Anita Williams Woolley
Associate Professor Carnegie Mellon University
- Pittsburgh PA
Anita Williams Woolley is an organizational psychologist who studies team collaboration in the workplace and collective intelligence.
Biography
Areas of Expertise
Media Appearances
Middle Managers Are Caught in RTO Cross Fire - Here's How They Can Handle the Clash
Business Insider online
2025-03-03
The return-to-office (RTO) debate is causing friction between leadership and Gen Z employees. Leaders push for in-office attendance. Gen Z workers value flexibility, autonomy, and transparency. Anita Williams Woolley (Tepper School of Business) said, "if the benefits are not that great for employees, leaders will need to decide if it's worth the staff reduction caused by RTO policies."
How to convince your U.S. employer to let you work abroad
Fast Company online
2025-02-02
Anita Williams Woolley (Tepper School of Business) discussed the benefits of working abroad in this piece that advocates for U.S. employers to allow their employees to do just that: “Organizations should actually encourage more of it. When you have individuals who have experience in multiple cultures, they contribute a tremendous amount to facilitating the work of any team that they’re on, whether those teams all share that same cultural experience or not,” Woolley said.
The workers quitting over return-to-office policies
BBC online
2022-05-24
"I'm not at all surprised – in fact, I'm surprised it took this long" for an executive at a high-profile company to quit over return-to-office, says Anita Williams Woolley, associate professor of organisational behaviour and theory at Carnegie Mellon University's Tepper School of Business, US. She says senior leaders at businesses she works with have all been "kind of watching each other to see who's going to do what first, and what the reaction is going to be" to tapering off remote work. "Now, they're getting the reaction."
6 ways to restructure IT for maximum productivity
CIO Magazine online
2022-02-15
Stop scheduling standing meetings, urges Anita Williams Woolley, an associate professor of organizational behavior and theory at Carnegie Mellon University’s Tepper School of Business. “Make asynchronous updates and discussions the default,” she advises.
Collective intelligence can be predicted and quantified, new study finds
Phys Org online
2021-05-17
"This paper introduces some computational metrics for evaluating collaboration processes that could be foundational for studying collaboration moving forward," says Anita Williams Woolley, Associate Professor of Organizational Behavior and Theory at Carnegie Mellon's Tepper School of Business, who co-authored the paper. "We also continue to find that having more women in the group raises collective intelligence, and in the supplement we specifically compare face-to-face and online collaborators and find few differences in the elements that lead to collective intelligence."
Zoom is actually less effective than a phone call for these types of meetings
Fast Company online
2021-03-29
“We found that video conferencing can actually reduce collective intelligence,” says coauthor Anita Williams Woolley, associate professor of organizational behavior and theory at Carnegie Mellon’s Tepper School of Business. “This is because it leads to more unequal contribution to conversation and disrupts vocal synchrony. Our study underscores the importance of audio cues, which appear to be compromised by video access.”
Research round-up: making the most of top female staff
Financial Times online
2016-06-26
“Gender diversity benefits do not materialise if the atmosphere is too cut-throat,” says Anita Williams Woolley, an associate professor at Carnegie Mellon’s Tepper School of Business, who led the research.
Media
Social
Industry Expertise
Accomplishments
Best Research Proposal, Academy of Management Managerial and Organizational Cognition Division "Cognition in the Rough" Workshop
2006
Bok Center Certificate of Distinction in Teaching, Harvard University
2007
Finalist, Best Paper of the Year, Small Group Research
2008
Best Paper Award, Managerial and Organizational Cognition Division, Academy of Management
2011
Roman Weil Prize for the Best Unpublished Paper on Problem-Solving
2010
Education
Harvard University
A.B.
Psychology
1995
Harvard University
A.M.
Social Psychology
2001
Harvard University
Ph.D.
Organizational Behavior
2003
Affiliations
- Editorial Review Board Member, Academy of Management Discoveries (2015 -)
- Editorial Review Board Member, Organization Science (2009 -)
- Editorial Review Board Member, Small Group Research (2008 -)
Links
Articles
The Components of Collective Intelligence and their Predictors
Academy of Management Proceedings2023
The ability of human groups to collaborate effectively is of growing economic and societal importance in more and more areas of daily life. Recent research on collective intelligence in human groups has offered robust evidence that group performance can be explained by one general “collective intelligence” (CI) factor (Riedl et al., 2021). More recent work has theorized a hierarchical structure, with some evidence suggesting a role for component processes such as collective memory, attention, and reasoning, but no analyses to date have examined the degree to which a hierarchical structure adequately fits CI measurement data.
Fostering Collective Intelligence in Human–AI Collaboration: Laying the Groundwork for COHUMAIN
Topics in Cognitive Science2023
Artificial Intelligence (AI) powered machines are increasingly mediating our work and many of our managerial, economic, and cultural interactions. While technology enhances individual capability in many ways, how do we know that the sociotechnical system as a whole, consisting of a complex web of hundreds of human–machine interactions, is exhibiting collective intelligence? Research on human–machine interactions has been conducted within different disciplinary silos, resulting in social science models that underestimate technology and vice versa. Bringing together these different perspectives and methods at this juncture is critical. To truly advance our understanding of this important and quickly evolving area, we need vehicles to help research connect across disciplinary boundaries.
Teaching agents to understand teamwork: Evaluating and predicting collective intelligence as a latent variable via Hidden Markov Models
Computers in Human Behavior2023
Rapid growth in the reliance on teamwork in organizations, coupled with advances in artificial intelligence, has fueled increased use of Human Autonomy Teams (HATs) involving the collaboration of humans and agents to complete work. Although there are many successful examples of HATs, researchers and technology developers can see additional applications if agents were better able to understand the mental states of humans to anticipate what a team is likely to do next. Creating this capability requires the creation of models of team interaction that enable agents to interpret a team’s current state and anticipate its future state.
Collective attention and collective intelligence: The role of hierarchy and team gender composition
Organization Science2023
Collective intelligence (CI) captures a team’s ability to work together across a wide range of tasks and can vary significantly between teams. Extant work demonstrates that the level of collective attention a team develops has an important influence on its level of CI. An important question, then, is what enhances collective attention? Prior work demonstrates an association with team composition; here, we additionally examine the influence of team hierarchy and its interaction with team gender composition. To do so, we conduct an experiment with 584 individuals working in 146 teams in which we randomly assign each team to work in a stable, unstable, or unspecified hierarchical team structure and vary team gender composition.
Correction: Speaking out of turn: How video conferencing reduces vocal synchrony and collective intelligence
Plos one2023
There are errors in the Funding statement. The correct Funding statement is as follows: This material is based upon work supported by the National Science Foundation under grant numbers CNS-1205539 (url: https://www. nsf. gov/awardsearch/showAward? AWD_ID= 1205539&HistoricalAwards= false) and OAC-1322278 (url: https://nsf. gov/awardsearch/showAward? AWD_ID= 1322278)(Author who received the awards: LD). This study was also supported by the Defense Advanced Research Projects Agency and the Army Research Office Grant Number W911NF-20-1-0006 (Author who received the award: AW).
