Learning to Ignore: A Case Study of Organization-Wide Bulk Email Effectiveness

Proc. ACM Hum. Comput. Interact.(2020)

引用 20|浏览56
暂无评分
摘要
Bulk email is a primary communication channel within organizations, with all-company emails and regular newsletters serving as a mechanism for making employees aware of policies and events. Ineffective communication could result in wasted employee time and a lack of compliance or awareness. Previous studies on organizational emails focused mostly on recipients. However, organizational bulk email system is a multi-stakeholder problem including recipients, communicators, and the organization itself. We studied the effectiveness, practice, and assessments of the organizational bulk email system of a large university from multi-stakeholders' perspectives. We conducted a qualitative study with the university's communicators, recipients, and managers. We delved into the organizational bulk email's distributing mechanisms of the communicators, the reading behaviors of recipients, and the perspectives on emails' values of communicators, managers, and recipients. We found that the organizational bulk email system as a whole was strained, and communicators are caught in the middle of this multi-stakeholder problem. First, though the communicators had an interest in preserving the effectiveness of channels in reaching employees, they had high-level clients whose interests might outweigh judgment about whether a message deserves widespread circulation. Second, though communicators thought they were sending important information, recipients viewed most of the organizational bulk emails as not relevant to them. Third, this disagreement was amplified by the success metric used by communicators. They viewed their bulk emails as successful if they had a high open rate. But recipients often opened and then rapidly discarded emails without reading the details. Last, while the communicators in general understood the challenge, they had a limited set of targeting and feedback tools to support their task.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要