I have been to a lot of conferences, both as an attendee and as a speaker: for associations, for industries, for practitioners. But last week I attended my first academic conference. It was for the Academy of Management, the annual gathering of all the people who study, want to study, have studied, may never study all things management, leadership, organizations, and so on. And, hoo boy! It was overwhelming in the best way.
Let’s start with some context!
Attendees: ~5,000 (apparently smaller than usual—last year in Copenhagen was close to 13K)
Next year: Vienna! It’s waiting for you!
Number of separate “divisions and interest groups” (e.g., Managerial and Organizational Cognition, Organizational Behavior, Organizational Development and Change, etc.): 26
Total sessions attended: 18
Number of different divisions represented by those sessions: 13
Specifically (top three by sessions attended are bolded):
CAR - Careers: 1
CTO - Communication, Digital Technology and Organization: 2
DEI - Diversity, Equity and Inclusion: 1
ENT - Entrepreneurship: 4
HR - Human Resources: 2
MOC - Managerial and Organizational Cognition (isn’t that name delicious?): 6
OB - Organizational Behavior (no surprise here, my MBA is in OB and Managerial Communications): 7
ODC - Organizational Development and Change: 4
OMT - Organization and Management Theory: 4
RM - Research Methods: 1
SAP - Strategizing Activities and Practices: 1
STR - Strategic Management: 4
TIM - Technology and Innovation Management (this is where most of the “how to talk about new ideas” tended to show up): 5
Pages of handwritten (well, on my Kindle Scribe) notes: 53
Group dinners: 2 (one with my AEGIS program cohort, alumni, and professors; one with my fellow Thinkers50 Radar members)
People on my follow-up list: 32 (look for those tomorrow!)
New papers added to my Zotero library, based on works and concepts mentioned in sessions: 99
New concepts and disciplines related to my research topic: 21 (more on those below)
So, yeah. As I said to multiple people, I was like a kid in a candy store. I only almost switched my dissertation topic twice! I did not put myself into a single track. As you can see, I was looking for anything and everything that I thought might relate to the various threads in my head that all tie together around my interest in accelerating the genuine adoption of new ideas.
And yes, I’m still putting my brain back together, but here’s what’s sticking (so far!).
For those used to attending conferences with big “plenary” or General sessions where everybody goes into one room at the same time to hear the same person speak, yeah, that doesn’t happen. In fact, there are no “keynotes” per se, at least none that all attendees hear. (There may be speakers at divisional plenary sessions, but I didn’t attend any of those).
In fact, in the sessions I attended, there were no solely one-way presentations of information. Every session had some form of cross-panel discussion, or roundtables amongst panelists and the audience, or moderated discussions of the group of papers presented. More conferences should do this—it’s a great way to learn in real time.
But also: the slides? OMG. Terrible. For a conference all about learning and scholars, much of what was presented on screen was almost ANTI-learning, or even comprehension. SO. MANY. WORDS.
Something I didn’t realize: People are presenting their research and ideas in progress (usually in “paper” form). As in: not done yet.
I mean, they literally stand up and say, “Here’s where I am with this paper (or, in one case, theory). I haven’t submitted it yet (or I’ve submitted it, and it’s in revision). What do you see? How can I make it better?”
(That is SO foreign to me and the world of professional speaking.)
But I gotta say: there are a lot of ideas that would be a lot better, and a lot stronger, if more aspiring “thought leaders” had to “defend their ideas in public” this way (to quote Lennart Nacke)
And this isn’t just students who do this! These sessions combined early-stage doctoral students through to LEGENDS in management scholarship (Rita McGrath! Adam Grant! Amy Edmondson! Sally Maitlis! Katy Milkman!)
There was even one session where the originator of a theory shared the stage with four or five other well-established academics who each took 15–20 minutes to critique and even disagree with the theory (!!!).
A fairly unspoken subtext to all of this: people are looking for collaborators—people to do papers and studies with. LOVE THAT.
There’s a huge interest (understandably!) in how people make sense of change, the future, and ideas they’ve never seen or heard before. What changes? What stays the same?
I loved Loizos Heracleous’s prediction, made in regard to discourse theory: “Concepts may remain, but assumptions may change“
No doubt heavily biased by my interest in one phenomenon across disciplines and levels, I saw a lot of conversations about what scholars generally identify as meso- or multilevel organizational behavior, which focuses on the connections between micro levels (individuals, dyads, teams) and macro ones (organizations, populations, fields, institutions).
For instance:
What works across them?
What’s the same?
What’s different?
What is, or will happen, in the face of new technologies like AI?
Can they be separable?
(I think no, but I loved how David Reetz put it: “Systemic knowledge generation is an organizational achievement, not just a cognitive one,” which pleases my little enactivist heart)
Showing my interest bias again here, but in a lot of sessions there was a theme around what holds or can hold the core steady when everything seems to be changing or changing too quickly.
This showed up using all sorts of different terms (see the first bullet):
Organizational “focus” and “direction” (Miriam Erez, Michael Beer)
Organizational center (Rita McGrath)
Organizing imprint (Milosevic and Bass)
Purpose, etc. Think: “north star,” to use a phrase I hear a lot with my organizational clients)
The final Tamsen-biased theme: particularly in the change and innovation tracks, was around how to get people “on board” with a new idea
It showed up as “buy-in,” “common agreement,” “finding common ground,” etc.
It led to sessions and discussions on “believability,” “plausibility,” “legitimizing” and “legitimation,” etc.
I also saw a lot of tensions that ran across content themes, such as those between and among:
Theory & theorizing
The future as an object we create & the future as the means (through motivation) to do something in the present
Sensemaking & meaning-making
The need for prospective (forward-looking) everything, amid traditionally retrospective (backward-looking) tools, theories, etc.
The desire and need for cross- and interdisciplinary work vs. in-discipline incentives and potentially incommensurable expectations of and standards for rigor, what “counts” as knowledge, etc.
Discovery & creation (and: is that a false choice?)
And yes, there was a ton (too much?) on AI, but I continue to hold that the more we understand about the above themes, the more we can make AI work better for all of us
First, there is a lot of what is known as “terminological confusion” and conflation, meaning there were a ton of occasions (see Tensions, above) when people used terms without defining them and/or interchanged them with other words that don’t exactly mean the same thing.
For instance, theory both does and doesn’t mean framework, which both does and doesn’t mean model, which both does and doesn’t mean strategy. Oy.
Other concepts and words that were treated as synonymous (and possibly hindering cross-pollination and study as a result):
Types of ideas (e.g., premises vs theories vs recommendations vs claims)
Agency and self-efficacy (which was never mentioned unless I asked a question about it, to clarify someone’s meaning behind their use of “agency”)
Simple/simplistic and accessible
Context and problem (and “problems” meaning simply “lack of specific solutions”)
Transparency and openness and visibility
Some of this is a (known) by-product of the AoM’s divisional structure, as well as that of academia’s analogous siloing.
That said, there’s also a lot of desire to figure out how to cross those disciplines—and even cross more effectively with “practice,” the official term for the organizational world itself.**
As a 30-year practitioner who is also a newly minted doctoral candidate, my ears were on high alert for assumptions that appeared to go unchecked or unchallenged. Things like:
“Change takes time.” (Definitely true at the macro levels of organization, but as you may know by now, I’m not so sure that’s always true at the micro levels)
“It’s necessary to change the culture to create change.”
“Imagining the future is heavy cognitive lifting.”
Multiple fields remain in love with story, storytelling, and narratives as the seeming answer to everything. What they don’t seem to acknowledge: that story is a form of framing, which means it carries or includes implicit assumptions that, themselves left unchecked, may get in the way of what someone hopes the story will accomplish
One of the few things that went uncited: a reliable source for story structure
Unfortunately, that leads (as it does in practice, as well) to presenters using faulty, over-simplified story structure as the basis for their research
More specifically, there seems to be a persistent tendency to conflate “problem-solution” structures with story structures (for example, mislabeling the presentation of a solution as a story’s emotional climax, rather than as the anagnorisis—the moment of truth)
At the same time, there was a strong focus on reasoning and “logic” as purely rational logic, without the acknowledgment or understanding that logic is in play with a-rational reasoning, well (this is one of my bugaboos, I know)
Dual-process theory (Systems 1 & 2) was only explicitly mentioned in the last panel I attended
Argumentative theory of reasoning
Theories of action and its related theories and constructs:
Reflection-in-action, reflection-on-action (at least by those names)
Espoused theories versus theories-in-use (though I recall triple-loop learning being mentioned)
Data-frame theory
Transcendental reasoning
Insight vs analytical problem-solving (especially in the innovation-focused session)
Full disclosure: the quick summaries are from Perplexity
Discourse theory (Foucault, 1972; Laclau & Mouffe, 1985; Fairclough, 1992): how language, categories, and recurring communicative practices constitute what people can treat as real, legitimate, knowable, and possible—rather than merely describing a pre-given reality.
The theory-based view (Felin & Zenger, 2017): strategy as an actor’s distinctive causal theory about a problem, the value it can create, and how activities and resources should be organized to create that value. In Felin and Zenger’s account, firms do not merely discover opportunities; they originate and test theories.
Future-making (Garud & Gehman, 2012; Kaplan & Orlikowski, 2013): the situated practices through which actors imagine, narrate, materialize, contest, and enact possible futures. It treats the future not as an external destination to forecast, but as something partially produced through present action.
Entrepreneurial narrative (Lounsbury & Glynn, 2001; Martens, Jennings, & Jennings, 2007): a story that makes a venture intelligible and credible by linking its founder, problem, solution, audience, and anticipated future. It is a resource for attracting attention, legitimacy, commitment, and resources under uncertainty.
Entrepreneurial framing (Cornelissen & Clarke, 2010; Cornelissen & Werner, 2014): the selective construction of meaning around a venture—defining what the problem is, why it matters, what solution is appropriate, and why particular audiences should act. It is rhetoric aimed at mobilizing support and minimizing resistance.
Entrepreneurial experimenting (Camuffo et al., 2020; Burnell et al., 2026): designing, conducting, and interpreting tests of hypothesized cause-and-effect relationships in order to learn and reduce uncertainty about a nascent venture.
Cultural entrepreneurship (Lounsbury & Glynn, 2001; Jones, Lounsbury, & Boxenbaum, 2013): creating economic or organizational value by constructing and institutionalizing meanings, identities, categories, and narratives—not simply by making a new product or deploying resources differently.
Expectancy theory (Vroom, 1964): motivation depends on whether a person believes effort can produce performance, performance can produce valued outcomes, and those outcomes are actually desirable.
Note that this connects strongly in my brain to self-efficacy!
Social exchange theory (Blau, 1964; Cropanzano & Mitchell, 2005): relationships persist when parties perceive reciprocal benefits and fair treatment; favorable treatment creates obligations that can elicit trust, commitment, helping, and discretionary effort.
Signaling theory (Spence, 1973; Connelly et al., 2011): under information asymmetry, an actor communicates otherwise unobservable quality, intent, or capability through observable signals that receivers interpret. Signals become credible when they are difficult, costly, or otherwise unattractive for low-quality actors to imitate.
Related: “demonstrative” signals versus “declarative” signals (yummy!)
Declarative signals are assertions—“we have superior expertise,” “our product is secure.”
Demonstrative signals make the claimed quality observable through costly or consequential action—independent certification, a working prototype, transparent performance data, a warranty, or putting capital/reputation at risk.
Fresh starts (Dai, Milkman, & Riis, 2014): temporal landmarks—New Year’s Day, a birthday, a new week, the start of a semester—that psychologically separate a present self from an imperfect past self and thereby increase motivation for aspirational action.
Semantic distance (Mednick, 1962; Beaty & Johnson, 2021): the conceptual remoteness between two ideas. Greater semantic distance can index novelty or associative breadth, although creativity requires usefulness as well as remoteness.
Narrow search effect (Sio, Kotovsky, & Cagan, 2022; Sio et al., 2024): the tendency to continue generating ideas near one’s initial cues, assumptions, or most recently activated concepts, rather than searching more remote regions of the problem space. It can be efficient for familiar problems but constrains insight and novelty when the initial representation is unproductive.
Double-interact (Weick, 1979): Weick’s minimal unit of organizing: an act, another person’s response, and the original actor’s adjustment. Repeated act–response–adjustment cycles reduce equivocality and stabilize shared interpretations.
Dematerializing (McGrath, 2026): in McGrath’s strategic usage, the movement of value creation away from physical assets and “stuff” toward intangibles such as software, data, capabilities, platforms, services, coordinated ecosystems, and experience.
Minimum viable social structure (said, but not necessarily claimed or theorized by Adam Grant): the smallest durable arrangement of roles, norms, routines, interfaces, and mutual expectations sufficient to coordinate a collective task.
Occupational schemas (Dane, 2010; Bechky, 2003): learned cognitive structures associated with an occupation that guide what practitioners notice, regard as relevant, classify as a problem, and see as an appropriate response. They improve efficient expert judgment but can also create cognitive entrenchment and reduce flexibility when situations change.
Black boxing of expertise (Latour, 1987; Trepos, 1996): embedding expert judgment into a tool, procedure, standard, algorithm, credential, or outsourced service so that users can rely on outputs without seeing—or being able to interrogate—the underlying reasoning. It enables scale and coordination, but can obscure assumptions, weaken reflexivity, and redistribute authority.
Tensility (Dutton & Heaphy, 2003; Carmeli, Brueller, & Dutton, 2009): a relationship’s capacity to bend under strain, accommodate changing conditions, and recover or strengthen after difficulty. It is one feature of high-quality connections, alongside emotional carrying capacity and connectivity.
Coordinated management of meaning (how did I not know about this one already!?; Pearce & Cronen, 1980; Pearce, 2007): a communication theory in which people create and manage social realities through the patterned coordination of conversation and action. The focus is not merely what individuals mean privately, but how interaction produces workable—or unworkable—shared worlds.
Engaged scholarship (Van de Ven & Johnson, 2006; Van de Ven, 2007): a participative approach to research in which scholars and relevant stakeholders jointly bring their distinct perspectives to a complex problem, with the aim of producing knowledge that advances both theory and practice.
“Theories accelerate learning.”
“Firms compete on their theories.”
“Go broad or go home.”
“AI doesn’t check facts. It tells you what it predicts as being a fact.”
“Legitimacy is retrospective.”
“What legitimacy is built on predicts durability.”
Lynne Vincent: “When we believe someone did something intentionally, we value it more.”
“The more you talk about the current state, the more you need to talk about the future state.”
“The presence of the problem and the structure have an effect.”
Drew Carton: “Descriptive norms tend to set prescriptive norms.”
Paraphrasing Alexy et al. 2021 (via Ivana Milosivic & Bass): “The logic tends to endure even with the expression changes.”
Matthew Yeaton: “Distinctive perspectives and cultures can support novel solutions to problems.
Aron Lindberg: “Qualitative research needs a strong dependent variable.”
Isha Dhruva: “Meaningfulness and economic livelihood are co-constitutive.”
“Anything [as in a concept] that’s been rebranded still has to make its way through the data.”
“Skepticism can be trained.”
“Humans are biased. But algorithmic bias is a training error that can be fixed.”
“You can’t de-bias humans. You can de-bias AI.”
Mary Tripsas: “Software is now a variable cost.”
When originally drafting this post, I had grand plans of also including:
The process that I used to take and summarize these notes
What I learned from the differences between the notes I wrote down and the notes Granola AI took
How I ran both my notes and the Granola notes to see which of my own assumptions went unchecked or unnoticed, or what potentially disconfirming information I either didn’t hear or didn’t attend to
But, I would say this is already plenty long, so I’ll save all of that for another day (or never!)
Until next time,
Tamsen
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