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The Effect of Rating Scales on Decision Quality and User Attitudes in Online Innovation Communities

机译:评分量表对在线创新社区决策质量和用户态度的影响

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摘要

Given the rise of the Internet, consumers increasingly engage in co-creating products and services. Whereas most co-creation research deals with various aspects of generating user-generated content, this study addresses designing ratings scales for evaluating such content. In detail, we analyze functional and perceptional aspects of two frequently used rating scales in online innovation communities. Using a multimethod approach, our experiments show that a multicriteria scale leads to higher decision quality of users than a single-criterion scale, that idea elaboration (i.e., idea length) negatively moderates this effect such that the single-criterion rating scale outperforms the multicriteria scale for long ideas, and finally that the multicriteria scale leads to more favorable user attitudes toward the Web site. To ensure robustness of our results, we applied a bootstrap-based Monte Carlo simulation based on our experimental data. We found that around 20 user ratings per idea are sufficient for creating stable idea rankings and that a combination of both rating scales leads to a 63 percent performance improvement over the single-criterion rating scale and 16 percent over the multicriteria rating scale. Our work contributes to co-creation research by offering insights as to how the interaction of the technology being used (i.e., rating scale) and the attributes of the rating object affects two central outcome measures: the effectiveness of the rating in terms of decision quality of its users and the perception of the scale by its users as a predictor of future use.
机译:随着Internet的兴起,消费者越来越多地参与共同创建产品和服务。尽管大多数共同创作研究都涉及生成用户生成的内容的各个方面,但本研究着眼于设计用于评估此类内容的等级量表。详细地,我们分析了在线创新社区中两个常用评级量表的功能和感知方面。使用多方法方法,我们的实验表明,多标准量表比单标准量表具有更高的用户决策质量,构想(即构想长度)会对这种影响产生负面影响,因此单标准评分表的表现优于多标准。扩展以适应长远的想法,最后,多标准扩展会导致用户对Web站点更满意的态度。为了确保结果的鲁棒性,我们根据实验数据应用了基于引导的蒙特卡洛模拟。我们发现,每个想法大约有20个用户评级足以创建稳定的想法排名,并且两个评级尺度的组合导致单标准评级尺度的性能提高63%,多标准评级尺度的性能提高16%。我们的工作通过提供有关所使用技术(即等级量表)和等级对象属性之间的相互作用如何影响两个主要结果指标的见解,为共同创建研究做出了贡献用户的使用情况,以及用户对其规模的感知,作为未来使用的预测指标。

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