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Marius Schmid

Wissenschaftlicher Mitarbeiter
Büro 52-6150
Müller-Friedberg-Strasse 8
9000 St Gallen
+41 71 224 3800


Despite the omnipresence of platforms and the breadth of related re-search, predicting the outcomes of platforms remains challenging. To reach a critical mass of complementary partners innovating on their platform, platform owners must foster both generativity and profitability on their platform. The con-ditions under which these objectives may be achieved have yet to be delineated, however. The study at hand theorizes that the impact of platforms’ promised ca-pabilities is delineated by the conditions in the competitive environment. Through simulation, explicitly designed to understand system-level behavior, this theorization can be tested. The developed agent-based simulation model cap-tures a platform within its surrounding business ecosystem. It accounts for plat-forms’ value proposition to lower search costs and resource costs associated with innovation, as well as related governance decisions faced by platform owners. Importantly, conditions in the competitive environment, namely environmental complexity, may also be adjusted for experimental testing. The simulation model thereby enables a flexible investigation of platform governance decisions and broader environmental conditions. Experimentation results reveal platforms as ineffective at fostering generativity and profitability in low complexity environ-ments, implying platforms to not be uniformly promising or disruptive in all kinds of competitive environments. Thus, this study’s main contribution is an agent-based simulation model to help understand and predict outcomes of plat-forms as complex phenomena.

Successfully innovating in business ecosystems requires firms to tame the complexity in their competitive environment. Firms must be generative enough to meet dynamically changing customer needs and reap profitability from their innovation efforts. Platforms promise to foster both generativity and profitability through their offered capabilities. Employing an agent-based simulation approach in modeling a platform within its ecosystem, we analyze the effectiveness of platform capabilities under varying conditions. We find complexity in the ecosystems to be a contingency for generativity and profitability effects of platforms. When complexity is low, firms are able to successfully innovate on their own without help from platforms. As complexity increases to medium and high levels, however, firms face higher adaptive tension and platform capabilities become more desirable and effective to generate profitable innovations. We thereby challenge the assumption of platforms being disruptive to all types of environments and delineate under which conditions platforms help taming complexity.

Firms struggle to meet dynamically changing customers’ needs. One challenge is to navigate a complex search space to find resources needed for innovations that meet customers’ needs. Another challenge is to acquire the resources at lower costs than revenue opportunities to yield profitability. Digital platforms promise to address these challenges better than the market by providing search matching capabilities and modular, reusable resources. We examine whether platforms improve innovation performance and profitability of firms better than the market, as assumed. Using agent-based modeling and simulation, we find that firms perform better in the market when environmental complexity is low. As environmental complexity increases, firms start to perform better on the platform than in the market, specifically when the platform owner remarkably invests in search matching and modularity capabilities. The study advances our understanding of the environmental conditions under which platforms could be superior or inferior to the market.

In the context of digital platforms, platform owners strive to maximize both their platform’s stability and generativity. This is complicated by the paradoxical relationship of generativity and stability, as well as associated tensions. To aid B2B platform owners in their design decisions, we aim to derive specific design principles that address the inherent tensions such that generativity and stability are maximized simultaneously. This requires a better understanding of when and to which extent a platform’s generativity and stability are paradoxical, and under which circumstances they can be maximized simultaneously. Thus, we first develop an agent-based simulation model to analyze the effects of an exemplary design decision regarding a tension (i.e. control vs. openness) on a platform’s generativity and stability. The developed simulation model enables predictive analyses of varying degrees of control and openness and their effect on generativity and stability. The simulation model must be further refined and applied to other tensions to thoroughly understand the impact of design decisions on a platform’s generativity and stability, and ultimately derive design principles.