We investigate the business model configurations associated with high and low firm performance by conducting a qualitative comparative analysis of firms competing in Formula One racing. We find that configurations of two business models-one focused on selling technology to competitors, the other one on developing and trading human resources with competitors-are associated with high performance. We also investigate why these configurations are high-performing and find that they are underpinned by capability-enhancing complementarities, accelerating firms' learning and supporting the development of focused firms' capabilities.

Until recently, scholars have customarily lumped multiple dimensions of environmental change into single constructs, and usually ascertained that the more the context changes, the more value firms derive from higher levels of exploration. In sync with more recent studies focusing on specific dimensions of change, in this paper we borrow theoretical elements from systems theory to examine the possibility that the reward to developing innovative product components may itself be eroded by implicit and yet burgeoning costs to fit the new component technology into existing architectures, thereby dampening system performance. Specifically, we theoretically assess how varying magnitudes of industry regulatory changes affect the optimum level of firm exploration, and propose—counterintuitively vis-à-vis past literature—that the more radical (i.e., competence destroying), as opposed to incremental (i.e., competence enhancing), these changes are, the more the optimum intensity of firm exploration recedes. Based on quantitative as well as qualitative empirical analyses from the Formula One racing industry, we precisely trace the observed performance outcomes back to the underlying logic of our theory, stressing that impaired capabilities to integrate the new component in the architecture redesign and time-based cognitive limitations both operate to inhibit the otherwise positive relationship between firm exploration and performance. In the end, we offer new insights to theory and practice

The concept of modularity has gained considerable traction in technology studies as a way to conceive, describe and innovate complex systems, such as product design or organizational structures. In the recent literature, technological modularity has often been intertwined with business model innovation, and scholarship has started investigating how modularity in technology affects changes in business models, both at the cognitive and activity system levels. Yet we still lack a theoretical definition of what modularity is in the business model domain. Business model innovation also encompasses different possibilities of modeling businesses, which are not clearly understood nor classified. We ask when, how and if modularity theory can be extended to business models in order to enable effective and efficient modeling. We distinguish theoretically between modularity for technology and for business models, and investigate the key processes of modularization and manipulation. We introduce the basic operations of business modeling via modular operators adapted from the technological modularity domain, using iconic examples to develop an analogical reasoning between modularity in technology and in business models. Finally, we discuss opportunities for using modularity theory to foster the understanding of business models and modeling, and develop a challenging research agenda for future investigations.

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Decision support systems (DSS) are sophisticated tools that increasingly take advantage of big data and are used to design and implement individual-and organization-level strategic decisions. Yet, when organizations excessively rely on their potential the outcome may be decision-making failure, particularly when such tools are applied under high pressure and turbulent conditions. Partial understanding and unidimensional interpretation can prevent learning from failure. Building on a practice perspective, we study an iconic case of strategic failure in Formula 1 racing. Our approach, which integrates the decision maker as well as the organizational and material context, identifies three interrelated sources of strategic failure that are worth investigation for decision-makers using DSS and big data: (1) the situated nature and affordances of decision-making; (2) the distributed nature of cognition in decision-making; and (3) the performativity of the DSS. We outline specific research questions and their implications for firm performance and competitive advantage. Finally, we advance an agenda that can help close timely gaps in strategic IS research.