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Proponents of open innovation have long argued that essential resources for sustained innovation lie beyond an organization’s boundaries with its customers and value chain partners and that organizations must work collaboratively with internal and external stakeholders to build creative solutions. Unlike the traditional internal Research and Development model, open innovation practices are implemented differently (e.g., crowdsourcing, innovation marketplace, user innovation, and open-source community). Previous studies have articulated the general logic of these models, described the workings of some well-known examples, and examined the logic of engaging external actors in new product development. While open innovation can potentially facilitate and enhance the innovation process and outcomes, the downside and the limits of this openness remain understudied. Further research on the limitations of open innovation is more needed than ever since in the last few years many companies closed down their customer innovation communities, open innovation marketplaces were abandoned, and innovation intermediaries filed for bankruptcies. Open innovation can fade due to many reasons. Open innovation models such as crowdsourcing and innovation marketplace usually hold tight control over the innovation process and activities that require external actors. These platforms still set structurally defined requirements and standard procedures for the actors to ideate based upon. Meanwhile, they often predefine the innovation scope with narrowly defined tasks for the innovative actors to complete. With the sponsors’ tight control, external actors often cannot freely ideate and become motivated to deliver a creative solution. Even with incentives, the traditional open innovation models sometimes cannot reach the most effective innovation results. For example, the cost of implementing and running open innovation model sometimes does not justify its benefits. Research also showed that open innovation does not necessarily reduce the risk or failure rate of new products. Unpredictability in the innovation results and uncertainty in the environment, including sociotechnical factors, can also become a part of why open innovation fails. Hence, it is crucial to understand the open innovation process and its interplay with success or failure in new product or service development. In this study, we used the case of Social Product Development (SPD) as an open innovation model to investigate and document the limitations of open innovation in consumer product development. The SPD model encompasses many key and common features of open innovation models while being more dynamic and less restrictive. The SPD model also has a high failure rate. Examining the SPD process model thus helped us identify the limitations in the open innovation with some generalizability to other open innovation models. We first identified key activities in the SPD model, including social engagement, ideation, experiential communication, social validation, co-development, and co-commercialization, at three different levels: innovation activities, innovation projects, and innovation community. Then we examined when and how the identified activities at each level may fail to deliver the expected outcomes. We categorized the results (open innovation failure factors) into three phases associated with invention initiation, development, and commercialization. For each group, we identified contributing groups namely Innovation sponsor, innovation partners, and problem-solver. Lastly, we proposed a 3 by 3 activity-phase matrix that includes open innovation success/failure factors such as technology affordances, reward systems, mass-screening, community culture, collaboration support, social validation, social selling, osmotic communication, manufacturing agility, and intellectual property right. For each group, we also identified the responsible actors namely innovation sponsors, innovation partners, and problem-solver. Our findings provide a richer picture of SPD failure factors that holds relevance for the design of open innovation platforms. Our results also provide practical recommendations on open innovation platform governance including rules and policies concerning reward systems, partnerships, and manufacturing.

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