By Katrin Prager
Where do the benefits of diverse teams come from and how can those benefits be unlocked? What are the pitfalls to watch out for in constructing a team that is greater than the sum of its parts?
To boost innovation and creativity in teams I suggest we need to develop diversity science, which has 5 elements:
- identifying the right kind of diversity
- avoiding homophily
- avoiding dominance hierarchies
- fostering appropriate leadership
- building and protecting trust.
Let’s unpack each of these elements.
- Identifying the right kind of diversity
Here I build on the work of Matthew Syed (2019) who argues that teams need to be purposefully diverse, as opposed to arbitrarily diverse, in order to be successful. Syed explains that one person can only ever know part of the overall problem space. If people in a group are similar, the parts of the problem space they know overlap. This means they may be collectively blind, both to the core of the problem and to creative solutions.Instead, what is needed are people who are likely to know different parts of the problem space and have different frames of reference; people who have differences in assumptions, divergent thinking and fresh perspectives.I suggest that we need to distinguish demographic diversity (race, gender, class, etc.) and cognitive diversity (differences in thoughts, insights, perspectives, frames of reference, thinking styles, etc.) and to work with both to maximise the benefits of diversity in a team.
- Avoiding homophily
Homophily is the (unconscious) enjoyment of being surrounded by people who think in the same way and share our perspectives. It pulls teams towards homogeneity and is comforting and validating. Working in diverse groups is cognitively demanding because there are different opinions and dissent, whereas working in homogenous groups is agreeable because members ‘mirror’ each other’s views rather than disagreeing. It takes more effort to listen to and follow someone who thinks and talks differently.In their blog post on Embracing tension for energy and creativity in interdisciplinary research, Clarke and Freeth develop this argument further, illustrating not only that higher levels of heterogeneity in an interdisciplinary team bring about higher levels of tension and potential for conflicts, but also that not embracing tensions productively leads to even more tension and restricts ability to address wicked problems.
- Avoiding dominance hierarchies
Dominance hierarchies are engrained in human societies and we are generally not aware of the emotions and behaviours associated with them. Dominance dynamics mean that team members will not speak up, even if they have crucial information, for fear of provoking retaliation from leaders or others in more powerful positions who might feel undermined. Expression of diverse perspectives can be shut down in hierarchies where dissent is perceived by those in dominant positions as a threat to their status.
- Fostering appropriate leadership
Team leadership needs to be empathetic and highly attuned to what others are thinking and saying. Such leaders create a culture of psychological safety where everyone feels safe to offer suggestions and divergent or dissenting viewpoints.
- Building and protecting trust
Trust is necessary for establishing psychological safety and being open to divergent and dissenting voices. It is also required to foster a ‘giving attitude’, ie., being willing to offer one’s insights to others and to feel that they will not be abused or taken advantage of.
Conclusions
Some of these elements intersect with key requirements of effective team science, such as the important role of trust and effective communication. What diversity science adds is an understanding of concepts necessary to pull together purposefully diverse teams and help them collaborate effectively.
Have you experienced the detrimental effects of homophily and dominance hierarchies, or the benefits of sharing different ideas?
I hope this blog post encourages exploration and testing of these ideas, so that we can build the knowledge base for diversity science.
Reference:
Syed, M. (2019). Rebel Ideas: The Power of Diverse Thinking. John Murray Publishers: London, United Kingdom.
Biography: Katrin Prager PhD is a Senior Lecturer at the University of Aberdeen, Scotland, UK. She is involved in inter- and transdisciplinary research on agri-environmental policy making and implementation, collaborative landscape management, and farmer decision making and behaviour. She investigates these topics through the lens of institutional analysis, knowledge management, adaptive capacity and organisational behaviour.
A number of comments on this blog post were made on the Science of Team Science listserv. Some of the highlights are reproduced below (and some are slightly edited).
From Tanya Mathew, Ohio State University
The blog is great – thank you for sharing. I love this topic from an organizational change and systems thinking perspective.
When it comes to strategies and tools, here are a few of my personal favorites.
1. DiSC® is a personal assessment tool used by more than one million people every year to help improve teamwork, communication, and productivity in the workplace. This is a good tool to see how you see the world and how others see the world based on the behavioral style you tend to function in. DiSC is an acronym that stands for the four main personality profiles described in the DiSC model: (D)ominance, (i)nfluence, (S)teadiness and (C)onscientiousness. DiSC® assessments are used in thousands of organizations around the world, from sprawling government agencies and Fortune 500 companies to nonprofits and small businesses. The reason is simple: DiSC® profiles help build stronger, more effective working relationships. The DiSC model of behavior was originally proposed by William Moulton Marston, a physiological psychologist with a Ph.D. from Harvard. His 1928 book, Emotions of Normal People, established the theories that were later expanded by many others.
https://www.discprofile.com/what-is-disc
2. FourSight is a valid, research-based assessment tool developed over the last 20 years by Gerard Puccio, Ph.D., director of the International Center for Studies in Creativity at the State University of New York College at Buffalo. Dr. Puccio is dedicated to teaching the “science” of creativity and innovation to graduate students and business professionals to help them gain awareness, skills and mastery of the creative process. “Teams trained in FourSight are more effective at innovation. – “Creating & Sustaining Innovation Teams” IBM study conducted by Dr. Casimer DeCusatis, IBM Master Inventor
https://foursightonline.com/
3. Search committees, recruitment & team “Invitations” (a.k.a. seat at the table): Groupthink kills innovation! “There is a familiar saying: “We recruit in our own image.” This bias doesn’t end with demographic distinctions like race or gender, or with the recruiting process, for that matter. Colleagues gravitate toward the people who think and express themselves in a similar way. As a result, organizations often end up with like-minded teams. When this happens, we have what psychologists call functional bias — and low cognitive diversity.”
https://hbr.org/2017/03/teams-solve-problems-faster-when-theyre-more-cognitively-diverse
4. Inclusive Intelligence competencies – I truly believe that you need high Emotional Intelligence (self-awareness and empathy), awareness of your implicit biases and unwavering commitment to inclusion if you want to create teams which foster trust and safety where each and every one, irrespective of title, degree, rank or position, feels heard, valued and respected. Inclusive leaders foster a sense of belonging in the teams they lead.
a. Emotional Intelligence 2.0 assessment: https://www.amazon.com/Emotional-Intelligence-2-0-Travis-Bradberry/dp/0974320625
b. Harvard Implicit Bias IAT tests: https://implicit.harvard.edu/implicit/takeatest.html
5. Equity policy to support scientific innovation by diverse teams: funding/selection criteria, processes and review panels: “Make diversity score-driving criteria, prioritize diverse teams for funding, and diversify review panels”.
(A) For R01 applications from 2014 to 2016, the award rate was 10.2% for Black PIs and 18.5% for white PIs (Erosheva et al., 2020).
Fund Black scientists
Kelly R. Stevens, Kristyn S. Masters, P.I. Imoukhuede, Hana El-Samad, Tejal A. Desai, Omolola Eniola-Adefeso
COMMENTARY| VOLUME 184, ISSUE 3, P561-565, FEBRUARY 04, 2021
DOI: https://doi.org/10.1016/j.cell.2021.01.011
https://www.cell.com/cell/fulltext/S0092-8674(21)00011-8
From Dan Stokols, University of California, Irvine
We’ve used this idea tree exercise to promote divergent thinking among the members of cross-disciplinary research teams.
https://i2insights.org/2019/03/12/idea-tree-brainstorming-tool/
The comments section at the end of the post list mention a number of other brainstorming and creativity-enhancing tools that are being used by research teams. The exercise we describe in the post is geared for in-person idea generation sessions, but can be adapted for online settings.
From Steve Fiore, University of Central Florida
Thanks for sharing your experience with these tools, Tonya. I think your points on 3, 4, and 5 are great examples for addressing diversity problems. Relevant to this, the National Academies has a current study on “Increasing Diversity and Inclusion in the Leadership of Competed Space Missions” (see https://www.nationalacademies.org/our-work/increasing-diversity-in-the-leadership-of-competed-space-missions). It is addressing these kinds of issues, by for example, engaging “with a wide range of experts in the relevant social science and space science communities”.
But I have a question about DiSC and FourSight. Can you provide any studies on the FourSight method? I saw the quote of Dr. Casimer DeCusatis about training teams with FourSight leading to innovation, but, when I tracked down that article, it was a case study (see https://onlinelibrary.wiley.com/doi/full/10.1111/j.1467-8691.2008.00478.x). I went to the FourSight website but did not see any empirical studies comparing their method to others. So can you please share any evidence that this technique has demonstrated predictive performance outcomes?
I’m more familiar with DiSC, and I understand that is a reliable instrument (consistent over time when assessing people). But I’m similarly curious to see if there is empirical data showing that team composition analyses using DiSC is predictive of team performance outcomes. More generally, the one personality assessment that is most robust is the Big 5 (or OCEAN) framework. But even with that measure, research finds that personality is not always the most predictive feature of team outcomes. Specifically, studies show that other factors associated with a team (attitudes like psychological safety), norms established, or process interventions, are more strongly associated with team performance.
Google did a comprehensive study of this a few years ago that brought this kind of research into the public arena. For those interested, Google has done a good job writing that research (https://rework.withgoogle.com/blog/five-keys-to-a-successful-google-team/). And they also provide a number of useful guides/handouts to help teams. The punchline from that study was that psychological safety predicted team innovation at Google while things like personality did not. I use this example in my team science workshops because, at Google, the teams making more mistakes were actually more successful. In line with psychological safety theory, these teams were more comfortable taking risks and knew they would not be punished for speaking up with ideas or concerns. Said another way, they learned from their mistakes and moved on. It’s good to keep these kinds of findings in mind because we are seldom able to choose a team based upon personality. So it is better to understand how to manage group processes and understand the factors behind success regardless of personality.
Response from Tanya Mathew
I’d like to thank you for sharing the work going on at the National Academies. I like their system mapping plan and considering “best practices from other agencies funding large, PI-led, multi-institutional projects to find common bottlenecks and to identify innovative methods for overcoming such barriers”. Those of us in the CTSA hubs are already doing the work of addressing extant bottlenecks and barriers to translation, so it wouldn’t be earth shattering to do the same to address racism and diversity. It’s a matter of finding a coalition of the willing.
Frankly, I had not done a lit review on FourSight yet but I was already in contact with Kelly Roberts from the company so I enquired on your behalf. [She provided a document] and she wrote, “It does list correlations between FourSight and other measures, including DiSC.”. I’d love to know what you think! Especially from a Cognitive Sciences point of view. I also got this invitation which I’ll pass on to you: “Please feel free to also reach out directly to FourSight Partner, Sarah Thurber with any additional research inquiries or questions.” mailto:sarah@foursightonline.com.
I need to look into the Big Five Personality Traits Model – thank you for sharing!
I particularly like Openness: sometimes called “Intellect” or “Imagination,” this measures your level of creativity , and your desire for knowledge and new experiences.
I often say that we need open hearts and open minds to be truly innovative and effective leaders. That includes a genuine interest and curiosity to learn from team members who represent diverse backgrounds/views/perspectives etc.
And I’m not surprised at all about the finding regarding psychological safety by the Google study. I think it’s a critical factor and deal breaker!
Thank you Tanya, Dan and Steve for sharing your experiences and resources! And to Gabriele for documenting them here.
Thanks Gianni, very interesting and I can see the parallels. Although I could not yet (only after a quick scan) find the detail on how to connect “the right people”. Are you saying the AI could be trained to identify them?
Thanks Katrin – generally, if used well, digital tools enable better connectivity of people. It is hard to believe given the social media mess we are in, but that’s because AI is trained on stickiness and propagation (good for advertising) and not quality of dialogue.
I also add that this is aligned with the output from my work on AI-augmented collective intelligence, inspired by my collaboration with Tom Malone at MIT’s Center for Collective intelligence. I identify a slightly longer set of possible parameters for diversity, particularly useful when attempting to practically form diverse networks – below. (full document at http://www.supermind.design). You will see that digital and AI in some cases can help these parameters to be used at scale.
· “Quality” metrics: like IQ, performance
· Personal competencies such as skills, or abilities as defined in labor taxonomies like US Department of Labor O*NET. Experience in different phases of the business cycle, i.e. design, build and run of new solutions, as well as general management (“run”) of established processes
· Generalist or specialist in a specific domain
· Workload, cognitive surplus available
· Collaborative ability
· Styles of communication such as those identified by natural language processing algorithms used by several research companies such as Crystal Reports
· Immediately creative or requiring pause and analysis to produce output. Those who need time to reflect will find it hard to contribute well in synchronous exercises, and that is especially problematic when time overlap between people is limited – because the iterations will take a long time to converge
· Social perceptiveness measured for instance by “Reading mind in the eyes (RME)”[i]
· Cognitive / emotional profile of individuals: Deductive vs inductive, better with Semantic (language) vs semiotics (visuals, symbols) driven, geospatial reasoning, intuitive vs analytical, level of curiosity
· Myers / Briggs traits and derivatives
· Practice of mindfulness
· Perceived levels of psychological safety[ii] – are one of the most important predictors of innovation and creativity in teams. Measuring them at individual level is useful. Google’s project Aristotle[iii], meant to discover the traits of successful teams, showed that this is a key metric.
· Favorite roles of individuals[iv], e.g. leaders vs active collaborators vs passive supporters; Sensors, People Connectors, Promoters, Idea connectors; Ideators; Energizers
· Formal roles of individuals. Google’s project Aristotle demonstrated that highly effective teams define individual roles well.
· Employee engagement levels, as measured through surveys (like TINYpulse or Pol.is, or LinkedIn’s Glint) or SNA (like Keencorp)
· Trust level (overall, and for specific people in specific situations)
· Physical location (including office-based vs remote), and time zone. These factors have a bearing on the ability to do in-person or at least synchronous work during enough of people’s waking hours, which is critical in some creative work.
· Organizational structure – and the position of the individual
· Organizational complexity of the group where the individual sits
· Network dexterity of the individual node (assessed through surveys or network centrality measures)
· Other proxies for quality and individual characteristics, like those from software companies that specialize in behavioral trait profiling (like Talocity or Knack, currently used in the recruiting space)
Thank you for expanding on the range of parameters!
I’ll just add the four footnotes in case readers are wondering what they were meant to refer to:
[i] well.blogs.nytimes.com/2013/10/03/well-quiz-the-mind-behind-the-eyes/ ; http://www.labinthewild.org/
[ii] https://en.wikipedia.org/wiki/Psychological_safety
[iii] https://rework.withgoogle.com/print/guides/5721312655835136/
[iv] Markova, McArthur “Collaborative Intelligence”, 2015
This thinking is very much in line with related work I have done – http://www.supermind.design (the longer guidebook). Significant applicability to both enterprise social media, and general social media aimed at innovation (e.g. LinkedIn).