PhD Defence: Crowdsourcing for Innovation - Unpacking Motivational, Knowledge and Relational Mechanisms of Innovative Behaviour in Crowdsourcing Platforms
A central step in enabling companies to harness the immense potential of crowdsourcing is to have a sound understanding of the behaviour of the individuals that make up the crowd. New research by Oguz Ali Acar enhances our understanding of crowd’s behaviour, and reveals that monetary rewards in crowdsourcing do not necessarily motivate the crowd and that crowd’s expertise does not always bring better solutions to complex innovation problems. Acar also discovered that young and male members of the crowd experience highest levels of fears of their ideas being misused.
With his dissertation ‘Crowdsourcing for Innovation: Unpacking Motivational, Knowledge and Relational Mechanisms of Innovative Behaviour in Crowdsourcing Platforms’, Oguz Ali Acar found that using larger amounts of monetary rewards to motivate the crowd is not effective at all under certain circumstances. Some crowd members are motivated by prosocial reasons, such as wanting to help others, in which case money will not be effective, and could even backfire.
In addition to helping others, crowds also engage in creative activities because of other non-monetary reasons: they enjoy solving problems and creating ideas, they want to learn new things and they want to gain reputation for their contributions. It is of great importance to acknowledge and take into consideration such diversity in motivations in order to effectively encourage the crowd to engage in creative activities.
Oguz Ali Acar also found that being an expert in a certain field of knowledge does not necessarily contribute to a better performance. Expertise only has a positive effect on performance when crowd members also look into a wide variety of knowledge domains for solving the problem. Although this kind of broad search was required for the performance-enhancing effects of expertise, breadth of the search was not enough on its own. For such positive effects, experts should also be shallow in the problem domain and those outside of it, but should be deep in domains that are related to the problem domain such as those at the boundaries of the problem domain. Organizers of crowdsourcing initiatives can benefit from these findings by encouraging specific approaches for solving problems.
Thirdly, Acar reveals some members of the crowd might fear potential opportunistic behaviour which can be a critical factor determining success or failure of crowdsourcing initiative as members of the crowd are unlikely to share their valuable knowledge when they fear their ideas being misused. Specifically, women and older people have significantly less fears due to potential opportunistic behaviour when disclosing their knowledge. In other words, young males experience the highest levels of fears, and this is the largest demographic group in most of the crowdsourcing platforms. What business can learn from this is that certain groups of users in crowdsourcing platforms need more attention to be motivated to disclose their ideas and solutions. Companies might benefit from being transparent about what happens with the ideas and solutions shared by the crowd, and from taking a proactive approach to inform male and younger participants regarding how the potential opportunistic behaviour will be avoided.
Oguz defended his dissertation in the Senate Hall at Erasmus University Rotterdam on Thursday, 25 September 2014. His Doctoral Committee members were Professor Gerrit van Bruggen, Professor Eric van Heck, Dr Daan Stam and his promoter was Professor Jan van den Ende. External members of the Doctoral Committee included Dr Oliver Baumann (University of Southern Denmark) and Professor Michael Jensen (University of Michigan).
About Oguz Ali Acar
Oguz Ali Acar was a PhD candidate in the Management of Technology and Innovation Department at Rotterdam School of Management. His research focused on management of distributed innovation in firms and communities. He particularly specializes in rewards, motivations and contributions in the innovation related use of online communities.
Oguz holds MSc and BSc degrees in Management Engineering from the Istanbul Technical University. He has been a visiting master student at Eindhoven University of Technology in Innovation Management in 2007 for a year. He also has received several grants and scholarships in his masters and bachelor studies including master scholarship of The Scientific and Technological Research Council of Turkey and grant of Center for European Union Education and Youth Programs. Before joining to his PhD position in RSM, he had 1,5 year of industry experience in the field of marketing and business development.
Abstract of Crowdsourcing for Innovation: Unpacking Motivational, Knowledge and Relational Mechanisms of Innovative Behaviour in Crowdsourcing Platforms
The Internet and the advance of communication technologies have brought unprecedented opportunities for harnessing the creative potential of people all over the world. In an attempt to utilize this potential to explore breakthrough new product ideas and find solutions to challenging innovation problems, companies make extensive use of crowdsourcing practices. The main purpose of this dissertation is to contribute to a greater understanding of the dynamics of crowdsourcing by providing a comprehensive investigation of the behavioral factors that influence innovative behavior and the performance of the crowd. In particular, we examine motivational, knowledge and relational mechanisms of crowd engagement, creativity and knowledge-sharing behavior.
We demonstrate that crowd members engage in new product ideation and innovative problem solving for different reasons (i.e., intrinsic, extrinsic, prosocial and learning motivation), and monetary rewards impact creativity in different ways, according to individuals’ prosocial motivation. In addition, we find that a crowd member’s performance in solving innovation problems is a consequence of the interplay between his/her expertise and how broadly and deeply he/she searches for solutions. Finally, we show that fear of opportunism by others – the main relational risk attached to disclosing knowledge in crowdsourcing platforms – is not uniform among crowd members, and trust in the owner of the crowdsourcing platform is central in assuaging such fears.
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