Emotion in enterprise social media systems

Abstract

Purpose

The purpose of this paper is to investigate enterprise social media systems and quantified gender and status influences on emotional content presented in these systems.

Design/methodology/approach

Internal social media messages were collected from a global software company running an enterprise social media system. An indirect observatory test using Berlo’s “source–message–channel–receiver” model served as a framework to evaluate sender, message, channel and receiver for each text. These texts were categorized by gender and status using text analytics with SAP SA to produce sentiment indications.

Findings

Results reveal women use positive language 2.1 times more than men. Senior managers express positive language 1.7 times more than non-managers, and feeling rules affect all genders and statuses, but not necessarily as predicted by theory. Other findings show that public messages contained less emotional content, and women expressed more positivity to lower status colleagues. Men expressed more positivity to those in higher positions. Many gender and status stereotypes found in face-to-face studies are also present in digital enterprise social networks.

Research limitations/implications

Limitations include generalizability: all data were collected from a single enterprise social media system.

Practical implications

Managers establishing codes of conduct for social media use will find this research useful, particularly when promoting awareness of emotional expressiveness in online venues with subordinate colleagues.

Originality/value

This study offers a behavioral measurement approach free from validity issues found in self-reported surveys, direct observations and interviews. The collected data offered new perspectives on existing social theories within a new environment of computerized, enterprise social media.

Citation

Reychav, I., Inbar, O., Simon, T., McHaney, R. and Zhu, L. (2019), "Emotion in enterprise social media systems", Information Technology & People, Vol. 32 No. 1, pp. 18-46. https://doi.org/10.1108/ITP-05-2018-0213