How organization designs evolve between adaptation to changing conditions and the pressures toward persistence of the designs adopted at founding remains an understudied phenomenon. To fill this lacuna, we conducted a longitudinal, multicase study of eight young ventures. We find that, in these ventures, specific organization design solutions changed frequently, triggered by various internal and external developments, although the changes were typically incremental and myopic. However, the more abstract principles of design, captured in the founders’ logics of organizing, were less amenable to change. This explains why observations of imprinting effects in logics of organizing may be consistent with observations of dynamic change to organization designs.

Critical Factors Affecting Strategy in the Future

Several trends at work, amplified by the COVID-19 pandemic, are likely to shape the agenda of research in strategic management going forward. These include the growing importance of the “four D’s”- deglobalization, digitalization, diversity and durability (sustainability). This chapter overviews the critical issues that are a result of these trends that are likely to play an important role in the future of the field. It also outlines a few research topics that arise from the impact of these trends on the strategic management field, with the proviso that the topics discussed are by no means a complete list.

Algorithm Supported Induction for Building Theory: How Can We Use Prediction Models to Theorize?

Across many fields of social science, machine learning (ML) algorithms are rapidly advancing research as tools to support traditional hypothesis testing research (e.g., through data reduction and automation of data coding or for improving matching on observable features of a phenomenon or constructing instrumental variables). In this paper, we argue that researchers are yet to recognize the value of ML techniques for theory building from data. This may be in part because of scholars’ inherent distaste for predictions without explanations that ML algorithms are known to produce. However, precisely because of this property, we argue that ML techniques can be very useful in theory construction during a key step of inductive theorizing—pattern detection. ML can facilitate algorithm supported induction, yielding conclusions about patterns in data that are likely to be robustly replicable by other analysts and in other samples from the same population. These patterns can then be used as inputs to abductive reasoning for building or developing theories that explain them. We propose that algorithm-supported induction is valuable for researchers interested in using quantitative data to both develop and test theories in a transparent and reproducible manner, and we illustrate our arguments using simulations.

Division of Labor Through Self-Selection

Self-selection–based division of labor has gained visibility through its role in varied organizational contexts such as nonhierarchical firms, agile teams, and project-based organizations. Yet, we know relatively little about the precise conditions under which it can outperform the traditional allocation of work to workers by managers. We develop a computational agent-based model that conceives of division of labor as a matching process between workers’ skills and tasks. This allows us to examine in detail when and why different approaches to division of labor may enjoy a relative advantage. We find a specific confluence of conditions under which self-selection has an advantage over traditional staffing practices arising from matching: when employees are very skilled but at only a narrow range of tasks, the task structure is decomposable, and employee availability is unforeseeable. Absent these conditions, self-selection must rely on the benefits of enhanced motivation or better matching based on worker’s private information about skills, to dominate more traditional allocation processes. These boundary conditions are noteworthy both for those who study as well as for those who wish to implement forms of organizing based on self-selection.

Disseminated Nocardia farcinica in an immunocompetent patient

Nocardia farcinica is a gram-positive, partially acid-fast, methenamine silver-positive aerobic actinomycete that is infrequently associated with nocardiosis. The relative frequency of Nocardia farcinica isolates in nocardiosis is unknown but thought to be under diagnosis. It is increasingly been recognized in immunocompetent patients.

We report a case of disseminated Nocardia farcinica causing brain abscess in 55 year old immunocompetent man who was successfully treated with long term antibiotics.

The present report illustrates that early detection and treatment of disseminated Nocardia farcinica can lead to a good outcome.