Open innovation systems represent an emerging collective intelligence success story. In such systems, a customer describes a problem they want to solve (for example, "we want ideas for new beverage products") and provides an online tool that allows the crowd to submit proposed solutions, as well as rate (and sometimes critique) other people's proposed solutions. Many open innovation platforms have emerged (such as ideascale, spigit, and imaginatik) and have been used widely in contexts that range from IBM to Starbucks, from the Danish central government to the White House. One recent survey2 found that one in four companies plan to utilize open innovation systems within the next 12 months, and this figure is growing. Such systems have proven they can elicit substantive contributions at a very large scale and very low cost. In the early weeks of his first term, for example, President Obama asked U.S. citizens to submit and vote on questions on the website change.gov, and promised to answer the top five questions in each category in a major press conference. This initiative engaged over 100,000 contributors, who submitted over 70,000 questions and four million votes. Google's 10 to the 100th project received over 150,000 suggestions on how to channel Google's charitable contributions. In IBM's Idea Jam in 2006, 46,000 ideas for possible IBM products and services were generated by 150,000 contributors. Such large-scale participation enables in turn, such powerful emergent phenomena as:
Open innovation systems face, however, serious challenges that, paradoxically, are largely a result of how successful they have been at eliciting huge volumes of participation. In this Viewpoint, we review these challenges and propose some promising directions for moving forward.
The challenges faced by open innovation systems occur both with idea generation and idea evaluation. Key challenges with idea generation include:
Open innovation systems also face challenges with crowd-sourced idea evaluation: there is often a disconnect between what the customer wants and what the crowd selects. This can occur for several reasons:
The challenges faced by open innovation systems occur both with idea generation and idea evaluation.
Open innovation systems thus face critical challenges in terms of ensuring the potentially massive contributions of the crowd provide high value to the customer without incurring prohibitive harvesting costs.
How can we meet these challenges and more fully achieve the promise of open innovation systems? Progress will require, we believe, advances on the following two key fronts.
Better open innovation processes. New open innovation processes are needed that provide more guidance about how the crowd can best contribute, help crowd members build on each other's inputs, and make it easier to harvest their contributions,
Deeper computer support. Crowds (of people) and clouds (computers) have synergistic capabilities. Crowds are able to create, understand and evaluate ideas in ways that computers cannot match, but are best suited for performing relatively small and quick tasks that require little context. Computers, by contrast, excel at rapid analysis of large swaths of data to get "the big picture" of what is (and is not) happening in a crowd. Combining these strengths will require bridging the semantic gap between the natural language that crowds use, and the formal languages that computers require. Someday, this will be achieved by advanced algorithms that allow computers to deeply understand natural language. But this achievement seems to remain far off. In the meantime our goal, we believe, must be to find ways that crowds can do the minimum formalization needed to enable significant computer support, for example by:
As the semantic gap between crowds and clouds narrows, we can create powerful new forms of computer support for open innovation.
As the semantic gap between crowds and clouds narrows, we can create powerful new forms of computer support for open innovation, such as:
Open innovation systems, as we have seen, have the potential to harness the collective intelligence of the crowd for problem solving in areas ranging from business to government, from science to education. This potential is far from fully realized, however, largely because of our inability to deal effectively with the massive levels of user contributions that these systems can elicit. Advances in this area will require contributions from many disciplines, including computer science, cognitive science, social psychology, computational linguistics, and economics. Will you join us in addressing these important challenges?
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