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Teams and their leaders may have conflicting networking goals. That needn't be a problem, but often is.
When it comes to teamwork, the role of team leaders is not only about setting goals and allocating resources, but also organising the work of their team members and forming networks among them, such as who works with whom, where and when. To get their work done, one team might need a leader to coordinate otherwise unconnected team members (for example, with separate, specialised sub-teams such as task forces), while another team might need to be more cohesive, tightly knit group collaboration.
The leader, therefore, often walks a tightrope between acting as a broker to channel information and connections from multiple unconnected team members and creating a highly cohesive group to shield the team from distrust. Leaders play a balancing act between brokerage and closure. But this presents a dilemma. In addition to managing the team, a leader also has other aims, such as furthering his or her own career. He or she may use and abuse "the power of networks" to manipulate team members and advance up the corporate ladder at their expense (for example, by "dividing and conquering" and playing sub-teams against each other, or not diffusing information and using it for his or her own advantage).
This means leaders might make certain trade-offs for their own or their team's benefit, which can affect their team's performance. Teams and their leaders may thus be at odds, creating a social dilemma.
Broker or protector?
I studied this phenomenon in my latest theoretical research paper with Fabrice Cavarretta and Matthias Thiemann, "Task complexity and shared value orientation: exploring the moderators of a social dilemma in team social networks", to explore the costs and benefits of brokerage network strategies within teams and what we call closure strategies, i.e. maintaining tighter connections to keep the team cohesive. We show that the extent of the dilemma between the two depends on the complexity of the team's work, i.e. how dependent they are on one another, and whether the team is individualistic or collectivistic in its nature.
Unlike previous research, our theories focus on the divergence of interest between the team leader and their team, depending on various characteristics of the latter.
Based on research of the literature in this area, we propose Figure 1 as a visualisation, which shows different levels of performance for team leaders and their teams, depending on their level of brokerage or closure:
Figure 1. Social dilemma as the multilevel gap between optimal structures for closed teams
We will use this figure in future empirical work to try and test it. As shown, the benefits for the leader and the team vary. The gap between the needs of the team leader and those of the team is visible in our chart for brokerage or closure (which can be, in most cases, considered opposites). An optimum point for the leader is never exactly the same as for the team, as seen in Figure 1. When a broker leader (as seen in (a)) performs best, the team's performance (c) starts to decline in performance beyond a certain point. When the cohesive leader (b) performs best, the team (d) is far from its best performance. The social dilemma can be seen in these gaps.
Brokered and cohesive teams also have other characteristics to consider, in terms of impact on performance. For high task complexity, when multiple team members are required to work closely together to meet a challenging common goal, the optimum team structure is a cohesive one. In this way, the team members can trust and rely on each other and act quickly. As brokerage by the team leader increases, the team that is heavily reliant on one another to complete a task starts to crumble and the social dilemma – the rift – between the team leader and team increases.
For individualistic teams in which team members value the individual's autonomy, brokerage is accepted and even possibly encouraged. Leaders can get away with brokerage and self-serving behaviours in this environment, although it is precisely then that more closure could help the team deal with its challenges.
Nationality can have an impact on the individualistic/collectivistic nature of teams. Americans, for example, are more likely to be more comfortable in individualistic teams and other nationalities which are more accustomed to a well-defined group structure, like Japanese, may be more comfortable in collectivistic teams.
Social networking strategies for the leader
Team leaders, especially in some contexts such as when there is a high complexity of tasks, quickly find out that networking for self-serving purposes is not a viable option for a successful team, although they may personally benefit from it.
Solving the dilemma would imply that the leader actually goes against his or her own interests from time to time or that team members decide that they should work together and put pressure on the team leader. Human resources departments or bosses can also enforce "rules" directing team leaders to focus on the needs of the team or find ways to reduce the tension between the team members and the leader.
Organisational adjustments
Organisations can act to ensure that leaders and their teams are both successful through three levers: hiring, developing the right culture and aligning incentives. The first solution should be putting the right people in place, hiring and promoting managers who are aware of these issues and who make the success of the team their own success.
The second option is to cultivate a culture that fosters collaboration, reinforcing this culture with training and teamwork exercises for example. Team leaders should understand when there is a problem and make it their duty as a manager is to solve social dilemmas, even if it goes against their own best interests.
Finally, team leaders are often motivated people, and aligning the leader's incentives to reflect the success of the team should be a priority. Determining how to best incentivise them – with rewards or punishment – depends on other factors like national and company cultures. Understanding the nature of their teams, their goals and primarily themselves, team leaders need to find the balance between their own networking needs and those of their team to achieve the best possible team performance.
Frédéric Godart is an Assistant Professor of Organisational Behaviour at INSEAD.
Whether predicting demand for a product or forecasting spot prices for a resource or currency, we invariably seek out subjective opinions - expert viewpoints - to assist in the information gathering process in order to make informed decisions.
At times a decision maker may have access to plenty of relevant historical data on which to establish robust statistical models. However, in many instances, even with such data, an overlay of human judgment is inevitable to counter ever-changing conditions. In predicting the demand for a new fashion product, for example, it is important to account for not only issues such as rapidly changing tastes and competing products, but also the possibility of inducing demand for the product that might not otherwise exist.
Thus, even with the advent of new approaches such as artificial neural networks, fuzzy logic and machine learning, human judgment remains a key element in making predictions.
The impact of experts' interrelationships
It makes sense to assume that the more judgments and viewpoints we seek, the more information we will amass and the more informed our final decision will be. While it is tempting to give greater weight to the opinions of experts who we feel are more trustworthy or have a higher level of expertise, research suggests that simply averaging the collective opinion of a group of individuals will give a much more robust finding. In other words, unless you have a very good reason to believe otherwise, you should give equal weight to all experts, ignoring their levels of experience or knowledge.
What cannot be ignored, however, is the level of dependence between their opinions or forecasts. If two or more experts share similar information, use similar tools and techniques, talk to each other and generally move in the same circles, they will tend to have very similar opinions and the amount of information garnered from their insights may be considerably less than could be expected. There is also a greater chance their forecast range will underestimate uncertainty.
The risk of too little information
The range, or spread, between individual subjective forecasts (which often come in the form of point forecasts) gives decision makers an indication of how uncertain the final price or demand for the product might be. When predictions are more spread out, it could be surmised that the uncertainty about the underlying quantity of interest is higher. On the other hand, as the forecasts become more closely clustered, businesses may speculate that there is consensus and hence less uncertainty.
However, this fails to take into consideration the impact of the level of correlation between the experts. If there is a high dependency amongst forecasters, businesses could find they are working with far less information than they see. The opinion of 10 similar experts, for example, may give the same information as that of two or three independent sources, and the spread of their individual predictions may considerably understate uncertainty, thus increasing the likelihood of businesses making costly mistakes.
Avoiding costly mistakes
To address this, we developed a new parsimonious and practical approach, building upon previous work on combining opinions that not only takes into consideration the correlation between experts but also minimises the estimation of model parameters to just one. In "Assessing Uncertainty from Point Forecasts", Dana Popescu, INSEAD Professor of Operations Management , Zhi Chen, a PhD student in Decision Sciences at INSEAD, and I test our model against existing methods. The study used the example of a newsvendor faced with the typical problem of identifying how much stock to order ahead of the selling season without knowing demand. If the vendor under-orders they will forgo potential profits, if they over-order they will be left with inventory which goes to waste – losing money. Taking into account the unit cost, selling price and salvage value, we compared the accuracy of the different approaches by testing the impact of each one on the order quantity and expected profit. What we discovered was that while many of the existing methods tended to be overconfident in their assessments, our approach (which took into account the correlation between point forecasts) erred on the side of caution. It resulted in orders which were biased in a less costly direction, and led to an increase in expected profits which, in some cases, exceeded 20 percent.
More informed decisions
Clearly, simply ignoring the dependence among experts is not a good option. Despite all efforts to create a group of independent experts, some form of dependence between their forecasts is inevitable. Previous research has noted an average correlation between business sales forecasts by managers of 0.6 while other research included the observation that a more articulate and assertive participant in a forecasting deliberation process could sway colleagues to such an extent that the final decision represented their preferences rather than the collective wisdom.
Loss of information due to dependence between experts cannot be overcome by simply increasing the number of experts, even to an extreme. To better assess uncertainty, it is important business managers assess the correlation of experts when weighing up information, opening the way for better and more informed decisions.
Anil Gaba is a Professor of Decision Science at INSEAD, the Orpar Chair Professor of Risk Management and the Academic Director of the INSEAD Centre for Decision Making and Risk Analysis.