Abstract

Microtask crowdsourcing marketplace as a novel platform has provided firms with a new way to recruit users and people from the public on various projects for a reasonable cost and fast turnaround. A Microtask is often combined with other Microtasks to solve a larger problem. Through Microtask crowdsourcing activities, a large variety of creative ideas can be collected which help resolving a company’s problem, resulting in better decision-making and outcomes. Despite the great potential of crowdsourcing offerings, it remains unclear how to create and develop the necessary community environment to drive and sustain individuals’ continued participation intention. Past studies on Microtask crowdsourcing focused on the system design to enrich its function, feature and performance of the crowdsourcing intermediate platform, and the implementation efficiency of crowdsourcing contests in order to enhance participants’ motivation and individual behavior to contest outcomes. This research intends to explore how intrinsic motivation affects individuals’ continued participation intention in Microtask crowdsourcing contests, taking into account the multi-level moderating effects of distributed justice and self-efficacy. Our theoretical model (Figure 1) builds on Expectancy theory, Self-determination theory (SDT) and Organisational justice theory. To validate the theoretical model, over 1,000 crowd workers participating in Amazon’s Mechanical Turk (MTurk) have completed an online questionnaire. According to the findings, respondents were motivated by intrinsic motivation and its influence on continued crowdsourcing participation is stronger when self-efficacy moderation is high. However, the interaction effect of distributive justice upon self-efficacy and continued participation intention was found insignificant. Nevertheless, it is interesting to uncover that when assert both self-efficacy and distributive justice upon the positive relationship between intrinsic motivation and continued participation, low distributive justice triggers intrinsic motivated and high self-efficacy solvers to sustain their participation. This result reflected that excessive or ‘over-justification’ distributive justice will create a significant undermining effect to high self-efficacy solvers with intrinsic motivation on their continued participation intention. From the practical perspective, task should be set at the appropriate complexity level to ensure motivated solvers are motivated and the crowdsourcing platform operator may consider not only award the winners but also award those who contribute to the tasks to keep their continued participation.

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An Empirical Study of Motivation, Justice and Self-Efficacy in Solvers’ Continued Participation Intention in Microtask Crowdsourcing

Microtask crowdsourcing marketplace as a novel platform has provided firms with a new way to recruit users and people from the public on various projects for a reasonable cost and fast turnaround. A Microtask is often combined with other Microtasks to solve a larger problem. Through Microtask crowdsourcing activities, a large variety of creative ideas can be collected which help resolving a company’s problem, resulting in better decision-making and outcomes. Despite the great potential of crowdsourcing offerings, it remains unclear how to create and develop the necessary community environment to drive and sustain individuals’ continued participation intention. Past studies on Microtask crowdsourcing focused on the system design to enrich its function, feature and performance of the crowdsourcing intermediate platform, and the implementation efficiency of crowdsourcing contests in order to enhance participants’ motivation and individual behavior to contest outcomes. This research intends to explore how intrinsic motivation affects individuals’ continued participation intention in Microtask crowdsourcing contests, taking into account the multi-level moderating effects of distributed justice and self-efficacy. Our theoretical model (Figure 1) builds on Expectancy theory, Self-determination theory (SDT) and Organisational justice theory. To validate the theoretical model, over 1,000 crowd workers participating in Amazon’s Mechanical Turk (MTurk) have completed an online questionnaire. According to the findings, respondents were motivated by intrinsic motivation and its influence on continued crowdsourcing participation is stronger when self-efficacy moderation is high. However, the interaction effect of distributive justice upon self-efficacy and continued participation intention was found insignificant. Nevertheless, it is interesting to uncover that when assert both self-efficacy and distributive justice upon the positive relationship between intrinsic motivation and continued participation, low distributive justice triggers intrinsic motivated and high self-efficacy solvers to sustain their participation. This result reflected that excessive or ‘over-justification’ distributive justice will create a significant undermining effect to high self-efficacy solvers with intrinsic motivation on their continued participation intention. From the practical perspective, task should be set at the appropriate complexity level to ensure motivated solvers are motivated and the crowdsourcing platform operator may consider not only award the winners but also award those who contribute to the tasks to keep their continued participation.