शैडोइंग अभ्यास: Decision-Making and Entrepreneurial Performance: Evidence from a Field Experiment - वीडियो के साथ अंग्रेजी बोलना सीखें

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thank you thank you very much ryan can you hear me and can you see the slides perfect

perfect thank you so much so i'm elena novelli from base business school

and as you mentioned today i'm presenting we are presenting together with chiara from inside this paper

and we would like to first of all thank sms for organizing this session we are delighted to be here

and also i can see there are many friends in the audience

so thank you

so much for taking the time to attend our presentation we

are really at a stage where we would certainly benefit from feedback

and comments so we look forward to hearing from you later

so this paper builds on the observation that innovation settings

and entrepreneurial settings in particular are characterized by a high degree of uncertainty

so part of which is endogenous to firm action

so a very important question in these settings is how can firms navigate this uncertainty

and in particular what kind of decision -making processes can help them navigate uncertainty.

So if we look at prior research there are essentially two main approaches

that have been identified that can help firms navigate uncertainty.

First of all what we can call a cognitive -based approach.

A cognitive -based approach is essentially based on the idea of addressing uncertainty by reasoning through theory,

frameworks or rules that can help decision makers understand what to do.

Alternatively, decision makers can use what we could call evidence -based approaches.

These approaches are essentially based on the idea of addressing uncertainty via the systematic collection of evidence and its discipline assessment,

with the idea that in this way they can learn from the environment.

Now, more recent evidence has suggested that there are actually important synergies that exist between cognitive and evidence -based approaches.

For example, the work of Cathy Eisenhardt

and Chris Bingham suggests about the importance of combining thinking and doing to devise superior strategies.

Or, for example, the work of Rory McDonald and Cathy Eisenhardt shows that entrepreneurs who combine cognition with evidence,

for example, using experimentation to test the key assumptions underlying a business model, can accelerate their learning.

Or finally, the work of Camufo and colleagues shows the benefits of a scientific approach to decision making,

an approach that combines elements of cognition, as well as elements of experimentation.

So we use the terminology used by these authors,

and we will talk about the scientific approach to decision -making to refer to an approach that uses theory,

hypothesis, but then tests these hypotheses with evidence and conducts a discipline evaluation of that evidence.

The expression scientific approach refers to the fact that approach that uses theory,

hypothesis, evidence and evaluation really resembles that used by scientists in the development of knowledge.

Now, let me give you an example about how such an approach might actually work in practice, especially in the context of entrepreneurial decision making.

So here is an example of an entrepreneur we worked with.

Essentially, this entrepreneur identified and developed a theory about the fact

that there is actually some unmet demand in electronics that comes from non -tech savvy consumers.

These consumers are really more interested in the usability of products, in the aesthetics of products.

They don't really know the details about the technical specifications.

So they know what they want to achieve, but they don't know exactly what technical specifications they need to achieve that.

So this entrepreneur developed a theory about these types of consumers

and how their demands could be met and developed a set of hypotheses.

An example of one of these hypotheses was,

for example, that providing customers with information on product compatibility was more relevant

when they were making a purchase than providing them with very detailed technical specifications

so they devised a set of tests to for their hypothesis

and for example in this case they ran an online test

and a b tests for two weeks offering customers different product

specifications on the one hand information about product compatibility on the

other hand information about technical specifications of products they found

that their hypothesis was supported and so

So they continued iteratively to test hypothesis about their theory until

they reached the stage where they developed a very solid and robust value proposition that enabled them to grow and succeed. So...

Let me synthesise what a scientific entrepreneur is.

A scientific entrepreneur is one who starts with a mental model that explains how a business creates value.

He or she then proceeds to articulate, to decompose that theory into testable propositions that logically flow from the theory.

They then perform rigorous tests with representative samples of customers, and then they assess the evidence collected in a disciplined way.

If this is a scientific entrepreneur, who is then a non -scientific entrepreneur?

A non -scientific entrepreneur is one who draws conclusions based on testing, but with no counterfactuals.

It's someone who tests multiple versions of a product, but without being driven by a theory.

Therefore, there is a higher degree of causal ambiguity.

Or someone who performs lower quality tests, for example, multiple versions of a product or a service are tested, but they differ more than one characteristics.

So the signal that those tests can reveal is certainly more noisy.

So the key research question that this paper addresses is to what extent

and under what conditions is a scientific approach to decision -making,

so an approach that combines cognition and evidence, associated with superior performance.

And in particular, what we want to understand is whether a scientific approach can benefit all firms equally.

Now, we believe that this is a very important question, especially because the main evidence

that has identified a positive relationship more in general between systematic decision -making approaches

and performance tends to be focused on more established firms.

So we want to understand this question more closely.

Thanks, Elena.

So this leads us to the focus of the paper, which is how do we expect the scientific approach to impact the performance of entrepreneurial firms and of different firms in particular?

So we're going to look at the impact of a scientific approach on performance first.

If you could please go to the next slide.

And we have a baseline hypothesis.

What we are expecting building on literature on strategy, entrepreneurship, decision -making, and organizational learning is that overall,

the scientific approach will improve this process of iteration that entrepreneurs usually go through.

This will result in better decisions and in better performance.

And we expect this because we are considering what the scientific approach does as entrepreneurs are making decisions.

On the one hand, as Elena was describing, the scientific approach is very beneficial

because entrepreneurs have a mental model they have a theory

and this implies that they have a clear representation of the problem they're facing and they're then able to articulate hypotheses

what theory and hypotheses do is

that they lead to more precise tests they're basically spotlighting what are the key assumptions

that entrepreneurs have and that they need to test in order to make sure

that a business is viable now this increased precision results in tests

which are more focused and therefore are more likely to result in learning in addition to

that the entrepreneurs are also gathering evidence

that they are evaluating in a very systematic manner and

so what evaluation does is

that it helps entrepreneurs first of all understand whether the evidence is supporting their initial assumptions

but also it's very beneficial when entrepreneurs do not find evidence actually support their hypothesis

which happens quite frequently and when that is the case the fact

that entrepreneurs have a clear framework is actually very beneficial in helping them in understanding what to do next

so for these reasons we're expecting

that the scientific approach will have a positive effect on decision making

and performance now we are also interested as we mentioned earlier in understanding what happens with firms

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that are different levels of business development and what do we mean by that

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we have looked at the literature and interestingly

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when it comes to entrepreneurship entrepreneurship Entrepreneurship is often thought about as a journey

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that requires firms to go through various steps and go through an evolution process.

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So you can think of an entrepreneurial firm as some scholars have used the parallel of the human life.

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So when a firm is born, like a baby, it's not fully formed.

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It's in a very embryonic stage, if you will, which we can consider the startup phase, which is also described as a progressing nascent venture.

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But this can progress over time, going all the way to a more established phase where the firm becomes a more sustainable and continued market participant.

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Now, the reason why this level of business development, and this is the construct that we're focusing on here, is particularly important is because these firms are in fact facing different problems.

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The fact that the firm is more or less developed actually implies that they've conducted slightly different activities, but they're facing different problems.

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And the fact that they're facing different problems has important repercussions for the type of decisions that they're making.

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to make this a little bit more concrete let me provide some definitions and also give an example

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when we are looking at firms that are lower level of development

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and remember that this concept is a continuum so the example

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that we have here is just a simplification

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so we're providing an example of a low and high level

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but of course there is a continuum you can think of

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a firm with a low level of development as a firm

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that is still quite early on in the process of venture creation and

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so doesn't have a particularly well -defined value proposition just yet

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on the other hand of the spectrum you can have a firm

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which is at a higher level of business development meaning

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that it's later on in the stage of development as a firm

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and this means that the value proposition is more defined and

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so the entrepreneur is more fine -tuning this value proposition now to be very specific let's look at an example

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and the example that we have here is one simple business idea help busy individuals stay fit

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so these are people who usually don't have time to go to the gym

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or attend some classes so you can have an entrepreneur

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that is trying to develop this business idea and here we have images

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that represent these two different levels of development on the left

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hand side what you are seeing is the low level of

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development where the entrepreneur has not decided yet how she's going

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to deliver this value to the customer this could happen through sessions

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which are close to for instance a park where the person lives they could be delivered online

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or they could be offered through a gym in a van on the right hand side what you're seeing is

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that the entrepreneur instead has decided

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that the offer will happen through a gym in a van

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and so this gives you a sense of what it means to have a more

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or less developed business idea now what this does is

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that it implies

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that these firms as we were talking earlier about the level of development are facing different problems And in particular, when we're looking at firms which have a lower level of business development,

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they're facing broader problem.

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By their very own nature, these problems are more open, they're more complex, there are more aspects that need to be taken into account.

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So in this case, what we are expecting, building on literature on organizational learning in particular, is that there will be more ambiguous learning and a slower pace of feedback,

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which is something that usually happens when actors are dealing with problems, actions, and consequences, which are quite broad in scope.

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As a consequence, this implies that it's going to be harder for these entrepreneurs to learn

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because the information that are emerging will be noisier.

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On the other side, when we're looking at a higher level of business development instead, what happens is that these firms have different types of problems

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that they're facing they are narrow in scope and what happens

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when actors are facing narrow problems is

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that the process of decision making is simplified and this implies

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that it will be easier for these entrepreneurs to apply the scientific approach

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and here you can think of some studies like eti raj

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and levin tall but also philip xsr that show how

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when you simplify this landscape of decision making for instance by keeping some aspects of decision making constant

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and just varying some aspects of the problems to change then these results in simplified processes of decision making

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and more effective decision making so the argument is

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that there will be enhanced learning for these firms

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which are the more advanced level of business development and

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so for these reasons we're expecting

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that the level of business development is moderating positively the impact of a scientific approach on performance

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how do we test these ideas well we ran a field

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experiment as it was alluded to earlier with the title

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and we use actually business support program to run this experiment

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you can see some pictures here from the study you can

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see elena myself the entrepreneurs the program were broadly it was a project

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that was made possible thanks to generous funding from the uk government

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which allowed us to create a program just for the purpose of this particular study

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and this meant

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that we recruited research assistants instructors in we created a demo

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day with investors i'm going to provide a little bit more details about

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that but most importantly we have firms

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which are at various level of business development participating to the

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study here's our review of the rct uh is i'm just

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going to provide some high level details of the study of course there are a lot of details with experiments, so feel free to ask any questions that you might have.

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What we did is that we started off with a call for applications.

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So we created a website and we started a marketing campaign.

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We managed to recruit 261 firms.

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After that, we randomly allocated them to either treatment or control condition.

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And what's important to point out here is that they both attended training.

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Both groups received the same amount of training for the same amount of hours with the same instructors on the same topics.

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The only difference is that 3D entrepreneurs learned about a scientific approach and entrepreneurs in the control group didn't.

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As a final step, we conducted data on these entrepreneurs for about 10 months using some phone interviews, as we're going to show you more details.

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This is a slide that gives you a sense of the timeline of the project.

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The project overall from design to end lasted for 12 months, but here what you can see is that we started off the training in month one,

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which was February 2019, and we ended in April 2019.

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During this time, we had 21 hours of training split over seven sessions, and this is where the treatment was.

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After that, until December 2019, we had monthly events.

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The monthly events were exactly the same for the treatment and the control group.

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We actually ran the same event twice on different days

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or different times of the day to prevent these people from talking and potentially disclosing elements of the treatment.

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As you can see, the data collection goes on from right before the treatment started all the way until the end, which was December 2019.

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to give you a little bit more details about the participants what we did as i mentioned we recruited firms

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that were at different levels of business development there were no prerequisites in terms of industry

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so we ended up with a beautiful variety in terms of the type of businesses

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that attended the program from those that were interested in fashion retail fintech

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security environment education really different type of businesses what they all

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had in common however was the size of the firm they all the firms could have at most 10 employees

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and the reason why we made that particular choice is

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that we actually wanted whoever participated into the program to be

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heavily involved in the decision making process of the firm

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and this condition is more accurately met in the context of these small firms the table

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that you're seeing on this slide is showing the balance checks

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so as i mentioned after recruiting these entrepreneurs we collected some data on the firm

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and then we use this to check whether the randomization actually resulted in balanced groups

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and what this table is showing you is

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that there are no statistically meaningful differences on a range of covariates

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which is looking at both aspects which are more demographic

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when it comes to the firm such as what's the age the team size what's the gender ratio within the company,

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to some which have more to do with decision making and performance.

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To give you some details about the treatment versus the control condition,

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as I mentioned earlier, we use the training in the same way for treatment and control firms with one important difference.

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You can see that both the control and the treatment group are exposed to strategy frameworks, for instance, the business model canvas or balance scorecards,

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and also data gathering techniques.

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And so this includes, for instance, how do you run a survey?

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How do you conduct A -B testing?

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How do you talk to your customers?

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How do you conduct good customer interviews?

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The only difference is that the treatment group was also taught for each of these topics

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and each of these sessions to use a scientific approach to decision -making.

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And so what this meant is that for each step, they had to have a very clear theory of a mental model that explain how their business generates value.

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They had to then develop hypotheses or testable propositions to test what their key assumptions were, and then conduct tests in a rigorous manner and evaluate the results.

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So to give you a better sense of how we did this in practice, the classes were highly experiential.

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So we had usually a fairly long lecture, so it was around one hour, one and a half hour.

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We had various in -class activities to really make sure

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that the entrepreneurs would understand the content that was explained to them right away

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and we got them to apply the content of the course to their business right away

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so the session also included some time for the entrepreneur to apply this content to their business

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and their business idea while they were in class

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and clarify with instructor whether they have any questions in the

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case of the first session where we teach the business model canvas for instance what we did is

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that we teach the business model canvas to both groups they

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conduct the same activities however there is a difference in how they're taught to use the business model canvas.

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control group follows a more standard approach

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which is used in a lot of universities and accelerators

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and business schools as well where they learn to use the

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business model canvas as a representation of their business where various elements should be connected between them

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and it contains some elements

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that will be tested later on in the case of the

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scientific approach they still learn about the business model canvas however they learned

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that that represents the basis for their theory of their firm

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and so they are asked to also

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and taught how to write a theory from their business model canvas

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and they consider each element of the business model canvas as hypothesis

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that will be tested at a later stage

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and then we follow this logic

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and implement similar changes throughout the course to make sure

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that the content remains the same but the only difference is the scientific approach

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and uh to give you also a little bit more details about the data collection process what we did is

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that we follow an approach which was championed by bloom

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and renan in economics with their study of management practices where

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we decided to conduct telephone interviews with the cell phone interviews we had a team of research assistants

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that were extensively trained on a predefined interview script.

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All interviews were recorded and this was helpful for us to make sure that we could provide feedback to the research assistants.

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And all these interviews ran for about once a month from February until December 2019.

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And the research assistants were asking entrepreneurs about various topics.

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There were both some close -ended questions

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that were primarily measuring performance such as how many revenue did

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your business generate in the last month what kind of cost

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did you make any changes to your business as we're going

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to see in in the next few slides with the results

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and then there were some more open -ended questions as well

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which aimed at measuring more the decision -making process that the entrepreneurs were going through

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i think this is the end of the design part so i can leave the floor to elena for the results

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Thank you.

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So let me give you an overview of what we find.

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So the first question that we wanted to address is whether treated entrepreneurs achieve better performance.

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So when it comes to estimating performance,

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prior research has extensively documented that firm performance is not a unidimensional construct

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and that difference performance measures really capture different nuances of performance

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so when we look at performance we look first of all at revenue as an established measure of performance

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but we also look at firms growth and

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because we know from private work that some firms focus on growth first

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because by doing this they basically go on a better performance trajectory

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so we wanted to capture both effects and

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so we wanted to capture both dimensions

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and therefore we look at both our analysis follows a classic

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difference in different specification we therefore estimate our performance variable as a function of treatment post

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which is an indicator variable that is valued as one for firms after the training

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and the interaction between the two so here what we see is

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that when we look at size of firms actually uh what we find is

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that the treatment had a positive effect on perform on performance

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as measure as growth in particular about a 12 % increase on performance.

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However, interestingly, if we look at revenue, we see a non -significant effect.

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Now, in looking at these results, we must acknowledge that our observation window was quite limited,

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about 10 months, which is not necessarily a huge amount of time for a firm to observe a change in performance.

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Now, ideally, we would want to observe a longer time horizon

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that this was not possible at the moment due to obviously finding funding constraints.

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However, to the extent that, as prior research has suggested, growth can be interpreted as an early performance measure,

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we could interpret this result to indicate that firms

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that are treated are led to invest in the size of the firm by hiring more employees

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and investing in the business model

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and this might later on lead them to achieve a superior performance in terms of revenue as well.

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But we will go back to this point as we see more results.

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We move next to try and understand the extent to which the scientific approach benefits all firms equally.

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So what we do here, we use a triple difference specification, so we want to see whether the stated difference

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that we observed between the performance before and after the treatment for treated and controlled firms,

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how it is shaped by our

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key variable which is the degree of business development so what we see here was that

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if we look at different types of firms a different degree of development

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if we look at firms more developed the treatment had an additional incremental effect on their size so

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if we observed a impact of the treatment on the size

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of the firm of about 11 percent what we see here is

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that one standard deviation increase in the degree of business development

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leads to an additional three percent increase in the size of

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the firm what is extremely interesting for us is to observe what happens

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when we look at revenue so you will remember

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that we didn't find a significant effect of the treatment on performance as measured in terms of revenue.

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Now, if we make now a distinction between firms at a low level of business development

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and firms at a high level of business development, what we see is really interesting.

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We see that for firms at a very low level of business development, the treatment actually had a negative effect on revenue.

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In particular, a negative effect of about 30%.

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On the other hand, if we look at the case of firms with a high degree of business development,

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the treatment had a positive effect. In particular,

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one standard deviation increase in the degree of business development is associated with a substantial increase in the performances measured in revenue,

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a 60 % increase, which is obviously quite substantial.

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Now, the way we interpret these results is the following.

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We interpret these results to basically indicate that for less established firms, the method brings them back to the drawing board.

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And so they start using the method to work again on their business model, to work again on their value proposition,

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to change things, to do things perhaps differently.

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And essentially, we see these being represented by the fact that they invest in their company by growing the number of employees.

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However, they are not yet at a stage, an exploitation stage, where we see an impact on revenue.

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On the other hand, if we look at more established firms, these are at a stage where they are already in an exploitation phase in their business model.

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So they use the method differently.

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They use the method to fine tune their value proposition

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and make even more revenue and make it even and make it basically even more successful.

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Now, this is very much in line with the findings of other scholars.

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For example, if we think about the work of Joshua Gantz,

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Scott Stern and colleagues, they suggest in line with these results that when firms are still in an early low commitment phase,

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any positive signal leads them to an exploration search phase.

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While when firms have already made significant commitment, a positive signal leads them to higher exploitation.

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Now, these results are also robust to winsorizing alternative measures of development and fixed effect specification.

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In a way, looking at comparing what happens to firms at a low degree of business development

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and firms at a high degree of business development helps us

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perhaps providing an answer to our earlier question about what happens in the longer term,

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because what we see here is a comparison between firms that are at a different stage of the journey.

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So this gives us a sort of snapshot at different points in time, which might suggest that in the longer term,

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the method also helps firms exploiting and benefiting in terms of revenue.

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More in general, we are also in conversation with the government to explore the

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possibility of observing these firms in the longer term by basically having a longer time horizon,

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by merging our data with the data collected by the government.

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So this would answer our question regarding longer term even more extensively of course meanwhile we had a relevant shock

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which was of course the pandemic

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but this might also offer some the interesting opportunity to see whether the methods made these firms for example more resilient

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Let me show you also some model free evidence

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that might give us another snapshot of what happens if we look at the data.

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So what we have here on the left hand side is

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the average of revenue at the baseline for firms at a low degree of development,

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lower than the average, and a high degree of development, higher than the average.

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And we compare control and treated firms.

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So what we see here is

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that the revenue of firms at a low degree of development were very similar in the control and in the treatment group.

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And also the revenue of firms at a high degree of development were very similar for the control and treated firms.

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If we observe the averages of the revenue at the end of the period, we basically see that, of course,

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all the revenue of all firms grew, but in the low development group,

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firms in the control group grew more in terms of revenue compared to treated firms,

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while the opposite is true in the case of firms at a higher degree of development.

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So this provides us another snapshot of the results that we showed in our econometric analysis.

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Now we are at a stage where we are trying to provide more evidence

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about the mechanism that we suggest are behind our results and that we mentioned earlier.

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So what we have started looking at is we asked the entrepreneur before starting our program, what were the issues that they faced?

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And what we see is that the issues

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that entrepreneurs at different levels of business development face are indeed different as predicted by our theory. In particular,

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we've seen that firms at low levels of business development are significantly more likely to indicate at the baseline

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that the main issues that they face are related to product development

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or to the identification of the product market fit and really to the market validation.

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So they're really facing higher level questions which concern the nature of the business model and the value proposition.

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On the other hand, firms at high levels of business development were significantly more likely at the baseline to indicate

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that the main issues that they were facing were relating to expanding the business and customer base.

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So this is very much in line with our interpretation of the findings

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that the scientific methods helped entrepreneurs at a lower stage of development to address questions

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that were probably broader and that concerned the value proposition.

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But in line with this, the outcome of that application, the method didn't necessarily lead them to obtain immediately revenue,

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but probably to improve their business model,

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to grow their, like the strength of that business model as represented by an increase in the number of employees.

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Similarly, we are also conducting some text analysis regarding the way in which these entrepreneurs talked about their value proposition at the baseline.

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And what we are seeing is

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that entrepreneurs at a lower degree of business development were significantly

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more likely at the baseline to describe their value proposition in a way that expressed a higher degree of uncertainty and hesitation.

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We also try to provide evidence of the mechanisms by looking at what these entrepreneurs do after the training.

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In particular, we look at the extent to which they make changes to their value proposition.

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To do so, we use the business model canvas as a way of representing the entrepreneurial business model.

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And I'm sure that this audience is quite familiar with the business model canvas

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and the fact that it's composed by different elements, for example, the value proposition, customer segment, customer relationships, and so on.

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So we look at whether entrepreneurs made any changes during the observation window to any elements of the business model canvas.

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And we also look at the extent to which these changes were radical versus incremental.

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And consistently with our theory and our interpretation of the results, what we see is

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that firms at a high levels of business development are significantly more likely to engage in incremental versus radical pivot.

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Once again, in line with the idea

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that entrepreneurs at a low stage of development use the method to go back to the drawing board

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and reconsider the way they're doing business.

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but this does not necessarily lead them immediately to observe revenue.

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On the other hand, firms that are at a more advanced stage of development have a more established value proposition, so use the method to improve it, to fine tune it.

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And so they're already making revenue and this leads them to essentially make even more revenue.

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We also try to exclude alternative explanations. For example, one that we consider was that maybe these results

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that was associated with a higher

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or lower degree of this development was perhaps driven by the experience of entrepreneurs in the industry

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or experiencing running startups or managerial experience.

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But none of these explanations was related to our results.

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None of these alternative explanations essentially held.

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Alternatively, we thought maybe this is is confidence that is really driving these results.

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Maybe the more developed firms are more confident and so maybe this is what is actually driving our results.

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But once again, we also ruled out this alternative explanation.

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So to conclude, what we find is that overall

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treated firms grow more than control firms and

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if we look at the distinction

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when it comes to performance as measured in terms of revenue

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if we look at the distinction between less developed

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and more developed businesses what we see is

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that less developed business actually for them the treatment has a negative effect on performance

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but it has a very positive effect on performance for more developed businesses now we believe

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that these results show um contribute to existing literature on decision making

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and strategy by showing an important boundary conditions for the application of the scientific decision making approach

413

and more in general about systematic approaches to decision making

414

and in particular it also highlights the complementarity between between the development of a theory about the firm and experimentation,

415

which is in contrast with the idea that entrepreneurs can really delay important choices and experiment at any stage of their journey.

416

We also believe that these results have important practical implications and they provide actionable tools for entrepreneurs.

417

We are in the process of developing some case studies as well and tools for entrepreneurs.

418

And we also believe that there's an important practical implication

419

that is particularly dear to us as educators in addition to being scholars,

420

which is basically that these results show that it's not just the content

421

that we teach in the classroom that makes the difference, but it's also the way in which the content,

422

it's also about teaching students or entrepreneurs how to use that content.

423

Because if we think about it, what we delivered in our control classroom was very similar to what we deliver in any management course.

424

But the difference in the treatment in this case was really about teaching entrepreneurs not only the tools, but also how to use them to develop a theory,

425

how to use them to test the theory.

426

So we believe this is something that is relevant for us as educators.

427

So what are the next steps?

428

We have presented today this paper, but this paper is really a part of a larger project

429

titled Scientific Approach to Innovation Management that we are conducting together with Alfonso Dambardella, Arnaud Camufo and other colleagues at Bucconi,

430

at the University of Maryland, at Erasmus, at the University of Lisandes, at the India School of Business, Coen University and Shanghai University.

431

More in general what we are trying to do with this project is

432

that of understanding and answering a broader question

433

which is what type of decision -making processes can help firms navigate uncertainty

434

and we hope with this project to be able to explore the antecedents associated with these decision making processes

435

and the implications

436

so we are in the process of setting up multiple randomized

437

control trials in different countries italy the netherlands uk colombia brazil china india

438

and tanzania and we hope

439

that this will give us the opportunity to further validate the robustness of the results

440

but also to explore other nuances and boundary conditions to improve our knowledge.

441

about decision making in the context of uncertainty.

442

So I would like you to thank you very much

443

and I will pause here and look forward to any feedback and suggestions.

444

Thank you.

445

So Elena, we actually have one question in the chat

446

so maybe we can start with that one and in the meantime

447

if people think about any other questions we can take them um

448

so since we are live online ziyan would you like to unmute yourself and ask the question

449

oh yeah can you hear me yes we can all right yeah thanks elena

450

and the care for the wonderful research is very interesting

451

and rigorous so i just have a quick question regarding the h2 uh especially in terms of the theory

452

so i think the research argument now centers on the benefits of scientific approach

453

but it also has the cost side of that approach for

454

so for the higher development of business it also means

455

that it's more costlier for them to do to adopt

456

that approach because they have larger sum costs if they use that approach.

457

So I wonder if

458

that also means the higher development also means they are unlikely to employ a scientific approach in reality

459

and that will also lead to the opposite prediction.

460

So it's about whether they use it or not rather than the approach is beneficial or not.

461

Thank you for this question.

462

We also look at costs actually in some supplementary analysis

463

and what we see is

464

that indeed the scientific approach leads more developed firms to basically higher expenses

465

so it is more expensive like they spend more

466

when they use the scientific approach so this is in line with with the prediction

467

that the scientific approach leads to higher costs not necessarily higher sunk costs but certainly higher costs

468

We also look at whether the value added, so whether, essentially, because we see an increase in revenue, and we see an increase in cost,

469

so a follow -up question could be, is it possible that increasing revenue is simply driven by the increasing cost, and so the value is simply transferred to the customer,

470

there's no value retention for the firm, and what we see is that we see an effect on value added.

471

So the scientific method leads more developed firms to also generate more value, which we believe is quite interesting.

472

I hope that answers your question.

473

Let me also add something about, right, because I think Elena was writing in talking a lot about empirics, but this is also a conceptual point, right?

474

And so the literature that looks at business development is really emphasizing how the level of development is not related to, for instance, age or cost.

475

So, of course, I think the example

476

which we showed might have primed you in that way because that shows two extremes on the continuum.

477

But usually the level of development is really about what are the problems that these firms are facing.

478

And so costs, and especially some costs which are irreversible, might not really be central in that particular case.

479

But I think it's interesting what you're pointing out about sort of what is the cost of using a scientific approach,

480

right? so i think this is also a very interesting question for for future research i see

481

that we also have a we have a question in the chat

482

and we have a hand raised and i believe

483

that the hand was raised first so why don't we hear from alexander

484

we can all hear you Alexander

485

oh it was a mistake okay

486

so there is another question in the chat from Alfonso Alfonso

487

would like to mute yourself okay just very simply uh i

488

would be curious to know how do you rule out

489

that higher developed firms are not just more skilled or experienced to adopt the approach

490

okay thank you for the question so um what we what we look at is um whether the experience of

491

more developed firms i mean obviously more developed firms are also tend to be uh more experienced

492

and we we basically uh try to see whether the effect

493

that we observe is is explained by experience whether experience leads to the same outcome

494

and we don't see So we don't find that more experienced firms

495

have a positive and more positive um like result when they use the approach

496

on the other hand when we look at the level of development we see the effect

497

does that answer your question yes thank you

498

I see Addis being spotlighted.

499

Addis, do you have a question?

500

Yes.

501

I actually left a question on the Q &A part.

502

There's a chat, so I think it was not clear.

503

It was an interesting presentation.

504

Do you hear me?

505

Can you hear me?

506

Yes.

507

Hello.

508

Hello.

509

Good to see you and it is an excellent project

510

so I have just one simple clarification question so

511

when you go on the post hoc analysis identifying the mechanisms you mentioned a few mechanisms

512

that kind of distinguish less developed and more developed businesses

513

and it felt to me more like they are the characteristics of the business themselves than mechanisms

514

so I was kind of yeah can you say a little more, as you say, in less developed business ideas, you know, owners were not very sure,

515

they had certain uncertainty, and I felt that was the very definition of less developed business idea.

516

So how do you kind of differentiate between the nature of the business idea itself, so it's not developed, versus the mechanism by which,

517

you know, the treatment helps them to, you know, to perform better? so just to check

518

that we understood the question um we understood the premise can

519

you maybe elaborate a bit uh more on the the question please

520

so yeah my question is are the characteristics the characteristics of the business idea

521

so highly developed versus less developed business idea

522

different from the mechanism you say as you know drivers for the performance outcome you observe

523

So I kind of see a mix between, you know, the characteristic of the business ideas and the mechanisms that connect the,

524

you know, the business ideas to the success, the performance.

525

Okay, so let me elaborate a bit on that, on the answer to this question and Chiara maybe can elaborate

526

and then you will tell us whether it answers or not your question.

527

So essentially our idea is that firms at a different level of business development face different problems.

528

So firms at a lower degree of business development are still validating the value proposition.

529

And this is the first point that you were suggesting.

530

And firms at a later stage are fine -tuning the value proposition.

531

So your question is, we understand that these are the two characteristics.

532

Tell us more about the mechanism.

533

And the idea for us is that for firms that are still developing the value proposition,

534

the method helps them make some higher level decisions, which can be, you know,

535

what is the customer, the target customer that we want to face.

536

So obviously, the method at this stage is relevant.

537

It helps them decide.

538

But this is like one out of several important choices that they're making.

539

So this might lead them to make more investments in a certain customer segment.

540

It might lead them to hire someone who is an expert in that customer segment,

541

but it might not necessarily translate into revenue immediately.

542

But the other thing that we are also thinking about in terms of the mechanism is

543

that this is like a search process in like a broader space, because many there are still many degrees of freedom.

544

So the entrepreneur has to go to I mean, the scientific method works in an iterative way.

545

So obviously each divided theory is further developed over time through these iterations.

546

And so when there are still many high degrees of freedom, first of all, it takes more time to cover the search space.

547

But also each test can be in a way less informative because, or let's put it this way,

548

might be more difficult to run all the like to develop the theory

549

and run all the different tests because if you think about the number of combinations that this entails is actually quite high.

550

On the other hand, if you compare the opposite situation, high degree of business development, very fine -tuned preposition, the search space is much smaller.

551

So the method, in terms of mechanism, to answer your question about the mechanism, can be applied in a much more focused way.

552

And so it quickly serves in providing answers that are like the missing pieces of the puzzle.

553

And then there is a complete picture,

554

which is the finalized fine tuned value proposition which can directly generate revenue.

555

I don't know if Chiara wants to add anything to this.

556

Sure, I think what I'd like to add is

557

that I think Addis went straight to the most difficult part of any field experiment

558

which is you know showing what the mechanism is

559

and obviously as this presentation shows these are preliminary results

560

so this is really the part of this project where ellen

561

and i are spending more time now

562

so really trying to show more clearly what is the journey

563

that these firms are going through

564

so i think as you mentioned the idea with the problems is not not

565

so much to show that they are the mechanism

566

but rather to show that that's the starting condition

567

so these firms actually face different problems which is something

568

that is consistent with the literature but then the mechanism are these different changes

569

that they're making so we're trying to work more on pivot

570

and other intermediate outcomes to show that a little bit more clearly

571

so i think this question is very helpful for us to to really clarify

572

that we need to focus on that a little bit more

573

and i think at this point is also closely related to the question

574

that miriam has that i see in the chat

575

miriam would you like to unmute and ask your question okay now you can hear me right yes Yes,

576

my question is very much related to the previous one and

577

so the first part of my question actually is a comment

578

and it's to make sure that I understood basically the main conclusions of the paper

579

which is one size basically does not fit all.

580

So it depends on the stage of the business cycle of the specific firm, how this firm adopts the scientific method,

581

the cognitive method and it depends on the outcomes that you're looking at.

582

Now, my point here is whether it's about the way the different firms,

583

according to the stage of the business development, use the scientific method that is different,

584

or whether they make different decisions because they are in a different stage of the business cycle,

585

and therefore they have to make decisions that are different compared to firms

586

that are more like ahead in terms of the business development.

587

So if you want, my question is confused because it's not clear to me what somehow what I want to ask,

588

but so whether this is an issue concerning the way firms use the scientific method,

589

whether it's a matter of the decisions that different firms at the different stage have to make.

590

So what comes first here that explains your finding?

591

yeah i think it's a very clear question actually it's not it's not confused at all um

592

so maybe i can start and then uh elena please feel free to add

593

because it's quite a broad question anyway um

594

so i really like how you summarize this idea of one size doesn't fit all

595

which might be one of the key takeaways

596

and i think elaborating on this point what is interesting is

597

that especially for policy makers for accelerators for us as educators

598

this suggests there might be better ways to help these entrepreneurs

599

which are really early in their journey of developing a new business now i think the caveat also

600

that we need to keep in mind when we interpret these results is

601

that the study has a fairly limited duration right

602

so precisely for the reasons

603

that you're discussing about the different type of problems it might be

604

that these early stage entrepreneurs need more time to reach a

605

stage where we can really see the benefits of using a scientific approach

606

and so the fact that they're facing these different problems might imply that they need more time.

607

So hopefully this is something that we will be able to measure later on at some point, as Elena mentioned earlier, with additional data.

608

But I think this is an important caveat and something

609

that we're thinking about in terms of how to interpret the results in a fairly cautious way.

610

But Elena, I'd love to hear your thoughts about this.

611

What comes first, problems, decisions?

612

Absolutely.

613

Thank you, Miriam.

614

It's obviously a very important question, and I particularly think that from a practical standpoint,

615

your suggestion in the chart of looking at intermediate decisions,

616

because I mean, essentially what we are saying here is also that between growing the firm,

617

like defining the value proposition and making revenues and exploiting that value proposition, there's really a lot.

618

And so it would be really nice to be able to map.

619

the data generation process there with possibly looking at intermediate outcomes,

620

so this is in fact why we in the following studies,

621

these are things that hopefully we will have the opportunity to observe so that it will provide additional evidence in this direction.

622

With this specific study, we obviously are limited to what we measured, so we are trying to provide some evidence in that direction,

623

but it's certainly where I mean it's certainly the direction to go in

624

because it's a very important opportunity to map the decision making process

625

and understand more about how I mean about the process

626

that goes from making the decision to observe some intermediate outcome and then investing more until performance is observed.

627

Thank you.

628

Thank you.

629

I think we're coming very close to the end of the session.

630

So I'll leave it to Emily or Ryan to wrap up.

631

But what I want to say for sure is

632

that I would to thank everyone for being with us today and taking the time to attend our presentation

633

and we would really love to to hear any thoughts um any ideas

634

or suggestions so please feel free to reach out to us

635

and we would be delighted to continue this conversation thank you thank you everyone.

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इस वीडियो में शैडोइंग के लिए 635 वाक्य और 8602 शब्द हैं। बोला गया हिस्सा 56:26 लंबा है। वक्ता लगभग 152 शब्द प्रति मिनट की स्वाभाविक गति से बोलता है, जो रोज़मर्रा की बातचीत के करीब है। 86% शब्द अंग्रेज़ी के 3,000 सबसे आम शब्दों में से हैं; बाकी शब्द अभ्यास से पहले देख लेना अच्छा रहेगा।

इस वीडियो की मुख्य शब्दावली

वीडियो के 15 सीखने लायक शब्द, उच्चारण और अर्थ के साथ:

शब्द	उच्चारण	अर्थ
entrepreneur संज्ञा	/ˌɑn.tɹə.pɹəˈnɝ/	व्यवसायी, उद्यमी
mechanism संज्ञा	/ˈmɛk.ə.nɪ.zəm/	तंत्र, यंत्रावली
observe क्रिया	/əbˈzɜːv/	देखना
uncertainty संज्ञा	/ʌnˈsɜːtənti/	संदेह, संशय
hypothesis संज्ञा	/haɪˈpɒθɪsɪs/	परिकल्पना
tune संज्ञा	/tjuːn/	धुन
necessarily क्रिया विशेषण	/ˌnɛs.əˈsɛɹ.ə.li/	अवश्य, अनिवार्यतः
extent संज्ञा	/ɪkˈstɛnt/	विस्तार
experiment संज्ञा	/ɪkˈspɛɹ.ə.mənt/	प्रयोग, तजुर्बा
interpret क्रिया	/ɪnˈtɜː.pɹɪt/	निर्वचन करना, अर्थ लगाना
explanation संज्ञा	/ˌɛkspləˈneɪʃən/	स्पष्टीकरण, समझाना
indicate क्रिया	/ˈɪndɪˌkeɪt/	सूचित करना, संकेत करना
feedback संज्ञा	/ˈfiːdˌbæk/	प्रतिपुष्टि, प्रतिक्रिया
beneficial विशेषण	/ˌbɛn.ɪˈfɪʃ.əl/	लाभदायक, हितकारी
systematic विशेषण	/ˌsɪs.tɪˈmæt.ɪk/	व्यवस्थित, नियोजित

ध्यान देने योग्य उच्चारण

वक्ता 55 बार संक्षिप्त रूपों का प्रयोग करता है, जैसे they're, we're, don't। इन्हें वैसे ही छोटा बोलें जैसा आप सुनते हैं।

“th” की ध्वनियाँ: hypothesis /haɪˈpɒθɪsɪs/, gather /ˈɡæðɚ/
“sh” और “zh” की ध्वनियाँ: proposition /ˌpɹɑpəˈzɪʃən/, essentially /ɪˈsɛnʃəli/, explanation /ˌɛkspləˈneɪʃən/, presentation /ˌpɹɛzn̩ˈteɪʃn̩/, beneficial /ˌbɛn.ɪˈfɪʃ.əl/
लंबे शब्द — बलाघात सही रखें: entrepreneur /ˌɑn.tɹə.pɹəˈnɝ/, proposition /ˌpɹɑpəˈzɪʃən/, mechanism /ˈmɛk.ə.nɪ.zəm/, essentially /ɪˈsɛnʃəli/, uncertainty /ʌnˈsɜːtənti/

इस वीडियो से अभ्यास कैसे करें

पहले बिना बोले पूरा वीडियो एक बार सुनें और जो शब्द नहीं आते उन्हें लिख लें।
0.75× गति से शुरू करें, एक-एक वाक्य की शैडोइंग करें, और सहज लगने पर सामान्य गति पर लौटें।
अपनी आवाज़ रिकॉर्ड करें और मूल से तुलना करें, ख़ासकर entrepreneur, mechanism, observe जैसे शब्दों पर ध्यान दें।

इस वीडियो का व्याकरण

वक्ता जिन संरचनाओं का सबसे अधिक प्रयोग करता है, वीडियो के असली शब्दों के साथ:

संरचना	वीडियो में
Present perfect have/has + past participle — बीता हुआ काम जिसका असर अभी भी है	have been identified · has suggested · has identified
Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता है	are characterized · have been identified · could be met
Relative clauses who / which + उपवाक्य — व्यक्ति या वस्तु के बारे में अतिरिक्त जानकारी	one who starts · one who draws · someone who tests

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शैडोइंग (Shadowing) एक विज्ञान-समर्थित भाषा सीखने की तकनीक है जो मूल रूप से पेशेवर दुभाषिया प्रशिक्षण के लिए विकसित की गई थी। विधि सरल लेकिन शक्तिशाली है: आप मूल अंग्रेज़ी ऑडियो सुनते हैं और तुरंत इसे ज़ोर से दोहराते हैं — जैसे वक्ता की छाया 1-2 सेकंड की देरी से। शोध से पता चलता है कि यह उच्चारण सटीकता, स्वर, लय, जुड़ी हुई ध्वनियाँ, सुनने की समझ और बोलने की प्रवाहशीलता में काफ़ी सुधार करता है।

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शैडोइंग अभ्यास: Decision-Making and Entrepreneurial Performance: Evidence from a Field Experiment - वीडियो के साथ अंग्रेजी बोलना सीखें

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