Analyzing the relationship between productivity and human communication in an organizational setting

• Published in: PloS one Vol. 16; no. 7; p. e0250301
• Main Authors: Dutta, Arindam, Steiner, Elena, Proulx, Jeffrey, Berisha, Visar, Bliss, Daniel W, Poole, Scott, Corman, Steven
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 14.07.2021; Public Library of Science (PLoS)
• Subjects: Audio data; Biology and Life Sciences; Communication; Computer and Information Sciences; Datasets; Economic aspects; Emotions; Empirical analysis; Employee performance; Employees; Engineering and Technology; Group dynamics; Human communication; Organizational communication; Physical Sciences; Productivity; Social Sciences; Software engineering; Speech; Speech recognition; Topology; Working hours; United States; United States > US; Illinois; Arizona
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0250301

Though it is often taken as a truism that communication contributes to organizational productivity, there are surprisingly few empirical studies documenting a relationship between observable interaction and productivity. This is because comprehensive, direct observation of communication in organizational settings is notoriously difficult. In this paper, we report a method for extracting network and speech characteristics data from audio recordings of participants talking with each other in real time. We use this method to analyze communication and productivity data from seventy-nine employees working within a software engineering organization who had their speech recorded during working hours for a period of approximately 3 years. From the speech data, we infer when any two individuals are talking to each other and use this information to construct a communication graph for the organization for each week. We use the spectral and temporal characteristics of the produced speech and the structure of the resultant communication graphs to predict the productivity of the group, as measured by the number of lines of code produced. The results indicate that the most important speech and network features for predicting productivity include those that measure the number of unique people interacting within the organization, the frequency of interactions, and the topology of the communication network.