Innovation & Technology
• 4 minute read
Open Collaboration: a Robust Tool for Innovation
How does open collaboration change the way we work and bring about robust performance?
By Qin Yi, PhD Candidate, Department of Management, CUHK Business School
When open collaboration platforms like Wikepedia, Linux, Android and SourceForge first came into existence, skeptics and naysayers abound, wondering how on earth serious products could be offered like free lunches, with volunteers donating their time and energy toward a goal of “common good.” Sure enough, the results speak louder than words, and today we are witnessing some of the most robust products and services as a result of this bold concept.
Increasingly, open collaboration is changing the business models in even some of the most traditional industries. A good example is Local Motor, a revolutionary vehicle innovator founded in 2007. It challenges the automobile giants in Detroit by using open collaboration in the process of designing cars and other transportation solutions. The company has only 59 employees, but it has built a community of 36,500 members, who are collaborating on 4,800 designs and 1,200 ideas across 403 projects. Compared with traditional motor companies, open collaboration enables Local Motor to produce cars in a shorter time and at lower costs.
How does open collaboration work and what is the best condition under which such collaboration would perform most effectively?
Sheen Levine, Principal Investigator of the Institute for Social and Economic Research and Policy at Columbia University, New York, and Senior Editor of Management and Organization Review, recently presented his findings on the subject at CUHK Business School. His paper “Open Collaboration for Innovation: Principles and Performance” has been published in a top-tier management journal, “Organization Science,” explores the underlying mechanism of an open collaboration system through a study based on computer simulation.
According to Levine, open collaboration is a system of innovation or production that relies on goal-oriented yet loosely coordinated participants who interact to create a product (or service) of economic value, which they then make available to contributors and consumers.
“Open collaboration creates a puzzle that challenges the common sense of people,” said Levine. “In common sense, people who have created a product do not want to share the fruits with people who have not contributed to the product. It’s hard to understand why an organization would allow free riders to have full access to its products.”
Levine and his co-author, Michael Prietula of Emory University, conducted a study to explore how open collaboration happens. They created a computer simulation model of group interactions to observe how various conditions under which open collaboration takes place influence the performance of such collaboration.
The researchers divided the participants in a cooperative system into three groups according to their tendency of cooperation. The first group, known as cooperators, tends to contribute to the group regardless what other people do. The second group, reciprocators, contributes only when others are also contributing. In other words, they see mutual benefits in such collaboration. The third group, free riders, hardly contributes at all regardless of whether others contribute or not.
According to the authors, among the general human population, an estimated 13 percent are cooperators, 63 percent reciprocators, 20 percent free riders and 4 percent are inconsistent in their tendencies. However, in real organizations, leaders can control the distribution of these types of people through recruitment and punishment.
Would the distribution of the cooperators, reciprocators and free riders influence the performance of an open-collaboration system? If so, how? Intuitively, one may think that the more contributors there are, the higher the performance. The result of the study reveals a more complicated picture. The researchers found that performance increased as the proportion of contributors increased. However, large increments of improvement occurred at the lowest level of cooperators. For example, when only 13 percent of a group are cooperators, the performance level reaches as much as 50 percent of that of a collaboration system that consists of only cooperators and no reciprocators or free riders. This implies that an efficient collaboration system requires only a small proportion of cooperators.
An even more interesting finding is the influence of free riders on the overall performance of the system. It was found that the negative impact of free riders on performance is limited — performance dropped by only 3 percent when the proportion of free riders rose from zero to 40 percent. It is only when the ratio of free riders increased to an extremely high percentage (e.g. 70 percent), and when the ratio of cooperators is very low (e.g. 5 or even 1 percent), that performance collapsed.
The two sets of findings above show that open collaborative systems are robust because the positive impact of cooperators — albeit their small number — is able to override the negative impact of free riders.
In other words, even when there are some free riders, a collaborative system can still be productive thanks to the positive influence of cooperators, who contribute to the system and establish role models.
According to the study, open collaboration can be used widely due to its robust performance. This may explain why organizations using the open collaboration model have stronger advantages over traditional organizations that depend on the collaboration of people in a closed system.