I published the first “Hello World” on OrgHacking.com on May 19th, 2014. I quickly settled on a 1 post/week cadence and was able to sustain it for more than 5 years and 275 posts with a little bit of planning and thoughtful queuing.
Sometimes I had several posts already lined up in my head and writing just flowed, and sometimes I had to push myself to figure out what to write about next and then to sit down and write it. Regardless of what was going on inside my head or in my life at the time, I remained committed to the 1 post/week pace.
This month I’m making a deliberate decision to do something different.
When this post will get published, I will already be on an almost month-long adventure, backpacking the John Muir Trail. But this time around, I deliberately haven’t queued up anything for the weeks I’ll be away from civilization and with no internet connection 🙂
I’m excited to see what insights emerge from deliberately stopping (and observing), and remain committed to resume posting again in August.
While “new managers” programs are becoming more and more popular these days, I still see a big opportunity in developing programs that are tailored to new executives — people who are going to be on the executive team for the first time, and actually, if we’re being honest, will most likely benefit many existing executives. And being a business school grad, I’m definitely not talking about what’s typically offered in their “executive programs”.
I’m making a conscious decision to stay within the existing hierarchical paradigm in my framing here, but in many ways, this is an advanced “How can we work better together?” curriculum. Cross-functional work is becoming more and more the norm in organizations but remains the exception rather than the rule for many people in many roles. That changes when members join the executive time, and for many, this is the first time where their primary, long-last peer team is truly cross-functional.
If I were to ever design a curriculum that’s meant to prepare people for doing this type of collaborative cross-functional work, these are the primary sources that I’d draw content and inspiration from:
I get excited when I notice that I’ve changed my mind about something. As is the case for me here.
Having a thoughtful career levels system (which ties to a compensation levels system) is often an important ingredient in creating both a clear roadmap for professional growth and a fair compensation structure.
A key question in designing such a system is which of its components should be made transparent to all member of the organization. In this post, I’ve explored the spectrum of transparency around the compensation component. Today, I want to focus more on the leveling itself.
We can envision a transparency spectrum around levels with the following milestones (multiple in-between stages can exist as well):
The member’s manager knows their level but the member doesn’t.
The member and the manager know the member’s level but anyone else (outside the reporting chain, finance, and HR) doesn’t.
Everybody knows everybody else’s level.
Google is a good example of an organization that’s on one far end of the spectrum (#3).
About three years ago, I used to believe that a more middle-ground position (#2) made more sense, for the following reasons:
Full level transparency will result in less equitable/meritocratic conversations, giving more weight to the opinions of the more senior participants, given that their seniority is known (this has been an issue at Google).
Full level transparency will result in counter-productive social-comparisons and encourage a more promotion-centric culture (also an issue at Google).
While both arguments are probably true to an extent (positive correlation between cause and effect), I’ve since then changed my mind and now leaning a lot closer to #3. Here’s what my experience since then taught me:
Seniority can still be inferred and/or established in many other ways. Thoughtfully managing power and its implications requires much deeper cultural work.
People who assess their self-worth and sense of accomplishment by social-comparison/external validation will continue to do so. Hiding levels won’t make them stop. Real investment in helping them grow out of the way they currently make sense of the world might.
Creating a “knowledge imbalance” where some individuals have access to some information when others don’t, generate a trust deficit and a tax on the culture.
Maintaining the “knowledge imbalance” has a real operational tax: complex permissions, data anonymization, double-checking data before sharing it broadly, etc.
I expect my opinion to continue to evolve as I continue to learn, but it’s nice to pause and notice the change.
She identifies three key reasons why we all struggle with communication:
We think we know how communication works, and we’re wrong (first image above)
When communication fails, we blame everyone but ourselves
The way communication works is fairly-complex (second image above)
She illustrates the latter with a good example:
Thought: I have a vague feeling of hunger and a desire for something tasty.
Encode: I write a message on Slack, “I’m so hungry.”
Transmit: I drop that message with a Cookie Monster gif into my team’s Slack channel.
Receive: People on my team get a message indicator on Slack.
Decode: My teammates read it and see my gif.
Interpret: One person thinks I want cookies. One person sees it’s 11:30am in San Francisco and assumes I’m ready for lunch. One person thinks I’m obsessed with Cookie Monster gifs because it’s the fourth one I’ve posted in three hours.
Understanding: One person thinks I have a sweet tooth. One person thinks I should probably eat a bigger breakfast. One person wonders where I find all of these amazing gifs.
In between our thoughts and the way they are eventually become someone else’s understanding, many things shape and morph the signal:
The emotional state of the participants (e.g., angry, happy, sad).
The relationship between the participants (e.g., siblings, married couples, coworkers).
The expectations of the participants (e.g. you assume positive intent, they expect bad news).
The context of the participants (e.g. confused, busy, distracted, impatient, underprepared, biased).
The language abilities of the participants (e.g., you don’t know the language of the speaker).
The capabilities of your transmission medium (e.g., unreadable handwriting, bad wifi for video calls).
Literal noise (e.g, the cafe where you’re talking is crowded and loud).
And finally, she offers some strategies to improve our communication in the workplace (of varying quality, imho):
Expect communication breakdowns and view them as opportunities to refine your message
Feel responsible and accountable for your communications being successful
Tailor your message and your medium to your audience
Reading Manuel’s post, it became quickly apparent to me that it contains some real gems. Yet something in the overall editorial and logical flow didn’t quite click for me (reminded me a lot of my grappling with the Heifetz book). So it took me longer than I expected to get to a distilled version. It’s still far from perfect, and I’m sure I’ll come back to this topic in the future, but it’s a better baseline to work from.
Manuel’s piece covers several topics:
The distinction between accountability and responsibility
The accountability process
Reliable promises and predictable results
The distinction between consequence and punishment
Support & rescue
Scaling accountability for multiple teams
Pre-requisites for accountability
As you can probably tell, that’s A LOT to digest. I’m going to focus on 2&3 here which are the core elements, in my opinion. #6 is also key but already has good coverage here, here, here, and here.
What is accountability and how does it work?
Collaboration is a network of people making promises for delivering certain results to each other. The promises make the results possible, but can never guarantee them.
When collaborators repeatedly uphold their promises, trust is established.
When trust is present, people interact with each other without cautious defense mechanisms that drain part of their energy and the collaboration becomes more and more effective.
However, when collaborators repeatedly don’t achieve the intended results, trust erodes and can easily turn into resentment.
Accountability is a refinement of the collaborative social contract that’s intended to help all parties involved maintain trust: the collaborator making the promise authorizes the other party to evaluate the result they’ll deliver, and enforce a logical consequence for that result. The consequence was ideally agreed-upon when the promise was made.
The key things to note here are the clarity on the role that accountability plays in supporting effective collaboration efforts, and its directionality: accountability is initiated by the collaborator that’s making the promise, authorizing the other party to hold them accountable.
Another powerful lever in delivering predictable results that reinforce trust is making reliable promises in the first place, which reduces the need to utilize accountability. Part of it has to do with being able to accurately assess our own level of skill/capability in regards to the particular result that we’re going to make a promise about. A notion that traces back to Andy Grove’s Task-Relevant-Maturity and most likely even further than that. But the other piece of that puzzle is being mindful of the level of predictability of the context in which we’ll be operating (inversely correlated to its complexity) which can also put a strong constraint on the type of result that we should be promising:
I find the distinction between these 4 types of results: input, output, outcome, and intention; and the way they interact with the context extremely powerful. Recognizing the relationship between result type and context type seems to be missing from many of the conversations around goals setting, accountability and effective collaboration.
It’s funny and sad at the same time when a Dilbert cartoon comes to life in your company. As Brendan Schwartz, CTO of Wistia recently learned.
I’ve explored goal setting in the past and it’s definitely a topic that’s due for a more comprehensive post with my updated thinking on it. But I’ll keep this one short and more anecdotal. Schwartz’s piece is super short as it is so there’s not much summarization or synthesis to be done:
At least in Tech, it’s become accepted as conventional wisdom that people are most motivated when their goals are “stretch” goals — targets that lay just beyond the rational limits of what’s possible. But Schwartz found the opposite at Wistia. Setting stretch goals led to two counter-productive symptoms:
Short term thinking: stretch goals put the team in a continuous catch-up mode, desperately looking for ways to meet the (short-term) key results, often times, at the expense of values and long-term consequences.
Demotivation: consistent with some supporting academic literature, the Wistia team found that when everyone sets goals they know they can hit with hard work, it creates a cycle of positive reinforcement, keeping people motivated and marching forward. Motivated employees don’t just sit back on their laurels after they achieve their goals. They set new ones, exceed those, and expand their view of what is possible along the way. As time goes on, they accomplish bigger and better things.
Schwartz’s conclusion really hits the nail on the head:
Setting intentionally out-of-reach goals reflects a cynical way of thinking about human nature and motivation. It’s driven by a belief that people are lazy, and by default won’t be ambitious or creative or try to do more than they think is possible. Goals, therefore, become a way to correct for that laziness. This way, even if people fail, their output can still be decent.
There’s definitely some missed nuance here. What Schwartz is describing sounds more like “impossible” goals than “stretch” goals, which were always described to me as “possible but not probable”.
The challenge in this definition is that it’s a judgment call, to begin with, and assumes perfect ability to predict the difficulty of a goal which is a fool’s errand in a complex world. So we can certainly make the case for the “art of goal-setting” but we can also question the value of the practice to begin with.
Thoughtful behavioral design is a required prerequisite for the effectiveness of any organizational policy or program. Mindful use of defaults is a hallmark of good behavioral design. But not all defaults are created equal.
A team led by Jon Jachimowicz set out to conduct a meta-analysis on the effectiveness of defaults and summarized their academic paper in this more readable blog post:
Overall the team looked at 58 default studies with a total sample size of 73,675 participants. On average, they found defaults were a strong choice architecture tool, shifting decisions by 0.63 to 0.68 standard deviations: in decisions where there are two possible options, the option that is preselected is on average chosen 27 percent more often than the option that is not preselected. Given that other behavioral interventions tend to shift decisions by 0.2 to 0.3 standard deviations, defaults, on average, were two times more effective. But averages only tell part of the story, since the effectiveness of specific default interventions varied greatly. From significantly more than average effectiveness to not effective at all.
They reflect an implicit endorsement from the choice architect.
Staying with the defaulted choice is easier than switching away from it.
They endow decision makers with an option, meaning they’re less likely to want to give it up, now that it’s theirs.
In their analysis, they found that studies that were designed to trigger endorsement or endowment were more likely to be effective. The other aspect that impacts the effectiveness of default is the intensity and the distribution of the decision makers’ underlying preferences. When decision makers care less about a particular choice, a default may be more persuasive in swaying their decision. Likewise, when preferences within a population are more varied, such that some people may have preferences that align with the default, but many people may not, then a default may be less effective.