| Summary: | 博士 === 國立高雄科技大學 === 管理學院博士班 === 107 === Crowdsourcing (CS), through the cloud service online platform, enables companies to match the technicians from all over the world at the lowest cost. However, when the company and the masses of social workers are successfully paired, whether the technology and professionalism of workers meet the demand, but it increases the uncertainty of the performance and satisfaction of the outsourcing work, it includes the mismatch of tasks and cooperative behaviors in learning. In the past research, due to the lack of long-term cooperative, the outsourcing technology could not adapt to the work process of the enterprise and the lack of understanding of the outsourcing technology and the influence of in-depth learning. Based on the theory of social cognition, this study develops a new model validation to explore how to effectively improve the performance and satisfaction of the crowdsourcing. This study uses the antecedents such as task technology adaptation, workplace flexibility and team learning atmosphere to reflect the characteristics of the external task itself, using the workplace context factor to influence the social self-regulated learning, and conceptualize the knowledge needs and cooperative behaviors of social cognition through self-learning. In the workplace output, it is measured by two factors: Job performance and crowdsourcing satisfaction. This study was conducted for respondents with crowdsourcing experience. A total of 380 valid questionnaires were validated using Smart PLS. The results show that workplace flexibility and team learning climate have a positive impact on social self-regulated learning, while task-technology fit adaptation has an indirect impact. It also verifies that social self-regulation learning positively affects job performance and satisfaction.
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