I spend a lot of time thinking about teamwork; how teams function, how they operate, how they fail or succeed, and how the most successful teams make their success repeatable in the face of changing conditions. This post is my attempt to synthesize what I have learned so far.¹

What is a team? For the purpose of this blog post I will define a team as: A group of people with complementary skills who choose to work collaboratively together towards accomplishing a shared vision and a common objective, within an environment of mutual support in which each team member is empowered to independently set goals, solve problems and make decisions with support from other members of the team, based on an agreed-upon framework, under severe resource constraints.

A team will perform better than an individual in situations where the task at hand can only be completed through:

1. the creation of new knowledge, or
2. a novel and unique application of existing knowledge, or
3. the meshing of different disciplines and subject matter areas, and
4. there isn’t a single person who possesses all the knowledge and skills that would be required to accomplish the task within a period of time acceptable to the parties involved.

The presence of the following phenomena help us identify a team²

1. Interdependence and Social interaction: Team members depend one one another in order to meet the teams goals and objectives, their interdependence results in social interaction through communication with one another.
2. Perception of a group and Commonality of Purpose: Team members agree they are part of the team, and they buy into the team’s purpose, its goals, and its objectives.
3. Favoritism: Members of the group demonstrate positive prejudice towards one another, and discriminate in favor of other members of the team.

In research using Letters-to-Numbers Problems, a task-grouping that combines elements of hypothesis testing, mathematical and logical reasoning, cryptographic reasoning, and collective induction: Groups of size three, four, and five performed better than the best of an equivalent number of individuals, but groups of size two performed at the level of the best of two individuals. Groups of size three, four, and five performed better than groups of size two but did not differ from each other. These results suggest that groups of size three are necessary and sufficient to perform better than the best of an equivalent number of individuals on intellective problems.³

Other research found that: “Groups are better than individuals in making difficult decisions, but the opposite effect is found when decisions are easy. The model suggests that the reason lies in the different assessment mechanisms operating at the level of individuals and colonies. For a difficult choice, solitary ants have a relatively high probability of accepting the worse nest, because they rely on quality dependent acceptance probabilities that differ little for similar nests. Successive comparisons cause these probabilities to diverge, but the ant is likely to make her decision before this slow process has had much effect. Whole colonies, on the other hand, do much better at difficult choices, because they use social information to accentuate the quality difference between sites. Rather than rely on individual comparisons, the colony’s choice emerges from a competition between recruitment efforts. Recruitment generates positive feedback on the number of ants at each site, with the better site slightly favored by its higher acceptance rate. The quorum rule amplifies this difference, allowing the colony to settle on the better site more frequently.”⁴

The importance of the team that is working to build a startup cannot be overstated. The team is the most important aspect of a startup during the earliest stages of its existence, while it is searching for a repeatable, scalable, and profitable business model. Once that business model has been found, the startup has a better chance of surviving team instability. Before that, team instability can be fatal. Also, the traits of the people in that early team determine the culture of the company that might evolve out of that startup.

Lesson # 1 – Every team goes through Development Stages: Bruce W. Tuckman’s model of how groups form is the foundational work on which our understanding of how teams develop and function is built. His paper ‘Developmental sequence in small groups’ was first published in 1965.⁵

1. Forming: “Groups initially concern themselves with orientation accomplished primarily through testing. Such testing serves to identify the boundaries of both interpersonal and task behaviors. Coincident with testing in the interpersonal realm is the establishment of dependency relationships with leaders, other group members, or pre-existing standards. It may be said that orientation, testing and dependence constitute the group process of forming.”
2. Storming: “The second point in the sequence is characterized by conflict and polarization around interpersonal issues, with concomitant emotional responding in the task sphere. These behaviors serve as resistance to group influence and task requirements and may be labeled as storming.”
3. Norming: “Resistance is overcome in the third stage in which in-group feeling and cohesiveness develop, new standards evolve, and new roles are adopted. In the task realm, intimate, personal opinions are expressed. Thus, we have the stage of norming.”
4. Performing: “the group attains the fourth and final stage in which interpersonal structure becomes the tool of task activities. Roles become flexible and functional, and group energy is channeled into the task. Structural issues have been resolved, and structure can now become supportive of task performance. This stage can be labeled as performing.”
5. Adjourning or Mourning: This stage is experienced by teams that go through the process of dissolution; planned or unplanned, voluntary or involuntary. It was added to the preceding four stages in 1977.

Lesson #2 – To sustain success, leadership matters: Anita Elberse conducted research on Manchester United Football Club’s legendary leader, Sir Alex Ferguson. About Sir Alex Ferguson, she writes “Some call him the greatest coach in history. Before retiring in May 2013, Sir Alex Ferguson spent 26 seasons as the manager of Manchester United, the English football (soccer) club that ranks among the most successful and valuable franchises in sports. During that time the club won 13 English league titles along with 25 other domestic and international trophies—giving him an overall haul nearly double that of the next-most-successful English club manager.” Following are some observations based on her research.⁶

1. Sir Alex Ferguson on building an organization that will last, starting with the foundation: “From the moment I got to Manchester United, I thought of only one thing: building a football club. I wanted to build right from the bottom. That was in order to create fluency and a continuity of supply to the first team. With this approach, the players all grow up together, producing a bond that, in turn, creates a spirit.”
2. Successful teams are led by people who set high standards, and hold everyone accountable to meeting and even exceeding those standards: “He recruited what he calls “bad losers” and demanded that they work extremely hard. Over the years this attitude became contagious—players didn’t accept teammates’ not giving it their all. The biggest stars were no exception.”
3. Team leaders, and other team members, should encourage one another as often as possible, especially when a team member’s effort has matched or exceeded the group’s expectations. Sir Alex Ferguson: “Few people get better with criticism; most respond to encouragement instead. So I tried to give encouragement when I could. For a player—for any human being—there is nothing better than hearing “Well done.” Those are the two best words ever invented. You don’t need to use superlatives.”
4. The most successful teams prepare to win. Under Sir Alex Ferguson, Manchester United was always prepared to adapt its tactical play in order to increase its chances of winning the game; how to play if a goal was needed in the late stages of a match, training to force a favorable outcome when the going got tough. They used training sessions as opportunities to learn and improve. Sir Alex Ferguson: “Winning is in my nature. I’ve set my standards over such a long period of time that there is no other option for me—I have to win. I expected to win every time we went out there. Even if five of the most important players were injured, I expected to win. Other teams get into a huddle before the start of a match, but I did not do that with my team. Once we stepped onto the pitch before a game, I was confident that the players were prepared and ready to play, because everything had been done before they walked out onto the pitch.”

Lesson #3 – Great teams learn how to adapt their leadership structure to match the intensity and difficulty of the task at hand: In a study of 5,104 mountain-climbing expeditions that took place between 1905 and 2012 on more than 100 mountains around the world, researchers found that: “In sum, hierarchical cultural values predicted summiting and fatality rates only for group expeditions. Hierarchy did not predict summiting or fatality rates in solo expeditions, providing evidence that group processes are a critical driver of the observed effects.”⁷ In other words, groups characterized by a higher degree of “command-and-control” style leadership – and a lower degree of egalitarian leadership, were more likely to summit but also faced more deaths than groups with a higher degree of egalitarian leadership – and a lower degree of command-and-control style leadership. Commenting on the study, Cecilia Ridgeway, a professor at Stanford University observed that:

1. The crucial factor in a team’s success or failure under conditions such as those the researchers examined is the leader’s competence. Perhaps that competence is compromised in certain situations due to ingrained social structures, norms and behavioral patterns.
2. Egalitarian teams are better positioned to survive in the face of potentially dooming conditions which would overwhelm the single decision maker in a non-egalitarian team. “The reason for that is when they hit these complex situations, under best circumstances they share their information, the ideas bounce off, and they come up with things that none of them would have thought of alone about how to survive.”

Related questions raised by this study:

1. How can a team find an optimal balance between egalitarianism and non-egalitarianism, and
2. How can the team learn to identify the situations in which it should adopt one leadership approach over the other?

Cecilia Ridgeway offers this advice: “The team would have to know itself well and all the members would really have to trust one another and be willing to go with their boss but also pull back from that in a kind of kaleidoscopic way. It’s not impossible but it wouldn’t be easy to do. It would depend a lot on the interpersonal skills, not just the climbing skills, of everybody involved.”

The best teams shift fluidly from one organizational form to another, depending on the circumstance, and depending on the nature of the task at hand. This is a function of the effectiveness of the team’s leadership, and reflects the complex nature of the environment in which startups and other businesses operate today.

1. Teams can be organized such that interaction between each member and the team leader is the key characteristic of how the team gets its work done. The degree of collaboration between team members is low. The effectiveness of a team organized in this way is largely dependent on the effectiveness of the team leader.
2. They may be organized such that responsibilities are shared to a large extent, with each team member exerting significant authority and decision-making responsibility for some aspect of the team’s work. Team leadership is not a shared responsibility. The degree of collaboration is high.
3. A team can also be self-directed, with no official leader. However, such a team will often have one person responsible for coordinating the activities of team members.

Lesson #4 – Great teams are made up of people who each strive for true mastery in their area of specialization. The greatest soccer teams usually have players who each would be selected amongst the very best players in the world for the position that they fill on the team.⁹ To become the best each member of the team must hold a worldview that is keeping with what the Japanese describe as Shokunin kishitsu (????) – translated roughly as the “craftsman spirit” and commit to the following five principles:

1. They must be committed to the art, and committed to always functioning in their role on the team at the highest possible level. Commitment to hard work, and dedication to consistently executing at a high level is what sets great teams apart from their peers.
2. They must aspire to improve themselves and their work, individually and collectively.
3. They must pay attention to the cleanliness and freshness of their work environment. “Work environment” applies to the physical space in which the team gets its work done, but it also applies to the intangible work environment; Do team members feel free to express opinions that might be unpopular without fear of the consequences? Does every member of the team feel a sense of belonging and inclusiveness? Have cliques formed within the team, how does this affect the team’s overall effectiveness?
4. The team’s leader is stubborn and obstinate in the pursuit of excellence. This does not mean that the leader has to be a jerk towards other members of the team, but it implies that the team’s standards for excellence, its vision, its mission . . . those are not sacrificed for the sake of consensus building.
5. They each must be passionate and enthusiastic about mastering their skill, and in doing so they each cause their team to improve and become every day. They must be passionate about their individual and collective pursuit of perfection.

Lesson #5 – A team should strive to become collectively more intelligent than any single member of that team could be acting alone. 

1. Gender diversity helps, or find team members with high social sensitivity. Researchers assigned subjects randomly into teams after each individual had been administered a standard intelligence test, and then the researchers asked the teams to solve several tasks which included brianstorming, decision making, and visual puzzles, as well as one complex problem. The team’s collective intelligence was scored on the basis of their performance on the tasks. Teams with members with higher IQs did not perform much better than the other teams. However, teams that had more women did. The researchers suggest that the higher social sensitivity of women relative to men, explains the higher scores attained by teams with more women. Teams that include people who have high social sensitivity will perform teams that do not.
2. In the face of complex problems, teams that solve the problem together will improve their chances of success over teams that rely on a star individual performer. “Swarm intelligence, which brings to mind the image of a hive of bees working together, requires people to gather information independently, process and combine it in social interactions, and use it to solve cognitive problems, according to behavioral biologist Jens Krause.  It has an advantage over other systems in that individuals get the opportunity to lead the swarm and affect what it does.  Moreover, because people act collectively, they can consider more factors, come up with more solutions, and make better decisions.” There are 4 things teams can do to accomplish this:
   1. Create a common vision,
   2. Leaders should be teachers, not bosses,
   3. Set collective objectives, and
   4. Leaders must be full-time leaders.
3. Create, maintain, and nurture the team’s identity. Together with culture, identity can provide a powerful means of driving performance. Identity is different from culture. Identity tells a team “who we are.” Culture tells the team “what we do” or “how we behave.”¹³

A good team has learned how to make one plus one equal two. A great team has learned how to make one plus one equal three.1

Lesson # 6 – In order to sustain performance teams should be aware of the problems related to intra-group collaboration and intra-group creativity. 

1. The process of collaboration can lead teams to perform worse than an individual. Julia A. Minson and Jennifer S. Mueller found that teamwork can exacerbate overconfidence, and lead team members to reject outside information.¹⁴
   1. The study examined the assumption that collaboration leads to superior decisions than decisions made by an individual.
   2. The study found that teams of two people were more reluctant to change their judgements when presented with new information than an individual working alone. As a result the teams made poorer decisions than they would have if they had more willingly incorporated outside information in their decision making.
   3. The researchers found that teams’ tended to be more confident in the inherent ability of the team to reach a decision without outside input, this led them to be less willing to accept outside information. They suggest that the process of collaboration itself, not the quality of collaboration, makes team members over-confident in their collective expertise and leads to the higher degree of rejection of outside input.
   4. This is especially detrimental when the team is confronting a novel problem or task, but fails to explore alternatives that might lead to an improved decision.
2. The best teams are those in which each member of the team shares the same team mental model, and the team mental model is correct. Beng-Chong Lim and Katherine J. Klein found that team performance is enhanced when team members share the same mental model.
   1. A mental model is “a ‘mechanism whereby humans generate descriptions of system purpose and form, explanations of system functioning and observed system states, and predictions of future system states.’ Mental models are organized knowledge frameworks that allow individuals to describe, explain, and predict behavior. Mental models specify relevant knowledge content as well as the relationships between knowledge components. An individual’s mental model (of, for example, a car, a disease, or a process such as child development) reflects the individual’s perception of reality.”
   2. They found “a direct relationship between team mental model similarity and team performance. This may reflect the context in which the teams that we studied are trained to operate. They are expected to perform under high stress and intense time pressure. Under such circumstances, there is very little time for explicit coordination and communication. To succeed in their tasks (e.g., reacting to an enemy’s ambush), team members must have a shared understanding of the emerging situation and the collective action required. It is precisely in this type of context that shared mental models have been hypothesized to be most predictive of team performance.” Mental model similarity is a measure of the degree to which each team member’s perception of reality differs from the perception of other individuals on the team.
   3. They also found “that team mental model accuracy is also instrumental for team performance. Teams whose average mental models were most similar to experts’ mental models performed better than did teams whose average mental models were less similar to experts’ mental models. We speculate that teams whose mental models were most accurate pursued more effective task performance strategies than did teams whose mental models were less accurate.” In other words, the more correct a team’s mental model, the better the team performed.
3. The team might fail to benefit from the knowledge of its most knowledgeable member because of pressure to conform with the majority position.  In 1956 Solomon E Asch found that even when one member of a team is more knowledgeable than the rest of the team about a specific task, that individual might choose to agree with the team even if the team is wrong, and that individual would have disagreed with the team’s decision under different circumstances. This happens because that individual feels pressure to conform with the team’s position. This is especially the case if that individual’s self-perception of the power-dynamic on the team places that individual in a position of weakness which makes it advantageous for that individual to protect the social relationships that exist between that individual and the other members of the team.
4. The behavioral biases present in individual team members can be amplified within a group setting, particularly, the biases of dominant group members can become amplified by the group. Behavioral psychology is the study of observable and quantifiable aspects of human behavior. Behavioral biases refer to the tendency that people have to behave in certain ways under certain conditions. Behavioral biasescan be divided into two groups; Cognitive biases and Emotional biases. Anindividual’s behavioral biases can interfere with that person’sdecision making, and cause theindividual to makesuboptimal choices. The impact behavioral biases have on the quality ofdecision makingcan be worse in the context of a group asindividual team members might have a multiplying effect on oneanothers’ biases instead of reducing bias within the group. For example, two over-confident people might form a team that exhibits a higher degree of overconfidence than either individual acting alone.
   1. Groupthink occurs when teams make consistently suboptimal decisions because members of the team have a strong desire to maintain harmony within the group. Groupthink can lead the team to consistently reject creative ideas.
   2. Groupshift occurs when the group adopts a position that is more extreme than the position that any of the individual members of the team would have taken. It is the example I described above of a team of individually overconfident people forming a team that is even more extreme in its overconfidence than any single individual member of the team would be if acting alone, under any circumstance.
   3. Deindividuation occurs as the group gradually becomes self-unaware as individual members of the group engage in less self-evaluation and self-critiquing, subsuming their self-awareness in the face of the behavior of the group. This phenomenon is exemplified in the real world through phenomena like lynch mobs, peaceful demonstrations that turn violent for no identifiable or obvious reason, or groups that form spontaneously and cause destruction, say while celebrating a sport’s teams victory in some sports tournament like the FIFA World Cup.
5. As a team grows individuals in the team can perform worse than they would have if they were acting alone. Jennifer S. Mueller found that as teams grow larger the performance of individuals on the team can suffer because the social bonds between members of the team grow weaker. In her study relational loss outweighed extrinsic motivational loss and perceived coordination loss in explaining the tendency for individuals on large teams to perform worse. Relational loss is a measure of the likelihood of one team member to obtain task-related help from another team member when it is needed. Extrinsic motivation is the tendency of team members to perform actions because of the likelihood of recognition from  other members of the team. Coordination loss is the tendency for team members to become less capable of taking synchronistic action towards completing a task as the team grows. She states: “This study identifies that, in modern contexts, coordination losses and motivation losses provide an incomplete story in explaining why individuals in larger teams perform worse. Instead, the current study shows that relational losses play an important role in explaining why individuals experience performance losses in larger teams. Better understanding of process in larger teams moves the field past an obsession with finding the ‘‘optimal team size,’’ a line of questioning which has yielded little understanding about performance in larger groups. Indeed, the optimal team size may be completely dependent upon the exact nature of the group task which may have as many variations as there are teams. Focusing on process also moves the field past blanket recommendations to simply keep group sizes small. The reality is that managers tend to bias their team size towards overstaffing, and theory would suggest that larger teams have more potential productivity that can lead organizations to increased competitive advantage if managed correctly.
6. Individual team members, and thus teams in general can have an implicit bias against creative ideas. The best teams are those that recognize this and introduce mechanisms to guard against discarding creative ideas that later go on to become the basis for phenomenally successful products and businesses. A study by Jennifer S. Mueller, Shimul Mewani and Jack A. Goncalo suggests that this might happen because people and teams try to reduce uncertainty, and creative ideas are those that confront us with extreme uncertainty.

Concluding Thoughts

If you want to go fast go alone. If you want to go far go together.

The Big 5 of Team Work and The Coordinating Mechanisms of Teamwork: In Is There a “Big Five” in Teamwork? Eduardo Salas, Dana E. Sims and C. Shawn Burke provide a helpful summary of The Big Five and the Coordinating Mechanisms of Team Work. I am reproducing part of that summary below.²⁰

The Big Five of Teamwork

1. Team Leadership
   1. Definition: Ability to direct and coordinate the activities of other team members, assess team performance, assign tasks, develop team knowledge, skills, and abilities, motivate team members, plan and organize, and establish a positive atmosphere.
   2. Behavioral Markers: Facilitate team problem solving. Provide performance expectations and acceptable interaction patterns. Synchronize and combine individual team member contributions. Seek and evaluate information that affects team functioning. Clarify team member roles. Engage in preparatory meetings and feedback sessions with the team.
2. Team Orientation
   1. Definition: Propensity to take other’s behavior into account during group interaction and the belief in the importance of team goal’s over individual members’ goals.
   2. Behavioral Markers: Taking into account alternative solutions provided by teammates and appraising that input to determine what is most correct. Increased task involvement, information sharing, strategizing, and participatory goal setting
3. Shared Mental Models
   1. Definition: An organizing knowledge structure of the relationships among the task the team is engaged in and how the team members will interact.
   2. Behavioral Markers: Anticipating and predicting each other’s needs. Identify changes in the team, task, or teammates and implicitly adjusting strategies as needed.
4. Mutual Trust
   1. Definition: The shared belief that team members will perform their roles and protect the interests of their teammates.
   2. Behavioral Markers: Information sharing. Willingness to admit mistakes and accept feedback.
5. Closed-loop Communication
   1. Definition: The exchange of information between a sender and a receiver irrespective of the medium.
   2. Behavioral Markers: Following up with team members to ensure message was received. Acknowledging that a message was received. Clarifying with the sender of the message that the message received is the same as the intended message.

The Coordinating Mechanisms of Teamwork

1. Mutual Performance Monitoring
   1. Definition: The ability to develop common understandings of the team environment and apply appropriate task strategies to accurately monitor teammate performance.
   2. Behavioral Markers:Identifying mistakes and lapses in other team members’ actions. Providing feedback regarding team member actions to facilitate self-correction.
2. Backup Behavior
   1. Definition: Ability to anticipate other team members’ needs through accurate knowledge about their responsibilities. This includes the ability to shift workload among members to achieve balance during high periods of workload or pressure.
   2. Behavioral Markers: Recognition by potential backup providers that there is a workload distribution problem in their team. Shifting of work responsibilities to underutilized team members. Completion of the whole task or parts of tasks by other team member.
3. Adaptability
   1. Definition: Ability to adjust strategies based on information gathered from the environment through the use of backup behavior and reallocation of intrateam resources. Altering a course of action or team repertoire in response to changing conditions (internal or external).
   2. Behavioral Markers: Identify cues that a change has occurred, assign meaning to that change, and develop a new plan to deal with the changes. Identify opportunities for improvement and innovation for habitual or routine practices. Remain vigilant to changes in the internal and external environment of the team.

1. Let me know if you feel I have failed to attribute something appropriately. Tell me how to fix the error, and I will do so. I regret any mistakes in quoting from my sources.
2. Adapted from Group Behavior, accessed on Jun 29, 2015 at https://www.boundless.com/psychology/textbooks/boundless-psychology-textbook/social-psychology-20/social-influence-104/group-behavior-393-12928/
3. “Groups Perform Better Than the Best Individuals on Letters-to-Numbers Problems: Effects of Group Size”, Patrick Laughlin, Erin Hatch, Jonathan Silver, and Lee Boh, University of Illinois at Urbana Champaign; Journal of Personality and Social Psychology, Vol. 90, No. 4. Accessed online on Jun 27, 2015.
4. Takao Sasaki et al, Ant Colonies Outperform Individuals When A Sensory Discrimination Task is Difficult But Not When it is Easy.Proceedings of the National Science Academy of Sciences of the United States 2013 110(34). Accessed on Jun 27, 2015 at http://www.pnas.org/content/110/34/13769.full.pdf+html
5. Tuckman, Bruce W. (1965) ‘Developmental sequence in small groups’, Psychological Bulletin, 63, 384-399.
6. Anita Elberse, Ferguson’s Formula. HBR October 2013 Issue accessed on Jun 27 at https://hbr.org/2013/10/fergusons-formula. Also, Anita Elberse and Thomas Dye, Sir Alex Ferguson: Managing Manchester United. Harvard Business School Case N9-513-051, Sep 2012.
7. Eric M. Anicich et al, Hierarchical Cultural Values Predict Success and Mortality in High-Stakes Teams. Proceedings of the National Science Academy of Sciences of the United States 2015 112(5). Accessed on Jun 27, 2015 at http://media.outsideonline.com/documents/himalaya-expedition-study.pdf.
8. Devon O’Neil, Summit or Death! Accessed on Jun 27 at http://www.outsideonline.com/1928751/summit-or-death
9. On such teams young players must commit to trying to become one of the best, and the example must be set by the more experienced members of the team.
10. Adapted from Garr Reynold’s Shokunin Kishitsu & The Five Elements of True Mastery. Accessed on Jun 27, 2015 at http://www.presentationzen.com/presentationzen/2015/05/the-five-secrets-to-mastery.html
11. Anita Wooley and Thomas W. Malone, What Makes a Team Smarter? More Women. Harvard Business Review, Jun 2011. Accessed on Jun 27, 2015 at https://hbr.org/2011/06/defend-your-research-what-makes-a-team-smarter-more-women/ar/1
12. Wolfgang Jenewein et al, Learning Collaboration from Tiki-Taka Soccer. Harvard Business Review, Jul 2014. Accessed on Jun 27, 2015 at https://hbr.org/2014/07/learning-collaboration-from-tika-taka-soccer/
13. Andres Hatum and Luciana Silvestri, What Makes FC Barcelona Such a Successful Business? Harvard Business Review, Jun 16 2015. Accessed on Jun 27, 2015 at https://hbr.org/2015/06/what-makes-fc-barcelona-such-a-successful-business ?
14. Julia A. Minson and Jennifer S. Mueller, The Cost of Collaboration: Why Joint Decision Making Exacerbates Rejection of Outside Information. Psychological Science, Mar 16, 2012. Accessed on Jun 29, 2015 at http://opim.wharton.upenn.edu/DPlab/papers/publishedPapers/Minson_2011_The%20cost%20of%20collaboration.pdf ?
15. Beng-Chong Lim and Katherine J. Klein, Team mental Models and Team Performance: A Field Study of The Effects of Team Mental Model Similarity and Accuracy.J. Organiz. Behav. 27, 403–418 (2006) Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/job.387. Accessed on Jun 29, 2015 at http://www-management.wharton.upenn.edu/klein/documents/Lim_Klein_Team_mental_models_2006.pdf ?
16. Coincidentally, this is an area of investigation I often pursue when I study early stage startups – does the startup team’s mental model match the mental model that customers have of the startup? What are the implications if it does not? ?
17. Solomon E. Asch, Studies of Independence and Conformity: 1. A Minority of One Against A Unanimous Majority. Psychological Monographs: General and Applied, Vol. 70, No. 9, Whole No. 416, 1956. Accessed online on Jun 28, 2015. I have saved a copy here Minority v. Majority – asch1956. A more accessible discussion of Samuel Asch’s research on Decision Making and Social Conformity can be found here
18. Adapted from Group Behavior, accessed on Jun 29, 2015 at https://www.boundless.com/psychology/textbooks/boundless-psychology-textbook/social-psychology-20/social-influence-104/group-behavior-393-12928/
19. Mueller, J. S. Why individuals in larger teams perform worse. Organizational Behavior and Human Decision Processes (2011), doi:10.1016/j.obhdp.2011.08.004. Accessed on Jun 29, 2015 at https://1318d3f964915c298476-71207924aec76187d46cf4d3ee8ac05a.ssl.cf2.rackcdn.com/or-mueller_2012_obhdp_why-indivdiuals-in-larger-teams-perform-worse.pdf
20. Eduardo Salas, Dana E. Sims and C. Shawn Burke, Is There a “Big Five” in Teamwork?SMALL GROUP RESEARCH, Vol. 36 No. 5, October 2005 555-599 DOI: 10.1177/1046496405277134. Accessed on June 29, 2015 at http://www.uio.no/studier/emner/matnat/ifi/INF5181/h14/artikler-teamarbeid/salas_etal_2005_is_there_a_big_five_in_teamwork—copy.pdf. I have saved the relevant pages Salas et al – Big 5 in Team Work.

As infrastructure improvements to both mobile and broadband technologies continue, the global telecommunication sector continues its transformation process in the upcoming year as well. New revenue streams relating to the apps and services are generated continuously and are being pursued by the operators of these technologies. Throughout 2016, the industry has been focused on plotting the future of 5G. Whilst standards are yet to be defined, we have seen a number of operators and vendors moving aggressively to conduct trials of the technology, as they look to shape how it will be introduced to the market. Yet 4G has also continued to evolve, with LTE-Advanced Pro deploying new features such as higher levels of carrier aggregation and interference management, which provide faster data rates and better performance at the cell edges.

The key global trends impacting the telecoms sector in 2017 and beyond are:

• Development in 5G technology:

5G is a very important area of development for telecom and wireless companies. 5G has been predicated as an enabler of next-generation IoT and M2M applications such as autonomous vehicles and virtual or augmented reality, which will need the low latency it promises. 5G will also be necessary to meet the ever-increasing demand for higher data rates and capacity. The new use cases that will be supported by 5G will depend on proving that new radio interface technologies can deliver the throughput, latency and capacity that will be required, and therefore validating the user experience for new applications will be a key consideration. New and sophisticated testing and validation techniques are already being developed for both the network infrastructure and the interoperability of the devices themselves. By adopting the ‘testing by design’ methodology, it is possible to build in quality and an understanding of system design and performance right from the start.

• Operators accelerate the deployment of NB-IoT:

In 2017, we will see major operators and standards bodies pushing forward with the commercialization of narrowband IoT (NB-IoT). This is a Low Power Wide Area (LPWA) technology that transmits data intermittently, enabling connected devices that use only a small amount of data to operate with low current consumption. This can greatly improve the battery life of IoT devices.

Compared to propriety LPWA technology such as LoRa and Sigfox which operate in the unlicensed spectrum, NB-IoT operates either in the LTE licensed spectrum or in re-farmed GSM spectrum. This means it uses only a narrow bandwidth, leading to spectral efficiency and allowing carriers to prioritize data-intensive internet services and applications. Testing will play a vital role in ensuring the delivery of a high quality service that operates effectively within LTE bands, while mitigating interference from other devices.

As a cellular-based standard, it is critical to ensure the ability of the network to cope with the huge number of additional devices, potentially exceeding that of current networks by an order of magnitude or more. NB-IoT devices can be flexibly deployed and scheduled within any legacy LTE system, sharing capacity and cell-site resources with other connected devices, and even using re-farmed GSM frequencies. This introduces new test challenges due to the diverse frequencies and the potential to interfere with other LTE traffic, as well as a proliferation of IoT device types with very different traffic and application profiles.

• The cyber security war on IoT devices hits closer to home:

In 2017, the number of DDoS attacks targeting IoT devices will increase, as hackers look to exploit service provider and business networks. The recent DDoS attack on DNS provider Dyn, driven by the MIRAI malware, was the world’s largest orchestrated hack via IoT devices. It brought down Twitter, Spotify and Reddit. However, next year will likely see an escalation of DDoS attacks, with hackers targeting higher risk services and institutions with far more severe consequences.

Imagine hospitals being cut off from internet-enabled life-saving devices, or power grids plunged into darkness, leaving towns and cities without access to crucial utilities like heating and electricity. Public transport systems could grind to a halt, and traffic light systems could stop working, causing havoc on roads. However, service providers and enterprises can put measures in place to prevent attacks of this scale. This can be achieved by implementing a modern security strategy which involves stress testing networks using the emulation of malware threats to identify weaknesses which would be targeted by cyber hackers. As a result, firms can protect themselves and their customers from IoT-driven threats.

• High-end smartphones will enable an ecosystem of devices using WiGig:

In 2017, we will see high-end smartphones incorporating WiGig technology – Wi-Fi in the 60GHz spectrum. This will create an ecosystem for short-range applications which can make use of this spectrum, allowing for developments and improvements in wireless video streaming, AR and VR, gaming, and networking applications. The 60GHz spectrum offers much wider bandwidth than the current 2.4GHz and 5GHz bands, which are becoming very congested. The technology allows devices to connect at speeds of up to 8Gbps, though these will typically be over a short distance as a 60GHz signal has a range of around 10 meters at this power level.

WiGig will also bring improvements to home entertainment, allowing media to be shared across devices – for example, streaming content from a smartphone to a TV, or music from a smartphone to a speaker – without the need for and hassle of multiple wires and connectors. Productivity at work could also be improved, as WiGig could enable sharing of resources between colleagues. In 2017 the number of WiGig enabled smartphones will increase as more device manufacturers look to leverage the technology.

• Virtualization accelerates the ‘Lab-as-a-Service’ market:

Virtualization has taken the telecoms industry by storm this year, enabling business to leverage the ability of host software solutions in the cloud. It enables companies to break free from the shackles of rigid hardware solutions, which are typically expensive and a scarce resource. Conversely, virtualized software solutions can be accessed from anywhere at a much cheaper price point, and also have the flexibility to be adapted based on the requirements of a business. These benefits will be reaped by network equipment providers and operators in 2017, particularly when it comes to testing their networks and solutions.

Virtual testing solutions will create demand for ‘Lab-as-as-Service’ solutions, where operators and NEMs can license testing solutions on a subscription basis, rather than paying for physical equipment. This can enable them to centrally manage and allocate their testing resources in a data center or cloud environment, testing services rapidly and cheaply, meaning developments can progress in parallel. It will also enable them to more frequently test their networks against cyber security threats, preventing potential damage to their businesses. We can expect some of the biggest operators in the world to move away from fixed lab environments and take advantage of emerging Lab-as-a-Service solutions as they look to reduce costs and centrally manage their resources.

Author: Mr. Stephen Hire is Vice President, Asia Pacific for Cobham Wireless. He has written this article keeping in mind the advancements in infrastructure, both mobile and broadband technologies that are transforming the global telecommunication sector in the years to come.