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RESEARCH ARTICLE

Analyzing the relationship between

productivity and human communication in an

organizational setting

Arindam DuttaID 1*, Elena Steiner1, Jeffrey Proulx2, Visar Berisha1, Daniel W. Bliss1,

Scott Poole 2 , Steven Corman

1

1 Arizona State University, Tempe, Arizona, United States of America, 2 University of Illinois at Urbana

Champaign, Champaign, Illinois, United States of America

* [email protected]

Abstract

Though it is often taken as a truism that communication contributes to organizational pro-

ductivity, 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 par-

ticipants 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 approxi-

mately 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 mea-

sure the number of unique people interacting within the organization, the frequency of inter-

actions, and the topology of the communication network.

Introduction

The “structural imperative” in network research [1] suggests that we can represent any organi-

zation as a network and look at the network as a determinant of behavior, culture, and the

individuals within the organization. Organizational networks are generated and populated by

human beings who are active agents with intentions, knowledge, and the ability to rationalize

their actions. From interactions between individuals in an organization we can derive certain

qualitative aspects like behavior, intentions, emotions and inter-employee relations of a work-

place. These aspects play a large role in the effectiveness and productivity of an organization.

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OPEN ACCESS

Citation: Dutta A, Steiner E, Proulx J, Berisha V,

Bliss DW, Poole S, et al. (2021) Analyzing the

relationship between productivity and human

communication in an organizational setting. PLoS

ONE 16(7): e0250301. https://doi.org/10.1371/

journal.pone.0250301

Editor: Nersisson Ruban, Vellore Institute of

Technology, INDIA

Received: December 31, 2020

Accepted: April 1, 2021

Published: July 14, 2021

Peer Review History: PLOS recognizes the

benefits of transparency in the peer review

process; therefore, we enable the publication of

all of the content of peer review and author

responses alongside final, published articles. The

editorial history of this article is available here:

https://doi.org/10.1371/journal.pone.0250301

Copyright: © 2021 Dutta et al. This is an open access article distributed under the terms of the

Creative Commons Attribution License, which

permits unrestricted use, distribution, and

reproduction in any medium, provided the original

author and source are credited.

Data Availability Statement: Anonymized data is

available from openICPSR: https://doi.org/10.3886/

E130041V1. Non-anonymized data are not released

because they could be used to identify individual

In this paper we aim to directly study this relationship between productivity and communica-

tion, and report new methods for doing so.

While productivity is relatively straightforward to measure, existing studies measure com-

munication indirectly, either through member self-reports of communication on rating scales

[2, 3], through external raters’ evaluation using global scales that assess communication behav-

ior [3], as communication technology investment [4], or through questionnaires measuring

more distal constructs such as communication satisfaction or perceived effectiveness [5, 6].

While these studies are useful, they can be challenged on the grounds that perceptions of com-

munication do not correspond to actual communication behavior [7]. Direct observation is

the “gold standard” for measuring communication and provides the most rigorous test of

the communication-productivity relationship. Though several studies involving direct obser-

vation of communication behavior have been completed (for a review see [8]), these typically

involved methods of human observation of small groups for short periods or unusual settings

(for example Ham radio operators) where communication is routinely logged. Long-term

studies based on objective observation are needed to supplement and validate current under-

standing of the relationship between communication and productivity.

Our general research question is:

What is the relationship between the amount of communication in an organization and its

productivity? What are the factors that may moderate this relationship?

Several factors may moderate the productivity-communication relationship. One particu-

larly important factor is the type of work the organizational unit in question does. For units

engaged in the production of verbal outputs-such as plans, reports, audits and in those whose

primary work involves interacting with clients or customers-such as those delivering educa-

tion, therapy, or advice-an argument can be made that the greater the amount of communica-

tion, the higher the productivity. For units engaged in action or production, however, a

different relationship would be expected: communication is good up to a point, but too much

communication interferes with action or production. Moreover, in these units, high levels of

communication may signal that they are experiencing difficulties and hence must engage in

problem solving that requires high levels of communication. In this case, we can expect a non-

linear relationship between communication and productivity, communication is positively

related to productivity up to a point, past which it is negatively related. Since the organizational

unit we are studying is engaged in producing software, we would expect an inverted-U shaped

(2nd order polynomial) relationship between communication and productivity.

In this work, we estimate inter-employee communication networks in a software engineer-

ing organization using speech recordings. For a period of 3 years, all employees wore audio-

recorders during their hours of work which recorded their conversations, and weekly commu-

nication graphs were estimated based on the detected speech. We use a simple speech activity

detector, combined with inter-recorder correlations, to detect interactions between individuals

and to construct daily communication graphs. In addition, we also measure several speech fea-

tures that describe the speaking style of each individual. These features, which are defined in

more detail in the S1 Appendix, include, pitch, temporal features (energy, zero crossing rate),

spectral features (spectral centroid, spectral flux etc), and cepstral features (mel-scale frequency

cepstral coefficients-MFCCs). Numerous studies have used these speech features to detect

speakers and speech features such as emotions with high accuracy [9–17]. Each research has,

in turn, linked various speech features to emotion. At the neurological level, emotions are

known to have an impact on individual task performance [18, 19]. Emotion also influences

individual behavior in task performance, citizenship and deviance [20]. Ashforth and Hum-

phrey [21] reviewed the importance of emotion in organizational contexts, including its

effects on motivation, leadership, and group dynamics. All of these have been associated with

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participants. Researchers can request access to

the non-anonymized data by contacting Dr. Steven

Corman ([email protected]), PI of this

project, or the ASU IRB (Phone: 480-965-6788 |

Fax: 480-965-7772 | Email: research.integrity@asu.

edu).

Funding: Dr. Steven Corman NSF PD 11-8031

National Science Foundation https://www.nsf.gov/

publications/pub_summ.jsp?ods_key=

gpg15001&org=NSF The sponsors played no role

in this manuscript.

Competing interests: The authors have declared

that no competing interests exist.

performance in empirical research, for example, motivation, [22], leadership [23] and group

dynamics [24]. It is important to study emotion alongside network structure because networks

are a substrate of emotional contagion, and such contagion has been shown to influence group

dynamics [25]. Therefore, we use a combination of networks and speech analysis to analyze

the relationship between productivity and human communication in an organization. The

method for this study was not intended to be applied by other organizations for practical pur-

poses. Our immediate purpose in comparing productivity to detected interaction was to vali-

date our detection method, i.e. to prove that the communication we detected has expected

relationships to organizational outcomes. An additional purpose was to support a larger spon-

sored project, focused on discrepancies between observable and perceived communication

[26].

Method

Organization setting and data collection

This study was approved by the Arizona State University IRB (Approval number:

STUDY00003138), and written consent forms were obtained for participants. The setting for

this research was the Software Factory (SF), a service unit at a large southwestern university

providing software engineering services for funded research projects and university technol-

ogy spinouts. SF had directors and work was led by a professional software engineer who man-

aged student programmers using industry-standard engineering processes and were organized

in forma, project-based teams. These characteristics put it squarely in the category of a profes-

sional organization [27]. It operated for 144 weeks from late 2002 to early 2005, and had 79

participants, including the manager, employees, clients, and researchers. Over this time, SF

worked on 31 separate projects, developing applications for the social sciences, natural sci-

ences, and education, and for internal use (such as an activity reporting system). The major

steps of handling a project at the Software Factory consisted of four major processes:

• The business process,

• The development process,

• The design process, and

• The implementation process.

Typically, the initial business process involved the most senior people on the customer side

(including the decision maker) and the highest-level SF personnel (one or more directors and

a project manager). When the client had already identified one or more students to work on

the project, they may also be in attendance. The development process included collaboration

between the customer, project manager and the technical lead of the project. The major activi-

ties in this process involved validating with the customer, setting realistic customer expecta-

tions, and communicating to all SF personnel working on the project. The design process

included the project manager, technical lead and the developers, and lastly the implementation

stage involved the technical lead and the developers. These projects varied in terms of time-

scale and the number of SF personnel involved. Over the course of 144 weeks, there were

instances where multiple projects existing at the same time, involving multiple employees, and

some instances with an employee being involved in multiple projects at the same time. This

study used only records from the 54 SF employees, because only employees made entries in a

code repository and activity reporting system, data used in this paper.

The SF data is a unique dataset that aimed to accomplish, as nearly as possible, ubiquitous

observation of a set of 79 employees and clients of the organization. The dataset contains

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recorded audio data from participants between September-2002 and June-2005. Whenever

they entered the dedicated SF facility, participants attached a digital recorder and lapel micro-

phone, and logged in to a server which placed a time stamp on the recording. When leaving,

they uploaded the recorded audio to a server for storage. The resultant dataset contains daily

recordings of all SF employees and visitors (primarily clients) comprising approximately 7000

hours of time synchronized recordings. There was no evidence if employees ever chose to

delete or not turn in recordings, it would have been reflected in our time-aligning analyses for

cross-correlation mentioned in the later section. Also, people involved in SF said that after the

first week or so, members tended to forget the recorders. The same has been reported in other

studies doing long-term recording of participants. The participant recordings were created in

digital speech standard (DSS) file formats, a compressed proprietary format optimized for

speech. They were converted to an uncompressed WAV format using the Switch Sound File

Converter software. The files were stored using a 6kHz sampling rate with 8-bits/sample.

In addition to the recordings, we analyzed the code written by employees at the SF. All

codes were stored and managed using a Visual Source Safe (VSS) 6.0 repository. We used the

VSS API to extract records from the repository. Each record included the filename, date, user,

version, and changes, insertions, and deletions at check-in. From this information we were

able to compute the number of lines of code at each check-in. In particular, we computed the

total number of inserted, deleted and changed lines of code per employee per week. A total of

11276 entries of changes in LOC were recorded staring from the first week of March-2003.

The SF dataset affords a unique opportunity to obtain a holistic picture of work activity and

communication in a small organizational unit over an extended period. In this analysis, we

have used the audio recording from March-2003 to June-2005 (124 weeks), to build communi-

cation networks and extract speech features to predict the effective lines of codes obtained

using VSS analysis.

Other studies in the literature have found that LOC is an effective measure of productivity

in software organizations [28, 29].

Approach

All analyses were done on a weekly basis. In case of communication graphs, individual interac-

tions between any two individuals were detected using a simple cross-correlation scheme.

Individual interactions were converted to a communication graph representing the frequency

of interactions between any two individuals over the course of a week. From this graph, we

extracted a set of features that describe the topology of the resultant network and denote that

by, Gw 2R 1�fg , where fg is total number of graph features. In addition, we also extracted several

speech features from the daily recordings and calculate two statistics (mean and variance) for

these features across the whole week for all participants. These are defined as, Sw 2R 1�ð2�fsÞ,

where fs is total number of speech features. Thus, we had a total communication feature space defined by Cw : ðGwkSwÞ 2R

1�ðfgþ2�fsÞ (where k is the concatenation operator).

We describe the details of how we estimate the communication graph and the feature

extraction in the sections below. We then describe how we predict productivity using these

features.

Communication graph analysis

Pair-wise communication detection. To construct the communication graphs, we used

cross channel signal analysis. The entire process of graph analysis can be subdivided into two

main blocks, the construction of speech cross-correlation graphs and graph feature extraction

as shown is Fig 1.

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Speech cross correlation graph. As a pre-processing step we normalized the data by the

mean to remove DC offset (caused by the analogue parts of the system that add a DC current

to the audio signal), that causes significant interference with the audio signal, especially during

signal processing. We investigated preliminary conversation detection performance on the SF

data by using a two-stage approach. The first stage identified continuous segments of speech

using an energy and spectral based detector; in the second stage, we use a pair-wise cross-cor-

relation between one speaker’s channel and the remaining channels to detect with whom that

person was speaking. The basic idea behind this approach is that, if two individuals are speak-

ing, their microphone will pick up each-other’s speech and cross correlation will be high. A

cross-correlation matrix was constructed using mean correlation weights between participant

pairs across each day. The weights were calculated based the quantity of communication

between participant pairs for an entire working day. The correlation matrix represents a proxy

for the frequency of interactions between any two individuals. The same data can also be used

to detect individual interactions and compare against manually coded data. Pairwise conversa-

tions between two speakers were detected by the algorithm and were presented to research

assistants for manual coding. The daily cross-correlation matrices, which represent a proxy

for frequency of interaction between two speakers, were averaged over the week to construct

weighted communication graphs, with participants as nodes and the correlation weights as

edges.

In the automated interaction detection, we used simple speech processing techniques from

audio segments of both employees in a dyad to detect communication. First, we computed the

short-time speech energy and spectral centroid (See S1 Appendix) for every 15 seconds frame

and estimated thresholds to detect speech from the two features. Speech portions were

detected using the two thresholds and non-speech portions were removed.

Next, we computed the covariance matrix between energy of speech segments from both

microphones in a dyad. Two sets of thresholds were estimated based on the diagonal elements

of the matrix, (a) Th1, to determine if communication occurred (0 or 1, 2, 3) and (b) Th2, to determine the direction of communication (1, 2 or 3).

Validation of detection. Before constructing the communication graphs based on pair-

wise cross-correlation, we validated the detections by comparing them to human coder classi-

fications of the audio recordings as indicating network connections. We extracted 10 minute

audio segments from a dyad from random working days. First we determined the total num-

ber of segments required to assess validity. Based on this we extracted that number of segments

through random sampling from the audio corpus. External raters then coded the 15 second

Fig 1. Process chain for communication graph analysis.

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segments regarding whether there was talk or silence in the segment and who was talking to

whom. The specific classifications they could make were:

• Silence/noise (0)

• Employee 1 speaking (1)

• Employee 2 speaking (2)

• Both employees speaking (3)

We determined the minimum number of audio segments required to assess validity using

the confidence interval equation,

N � p̂ð1 � p̂Þ

�2

where N is the minimum number of samples, p̂ is the estimated population proportion and � is the margin of error. With an error margin (variance per sample) of 5% and a p̂ of 0.8, the mini- mum number of samples required is 64. In our analyses, a total of 75 ten minutes audio seg-

ments from random working days and between random dyads were used for communication

validation. As Fig 3 indicates, there was 88% agreement between the coders and the automated

detection (see next section for more details).

Graph feature extraction. After the graph was constructed using pairwise speech correla-

tion, we extracted several topological features that aim to describe the nature of daily interac-

tions. A total of 11 graph features were investigated in this work, which are described in more

details in S1 Appendix.

Basic graph descriptors. We calculated the following basic graph descriptors:

• Number of edges. The total number of communication links present between employees in the network.

• Number of nodes. The total number of active employees present in the network.

• Average degree. Defined as the number of links that are incident on a particular employee. It is informative of total communication for individual employees.

• Number of connected triples. A count of the number of connected triples in the graph.

• Number of cycles in a graph. Defined as m − n + c, where m is the number of links, n is the number of employees and c is the number of connected components. This indicates how connected the network is.

• Graph energy. The sum of the absolute values of the real components of the eigenvalues of the graph. They tell us about the structural complexity of the network. A structurally com-

plex network has more differentiated interactions, which suggests members are working on

different tasks in smaller groups and also that there is some interchange among these small

groups.

Graph centrality measures. We computed the following graph centrality measures:

• Degrees. The average number of links adjacent to an employee node. This is an effective mea- sure of the influence or importance of individual nodes on the network.

• Average neighbor degree. The average degree of adjacent or neighboring nodes for every ver- tex. We took the average of this measure across all nodes. This indicates the flow of commu-

nication around the organizational unit.

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• Eigen centrality. The i-th component of the eigenvector of the adjacency matrix gives the centrality score of the i-th node of the network. The average eigen centrality across all nodes was computed for this study. This measure tells us about the quality of communication of an

employee with others. This indicates the influence an employee over other employees in the

organization.

Laplacian features. We also calculated two Laplacian graph features.

• Graph spectrum. Defined as the eigenvalues of the Laplacian of the graph. This tells us about the frequency of communication in the organizational unit and its relationship to the nodes

and link attributes.

• Algebraic connectivity. The magnitude of this value reflects how well connected the overall network is. It has been used in analyzing the robustness and synchronizability of networks.

These features are estimated based on daily graphs. We average over the week to compute a

weekly graph feature vector, Gw 2R 1�11

, where 11 is total number of graph features investigated.

Speech analysis

In addition to the graph features, we extracted speech features for every speaker from the data.

These features carry information about speaker identity and various aspects of affect, which

are important characteristics for predicting productivity.

Speech feature extraction. Speech features are extracted independently for every speaker

(e.g. every recording channel). Prior to feature extraction, we remove the DC offset, and split

the data into 1-second speech segments using hamming windows. All features are extracted at

this scale.

A total of 35 different features were obtained from the audio data. Some of these pertained

to whether there was a network linkage between actors and others pertained to properties of

the linkages. In view of the exploratory nature of this research, we included the latter in order

to capture a richer description of the nature of the links than a simple linked-not linked

description would provide. As mentioned before, emotion affects productivity and these emo-

tions can be recognized from variations in various aspects of speech. The speech features used

for this study are mentioned below and described in details in S1 Appendix,

• Pitch. Features related to pitch contain information related to speaker emotions [9, 10, 13]. Fundamental pitch frequency, 12 harmonics and harmonic ratio were the pitch-related fea- tures that were investigated in this study.

• Temporal features. These features capture certain aspects of speaker emotion, like stress level, joy, excitement etc [9, 10]. We calculated the zero-crossing rate, shot-time energy and energy entropy from every one-second speech frame.

• Spectral features. These features carry the particulars of the frequency content of speech. They carry information about speaker identity and can help classifying a wide range of emo-

tions [10, 11]. The spectral features investigated in this study are the spectral centroid, spectral spread, spectral entropy, spectral flux and spectral rolloff.

• Cepstral features. These features capture the characteristics of our auditory system based on changes in emotions, irrespective of language or gender. A significant number of speech

emotion recognition (SER) research papers have identified these as one of the most efficient

features for emotion classification [9–11, 13, 16]. Thirteen Mel-frequency cepstrum coeffi- cients (MFCC) were extracted from 20 ms frames and averaged over 1 sec window.

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We calculated the mean and variance of these features over the working days of a week to

compute weekly speech feature vectors defined as, Sw 2R 1�ð35�2Þ

, where 35 is total number of

graph features investigated and 2 is the number of statistics computed for each speech feature.

Thus, together with the graph and speech features we had a combined communication feature

set defined by, Cw 2R 1�ð11þ70Þ

¼R1�81.

Measure of productivity

In this paper, the overall organization productivity, defined by the total lines of codes per week

per employee (LOCw) was used as the measure of productivity in the SF. The total LOC was calculated for each week as the sum of ‘changed’, ‘inserted’ and ‘deleted’ LOC, as, LOCw = Changed + inserted + deleted LOC. The weekly LOC measures were converted to log scale to

reduce the variable dynamic range. The average LOC per employee was calculated bu normal-

izing the LOC measure by the number of employees present during the particular week.

Predicting productivity from communication

Regression methods allow us to summarize and study relationships between two continuous

(quantitative) variables. One variable is regarded as the predictor, explanatory, or independent

variable (in this case the weekly ‘communication features, Cw’), and the other variable, is regarded as the response, outcome, or dependent variable (in this case weekly ‘productivity, LOCw’). We mentioned before that we should expect an inverted U-shaped relationship (poly- nomial of order 2) between communication and productivity. To apply this hypothesis, we

first selected the communication features that exhibited such relationship. The selected com-

munication features were then used to predict the organizational productivity. Since the vari-

ables are consecutive and evenly-spaced observations in time, it is a sequence of discrete-time

data, where each data point is dependent on previously observed values. Consequently, We

used a time-series regression model to predict productivity. In general, our regression model

assumes productivity and the communication features are related to one another by

LOCw ¼ FðCw; tÞþ �

where FðCw; tÞ is some mathematical operation (or model) showing productivity as a function of the input communication features and time (weeks), and � is the prediction error. Fig 2

shows the block diagram of the prediction process and each block is described.

Pre-whitening. Pre-whitening is required to remove autocorrelation and trends from the

time-series variables, so that a meaningful relationship between the variables can be assessed.

It concentrates the main variance in the data in a relatively small number of dimensions,

and removes all first-order structure from the data. We implemented the ZCA whitening

Fig 2. Prediction process chain.

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transformation,

X̂ ¼ X � mðXÞ ffiffiffiffiffiffiffiffiffiffiffiffiffiffi covðXÞ

p

where, μ(X) and cov(X) are the mean and covariance matrix of time-series variable X. X̂ is the transformed variable whose covariance matrix is the identity matrix. We pre-whitened all the

independent variables (Cw) and the dependent variable (LOCw). Feature selection. We used a rank based feature selection method with a regression

model (FðCwÞ) to evaluate correlation weights of each communication feature independently with 10-fold cross validation (in a 10-fold cross validation, the entire set is divided into 10 sub-

sets, where 9 of them are used to train the regression model and one set for prediction). A sup-

port vector regression (SVR) model (see S1 Appendix) with a second order polynomial kernel

(according to hypothesis) was used to find the association of each feature with the measure of

productivity. Features with correlation weights above zero were selected for prediction analy-

sis. Fig 2 shows that 27 communication features were selected from 81, which were given as

input to the regression model.

Time-series regression. After selecting the most correlated features, they were used to

predict productivity (LOCw) using a time-series regression model. The SVR model with second order polynomial kernel was used as the base regression model. We can write the final model

as

FðCw; tÞ¼ X

k

FðCwðt � kÞÞ

To test the accuracy of the model k-steps ahead predictions were made at each data point, for k = 0, 1, 2, 3, . . ., 8. Prediction for various time lags (1–8 weeks) were evaluated, to assess the dependency of productivity on past data.

Results

Pair-wise communication detection results

In the pair-wise communication detection, the four main classes were, “Silence/noise” (0), “Employee 1 speaking” (1), “Employee 2 speaking” (2), and “Both employees speaking” (3). The receiver operating characteristics (ROC) curve (see Fig 3) was used to illustrate the communi-

cation detection accuracy (0 or 1, 2, 3). The ROC curve was constructed by varying the thresh-

old Th1, and the optimum value of Th1 was determined. Threshold Th2 was determined after constructing confusion matrices for various Th2 values. The threshold parameters for the best model were Th1 = 2.53e

−5 and Th2 = 2.02e

−5 . We have shown the confusion matrix of the best

detection model in Table 1.

Our method produced a good communication detection rate (AUC: 0.88), and on review-

ing the results, we noticed that most of the false positives resulted because of the presence of

other employees. We then constructed the daily communication graph using the above detec-

tion method, with correlation weights as edges connecting the employees present in the day.

Thus in case of a communication scenario with more than two employees, the correlation

weights will be high for any dyad with the speaker in it, while the correlation weights between

other employees will be relatively low. For any focal individual the correlation weights between

that individual will be high with anyone they address, while those between other speakers who

might be detected in the background is lower.

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Predicting productivity from communication

Feature selection. We computed the correlation weights for each communication feature

while predicting productivity. Fig 4 shows the average merit of the features based on correla-

tion weights achieved while predicting LOCw. It can be seen that almost all the graph features (10 out of 11) had positive correlation weights. Among the weekly speech features, the MFCC

coefficients (1, 2, 3, 4, 5, 6, 8), the spectral and energy entropy (mean), fundamental frequency

Fig 3. Receiver operating characteristics curve for communication detection; area under curve (AUC) = 0.88.

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Table 1. Confusion matrix for the best detection model; each element is shown in terms of number of 15 seconds

segments.

Coder

Class 0 1 2 3

Tool 0 1390 183 227 74

1 51 201 32 105

2 70 14 309 98

3 16 42 50 138

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(variance), spectral roll-off (mean) and spectral centroid and spread (mean) were positively

correlated. Comparing the two types of communication features, the graph features had higher

correlation weight than the speech features. The number of nodes, average neighbor degree,

algebraic connectivity, graph energy and graph spectrum were the features with highest aver-

age merit.

Time-series prediction of productivity. To analyze the communication-productivity

relationship we made k-steps time-series prediction of LOCw at each data point using the selected communication features. We used lags of upto six weeks to analyze how much the

productivity depend on previous weeks’ communication. The mean absolute error (MAE),

mean absolute percentage error (MAPE), root mean squared error (RMSE) and direction

accuracy (DA) were measured to evaluate the accuracy of the time-series model. The time-

series model implementation was done in WEKA 3.8 [30]. Fig 5 shows the k-steps (k = 1, 2, 4, 8) prediction result using a lag of one week. The accuracy parameters are shown in Table 2 for

1 week and 6 weeks lags. Fig 6 shows the MAPE for different lags (1 to 8 weeks).

It can be seen that, using 1-week previous information, we can predict productivity (LOCw) with an error of 7.2–9.8% (1–8 steps ahead prediction). This is error is reduced to 2.2–5.6%,

when we use information from the previous 6 weeks. The direction accuracy also improves

from 71–77% to 83–92%.

Fig 4. Correlation coefficients for each selected communication feature.

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Discussion

From the results we can conclude that communication is strongly related to productivity in an

organization. Table 2 suggests that we can predict organizational productivity with high accu-

racy with mean absolute error less than 10%. We hypothesized before, that communication

and productivity share a non-linear relationship (polynomial of order 2), and we made use of

that relationship in the regression model. With the use of a second order polynomial kernel

SVR model, we selected the communication features and used to same model to do a time-

series forecasting of productivity. The results are also suggestive of the fact that the prediction

accuracy improved as we used more previous information. Though comparisons are difficult

due to differences in methods and measures, this study shows a stronger correlation between

communication and performance than previous research. In [6], the authors found a relation-

ship of r = 0.27 between two-way interaction and effectiveness. In [31], only a small r = 0.02 correlation between communication satisfaction and productivity was reported. It is possible

Fig 5. 1, 2, 4, 8 steps ahead prediction for productivity (LOCw) using 1-week past information.

https://doi.org/10.1371/journal.pone.0250301.g005

Table 2. Accuracy of time-series model used to predict productivity (LOCw) using communication features (Cw).

MAE RMSE MAPE % DA %

1 week lag 1-step 0.64 1.36 7.2 77.5

2-steps 0.83 9.18 9.2 74.5

4-steps 0.96 1.80 9.9 71.3

8-steps 0.97 1.79 9.8 73.1

6 weeks lag 1-step 0.17 0.58 2.2 92.5

2-steps 0.25 0.75 2.8 89.5

4-steps 0.34 0.83 3.2 89.4

8-steps 0.51 1.07 5.9 83.8

MAE: Mean absoulte error; RMSE: Root mean square error; MAPE: Mean absolute percentage error; DA: Direction accuracy.

https://doi.org/10.1371/journal.pone.0250301.t002

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that the more long-term, detailed, objective measurement of both communication and pro-

ductivity in this study allowed the relationship between the two variables which to most is

common sense to be more accurately estimated.

The results from Fig 4 indicate the communication graph features played a more important

role than speech features in predicting the dependent variables. Among the top graph features,

algebraic connectivity, number of nodes and average neighbor degree signify the total number

of employees and frequency of interactions between them and graph energy and graph spec-

trum tells us about the structural complexity of the network. From the speech features, the

mean MFCC coefficients are likely tapping into the number of speakers in the graph; the spec-

tral and the energy variability features are likely measuring the number of speakers and fre-

quency of interactions. It is interesting that the fundamental frequency variability is a measure

of productivity. This could be a proxy for gender diversity in the organizational unit, although

this most certainly requires additional study.

It is important to note that while this study reveals some relationship between communica-

tion and productivity, it does not mean that this relationship is causal. It is unknowable from

out data whether it is the productivity that induces a change in the network or whether the net-

work induces a change in productivity.

Fig 6. Mean absolute percentage error while predicting productivity (LOCw) for different lags (1–8 weeks).

https://doi.org/10.1371/journal.pone.0250301.g006

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The method described in this paper makes it possible to convert audio-recordings among

members of an organization into communication network measures. As such it should be use-

ful to group researchers, who often record all members of a group, and to those organizational

researchers who record an entire unit or organization. While the data requirements for the

method are demanding, it yields a much more accurate and potentially more valid measure of

communication networks than do currently utilized questionnaire methods.

The best choice of a productivity measure can be argued here. Both changed and inserted

lines of codes are important measures that cannot be neglected, when it comes to programmer

productivity. The inclusion of deleted lines of codes is debatable, as those can be errors or bugs

in previously-written codes, that can said to be counter-productive. But at the same time, it

can argued that deletion mean shortening of code or making it more compact using improved

logic, which is an important aspect of productivity.

This study is unique in terms of organizational communication as it involves long-term,

objective, quantitative analysis showing the relationship between a human communication

network and productivity in an organization. We have used speech recordings from employees

in a software organization to estimate communication networks and extract speech features

over a period of 3 years. Effective lines of code was used as the measure of productivity which

we attempted to predict using both communication network and speech features. It was found

that there exists a moderate relationship between communication and productivity in an orga-

nization and it depends on the number of employees, the frequency of conversation between

them and the topography of the network. Further investigation can be done by including other

forms of communication like, email, texts etc. Besides that, more complex graphs with multi-

ple modules (employee, project, task) can be investigated, which can be a better representative

of an organizational setting model. Although, project deadlines were not a prominent feature

of SF work because it used extreme programming (XP) as its software development process, it

could be interesting to study the communication productivity relationship for different project

types and deadline situations. This study does not capture how the communication quantity

or speech patterns are affected by specific job stages of a project and how the job stages drive

the overall productivity. Since multiple projects overlapped over the whole timeline with

employees working on multiple projects at the same time, analyzing various job stages remains

a limitation of this study. It requires a more precise analysis of the communication pattern and

productivity at various job stages in a project and compare the relationship across various job

stages. Furthermore, we can also investigate on productivity on a personal level by analyzing

the relationship between communication and productivity for individual employees in the

organization.

Supporting information

S1 Appendix. Speech signal features.

(PDF)

Author Contributions

Conceptualization: Arindam Dutta, Visar Berisha, Daniel W. Bliss, Scott Poole, Steven

Corman.

Data curation: Elena Steiner, Jeffrey Proulx, Steven Corman.

Formal analysis: Arindam Dutta, Visar Berisha.

Funding acquisition: Scott Poole, Steven Corman.

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Investigation: Elena Steiner, Jeffrey Proulx, Visar Berisha, Daniel W. Bliss, Scott Poole, Steven

Corman.

Methodology: Arindam Dutta, Visar Berisha, Daniel W. Bliss, Scott Poole.

Project administration: Scott Poole, Steven Corman.

Resources: Elena Steiner, Jeffrey Proulx, Scott Poole, Steven Corman.

Software: Arindam Dutta.

Supervision: Visar Berisha, Daniel W. Bliss, Scott Poole, Steven Corman.

Validation: Elena Steiner, Jeffrey Proulx, Visar Berisha, Steven Corman.

Visualization: Arindam Dutta.

Writing – original draft: Arindam Dutta, Visar Berisha.

Writing – review & editing: Arindam Dutta, Elena Steiner, Visar Berisha, Daniel W. Bliss,

Scott Poole, Steven Corman.

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