SRM_R: A Web-Based Shiny App for Social Relations Analyses

Wong, M. N., Kenny, D. A., & Knight, A. P. (In Press). SRM_R: A Web-Based Shiny App for Social Relations Analyses. Organizational Research Methods.

Abstract. Many topics in organizational research involve examining the interpersonal perceptions and behaviors of group members. The resulting data can be analyzed using the Social Relations Model (SRM). This model enables researchers to address several important questions regarding relational phenomena. In the model, variance can be partitioned into group, actor, partner, and relationship; reciprocity can be assessed in terms of individuals and dyads; and, predictors at each of these levels can be analyzed. However, analyzing data using the currently available SRM software can be challenging and can deter organizational researchers from using the model. In this article, we provide a “go-to” introduction to SRM analyses and propose SRM_R (, an accessible and user-friendly, web-based application for SRM analyses. The basic steps of conducting SRM analyses in the app are illustrated with a sample dataset of 47 teams, 228 members, and 884 dyadic observations, using the participants’ ratings of the advice-seeking behavior of their fellow employees.

Addressing performance tensions in multiteam systems: Balancing informal mechanisms of coordination within and between teams

Ziegert, J. C., Knight, A. P., Resick, C. J., & Graham, K. A. (2022). Addressing performance tensions in multiteam systems: Balancing informal mechanisms of coordination within and between teams. Academy of Management Journal, 65, 158-185.

Abstract. Due to their distinctive features, multiteam systems (MTSs) face significant coordination challenges—both within component teams and across the larger system. Despite the benefits of informal mechanisms of coordination for knowledge-based work, there is considerable ambiguity regarding their effects in MTSs. To resolve this ambiguity, we build and test theory about how interpersonal interactions among MTS members serve as an informal coordination mechanism that facilitates team and system functioning. Integrating MTS research with insights from the team boundary spanning literature, we argue that the degree to which MTS members balance their interactions with members of their own component team (i.e., intrateam interactions) and with the members of other teams in the system (i.e., inter-team interactions) shapes team- and system-level performance. The findings of a multimethod study of 44 MTSs composed of 295 teams and 930 people show that as inter-team interactions exceed intrateam interactions, team conflict rises and detracts from component team performance. At the system level, balance between intra- and inter-team interactions enhances system success. Our findings advance understanding of MTSs by highlighting how informal coordination mechanisms enable MTSs to overcome their coordination challenges and address the unique performance tension between component teams and the larger system.

Spring 2022 Courses

People Metrics
Open to BSBA, MBA, PMBA, & SMP Students

Metrics are at the core of people analytics. The purpose of this course is to introduce you to the    foundations of assessing people in organizations, particularly through the use of novel measurement technologies. Through out-of-class preparation, in-class discussions, and real-world applications, this course will enable you to add value to organizations as a developer of new metrics and as a critical consumer of existing metrics. After taking this course, you will be able to:

  • Develop a clear and logical conceptual measurement model to assess a given construct. A conceptual measurement model is the foundation of creating novel and useful new approaches for assessing individual attitudes and characteristics (e.g., personality, intelligence, well-being, engagement) and interpersonal characteristics and behavior (e.g., knowledge sharing, inclusion, teamwork).
  • Scrutinize the quality of measures and measurement approaches. By understanding the principles of good measurement, you will be able to critically assess the quality of measures being applied to you (e.g., your performance) and the value of new potential measures.
  • Understand the use of novel technologies for assessing people in organizations. Organizations and, more generally, society are awash in the traces of individual behavior and social interactions. Decoding how data that already exist in an organization can be used to understand behavior is an essential skill for adding value in the field of people analytics.
  • Think critically about how to put new measures into practice in organizations. Because what gets measured gets attention, metrics are often among the most politically-charged phenomena in organizations. To have impact by developing a new measure, it is necessary to be skilled in implementing it and communicating its value within an organizational context.

Teamwork & Leading Organizations
Open to Online MBA Students

Skillfully contributing to, building, and leading collaborative efforts—from small project-based teams to larger functions and divisions—will enable you to have an impact throughout your career. The purpose of this course is to lay a foundation of interpersonal skills and systems thinking that will enable you to differentiate yourself as a valued-adding member and leader of organizations. The specific learning objectives for this course are to:

  • Develop your skills as a contributor to and leader of project-based teams. This includes sharpening your understanding of the core elements of team design and how leaders and team members alike can promote effective team processes.
  • Develop your skills as a leader in and of organizations. This comprises being able to architect a system—its structure, work design, culture, and people management practices—to execute a given strategy, within a given environment.

At the conclusion of this course, you will be able to independently transfer your learning to:

  • design, launch, and lead project-based teams in a manner that (a) meets or exceeds stakeholder’s expectations for task performance; (b) contributes to the growth of individual team members; and, (c) leaves team members willing to work together again in the future;
  • systematically analyze an organization’s architecture, assessing its internal congruence and its utility for executing a given strategic approach, either when engaged in early organizational design (e.g., scaling a start-up team) or when diagnosing the reasons for unsatisfactory organizational performance.

From the editors: Publishing impactful research in AMJ: Winners of the 2020 and 2021 Impact Awards

Umphress, E. E., Greer, L. L., Muir (Zapata), C. P., & Knight, A. P. (2021). From the editors—Publishing impactful research in AMJ: Winners of the 2020 and 2021 Impact Awards. Academy of Management Journal, 64, 1648-1653.

Abstract. All scholars set out to publish high-quality and important research. The attributes of high-quality research have been discussed at length in past commentaries (e.g., the “Publishing in AMJ” series of FTEs). What does it mean, though, for research to be important?

Summer 2021 Courses

Foundations of Impactful Teamwork
Open to Full-Time MBA Students

Skillfully contributing to, leading, and building teams will enable you to have an impact throughout your career—from a front-line position all the way to the C-suite. The purpose of this course is to lay a foundation of interpersonal skills that will enable you to differentiate yourself as an effective leader and member in team-based work environments. The specific learning objectives for the course are:

  • Develop your skills as a leader of project-based teams. This includes sharpening your understanding of critical elements of team design, as well as leader behaviors that enable learning in project-based teams over time.
  • Enhance your capacity to make valuable contributions as a member of project-based teams. This includes understanding how to integrate diverse perspectives and overcome socioemotional and relational barriers to team effectiveness.
  • Develop a systems view of organizations, which envisions the interconnected parts of an organization (e.g., people, roles, teams, divisions) as the machinery that enables it to execute a given strategy within a given overarching environment (e.g., societal, industry).
  • Advance your skills as a leader of organizations. This involves seeing how, with a given strategy in mind, leaders act as architects of organizational systems, shaping their formal (e.g., structure, personnel practices, policies) and informal (e.g., norms, routines, culture) elements.

How to download YouTube videos

As a professor, I frequently use YouTube videos (and other streaming videos) in my courses. For a variety of reasons, I do not want to stream the video during a given class session. For example, if I am teaching a session virtually on Zoom, I do not want to simultaneously download and upload the video. Additionally, if I am teaching a course in China, I may not have access to YouTube. Finally, YouTube videos sometimes disappear, are blocked, or have advertisements that I don’t want to play in class.

For all of these reasons, I almost always download YouTube videos before class sessions and play them through my local machine. I never stream them directly from YouTube.

While there are a range of semi-functional browser plug-ins for downloading YouTube videos, I have never been satisfied with them. Instead, I use a command line tool — youtube-dl–for pulling videos from YouTube. It is an outstanding piece of (free) software that will enable you to quickly pull down videos to include in your classes.

The best way to install youtube-dl, if you’re on a Mac, is to use Homebrew. After doing so, you can run the following command in your shell (replacing {url} with the website address of the video you want to download:

youtube-dl "{url}"

For example, the following command would pull down an *amazing* video about recurrence analysis 🙂

youtube-dl ""

There are so many other options for this software. I think this is a particularly useful guide to using youtube-dl.

zoomGroupStats released on CRAN

zoomGroupStats is now available as a package on CRAN.

Title: zoomGroupStats: Analyze Text, Audio, and Video from ‘Zoom’ Meetings
Description: Provides utilities for processing and analyzing the files that are exported from a recorded ‘Zoom’ Meeting. This includes analyzing data captured through video cameras and microphones, the text-based chat, and meta-data. You can analyze aspects of the conversation among meeting participants and their emotional expressions throughout the meeting.

# To use the stable release version of zoomGroupStats, use:

# To use the development version, which will be regularly updated with new functionality, use:

You can stay up-to-date on the latest functionality on

Package version of zoomGroupStats

Thank you all for the encouragement and feedback on the initial version of zoomGroupStats. I can’t believe it’s been a little over a year since I posted the first set of functions in the early days of COVID-19. Following the suggestions of several users, I took some time this past week to build this out as a more structured R package.

Accompanying the package, you will find a multi-part guide for conducting research using Zoom and using zoomGroupStats to analyze Zoom meetings using R.

The best way to use this resource currently, because I am actively building out new functionality, is to install it through my github repository. To do so:

install_github("", force=TRUE)

I’ll be updating the documentation, guidance videos, and adding further functionality in the weeks ahead. The best resource for zoomGroupStats going forward will be a dedicated package site, which you can access at

Please keep the feedback and suggestions coming!

Materials from zoomGroupStats Tutorial Session

I facilitated a short workshop to give an introduction to using the zoomGroupStats set of functions. The tutorial covered three issues. First, I offered recommendations for how to configure Zoom for conducting research projects. Second, I described how to use the functions in zoomGroupStats to parse the output from Zoom and run rudimentary conversation analysis. Third, I illustrated how to use zoomGroupStats to analyze the video files output from Zoom.

If you weren’t able to make it, here are a few artifacts from the session:

Presentation materials, which provided the structure for the session (but do not include the demonstrations / illustrations)

zoomGroupStats Tutorial Code, which walked through using different functions in zoomGroupStats

Supplementary Tutorial Guide, which accompanied the session to provide additional recommendations

A faster way to grab frames from video using ffmpeg

In the zoomGroupStats functions that I described in this post, there is a function for identifying and analyzing faces. The original version that I posted uses ImageMagick to pull image frames out of video files. This is embedded in the videoFaceAnalysis function. In practice, this is a very inefficient method for breaking down a video file before sending it off to AWS Rekognition for the face analysis. I’ve found that ImageMagick takes quite a long time to pull images from a video.

As an alternative, I’ve been using ffmpeg to process the video files before using the zoomGroupStats functions. I love ffmpeg and have used it for years to manipulate and process audio and video files. After you have installed ffmpeg on your machine, you can use system(“xxxx”) in the stream of your R code to execute ffmpeg commands. For example, here’s what I include in a loop that is working through a batch of video files:

ffCmd = paste("ffmpeg -i ", inputPath, " -r 1/", sampleWindow, " -fimage2 ", outputPath, "%0d.png", sep="")

Then, you can just run system(ffCmd) to execute this line. In the line, inputPath is the path to the video file, sampleWindow is the number of seconds that you would like between each frame grab, and outputPath is the path to the directory, including an image name prefix, where you want the images saved.

Using a computer that isn’t very powerful (a Mac Mini), I was able to break down 20 ~2 hour videos (about 2400 minutes of video) into frame grabs every 20 seconds (around 7000 images) in less than an hour.

I will end up replacing ImageMagick with ffmpeg in the next iteration of the videoFaceAnalysis function. This will also come with the output of new metrics (i.e., face height/width ratio and size oscillation). Stay tuned!