research paper on communication in organization

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Peer-reviewed

Research Article

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

Roles Conceptualization, Formal analysis, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Arizona State University, Tempe, Arizona, United States of America

Roles Data curation, Investigation, Resources, Validation, Writing – review & editing

Roles Data curation, Investigation, Resources, Validation

Affiliation University of Illinois at Urbana Champaign, Champaign, Illinois, United States of America

Roles Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Validation, Writing – original draft, Writing – review & editing

Roles Conceptualization, Investigation, Methodology, Supervision, Writing – review & editing

Roles Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – review & editing

Roles Conceptualization, Data curation, Funding acquisition, Investigation, Project administration, Resources, Supervision, Validation, Writing – review & editing

Arindam Dutta,
Elena Steiner,
Jeffrey Proulx,
Visar Berisha,
Daniel W. Bliss,
Scott Poole,
Steven Corman

Published: July 14, 2021
https://doi.org/10.1371/journal.pone.0250301
Peer Review
Reader Comments

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.

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

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: 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 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: [email protected] ).

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.

Introduction

The “structural imperative” in network research [ 1 ] suggests that we can represent any organization 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 workplace. These aspects play a large role in the effectiveness and productivity of an organization. In this paper we aim to directly study this relationship between productivity and communication, and report new methods for doing so.

While productivity is relatively straightforward to measure, existing studies measure communication indirectly, either through member self-reports of communication on rating scales [ 2 , 3 ], through external raters’ evaluation using global scales that assess communication behavior [ 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 communication 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 observation 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 understanding 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 particularly 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 education, therapy, or advice-an argument can be made that the greater the amount of communication, 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 engineering 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 communication 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 features 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 Humphrey [ 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 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 purposes. Our immediate purpose in comparing productivity to detected interaction was to validate 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 sponsored project, focused on discrepancies between observable and perceived communication [ 26 ].

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 technology spinouts. SF had directors and work was led by a professional software engineer who managed 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 professional 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 sciences, 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 activities in this process involved validating with the customer, setting realistic customer expectations, 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 timescale 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 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 microphone, 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 communication 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 ].

research paper on communication in organization

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 .

PPT PowerPoint slide
PNG larger image
TIFF original image

https://doi.org/10.1371/journal.pone.0250301.g001

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-correlation 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 speaking, 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 conversations 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) Th 1 , to determine if communication occurred (0 or 1, 2, 3) and (b) Th 2 , 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 classifications 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 number 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 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)

Graph feature extraction.

After the graph was constructed using pairwise speech correlation, we extracted several topological features that aim to describe the nature of daily interactions. 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 complex 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 measure of the influence or importance of individual nodes on the network.
Average neighbor degree . The average degree of adjacent or neighboring nodes for every vertex. We took the average of this measure across all nodes. This indicates the flow of communication around the organizational unit.
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.

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 emotions 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 features 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 emotions [ 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 coefficients (MFCC) were extracted from 20 ms frames and averaged over 1 sec window.

Measure of productivity

In this paper, the overall organization productivity, defined by the total lines of codes per week per employee ( LOC w ) 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, LOC w = 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 normalizing the LOC measure by the number of employees present during the particular week.

Predicting productivity from communication

https://doi.org/10.1371/journal.pone.0250301.g002

Pre-whitening.

Feature selection.

Time-series regression.

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 communication detection accuracy (0 or 1, 2, 3). The ROC curve was constructed by varying the threshold Th 1 , and the optimum value of Th 1 was determined. Threshold Th 2 was determined after constructing confusion matrices for various Th 2 values. The threshold parameters for the best model were Th 1 = 2.53 e −5 and Th 2 = 2.02 e −5 . We have shown the confusion matrix of the best detection model in Table 1 .

https://doi.org/10.1371/journal.pone.0250301.g003

https://doi.org/10.1371/journal.pone.0250301.t001

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

We computed the correlation weights for each communication feature while predicting productivity. Fig 4 shows the average merit of the features based on correlation weights achieved while predicting LOC w . 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 (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 average merit.

https://doi.org/10.1371/journal.pone.0250301.g004

Time-series prediction of productivity.

To analyze the communication-productivity relationship we made k -steps time-series prediction of LOC w 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).

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

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

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

It can be seen that, using 1-week previous information, we can predict productivity ( LOC w ) 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%.

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 accuracy 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 relationship 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 that the more long-term, detailed, objective measurement of both communication and productivity 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 spectrum 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 spectral and the energy variability features are likely measuring the number of speakers and frequency 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 communication 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 network induces a change in productivity.

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 useful 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 organization 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 multiple 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..

https://doi.org/10.1371/journal.pone.0250301.s001

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Current Trends and Issues in Internal Communication pp 1–18 Cite as

Evolving Research and Practices in Internal Communication

Linjuan Rita Men 5
First Online: 29 September 2021

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9 Citations

Part of the book series: New Perspectives in Organizational Communication ((NPOC))

Internal communication, sometimes referred to as employee communication, internal relations, or internal public relations, has witnessed significant growth in the past decades as a discipline and profession. The introduction chapter revisits the definitions of internal communication, provides an overview of the recent developments in research and practice in this domain, particularly positioned in the field of public relations, along with a discussion of emerging trends and issues that are shaping the practice. The chapter ends with the discussion of the vision and goals of the book and an overview of the book’s structure and content.

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Men, L.R. (2021). Evolving Research and Practices in Internal Communication. In: Men, L.R., Tkalac Verčič, A. (eds) Current Trends and Issues in Internal Communication. New Perspectives in Organizational Communication. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-78213-9_1

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

Arindam Dutta

1 Arizona State University, Tempe, Arizona, United States of America

Elena Steiner

Jeffrey proulx.

2 University of Illinois at Urbana Champaign, Champaign, Illinois, United States of America

Visar Berisha

Daniel w. bliss, scott poole, steven corman, associated data.

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 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: [email protected] ).

Introduction

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?

Organization setting and data collection

The business process,
The development process,
The design process, and
The implementation process.

Other studies in the literature have found that LOC is an effective measure of productivity in software organizations [ 28 , 29 ].

All analyses were done on a weekly basis. In case of communication graphs, individual interactions 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, G w ∈ R 1 × f g , where f g 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, S w ∈ R 1 × ( 2 × f s ) , where f s is total number of speech features. Thus, we had a total communication feature space defined by C w : ( G w ‖ S w ) ∈ R 1 × ( f g + 2 × f s ) (where ‖ 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.

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Speech cross correlation graph

Validation of detection

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,

where N is the minimum number of samples, p ^ is the estimated population proportion and ϵ is the margin of error. With an error margin (variance per sample) of 5% and a p ^ of 0.8, the minimum number of samples required is 64. In our analyses, a total of 75 ten minutes audio segments 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

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 complex 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 measure of the influence or importance of individual nodes on the network.
Average neighbor degree . The average degree of adjacent or neighboring nodes for every vertex. We took the average of this measure across all nodes. This indicates the flow of communication around the organizational unit.
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, G w ∈ R 1 × 11 , where 11 is total number of graph features investigated.

Speech analysis

Speech feature extraction

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 features 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 emotions [ 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 coefficients (MFCC) were extracted from 20 ms frames and averaged over 1 sec window.

We calculated the mean and variance of these features over the working days of a week to compute weekly speech feature vectors defined as, S w ∈ R 1 × ( 35 × 2 ) , 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, C w ∈ R 1 × ( 11 + 70 ) = R 1 × 81 .

Measure of productivity

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 , C w ’), and the other variable, is regarded as the response, outcome, or dependent variable (in this case weekly ‘ productivity , LOC w ’). We mentioned before that we should expect an inverted U-shaped relationship (polynomial of order 2) between communication and productivity. To apply this hypothesis, we first selected the communication features that exhibited such relationship. The selected communication features were then used to predict the organizational productivity. Since the variables 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

where F ( C w , t ) is some mathematical operation (or model) showing productivity as a function of the input communication features and time (weeks), and ϵ is the prediction error. Fig 2 shows the block diagram of the prediction process and each block is described.

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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 transformation,

where, μ ( X ) and cov ( X ) are the mean and covariance matrix of time-series variable X . X ^ is the transformed variable whose covariance matrix is the identity matrix. We pre-whitened all the independent variables ( C w ) and the dependent variable ( LOC w ).

Feature selection

We used a rank based feature selection method with a regression model ( F ( C w ) ) 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 subsets, where 9 of them are used to train the regression model and one set for prediction). A support 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 analysis. 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 ( LOC w ) 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

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.

Pair-wise communication detection results

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Time-series prediction of productivity

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

Supporting information

S1 appendix, funding statement.

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.

Data Availability

PLoS One. 2021; 16(7): e0250301.

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

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Reviewer #1: This paper uses very detailed voice recording data from what I think is a kind of student software consulting organization at a University over the course of almost 3 years to study whether measures of voice communication can predict productivity. The voice data used in the study is very unique in how complete it is and how long a time period it covers and it allows the authors to generate measures of communication between pairs of individuals inside the organization (employees and clients). The authors find that speech and communication network characteristics can predict software programmer productivity.

I do not have a sufficient understanding of voice measures to comment on the validity what seems to be heroic work by the authors to generate useful measures of communication networks and speech characteristics.

While I think a better understanding of how communication relates to productivity in organizations is a very important question, I am not convinced this study advances our understanding of this question. There are several reasons for this, which I separate out below.

1. The authors do not provide sufficient information about the organization they are studying, except that the work being performed by employees is software programming. The authors argue that the type of work being performed should affect the relationship between communication and productivity, which I agree with, but so should, for instance, the organizational structure, and the sales or work cycles. From what I can gather on my own, it seems as though the software factory is a student run software consultancy which suggests a very flat organizational structure in which most conversations may be between “equals”. It also suggests that work occurs when there is a project available, and otherwise no work occurs. Without understanding how the organization functions and how often work occurs in the organization, it is difficult to know the extent to which the findings generalize to other organizations.

Moreover, the authors mention that their voice data includes conversations between programmers and between programmers and clients. It seems to me like the type of communication that is desirable differs depending on whether a programmer is speaking with a fellow programmer or with a client. I may have missed a discussion of whether the authors drop the client-facing conversations, but I was surprised this wasn’t emphasized more. Given that the measure of productivity used in the paper does not directly capture how satisfied a client was or how big a contract the programmers got, I don’t think the conversations with clients should be included in the study.

2. As the authors point out, the study is not able to determine whether different types of communication patterns cause more or less productivity, but rather whether they can be useful to predict how well the organization is doing (in this case, how productive workers will be). For instance, communication frequency may be a proxy for how excited or engaged workers are in the job or how frustrated they are with the client’s demands. If speech and communication network characteristics are easier to observe and measure than other things like employee attitudes, that using them to predict productivity seems like it could be a very useful tool for employers to improve performance by helping them catch potential problems before they become disasters.

However, I do not think the authors did enough to explain how feasible or practical it is for employers to adopt this in practice. I imagine many employees would object to being recorded while working, and it may be hard to retain high quality employees if they were required to record themselves while at work. Given the importance of communication frequency for predicting productivity, partial recordings may not be sufficient. Generating useful data out of the voice recordings would also be expensive time-wise. Thus, it is unclear to me what the practical takeaways of the paper are.

3. I don’t think my previous comment would be important if the study improved our understanding of why people communicate the way they do when they are more or less productive. As I mention above, there are many reasons why communication patterns may change at the same time as productivity changes but the authors do not tell us anything about these potential mechanisms. I think the authors could do more with their data in this regard. For instance, perhaps they could get data on when project deadlines are and test whether the relationship between communication out productivity changes when workers are facing more urgency. Similarly, perhaps the authors could get information on when students are working on different types of jobs (e.g. maybe students participated in hack-a-thons at some points that were not contracted for). These types of tests may move us forward in our understanding of how speech and communication patterns differ depending on the circumstances, and how those differences relate to productivity.

4. This is a more minor comment, but I wondered if employees would choose to delete or not turn in recordings of less appropriate discussions, including disagreements. If this is possible, perhaps this might explain the lack of findings on speech signal features? Some discussion of possible measurement error of these features might be helpful for understanding how much we can take away from the findings.

5. I am also a bit concerned about mismeasurement of productivity in the lines of code changed may not capture code quality. The authors acknowledge that deletions may or may not improve code quality, but on the converse, additions may or may not improve code quality. At the extreme, if one programmer is particularly frustrated with a co-worker or client, they may write a number of confusing lines that could make it hard for subsequent programmers to successfully edit the code. There are some relatively standard measures of code quality that the authors could have external programmers evaluate the organization’s code on (e.g. complexity, coupling).

Reviewer #2: I think this is great. I like the analyses to measure communication among this group of employees. The sample size is a bit small for my taste, but overall it's quite sound. I think this could be an important paper that change the way we measure communication in the future.

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The Impact Of Effective Communication On Organizational Performance

Related Papers

Ambo University , Fikre Fikadu Fufa

Communication for development is a broad cognitive field of enormous international, national and regional interest attracting attention as a special field of study by students and researchers across disciplines. All those involved in the analysis and application of communication for development - or what can broadly be termed ―development communication‖ - would probably agree that in essence development communication is the sharing of knowledge aimed at reaching a consensus for action that takes into account the interests, needs and capacities of all concerned. It is thus a social process. Communication media are important tools in achieving this process but their use is not an aim in itself—interpersonal communication too must play a fundamental role.

International Journal of Advance Research in Computer Science and Management Studies [IJARCSMS] ijarcsms.com

Dr. S H I N E David

Manju Singh

Benson James Lyimo

The main objective of this study was to assess the effective communication in attaining organizational goals; this study has been conducted for three main specific objectives. The first one was to investigate the influence of effective communication on employee performance objective in attaining organizational goals. The second objective was assess the effectiveness of staff objectives on attaining organization goals and lastly was to find out relation between change management objective and attainment of organizational goal, in Iringa regional commissioner office, Tanzania The study adopted both research methods, qualitative and quantitative approaches. Both were used so as to help the researcher to complement the weakness of each, therefore provide an extended room for triangulation of both instruments for data collection and approaches. Respondents were obtained by using Non-probability sampling. The sample comprised of 50 respondents. Data was collected through questionnaires, both open ended questionnaires and open ended questionnaires. The quantitative data was analyzed with the help of Microsoft Excel 2010 and Statistical Package for Social Science (SPSS) software program version 22 and were summarized in tables of frequencies, percentages, correlations, regression and charts. Study findings unveiled that effective communication creates mutual understanding between management and workers which helps in attainment of organization’s goals also management need to communicate with employees regularly to get feedback and offer suggestions in other to prevent confusion about future job assignments; this will help improve workers performance and organizational productivity. Thus the study recommends organization to embraces timely feedback and proper, immediate communication. Staff appraisal should also be improved so as to inform on areas of improvement and identify training needs/gaps. Communication and feedback should include elements of reward, commendation, recognition and praise. These elements reinforce behavior which in turn motivates staff to greater individual and organizational performance.

antony waihenya

The study was based on the role of communication in enhancing organizational growth. The objectives of the study was to determine the most preferred communication means used at equity bank, to determine the importance of communication at equity bank, to find out factors affecting communication channels at equity bank and to determine challenges faced by equity bank in facilitating efficient communication mode. The study was limited to Equity Bank Eldoret branch only. The study adopted a case study as a research design. The researcher used questionnaires to collect data from a sample size of 40 respondents out of targeted population of 80 respondents. The researcher used a stratified sampling technique then followed by a simple random sampling to come up with the sample size. Data was then analyzed and presented using descriptive statistics including frequency tables, and percentages. From the findings the study established that communication has a greater importance in enhancing organizational growth it lead to better understanding in the bank, it improves efficiency, it also leads to effective coordination and avoid loses. However, the company’s greater challenges such as financial resources being insufficient, competition from other companies and poor management. To overcome all these challenges the company has to train its employees on communication skills and training of personnel should be put in place as part of the recommendation of the study. Finally the study suggested that further research should be conducted on the effects of multi directional communication on organizational development. Also study should be carried on the best communication type an organization can adopt to gain competitive advantage.

shewayirga assalf

Euro Asia International Journals

There are varying views about Organizational Communication. Contingency approach assumes that organizational communication effects can be explained only in the context of the constraints of different contingencies But most thinkers opine that through communication, everyone knows his /her role place, and task within the organization and the different parts of that organization are adequately coordinated. When communication stops, organized activity ceases to exist, individual, uncoordinated activity returns. Communication is effective when members of an organization share information with each other and all parties involved are relatively clear about what this information means. Communication is not an end in itself but a means to an end. Proper communication lays the foundation of a sound organizational culture, builds high employee morale to the extent that in some cases listening the actual problem of the employees itself can give them the impression that proper action will be taken. Management has to ensure that it leaves the impression that communication efforts guarantee results.

Organizational Communication Research Paper Topics

Bona Fide Groups
Bureaucracy and Communication
Communication in Organizational Crises
Communication Networks
Control and Authority in Organizations
Critical Approaches to Organizational Communication
Cultural Diversity in Organizations
Decision-Making Processes in Organizations
Dialogic Perspectives
Dissent in Organizations
Emotion and Communication in Organizations
Feedback Processes in Organizations
Functional Theory of Group Decision-Making
Globalization of Organizations
Group Communication
Group Communication and Problem-Solving
Group Communication and Social Influence
Institutional Theory
Interorganizational Communication
Knowledge Management
Leadership in Organizations
Learning Organizations
Meeting Technologies
Organizational Assimilation
Organizational Change Processes
Organizational Conflict
Organizational Culture
Organizational Discourse
Organizational Ethics
Organizational Identification
Organizational Metaphors
Organizational Structure
Participative Processes in Organizations
Postmodern Approaches Organizational Communication
Sense-Making
Structuration Theory
Supervisor–Subordinate Relationships
Symbolic Convergence Theory

Most historians of the field place the beginning of the modern discipline in the middle of the twentieth century. Redding and Tompkins (1988) provide a typical recounting of this history in discussing three overlapping formative phases. The first, from 1900 and 1950, is the “era of preparation.” During this period, concerns revolved around skills-based training that would achieve “effective” communication in organizations. The second phase (1940–1970), the “era of identification and consolidation,” was marked by an emphasis on the scientific method. with empirical attention focused on supervisor–subordinate relationships, employee satisfaction, and group decision-making. Redding and Tompkins argue that organizational communication reached the third era (“era of maturity and innovation”) in the 1970s. At this point, organizational communication was recognized as an established discipline with large divisions in the ICA (International Communication Association) and the National Communication Association (NCA) in the US, graduate programs across the globe, and scholarship represented in disciplinary and interdisciplinary outlets, as well as specialized journals such as Management Communication Quarterly.

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Get 10% off with 24start discount code, theoretical and methodological approaches.

In recent decades, the discipline has been marked by several major intellectual shifts and conceptual debates. Thus, organizational communication is now an eclectic discipline in terms of theory and method. Three important metatheoretical strands are now prevalent in organizational communication. Following Corman and Poole (2000), these strands are labeled ‘post-positivist,’ ‘interpretive,’ and ‘critical.’

The post-positivist approach was dominant as organizational communication reached maturity in the 1970s. The ontological focus was a realist conception of both ‘organization’ and ‘communication’ – organizations were seen as ‘containers’ within which people worked and communication followed prescribed routes and included defined content. Early examples of post-positivist research included topics such as supervisor–subordinate communication, information flow, feedback, communication climate, and communication networks. Post-positivist scholars today consider crucial questions of organizing in the late modern and postmodern world, including communication and decision-making technologies, globalization, nonprofit organizations, and self-organizing systems.

During the 1970s and 1980s, many organizational communication scholars began to reject realist conceptions of organizations and communication and turn away from positivistic epistemological assumptions and scientific research methods. Within organizational communication, the interpretive turn (Putnam and Pacanowsky 1983). The intellectual roots of the interpretive turn in organizational communication can be found in movements such as symbolic interactionism, hermeneutics, phenomenology, and ethnomethodology. This approach is marked by a social constructionist ontology and epistemologies that emphasize the relationship between the knower and the known and the value of emergent forms of knowledge. Instead of following the ‘container’ metaphor, interpretive scholars considered the role of communication in processes of organizing and sense-making (Weick 1979); scholars shifted from a mechanistic view to a constitutive view of oganizing and communicating (Putnam & Nicotera 2009).

During the same time period as the interpretive turn, many scholars were also moving toward a critical approach in which organizations were viewed as systems of power and control. In organizational communication, critical scholarship can be traced to formative influences including Karl Marx, Frankfurt School critics, Louis Althusser, Antonio Gramsci, Jürgen Habermas, Michel Foucault, and Anthony Giddens. The turn to critical scholarship involved analyzing organizations as ‘sites of oppression,’ considering the discursive construction of managerial interests, examining how workers are complicit in processes of alienation, and highlighting processes of resistance. Critical organizational communication scholars’ concern with praxis has led to the scholarship considering alternative organizational forms, participatory practices, and opportunities for employee dissent.

With the critical turn also came a move to feminist scholarship (Ashcraft & Mumby 2004). This research has roots in both the critical theory and the political activism at the heart of feminism. Feminist scholarship did not gain a foothold in the discipline until the 1990s, though there had been earlier studies of gender and biological sex in organizational communication processes. In recent decades, feminist scholarship has included the public/private divide, feminist ways of organizing, emotionality in the workplace, feminist approaches to conflict, and embodied organizational experience with the late twentieth century also marked the emergence of postmodern theorizing that differentiates organizations and communication in the modern epoch (e.g., centralized authority, rationality, standardization) from the postmodern epoch (e.g., lateral relationships, consensus-based control, interactivity, and change).

Contemporary Frames and Research Topics

Putnam et al. (1996) provide a helpful framework that considers the metaphors of communication and organization. In the ‘conduit metaphor’ communication is seen as transmission that occurs within the container of the organization. Research in this tradition considers formal and informal communication flow, adoption of communication technology, and information load.In the ‘lens metaphor’ approach, communication is a filtering process and the organization is the eye. This metaphor highlights the possibility of distortion and the importance of message reception. The ‘linkage metaphor’ shifts emphasis to the connections among individuals and organizations including communication networks, patterns, and structures. The ‘performance metaphor’ considers organizations as emerging from coordinated actions (processes including storytelling and organizational image). The ‘symbol metaphor’ sees communication as a process of representation through which the organizational world is made meaningful and includes scholarship in organizational culture and socialization. The ‘voice metaphor’ considers how organizational voices are expressed or suppressed through processes including ideology, hegemony, democratization, and cultural difference. Finally, ‘discourse metaphor’ sees communication as a conversation, as collective action, and as dialogue.

References:

Ashcraft, K. L. & Mumby, D. K. (2004). Reworking gender: A feminist communicology of organization. Thousand Oaks, CA: Sage.
Corman, S. R. & Poole, M. S. (2000). Perspectives on organizational communication: Finding common ground. New York: Guilford.
May, S. & Mumby, D. K. (2005). Engaging organizational communication theory and research: Multiple perspectives. Thousand Oaks, CA: Sage.
Mumby, D. K. & Stohl, C. (1996). Disciplining organizational communication studies. Management Communication Quarterly, 10, 50–72.
Putnam, L. L. & Mumby, D. K. (eds.) (2013). The Sage handbook of organizational communication, 3rd edn. Thousand Oaks, CA: Sage.
Putnam, L. L. & Nicotera, A. (eds.) (2009). Building theories of organization: The constitutive role of communication. London: Routledge.
Putnam, L. L. & Pacanowsky, M. E. (eds.) (1983). Communication in organizations: An interpretive approach. Beverly Hills, CA: Sage.
Putnam, L. L., Phillips, N., & Chapman, P. (1996). Metaphors of communication and organization. In S. R. Clegg, C. Hardy, & W. R. Nord (eds.), Handbook of organization studies. Thousand Oaks, CA: Sage, pp. 375–408.
Redding, W. C. & Tompkins, P. K. (1988). Organizational communication: Past and present tenses. In G. Goldhaber & G. Barnett (eds.), Handbook of organizational communication. Norwood, NJ: Ablex, pp. 5–34.
Weick, K. E. (1979). The social psychology of organizing, 2nd edn. Reading, MA: Addison-Wesley.

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6 Common Leadership Styles — and How to Decide Which to Use When

Rebecca Knight

Being a great leader means recognizing that different circumstances call for different approaches.

Research suggests that the most effective leaders adapt their style to different circumstances — be it a change in setting, a shift in organizational dynamics, or a turn in the business cycle. But what if you feel like you’re not equipped to take on a new and different leadership style — let alone more than one? In this article, the author outlines the six leadership styles Daniel Goleman first introduced in his 2000 HBR article, “Leadership That Gets Results,” and explains when to use each one. The good news is that personality is not destiny. Even if you’re naturally introverted or you tend to be driven by data and analysis rather than emotion, you can still learn how to adapt different leadership styles to organize, motivate, and direct your team.

Much has been written about common leadership styles and how to identify the right style for you, whether it’s transactional or transformational, bureaucratic or laissez-faire. But according to Daniel Goleman, a psychologist best known for his work on emotional intelligence, “Being a great leader means recognizing that different circumstances may call for different approaches.”

RK Rebecca Knight is a journalist who writes about all things related to the changing nature of careers and the workplace. Her essays and reported stories have been featured in The Boston Globe, Business Insider, The New York Times, BBC, and The Christian Science Monitor. She was shortlisted as a Reuters Institute Fellow at Oxford University in 2023. Earlier in her career, she spent a decade as an editor and reporter at the Financial Times in New York, London, and Boston.

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Tracking Firm Use of AI in Real Time: A Snapshot from the Business Trends and Outlook Survey

Timely and accurate measurement of AI use by firms is both challenging and crucial for understanding the impacts of AI on the U.S. economy. We provide new, real-time estimates of current and expected future use of AI for business purposes based on the Business Trends and Outlook Survey for September 2023 to February 2024. During this period, bi-weekly estimates of AI use rate rose from 3.7% to 5.4%, with an expected rate of about 6.6% by early Fall 2024. The fraction of workers at businesses that use AI is higher, especially for large businesses and in the Information sector. AI use is higher in large firms but the relationship between AI use and firm size is non-monotonic. In contrast, AI use is higher in young firms although, on an employment-weighted basis, is U-shaped in firm age. Common uses of AI include marketing automation, virtual agents, and data/text analytics. AI users often utilize AI to substitute for worker tasks and equipment/software, but few report reductions in employment due to AI use. Many firms undergo organizational changes to accommodate AI, particularly by training staff, developing new workflows, and purchasing cloud services/storage. AI users also exhibit better overall performance and higher incidence of employment expansion compared to other businesses. The most common reason for non-adoption is the inapplicability of AI to the business.

This research paper is associated with the research program of the Center for Economic Studies (CES) which produces a wide range of economic analyses to improve the statistical programs of the U.S. Census Bureau. Research papers from this program have not undergone the review accorded Census Bureau publications and no endorsement should be inferred. Any opinions and conclusions expressed herein are those of the authors and do not represent the views of the U.S. Census Bureau. The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. P-7529868, Disclosure Review Board (DRB) approval numbers: CBDRB-FY23-0478, CBDRB-FY24-0162, and CBDRB-FY24-0225). We thank Joe Staudt and John Eltinge for helpful comments. John Haltiwanger was also a Schedule A employee of the Bureau of the Census at the time of the writing of this paper. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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Is communication around climate change just hot air?

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By Joe Arney Photos by Kimberly Coffin (CritMedia, StratComm’18)

As an undergraduate student, Emily King Kinsey most enjoyed professors who brought work experience to the classroom.

That’s not the only reason she sought professional experience before enrolling in the doctoral program at the University of Colorado Boulder’s College of Media, Communication and Information. But when it comes to the impact her work is having, especially as a researcher, that professional experience is every bit as valuable as she expected it would be.

“I like being able to share those connections I’ve developed—to be able to show some of my own work and talk about my own experiences, and to help students as they prepare for their own professional journeys,” she said.

“I got to see this whole other decision-making component that has to do with how you set policies, how you get people on board with them, how you get the public to understand why these advancements and policies are important.” Emily King Kinsey

Her own scholarly journey is rooted in the intersection between political science and public relations. After completing her master’s in communication at the University of Tennessee, King Kinsey worked at a prominent materials science research group, where she got to see up close the technical advancements needed to create things like lightweight cars or recyclable wind turbines that could help stabilize climate change.

But those developments weren’t the whole story.

Creating meaningful impact

As growing public pressure mounts on businesses to take a more active role for their responsibility for the changing climate, King Kinsey is interested in understanding how corporations and governments can effectively set policies to create meaningful impact. Finding that intersection of environmental matters, corporate governance and public diplomacy will help her create the impact she seeks, according to her advisor.

“In a grad program, you shouldn’t just be a replica of your advisor. You should be your own person, your own scholar, and she is able to do that because she has that dedication and sense of direction.”

King Kinsey made her program her own by taking political sciences classes outside of CMCI, which helped her bring an international flair to her public relations focus in a way that PhD programs elsewhere didn’t readily offer.

That focus has helped her build on her experience in materials science and innovation to do research with global impact. Her dissertation incorporates renewable energy and climate change as it’s playing out in larger competition between the United States and China.

‘Saying things just to say them’

Both states, she said, are investing in renewable energy development worldwide; in Indonesia, both have poured billions of dollars—China significantly more—into these projects. King Kinsey looks at the consistency of messaging being shared around such investments to better understand the role communication plays in influencing public diplomacy around climate change.

Emily King Kinsey standing on the trail at Chautauqua Park, with the Flatirons in the background.

Fisher said mentoring students is her favorite part of being a CMCI professor, and she said King Kinsey’s experiences beyond work—including pursuing a PhD during COVID-19 lockdowns and having a daughter part way through her degree—will make her “a fantastic role model for her students.”

“One thing I admire about Emily is she is figuring out how to find balance—she’s a great parent, she’s doing this intensive research work and she’s navigating a job search,” Fisher said. “That’s so hard for PhD students, especially women, and her experience navigating these things and staying true to herself will make her a great mentor one day. I’m excited to see where she goes and what she does—and I’m excited to keep learning from her.”

Becoming a parent while pursuing a PhD is a daunting proposition. Spend a few minutes in her company, though, and it’s clear that when King Kinsey sets her heart on something, she’s going to achieve it.

In her case, it will be getting to hug her daughter after she is hooded at commencement in May.

“My advisor and the faculty at CMCI were really supportive of me and advocated for me throughout my journey,” she said. “I’m very motivated to get things done, and they matched that, were supportive and helped me get things done.”

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Paper: To understand cognition—and its dysfunction—neuroscientists must learn its rhythms

Thought emerges and is controlled in the brain via the rhythmically and spatially coordinated activity of millions of neurons, scientists argue in a new article. Understanding cognition and its disorders requires studying it at that level.

It could be very informative to observe the pixels on your phone under a microscope, but not if your goal is to understand what a whole video on the screen shows. Cognition is much the same kind of emergent property in the brain . It can only be understood by observing how millions of cells act in coordination, argues a trio of MIT neuroscientists. In a new article , they lay out a framework for understanding how thought arises from the coordination of neural activity driven by oscillating electric fields—also known as brain “waves” or “rhythms.”

Historically dismissed solely as byproducts of neural activity, brain rhythms are actually critical for organizing it, write Picower Professor Earl Miller and research scientists Scott Brincat and Jefferson Roy in Current Opinion in Behavioral Science . And while neuroscientists have gained tremendous knowledge from studying how individual brain cells connect and how and when they emit “spikes” to send impulses through specific circuits, there is also a need to appreciate and apply new concepts at the brain rhythm scale, which can span individual, or even multiple, brain regions.

“Spiking and anatomy are important but there is more going on in the brain above and beyond that,” said senior author Miller, a faculty member in The Picower Institute for Learning and Memory and the Department of Brain and Cognitive Sciences at MIT. “There’s a whole lot of functionality taking place at a higher level, especially cognition.”

The stakes of studying the brain at that scale, the authors write, might not only include understanding healthy higher-level function but also how those functions become disrupted in disease.

“Many neurological and psychiatric disorders, such as schizophrenia, epilepsy and Parkinson’s involve disruption of emergent properties like neural synchrony,” they write. “We anticipate that understanding how to interpret and interface with these emergent properties will be critical for developing effective treatments as well as understanding cognition.”

The emergence of thoughts

The bridge between the scale of individual neurons and the broader-scale coordination of many cells is founded on electric fields, the researchers write. Via a phenomenon called “ephaptic coupling,” the electrical field generated by the activity of a neuron can influence the voltage of neighboring neurons, creating an alignment among them. In this way, electric fields both reflect neural activity but also influence it. In a paper in 2022 , Miller and colleagues showed via experiments and computational modeling that the information encoded in the electric fields generated by ensembles of neurons can be read out more reliably than the information encoded by the spikes of individual cells. In 2023 Miller’s lab provided evidence that rhythmic electrical fields may coordinate memories between regions.

At this larger scale, in which rhythmic electric fields carry information between brain regions, Miller’s lab has published numerous studies showing that lower-frequency rhythms in the so-called “beta” band originate in deeper layers of the brain’s cortex and appear to regulate the power of faster-frequency “gamma” rhythms in more superficial layers. By recording neural activity in the brains of animals engaged in working memory games the lab has shown that beta rhythms carry “top down” signals to control when and where gamma rhythms can encode sensory information, such as the images that the animals need to remember in the game.

Some of the lab’s latest evidence suggests that beta rhythms apply this control of cognitive processes to physical patches of the cortex, essentially acting like stencils that pattern where and when gamma can encode sensory information into memory, or retrieve it. According to this theory, which Miller calls “ Spatial Computing ,” beta can thereby establish the general rules of a task (for instance, the back and forth turns required to open a combination lock), even as the specific information content may change (for instance, new numbers when the combination changes). More generally, this structure also enables neurons to flexibly encode more than one kind of information at a time, the authors write, a widely observed neural property called “mixed selectivity.” For instance, a neuron encoding a number of the lock combination can also be assigned, based on which beta-stenciled patch it is in, the particular step of the unlocking process that the number matters for.

In the new study Miller, Brincat and Roy suggest another advantage consistent with cognitive control being based on an interplay of large-scale coordinated rhythmic activity: “Subspace coding.” This idea postulates that brain rhythms organize the otherwise massive number of possible outcomes that could result from, say, 1,000 neurons engaging in independent spiking activity. Instead of all the many combinatorial possibilities, many fewer “subspaces” of activity actually arise, because neurons are coordinated, rather than independent. It is as if the spiking of neurons is like a flock of birds coordinating their movements. Different phases and frequencies of brain rhythms provide this coordination, aligned to amplify each other, or offset to prevent interference. For instance, if a piece of sensory information needs to be remembered, neural activity representing it can be protected from interference when new sensory information is perceived.

“Thus the organization of neural responses into subspaces can both segregate and integrate information,” the authors write.

The power of brain rhythms to coordinate and organize information processing in the brain is what enables functional cognition to emerge at that scale, the authors write. Understanding cognition in the brain, therefore, requires studying rhythms.

“Studying individual neural components in isolation—individual neurons and synapses—has made enormous contributions to our understanding of the brain and remains important,” the authors conclude. “However, it’s becoming increasingly clear that, to fully capture the brain’s complexity, those components must be analyzed in concert to identify, study, and relate their emergent properties.”

Study reveals a universal pattern of brain wave frequencies.

Anesthesia blocks sensation by cutting off communication within the cortex

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(PDF) Communication in Organizations
a communicative lens for the study of leadership. Fairhurst & Connaughton (2014) identify six. points: ( a) Leadership communication is transmissional and meaning centered; (b) leadership is ...
The Role of Communication in Enhancing Work Effectiveness of an
communication has a triple role: ‒ Interpersonal role: managers act as. leaders of the organization, interacting with peers, subordinates, customers from the organization and. from outside. S ...
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Communication in the workplace is critical to establishing and maintaining quality working relationships in organisations. This paper discusses the communication process, barriers to communication ...
The Role of Internal Communication on Employee Outcomes: A Review and
Despite the importance of internal communication (IC) in organization and ... & Walker K. (2019). Using innovative internal communication to enhance employee engagement. In Handbook of research on strategic communication, leadership ... Best Reviewer Award from the Journal of Business Venturing (an FT Top 50 Journal), 'Best Paper ...
Analyzing the relationship between productivity and human communication
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 ...
Organizational Communication: A Review and Analysis of Three Current
8. For views on current problems in communication theory and prac tice see Karlene H. Roberts, "Some Conceptual Issues About Organiza tional Communication Research," in Purdue Lecture Series on "theoretical perspectives of organizational communication," Precis of lecture presenta tions, Dept. of Communication, Purdue University, Fall, 1979; see also Lee Thayer, "Communication-Organization.
Understanding the Internal and External Communicative Drivers of
Although an abundance of research has examined the drivers of organizational innovativeness in organization studies, limited research has examined it in communication research. Yet communication is critical to organizational innovation (Leonardi, 2014; Monge et al., 1992). Hence, this research will have important practical implications for ...
PDF Organizational Communication as an Effective Communication Strategy in
The role and importance of leadership and leaders in the effective organizational communication process is an undeniable fact. Leaders play important roles in the effective management of organizational communication with their identity as a good communicator as well as many of their characteristics. In this study, it is aimed to examine the ...
Evolving Research and Practices in Internal Communication
Regardless, the evolving environment, new trends, issues, and audiences are calling for updated internal communicationtheories, models, research, and practices. Internal communication research has long lagged behind practice until the recent decade (Lee & Yue, 2020; Tkalac Verčič et al., 2012 ).
Role of Communication Strategies in Organizational Commitment
Theoretical Underpinning. This study is inspired by relationship management theory by Ledingham and the public relations literature on faculty-organizational relationships.Cultural and societal roots, relationship development tactics, communications, and outcomes of interactions make up the relationships (Men, 2014).The relationships, hence developed, are the changeable outcomes of ...
Analyzing the relationship between productivity and human communication
Introduction. The "structural imperative" in network research [] suggests that we can represent any organization 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.
Communication in Organizations
The next section points to specific areas of individual-, dyadic-, group-, and organizational-level communication research in which communication and organizational psychology and organizational behavior (OPOB) share similar interests. The article concludes by describing practical implications of this area of scholarship (i.e., what can ...
(PDF) Organizational Communication as an Effective Communication
First, the conceptual framework for organizational communication and leadership is presented in the study, and then the relational context is explained. ... Discover the world's research. 25 ...
(PDF) The Impact Of Effective Communication On Organizational
Organizational communication research has mainly been conducted both in the business management field and in the communication field; however, researchers in the public administration field have provided little knowledge about organizational communication and its roles and effects. ... Pg:1904-1914 For the purpose of this paper, communication ...
The Role of Communication in Organizations: Ethical Considerations
This paper proposes a theoretical framework of ethics, power, and communication in the workplace. First, a model of conceptual ethics is developed to provide a backdrop for viewing ethical decisions. Second, ethics in the workplace is discussed by showing how managers are often caught in a dilemma between the pressures of the job and their own ...
Internal communication in organizations and employee engagement
While research on internal communication spans only a few decades, it has experienced a number of organizational shifts in that short time. In 1982, D'Aprix wrote of a critical time for communicating with employees and called for the reevaluation of internal communication. In regard to communication within organizations, he believed
Full article: Organizational Voicing Architecture in the Age of Social
Strategic communication management, voicing and listening in organizations. According to Zerfass et al. (Citation 2018, p. 497), "strategic communication management is the attempt to manage the communication of strategic significance with regard to a focal entity."Following this definition, strategic communication management in an organizational context pertains to the orchestration of ...
Organizational Communication Research Paper Topics
Organizational Communication Research Paper Topics. Most historians of the field place the beginning of the modern discipline in the middle of the twentieth century. Redding and Tompkins (1988) provide a typical recounting of this history in discussing three overlapping formative phases. The first, from 1900 and 1950, is the "era of ...
6 Common Leadership Styles
Summary. Research suggests that the most effective leaders adapt their style to different circumstances — be it a change in setting, a shift in organizational dynamics, or a turn in the business ...
Navigating Crisis: The Role of Communication in Organizational Crisis
Abstract. This article introduces the special issue on crisis communication, whose aim is to bring together diverse approaches and methods of analysis in the field. The article overviews the field by discussing two main frameworks, dealing with postcrisis (reputation management) and precrisis (issue management) communication, respectively.
Extreme work in organizations: mapping the field and a future research
The paper, which is based on a thematic analysis of interviews with 21 Australian HR managers, found that they experienced major organizational intensity and role complexity due to the requirement to manage multiple stakeholders, having to deal with dark side HR issues and major variations in the amount of work to be undertaken.
Harris to present paper at Visual Communication Conference
Apr 11, 2024. Heather Harris, associate professor in the Jandoli School of Communication and director of the master's in communication program, was accepted to present her paper "Shape Language: Mood, Emotion, Power and Appeal" at the 38th annual Visual Communication Conference, June 17-20, 2024. Harris is one of a select group of 30 ...
Heterogeneity and Endogenous Compliance: Implications for Scaling Class
Founded in 1920, the NBER is a private, non-profit, non-partisan organization dedicated to conducting economic research and to disseminating research findings among academics, public policy makers, and business professionals.
Harris takes Best Paper honor at business research gathering
Apr 11, 2024 Dr. Heather Harris, associate professor in the Jandoli School of Communication and director of the master's in communication program, presented her paper titled "Visuals, Power Stances, and Position: Power Messages in Disney Marketing Media" at the Academy of Business Research spring conference in New Orleans, Louisiana, on March 22.
Tracking Firm Use of AI in Real Time: A Snapshot from the Business
We provide new, real-time estimates of current and expected future use of AI for business purposes based on the Business Trends and Outlook Survey for September 2023 to February 2024. During this period, bi-weekly estimates of AI use rate rose from 3.7% to 5.4%, with an expected rate of about 6.6% by early Fall 2024.
(PDF) The Impact of Effective Communication on Organizational
The Impact of Effective Communication on Organizational Performance with Moderating Role of Organizational Culture December 2020 DOI: 10.13140/RG.2.2.22101.35048
Is communication around climate change just hot air?
Her own scholarly journey is rooted in the intersection between political science and public relations. After completing her master's in communication at the University of Tennessee, King Kinsey worked at a prominent materials science research group, where she got to see up close the technical advancements needed to create things like lightweight cars or recyclable wind turbines that could ...
The Contribution of Communication to Employee Satisfaction in Service
Social exchange theory claims that a set of three dimensions lead to employee satisfaction: the organization, the leader and peers (Wang et al., 2018, 2020).Employees' relationships with leaders, peers and the organization allow them to exchange intangible resources through communication (Cropanzano & Mitchell, 2005).Communication is a process of mutual influence and reciprocity that leads ...
Paper: To understand cognition—and its dysfunction—neuroscientists must
"Thus the organization of neural responses into subspaces can both segregate and integrate information," the authors write. The power of brain rhythms to coordinate and organize information processing in the brain is what enables functional cognition to emerge at that scale, the authors write.
(PDF) Barriers to Effective Communication
Communication is recognized as the key factor for the success of any organization, within any type of organizational structure, individuals must work closely in cooperation, must hold meetings ...

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

Introduction

Organization setting and data collection

Communication graph analysis

Speech cross correlation graph.

Validation of detection.

Graph feature extraction.

Speech analysis

Speech feature extraction.

Measure of productivity

Predicting productivity from communication

Pre-whitening.

Feature selection.

Time-series regression.

Pair-wise communication detection results

Time-series prediction of productivity.

Supporting information

Evolving Research and Practices in Internal Communication

Buying options

Author information

Corresponding author

Editor information

Rights and permissions

Copyright information

About this chapter

Download citation

Share this chapter

Arindam Dutta

Elena Steiner

Visar Berisha

Introduction

Organization setting and data collection

Communication graph analysis

Speech cross correlation graph

Validation of detection

Graph feature extraction

Speech analysis

Speech feature extraction

Measure of productivity

Predicting productivity from communication

Pre-whitening

Feature selection

Time-series regression

Pair-wise communication detection results

Time-series prediction of productivity

Supporting information

Data Availability

Decision Letter 0

Author response to Decision Letter 0

Decision Letter 1

Author response to Decision Letter 1

Decision Letter 2

Acceptance letter

The Impact Of Effective Communication On Organizational Performance

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