Opting out in computer-supported sequential collaboration

IF 8.9 1区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL Computers in Human Behavior Pub Date : 2025-04-01 Epub Date: 2024-12-11 DOI:10.1016/j.chb.2024.108527
Maren Mayer , Daniel W. Heck , Joachim Kimmerle
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Abstract

Many online collaborative projects such as Wikipedia and OpenStreetMap organize collaboration among their contributors sequentially. When engaging in sequential collaboration, one contributor creates an initial entry which is then consecutively adjusted or maintained by the following contributors. Thereby, only the latest version of this entry is presented to subsequent contributors. Sequential collaboration was recently examined as a method for aggregating numerical judgments compared to averaging independently provided judgments (i.e., wisdom of crowds). Sequential collaboration was shown to yield increasingly accurate judgments that result in estimates that are at least as accurate as those obtained from aggregating independent judgments. However, sequential collaboration differs from simply aggregating independent judgments in the sequential nature of the process of providing judgments and in the possibility of contributors to opt out of providing a judgment by maintaining it. How these different features contribute to the accuracy of provided judgments is still unknown. In two experiments, we found that the most accurate judgments were provided by participants who engaged in standard sequential collaboration (with an opt-out option), whereas participants who performed sequential collaboration without opt-out gave less accurate judgments; and independent judgments were least accurate. Thus, both the sequential-collaboration process per se as well as the possibility to opt out and not provide a judgment contribute to the accuracy of contributions. These two features come together in a typical sequential-collaboration paradigm. Allowing contributors to use sequential collaboration in collaborative online projects or at least implement some features of sequential collaboration can be beneficial for the resulting entries and information.
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选择退出计算机支持的顺序协作
许多在线协作项目,如维基百科和开放地图,按顺序组织贡献者之间的协作。当参与顺序协作时,一个参与者创建一个初始条目,然后由后续参与者连续调整或维护。因此,只有该条目的最新版本才会呈现给后续贡献者。与独立提供的平均判断(即群体智慧)相比,顺序协作最近被作为一种汇总数字判断的方法进行了研究。连续的协作被证明可以产生越来越准确的判断,其结果估计至少与从汇总的独立判断中获得的估计一样准确。然而,在提供判断过程的顺序性质和贡献者通过维持判断而选择不提供判断的可能性方面,顺序协作不同于简单地汇总独立判断。这些不同的特征是如何影响所提供判断的准确性的,目前尚不清楚。在两个实验中,我们发现,从事标准顺序协作(有退出选项)的参与者提供了最准确的判断,而没有选择退出的顺序协作参与者给出的判断较不准确;独立判断是最不准确的。因此,顺序协作过程本身以及选择退出和不提供判断的可能性都有助于贡献的准确性。这两个特性在一个典型的顺序协作范例中结合在一起。允许参与者在协作在线项目中使用顺序协作,或者至少实现顺序协作的一些特性,这对生成的条目和信息是有益的。
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来源期刊
CiteScore
19.10
自引率
4.00%
发文量
381
审稿时长
40 days
期刊介绍: Computers in Human Behavior is a scholarly journal that explores the psychological aspects of computer use. It covers original theoretical works, research reports, literature reviews, and software and book reviews. The journal examines both the use of computers in psychology, psychiatry, and related fields, and the psychological impact of computer use on individuals, groups, and society. Articles discuss topics such as professional practice, training, research, human development, learning, cognition, personality, and social interactions. It focuses on human interactions with computers, considering the computer as a medium through which human behaviors are shaped and expressed. Professionals interested in the psychological aspects of computer use will find this journal valuable, even with limited knowledge of computers.
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