Many human computation systems use crowdsourcing markets like Amazon Mechanical Turk to recruit human workers. The payment in these markets is usually very low, and still collected demographic data shows that the participants are a very diverse group including highly skilled full time workers. Many existing studies on their motivation are rudimental and not grounded on established motivation theory. Therefore, we adapt different models from classic motivation theory, work motivation theory and Open Source Software Development to crowdsourcing markets. The model is tested with a survey of 431 workers on Mechanical Turk. We find that the extrinsic motivational categories (immediate payoffs, delayed payoffs, social motivation) have a strong effect on the time spent on the platform. For many workers, however, intrinsic motivation aspects are more important, especially the different facets of enjoyment based motivation like “task autonomy” and “skill variety”.
Our proposed model is based on motivation theory (Deci & Ryan 1985, 2000 and Lindenberg 2001), work motivation theory (Hackman & Oldham 1980), and open source software development (Lakhani & Wolf 2003). It composes motivating factors which can be classified either intrinsic or extrinsic of type. Each category is influenced by one or more constructs that affect the overall motivation of workers.
Intrinsic motivation exists if an individual is activated because of its seeking for the fulfillment generated by the activity (e.g. acting just for fun). Within the group of intrinsic motivation, two categories are differentiated: Enjoyment Based and Community Based Motivation. The category of Enjoyment Based Motivation contains factors that lead to that lead to the sensation of “fun” that might be perceived by the workers. These factors are measured by the constructs:
- Skill Variety: Usage of a diversity of skills that are needed for solving a specific task and fit with the skill set of the worker; e.g. a worker picks a translation task because he likes translating and wants to use his skills in his favorite foreign language.
- Task Identity: Refers to the extent a worker perceives the completeness of the task he has to do. The more tangible the result of his work is, the higher will be his motivation; e.g. a tasks that allows him to see how the result of his work will be used.
- Task Autonomy: Refers to the degree of freedom that is allowed to the worker during task execution; e.g. a worker who is motivated because a certain task allows him to be creative.
- Direct Feedback from the Job: Covers to which extent a sense of achievement can be perceived during or after task execution. This is explicitly limited to direct feedback from the work on a task, not by other persons.
- Pastime: Covers acting just to “kill time”. It appears if a worker does something in order to avoid boredom.
The category of Community Based Motivation covers the acting of workers guided by the platform community:
- Community Identification: Covers the acting of workers guided by the subconscious adoption of norms and values from the crowdsourcing platform community, which is caused by a personal identification process.
- Social Contact: Covers motivation caused by the sheer existence of the community that offers the possibility to foster social contact; i.e. meeting new people.
In the case of extrinsic motivation the activity is just an instrument for achieving a certain desired outcome (e.g. acting for money or to avoid sanctions). Three motivational categories are counted to the extrinsic motivation: Immediate Payoffs, Delayed Payoffs and Social Motivation. The category of Immediate Payoffs covers all kinds of immediately received compensations for the work on crowdsourcing tasks:
- Payment: Motivation by the monetary remuneration received for completing a task.
Delayed Payoffs address all kind of benefits that can be used strategically to generate future material advantages. This type of motivation is measured by:
- Signaling: Refers to the usage of actions as strategic signals to the surroundings; e.g. selects tasks in order to show presence and advance his chance of being noticed by possible employers.
- Human Capital Advancement: Refers to the motivation through the possibility to train skills that could be useful to generate future material advantages.
The category of Social Motivation is the extrinsic counterpart of intrinsic motivation by community identification. It covers socially motivated extrinsic motivation out of values, norms and obligations from outside the platform community as well as indirect feedback from the job and the need for social contact:
- Action Significance by External Values: Captures the significance of an action concerning the compliance with values from outside the crowdsourcing community that is perceived by the worker when contributing to the community or working on a task.
- Action Significance by External Obligations & Norms: Motivation induced by a third party from outside the platform community that traces back to obligations a worker has or social norms he wants or to comply with in order to avoid sanctions (does not include material obligations).
- Indirect Feedback from the Job: Covers motivation caused by the prospect of feedback about the delivered working results by other individuals; e.g. working on tasks to get positive feedback from requesters.
A more detailed overview of the individual constructs including examples and references is listed in the appendix of our HCOMP paper. We have tested the model with a survey on Amazon Mechanical Turk. Details about the survey design, data collection, data analysis, and details about the influence of demographics on motivation can be found in our AMCIS 2011 paper which in an extended version of the paper that will be presented at HCOMP 2011 in August. On MTurk, many intrinsic motivation factors seem to dominate the extrinsic ones. Task related factors play a major role in the continuum of factors that motivate the workers which includes the usage of a variety of skills, deciding on the own how to solve a task or the “feasibility” of work results. Surprisingly, this list also and explicitly includes the (extrinsic) motivation to work on tasks to learn new or train existing skills, which related literature has not perceived to be that important yet.
A general model for the motivation of workers in paid crowdsourcing environments and human computation systems is a prerequisite for many further research directions in that area. An interesting research question is the connection between properties of tasks and platforms and the resulting motivation. The question how workers can be motivated to contribute better results is also very promising. We welcome researcher to apply and extend our model to different platform and to use the constructs for demographic data in related research on Amazon Mechanical Turk.
Kaufmann, N., Schulze, T., Veit, D. (2011). ”More than fun and money. Worker Motivation in Crowdsourcing – A Study on Mechanical Turk”. AMCIS 2011 Proceedings. (forthcoming) PDF HCOMP Paper (including Appendix)