El caso especial de los consejeros ejecutivos

Our findings confirm our three hypotheses and demonstrate that participation in the business simulation leads individuals to behave in a manner inconsistent with their stated, baseline risk preferences. We found some changes in risk perceptions and attitudes towards employment and salary options, post simulation. Also, while the inability to align personal and team risk preferences leads to poor team performance and lower self-confidence, we found that contrary to our assumption, these factors do not lead to increased team conflict amongst poorly performing teams. Losing teams do make risk-averse decisions as a group but individuals on the team clamor for more risk if they were operating alone.

Summarizing our major findings from equations 1-4, there are distinct differences between how winning and losing teams behave and operate in a dynamic, competitive

environment. Losing teams do not have much agreement on team strategy direction and tend to adopt a more risk-averse approach as a group. Individuals on losing teams have strategy

disagreements but do not share their concerns with the entire team in an effort to avoid conflict. Not only do last placed teams avoid conflict, they change strategies based on internal inclinations versus external competitive indicators. These individuals also need a combination of competitive and internal team discontent to make the tough decisions that fall outside their usual comfort zones.

First placed teams behave in an opposite manner, thus giving us a blueprint on how teams should perform when operating in an ever-changing business environment. A key educational outcome from our simulation is that the losing teams were not oblivious to the difference in performance and attitudes. Upon reflecting on their performance during the feedback session following the end of the simulation, individuals in losing teams acknowledged that they should

have adopted a more risk-seeking approach to decision making, including having more open discussions on team strategy. They also stated that if they were to partake in the simulation again, they would spend more time formulating a strong initial strategy and analyzing competitor moves. Somewhat sheepishly, losing teams also admitted that they were overconfident in their own abilities and underestimated the dynamic nature of the simulation. After falling to the back of the leaderboard early in the simulation, some lost hope for winning and redefined winning as not finishing in last place. This led them to try many different strategies to avoid being in last place, giving us the ‘yo-yo’ effect we find in our results.

3.12.1 Relevance To Entrepreneurship

These attitudinal differences are one of the critical findings of our research, because they may lead to a method for quickly pinpointing losing teams. Simply by looking at the first two rounds we can quickly identify teams that have poor decision-making processes based on the specific correlation coefficients from table 6. We see that the potential for being the losing teams is grounded in severe disagreement on individual versus team risk preferences. As the simulation progresses, these last placed teams find themselves struggling to recover (in 4th or 5th place), collectively get more polarized and the disagreements in team versus individual risk attitudes become more pronounced. Since all teams start off in the same place with the same financial metrics and no new products, we can confidently state that poor team performance is a direct result of poor interpersonal dynamics and team decision-making.

Conversely, when we look across the correlations, we notice that first place teams experience disagreements in risk preferences earlier in the simulation, which helps them openly debate, and agree upon a group risk attitude. This allows the top teams to move forward “in

sync” better than their competitors. These results also hold true from the results for hypothesis 3 and highlight the importance of aligning individual and team risk attitudes in a business setting.

It is here that we can draw a parallel to entrepreneurship and startup teams. Most university startup teams tend to be small, results focused, and operate under severe pressures (time, funds etc.) and constraints. The one critical issue that these founders or their faculty advisors tend to ignore is individual risk preferences and whether the team can quickly align its risk preferences moving forward. Differences in operation strategy (for example), essentially arise from differing risk preferences and how much risk an individual is willing to take operating alone. We not only can simulate many of these pressures and get insights into team preferences, we can also allow the team to gauge how they performed under pressure. Politis & Gabrielsson (2009) found that business failure due to poor performance is a valuable source of learning and we contend that such an exercise can be a valuable resource for academic incubators. We validate that risk attitudes play a critical role in determining team success where one strategic error could prove fatal for a startup. Thus, the importance of aligning risk preferences and embracing conflict to enable better decision-making cannot be overstated.