This paper explores the idea that well‐aligned HR practices may produce varied and even negative effects on innovation performance. To do so, we examine the interaction effect between rewards for and appraisal of knowledge behaviours on radical and incremental innovation outcomes. Drawing on the insights from the strategic HRM literature on the internal fit between HR practices, as well as the developments of the knowledge governance approach, we argue that rewards and appraisal applied together produce a setting that is conducive for deepening existing knowledge bases, but hindering for more distant and diverse knowledge search. Empirical test of these hypotheses using the data from 259 Finnish companies lends partial support for this argument. Intensive usage of appraisal of knowledge behaviours reduces the positive impact that rewards for such behaviours have on radical innovation. At the same time, rewards and appraisal do not intensify each other's effect on incremental innovation.

The introduction of machine learning (ML)in organizations comes with the claim that algorithms will produce insights superior to those of experts by discovering the “truth” from data. Such a claim gives rise to a tension between the need to produce knowledge independent of domain experts and the need to remain relevant to the domain the system serves. This two-year ethnographic study focuses on how developers managed this tension when building an ML system to support the process of hiring job candidates at a large international organization. Despite the initial goal of getting domain experts “out the loop,” we found that developers and experts arrived at a new hybrid practice that relied on a combination of ML and domain expertise. We explain this outcome as resulting from a process of mutual learning in which deep engagement with the technology triggered actors to reflect on how they produced knowledge. These reflections prompted the developers to iterate between excluding domain expertise from the ML system and including it. Contrary to common views that imply an opposition between ML and domain expertise, our study foregrounds their interdependence and as such shows the dialectic nature of developing ML. We discuss the theoretical implications of these findings for the literature on information technologies and knowledge work, information system development and implementation, and human–ML hybrids.

In response to the calls for more context-aware theorizing, in this essay we review the empirical research on individual knowledge sharing behavior in organizations, with a specific focus on the context in which employees share knowledge. We build on the "Who? / Where? / Why? / What?" framework to "flesh out" the contexts of the empirical studies on individual knowledge sharing published in top-level journals. Mapping the researched contexts, we indicate several biases of the literature as well as point to under-investigated spaces, suggesting theoretical dimensions, their contrasts, and new empirical settings that are missing from the major stream of knowledge sharing studies. We also find that context has been scarcely accounted for in the existing literature, discuss the reasons for it, show how accounting for context can be used to re-interpret some contradictions in existing literature, and suggest some ways to move forward.