Sensor data provide an objective view of reality but fail to capture the subjective motivations behind an individual's behavior. This latter information is crucial for learning about the various dimensions of the personal context, thus increasing predictability. The main limitation is the human input, which is often not of the quality that is needed. The work so far has focused on the usually high number of missing answers. The focus of this paper is on \textit{the number of mistakes} made when answering questions. Three are the main contributions of this paper. First, we show that the user's reaction time, i.e., the time before starting to respond, is the main cause of a low answer quality, where its effects are both direct and indirect, the latter relating to its impact on the completion time, i.e., the time taken to compile the response. Second, we identify the specific exogenous (e.g., the situational or temporal context) and endogenous (e.g., mood, personality traits) factors which have an influence on the reaction time, as well as on the completion time. Third, we show how reaction and completion time compose their effects on the answer quality. The paper concludes with a set of actionable recommendations.

翻译：传感器数据提供了客观的现实视角，但无法捕捉个体行为背后的主观动机。这类主观信息对了解个人情境的多维特征至关重要，能有效提升可预测性。其主要局限在于人类输入常缺乏所需质量。现有研究多关注回答缺失率较高的问题，而本文聚焦于回答过程中产生的错误数量。本文有三项核心贡献：首先，我们证明用户反应时间（即开始作答前的耗时）是导致回答质量低下的主因，其影响兼具直接性与间接性——间接效应体现在对完成时间（即撰写回答的总耗时）的干扰上。其次，我们识别出影响反应时间与完成时间的具体外源因素（如情境或时间语境）与内源因素（如情绪、人格特质）。第三，我们揭示了反应时间与完成时间如何共同作用于回答质量。本文最后提出了一系列可操作性建议。