Qualitative Research on Incumbents’ Responses to Discontinuous Technologies: Distilling an Integrative Framework of Context
Juan Carlos Rivera-Prieto et al.
Abstract
Scholars have long and effectively used qualitative-inductive methodologies to understand the heterogeneous responses of established organizations—so-called incumbents—to emerging discontinuous technologies. However, after nearly 30 years of nuanced, contextualized qualitative research in this area, there is a unique opportunity—and essentially a need—for its distillation, integration, and critical reflection. In particular, while qualitative studies on heterogeneous incumbent responses aspire to develop “theories of the middle range,” and, thus, to acknowledge boundary conditions and contextual nuances, in practice, they rarely discuss a given theory’s range systematically. This disconnect limits the comparability, generalizability, and integration of findings. To address this instability, we inductively and critically review 127 qualitative studies on incumbents’ responses to discontinuous technologies published between 1998 and 2024. The central outcome of our review is an integrative framework of core contextual dimensions of incumbent responses to emerging discontinuous technologies, organized along six attributes related to the overarching domains of technology, market, and organization and institutions. Our distilled framework provides a taxonomic map for systematically comparing different empirical contexts of incumbent adaptation to discontinuous technologies and critically considering the boundary conditions of qualitatively induced theorizing in this regard. Our framework also enables us to present an encompassing program for future qualitative research on incumbent heterogeneity, one of the core phenomena underlying the overall process of creative destruction.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.