Managing Email Overload in the Workplace

2014 ◽  
Vol 53 (7) ◽  
pp. 31-37 ◽  
Author(s):  
Kim McMurtry
Keyword(s):  
Email Overload

Email Overload

2017 ◽  
pp. 20-43 ◽  
Author(s):  
Ana Lúcia Terra
Keyword(s):  
Literature Review ◽  
Information Management ◽  
Time Management ◽  
Information Overload ◽  
Personal Characteristics ◽  
Training Time ◽  
Email Overload ◽  
Organizational Problem ◽  
Management Techniques ◽  
Technological Options

In this chapter email overload is presented as a component of information overload and some of its causes and consequences are identified. Furthermore, an analysis on the skills required to deal with information overload is made. Then, a critical literature review about the concept of email overload is realized, stressing aspects such as the amount of messages, personal characteristics and skills or technological issues. Solutions for this organizational problem are presented based on relevant case studies from the literature review. Key components to consider in email overload management are also identified, including information management techniques and technological options, training, time management and information behavior (individual and organizational).

Email Overload

2018 ◽  
pp. 67-86
Author(s):  
Julie Rennecker ◽  
Daantje Derks
Keyword(s):  
Email Overload

scholarly journals The Design and Evaluation of User Interfaces for the RADAR Learning Personal Assistant

AI Magazine ◽  
2009 ◽  
Vol 30 (4) ◽  
pp. 74 ◽  
Author(s):  
Andrew Faulring ◽  
Ken Mohnkern ◽  
Aaron Steinfeld ◽  
Brad Myers
Keyword(s):  
User Interfaces ◽  
Interaction Design ◽  
Office Workers ◽  
Multi Agent System ◽  
Negative Effects ◽  
Email Overload ◽  
Novice Users ◽  
Multi Agent ◽  
Personal Assistant ◽  
Mixed Initiative

The RADAR project developed a large multi-agent system with a mixed-initiative user interface designed to help office workers cope with email overload. Most RADAR agents observe experts performing tasks and then assist other users who are performing similar tasks. The interaction design for RADAR focused on developing user interfaces that allowed the intelligent functionality to improve the user’s workflow without frustrating the user when the system’s suggestions were either unhelpful or simply incorrect. For example with regards to autonomy, the RADAR agents were allowed much flexibility in selecting ways to assist the user, but were restricted from taking actions that would be visible to other people. This policy ensured that the user remained in control and mitigated the negative effects of mistakes. A large evaluation of RADAR demonstrated that novice users confronted with an email overload test performed significantly better, achieving a 37% better overall score when assisted by RADAR. The evaluation showed that AI technologies can help users accomplish their goals.

scholarly journals Evolution of Conversations in the Age of Email Overload

2015 ◽  
Author(s):  
Farshad Kooti ◽  
Luca Maria Aiello ◽  
Mihajlo Grbovic ◽  
Kristina Lerman ◽  
Amin Mantrach
Keyword(s):  
Email Overload

scholarly journals Combining content and social features in a deep learning approach to Vietnamese email prioritization

2021 ◽  
Author(s):  
Ha Thanh Nguyen ◽  
Quan Dinh Dang ◽  
Anh Quang Tran
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
New Method ◽  
Learning Approach ◽  
Field Of Study ◽  
Learning Techniques ◽  
Email Overload ◽  
Social Features

The email overload problem has been discussed in numerous email-related studies. One of the possible solutions to this problem is email prioritization, which is the act of automatically predicting the importance levels of received emails and sorting the user’s inbox accordingly. Several learning-based methods have been proposed to address the email prioritization problem using content features as well as social features. Although these methods have laid the foundation works in this field of study, the reported performance is far from being practical. Recent works on deep neural networks have achieved good results in various tasks. In this paper, the authors propose a novel email prioritization model which incorporates several deep learning techniques and uses a combination of both content features and social features from email data. This method targets Vietnamese emails and is tested against a self-built Vietnamese email corpus. Conducted experiments explored the effects of different model configurations and compared the effectiveness of the new method to that of a previous work.

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