Innocentive

Innocentive brings together companies in need of creative scientific and technical problem-solving, and the free cooperation of scientists, engineers and creators generally, at http://www.innocentive.com/ .

It had 120,00 cooperating scientists by February 2007 and hundreds of corporate users paying a $80,000 membership fee, such as Boeing and Procter & Gamble.

Pools like Innocentive are also called Ideagoras ([1])

Description

From an article in Wired magazine [2] about Crowdsourcing and Distributed Labor Networks

"Pharmaceutical maker Eli Lilly funded InnoCentive’s launch in 2001 as a way to connect with brainpower outside the company – people who could help develop drugs and speed them to market. From the outset, InnoCentive threw open the doors to other firms eager to access the network’s trove of ad hoc experts. Companies like Boeing, DuPont, and Procter & Gamble now post their most ornery scientific problems on InnoCentive’s Web site; anyone on InnoCentive’s network can take a shot at cracking them.

The companies – or seekers, in InnoCentive parlance – pay solvers anywhere from $10,000 to $100,000 per solution. (They also pay InnoCentive a fee to participate.) Jill Panetta, InnoCentive’s chief scientific officer, says more than 30 percent of the problems posted on the site have been cracked, “which is 30 percent more than would have been solved using a traditional, in-house approach."

The solvers are not who you might expect. Many are hobbyists working from their proverbial garage, like the University of Dallas undergrad who came up with a chemical to use inart restoration, or the Cary, North Carolina, patent lawyer who devised a novel way to mix large batches of chemical compounds.

This shouldn’t be surprising, notes Karim Lakhani, a lecturer in technology and innovation at MIT, who has studied InnoCentive. “The strength of a network like InnoCentive’s is exactly the diversity of intellectual background," he says. Lakhani and his three coauthors surveyed 166 problems posted to InnoCentive from 26 different firms. “We actually found the odds of a solver’s success increased in fields in which they had no formal expertise," Lakhani says. He has put his finger on a central tenet of network theory, what pioneering sociologist Mark Granovetter describes as “the strength of weak ties." The most efficient networks are those that link to the broadest range of information, knowledge, and experience" (http://www.wired.com/wired/archive/14.06/crowds.html?)

Example

"Take Colgate-Palmolive (CL). The company needed a more efficient method for getting its toothpaste into the tube—a seemingly straightforward problem. When its internal R&D team came up empty-handed, the company posted the specs on InnoCentive, one of many new marketplaces that link problems with problem-solvers. A Canadian engineer named Ed Melcarek proposed putting a positive charge on fluoride powder, then grounding the tube. It was an effective application of elementary physics, but not one that Colgate-Palmolive's team of chemists had ever contemplated. Melcarek was duly rewarded with $25,000 for a few hours work." (http://www.businessweek.com/innovate/content/feb2007/id20070215_251519.htm?)

Discussion

Innocentive isolates the problem solvers from each other

Commentary by Sami Viitamaki:

"Innocentive fits the category of crowdsourcing that does not fully utilize the community’s ‘wisdom of crowds’. The solvers pursue the solution in isolation from each other, and the possibility of using the community to gather comments on the alternatives, build on others’ ideas, find a winning solution by community rating, etc. is absent.

In my thesis I call these kind of companies ‘Crowdsourcing Brokers’, for they really simply gather up alternative solutions from a large member base for their own clients’ needs and leave deciding on the winning solution to the clients. Given the nature of the competition in these kind of efforts and the considerable monetary rewards involved, the approach is naturally understandable. Other ‘broker’ approach companies are e.g. iStockphoto and Holotof advertising."

An alternative to this approach is the FLIRT Model of Crowdsourcing

Crowdsourcing tough analytical problems is workable

Summary of a study by Karim Lakhani at http://diamondinfoanalytics.com/blog1/2007/02/23/crowdsourcing-analytics/

"Karim Lakhani and his team at Harvard Business School have been studying this phenomenon in the context of scientific problem solving (working paper here) based on data from Innocentive’s winning entries (30% of all problems on Innocentive have been solved). Below are some of their findings of the study followed by my commentary on its applicability to analytics:

Those problems were more likely to be solved which had a presence of heterogeneous scientific interests amongst scientists submitting solutions.

In short, diversity of problem solvers area of expertise was key. Analytics lends well to diversity as it is a very multi-disciplinary field with potential applications from a various branches of science i.e. economics, mathematics, engineering, psychology, operations research etc.

72.5% of winning solvers stated that their submissions were partially or fully based on previously developed solutions,

There is some significant research work in analytics that goes on in the universities and even some companies, and there are a lot of models and techniques out there looking or applications.

In addition the study also analyzes the winning solvers and found that:

Probability of being a winning solver is significantly and positively correlated with both a desire to win the award money and intrinsic motivations like enjoying problem solving and cracking a tough problem

Having free time to actually participate in the problem solving effort significantly and positively correlates with being a winning solver.

Both these aspects are essential for any open-source model to work.

The thing to note in the study is that companies only post those problems to Innocentive, where the internal R&D team has not been able to come up with a solution. So these are really tough problems. And that is an interesting point, because crowdsourcing a simple problem is not efficient. There is much less control over time frames and the overhead associated with managing the process will not make it worthwhile.

In conclusion:

1. An open-source strategy for solving tough analytical problems is certainly worth exploring for companies and initial research suggests that analytical problems can be good candidates for this model.

2. There is an opportunity for an intermediary (like Innocentive) to develop an open-source community for analytics." (http://diamondinfoanalytics.com/blog1/2007/02/23/crowdsourcing-analytics/)

More Information

See our entries on Crowdsourcing and Co-Creation

Background on the initiative in an interview with the founder Alpheus Bingham