Post-implementation challenges in innovation management: a qualitative case study of a scientific institution in Germany

Abstract

While substantial scholarly attention has been devoted to the implementation phase of innovation management, the post-implementation phase—the period in which established innovation processes must be sustained, refined, and scaled—remains comparatively underexplored, particularly for small scientific institutions and resource-constrained organizations. This study investigates the distinct challenges that emerge after the initial deployment of innovation management strategies at a small, industry-focused scientific institution in Germany, with the aim of distinguishing post-implementation barriers from those that characterize earlier stages and providing a structured understanding of their relative severity. A single-case, qualitative methodology was employed, drawing on 17 semi-structured in-depth interviews with three stakeholder groups: department heads within the focal institution, employees who engage indirectly with the innovation process, and external innovation managers from other German organizations with established innovation management implementations. Interview data were analyzed using Qualitative Content Analysis (QCA) via QCAmap software. Findings reveal that post-implementation challenges constitute a distinct and non-trivial category of barriers that differ qualitatively from the foundational challenges of the implementation phase. High-priority barriers include insufficient customer engagement and limited employee time and capacity, each cited across seven interviews. Medium-priority barriers encompass knowledge deficits among employees, absent feedback loops, lack of agility, financial constraints, and an innovation culture insufficiently tolerant of risk and experimentation. Low-priority but structurally significant barriers include incentive scarcity, communication gaps, and the absence of a designated innovation process coordinator. The study's contribution lies in demonstrating that sustaining innovation after implementation demands capabilities—structured feedback architectures, flexible governance, dedicated accountability roles, and culture-level adaptation—that are qualitatively distinct from those required for initial implementation. These findings provide a theoretically grounded and practically oriented framework for small scientific organizations seeking to sustain their innovation capacity under structural and resource constraints.