F or many sectors like health care, financial services, or renewable energy, new products and services are generated by an ecology of business firms, nonprofit foundations, public institutions, and other agents. Knowledge to innovate is dispersed across ecologies, so no single firm or small group of firms can innovate alone. Moreover, many new products and services in ecologies such as health care or energy are complex or comprise many parts with unknown interactions. New products, knowledge, business models, and applications all emerge unpredictably over considerable time periods, as various agents in the ecologies of innovation interact with and react to the actions of others. However, the existing organizing structure in these ecologies stifles emergence and precludes much innovation, simply because theory and practice do not adequately address how to organize for complex innovation. We develop a preliminary model for organizing ecologies of complex innovation. We suggest that innovations can continually emerge productively if people work locally in ecologies to set and solve problems of orchestrating knowledge capabilities across the ecology, strategizing across the ecology to create new businesses and applications, and developing public policies to embrace ambiguity. Using examples from biopharmaceuticals and alternative energy, we develop specific organizing ideas that can be examined and elaborated upon. This new direction for organization science integrates existing ideas around a new kind of organizing and shows how organization science can add real value in addressing major challenges of public welfare and safety in the 21st century.
Drug discovery is a complex innovation process in which scientists need to make sense of ambiguous findings and grapple with numerous unpredictable interdependencies over many years of product development. Digitalization has combined with expanding science to address this complexity, creating new ways to measure, analyze, and model chemical compounds, diseases, and human biology. We interviewed 85 scientists and managers working on drug discovery to understand how they deal with complexity. We find a major knowledge fault line between digital scientists, who use computers as laboratories and manipulate signs, and therapy scientists, who use conventional laboratories and manipulate physical material. We build on research on epistemic cultures and knowing in practice to develop empirically grounded theory for the role of digital science in complex innovation. We propose that digitalization creates a new form of knowledge that provides essential complementary insights for complex innovation that cannot exist otherwise. However, digitalization also creates new knowledge boundaries that concern central activities of innovation. These boundaries highlight challenges of complex innovation that digital sciences can help address, but only if the innovation activities are transformed so that digital and therapy sciences can integrate their complementary knowledge.
Complex innovation processes such as drug discovery present challenges to innovators because they must proceed with limited feedback but face a system that involves enormous amounts of information and unknown interdependencies. Organizational scholars suggest that abductive reasoning fits complex situations and may address many of the challenges of complexity. Abductive reasoning is a form of reasoning that generates and evaluates hypotheses in order to make sense of puzzling facts. Existing research on abductive reasoning makes a number of important contributions, but does not explain how innovators can use abductive reasoning to formulate hypotheses for possible new products and then use these hypotheses to navigate in the labyrinth of complex product innovation. We interviewed 85 scientists and managers working in the biopharmaceutical industry and use grounded theory building to develop a new framework. Our framework identifies three social mechanisms that explain how innovators use abductive reasoning to detect useful information despite the noise, avoid competency traps and local optima, and accumulate insights in a holistic way. We contribute to existing research by explaining the systematic process that enables innovators to overcome the challenges of complex innovation and navigate effectively in the labyrinth.
Time pacing, which refers to the regulation of intensity and direction of people's attention and effort, is central to innovation management. However, in a study of complex product innovation in pharmaceuticals, we find that time pacing is a major source of conflict between managers and scientists over innovation management. Our analysis of this tension reveals that two very different forms of time pacing operate in this complex innovation. Clock-time pacing, which gauges progress by the predictable passage of clock time, is used by strategic managers to reduce unnecessary exploration, focus on necessary questions, and speed up the execution of steps. Event-time pacing, which gauges progress by the unpredictable achievement of learning events, is used by the scientists to develop a deep understanding of how a drug might behave in the body against a disease, to focus on learning by asking many questions, and to integrate emergent results into plausible patterns. We identify four dimensions that differentiate clock-time pacing from event-time pacing, which drive the tension between the two. We summarize negative effects that this tension can have on innovation if left unaddressed, and then suggest ways to integrate clock-time pacing with event-time pacing. We also discuss implications for Chinese management.
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