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This paper responds to the lack of research investigating procedural differences in open innovation collaboration. Based upon a sample of intermediaries, we analyze the costs of different setups for open collaboration. Our focus is on the mechanism of search initiating the collaboration. Differentiating direct search, i.e. actively scanning information sources for relevant knowledge, and indirect search, i.e. revealing an innovation problem to a potential pool of contributors, we find that variations in search behavior lead to different coordination cost. Our analysis allows us to derive implication for the theory and management of open innovation.

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... Problem complexity can also be expressed by the degree of problem decomposability, that is, the extent to which the innovation problem can be decomposed into independent and non-related tasks (Argyres & Silverman, 2004;Baldwin & von Hippel, 2011). Complex problems exhibit low degrees of decomposability due to a high degree of task interdependence: the more interdependent tasks are, the less autonomy actors have in carrying out their own activities, the more coordination is required among collaborators, the higher will be the transaction costs (Diener et al., 2015). Thus, in complex innovation problems, coordination costs arising from low autonomy tend to be highly relevant. ...
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Collaborations between actors from different sectors (governments, firms, nonprofit organizations, universities, and other societal groups) have been promoted or mandated with increasing frequency to spur more innovative activities. This article argues that there is an essential gap in evaluating the issues of these collaborative arrangements on innovation and a need to theorize the costs of these arrangements systematically. This article identifies three implicit assumptions in current research that prevent a sound analysis of the costs of collaborative innovation and advances a new cost theory based on the integration of studies from several research fields and explanations provided by three main economic theories: transaction cost economics, game theory, and the knowledge-based view. In particular, four overarching factors are posited to impact the effectiveness of collaboration for innovation: governance (the number of collaborators and the hierarchical relationships among them); compactness (the degree of relationship formality that binds collaborators together); reliability (the quality of the relationships); and institutionalization (the extent to which the relationships have been pre-established by practice). We discuss the importance of leveraging these factors to determine an optimal governance structure that allows collaborating actors to minimize transaction, cooperation, and knowledge costs, and to reward participants proportionally to the cost they bear, in order to foster conditions of reciprocity, fair rates of exchange, and distributive justice.
With the rapid development of social platforms and human activities, people are now interested to solve their problems with the help of the social crowd-powered system. So, crowdsourcing has become a promising way of solving problems in a distributed manner within a specific time. Crowd workers pick up a task (simple or complex) and solve it with competing interest or collaboratively and receive an incentive (as monetary or non-monetary). In paid crowdsourcing platforms, crowd workers solve complex tasks and get remunerations through prior bidding or prior announcement of fee decided by the requester (task provider). For decomposable tasks, a single winner may not provide a significant solution to the requester due to the insufficient knowledge. So, we induce collaboration in competitive crowdsourcing markets to better handle decomposable-type tasks. In this paper, we propose an envelope game-based mechanism that ensures if the tasks are decomposable then the workers will be encouraged to collaborate with their subtasks and share their remunerations. This mechanism also increases the chance of receiving more number of cost-effective solutions. Thus both the requester and crowd workers get benefits from the system. The effectiveness of the proposed mechanism is evaluated through empirical analysis on simulated environments.
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Die Gestaltung offener Innovationsprozesse gilt heute für viele Organisationen als Chance, erfolgreich zu innovieren und die Effektivität (‘fit-to-market’) wie Effizienz (‘time-to-market’ oder ‘cost-to-market’) im Entwicklungsprozess zu erhöhen. Open Innovation bedeutet in diesem Zusammenhang, durch gezielten Einsatz offener Such- und Ausschreibungsmethoden externes Wissen in den Innovationsprozess zu integrieren und durch neue Kooperationsformen mit externen Partnern, oft außerhalb der eigenen Industrie, zusammenzuarbeiten.
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