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How does this message make you feel? A study of user perspectives on software update/warning message design

机译:此消息使您感觉如何?用户对软件更新/警告消息设计的观点研究

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Software update messages are commonly used to inform users about software updates, recent bug fixes, and various system vulnerabilities, and to suggest recommended actions (e.g., updating software). While various design features (e.g., update options, message layout, update message presentation) of these messages can influence the actions taken by users, no prior study can be found that investigated users opinions regarding various design alternatives. To address this void, this paper focuses on identifying software update message design features (e.g., layout, color, content) that may affect users positively or negatively. Toward that, we conducted a user study where users are shown 13 software update messages along with 1 virus warning message. We collect responses from 155 users through an online survey. Participants gave a total of 809 positive comments and 866 negative comments along with ranking of each image in terms of perceived importance, noticeability, annoyance and confusion. As many of the comments are repetitive and often contain multiple themes, we manually analyzed and performed a bottom-up, inductive coding to identify and refine the underlying themes. Over multiple iterations, positive comments were grouped into 52 categories which were subsequently grouped under four themes. Similarly, negative comments were first grouped into 38 categories which were subsequently grouped under four themes. Based on our analysis, we present the list of design features that are found to be highly correlated to confusion, annoyance, noticeability, and importance, either positively or negatively.
机译:软件更新消息通常用于通知用户有关软件更新,最近的错误修复以及各种系统漏洞的信息,并建议采取的措施(例如,更新软件)。尽管这些消息的各种设计功能(例如,更新选项,消息布局,更新消息表示)可能会影响用户采取的行动,但没有发现以前的研究可以调查用户对各种设计替代方案的意见。为了解决这一空白,本文着重于确定可能对用户产生正面或负面影响的软件更新消息设计功能(例如,布局,颜色,内容)。为此,我们进行了一项用户研究,向用户显示13条软件更新消息以及1条病毒警告消息。我们通过在线调查收集了155个用户的回复。与会人员共给出了809条正面评论和866条负面评论,并对每张图片的感知重要性,引人注目,烦恼和困惑进行了排名。由于许多评论都是重复性的,并且通常包含多个主题,因此我们手动分析并执行了自下而上的归纳编码,以识别和完善基础主题。在多次迭代中,正面评论分为52个类别,随后分为四个主题。同样,负面评论首先被分为38个类别,随后又分为四个主题。根据我们的分析,我们列出了正与负与混乱,烦恼,引人注目的和重要性高度相关的设计特征列表。

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