形成用户规模是我国处于起步阶段的科研众包平台获得长足发展的关键,所以以满足用户需求为出发点,准确分析用户心理能为平台优化发展提供最有效的研究支撑。本文利用协同激励理论的构念和研究方法,以UTAUT为基础研究模型,从用户激励主体来源、激励动力来源、激励内容、激励作用方向、社会评价模式等多维度入手,构建科研众包平台双边用户(发包方和接包方)的协同激励效应影响因素评价指标体系,并通过对广东省科技厅首批认定的14家科研众包平台双边用户发放调查问卷的方式获取实证检验结果。结果显示,绩效期望、社交影响、促进条件等都对双边用户有显著的正向激励作用,付出期望对接包方用户有显著的正向激励作用。基于双边用户协同激励影响因素的分析折射出用户对于科研众包平台的使用期待,并指出平台未来发展必须从智能化、市场化、社群化三方面重点发力强化用户黏性,保持竞争优势。

The formation of user scale is the key to the rapid development of China"s research crowdsourcing platforms in their early stages. Therefore, starting from meeting user needs, accurate analysis of user psychology can provide the most effective research support for platform optimization and development. This article utilizes the concept and research methods of collaborative incentive theory, based on UTAUT, to study the model. Starting from multiple dimensions such as the source of user incentive subjects, incentive motivation sources, incentive content, incentive direction, and social evaluation mode, it constructs an evaluation index system for the synergistic incentive effect influencing factors of bilateral users (employers and contractors) of scientific research crowdsourcing platforms, And the empirical test results were obtained by distributing survey questionnaires to the first batch of 14 bilateral users of research crowdsourcing platforms recognized by the Guangdong Provincial Department of Science and Technology. The results show that performance expectations, social influence, and promotion conditions all have a significant positive motivating effect on bilateral users, while paying expectations has a significant positive motivating effect on outsourcing users. The analysis based on the factors influencing the collaborative incentives of bilateral users reflects users" expectations for the use of research crowdsourcing platforms, and points out that the future development of the platform must focus on strengthening user stickiness and maintaining competitive advantages from three aspects: intelligence, marketization, and socialization. Keywords: Research crowdsourcing platform; Collaborative incentive effect; Bilateral perspective

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