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MGT 18: MANAGING DIVERSE TEAMS Table of Contents for Assigned Readings

PROFESSOR: Mary A. McKay SUMMER I AND II 2016 All bolded items are in the reader. Others can be found via links embedded here and via TED (see Content folders by WEEK).

CLASS 1: THE BUSINESS CASE FOR DIVERSITY

1. Page, Scott E., “Making the Difference: Applying a Logic of Diversity.” Academy of Management

Perspectives (2007, November).

2. Banaji, M. R., Bazerman, M. H., & Chugh, D. (2003, December). “How (Un) Ethical Are You?” Harvard

Business Publishing Product #R0312D-PDF-ENG (skim for CLASS 1 but read thoroughly before CLASS 2)

3. Goldsmith, M. (2010, June 16). “Learn to Embrace the Tension of Diversity.”

http://blogs.hbr.org/goldsmith/2010/06/learn_to_embrace_the_tension_o.html

CLASS 2: SOCIAL IDENTITY THEORY: UNDERSTANDING INDIVIDUAL BEHAVIOR IN GROUPS AND TEAMS

4. Davidson, M. N. (2002, August). “Primer on Social Identity: Understanding Group Membership.”

Harvard Business Publishing Product #: UV0644-PDF-ENG

5. Sucher, S. J. (2007, November). “Differences at Work: The Individual Experience.” Harvard Business

Publishing Product # 608068-PDF-ENG

6. Sucher, S. J. (2007, November). “Social Identity Profile.” Harvard Business Publishing Product # 608091-

PDF-ENG

7. Ely, R. J., Vargas, I. (2004, December). “Managing a Public Image: Kevin Knight.” Harvard Business

Publishing Product # 405053-PDF-ENG

8. Polzer, J. T., Elfenbein, H. A. (2003, February). “Identity Issues in Teams.” Harvard Business School

Product # 403095-PDF-ENG

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CLASS 3: AN INTRODUCTION TO GROUPS AND TEAMS

9. Katzenbach, Jon R., Smith, Douglas K. (2005, July). “The Discipline of Teams.” Harvard Business

Publishing Product # R0507P-PDF-ENG

10. Hackman, J. (2011, June 7). “Six Common Misperceptions About Team Work.”

http://blogs.hbr.org/cs/2011/06/six_common_misperceptions_abou.html

11. Coutu, D., & Beschloss, M. (2009, May). “Why Teams Don't Work.” Harvard Business Publishing Product

# R0905H-PDF-ENG

12. Huckman, R. S. and Staats, B. R. (2013, December). “The Hidden Benefits of Keeping Teams Intact.”

Harvard Business Publishing Product # F1312A-PDF-ENG

CLASS 4: UNDERSTAND BEFORE YOU ARE UNDERSTOOD: ESSENTIAL SKILLS FOR TEAM MEMBERSHIP

13. Edmondson, A. C. & Roloff, K. S. (2009, September). “Leveraging Diversity Through Psychological

Safety.” Harvard Business Publishing Product # ROT093-PDF-ENG.

14. Davidson, M. N. (2001). “Listening.” Darden Business Publishing Product # UVA-OB-0736.

15. Rosh, L. and Offermann, L. (2013, October). “Be Yourself, But Carefully.” Harvard Business Publishing

Product # R1310J-PDF-ENG

16. Connor, Jeffrey C. “It Wasn’t About Race. Or Was It?” Harvard Business Publishing # R00502-PDF-ENG.

CLASS 5: INTELLIGENCES: EMOTIONAL, SOCIAL AND CULTURAL

17. Goleman, Daniel (2004, January). “What Makes a Leader?” Harvard Business Publishing Product #

R0401H-PDF-ENG

18. Ross, Judith A. (2004, December). “Make Your Good Team Great.” Harvard Business Publishing Product

# U0812B-PDF-ENG

19. Goleman, D. & Boyatzis, R. (2008, September). “Social Intelligence and the Biology of Leadership.”

Harvard Business Publishing Product # R0809E-PDF-ENG

20. Earley, P. C. & Mosakowski, E. (2004, October). “Cultural Intelligence.” Harvard Business Publishing

Product # R0410J-PDF-ENG

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CLASS 6: MIDTERM AND VIRTUAL/REMOTE TEAMS

21. Siebdrat, F., Hoegl, M., Ernst, H. (2009, July). “How to Manage Virtual Teams.” Harvard Business

Publishing Product # SMR322-PDF-ENG (CLASS 6 reading is covered on the final exam, not the midterm.)

CLASS 7: LEADING 21ST CENTURY TEAMS

22. Cardona, P. & Miller, Paddy. (2004, July). “Leadership in Work Teams.” Harvard Business Publishing

Product # IES087-PDF-ENG

23. Sitkin, S. B. & Hackman, J.R. “Developing Team Leadership: An Interview With Coach Mike Krzyzewski.”

Academy of Management Learning & Education, 2011, Vol. 10, No. 3, 494–501.

24. Useem, Michael. (2001, October). “Leadership Lessons of Mount Everest.” Harvard Business Publishing

Product # R0109B-PDF-ENG

25. Gallo, A. (2010, June 9). “Get Your Team to Stop Fighting and Start Working.”

http://blogs.hbr.org/hmu/2010/06/get-your-team-to-stop-fighting.html

26. Ellington-Booth, B. & Cates, K. L., “Growing Managers: Moving From Team Member to Team Leader.”

Harvard Business Publishing Product # KEL629-PDF-ENG.

CLASS 8: CULTURAL COMPETENCE

27. Corkindale, G. (2007, June 14). “Navigating Cultures.”

http://blogs.hbr.org/corkindale/2007/06/navigating_cultures.html

28. Brett, J. Behfar, K., Kern, M.C. (2006, November). “Managing Multicultural Teams.” Harvard Business

Publishing Product # R0611-PDF-ENG

29. Meyer, Erin (2014, May). “Navigating the Cultural Minefield.” Harvard Business Publishing Product #

R1405K-PDF-ENG

30. Meyer, Erin (2014, February). “How to Say This is Crap in Different Cultures.”

https://hbr.org/2014/02/how-to-say-this-is-crap-in-different-cultures/

31. Meyer, Erin. (2014, July). “Multicultural Teamwork: Accommodate Multiple Perspectives.”

http://knowledge.insead.edu/blog/insead-blog/multicultural-teamwork-accommodate-multiple-

perspectives-3489

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CLASS 9: THE FUTURE OF TEAMS

32. Pentland, A. (2012, April). “The New Science of Building Great Teams.” Harvard Business School Product

# R1204C-PDF-ENG

33. Edmondson, A. (2012, April). “Teamwork on the Fly.” Harvard Business Publishing Product # R1204D-

PDF-ENG

34. Duhigg, C. “What Google Learned From Its Quest to Build the Perfect Team” (February 25, 2016).

http://www.nytimes.com/2016/02/28/magazine/what-google-learned-from-its-quest-to-build-the-

perfect-team.html?_r=0

CLASS 10: BECOMING A GLOBAL TEAM LEADER

35. Groysberg, B. and Connolly, K. (September 2013). “Great Leaders Who Make the Mix Work.” Harvard

Business Publishing Product # R1309D-PDF-ENG

36. Klau, M. “Twenty-first Century Leadership: It’s All About Values” (May 27, 2010).

http://blogs.hbr.org/imagining-the-future-of-leadership/2010/05/whose-values-the-gandhihitler.html

*Permission to reprint all selections granted to University Readers by the publishers for this individual course reader.

Please don’t photocopy – to do so would be a violation of copyright law.

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E X C H A N G E

Making the Difference: Applying a Logic of Diversity by Scott E. Page

Executive Overview Each year, corporations spend billions of dollars on diversity training, education, and outreach. In this article, I explain why these efforts make good business sense and why organizations with diverse employees often perform best. I do this by describing a logic of diversity that relies on simple frameworks. Within these frameworks, I demonstrate how collections of individuals with diverse tools can outperform collections of high “ability” individuals at problem solving and predictive tasks. In problem solving, these benefits come not through portfolio effects but from superadditivity: Combinations of tools can be more powerful than the tools themselves. In predictive tasks, diversity in predictive models reduces collective error. It’s a mathe- matical fact that diversity matters just as much as highly accurate models when making collective predictions. This logic of diversity provides a foundation on which to construct practices that leverage differences to improve performance.

Along the moving sidewalks inside Paris’Charles de Gaulle airport, you cannot helpbut notice a sequence of HSBC advertise- ments meant to show diverse perspectives. One shows two identical pictures of a half-full glass of water. Across one glass, the caption reads moitié vide, under the other moitié plein. A second adver- tisement shows two identical pictures of an apple with a bite taken out. Défendu scrolls across one apple and recommandé across the other. These ads encourage us to think of HSBC as a firm that sees a problem from more than one perspective—and they also provide a welcome diversion from the inefficiencies of the airport. This multiple per- spective taking allows HSBC to add value, or so we are intended to believe.

The HSBC ads reflect a broader trend. Each

year, corporations spend billions related to pro- moting positive messages about diversity both in- ternally and externally. Why profit-seeking busi- nesses commit so many resources to constructing diverse workforces and creating welcoming orga- nizational cultures stems from two trends. First, businesses have become more global and hence more ethnically diverse. Firms sell to diverse con- sumers and hire from a diverse pool of candidates. The world, as has been said, is now flat, and consequently, organizations must cope with diver- sity. Second, the practice of work has become more team focused. The fixed hierarchy has given way to the evolving matrix (Mannix & Neale, 2006). In the past, welders positioned two stations apart on an assembly line need not get along. They need not validate one another’s worldview. The same cannot be said of a team of scriptwriters or oncologists, who must learn to understand the language and actions of one another.

This article is adapted from Scott E. Page’s book The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies, published in 2007 by Princeton University Press.

Scott E. Page (spage@umich.edu) is Professor of Complex Systems, Political Science, and Economics at the University of Michigan-Ann Arbor. He is also an external faculty member of the Santa Fe Institute.

6 NovemberAcademy of Management Perspectives

Copyright by the Academy of Management; all rights reserved. Contents may not be copied, e-mailed, posted to a listserv, or otherwise transmitted without the copyright holder’s express written permission. Users may print, download, or e-mail articles for individual use only.

1 This course pack is for use in Professor McKay’s MGT 18 Summer 2016 course. Further reproduction is prohibited.

This coincident emergence of diverse work- forces and team-based work makes leveraging di- versity a central concern of most organizations. A first question to ask is whether it’s a good thing from a business perspective. Does it hurt or help the bottom line? A substantial empirical literature addresses the question of whether diversity im- proves team performance (Williams & O’Reilly, 1998). A brief summary of that literature reveals that the answer depends on several factors. Par- ticularly important is what people believe (Ely & Thomas, 2001). If people do not believe in the value of diversity, then when part of a diverse team they’re not as likely to produce good out- comes. That expectations shape behavior and that behavior shapes outcomes should not come as a big surprise. How though to change expectations? How does an organization get its employees to believe that diversity leads to better outcomes? Taking out advertisements or printing up human resources documents with elaborate graphics and catchy tag lines won’t make it so. Managers and employees need, to quote Springsteen, “a reason to believe.”

Simple, clean logic can provide that reason. In this brief article, I derive links between cognitive differences among team members and better col- lective outcomes at specific tasks: problem solving and prediction. I build those links using conceptual frameworks that borrow from psychology, com- puter science, and economics. These links not only provide a foundation for understanding when, how, and if diversity produces benefits— the reason to believe—they also point toward specific policies and practices that can leverage the power of diversity.

The bottom line: Diversity can improve the bottom line. It may even matter as much as abil- ity.

DiversePerspectivesandHeuristics

I begin by formalizing the loose notion of a per-spective. No end of brochures and advertisementssing the praises of diverse perspectives, but what are they? Here, I define a perspective to be a representation of the set of the possible: the set of the semiconductor designs, welfare policies, or fall leather coats. Two people possess diverse perspec-

tives if they mentally represent the “set of the possible” differently. For example, one person might organize a collection of books by their au- thors’ last names; another person might organize those same books by color and size. One professor might arrange students’ names by class rank; an- other professor might order those same students alphabetically.

How a person represents the set of the possible determines “what is next to what.” For example, The Catcher in the Rye may seem rather discon- nected from Mao’s Little Red Book, but they are adjacent in a perspective that organizes books by color and size. Perspectives matter because “what is next to what” determines how a person locates new solutions. The linkage between perspectives and locating solutions can be clarified with an example. Suppose you are making butternut squash soup. You’ve pureed the sautéed onion and added the cream and baked squash, but the result tastes bland. Arrayed before you is an enormous spice rack. You’re thinking that perhaps you’ll add cumin. You sniff the cumin. It smells fine, but next to it, you see curry. So you smell the curry and decide it will be wonderful. You only try the curry because it sits adjacent to the cumin. Had the spices been arranged differently, say by color, you might have added cinnamon instead. What is next to what—in this case curry is next to cumin—determines where you look.

This same logic extends to almost any problem- solving situation: Two people with different perspec- tives test different potential improvements and increase the probability of an innovation.

Diverse perspectives may be the cause of most breakthroughs, but this does not mean that all diverse perspectives prove helpful. Someone who sees a problem from a different perspective will notice different candidate solutions. But those can- didate solutions need not improve the status quo. Diverse perspectives prove most valuable if they embed information relevant to the problem being solved. For example, in trying to increase fuel efficiency, a perspective that focuses on the weight of parts will likely yield good ideas. A perspective that considers their color probably won’t. Therefore, while organizations should en- courage bringing diverse perspectives to a prob-

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2 This course pack is for use in Professor McKay’s MGT 18 Summer 2016 course. Further reproduction is prohibited.

lem, they must also have some method for iden- tifying useful perspectives.

Perspectives describe how people see a prob- lem, but they do not fully capture the act of problem solving. When solving problems, people also use heuristics. Heuristics are methods or tools to find solutions. In my description of searching for a spice to add to the soup, I’ve assumed that you looked at adjacent jars. This is an example of a heuristic. Heuristics take many forms and vary in their sophistication from simple rules of thumb to complicated algorithms. To give a flavor for how heuristics operate, I describe here a famous simple heuristic known as do the opposite. In a classic episode of the television show Seinfeld, Jerry’s bumbling friend George Costanza comes to the realization that every decision he has made in his life has been the wrong one. This realization re- sults in an epiphany: He should do the opposite. He should do the reverse of whatever he thinks is best. If the rules in his head tell him to be kind, he should be rude. If they tell him to arrive early, he should show up late. If they tell him to dress casually, he should dress formally. The irony, of course, is that doing the opposite of what he thinks is right is the only “right” thing George has ever done, and by the end of the show he achieves personal and professional success. Diverse heuris- tics, like diverse perspectives, improve problem solving, but they do so in a different way.

Whereas perspectives change “what is next to what,” heuristics change how a person searches for solutions. Imagine two engineers working for a manufacturing company trying to improve the speed of an assembly line. The first engineer’s heuristic might be to try to break down individual tasks into smaller tasks. The second engineer’s heuristic may be to switch the order of the tasks. The two heuristics differ, and because they differ, they identify different candidate solutions, in- creasing the probability of a breakthrough.

This brief description of diverse perspectives and heuristics and how they operate reveals only part of the power of diversity. What I’ve shown is that by seeing problems differently (diverse per- spectives) and by looking for solutions in different ways (diverse heuristics), teams, groups, and orga- nizations can locate more potential innovations. I

now show that these individual improvements can be combined, creating superadditivity. Superaddi- tivity exists when the total exceeds the sum of the parts, when 1�1 � 3.

The idea that 1�1 � 3 may seem counterin- tuitive. Yet, when we add heuristics, either the two heuristics are the same (i.e., each points to the same solution, and therefore 1�1 � 1) or the two heuristics differ (in which case 1�1 � 3). Why three? Let’s do the math. Let’s go back to our assembly line problem. The first heuristic might advocate dividing a task that consists of six spot welds into two tasks. The second heuristic might advocate gluing on a piece of trim prior to the welding. The third heuristic comes from doing both—dividing the task and switching the order. Thus, any time you have two heuristics, you can create a third by combining the two heuristics. A similar logic shows that 1�1�1 � 7. Far from being a meaningless buzzword, superadditivity can be real, but only if people bring diverse perspec- tives and solutions to a problem.

The logic that diversity creates superadditive benefits differs from the standard portfolio analogy for diversity. According to the portfolio analogy, a firm wants diversity so as to be able to respond to diverse situations just as a stock investor wants a diverse portfolio of stocks. Just as a diverse port- folio guarantees a good payoff regardless of the state of the world, a diverse set of employees ensures that someone exists within the firm to handle any situation that arises. The portfolio analogy, though accurate in some cases, breaks down when applied to team-based problem solv- ing. There’s no give and take between stocks in a portfolio. One stock doesn’t say to another stock, “I never thought of the problem that way.” Nor can stocks build on solutions thought of by existing stocks. That just doesn’t make any sense.

I do not mean to imply that diversity does not provide insurance as suggested by the portfolio analogy. It does. However, the value of insurance against risk should not obscure the potentially larger superadditive benefits that accrue from hav- ing employees with diverse perspectives and heu- ristics.

Before moving on to more theoretical results, I

8 NovemberAcademy of Management Perspectives

3 This course pack is for use in Professor McKay’s MGT 18 Summer 2016 course. Further reproduction is prohibited.

want to inject a brief comment about identity diversity. In the framework that I have described, diverse perspectives and heuristics underpin diver- sity’s benefits. These more cognitively based no- tions of differences are distinct from identity- based distinctions such as race, gender, age, ethnicity, and so on. Though conceptually dis- tinct, cognitive and identity diversity often corre- late empirically. This correlation arises because perspectives and heuristics that people apply to problems do not come from thin air. They are the product of training, practice, and life experiences. How we see the world is informed and influenced by our values, our identities, and our cultures. People often reason by analogy. Each person’s unique set of life experiences provides the engine for these analogies. Diverse identities, therefore, often translate into diverse perspectives and heu- ristics.

ProblemSolving:Diversity TrumpsAbility

I have just outlined the basic logic for how di-verse perspectives and heuristics can improveproblem solving. I now want to push this logic a little further and touch on some formal results. First, I want to describe some experiments that I ran while an assistant professor at Caltech. For fun, I constructed a computer model of diverse problem solvers confronting a difficult problem. In my model, I represented diversity as differences in the ways problem solvers encoded the problem and searched for solutions, i.e., diverse perspectives and heuristics. I then stumbled upon a counterin- tuitive finding: Diverse groups of problem solv- ers—groups of people with diverse perspectives and heuristics—consistently outperformed groups composed of the best individual performers. So, if I formed two groups— one random (and therefore diverse) and one consisting of the best individual performers—the first group almost always did better. In other words, diversity trumped ability.

This counterintuitive finding led me to try to identify sufficient conditions for this to be true. What assumptions did I have to make for diverse groups, on average, to outperform groups of the best individuals? That turned out to be a rather

difficult task. So, following the logic of my own model, I enlisted the help of someone else, Lu Hong, a person with a different set of perspectives and heuristics than my own, to help me identify those conditions. Together, we found a set of conditions that, if they hold, imply that diversity trumps ability.

To show what these conditions are and why they matter, I will describe a simple model. Sup- pose that I begin with an initial pool of problem solvers from which I draw a random (e.g., diverse) team and a team of the best individual problem solvers. Each of these teams will have some mod- erate number of people, whereas the initial pool of people could be quite large. It could consist of everyone who works for a firm or every faculty member at a university. I then compare the col- lective performance of the team of the best prob- lem solvers against the collective performance of the randomly selected problem solvers.

Before I go too far, I want to remind you of the goal. Keep in mind that the diversity-trumps-abil- ity result won’t always hold. It holds given certain conditions. If, for example, the teams have only a single member, the team of the best problem solv- ers will consist of the best individual, and the team of random problem solvers will consist of a random person. Therefore, the first team will out- perform the second. Of course, in this case ability doesn’t trump diversity because the second team isn’t diverse. It has only one person. Thus, having the teams have more than one person will be a condition for the result to hold.

The question Lu and I asked was, what other conditions are needed? If those conditions are unrealistic, then we should not expect diversity to trump ability in practice. If those conditions seem mild, then perhaps we should. That’s one reason that we “do the math,” so that we can see when logic holds and when it doesn’t. Doing the math has other benefits as well, not the least of which is that we better understand how diversity produces benefits, which better enables us to leverage it in practice.

The first condition we identified relates to the difficulty of the problem. Easy problems don’t require diverse approaches.

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Condition 1: The Problem Is Difficult: No individual problem solver always locates the best solution.1

Without this condition, diversity cannot trump ability. If any individual problem solver always finds the best solution, then the collection of the best problem solvers (which by definition con- tains the best problem solver) always locates the best solution. For example, if we need to find the answer to a standard engineering problem, we can just ask an engineer who can give us the correct answer. We have no need to put together an interdisciplinary team. For harder problems, like designing an aircraft engine, we need a team. And that team needs diverse thinkers.

Condition 2: The Calculus Condition: The local optima of every problem solver can be written down in a list. In other words, all problem solvers are smart.

The second condition concerns the ability of the problem solvers. All of the possible problem solv- ers must have some ability to solve the problem. We cannot set loose a bunch of anthropologists and economists in the physics lab and hope they produce cold fusion. To formalize the idea that the problem solvers must have relevant cognitive tools, Lu and I assumed that the problem solvers got stuck in only a reasonable number of places. In the language of mathematics, such points are called local optima. We decided to call this restric- tion the Calculus Condition. We did this because people who know calculus can take derivatives, and therefore have a reasonable number of local optima. Here’s why. Think of a problem as creat- ing a mathematical function in which high values are good solutions. The derivative equals the slope of that function, which like the slope of a moun- tain is either positive (uphill), negative (down- hill), or zero (on a peak or a plateau). On a peak the derivative equals zero; the slope goes neither up nor down. Calculus enables a person to find points with derivatives equal to zero. Therefore, people who know calculus can find peaks. Econ- omists don’t know calculus when it comes to phys-

ics, but they probably do know calculus when asked about tax policy.

Condition 3: The Diversity Condition: Any solution other than the global optimum is not a local optimum for some non-zero percentage of problem solvers.

The third condition requires that for any proposed solution other than the global optimum, some problem solver can find an improvement on that solution. In formal terms, this means that the intersection of the problem solvers’ local optima contains only the global optimum. We call this the Diversity Condition, as it assumes diversity among the problem solvers. This condition does not say that given any solution some problem solver can immediately jump to the global opti- mum. That assumption would be much stronger and would rarely be the case. The assumption says, instead, that some problem exists who can find an improvement. That improvement need not be large. It need only be an improvement.

Condition4:Reasonably SizedTeamsDrawn from Lots of Potential Problem Solvers: The initial population of problem solvers must be large, and the teams of problem solvers working together must consist of more than a handful of problem solvers.

The final condition requires that the initial pool of problem solvers must be reasonably large and that the set of problem solvers who form the teams must not be too small. The logic behind this condition becomes clear in extreme cases. If the initial set consists of only 15 problem solvers, then the best ten should outperform a random ten. With so few problem solvers, the best ten cannot help but be diverse and therefore have different local optima. At the same time, the teams that work together must be large enough that the ran- dom collection can be sufficiently diverse. Think of it this way: We need to be selecting people from a big pool, and we need to be constructing teams that are big enough for diversity to come into play.

These four conditions—(a) the problem has to be hard, (b) the people have to be smart, (c) the people have to be diverse, and (d) the teams have to be reasonably big and chosen from a large pool—prove sufficient for diversity to trump abil-

1 If the best problem solver finds the optimal solution 99.9% of the time, the collection of randomly selected problem solvers will not outper- form the group of the best.