电商客服对话提示信息的双任务操作注意分配研究
Research on Dual-task Operational Attention Allocation of Dialogue Prompts for E-commerce Customer Service
孙林辉 1罗诗凡 1袁晓芳 1朱梦婷2
作者信息
- 1. 西安科技大学管理学院,西安 710054;西安科技大学人因与管理工效学研究中心,西安 710054
- 2. 西安科技大学管理学院,西安 710054
- 折叠
摘要
目的 为研究双任务操作下,不同类型对话提示信息对电商客服注意分配的影响.方法 选取视觉呈现位置和三种常见提示音为自变量,设计了三种实验情景:只呈现两种位置差异的视觉信息、只呈现三种不同的听觉信息、同时呈现视听觉组合信息,对被试人员操作过程中的反应时及正确率进行了分析.结果 电商客服人员在同时接收视听觉对话提示信息时,存在注意分配,视觉上更偏向左侧,听觉上更偏向于机械提示音,当位于左侧的视觉对话提示信息与钢琴音同时出现时,电商客服的操作表现优于其他组合的视听觉对话提示信息.结论 研究对话提示信息双任务操作的注意分配,为电商企业提升客服注意力培训、设计对话提示信息具有一定指导意义.
Abstract
Objective To study the influence of different types of dialogue prompts on the attention distribution of e-commerce customer service under dual-task operation.Methods By selecting the visual presentation position and three common cue tones as independent variables,three kinds of experimental scenarios were designed:only two different visual information,only three different auditory information,and visual and auditory combination information were presented at the same time.The reaction time and accuracy of the subjects during operation were analyzed.Re-sults When the e-commerce customer service staff received the audio-visual dialogue prompt information at the same time,there was attention distribution,the vision was more inclined to the left,and the hearing was more inclined to the mechanical prompt sound.When the visual dialogue prompt message on the left appears at the same time with the piano sound,the operation performance of e-commerce customer service is better than that of other combinations of audio-visual dialogue prompts.Conclusion The research on the attention distribution of dual-task operation of dialogue prompts has a certain guiding significance for e-commerce enterprises to improve customer service attention training and design dialogue prompts.
关键词
人机交互/互联网/电商客服/提示信息/注意分配/双任务/管理工效Key words
man-machine interaction/internet/e-commerce customer service/prompt message/attention allo-cation/dual task/management ergonomics引用本文复制引用
基金项目
国家自然科学基金青年基金(71701025)
出版年
2024
NETL
NSTL
万方数据