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Organizations typically employ a division of labor between specialist creator roles and generalist business roles in a bid to orchestrate innovation. We seek to determine the extent to which individuals dividing the work across roles can also benefit from dividing their network. We argue that collaborating individuals benefit from connecting to the same groups but different individuals within those groups—an approach we label dual networking—rather than from a pure divide-and-conquer approach. To test this argument, we study a dual career-ladder setting in a large multinational in which R&D managers and technologists partner up in their quest for innovation. We find that collaborators who engage in dual networking attain an innovation performance advantage over those who connect to distinct groups. This advantage stems from the opportunity to engage in the dual interpretation of input the partners receive, as well as from dual influencing that helps them to gain momentum for their proposed innovations, and it leads to more effective elaboration and championing of their ideas. In demonstrating these effects, we advance understanding of how collaborators organize their networking activities to best achieve innovative outcomes.

A central part of the innovation process concerns the way firms go about organizing search for new ideas that have commercial potential. New models of innovation have suggested that many innovative firms have changed the way they search for new ideas, adopting open search strategies that involve the use of a wide range of external actors and sources to help them achieve and sustain innovation. Using a large-scale sample of industrial firms, this paper links search strategy to innovative performance, finding that searching widely and deeply is curvilinearly (taking an inverted U-shape) related to performance.

To develop innovations in large, mature organizations, individuals often have to resort to underground, “bootleg” research and development (R&D) activities that have no formal organizational support. In doing so, these individuals attempt to achieve greater autonomy over the direction of their R&D efforts and to escape the constraints of organizational accountability. Drawing on theories of proactive creativity and innovation, we argue that these underground R&D efforts help individuals to develop innovations based on the exploration of uncharted territory and delayed assessment of embryonic ideas. After carefully assessing the direction of causality, we find that individuals’ bootleg efforts are associated with achievement of high levels of innovative performance. Furthermore, we show that the costs and benefits of bootlegging for innovation are contingent on the emphasis on the enforcement of organizational norms in the individual’s work environment; we argue and demonstrate empirically that the benefits of an individual’s bootlegging efforts are enhanced in work units with high levels of innovative performance and which include members who are also engaged in bootlegging. However, during periods of organizational change involving formalization of the R&D process, individuals who increase their bootlegging activities are less likely to innovate. We explore the implications of these findings for our understanding of proactive and deviant creativity.

This article reviews the intellectual contributions of Professor Sidney G. Winter, who is the recipient of the 2015 Global Award for Entrepreneurship Research. Professor Winter has contributed through his theoretical as well as empirical understanding of Schumpeterian processes of dynamic competition, the generation of differential technological opportunities through appropriability conditions and the mechanisms driving dynamic capabilities in firms. His work, especially the joint work on evolutionary economics with Richard R. Nelson, has led to a revival of interest in theories based upon Schumpeterian economics within the study of both entrepreneurship and innovation. His work on dynamic capabilities has been highly influential in management. Professor Sidney G. Winter is Deloitte and Touche Professor Emeritus of Management, The Wharton School, University of Pennsylvania.

Although academics are under increasing pressure to engage industry in their research, they often find it difficult to do so. Conflicts with industry over the timing of disclosure and the choice of topics are common. Moreover, collaborations with industry may require academics to negotiate formal contracts about the ownership of intellectual property. To help understand the factors that might mitigate these conflicts, this paper examines how the professional and collaborative experiences of academics shape their perceptions of the barriers to industry collaboration. Using a rich dataset of UK academics, we find that perceived barriers to collaboration are lower for academics with industrial and collaborative experience and for those who trust their industry partners. However, for the transactional costs of industry engagement, we find entrepreneurial experience and the diversity of methods used to collaborate with industry increases the perceived barriers to collaboration.

University-business collaborations are an increasingly important source of research and development for many companies. Yet despite their importance, the authors argue that many companies take much less care managing these relationships than they do those with their vendors or customers. As a result, business-academic collaborations often fail to achieve as much as they might. By taking a more structured approach, companies can improve the performance of their academic research partnerships, the authors say. To leverage value from universities, the authors argue that business executives need to consider two key dimensions: whether the time horizon of the collaboration is short-term or long-term, and the degree of disclosure of the results of the partnership. Openness facilitates rapid publishing, which constitutes the lifeblood of public science and has the advantage of reducing transaction costs related to intellectual property. For companies, however, protection of research results facilitates the commercialization of discoveries. Typically, the authors suggest, there are four basic models of successful collaboration: 1. The idea lab, where managers put aside their desire for secrecy and work with academics on short term projects to create new options and contacts; 2. The grand challenge, where managers and academics work together on long-term projects to create a new knowledge base that will be shared in the public domain; 3. The extended workbench, where managers work rapidly with university partners on proprietary problems and solutions; and 4. Deep exploration, where the company creates rich and long-lasting relationships with university partners that, in turn, offer the business rights of first refusal to license collaboration results. The authors describe the most important characteristics of each model, give examples of companies that have used such a model and suggest situations where each would work best, as well as managerial best practices that can improve the odds of a successful collaboration.

While the protection of intellectual property, or IP, seems to be at odds with a company's pursuit of open innovation, or OI the selective use of research carried out elsewhere businesses in the know can align these two approaches. An appropriate IP strategy can actually be an enabler of OI activities. In fact, an increasing number of companies, such as International Business Machines Corp., are involved in interconnected ecosystems critically dependent on cooperating with other parties to generate innovations and profits. The authors research has found that the enabling function of IP depends on the specific circumstances under which companies engage in OI. Two variables in particular have emerged as critical determinants: the technological environment in which the business is active, and the knowledge distribution among potential collaborators. Each variable is presented as having two possible values. The technological environment, for instance, is either calm or turbulent. Concerning the nature of innovative knowledge distribution, external knowledge can be thought of as residing either with the few (in puddles) or with the many (in oceans). By combining these two dimension sets, and thus creating four possible scenarios, we provide a better sense of a firm's most appropriate IP/IO strategy. Depending on the category into which the company falls, IP plays a different role as an enabler of OI. [PUBLICATION ABSTRACT]

Research summary Patent data is a valued source of information for strategy research. However, patent‐based studies may suffer from sample selection bias given that patents result from within‐firm selection processes and hence do not represent the full population of inventions. We assess how incidental and nonincidental data truncation resulting from firm‐level and inventor‐level selection processes may result in sample selection bias using a quasi‐replication approach, drawing on rich qualitative data and a novel, proprietary dataset of all 40,000 invention disclosures within a large multinational firm. We find that accounting for selection both reaffirms and challenges past work, and discuss the implications of our findings for work on the microfoundations of exploratory innovation activities and for strategy research drawing on patent data. Managerial summary Much of what is known about innovation in general, and in particular about what makes inventors prolific, comes from studies that use patent data. However, many ideas are never patented, meaning that these studies may not in reality talk about ideas or inventions, but only about patents. In this paper, we examine the question of whether patent data can accurately be used to represent inventions by using data on all inventions generated within a large multinational firm to explore how and to what degree the selection processes behind firms' patenting decisions may lead to important differences between the two. We find that accounting for selection changes many previously given managerial implications; for example, we show how junior inventors may often not get the credit they deserve.

Organizations face challenges in selecting the right research and development (R & D) projects for funding due to bias and process issues. Here, Grohsjean et al explore ways to improve the decision-making process and make more objective choices. They identify five main categories of issues in R & D selection panels: bias against novel ideas, lack of diversity, focus on technical aspects without considering business opportunities, biased decision-making process, and timing of the process. To address these issues, organizations can implement various measures. Before selection, they can remove names and demographic information from project submissions and standardize submissions to ensure comparability. During selection, they can seek diverse voices, use crowdsourcing principles, employ workshop approaches, and incorporate randomness in decision-making. After selection, organizations can provide feedback on proposals and track and learn from failures. By implementing these practices, organizations can improve their track record in selecting innovative projects and generate better outcomes.