With the rise of machine learning (ML), humans are no longer the only ones capable of learning and contributing to an organization’s stock of knowledge. We study how organizations can coordinate human learning and ML in order to learn effectively as a whole. Based on a series of agent-based simulations, we find that, first, ML can reduce an organization’s demand for human explorative learning that is aimed at uncovering new ideas; second, adjustments to ML systems made by humans are largely beneficial, but this effect can diminish or even become harmful under certain conditions; and third, reliance on knowledge created by ML systems can facilitate organizational learning in turbulent environments, but this requires significant investments in the initial setup of these systems as well as adequately coordinating them with humans. These insights contribute to rethinking organizational learning in the presence of ML and can aid organizations in reallocating scarce resources to facilitate organizational learning in practice.

Due to a huge amount of scientific publications which are mostly stored as unstructured data, complexity and workload of the fundamental process of literature reviews increase constantly. Based on previous literature, we develop an artifact that partially automates the literature review process from collecting articles up to their evaluation. This artifact uses a custom crawler, the word2vec algorithm, LDA topic modeling, rapid automatic keyword extraction, and agglomerative hierarchical clustering to enable the automatic acquisition, processing, and clustering of relevant literature and subsequent graphical presentation of the results using illustrations such as dendrograms. Moreover, the artifact provides information on which topics each cluster addresses and which keywords they contain. We evaluate our artifact based on an exemplary set of 308 publications. Our findings indicate that the developed artifact delivers better results than known previous approaches and can be a helpful tool to support researchers in conducting literature reviews.

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