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Venture team membership dynamics and new venture innovation

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Brian Fox discusses current understanding of the relationship between pre-entry experience and new venture innovation, as well as novel insights into the central but often overlooked role of team fluidity.

Image courtesy of interviewee. March 22, 2024

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This video video discusses the dynamic nature of team composition in new ventures and its impact on innovation. It emphasizes how the organic process of change in a startup involves the addition and loss of team members, which can be driven by various push and pull factors—such as personal commitments or relevant experience.

Key points include:

1. **Team Growth & Membership Fluidity**: As a startup evolves, initial team members are likely to be supplemented by new members with relevant experience or expertise, while others may leave due to personal or stability reasons.

2. **Impact on Innovation**: The experiences and backgrounds of team members significantly affect the organization's innovative capabilities. Previous studies indicated that team dynamics, specifically the hiring of experienced individuals, can foster greater innovation.

3. **Pre-Entry Experience**: The pre-entry experience of founders and team members is crucial, as their insights can lead to breakthrough innovation. The paper highlights studies showing how familiarity with industry marketing and regulatory environments contributes to successful innovation.

4. **Methodology Overview**: Utilizing a dataset from the Coffin Foundation, which surveyed over 4,900 firms from 2004 to 2012, the authors analyzed the relationship between the rates of member turnover and innovation behaviors, using statistical models to control for various factors.

5. **Findings**: The results suggest that fluctuating team membership—through both additions and departures—can enhance a company's innovative processes. As members come and go, they bring new perspectives and necessitate reevaluation of internal processes, which in turn fosters creativity.

6. **Theoretical Contributions**: The paper advocates for a dual focus on both stocks (existing knowledge and skills) and flows (movement of people and ideas) in understanding organizational innovation, while introducing the concept of membership fluidity that impacts long-lasting effects of founders' initial experiences.

Practically, it suggests that companies can boost innovation through strategic membership changes without necessarily relying on the experience of the founders alone. The study concludes that a fluid team dynamic is beneficial for fostering innovative practices within entrepreneurial ventures.

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Here are several similarly themed articles that discuss concepts related to team dynamics, innovation in startups, and the influence of team composition on organizational performance:

1. **"The Role of Team Composition in Innovation: Insights from Successful Startups"**
- This article explores how diverse team compositions contribute to innovative outcomes in startup environments. It emphasizes the importance of blending different skills and perspectives and how this diversity can drive creativity and problem-solving.

2. **"Startup Team Dynamics: The Impact of Member Turnover on Innovation Performance"**
- Focused on the effects of team member turnover, this article presents research findings that analyze how changes in team composition can influence a startup’s ability to innovate. It discusses strategies for managing transitions effectively to sustain creative outputs.

3. **"Exploring the Link Between Team Dynamics and Innovative Capability in New Ventures"**
- This paper investigates the connection between team dynamics—including interaction styles and collaboration patterns—and the innovative capabilities of startups. It provides evidence on how effective collaboration can lead to a competitive edge in technology-driven markets.

4. **"Innovation Through Membership Fluidity: A Study of Dynamic Teams in High-Tech Startups"**
- This article delves into how membership fluidity, where team members frequently join and leave, can stimulate innovative thinking and adaptability within high-tech startups. The findings highlight the necessity for organizations to embrace change rather than resist it.

5. **"Understanding the Impact of Founders’ Experience on Team Innovation"**
- This study examines how the prior experiences of founders influence the innovation processes within their startups. It emphasizes the importance of leveraging the diverse backgrounds of team members to enhance creative solutions and entrepreneurial success.

6. **"Agility and Innovation in Startups: The Role of Team Composition Changes"**
- Discussing the agile methodologies in startups, this article analyzes how changing team compositions can lead to improved responses to market shifts, supporting a culture of continuous innovation and adaptation.

7. **"Human Capital and Team Dynamics: A New Perspective on Startup Innovation"**
- This article investigates how human capital, in the form of team members’ skills and knowledge, affects innovation in startups. It discusses the significance of aligning team capabilities with innovation strategies.

These articles collectively reflect on the themes of team composition, dynamic member changes, and the resulting impact on innovation practices within entrepreneurial ventures. They might offer valuable insights and complementary viewpoints to the original video you .

X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

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For a new venture change is a very organic process, right?

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So if you kinda just imagine, let's say

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that you were gonna start up a company and it's you

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and perhaps one of your colleagues.

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Those aren't gonna be the only two kind of core members

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of the team as the company grows and becomes larger.

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Or you might go into a new market or something like that.

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So you can see how there'd be this very, at, at a minimum,

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there's a very natural tendency to acree members, right?

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To have more members come

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and join the team at a certain point in time.

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But equally as possible is, people realizing, well,

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you know, this isn't for me.

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Maybe I have, family commitments,

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or I need to have a more stable income.

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I can't wait for an equity event

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and I need to, you know, do something else with my time.

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'cause time is limited. And so this is often framed in terms

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of having push and pull factors, right?

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So members can be pulled in due

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to their relevant experience, right?

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I, I might meet somebody in a conference

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and say, wow, this is exactly what I'm looking for.

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This is the missing piece to our, our, our company

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to be able to create a new product or service.

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We need you to come into the team

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and you try to entice 'em to do so,

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or members can get pushed out

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for the reasons I mentioned earlier.

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You know, family demands, looking

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for alternative sources of income, maybe more stability.

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And the other thing to kinda keep in mind is, especially

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as ventures go from that kind of initial phase,

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maybe those first year or two to, you know, year 3, 4, 5,

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and six, particularly if they're trying to scale

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and become a larger organization, Ferguson

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and colleagues talk about this idea

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of structural elaboration.

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And so you have, you know,

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you might have had just like a CEO and a COO,

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but then you have a marketing function you need

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to bring a chief marketing officer on,

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or maybe they're just called the VP of marketing.

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You're like, the titles could change,

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but the idea is you start elaborating on the structure of

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that core team as you need to have more

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and more functions be performed by your company.

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In terms of kind of, how does all of this change

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and churn impact innovation?

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One thing that is clear is that the prior experiences

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of the team members, whoever they are,

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does have a clear impact on

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the innovative potential of the organization.

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And so, just to be clear, I don't mean just like the people

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who are sitting there in the garage, you know,

02:11.305 --> 02:13.505
if we're gonna use the proverbial garage on day one,

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but it's also the comings

02:14.905 --> 02:17.025
and goings of these, of these additional members that,

02:17.215 --> 02:19.145
that join over the time.

02:19.145 --> 02:23.385
That's very consistent with some of the, findings

02:23.385 --> 02:24.505
that are available in the literature.

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And I'm happy to kind of elaborate on those as necessary.

02:28.125 --> 02:32.105
But that's kind of the key connection between membership,

02:32.325 --> 02:33.625
how it's changing and innovation.

02:33.685 --> 02:35.465
So it's, it's the people who are in those roles

02:35.965 --> 02:37.425
and accumulated experience

02:37.425 --> 02:38.825
that they bring from their prior

02:38.825 --> 02:40.145
experiences into that venture.

02:40.845 --> 02:43.625
We draw primarily in terms of kind

02:43.625 --> 02:44.905
of the ground, our, our work.

02:44.925 --> 02:47.665
We, we draw on two big streams of work,

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and the first one is the

02:50.225 --> 02:51.265
literature on new venture innovation.

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And this particular emphasis on the concept,

02:53.435 --> 02:55.105
which is called pre-entry experience.

02:56.375 --> 02:58.675
And so pre-entry experience, you know, that can be

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With respect to multiple reference, meaning, you know,

03:01.465 --> 03:03.285
different parts of the organization.

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But when, when we're talking about it,

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we're speaking specifically to the founders

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and then the later new venture team members

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that come on after founding.

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And so there's a bunch of selective lists.

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I'm not, there's a, there's a bunch

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of other people who've contributed to this,

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but I'm thinking of people like Mark Gruber, Ron Chatterjee,

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Mary ssis, Daniel, sabar and, and Nathan Fur.

03:24.085 --> 03:25.805
They've all kind of looked at this space

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of pre-entry experience and how it can influence innovation,

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and that's innovation of different types.

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So, I think, Marvel

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and Lumpkin looked particularly at radical innovation,

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so not just, you know, the better mousetrap,

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but really a different product class.

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And they found that the pre-entry experience of founders,

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meaning they had either prior startup experience

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or deep expertise in the industry, that was, you know,

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in question where this, where this ventures being

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is operating in that was associated

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with higher levels of breakthrough innovation as measured

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by, you know, the presence of a, of a new product class

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or something along those ideas.

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And there's another interesting paper by Ron Je,

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which looked at, well, why is that the case?

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And one of the things that he found was it wasn't

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necessarily in his context.

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He was looking at a particular type

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of company called a spinout or a spinoff.

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And that's where you have maybe a group of people

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who are working in an established organization like Xerox,

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and they have a disagreement, right?

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There's a project that they think is really important.

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They, they want to push it forward, and management says no.

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And they say, okay, well then we're just gonna start our own

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company and, and do this product ourselves.

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And so there, what he found was it wasn't necessarily the

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technical experience that they were kind of extracting from

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that company, but it was actually their familiarity

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with the marketing and the regulatory environments,

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if I'm remembering, I know it's definitely marketing,

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I think it was regulatory as well.

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Um, and that that was actually

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what was predicting their success in

04:55.485 --> 04:57.965
creating innovations in the future was that not only was it,

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not so much was it the technical, like the idea,

05:00.585 --> 05:02.205
but it was the implementation of that idea.

05:03.185 --> 05:06.125
And, mark Ruber has found, similar,

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similar findings in, in, in different contexts.

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So that's one group of papers is this idea

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of pre-entry experience, providing the knowledge,

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the experience, the connections, the familiarity, like the,

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as Hayek would say, like the, the man on the spot,

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the local knowledge necessary, to be able to break

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through and actually deliver a

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successful, innovative product.

05:27.265 --> 05:29.725
The second literature is this one that I was kind

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of alluding to earlier, which is the vent,

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which is the new venture evolution literature.

05:34.125 --> 05:37.265
And that is basically pointing out the obvious, you know,

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phenomenologically obvious,

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but maybe not theoretically obvious fact

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that venture teams are constantly evolving and changing.

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And it could be for the, per the, for the, you know, number

05:46.605 --> 05:48.085
of reasons that we talked about before.

05:49.515 --> 05:51.915
A couple of, scholars

05:51.915 --> 05:54.275
that we drew on more extensively in the, in the development

05:54.275 --> 05:56.835
of this paper were, no

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Bosserman who's done,

05:58.055 --> 05:59.545
he's got a very interesting book called

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The Founder's Dilemmas.

06:01.545 --> 06:03.485
And that talks about a number of different kind

06:03.485 --> 06:06.045
of stage gates in the development of, of a venture

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and how it can modify, the composition of the team.

06:09.785 --> 06:12.605
So for example, deciding if you want to be, one of the kind

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of tensions he talks about is, do you want to be,

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do you wanna be rich or do you wanna be king?

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So it's a question, how much control are you willing

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to seed over your venture

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or do you wanna retain that control?

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But that tends to limit your options.

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Um, and so that's, you know, you can think about

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that in terms of bringing in external financing,

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but you could also think about that in terms

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of bringing in additional people in

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order to kind of get the work done.

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Um, and so that they, this literature talks about a number

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of different push and pull factors that will bring people in

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or drop people or push people out of a particular venture.

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And what this starts to foreground is the idea

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that we can't just kind of index a company by the stock

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of human capital they have at any given time.

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We have to think about the flows.

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The one thing that we did perceive was missing from these

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two streams of literature, and that was kind

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of the motivation for this paper was to say, it's all well

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and good to think about this as, you know, a a a company

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as a, as a warehouse of knowledge.

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And we can say, okay, there's what we knew at the beginning

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and we augment, or we,

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or we diminish from that by adding or subtracting people.

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But what about the actual process, right?

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Innovation's a process.

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It's not just, this thing where we feed knowledge in,

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we do some work on it, and we get these new products.

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There's kind of the ideation phase, there's interfacing

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with people within the organization, maybe the, the,

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the actual kind of, line workers, right?

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You know, the people that you're bringing in in order

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to execute on the product, working

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with external stakeholders in order to get buy-in to ideate

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and to be able to iterate, make the product fit the needs

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and demands of, of the, of your customers, for example.

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And so we said, well, what about just the comings

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and goings, right?

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So the actual comings

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and goings of people, is that gonna make a difference?

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And the organizational behavioral literature has this

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idea called team fluidity.

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And this is the act, just the gain and loss of membership.

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So an independent of what they know, just this,

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this processual act of having people come in

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and leave that is po you know,

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has been shown in other contexts

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to have an influence on the ability of the team to kind

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of evaluate where do we stand, what kind

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of mental models are we using?

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You know, are we kind of getting stuck in a rut?

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And so we took that perspective and implied it to this case.

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So that's kinda like the three, three areas

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that we, we drew upon.

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We relied on the coffin firm survey.

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Uh, so we didn't do the primary data collection ourselves.

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We're relying upon a very, well established,

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often utilized dataset, that was commissioned by the,

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the coffin Foundation.

08:52.215 --> 08:54.955
Uh, I forget when they, you know, commissioned the study.

08:55.065 --> 08:58.165
But the study took place from 2004 until 2012.

08:59.185 --> 09:02.005
And so in 2004, there were

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a little more than 4,900 firms that were surveyed,

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and they were resurveyed every year for seven years.

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And as you can imagine, obviously not every

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company survived, right?

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So over seven, you had a lot of those companies,

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they close up shop during that period.

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I forget exactly what the survival rate was,

09:17.785 --> 09:19.805
but it was very consistent with what the Bureau

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of Labor statistics, their, their statistics, which say

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that usually have about a 45% mortality rate

09:25.845 --> 09:28.405
after five years, I believe a little more than half

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of the companies in that original 4,900 plus

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or minus, had closed up shop

09:33.285 --> 09:34.445
during the, during the period.

09:35.105 --> 09:37.085
So we, we looked at the surviving companies.

09:37.745 --> 09:42.485
Um, the reason why our figure is in the 4, 4 40

09:42.485 --> 09:46.165
and the four 40 ballpark versus whatever half of 4,900 is,

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is because we had to have complete data on all

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of the variables that we have of interest.

09:52.475 --> 09:55.125
This was a survey that asked hundreds of questions

09:56.355 --> 09:57.525
each of these periods.

09:58.025 --> 10:00.725
And so we only were able to rely on the ones that

10:01.265 --> 10:03.805
had sufficient data on the material we were looking at.

10:03.805 --> 10:05.565
And we were looking at different aspects of companies,

10:05.565 --> 10:09.165
their financial data, their founder care, the, sorry,

10:09.165 --> 10:10.245
the owner characteristics.

10:10.245 --> 10:11.445
So at the beginning, the founders

10:11.465 --> 10:14.885
and then key key personnel going forward, as well

10:14.945 --> 10:16.405
as their innovation behaviors.

10:17.115 --> 10:18.765
That being said, even though, you know,

10:19.625 --> 10:21.605
in a panel like this, one of the things

10:21.605 --> 10:23.005
that you're concerned about is attrition.

10:24.145 --> 10:25.655
There are a number of very positive,

10:25.655 --> 10:27.455
attractive features of this data set.

10:27.595 --> 10:30.735
Uh, one of them is that it actually maps not only back,

10:30.805 --> 10:33.415
they, we have survey weights which map not only back

10:33.415 --> 10:35.055
to the original survey of 4,900,

10:35.395 --> 10:38.895
but what it actually maps back to is the original population

10:39.075 --> 10:43.015
of 33, 2 32 30 3000 ventures, all the ventures

10:43.015 --> 10:44.655
that were registered in 2004.

10:45.635 --> 10:47.895
So in other words, you could say, okay, at the end

10:47.895 --> 10:50.135
of the survey, I can say, how does this map back

10:50.135 --> 10:51.855
to my initial one so I can control

10:51.855 --> 10:54.255
for potential selection issues due to attrition, due

10:54.255 --> 10:55.535
to companies dying off on me,

10:55.795 --> 10:58.055
but also can I map back to the whole population?

10:58.835 --> 11:00.255
And the reason that's important is

11:00.255 --> 11:03.935
because this particular survey over sampled on things

11:03.935 --> 11:05.895
that we care about, you know, we're trying

11:05.895 --> 11:09.455
to understand innovation, and one of the characteristics

11:09.455 --> 11:10.895
that the survey overs sampled on was

11:10.895 --> 11:11.935
high high tech ventures.

11:13.015 --> 11:17.575
So we were more likely to see some of the, some

11:17.575 --> 11:18.775
of the characteristics that we're looking for.

11:18.835 --> 11:21.455
And so instead of having it be, we have 5,000 ventures

11:21.455 --> 11:23.575
and there are 10 that are pertinent

11:23.595 --> 11:25.575
for our study will actually have a couple hundred.

11:25.635 --> 11:28.535
So there's some statistical power to be able to, to,

11:28.535 --> 11:29.695
to look at what we wanted to look at.

11:29.715 --> 11:32.615
So in terms of the methodology, we, we had kind

11:32.615 --> 11:34.975
of our primary approach, which was simply

11:35.115 --> 11:37.535
to figure out if there was a correspondence

11:37.535 --> 11:38.975
between the number of innovation

11:39.295 --> 11:40.535
behaviors that these companies had.

11:41.195 --> 11:43.775
And so what, but I mean by that, is there,

11:43.845 --> 11:46.375
from the fifth wave of the survey to the conclusion of it,

11:46.625 --> 11:49.335
there were two questions that were asked whether there were

11:49.355 --> 11:50.975
new products introduced by that company in

11:50.975 --> 11:52.215
that calendar year or they started

11:52.215 --> 11:53.640
implementing a new process.

11:54.505 --> 11:58.955
And so these are kind of two very common, indicators

11:58.975 --> 12:00.035
of innovative activity.

12:00.455 --> 12:02.115
Uh, you know, if you kind of look at the,

12:02.115 --> 12:04.435
the extent innovation literature, those are, those are two,

12:04.655 --> 12:06.235
you know, there are obviously many more than that,

12:06.235 --> 12:07.795
but those are two kind of common core

12:08.085 --> 12:09.515
indicators that you would look at.

12:10.095 --> 12:14.955
And so we basically said, okay, can we use that as a, as a,

12:15.635 --> 12:17.555
a proxy for the innovativeness of the company

12:18.655 --> 12:21.435
and determine whether the different traits,

12:21.455 --> 12:24.035
the characteristics of, of, of this organization in terms

12:24.035 --> 12:25.675
of the stock of human capital they have

12:25.775 --> 12:27.355
and the flows of human capital in

12:27.355 --> 12:29.755
and out over time, as well as this membership dynamic thing,

12:29.825 --> 12:33.395
comings and goings of team members, is that predictive

12:33.455 --> 12:34.555
of the number of innovation

12:34.795 --> 12:35.915
behaviors that these companies exhibit.

12:37.055 --> 12:39.275
And so that we used a negative binomial model,

12:39.475 --> 12:41.785
'cause it's account, it's an image account,

12:41.925 --> 12:44.425
and that was what we used our to,

12:44.425 --> 12:45.745
to do our primary analysis.

12:46.605 --> 12:48.625
But we also used a number

12:48.865 --> 12:51.865
of different robustness techniques in order to account

12:51.885 --> 12:53.465
for different alternative explanations.

12:53.465 --> 12:55.865
Like is it because the surviving ventures are different from

12:55.865 --> 12:56.865
the ones that began?

12:57.365 --> 13:01.625
Or is it because companies are actually engaging in this

13:01.625 --> 13:03.705
fluidity process, like kicking people, you know,

13:03.705 --> 13:05.265
kicking people out or bringing people in

13:05.265 --> 13:06.825
because they're trying to be innovative.

13:06.925 --> 13:08.425
You know, is it, is it actually the other way?

13:08.445 --> 13:09.705
Is it kind of the car? Are we,

13:09.725 --> 13:10.985
do we have the causality wrong?

13:11.605 --> 13:14.185
Is it that innovative ventures do this and

13:14.185 --> 13:15.705
therefore we're gonna see that they're innovative?

13:15.765 --> 13:18.065
Or is it the actual act of coming and going?

13:18.085 --> 13:20.345
Is that predictive or not, not predictive,

13:20.345 --> 13:21.625
but somehow causing innovation?

13:22.325 --> 13:24.945
We can't make that definitive conclusion,

13:25.045 --> 13:26.825
but the evidence that we present in the paper,

13:26.885 --> 13:29.745
and that's what's in the, in the robust section, seems

13:29.745 --> 13:32.185
to indicate that there is this link

13:32.335 --> 13:34.305
between coming and going and membership.

13:34.305 --> 13:36.065
So membership fluidity and innovation

13:37.445 --> 13:39.875
after controlling for this potential reverse causality

13:39.875 --> 13:40.875
effect three different ways.

13:41.695 --> 13:44.035
One is just having a founder

13:45.045 --> 13:47.865
who has experience within this industry.

13:47.925 --> 13:50.025
So they have, they've already started up a venture in this

13:50.265 --> 13:52.545
industry, so they know the industry, they know

13:52.545 --> 13:53.785
how the startup process works,

13:54.325 --> 13:56.665
and they're in a better position to be able to take some

13:56.665 --> 13:58.025
of those ideas and implement them.

13:58.715 --> 14:00.895
So that's one way that we see.

14:00.895 --> 14:02.455
And, and then the data's consistent with that,

14:02.485 --> 14:07.135
that if you have an experienced founder startup experience

14:07.135 --> 14:09.935
experience in the industry, they're gonna have,

14:10.135 --> 14:11.535
a higher likelihood of it being innovative.

14:12.035 --> 14:13.335
So that's kind of pathway one.

14:14.735 --> 14:17.545
Pathway two is recruiting new members.

14:18.685 --> 14:21.185
So, you know, somehow pulling people in up, we know we,

14:21.325 --> 14:23.265
we don't have that insight into the actual process

14:23.265 --> 14:25.025
where we're just see we're seeing the outcomes,

14:25.085 --> 14:27.825
but when we see, evidence

14:27.825 --> 14:31.065
that a company has hired somebody with that experience

14:31.715 --> 14:34.905
after founding intuitively again, you,

14:35.045 --> 14:36.505
you expect to see a similar effect.

14:36.505 --> 14:39.025
And that's what we do. We see a similar magnitude of effect

14:39.485 --> 14:42.625
of hiring somebody and after the founding,

14:42.625 --> 14:44.145
and that seems to have a boost innovation

14:45.045 --> 14:46.505
so far, so obvious.

14:46.925 --> 14:48.945
And, but we just wanted to make sure that that, you know,

14:48.945 --> 14:50.905
this intuition was, was brought

14:50.905 --> 14:51.905
Out in the data.

14:52.385 --> 14:56.365
Our contribution was to show that holding those, so kind

14:56.365 --> 14:58.525
of allowing for those two things, holding that aside,

14:58.525 --> 15:00.685
setting a con, you know, holding it

15:00.685 --> 15:02.525
to be statist statistically constant,

15:03.225 --> 15:04.925
the change in the composition of the team.

15:04.985 --> 15:07.645
So additions of subtractions, of members of any stripe

15:08.545 --> 15:10.675
independent of the knowledge that they're bringing in

15:11.345 --> 15:13.115
that also is predictive of innovation.

15:13.735 --> 15:16.715
And our argument for why is that it basically allows the,

15:17.215 --> 15:19.435
the members, so either for additions or departures,

15:19.435 --> 15:20.875
and we tested them both independently.

15:20.895 --> 15:22.515
We said, what about just additions?

15:22.515 --> 15:23.595
What about just departures?

15:23.595 --> 15:26.355
And we saw a similar, main effect was

15:26.865 --> 15:30.555
that it allows the company to basically sit back

15:30.555 --> 15:32.195
and take stock and say, what are we doing here?

15:32.375 --> 15:33.875
How are we interacting with one another?

15:33.945 --> 15:36.315
What is our, whatever our implicit mental models

15:36.415 --> 15:38.275
or our, the transactive memory systems is,

15:38.275 --> 15:40.195
is is a term that sometimes used?

15:40.735 --> 15:44.555
How are we dis distributing, decision making processes?

15:45.055 --> 15:46.555
And should we, should we change that?

15:47.145 --> 15:48.405
So when we have somebody new come

15:48.405 --> 15:49.925
and we say, well, we have this new team member,

15:50.025 --> 15:52.165
how do we incorporate them into this, into this mix?

15:52.625 --> 15:55.925
Or if we lose somebody, we say, well, you know, Larry or Bob

15:55.925 --> 15:57.965
or whatever the name is, right, they, they're gone.

15:58.385 --> 16:00.365
We can't have them do this anymore. Somebody else.

16:00.425 --> 16:01.965
We have to reconfigure how this is working.

16:02.625 --> 16:06.605
And in both cases, we, we see that kind of the,

16:07.025 --> 16:10.605
the net effect is that it's boosting innovation, allowing us

16:10.605 --> 16:13.245
to recalibrate where we need to be and,

16:13.305 --> 16:16.005
and think about recombining our ideas in novel ways.

16:16.675 --> 16:19.175
So that's kind of our, our explanation for why,

16:19.175 --> 16:20.255
why we're seeing what we're seeing.

16:20.765 --> 16:22.575
I'll speak to it in two kind of two buckets.

16:23.195 --> 16:24.855
One for maybe more practical lens

16:24.855 --> 16:26.735
and then one from like a more theoretical lens

16:26.795 --> 16:29.735
or, you know, academic lens In terms

16:29.735 --> 16:30.855
of practical contributions.

16:30.935 --> 16:33.735
I mean, I think what we're, we're trying to indicate

16:33.735 --> 16:34.935
to people is that there are men,

16:34.935 --> 16:36.535
there are multiple ways in which your

16:36.535 --> 16:37.695
company can become more innovative.

16:38.235 --> 16:40.735
You know, it's just because you don't have somebody with,

16:41.155 --> 16:42.775
deep experience

16:42.915 --> 16:43.975
or, you know, it was a,

16:44.055 --> 16:45.895
a serial entrepreneur was a prior founder.

16:46.125 --> 16:47.935
That doesn't preclude you from being innovative.

16:50.495 --> 16:53.915
But it helps to make sure that if you don't have the kind

16:53.915 --> 16:56.515
of this reservoir of ready-made kind of ideas

16:56.545 --> 16:57.995
that you can draw from and, and,

16:57.995 --> 17:01.315
and implement straightforwardly, it helps to be able

17:01.315 --> 17:04.195
to really start getting the blender going right,

17:04.195 --> 17:05.795
and getting people churning inside

17:05.795 --> 17:07.635
and out of this, of this organization in order

17:07.655 --> 17:09.355
to get those creative juices flowing.

17:10.215 --> 17:12.795
The other thing that I think is kind of fascinating, and

17:12.795 --> 17:15.115
because this is a fairly large data set over a

17:15.115 --> 17:16.595
representative sample of both high-tech

17:16.595 --> 17:19.475
and non high-tech ventures, we get predictions.

17:19.575 --> 17:21.515
So, and we've isolated the effects.

17:21.695 --> 17:23.915
So we have the effect of having somebody at the beginning.

17:24.135 --> 17:25.955
We have the effect of somebody coming in later.

17:26.135 --> 17:27.955
We have the effect of people coming and going.

17:28.455 --> 17:29.475
And so you could say,

17:29.735 --> 17:31.275
and these are not independent things, right?

17:31.275 --> 17:33.995
You know, if I, if I look at a membership change event

17:34.865 --> 17:36.395
that might trigger multiple

17:36.395 --> 17:37.595
of those things happening simultaneously.

17:37.685 --> 17:39.955
Let's say somebody leaves, what if it's the founder

17:39.975 --> 17:41.035
who had that experience?

17:41.685 --> 17:42.815
What is the predicted effect?

17:42.975 --> 17:45.015
'cause they're gonna have kind of contradictory influences.

17:45.635 --> 17:46.775
And, one of the tables,

17:46.775 --> 17:49.325
I think it's table four in our paper, actually walks

17:49.325 --> 17:51.565
through all the different configurations of, of a change.

17:51.665 --> 17:53.445
You know, if it's an addition, if it's a subtraction,

17:53.745 --> 17:56.045
if it's a replacement, what does

17:56.045 --> 17:57.605
that mean in terms of the net effect?

17:58.365 --> 18:01.065
And so that is something that, you know, founders can use

18:01.065 --> 18:02.425
as a, as a guide to say,

18:02.895 --> 18:04.905
what do you know if we're mulling this change

18:05.245 --> 18:08.465
and we are concerned about being innovative, what is the,

18:09.125 --> 18:11.105
you know, to some order of magnitude,

18:11.105 --> 18:12.225
what is the predicted effect?

18:12.225 --> 18:15.465
Are we expecting this is gonna hurt us, help us do,

18:15.465 --> 18:16.945
is there a silver lining to this change?

18:17.145 --> 18:19.065
'cause the membership changes, they're, they're,

18:19.065 --> 18:21.385
they're kind, maybe traumatic is not the right word,

18:21.385 --> 18:23.305
but they're a material experience, right?

18:23.365 --> 18:24.505
So you kind

18:24.505 --> 18:26.625
of wanna an understanding what that might look like.

18:27.635 --> 18:31.975
Um, in terms of kind of the more our contributions to,

18:32.035 --> 18:34.655
to the theory that this liter to these literatures.

18:35.335 --> 18:38.095
I think one thing that we do is we show that it's important

18:38.095 --> 18:39.175
to focus on both the stocks

18:39.175 --> 18:40.615
and the flow simultaneously, right?

18:40.615 --> 18:42.495
That if, if we don't consider them jointly,

18:42.545 --> 18:43.855
we're missing part of the picture.

18:44.475 --> 18:48.655
Uh, the second thing is we add this, this idea

18:48.655 --> 18:49.695
of membership fluidity, right?

18:49.695 --> 18:51.175
I mean, that, that really hadn't been

18:51.175 --> 18:52.495
thought of in this literature.

18:52.555 --> 18:56.175
We, we thought a lot about, as I said, knowledge flowing in

18:56.175 --> 18:58.135
and out, but we didn't necessarily think about

18:58.135 --> 18:59.255
the people side as much.

18:59.675 --> 19:04.335
So bringing that lens, you know, as a predictive validity,

19:04.335 --> 19:06.295
but also just enriches this,

19:06.565 --> 19:08.375
this enrich enriches the space a bit.

19:09.195 --> 19:10.615
And then the third one,

19:10.685 --> 19:13.095
this is I guess maybe a little bit idiosyncratic and near

19:13.095 --> 19:15.625
and dear to myself

19:15.625 --> 19:16.865
and my co-authors, is

19:16.865 --> 19:17.865
that we've always been interested in

19:17.865 --> 19:18.865
this literature and imprinting.

19:19.485 --> 19:22.385
So imprinting is this concept that, you know, the,

19:22.605 --> 19:24.345
the conditions at the time of the founding

19:24.345 --> 19:26.745
of an organization will have enduring effects more

19:26.745 --> 19:27.785
than you would expect, right?

19:29.005 --> 19:31.785
And so what we find is, you know, one way to kind

19:31.785 --> 19:35.955
of think about the initial experience of a,

19:35.955 --> 19:37.355
of a founder coming in at the time

19:37.355 --> 19:38.675
of founding their experience,

19:38.675 --> 19:40.075
getting imparted to the venture.

19:40.695 --> 19:42.715
So it's not so much what's necessarily in their head,

19:42.715 --> 19:43.795
but kinda like the routines

19:43.795 --> 19:45.115
that they're pushing into the process.

19:45.255 --> 19:47.635
You know, as they're building out the organization

19:47.695 --> 19:48.995
and thinking about,

19:49.015 --> 19:50.555
how am I gonna structure this division?

19:51.255 --> 19:54.435
Or what pathway would I push this product down?

19:54.435 --> 19:57.235
You know, do I go to the, do I go to the customers first

19:57.235 --> 19:58.795
and then do I go to my collaborators

19:58.795 --> 19:59.955
versus the other way around?

20:00.135 --> 20:02.355
You can think about how some of these effects could persist.

20:03.305 --> 20:08.275
What we find is that the fluidity, if you do have fluidity,

20:08.275 --> 20:10.955
what it does is it, it diminishes that imprinting effect.

20:11.615 --> 20:15.395
So it makes it so that the enduring kind of, influence

20:15.395 --> 20:17.355
of the founders decays faster.

20:17.975 --> 20:20.995
And this is actually one of the things that we had kind

20:20.995 --> 20:22.755
of proffered the literature back in 2013.

20:22.815 --> 20:25.195
We, we did a, a review on imprinting

20:25.615 --> 20:27.555
and that was one of the things we said, this is something

20:27.555 --> 20:29.155
that probably warrants further study.

20:29.375 --> 20:32.515
And this is one piece of evidence towards, towards that end.

Team Dynamics Impacting Venture Innovation

00:00:00 - For a new venture change is a very organic process, right? So if you kinda just imagine, let's say that you were gonna start up a company and it's you and perhaps one of your colleagues. Those aren't gonna be the only two kind of core members of the team as the company grows and becomes larger. Or you might go into a new market or something like that. So you can see how there'd be this very, at, at a minimum, there's a very natural tendency to acree members, right? To have more members come and join the team at a certain point in time. But equally as possible is, people realizing, well,
00:00:34 - Maybe I have, family commitments, or I need to have a more stable income. I can't wait for an equity event and I need to, you know, do something else with my time. 'cause time is limited. And so this is often framed in terms of having push and pull factors, right? So members can be pulled in due to their relevant experience, right? I, I might meet somebody in a conference and say, wow, this is exactly what I'm looking for. This is the missing piece to our, our, our company to be able to create a new product or service. We need you to come into the team and you try to entice 'em to do so,
00:01:06 - for the reasons I mentioned earlier. You know, family demands, looking for alternative sources of income, maybe more stability. And the other thing to kinda keep in mind is, especially as ventures go from that kind of initial phase, maybe those first year or two to, you know, year 3, 4, 5, and six, particularly if they're trying to scale and become a larger organization, Ferguson and colleagues talk about this idea of structural elaboration. And so you have, you know, you might have had just like a CEO and a COO, but then you have a marketing function you need
00:01:38 - or maybe they're just called the VP of marketing. You're like, the titles could change, but the idea is you start elaborating on the structure of that core team as you need to have more and more functions be performed by your company. In terms of kind of, how does all of this change and churn impact innovation? One thing that is clear is that the prior experiences of the team members, whoever they are, does have a clear impact on the innovative potential of the organization.
00:02:09 - who are sitting there in the garage, you know, if we're gonna use the proverbial garage on day one, but it's also the comings and goings of these, of these additional members that, that join over the time. That's very consistent with some of the, findings that are available in the literature. And I'm happy to kind of elaborate on those as necessary. But that's kind of the key connection between membership, how it's changing and innovation. So it's, it's the people who are in those roles and accumulated experience
00:02:38 - experiences into that venture. We draw primarily in terms of kind of the ground, our, our work. We, we draw on two big streams of work, and the first one is the literature on new venture innovation. And this particular emphasis on the concept, which is called pre-entry experience. And so pre-entry experience, you know, that can be With respect to multiple reference, meaning, you know, different parts of the organization. But when, when we're talking about it, we're speaking specifically to the founders

Impact of Pre-Entry Experience on Innovation

00:03:09 - that come on after founding. And so there's a bunch of selective lists. I'm not, there's a, there's a bunch of other people who've contributed to this, but I'm thinking of people like Mark Gruber, Ron Chatterjee, Mary ssis, Daniel, sabar and, and Nathan Fur. They've all kind of looked at this space of pre-entry experience and how it can influence innovation, and that's innovation of different types. So, I think, Marvel and Lumpkin looked particularly at radical innovation, so not just, you know, the better mousetrap,
00:03:43 - And they found that the pre-entry experience of founders, meaning they had either prior startup experience or deep expertise in the industry, that was, you know, in question where this, where this ventures being is operating in that was associated with higher levels of breakthrough innovation as measured by, you know, the presence of a, of a new product class or something along those ideas. And there's another interesting paper by Ron Je, which looked at, well, why is that the case? And one of the things that he found was it wasn't
00:04:13 - He was looking at a particular type of company called a spinout or a spinoff. And that's where you have maybe a group of people who are working in an established organization like Xerox, and they have a disagreement, right? There's a project that they think is really important. They, they want to push it forward, and management says no. And they say, okay, well then we're just gonna start our own company and, and do this product ourselves. And so there, what he found was it wasn't necessarily the technical experience that they were kind of extracting from
00:04:44 - with the marketing and the regulatory environments, if I'm remembering, I know it's definitely marketing, I think it was regulatory as well. and that that was actually what was predicting their success in creating innovations in the future was that not only was it, not so much was it the technical, like the idea, but it was the implementation of that idea. And, mark Ruber has found, similar, similar findings in, in, in different contexts. So that's one group of papers is this idea of pre-entry experience, providing the knowledge,
00:05:19 - as Hayek would say, like the, the man on the spot, the local knowledge necessary, to be able to break through and actually deliver a successful, innovative product. The second literature is this one that I was kind of alluding to earlier, which is the vent, which is the new venture evolution literature. And that is basically pointing out the obvious, you know, phenomenologically obvious, but maybe not theoretically obvious fact that venture teams are constantly evolving and changing. And it could be for the, per the, for the, you know, number
00:05:49 - A couple of, scholars that we drew on more extensively in the, in the development of this paper were, no Bosserman who's done, he's got a very interesting book called The Founder's Dilemmas. And that talks about a number of different kind of stage gates in the development of, of a venture and how it can modify, the composition of the team. So for example, deciding if you want to be, one of the kind of tensions he talks about is, do you want to be,

Balancing Control and Innovation in Startups

00:06:19 - So it's a question, how much control are you willing to seed over your venture or do you wanna retain that control? But that tends to limit your options. and so that's, you know, you can think about that in terms of bringing in external financing, but you could also think about that in terms of bringing in additional people in order to kind of get the work done. and so that they, this literature talks about a number of different push and pull factors that will bring people in or drop people or push people out of a particular venture. And what this starts to foreground is the idea
00:06:51 - of human capital they have at any given time. We have to think about the flows. The one thing that we did perceive was missing from these two streams of literature, and that was kind of the motivation for this paper was to say, it's all well and good to think about this as, you know, a a a company as a, as a warehouse of knowledge. And we can say, okay, there's what we knew at the beginning and we augment, or we, or we diminish from that by adding or subtracting people.
00:07:21 - Innovation's a process. It's not just, this thing where we feed knowledge in, we do some work on it, and we get these new products. There's kind of the ideation phase, there's interfacing with people within the organization, maybe the, the, the actual kind of, line workers, right? You know, the people that you're bringing in in order to execute on the product, working with external stakeholders in order to get buy-in to ideate and to be able to iterate, make the product fit the needs and demands of, of the, of your customers, for example.
00:07:55 - and goings, right? So the actual comings and goings of people, is that gonna make a difference? And the organizational behavioral literature has this idea called team fluidity. And this is the act, just the gain and loss of membership. So an independent of what they know, just this, this processual act of having people come in and leave that is po you know, has been shown in other contexts to have an influence on the ability of the team to kind of evaluate where do we stand, what kind
00:08:25 - You know, are we kind of getting stuck in a rut? And so we took that perspective and implied it to this case. So that's kinda like the three, three areas that we, we drew upon. We relied on the coffin firm survey. so we didn't do the primary data collection ourselves. We're relying upon a very, well established, often utilized dataset, that was commissioned by the, the coffin Foundation. I forget when they, you know, commissioned the study.
00:08:59 - And so in 2004, there were a little more than 4,900 firms that were surveyed, and they were resurveyed every year for seven years. And as you can imagine, obviously not every company survived, right? So over seven, you had a lot of those companies, they close up shop during that period. I forget exactly what the survival rate was, but it was very consistent with what the Bureau of Labor statistics, their, their statistics, which say that usually have about a 45% mortality rate

Analyzing Company Survival and Innovation Dynamics

00:09:28 - of the companies in that original 4,900 plus or minus, had closed up shop during the, during the period. So we, we looked at the surviving companies. the reason why our figure is in the 4, 4 40 and the four 40 ballpark versus whatever half of 4,900 is, is because we had to have complete data on all of the variables that we have of interest. This was a survey that asked hundreds of questions
00:09:58 - And so we only were able to rely on the ones that had sufficient data on the material we were looking at. And we were looking at different aspects of companies, their financial data, their founder care, the, sorry, the owner characteristics. So at the beginning, the founders and then key key personnel going forward, as well as their innovation behaviors. That being said, even though, you know, in a panel like this, one of the things that you're concerned about is attrition. There are a number of very positive, attractive features of this data set.
00:10:30 - they, we have survey weights which map not only back to the original survey of 4,900, but what it actually maps back to is the original population of 33, 2 32 30 3000 ventures, all the ventures that were registered in 2004. So in other words, you could say, okay, at the end of the survey, I can say, how does this map back to my initial one so I can control for potential selection issues due to attrition, due to companies dying off on me, but also can I map back to the whole population?
00:11:00 - because this particular survey over sampled on things that we care about, you know, we're trying to understand innovation, and one of the characteristics that the survey overs sampled on was high high tech ventures. So we were more likely to see some of the, some of the characteristics that we're looking for. And so instead of having it be, we have 5,000 ventures and there are 10 that are pertinent for our study will actually have a couple hundred. So there's some statistical power to be able to, to, to look at what we wanted to look at.
00:11:32 - of our primary approach, which was simply to figure out if there was a correspondence between the number of innovation behaviors that these companies had. And so what, but I mean by that, is there, from the fifth wave of the survey to the conclusion of it, there were two questions that were asked whether there were new products introduced by that company in that calendar year or they started implementing a new process. And so these are kind of two very common, indicators of innovative activity.
00:12:02 - the extent innovation literature, those are, those are two, you know, there are obviously many more than that, but those are two kind of common core indicators that you would look at. And so we basically said, okay, can we use that as a, as a, a proxy for the innovativeness of the company and determine whether the different traits, the characteristics of, of, of this organization in terms of the stock of human capital they have and the flows of human capital in and out over time, as well as this membership dynamic thing, comings and goings of team members, is that predictive

Membership Changes and Innovation Dynamics

00:12:34 - behaviors that these companies exhibit. And so that we used a negative binomial model, 'cause it's account, it's an image account, and that was what we used our to, to do our primary analysis. But we also used a number of different robustness techniques in order to account for different alternative explanations. Like is it because the surviving ventures are different from the ones that began? Or is it because companies are actually engaging in this fluidity process, like kicking people, you know,
00:13:05 - because they're trying to be innovative. You know, is it, is it actually the other way? Is it kind of the car? Are we, do we have the causality wrong? Is it that innovative ventures do this and therefore we're gonna see that they're innovative? Or is it the actual act of coming and going? Is that predictive or not, not predictive, but somehow causing innovation? We can't make that definitive conclusion, but the evidence that we present in the paper, and that's what's in the, in the robust section, seems to indicate that there is this link between coming and going and membership.
00:13:37 - after controlling for this potential reverse causality effect three different ways. One is just having a founder who has experience within this industry. So they have, they've already started up a venture in this industry, so they know the industry, they know how the startup process works, and they're in a better position to be able to take some of those ideas and implement them. So that's one way that we see. And, and then the data's consistent with that, that if you have an experienced founder startup experience
00:14:10 - a higher likelihood of it being innovative. So that's kind of pathway one. Pathway two is recruiting new members. So, you know, somehow pulling people in up, we know we, we don't have that insight into the actual process where we're just see we're seeing the outcomes, but when we see, evidence that a company has hired somebody with that experience after founding intuitively again, you, you expect to see a similar effect. And that's what we do. We see a similar magnitude of effect
00:14:42 - and that seems to have a boost innovation so far, so obvious. And, but we just wanted to make sure that that, you know, this intuition was, was brought Out in the data. Our contribution was to show that holding those, so kind of allowing for those two things, holding that aside, setting a con, you know, holding it to be statist statistically constant, the change in the composition of the team. So additions of subtractions, of members of any stripe independent of the knowledge that they're bringing in
00:15:13 - And our argument for why is that it basically allows the, the members, so either for additions or departures, and we tested them both independently. We said, what about just additions? What about just departures? And we saw a similar, main effect was that it allows the company to basically sit back and take stock and say, what are we doing here? How are we interacting with one another? What is our, whatever our implicit mental models or our, the transactive memory systems is, is is a term that sometimes used? How are we dis distributing, decision making processes?

Impact of Team Dynamics on Innovation

00:15:47 - So when we have somebody new come and we say, well, we have this new team member, how do we incorporate them into this, into this mix? Or if we lose somebody, we say, well, you know, Larry or Bob or whatever the name is, right, they, they're gone. We can't have them do this anymore. Somebody else. We have to reconfigure how this is working. And in both cases, we, we see that kind of the, the net effect is that it's boosting innovation, allowing us to recalibrate where we need to be and, and think about recombining our ideas in novel ways.
00:16:19 - why we're seeing what we're seeing. I'll speak to it in two kind of two buckets. One for maybe more practical lens and then one from like a more theoretical lens or, you know, academic lens In terms of practical contributions. I mean, I think what we're, we're trying to indicate to people is that there are men, there are multiple ways in which your company can become more innovative. You know, it's just because you don't have somebody with, deep experience or, you know, it was a, a serial entrepreneur was a prior founder. That doesn't preclude you from being innovative.
00:16:53 - of this reservoir of ready-made kind of ideas that you can draw from and, and, and implement straightforwardly, it helps to be able to really start getting the blender going right, and getting people churning inside and out of this, of this organization in order to get those creative juices flowing. The other thing that I think is kind of fascinating, and because this is a fairly large data set over a representative sample of both high-tech and non high-tech ventures, we get predictions. So, and we've isolated the effects.
00:17:24 - We have the effect of somebody coming in later. We have the effect of people coming and going. And so you could say, and these are not independent things, right? You know, if I, if I look at a membership change event that might trigger multiple of those things happening simultaneously. Let's say somebody leaves, what if it's the founder who had that experience? What is the predicted effect? 'cause they're gonna have kind of contradictory influences. And, one of the tables, I think it's table four in our paper, actually walks through all the different configurations of, of a change. You know, if it's an addition, if it's a subtraction,
00:17:56 - that mean in terms of the net effect? And so that is something that, you know, founders can use as a, as a guide to say, what do you know if we're mulling this change and we are concerned about being innovative, what is the, you know, to some order of magnitude, what is the predicted effect? Are we expecting this is gonna hurt us, help us do, is there a silver lining to this change? 'cause the membership changes, they're, they're, they're kind, maybe traumatic is not the right word, but they're a material experience, right? So you kind
00:18:27 - in terms of kind of the more our contributions to, to the theory that this liter to these literatures. I think one thing that we do is we show that it's important to focus on both the stocks and the flow simultaneously, right? That if, if we don't consider them jointly, we're missing part of the picture. the second thing is we add this, this idea of membership fluidity, right? I mean, that, that really hadn't been thought of in this literature. We, we thought a lot about, as I said, knowledge flowing in

Impact of Founder Experience on Organizational Change

00:18:58 - the people side as much. So bringing that lens, you know, as a predictive validity, but also just enriches this, this enrich enriches the space a bit. And then the third one, this is I guess maybe a little bit idiosyncratic and near and dear to myself and my co-authors, is that we've always been interested in this literature and imprinting. So imprinting is this concept that, you know, the, the conditions at the time of the founding of an organization will have enduring effects more
00:19:29 - And so what we find is, you know, one way to kind of think about the initial experience of a, of a founder coming in at the time of founding their experience, getting imparted to the venture. So it's not so much what's necessarily in their head, but kinda like the routines that they're pushing into the process. You know, as they're building out the organization and thinking about, how am I gonna structure this division? Or what pathway would I push this product down? You know, do I go to the, do I go to the customers first
00:19:58 - versus the other way around? You can think about how some of these effects could persist. What we find is that the fluidity, if you do have fluidity, what it does is it, it diminishes that imprinting effect. So it makes it so that the enduring kind of, influence of the founders decays faster. And this is actually one of the things that we had kind of proffered the literature back in 2013. We, we did a, a review on imprinting and that was one of the things we said, this is something
00:20:29 - And this is one piece of evidence towards, towards that end.

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