Facing ever-increasing pressure to innovate, some companies turn to crowdsourcing for new ideas. Many crowdsourcing efforts, however, fall short of expectations or are abandoned. Amazon’s crowdsourced film script submission system, for example, was shut down after failing to attract scripts with global appeal. Quirky, a product invention startup, went bankrupt as it unsuccessfully attempted to crowdsource the entire product development process.
There is a common misconception that there is only one approach to crowdsourcing, but asking crowds to address problems that they’re poorly suited to solve leads to many crowdsourcing failures. Our research suggests instead that there are multiple approaches to crowdsourcing that are appropriate for tasks of differing scope and complexity. Our smart crowds framework of three distinct types of crowdsourcing provides guidance for managers wishing to address business problems and boost innovation opportunities through crowdsourcing.
The Smart Crowds Framework
Crowdsourcing tasks can be organized along a spectrum of problems from low to high complexity — from a simple description of a new product idea drawing on a limited number of knowledge domains, to a highly complex working prototype of, say, a new type of a spacecraft requiring integration across multiple knowledge domains. Creating connections among individuals to learn from one another within a wired crowd or forming crowd teams can enable smart crowds that are able to tackle problems of increased scope and complexity.
The best type of crowd depends on the scope and complexity of the problem you are seeking to solve. Our smart crowds framework proposed three distinct types of crowdsourcing — search crowds, wired crowds, and crowd teams — suited for different types of problems. (See “The Smart Crowds Framework.”)
Search Crowds: Defining Tasks and Rewards for Efficient Search
Search crowds are most effective at finding solutions to well-defined problems with a relatively small scope. The problem itself may be quite complex — say, addressing climate change — but the answer you seek may be a simple one-pager outlining an idea. Search crowds unfold their strengths when the best skills or technical approaches to employ in problem-solving are not obvious.
1. C. Riedl and V.P. Seidel, “Learning From Mixed Signals in Online Innovation Communities,” Organization Science 29, no. 6 (November-December 2018): 1010-1032.
2. C. Riedl and A.W. Woolley, “Teams vs. Crowds: A Field Test of the Relative Contribution of Incentives, Member Ability, and Emergent Collaboration to Crowd-Based Problem Solving Performance,” Academy of Management Discoveries 3, no. 4 (December 2017): 382-403.