Through not-the-most-scientifically-rigorous method (a survey on Twitter) Webber collected ~150 responses on different communities of practices capturing information about their community size, number of leaders and frequency of meetings. The results are captured in the following graphs:
Business communities of practice mimic natural social communities, sharing a similar fractal distribution of size. The intuition of drawing on insights from natural social communities when addressing issues with business organizations have existed for a while and provides some additional evidence for the validity of that analogy.
There’s a notable community threshold at about 40 participants. Below that threshold it’s more likely to see purely democratic (leaderless) communities and they meet fairly frequently (monthly or less). Above that threshold it’s more likely to see more definitive “leader” roles and meeting frequency increases substantially.
The 40 person threshold was interesting to me as it’s notably smaller than the Dunbar Number that’s estimated to be around 150. While it’s not discussed in the article, I’d hypothesize that the looser nature of the business community leads to the need to introduce structure in order to maintain them sooner (at smaller scale).
The other piece I want to cover today is by FabRiders titled:
It notes the recurring conflation of terms between “community” and “network” and posits that the short longevity of many efforts to create self-sustaining peer expertise exchanges is a result of unrealistic expectations for having those networks become real communities.
After providing a few “classical” formal definitions of community, they make a minimally compelling case for “network“ being a better framework for thinking about these groups than “community” arguing that:
We should not have expectations that a group of people coming together to share expertise will form a community, and in particular, that it will become self-sustaining… [peer expertise networks] provide an ability to establish connections that can deliver knowledge sharing in ways that strengthen the communities we are aiming to serve.
While there’s definitely some merit in this argument where the knowledge gain through that group is often used elsewhere, earlier in the post Paul Jame’s definition of a community is also covered:
It is a group of people who are connected by durable relations that extend beyond immediate genealogical ties, and who mutually define that relationship as important to their social identity and practice.
For many of us, our professional identity plays a big role in our overall sense of identity. While the criticism of it playing an outsized role is mostly justified, the activity that we spend such a large portion of our waking hours engaged in should play a meaningful role in our identity.
So while other references to “communities”, for example around certain product brands, easily fail both tests, the professional community seems more complex.
The distinction between a peer expertise network and a community is helpful, and I wish that the authors had provided clearer definitions of each. Yet as the article does point out, regardless of how the group is labeled, a clear and focused group purpose will be essential to its success.
Psychological safety is one of the hottest terms in the People field in recent years, yet there’s still a lot of ambiguity about what it means and how to create it. Shane Snow took a good stab at advancing this conversation in:
Snow starts off with Edmundson’s definition of “a shared belief held by members of a team that the team is safe for interpersonal risk-taking.”
A big chunk of the ambiguity around psychological safety stems from the various ways in which “safe for interpersonal risk-taking” can be interpreted so he offers two powerful distinctions to make reduce some of it:
Safety is not comfort (and discomfort is not danger)— You can be safe and uncomfortable. As a matter of fact, those are the required conditions for growth experiences. He illustrates that using the 2×2 above and offers a simple example of working with a personal trainer at the gym — you’re safe, but uncomfortable. Conflating the two terms leads to an overly broad definition of safety which reduces psychological safety: you view other’s disagreement with you as risking your safety, and/or are afraid to voice your disagreement in order to not jeopardize the safety of others. He references Haidt and Lukianoff’s work which discusses the downsides of mistaking cognitive friction for violence at length.
Not all interpersonal risk-taking is good — interpersonal risk-taking for the sake of interpersonal risk-taking is not helpful. Intentionally not delivering on a commitment, or shouting down someone who says something uncomfortable requires taking an interpersonal-risk, but it’s not taken in support of the overall benefit of the group so it’s not helpful. Suggesting a new idea, or voicing your disagreement also requires taking an interpersonal risk. But that risk is taken in support of the overall benefit of the group.
From there Snow goes to explore the relationship between psychological safety and trust:
I’m not sure that I’m bought into this distinction where trust is an attribute of the relationship between two people and psychological safety is an attribute of relationship between the whole group, since the relationship between the whole group is the sum of the relationship between every two people in the group. However, exploring that analogy does lead him from what psychological safety is to how it gets created, and the critical role that a benevolent or charitable disposition plays in that process.
But as we think about the behaviors that suggest high psychological safety, a charitable disposition seems to be insufficient. Here, I want to add and extend Snow’s work and I think the hint for the missing ingredient can be found in Google’s definition of psychological safety: “team members feel safe to take risks and be vulnerable in front of each other”. A charitable disposition gives you guidance on how to respond to others, but doesn’t provide you with much guidance on how to engage/show up yourself. This is where vulnerability and the importance of personal disclosures come in.
If I was to sum up the behavioral guidance for creating psychological safety, it would be: show up vulnerably; respond benevolently and charitably
Rephrasing some of Snow’s examples, this is how it’d look like:
Admit your mistakes; don’t hold others’ against them personally.
Speak up if you think something is wrong; don’t use others’ speaking up against them.
Ask for help when you need; support others when they ask for it.
Confess when you’ve changed your mind about something; applaud others’ intellectual humility when they change theirs.
Weigh the best interests of the group when making a decision; trust that they do the same.
This piece turned out to be trickier to write than I initially envisioned, since it required a delicate balancing act of not throwing the baby out with the bathwater. Since it’s a highly insightful “baby” and there’s quite a bit of “bathwater”.
Following one of Mark Murphy’s Forbes articles, I came across this more detailed blog post:
The team at Leadership IQ analyzed results from ~11,300 responses to uniquely-designed engagement surveys, exploring the correlation between an outcome engagement metric and two groups of engagement drivers:
Traditional engagement drivers — which focus on the support provided to the employee by their manager and the organization: “my manager recognizes my accomplishments”, “my job responsibilities are clearly defined”, etc.
Self-engagement drivers — 18 outlooks and attitudes over which employees have direct and personal control: “I expect that more good things will happen to me than bad things” (optimism), “I will succeed if I work hard enough” (internal locus of control), etc.
First, the outcome (dependent) metric they chose really resonates with me: “working at my company inspires me to give my best effort”.
While there’s no perfect way to describe engagement, this definition which focuses on discretionary effort maps neatly to the “Will” component in Andy Grove’s equations of:
If we are gearing our working on work efforts towards a specific end-state, a state in which we are all giving our work our best efforts fits the bill a lot more cleanly than an eNPS metric (“How likely are you to recommend your company as a place to work?”).
Second, despite some analytical shortcomings (more on that soon), their results provide some compelling evidence that several self-engagement drivers (optimism, internal locus of control, resilience, assertiveness, and meaning) correlate with the engagement metric more strongly than some traditional drivers (receiving recognition, openness to ideas, supervisor trustworthiness, teamwork, clear job responsibilities).
Third, the team does not view the internal outlooks and attitudes as fixed, but rather as elements that can be honed and changed through training and coaching.
Fourth, the team does not advocate for replacing the traditional view of engagement with the self-engagement one. It advocates for taking a holistic approach that addresses both traditional and self-engagement drivers.
There are a few critical gaps in the way the study was conducted that, on aggregate, reduce its overall level of rigor below the threshold that will automatically receive my stamp of approval.
First, there are places where the distinction between traditional drivers and self-engagement drivers get murky. For example, “I trust my immediate supervisor” is considered a traditional driver that’s purely a factor of the supervisor’s behavior, ignoring the way the employee’s overall trust disposition may mediate that perception.
Second, the decision to look at the correlation between the engagement metric and each one of the drivers in isolation is rather peculiar. A more insightful and rigorous analysis would regress all drivers against the engagement metric, and ideally, perform some factor analysis on drivers ahead of that to address any shared unobserved factors.
Third, the partial presentation of the results, discussing only 10 out of about 30 drivers, and constructing a narrative in which a specific traditional engagement metric is compared head-to-head against a specific self-engagement mertic, raises concerns around cherry-picking the results in a way that best supports the overall narrative.
It’s sad that the shortcomings in analysis limit the insights that can be drawn from the study. Yet, nonetheless, the following can be stated with a high degree of certainty:
Self-engagement drivers have a significant impact on overall engagement outcomes, namely our willingness to exert discretionary effort in doing our work.
Engagement surveys/reflections that leave out self-engagement drivers will reach partial insights that can only drive sub-optimal actions.
The criticality of self-engagement outlooks and attitudes coupled with their plasticity, creates an opportunity to strategically align learning and development efforts with the overall organizational effort to improve engagement.
Since many of you will not have the patience to read through a 68-page paper, below are the key highlights from this study.
The overall premise
The team starts off framing the challenge: various empirical studies have found that feedback interventions designed to illuminate employees’ blind spots don’t always yield the desired outcomes. Feedback from others is intended to motivate improvement but it often has a demotivating impact, even to high-performing employees. One meta-analysis found that one-third of feedback interventions actually resulted in lower post-feedback performance.
Academic research of the reasons for the mismatch between intention and outcome has centered around a few mediating factors:
Poor design of the instruments/programs — for example, utilizing performance review for both compensation changes and developmental purposes leading to positively skewed feedback.
Poorly executed feedback — the feedback itself is not captured/delivered effectively: the content itself is garbled or confusing, numerical ratings without behavioral guidance on how to improve, etc.
Contextual features affecting the feedback — things that are happening outside of the feedback itself, such as the organizational culture, or level of trust, or even things outside the organization such as the external economic environment leading to distorted feedback.
While these three factors do offer paths for improving feedback effectiveness, in aggregate there is little evidence that feedback interventions, even using some of the best practices on these fronts, have systematically led to organizational level benefits.
The team, therefore, proposes a fourth driver which they set out to explore: the discrepancies (blindspots) identified by the feedback are demotivating. The discrepancies, the gaps between the way the person views themselves, and the way they are reflected through the feedback received from others, represent threats to recipients’ positive self-concept. Because the self-concept is maintained and evolves through interactions with others, feedback recipients will try to avoid these threats by minimizing the way they collaborate with peers who provided them with disconfirming feedback, often in ways that lead to a reduction in performance (which heavily relies on effective collaboration).
The theory can be illustrated as follows:
To explore the theory in more detail, the team formulated the following hypothesis:
People are likely to perceive disconfirming feedback as more threatening to their self-concepts than feedback that is not disconfirming.
People are more likely to eliminate a discretionary relationship with a person providing disconfirming feedback than they are to eliminate a discretionary relationship with a person providing feedback that is not disconfirming.
The perceived threat to one’s self-concept mediates the relationship between disconfirming feedback and the elimination of a discretionary relationship.
The greater the number of a person’s obligatory reviews are disconfirming, the greater the negative change in future constraint.
Eliminating discretionary relationships with individuals who provided disconfirming feedback is negatively associated with subsequent performance.
Decreases in constraint in response to disconfirming feedback by obligatory relationships is negatively associated with subsequent performance.
For simplicity’s sake, I would describe the “decrease in constraint” mentioned in #4 and #6 as a change to the collaboration pattern between the recipient and the individuals who provided the feedback.
The team ran two experiments to test their hypothesis, a field study, and a lab experiment.
The team tested hypotheses 2,4, 5 and 6 using data collected from “a vertically integrated food manufacturing and agribusiness company located in the Western United States”, which given additional details about the way the organization runs, revealed it as The Morning Star Company.
Morning Star uses a fluid organizational structure where every season each employee signs a “Colleague Letter of Understanding” (CLOU) with the employees that they’ll be collaborating with during the season. These data, providing insights into the dynamic changes in collaboration patterns across the organization by applying Organizational Network Analysis techniques, was the critical piece connecting the more standard inputs (feedback data) and outcomes (bonus allocations as a proxy for change in performance).
The team tested hypotheses 1,2 and 3 through a well-crafted lab experiment.
Filtering for people who value creativity and view themselves as creatives, participants were invited to perform an online assignment in which they’ll be paired with another participant and randomly assigned to be either the writer or evaluator of the task. However, all participants were assigned to be writers, with the software playing the role of the evaluator.
In the first task, participants were given 5 mins to write a creative short story that is at least 200 words. There were then assigned to one of the two conditions, receiving either confirming or disconfirming feedback. They were then presented with the second task, answering 10 trivia questions under time pressure. If both they and their partner will answer both correctly — they’d be given a bonus payment for their participation.
BUT they were also given a choice: whether to stick with their current partner or be randomly assigned a new one.
Results and Conclusions
The field study found a strong positive relationship between disconfirming feedback and the likelihood that the individual receiving the negative feedback drops the relationship in the subsequent year (Hypothesis 2). And the greater the number of an employee’s non-discretionary reviews that are disconfirming, the lower that employee’s constraint in subsequent periods (Hypothesis 4). The results suggesting that the greater the extent to which individuals engage in the practice of dropping discretionary relationships that provide disconfirming reviews, the lower their performance will be in the subsequent year, were not statistically significant, therefore rejecting Hypothesis 5. The team did find evidence that decreases in constraint in response to disconfirming feedback by obligatory relationships were negatively associated with subsequent performance (Hypothesis 6).
The lab experiment showed the participants did find the disconfirming feedback as more threatening (Hypothesis 1, an average score of 2.7 vs. 1.5 on a 7-point scale), and were more likely to switch partners for the second task (Hypothesis 2, 30% vs. 9%). Finally, the perceived threat mediated the relationship between disconfirming feedback and the elimination of the discretionary relationship (Hypothesis 3).
The team articulates the conclusion from their study succinctly
Feedback processes are nearly ubiquitous in modern organizations. Managers employ these processes naively, assuming employees will respond to them with dutiful efforts to improve. But we find that disconfirming feedback shakes the foundation of a core aspect of employees’ self-concept, causing them to respond by reshaping their networks in order to shore up their professional identity and salvage their self-concept. This reshaping of employee networks contributes to lowered performance — a result ironically at odds with the ultimate goal of performance feedback. Our research offers an expanded view of social capital in interpersonal settings, and suggests that organizations must finds ways to fulfill employees’ need for a socially bolstered self-concept — that developmental feedback in the absence of this self-confirmation offers little hope for improving performance outcomes.
For the more textually inclined, full transcript below:
What makes for good leadership? That’s a question that’s in the air right now, and rightly so. I’ve got some thoughts that have been running through my mind lately, that I’d like to share. There are more nuanced ways to talk about this, but this is not the time for nuance. I think the kind of leader that you are or that I am has everything to do with the way we envision the ship or the vessel that we’re leading. And we might not have thought of this until now, but it is time, right now, that we do.
Let’s start with the word itself, leader-ship. There’s “leader” and there’s “ship”. In my view, the leader dimension has two elements: direction and connection. However he or she may come to it, a good leader offers guidance or direction for some kind of group journey. He or she points the way forward. “Let’s go this way! This would be a good way to go.” Then, there’s the connection part. A leader acts towards others and encourages people to act towards each other in a way he or she believes would make their journey go better. So while we’re going — let’s relate to each other a certain way, let’s connect with each other a certain way. Our journey will go better if we do, and I’m going to do my best to demonstrate that in my behavior. So that’s the “leader” side of leadership. it’s about direction and connection.
But what about the “ship” side of leadership? This is where it gets really interesting to me. To exercise leadership suggests that someone gives direction, etc. aboard some kind of ship, some kind of vessel. They are the leader of a ship. And when I think “ship”, I think of some kind of water-borne vessel, don’t you? You know, a ship.
Do we envision ourselves as leading a kind of cruise ship, where some people on board, the more privileged ones, can pretty much do whatever they want, whenever they want to do it? And the others are there mainly to serve them. Or do we envision our vessel as more like a small boat? A small boat where each person’s actions directly affect the others on the boat. And also impacts the stability and the seaworthiness of the boat itself.
I’ll leave it to your imagination to extend this metaphor further. And to apply it in some way if you think it’s useful. But this distinction between leadership according to the principles of a cruise line, say the Titanic, perhaps. And leadership according to the principles of a smaller boat, seems relevant to me. And I invite you to consider what makes more sense now. What fits better with the reality of the world as we’re experiencing and seeing it. Perhaps, with fresh eyes, right now.
Setting aside the irksome word-play (leader-ship) and my qualms with the “leader” definition, I find the boat metaphor quite compelling. The cruise ship, in particular, seems to capture many of the ills that sometimes plague large organizations, beyond the leisurely purpose of the journey itself… Specifically, the stratification of membership into two classes: staff and passengers. Though often the “staff” are the privileged ones: ignoring what the “passengers” can contribute to steering the boat towards its destination, and optimizing solely for the “passengers” satisfaction/happiness. Often this a byproduct of failing to evolve the “employees as users/customers” metaphor from metaphor to analogy.
As Fleming suggests, this is a fun one to play around with, reconciling contradictions, making distinctions, and drawing insights.