Monday, June 27, 2016

The Limitations of Self-Managed Teams


You no doubt have heard about Zappo’s, the on-line shoe retailer that offers $2000 to new employees who decide after their initial training that they no longer wish to continue with the company. A couple of years ago, Zappo’s founder, Tony Hsieh, decided to introduce a management concept called holacracy (a variation of the self-managed team concept) and offered all employees the choice of staying with the company and embracing this new concept or taking a generous severance package. Eventually, by the time the deadline had passed, 260 of the company’s roughly 1500 employees (about 18%) decided to take the offer – which was on average about 5.5 months’ worth of salary.

Zappo’s specific implementation of holacracy was developed by Brian Robertson, a former programmer, and is perhaps a more structured and extreme variation of the concept of self-managed teams.  Zappo’s is not the first company to experiment with some form of self-managed teams; Thorell (2013) claims to have identified 18 “bossless” companies in the U.S. (not including Zappo’s), with 6 of them operating internationally.

Another company, W. L. Gore, has become famous not only for its revolutionary fabric but also for its commitment to self-managed teams. Here is an excerpt from its web site:

“How we work at Gore sets up apart. Since Bill Gore founded the company in 1958, Gore has been a team-based, flat lattice organization that fosters personal initiative. There are no traditional organizational charts, no chains of command, nor predetermined channels of communication.

“Instead, we communicate directly with each other and are accountable to fellow members of our multi-disciplined team. We encourage hands-on innovation, involving those closest to a project in decision making. Teams organize around opportunities and leaders emerge. This unique kind of corporate structure has proven to be a significant contributor to associate satisfaction and retention.”

The distinguishing feature of such self-managed teams is the absence of any formal hierarchy. This means there are no formal titles or promotions, and no one has a “boss.” Team members negotiate their responsibilities with each other, employees rank each other to help with pay decisions, and team members can hire and fire.  These applications of self-managed teams are consistent with Carson et al.’s (2007) definition: those in which team members have greater responsibility for setting their own goals, monitoring their own progress, and making their own decisions than do team members in manager-led teams.

Note that self-managed teams are not, strictly speaking, leaderless. Rather, leadership is “shared” or distributed. In their meta-analysis on the impact of “shared” leadership on team effectiveness, Wang et al. (2014) conclude that the effects of this type of leadership is stronger for attitudinal outcomes and group processes than on team performance.

Actually, the majority of work organizations not only in the U.S. but globally are based on hierarchical principles. Some have tried to experiment with flat organizations and have not had much success. Even Google learned this the hard way, when its founders Larry Page and Sergey Brin flattened the organization early on but pulled the plug a few months later when they found that this was just not working.

According to Galinsky and Schweitzer (2015), hierarchy is the most dominant form of social organization across all species: “Hierarchy helps people know who does what, when, and how. These rules promote efficient interactions by setting clear expectations for the behaviors of people of different ranks. Essentially, hierarchy facilitates social interactions by simplifying them.” (p. 67)

Kenney and Anderson (2012) have summarized the literature on status hierarchies in groups, and they point to two theories of hierarchy: the functionalist and the dominant theories, and propose a third one, what they call the micropolitics theory. In brief, the functionalist theory says that hierarchies form to help groups make decisions more efficiently. They give some individuals who are seen to be the most competent more control over decisions. According to the dominance theory, hierarchies form as a result of competition and the assertiveness of some members over others. While the research shows overwhelming evidence for the functionalist theory over the dominance theory, the authors propose a blending of the two: that hierarchies form as a result of who can help the group most and also through individuals’ desire for status. Regardless of the evidence for each of these theories, the reality is that most social and work groups establish hierarchies early on, and they are relatively difficult to eliminate.

In my view, four factors need to be taken into account for any organization interested in moving towards self-managed teams: cultural preferences, individual differences, organizational context, and the nature of the work or task. First, cultural preferences. As Hofstede and others have demonstrated over the years, cultures that are high in uncertainty avoidance and high in power distance in particular have a strong preference for hierarchy. Several years ago, I was coaching a Taiwanese executive who was the general manager of a global company’s subsidiary. His company had just introduced the matrix form of organization, and Ron (not his real name) commented to me: “This is difficult. Most Asians want to know ‘who is my boss?’”

In a recent study, Herbert et al. (2014) found that perceptions of shared leadership varied depending on the collectivistic orientation of the participants in their study (over 350 members of various virtual project teams). And in a survey of over 200 Mexican executives, Nicholls et al. (1999) also found numerous challenges in the implementation of self-managed teams in the Mexican work setting. As demonstrated by Hofstede and others, Mexican cultural values are closely related to collectivism, high power distance, and high uncertainty avoidance. While a collectivistic orientation might help provide a positive reaction to self-managed teams, the last two certainly do not, as their interview findings suggest. Anicich et al. (2014) have been studying the effects of hierarchy both structurally and culturally. In their studies of expert mountain climbers from 56 countries on over 5,000 expeditions, they found that expeditions from more hierarchical countries had more climbers reaching the summit, but also more climbers dying along the way. These findings illustrate both the benefits and drawbacks of hierarchy, which can create efficiencies but, by suppressing participation and voice, can be dysfunctional.

Second, individual differences. People will vary in their need for structure and their own motivational level, as well as in their testosterone levels. I would predict that individuals who tend to have a strong desire to be open to experience (one of the personality characteristics of the Big Five theory of personality) are more likely to be comfortable with such self-managed teams. Furthermore, many studies have shown that high-testosterone individuals are dominant and like being in high-power positions. By implication, such individuals like to be in charge and prefer hierarchy. In fact, as Galinksy and Schweitzer (2015) and others have written, two baboons that have high testosterone levels become very competitive and when testosterone levels vary, the baboon with the lower testosterone will walk away. Similar findings have been found in research on other primates and animals. Perhaps this is why co-CEOs are rare and when they do happen, they do not last long. It is rare for two alpha dogs to cooperate over a long period of time! As an aside, Galinksy and Schweitzer (2015) suggest a simple test to determine a rough estimate of your testosterone level: compare your ring finger with your index finger. The longer the ring finger is relative to the index finger, the more you were exposed to greater levels of testosterone in the mother’s womb – and this exposure is an indicator of your testosterone.


Third, the organizational context. Moving from an authoritarian corporate culture to one that is self-managing is not an overnight transition, as anyone knows who has worked in companies trying to create large-scale change. In fact, there is something ironic in a leader who dictates that henceforth his or her company/division/group will implement self-managing teams. In addition, because there will inevitably be a period of confusion and uncertainty when this concept is implemented, it is generally not a good idea to introduce it when an organization is going through a number of changes at the same time (e.g., introducing a new business model while restructuring and downsizing).  

One of the successful examples of a self-managed team is the Orpheus Chamber Orchestra, an ensemble of musicians that has operated without a conductor for many years. The orchestra has been written about and studied over the years (e.g., Vredenburgh and He, 2003) and it has certainly thrived. Yet if you examine the context, several features are notable. First, it has only about 25 musicians, since it is a chamber orchestra, not a full orchestra. Second, it has established a culture of collaboration which is congruent with the self-managed team approach. Third, members have self-selected into the orchestra so that its musicians are motivated by self-leadership and reciprocal influence. The orchestra is, however, NOT leaderless; rather, leadership roles rotate. Here is one of their principles and a brief explanation: Working in shared leadership. Every musician has the opportunity to play leadership roles such as leading rehearsals or directing the performance of a new musical composition. The decision rests on the group; the leader is chosen according to expertise, strengths and interests (Luc, 2011).”

Fourth, the nature of the work or task. Self-managed teams seem to work best when members are working on tasks that are complex and require high levels of collaboration and information sharing. Ronay et al. (2012) performed a series of experiments in which they assigned participants to different groups working on tasks that were either high in procedural interdependence or low in procedural interdependence. Participants’ power levels were “primed” so that there were three different types of groups: groups of 3 high-power participants, groups of 3 low-power participants, and groups of 1 high-power, 1 low-power and 1 baseline participant. The mixed-power groups were significantly more productive than the other two groups but only for the tasks high in procedural interdependence. In a second experiment, they created different types of groups (groups of high-testosterone participants, groups of low-testosterone participants, and a mix of high-, low- and average-testosterone participants) and had them working on the interdependent tasks. The mixed groups were significantly more productive than the other two groups, with the high-testosterone groups experiencing much more intragroup conflict than the other two types of groups. The researchers conclude that “… the functional benefits of hierarchy are most pronounced under conditions of high procedural interdependence …” and “…intragroup conflict mediated the performance decrements for the high-testosterone groups, but not the low-testosterone groups.” (p. 675)

The intent of organizations promoting self-managing teams is certainly admirable. Too many organizations have created cultures where people are afraid to speak up, and where ideas (especially from the rank-and-file) are not listened to. The effects of too much hierarchy, combined with the presence of high-testosterone individuals, many of whom are also narcissistic, can be damaging for teams and organizations that not only need to be high performing, but also need to be innovative and resilient. Giving employees more of a voice and empowering them is in principle a great idea. And moving away from the command-and-control model to a more collaborative model will resonate with many employees today.

For organizations interested in making the move towards self-managing teams and less hierarchy, here are three pieces of advice. First, assess your organization’s readiness for this change (more on this in a subsequent blog). Second, do some experimenting and piloting, while making sure you involve organizational members and get their feedback. Third, allow for transitions, keeping in mind that the choice is not necessarily a binary one between an authoritarian, hierarchical model and a self-managed model – but may well lie in variations on this continuum.

Anderson, A., and J. Kennedy. (2012). Micropolitics: A New Model of Status Hierarchies in Teams, in Margaret A. Neale, Elizabeth A. Mannix (eds.) Looking Back, Moving Forward: A Review of Group and Team-Based Research (Research on Managing Groups and Teams, Volume 15.) Emerald Group Publishing Limited, 49 – 80.

Anicich, E.M., Swaab, R.I., & Galinsky, A.D. (2014) Hierarchical cultural values predict success and mortality in high-stakes teams. Proceedings of the National Academy of Sciences. doi: 10.1073/pnas.1408800112

Carson, J. et al. (2007). Shared Leadership in Teams: An Investigation of Antecedent Conditions and Performance. Academy of Management Journal, 50 (50: 1217-1234.

Galinsky, A. and Schweitzer, M. (2015). Friend & Foe. New York: Crown Business.

Garvin, D. (2013). How Google Sold Its Engineers on Management. Harvard Business Review.

Luc, E. (2011). The 8 Leadership Principles of Orpheus, the Conductor-less Chamber Orchestra. http://blogue.edithluc.com/the-8-leadership-principles-of-orpheus-the-conductor-less-chamber-orchestra/?lang=en

Nicholls, C. et al. (1999). Taking Self-Managed Teams to Mexico. Academy of Management Executive, 12 (2): 15-25.

Ronay, R. et al. (2012). The Path to Glory Is Paved with Hierarchy: When Hierarchical Differentiation Increases Group Effectiveness. Psychological Science, 23 (6): 669-677.

Thorell, L. (2013). How Many Bossless Companies Exist Today? http://www.innovatini.com/how-many-bossless-companies-are-there/

Vredenburgh, D., and I. He. (2003). Leadership Lessons from a Conductorless Orchestra. Business Horizons, September-October, 19-24.

Wang, D. et al. (2014). A Meta-Analysis of Shared Leadership and Team Effectiveness. Journal of Applied Psychology, 99 (2): 181-198.


Thursday, June 2, 2016

Leadership in the Age of Robots


In March of this year, a Google computer defeated the world’s reigning Go champion in four out of five matches. Several years earlier, IBM’s Watson computer defeated two of Jeopardy’s greatest champions and IBM’s Deep Blue competed successfully against he former world chess champion Garry Kasparov. We are all aware of the use of robots in hospitals and combat zones, and the evolution of autonomous cars.

The military uses robots for several purposes, including the recovery of improvised explosive devices. Hospitals are also increasingly using robots for guiding patients and delivering drugs. More recently, as reported in the Economist (2016), researchers have created “robodocs,” robot surgeons that successfully stitched up the intestines of piglets with a minimum of human supervision. Under a surgeon’s supervision, the Smart Tissues Autonomous Robot (STAR) was able to sew piglets’ guts together after the doctors had severed the piglets’ intestines. In fact, STAR was able to carry out about 60% of the procedure without human intervention and its stitches were more evenly spaced and the sutured guts less leaky than what surgeons would have done.

There is no question that we are in the midst of a new age of artificial intelligence and the use of robots. Bryjolffon and McAffee (2014) have argued that we are now in the second machine age. The first started with James Watt’s steam engine, which kicked off the Industrial Revolution. The second machine age started with using computers and digital tools. Carlopio (1988) has described the different phases of new technology a bit differently. In the 15th and 16th centuries, printing, silk-throwing machinery, the screw press and the windmill were seen as labor-saving devices. Traditional trades were not affected but were actually enhanced. Then the Industrial Revolution began and technology became labor-enslaving, with work processes becoming more standardized and specialized. In the early twentieth century, scientific management became popular, with the assembly line perhaps its most widely used application. Now we are in the third phase of technology which he has described as labor-replacing, where computers and robots will replace jobs.

There has been a lot written on the impact of automation on workers and jobs, and I am not an expert in this area. However, the consensus seems to be that computers are getting smarter. Many experts predict that computers will displace jobs, not just at low-end but also at the high-end. Acemoglu and Autor (2010) suggest that work can be divided into a two-by-two matrix: cognitive versus manual and routine versus nonroutine. Demand for routine tasks to be performed by humans has been falling due to automation, whether these are routine cognitive tasks (e.g., bank tellers, mail clerks) or routine manual tasks (e.g., machine operators, cement masons, dressmakers). But nonroutine cognitive and manual work has been growing, and there has been much debate on the extent to which computers and robots will be able to perform these tasks and replace humans.

In the meantime, we are seeing more and more robots working alongside humans. In 2012, Amazon bought Kiva Systems, a company that makes robots. These robots are used in Amazon’s warehouses to “pick” and bring goods from storage shelves to employees. What do we know about interactions between humans and robots in the work setting? Actually, research on human-robot interaction has been going on for a while. For example, Hinds et al. (2004) wrote about the rise of “professional service” robots (as distinguished from industrial robots) that share the workplace and help people perform their tasks, e.g., supplying troops with ammunition in the battlefield, delivering medications from pharmacies to nursing stations in hospitals. In the future, these robots will be more highly interactive with people. The researchers did a study to determine the effects of the robot’s appearance and its relative status on how people work with robots. They created a lab experiment that required subjects to interact with robots to accomplish some tasks. They manipulated three levels of appearance (human as the baseline; human-like where the robot had a face, torso, arms and legs, and wore an outfit; and machine-like, where the robot covering was metallic and angular). Then they manipulated status by telling the subjects that their robot partner was their supervisor, their peer, or their subordinate. They found that subjects interacting with a more machine-like robot had an increased sense of personal responsibility they felt for the task, with subjects feeling most responsible when interacting with a machine-like subordinate. Furthermore, subjects felt less responsible when collaborating with a robot supervisor as compared with a robot peer or subordinate.

As another example, Kim et al. (2014) reviewed the literature on social distance and developed some hypotheses on people’s reactions to interactions with robots. They had participants play a card-matching game on a computer with Wakamaru, a robot developed by Mitsubishi Heavy Industries, Ltd. As the researchers described it, here is what the robot did:

“In its interactions with the participants, the robot used three key behaviors: gaze, speech, and navigation. The robot’s three-degrees-of-freedom head allowed it to direct its gaze toward the participant and other targets in the environment. The robot communicated with the participants using synthesized natural language and moved toward and away from them at different points during the interaction.” (p. 786)

The participants played the game with the robot, which made suggestions on moves. The researchers manipulated power distance (with the robot either as supervisor or subordinate) and proxemic distance (how close or distant the robot was to the participant).  What they found was that participants who interacted with the supervisor robot at close distances performed better and reported a more positive experience and stronger rapport with the robot than those who interacted with the supervisor robot at far distances. They also found that participants who interacted with a close subordinate robot reported a more positive user experience and more rapport than those who interacted with a distant robot.

What these and other studies show is that there are interaction dynamics between humans and robots that we need to take into account when designing the work of the future. Now if robots can replace humans in many tasks, can they also replace humans as leaders? Here, there is very little research on this topic. I did find an article by Samani et al. (2012) who used the term robotics leadership to describe the work that robots can perform in stock brokering (robots handling stock trades) and avionics (robots replacing human pilots in airplanes). However, I don’t consider these as leader behaviors as much as expertise that robots can provide. There is also some emerging research on leader-like behaviors of robots among themselves; for example, using simulation techniques among foraging robots, Pugliese et al. (2015) found that the most skilled robots became “leaders” and that robot groups with leaders were more effective than robot groups without leaders.

Part of being a leader is influencing others to perform certain actions. We have seen evidence from research by Milgram (1963) and others on the effect that those perceived to be in positions of authority can have on getting others to comply and follow orders. Parasuraman et al. (1997) wrote about this in the context of automation, when they summarized the research on the dangers of automation. A robot leader who gives instructions (especially if the robot has a deep male voice and perhaps is made up to look dominant) might have others following orders unquestioningly. In fact, in an interesting experiment where participants worked with robots on a set of tasks, Gombolay et al. (2015) found that participants preferred robots who made decisions about how the tasks were to be allocated rather than the participants having to make the decisions themselves. In another experiment, Robinette et al. (2015) found that participants followed the robot in an artificially created emergency situation (where they had to be evacuated from a room) even when they had seen the robot make navigation mistakes earlier and continued to make mistakes in directing them to a wrong exit!   

We do know quite a bit about effective leadership, and what people look for in leaders. Kousez and Posner (2007) have been conducting surveys over the past 30 years on qualities most admired in leaders, and they have found that four qualities are what most of the people they surveyed around the world want in a leader: honest, forward-looing, inspiring, and competent.

In his now classic article, Kotter (2001) stated that leaders do things differently than managers: leaders set a direction, align people, and motivate them. More recently, Google found in its own research (Garvin, 2013) that outstanding managers (versus average managers): coach well, empower their teams and do not micro-manage, express interest in employees’ success and well-being, are productive and results-oriented, are good communicators and listen to their teams, help their employees with career development, have a clear vision and strategy for the team, and have key technical skills so they can help advise the team.
Can robots be as good as humans in performing these behaviors? Potentially, yes. I can envision robot leaders being programmed, for example, to express interest in employees’ success, to listen and provide a clear vision for the team. I can also envision robot leaders being programmed to be honest, forward-looking, inspiring, and competent.

However, effective leadership is not only about performing these behaviors. As Kousez and Posner (2007) argue, leadership is about establishing a relationship between leader and follower. While certain robot leadership behaviors might lead to compliance among followers, organizations also want to create high-performing cultures characterized by motivation and commitment. Can robots get us to trust them so that they inspire us to do our best and engage us? Can a robot ever cause our brains to release oxytocin, which is a chemical that helps promote many kinds of social behavior (Stix, 2014)? Based on the evidence, it seems that our brains are wired differently when it comes to reacting to those who inspire, engage, and motivate us versus those who simply get us to comply.

As Colvin (2015) has pointed out, we are asking the wrong question if we are trying to figure out only what computers cannot do that humans can: “Rather than ask what computers can’t do, it’s much more useful to ask what people are compelled to do – those things that a million years of evolution cause us to value and seek from other humans, maybe for a good reason, maybe for no good reason, but it’s the way we are.” (p. 53)

In my view, we we will continue to want human leaders in the work setting especially in three (and very “human”) areas where leadership is critical: making decisions on business and people issues (specifically around strategic decisions, and on who to select and promote), communicating those decisions and related issues, and inspiring and motivating. The value-add of human leaders is evident in these areas, where commitment rather than compliance is of critical importance to building a high-performance organization.

For each of the above, note the following continuing patterns:
·       Despite years of evidence that statistical methods of selecting job candidates are superior to human methods, almost every firm that I know still wants to see a candidate face-to-face before hiring him or her, especially for higher-end work and/or professional positions.
·       When managers congratulate someone for a job well done, or let them know that they have been promoted, they much prefer to do this face-to-face rather than sending them an e-mail or handing them a piece of paper with the news. The same is true for communicating bad news, such as when someone has to be let go. Evidence suggests that people also would much prefer to hear this information face-to-face from their leaders.
·       When a team needs to be inspired and motivated, the most effective managers engage in face-to-face meetings and “high-touch” actions to lift spirits up and boost morale.

Brynjolfsson, E. and McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. New York: W. W. Norton.

Carlopio, James. (1998). Implementation: Making Workplace Innovation and Technical Change Happen. Synergy Books International.

Colvin, G. (2015). Humans Are Underrated: What High Achievers Know That Brilliant Machines Never Will. New York: Penguin.

The Economist (2016). Who Wields the Knife? May 17, p. 74.

Garvin, D. (2013). How Google Sold Its Engineers on Management. Harvard Business Review.

Gombolay, Matthew C., et al. (2015). Decision-making authority, team efficiency and human worker satisfaction in mixed human–robot teams. Autonomous Robots 39 (3): 293-312.

Hinds, P., Roberts, R. and Jones, H. (2004). Whose Job Is It Anyway? A Study of Human-Robot Interaction in a Collaborative Task. Human-Computer Interaction, 19: 151-181.

Kim, Y. and Mutlu, B. (2014). How Social Distance Shapes Human-Robot Interaction. International Journal of Human-Computer Studies, 72: 783-795.

Kotter, J. (2001). What Leaders Really Do. Harvard Business Review.

Kousez, J. and Posner, B. (2007). The Leadership Challenge (Fourth Edition). New York: Wiley.

Milgram, S. (1963). Behavioral study of obedience. The Journal of Abnormal and Social Psychology67 (4), 371-378.

Parasuruman, R. and Riley, V. (1997). Humans and Automation: Use, Misuse, Disuse, and Abuse. Human Factors, 39 (2): 230-253.

Pugliese, F. et al. (2015). Emergence of Leaders in a Group of Autonomous Robots. PLoS ONE 10(9): e0137234. doi:10.1371/journal.pone.0137234.

Robinette, P et al. (2015). Overtrust of Robots in Emergency Evacuation Scenarios. http://www.cc.gatech.edu/~alanwags/pubs/Robinette-HRI-2016.pdf

Samani, H. et al. (2012). Towards Robotic Leadership. In SS. Par et al., (Eds.), ACHRS Part II. Hedelberg: Springer, pp. 158-165.


Stix, G. (2014). Fact or Fiction: Oxytocin Is the Love Hormone. Scientific American, September 8: http://www.scientificamerican.com/article/fact-or-fiction-oxytocin-is-the-love-hormone/