What makes a good manager?
Many managers can’t answer that question.
As we’ve noted several times over the past few weeks, the findings in our ‘Building Better Managers’ report are based on a survey of 2,001 managers across 12 industries.
Of these managers, 86% told us they know what skills they need to be effective in their roles. But when we interviewed a group of managers at the end of 2023, most of them had a hard time articulating what good management looked like in practice.
At a surface level, the job of a manager is simple — get other people to do things. But this obscures the complexity of a role that requires individuals to wear many hats. As the report states:
‘Managers are expected to lead others while showing vulnerability to those they answer to. They’re expected to deliver objectives while encouraging people to experiment with new ideas. They must address underperformance in their teams but develop trusting relationships with its members. Managers have to support decisions they didn’t make, translate organizational goals into actions, identify meaningful opportunities for people to develop, remember to acknowledge successes, regulate their emotions, and resolve team conflicts. They are coaches, learning champions, coordinators, motivators, disseminators, and decision makers.’
Framed in this way, it’s perhaps unsurprising that many managers struggle to provide a succinct description of the skills they need to do their jobs. ‘Good management’ looks different from one day to the next, and this picture is further complicated by organizational context.
For managers or, indeed, for L&D professionals, this poses a significant challenge. If a manager has to be all things to all people, exercising a broad range of skills in the process, how do you improve management capability? Where do you even start?
Based on our research, one of the top requests from managers is ‘I want to know what my organization thinks I should be focusing on’.
To help L&D teams respond to this request, we’ve drawn on scientific studies to identify twelve manager capabilities that have a significant impact on people outcomes (e.g., innovation and engagement), manager outcomes (e.g., decision making and performance) and business outcomes (e.g., profitability and customer satisfaction). These are:
Self-awareness/self-regulation
Social sensitivity
Empathy
Inclusive leadership
Recognition
Trust
Active listening
Guidance
Coaching
Transparent communication
Goal setting
Delegation
The research tells us that these capabilities move the needle. But to narrow the field even further, L&D professionals need to understand their organizations’ business objectives, and the nuances of good management in their unique context.
Recently, the Mind Tools Custom team helped one client do exactly that, designing a blended program measurable improvement across critical management capabilities.
We did this by conducting scoping sessions with key stakeholders to develop a set of assumptions about the problems we were trying to solve. Then, with the support of our Insights team, we validated these assumptions with our target audience, using focus groups and semi-structured interviews to explore learners’ perceptions, feelings, and experiences.
This process helped us clarify which capabilities we wished to prioritize in the program, informing our approach to content design.
Before and after the program, participants and members of a control group completed a valid and reliable behavioral survey, establishing a baseline against which we could measure changes in capability. The results of the survey showed a statistically significant improvement in performance amongst the participants, when measured against the control.
We recently submitted this project in the Learning Technologies Awards, and we hope to be able to share more details with you soon!
Before we wrap up this week’s edition, we have a favor to ask…
Our friends at Emotion at Work are currently doing some research into people’s ability to express feelings in the workplace. More specifically, they’re looking to understand how factors like demographic characteristics and organizational position influence our perception of which emotions are acceptable at work. They’re also interested in assessing the extent to which opportunities to express emotion at work impact factors like stress and sleep quality.
If you could take a couple of minutes to support the research by completing this anonymous survey, we’d be very grateful to you.
Want to share your thoughts on this week’s Dispatch? Interested in working with the Mind Tools Custom Team? Then get in touch by emailing custom@mindtools.com or reply to this newsletter from your inbox.
🎧 On the podcast
According to a recent study from Ipsos, and commissioned by Amazon, 86% of respondents said that career development is essential, very or fairly important to them. But, in our experience, it tends to become a lot less important when the day-to-day demands of work crop up.
So, in this week’s episode of The Mind Tools L&D Podcast, return guest Neil John Cunningham from Align Learn Do joins Ross G to ask why this is, and what to do about it.
Check out the episode below. 👇
You can subscribe to the podcast on iTunes, Spotify or the podcast page of our website. Want to share your thoughts? Get in touch @RossDickieMT, @RossGarnerMT or #MindToolsPodcast
📖 Deep dive
I stumbled across this week’s ‘deep dive’ thanks to a LinkedIn post from Dr Philippa Hardman.
In the post, Philippa spotlights a new study, which explores the impact of generative AI on learning, in the context of high-school mathematics classes.
Based on an experiment involving nearly a thousand students, the researchers evaluated two GPT based tutors — one that mimics a standard ChatGPT interface, and one with prompts designed to safeguard learning.
The results of the experiment show that access to GPT-4 significantly improves performance (48% improvement for the standard interface, and a staggering 127% improvement for the safeguarded interface). But they also show that, when access is subsequently taken away, students actually perform worse than those who never had access in the first place.
The authors write:
‘Our results suggest that students attempt to use GPT-4 as a "crutch" during practice problem sessions, and when successful, perform worse on their own. Thus, to maintain long-term productivity, we must be cautious when deploying generative AI to ensure humans continue to learn critical skills.’
While this is one interpretation, Philippa also raised several interesting questions in her post of LinkedIn:
‘Are there some skills that we want to teach to humans manually (i.e. without AI assistance), at the cost of enabling optimal mastery of that skill? If so, what are those skills? And what's the cost/benefit of not using AI in the process of teaching them, both for the execution of the skill and for the learner?’
Bastani, Hamsa and Bastani, Osbert and Sungu, Alp and Ge, Haosen and Kabakcı, Özge and Mariman, Rei. (2024). ‘Generative AI Can Harm Learning’.
👹 Missing links
😶 Combatting stakeholder silence
Picture the scene. You’ve been working hard on a strategy for a new learning program, and have finally pinned down a time to present your vision to the key stakeholders. The presentation goes well, the stakeholders ask interesting questions, and they thank you for your time. Now what? Can you move forward with the program? Have the stakeholders agreed to give you the resources you need to kick it off? As Jess Almlie points out in this issue of her newsletter, the meeting was your opportunity to ask those questions. Now, you’re stuck with the silence.
Over the past decade or so, Meta has invested billions of dollars in AI. And unlike its major competitors, the company is giving that technology away for free by open-sourcing it. The latest version of Meta’s LLM, Llama 3.1, reportedly matches GPT-4 Omni and Claude 3.5 Sonnet on some benchmarks, and isn’t far behind the frontier models on other measures. This has obvious implications for the business models of AI companies, but it also raises concerns over safety. In this edition of Platformer, Casey Newton asks ‘Should we be worried?’
☕ Making memes for the global ‘oat milk elite’
The following sentence in this New Yorker article left me feeling exposed: ‘In the Netherlands, as elsewhere, the oat-milk life style might come with a slew of other familiar consumption choices: bottles of natural orange wine, containers of expensive moisturizer, meals at small-plates restaurants.’ The article explores the rise of meme accounts on Instagram, which both satirize and codify the habits of ‘the oat milk elite’ consumer. You don’t drink dairy, do you, reader?
👋 And finally…
As a learning designer, I obviously can’t relate to this video. 😉
👍 Thanks!
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