Omar A El Sawy

The success of platform companies often depends on their ability to “scale” their customer and supplier base. Existing studies have focused on a variety of approaches that platforms may use to “scale” but have not systematically considered that platforms might acquire other companies as part of this growth strategy. In this paper, we study the acquisition patterns of digital platform companies and contrast these to the acquisition patterns of digital non-platform companies. We find that platform companies acquire earlier (shortly after founding) when compared with non-platform companies, and they often first acquire competing platform companies from the same market niche. As platform companies mature, they begin to acquire non-platform companies from other market niches. This contrasts to how acquisitions are made by non-platform companies, as shown in our analysis.

Artificial Intelligence for Sustainable Value Creation provides a detailed and insightful exploration of both the possibilities and the challenges that accompany widespread Artificial Intelligence

Faced with the challenge of multifaceted digital phenomena, researchers in IS and related fields have increasingly adopted qualitative comparative analysis (QCA). However, in the absence of explicit guidelines for how to use QCA for theory development, the popularity and proliferation of QCA possibly amplifies the risk of using QCA in an atheoretical manner, hindering theoretical advancement. In this paper, we offer a conceptual framework and prescriptive guidelines for applying QCA to develop causal recipes that account for complex digital phenomena marked by theoretical and configurational multiplicity. Causal recipes are formal statements explaining how causally relevant elements combine into configurations associated with outcomes of interest. We describe these causal recipes in terms of which causes matter (i.e., factorial logic)  and how these causes combine into configurations (i.e., combinatorial logic) to produce target outcomes, and propose an ecology of configurations that elucidates the explanatory power of multiple configurations as well as their explanatory overlap. Further, we offer two illustrative empirical examples to demonstrate the usefulness of our framework and step-by-step guidelines for applying QCA to deductive theory testing as well as inductive theory development on phenomena marked by multiplicity.