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Article
Publication date: 17 January 2022

Hua (Jonathan) Ye

In crowdsourcing contests, the capabilities and performance of individual workers (solvers) determine whether seeker firms can obtain satisfactory solutions from the platform. It…

Abstract

Purpose

In crowdsourcing contests, the capabilities and performance of individual workers (solvers) determine whether seeker firms can obtain satisfactory solutions from the platform. It is noted that solvers may learn such skills in crowdsourcing from doing (experiential learning) or observing (vicarious learning). However, it remains unclear if such learning can be materialized into improved performance considering the unique settings of crowdsourcing contests. The study aims to understand how experiential learning and vicarious learning enhance solver performance and under what conditions.

Design/methodology/approach

The model was tested using survey and archival data from 261 solvers on a large contest platform in China.

Findings

Results support the premise that experiential learning and vicarious learning separately and jointly enhance solver performance. Moreover, perceived task uncertainty strengthens the effect of vicarious learning but weakens the effect of experiential learning, whereas perceived competition uncertainty weakens the effect of vicarious learning.

Originality/value

The current study enriches the understanding of the impacts of experiential learning and vicarious learning and offers a more nuanced understanding of the conditions under which solvers can reap the performance benefits from learning in crowdsourcing contests. The study also provides practical insights into enhancing solver performance under perceived task uncertainty and perceived competition uncertainty.

Article
Publication date: 5 April 2022

Wei Wu, Qianwen Yang, Xiang Gong and Robert M. Davison

Crowdsourcing platforms have emerged as an innovative way to generate ideas and solving problems. However, promoting sustained participation among crowdworkers is an ongoing…

Abstract

Purpose

Crowdsourcing platforms have emerged as an innovative way to generate ideas and solving problems. However, promoting sustained participation among crowdworkers is an ongoing challenge for most crowdsourcing platform providers. Drawing on self-determination theory, this study investigates the impacts of job autonomy on crowdworkers' sustained participation intention.

Design/methodology/approach

A survey of 212 crowdworkers from a leading crowdsourcing platform in China was conducted to empirically validate the model.

Findings

The empirical results lead to several key findings. First, the taxonomy of job autonomy in crowdsourcing contains three archetypes: work-scheduling autonomy, work-task autonomy, and work-method autonomy. Second, work-scheduling autonomy and work-method autonomy have more significant positive effects on temporal value than work-task autonomy, and this increase in temporal value increases crowdworkers' sustained participation intention. Third, work-task autonomy exerts a stronger influence on hedonic value than work-scheduling autonomy or work-method autonomy, and this increase in hedonic value also increases crowdworkers' sustained participation intention.

Originality/value

This study extends the crowdsourcing literature by examining the formation of crowdworkers' sustained participation and highlighting the role of differential effects of multidimensional job autonomy on crowdworkers' sustained participation. We believe that this study provides actionable insights into measures that promote crowdworkers' sustained participation in the crowdsourcing platform.

Details

Information Technology & People, vol. 36 no. 2
Type: Research Article
ISSN: 0959-3845

Keywords

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