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Design of Small Multiples Matrix-based Visualisation to Understand E-mail Socio-organisational Relationships

Sathiyanarayanan, M. ORCID: 0000-0002-8598-1949, Turkay, C. ORCID: 0000-0001-6788-251X and Fadahunsi, O. (2019). Design of Small Multiples Matrix-based Visualisation to Understand E-mail Socio-organisational Relationships. 2018 10th International Conference on Communication Systems & Networks (COMSNETS), pp. 643-648. doi: 10.1109/COMSNETS.2018.8328288

Abstract

One of the fundamental organisational questions is how organisations identify anomalies, monitor and compare E-mail communications between staff-staff or staff-clients or staff- customers relationships on a daily basis. The tenacious and substantial relationships are built by the combination of timely replies, frequent engagement and deep interaction between the individuals. To watchdog this periodically, we need an interactive visualisation tool that can help organisational analysts to reconnect some lost relationships and/or strengthen an existing relationship or in some cases identify inside persons. From our point of view, Social Intelligence (SI) in an organisation is a combination of self-, social- and organisational-awareness that will help in managing complex socio-organisational changes and can be interpreted in terms of socio-organisational communication efficacy (that is, one’s confidence in one’s ability to deal with social and organisational information). We considered a case study, an Enron E-mail Scandal, to understand the relationships of staff during various parts of the years and we conducted a workshop study with legal experts to gain insights on how they carry out investigation/analysis with respect to E-mail relationships. The outcomes of the workshop helped us develop a novel small multiples matrix-based visualisation in collaboration with the company, Red Sift UK to find anomalies, monitor and compare how email relationships changes over time and how it defines the meaning of socio-organisational communication efficacy.

Publication Type: Article
Additional Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords: Email Communication; visualisation; social networks; anomaly detection; temporal features; D3;
Subjects: H Social Sciences > HE Transportation and Communications
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Mathematics, Computer Science & Engineering > Computer Science
URI: http://openaccess.city.ac.uk/id/eprint/22860
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