Background & problem statement In recent years online innovation contests have gained popularity since the internet facilitates access to problem-solvers. In these contests, individuals decide freely, whether to join a contest... [ view full abstract ]
Background & problem statement
In recent years online innovation contests have gained popularity since the internet facilitates access to problem-solvers. In these contests, individuals decide freely, whether to join a contest (self-select in), or leave a contest (self-select out). Extant research assumes that this self-selection process is beneficial based on the premise that self-selecting individuals know more about their capabilities and knowledge than the publisher of the task (Geiger & Schader, 2014; Afuah & Tucci, 2012). Crowdsourcing research has mainly focused on investigating individuals who submitted their ideas (e.g. Brabham, 2010; Lakhani, Jeppesen, Lohse, & Panetta, 2007). Nevertheless, self-selection in innovation contests is still a black box and little is known about the mechanisms and consequences of self-selection.
We argue that innovation contests are a series of self-selection decisions and cannot be reduced to one single point in time. There is no guarantee that the voluntary and open character of innovation contests leads to efficient self-selection of problem-solvers. Moreover, the problem-solving expertise needed might be lost along the way due to characteristics of the contest as such. As an example, self-selection might mean that individuals with overconfidence and little knowledge find their way into the contest, or, even worse, that individuals with the sought capabilities intentionally self-select out of the contest.
Research questions
The objective of this project is thus a fundamental analysis of the constant self-selection processes in innovation contests. In particular, we want to investigate if there are systematic differences between particular self-selection groups and which factors trigger self-selection into innovation contests to which extent. Enhancing our knowledge on this important aspect is not only of academic interest, it also allows organizers to design their innovation contests more effectively.
Method & expected results
We will investigate self-selection in a unique longitudinal and real research setting: a panel provider will host an online innovation contest, posting a problem related to his innovation-activities (finding innovative ideas for a new app) to a heterogeneous population of 25000 potential problem solvers (panelists). Based on interviews with crowdsourcing experts, we understand participant self-selection as a multi-stage decision process. Therefore, with the approval of the host, we will monitor each individual’s self-selection status in the course of the contest using distinct online indicators, such as cookie tracking. After determining their self-selection status, we will send out surveys to random samples of individuals of the same status via the panel provider, asking individuals to participate in a survey as part of their panel membership. This unique approach will enable us to compare different self-selection groups (self-selected in or out) systematically in different points in time during the period of the contest, shortly after self-selection decisions were made and without interfering with the subsequent self-selection behavior of individuals. In addition, in order to determine the extent of self-selection in dependence of contest parameters, we will induce different stimuli conditions and monitor the self-selection response of individuals (between-subject experimental design). Our goal is to finalize the experimental stimuli and survey constructs by August 2016.
The expected results will, on one hand, decode the process of self-selection, providing us with answers to the question which individuals self-select themselves in which direction (in or out) during an innovation contest. This will answer the important question whether self-selection actually works in innovation contests. On the other hand, the application of different contest stimuli will resolve the question of controllability of self-selection from a practical point of view. As a result, innovation contest organizers could enhance overall quality of their contests, if potential top-solvers are for example encouraged to self-select themselves into the contest and less-qualified participants prevented from participation.
Novelty and importance of the project
For the first time in crowdsourcing research, actual self-selection behavior of individuals in the course of an innovation contest will be observed in a longitudinal, real research setting which will enable us to interrelate individual-level data. We expect that this unique approach will allow us to make fundamental statements about the functioning, efficiency and consequences of participant self-selection in crowdsourcing systems.
160513_Self-selection_in_innovation_contests_Franke_Reith_Topic.pdf 