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

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 organizat...

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Autores principales: Arindam Dutta, Elena Steiner, Jeffrey Proulx, Visar Berisha, Daniel W Bliss, Scott Poole, Steven Corman
Formato: article
Lenguaje:EN
Publicado: Public Library of Science (PLoS) 2021
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Acceso en línea:https://doaj.org/article/d5372a9d45d247a28bc2e9c1257dd600
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spelling oai:doaj.org-article:d5372a9d45d247a28bc2e9c1257dd6002021-12-02T20:05:04ZAnalyzing the relationship between productivity and human communication in an organizational setting.1932-620310.1371/journal.pone.0250301https://doaj.org/article/d5372a9d45d247a28bc2e9c1257dd6002021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0250301https://doaj.org/toc/1932-6203Though 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.Arindam DuttaElena SteinerJeffrey ProulxVisar BerishaDaniel W BlissScott PooleSteven CormanPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0250301 (2021)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Arindam Dutta
Elena Steiner
Jeffrey Proulx
Visar Berisha
Daniel W Bliss
Scott Poole
Steven Corman
Analyzing the relationship between productivity and human communication in an organizational setting.
description 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.
format article
author Arindam Dutta
Elena Steiner
Jeffrey Proulx
Visar Berisha
Daniel W Bliss
Scott Poole
Steven Corman
author_facet Arindam Dutta
Elena Steiner
Jeffrey Proulx
Visar Berisha
Daniel W Bliss
Scott Poole
Steven Corman
author_sort Arindam Dutta
title Analyzing the relationship between productivity and human communication in an organizational setting.
title_short Analyzing the relationship between productivity and human communication in an organizational setting.
title_full Analyzing the relationship between productivity and human communication in an organizational setting.
title_fullStr Analyzing the relationship between productivity and human communication in an organizational setting.
title_full_unstemmed Analyzing the relationship between productivity and human communication in an organizational setting.
title_sort analyzing the relationship between productivity and human communication in an organizational setting.
publisher Public Library of Science (PLoS)
publishDate 2021
url https://doaj.org/article/d5372a9d45d247a28bc2e9c1257dd600
work_keys_str_mv AT arindamdutta analyzingtherelationshipbetweenproductivityandhumancommunicationinanorganizationalsetting
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AT danielwbliss analyzingtherelationshipbetweenproductivityandhumancommunicationinanorganizationalsetting
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