Stanford Meets Europe
Summary of YLD's 1st Leadership Workshop
Although I haven't been yet a manager or senior executive in any company, I grabbed the unique opportunity to participate in the first workshop of YLD's Leadership Series in partnership with Stanford University's Graduate School of Business. In the following, I summarise the workshop, my key learnings, and further exciting insights1.
The workshop was led by Francis J. Flynn, Professor of Organisational Behaviour, who received his Ph.D. from the University of California, Berkeley. His research focuses on employee collaboration, work group dynamics, and leadership in organisations. The Business School is one of seven schools at Stanford University and one of the best in the world. Its programs aim to enhance and deepen the understanding of management and create innovative leaders who change the world.
This series of workshops focuses on how to scale leadership excellence, this time in four interactive sessions of 80 minutes each, addressing topics such as effective decision-making, collective wisdom, employee motivation, and collaboration. Several breaks from time to time allowed the participants to deepen their knowledge in discussions and gain interesting insights into the challenges faced by other companies. The majority of the attendees enables teams to do their best work and were from various globally successful companies — like Salesforce, Google, DAZN, and JPMorgan. Since I am trying to do this as well, I fitted in quite well even without leading a team. Intimidating though.
The Key Learnings
Make Sound Decisions
In this first session, I have learned two things in particular that overlap in some areas: question the data provided and be aware of potential biases. So probably everyone knows that you should rely on data rather than your gut feeling when making decisions. However, this is easier said than done. Because even if data is available, it can still favour certain decisions depending on how the data is prepared, selected and presented.
For this reason, when you find yourself in a situation where data is presented for decision-making, challenge the data. Why were these specific metrics chosen? How was the data collected? Have all possible conditions been considered? Try to minimise biases as much as possible to make the more objective decisions.
So, what are potential biases, apart from limited resources (time, staff & money), loss aversion and novice-like overconfidence? I would like to introduce three key biases that have definitely opened my eyes. I have stumbled upon them a few times in my life, but have never been aware of them like this. Let's start with the Confirmation Bias, which essentially describes the sample on a dependent size. In other words, one seeks information that supports one's own opinion and omits those that do not fit. Next, the Misconception of Chance which is described as 'people expect that a sequence of events generated by a random process will represent the essential characteristics of that process even when the sequence is short'2. That's a tough one because it can trigger the first. Last but not least the Default Bias. Observations and experiments in various cultures have shown that the likelihood of an option being chosen can be significantly increased by setting it as default. Oops, right?
Harness the Collective Wisdom
Well, who doesn't know these frustrating & inefficient meetings? In the second session, we looked at how to make group decisions in meetings more efficient. How do you do that? Vote by show of hands? Nice idea, equal chances for everyone, right? In fact, this often leads to strong biases through anchoring effects and thus is not that successful.
Making assumptions is only an expression of lacking knowledge — asking the right questions may help.
The actual problem is that you don't know what others know. This is where biases like sympathies and values come along. Therefore, assumptions are made to fill gaps in information. If everyone makes these assumptions for oneself, without sharing them with others and also labeling them as such, the chaos is perfect.
As a result, the following approach was introduced: information is collected from each individual, then gathered, processed and presented by one person. It is important to share all the information. On this basis, discussions can be started and decisions made. Other ideas that may be helpful in this context: creating competing subgroups, having outside experts attend, and not always letting the same one be the devil's advocate.
After lunch, we tackled the topics I had been looking forward to the most. I've been studying collaboration for some time now, and I'm also trying to boost collaboration across teams and departments. Not the first time that day, Stanford opened our eyes and helped us view the problem from a different perspective.
To be honest, I have rarely perceived collaboration as helping each other but rather as knowledge sharing and involving different viewpoints and experiences. If you look at it from a different angle and dive a little deeper, one thing becomes immediately clear. People like to help but are not very keen on asking for help. The reason for this is that you have been conditioned. Expertise is associated with status and you don't want to appear weak. Ah, of course, people fear rejection. In fact, we are clearly overestimating the likelihood of someone saying no. According to Francis even at proposals.
However, this is even more complex. Something else we don't like is offering help without being asked. The question needs to come first. This needs to become part of the culture, where leadership sets a good example and asks others for help. Simply to underline that this is not bad but encouraged. Perhaps one should also think about reflecting this in performance reviews rather than just the willingness to help. I'll address that in my own review pretty soon.
It was funny that the topic of open space offices was also discussed. Anyway, everybody agreed on how important it is to integrate physical aspects into the design of the collaborative environment, because not all can be completely covered by virtual space. Exciting concepts here are circular buildings such as the Apple Park, where people tend to meet more often randomly. Or, at a smaller scale, the idea of placing several coffee machines on a round island, so that folks talk to others more frequently. As you can see, the basic idea is to promote cross-company interactions without networking events where people talk mainly to primary contacts. It is all about secondary contacts and chance meetings.
Currently, I'm reading Daniel Pink's book again, so I'm familiar with the intrinsic motivators of autonomy, mastery & purpose. Even with the impact of expected and unexpected extrinsic rewards. Nevertheless, there were a lot of new things to learn in the last session as well. There are three aspects that can reduce or even eliminate motivation: I can't do it, I won't get it, it's not worth it. In other words: morality, competence, and incentives. But how do you manage to motivate creative minds in the long term? Daniel Pink has already shown that intrinsic motivation plays a crucial role here, even if we largely underestimate it in others.
The aspect of Relatedness that was new to me and interesting. According to my research, it probably refers to Edward Deci & Richard Ryan who inspired Pink's work. Anyway, relatedness can be described by relationships and responsibility. Interesting, right? As it turns out, the responsibility we feel towards our fellow human beings is an intrinsic motivator that should not be underestimated. Nevertheless, instead of highlighting the impact on the lives of others, too many attempts are made to highlight the benefits for the individual. If you think about it that way, it also becomes obvious why it's effective. It feels like being part of something bigger.
Summing up was quite difficult here because this day left so many impressions and lessons learned that I actually need this article to recap and memorise everything. Everyone who participated knows that I outlined it only partly. I've read over a dozen books on these topics over the past few months, but I still experienced a lot of new things opening my eyes and adding new perspectives.
Thanks a million to Nuno Job and the YLD team for having me and supporting me in my personal development. Great company, people, organising, and venue — it was a truly unique experience that builds on my first contact with Stanford. Be proud of yourselves! The even more relaxed conversations during a yummy dinner capped the day. In this respect, I can only reconfirm the statements made in my last article and also want to add that YLD is a place where brilliant colleagues fuse with friends. It was great fun to witness this and to be a part of it for one day.
Furthermore, I would like to thank the Stanford team for their invaluable insights into the topics that fascinate me most. Also for his countless stories that linked theory and practice. A great enrichment and in any case recommended being there next time or dealing with the Stanford mindset in another way.
- Not only because we were told to share it with at least three others.↩
- 'Judgment under Uncertainty: Heuristics and Biases', Amos Tversky & Daniel Kahneman, 1974↩
- The 1st article published on a Monday. To my grandpa, who would have turned 78 today.↩